diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..b21def48e2d7f6cf9e3623fff25be1c21751bd87 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
*.zip filter=lfs diff=lfs merge=lfs -text
*.zst filter=lfs diff=lfs merge=lfs -text
*tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/driving_video.mp4 filter=lfs diff=lfs merge=lfs -text
+assets/teaser/teaser.png filter=lfs diff=lfs merge=lfs -text
diff --git a/LICENSE.txt b/LICENSE.txt
new file mode 100644
index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64
--- /dev/null
+++ b/LICENSE.txt
@@ -0,0 +1,201 @@
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
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diff --git a/ORIGINAL_README.md b/ORIGINAL_README.md
new file mode 100644
index 0000000000000000000000000000000000000000..ff842cf277d999093052d9e41033c338c5647abf
--- /dev/null
+++ b/ORIGINAL_README.md
@@ -0,0 +1,70 @@
+
+
+
+
+
X-Portrait: Expressive Portrait Animation with Hierarchical Motion Attention
+
+ You Xie,
+ Hongyi Xu,
+ Guoxian Song,
+ Chao Wang,
+ Yichun Shi,
+ Linjie Luo
+
+ ByteDance Inc.
+
+
+ 
+ 
+
+ )
+
+
+
+
+
+
+ 
+ |
+
+
+
+This repository contains the video generation code of SIGGRAPH 2024 paper [X-Portrait](https://arxiv.org/pdf/2403.15931).
+
+## Installation
+Note: Python 3.9 and Cuda 11.8 are required.
+```shell
+bash env_install.sh
+```
+
+## Model
+Please download pre-trained model from [here](https://drive.google.com/drive/folders/1Bq0n-w1VT5l99CoaVg02hFpqE5eGLo9O?usp=sharing), and save it under "checkpoint/"
+
+## Testing
+```shell
+bash scripts/test_xportrait.sh
+```
+parameters:
+**model_config**: config file of the corresponding model
+**output_dir**: output path for generated video
+**source_image**: path of source image
+**driving_video**: path of driving video
+**best_frame**: specify the frame index in the driving video where the head pose best matches the source image (note: precision of best_frame index might affect the final quality)
+**out_frames**: number of generation frames
+**num_mix**: number of overlapping frames when applying prompt travelling during inference
+**ddim_steps**: number of inference steps (e.g., 30 steps for ddim)
+
+## Performance Boost
+**efficiency**: Our model is compatible with LCM LoRA (https://huggingface.co/latent-consistency/lcm-lora-sdv1-5), which helps reduce the number of inference steps.
+**expressiveness**: Expressiveness of the results could be boosted if results of other face reenactment approaches, e.g., face vid2vid, could be provided via parameter "--initial_facevid2vid_results".
+
+## 🎓 Citation
+If you find this codebase useful for your research, please use the following entry.
+```BibTeX
+@inproceedings{xie2024x,
+ title={X-Portrait: Expressive Portrait Animation with Hierarchical Motion Attention},
+ author={Xie, You and Xu, Hongyi and Song, Guoxian and Wang, Chao and Shi, Yichun and Luo, Linjie},
+ journal={arXiv preprint arXiv:2403.15931},
+ year={2024}
+}
+```
diff --git a/assets/driving_video.mp4 b/assets/driving_video.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..d2c7349aaf5e2ed296b90b5cd7155c0bba3e1ec3
--- /dev/null
+++ b/assets/driving_video.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:030c10c861e9fd4f6395eede5e9d4005dafc3fa56569e6a7167337b1b3675c08
+size 3839556
diff --git a/assets/source_image.png b/assets/source_image.png
new file mode 100644
index 0000000000000000000000000000000000000000..2d143e81996ebaac391bb0bcdb5b14bf6cd4cdd1
Binary files /dev/null and b/assets/source_image.png differ
diff --git a/assets/teaser/teaser.png b/assets/teaser/teaser.png
new file mode 100644
index 0000000000000000000000000000000000000000..de5e1677d9d6d18313b80ad365b2a1836c0af33e
--- /dev/null
+++ b/assets/teaser/teaser.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:32c8e4475ed8b1db09711d4258c49ff03b9cb9b461557dd7f715bf056940b3c7
+size 5328323
diff --git a/checkpoint/checkpoint_location b/checkpoint/checkpoint_location
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/config/cldm_v15_appearance_pose_local_mm.yaml b/config/cldm_v15_appearance_pose_local_mm.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e8ab3300c187da12cc4ef6eb783fb311ace955d3
--- /dev/null
+++ b/config/cldm_v15_appearance_pose_local_mm.yaml
@@ -0,0 +1,130 @@
+model:
+ target: model_lib.ControlNet.cldm.cldm.ControlLDMReferenceOnly_Temporal_Pose_Local
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: "jpg"
+ cond_stage_key: "txt"
+ control_key: "hint"
+ image_size: 64
+ channels: 4
+ cond_stage_trainable: false
+ conditioning_key: crossattn
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: False
+ only_mid_control: False
+
+ appearance_control_stage_config:
+ target: model_lib.ControlNet.cldm.cldm.ControlNetReferenceOnly
+ params:
+ image_size: 32 # unused
+ in_channels: 4
+ hint_channels: 3
+ out_channels: 4
+ model_channels: 320
+ attention_resolutions: [ 4, 2, 1 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 4, 4 ]
+ num_heads: 8
+ use_spatial_transformer: True
+ transformer_depth: 1
+ context_dim: 768
+ use_checkpoint: True
+ legacy: False
+
+ pose_control_stage_config:
+ target: model_lib.ControlNet.cldm.cldm.ControlNet
+ params:
+ image_size: 32 # unused
+ in_channels: 4
+ hint_channels: 3
+ model_channels: 320
+ attention_resolutions: [ 4, 2, 1 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 4, 4 ]
+ num_heads: 8
+ use_spatial_transformer: True
+ transformer_depth: 1
+ context_dim: 768
+ use_checkpoint: True
+ legacy: False
+
+ local_pose_control_stage_config:
+ target: model_lib.ControlNet.cldm.cldm.ControlNet
+ params:
+ image_size: 32 # unused
+ in_channels: 4
+ hint_channels: 3
+ model_channels: 320
+ attention_resolutions: [ 4, 2, 1 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 4, 4 ]
+ num_heads: 8
+ use_spatial_transformer: True
+ transformer_depth: 1
+ context_dim: 768
+ use_checkpoint: True
+ legacy: False
+
+ unet_config:
+ target: model_lib.ControlNet.cldm.cldm.ControlledUnetModelAttn_Temporal_Pose_Local
+ params:
+ image_size: 32 # unused
+ in_channels: 4
+ out_channels: 4
+ model_channels: 320
+ attention_resolutions: [ 4, 2, 1 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 4, 4 ]
+ num_heads: 8
+ use_spatial_transformer: True
+ transformer_depth: 1
+ context_dim: 768
+ use_checkpoint: True
+ legacy: False
+
+ unet_additional_kwargs:
+ use_motion_module : true
+ motion_module_resolutions : [ 1,2,4,8 ]
+ unet_use_cross_frame_attention : false
+ unet_use_temporal_attention : false
+
+ motion_module_type: Vanilla
+ motion_module_kwargs:
+ num_attention_heads : 8
+ num_transformer_block : 1
+ attention_block_types : [ "Temporal_Self", "Temporal_Self" ]
+ temporal_position_encoding : true
+ temporal_position_encoding_max_len : 24
+ temporal_attention_dim_div : 1
+ zero_initialize : true
+
+ first_stage_config:
+ target: model_lib.ControlNet.ldm.models.autoencoder.AutoencoderKL
+ params:
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+
+ cond_stage_config:
+ target: model_lib.ControlNet.ldm.modules.encoders.modules.FrozenCLIPEmbedder
\ No newline at end of file
diff --git a/core/test_xportrait.py b/core/test_xportrait.py
new file mode 100644
index 0000000000000000000000000000000000000000..971b33b1d62179d46e69a2a8b9f6fdf81aece92c
--- /dev/null
+++ b/core/test_xportrait.py
@@ -0,0 +1,506 @@
+# *************************************************************************
+# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
+# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
+# ytedance Inc..
+# *************************************************************************
+import os
+import argparse
+import numpy as np
+# torch
+import torch
+from ema_pytorch import EMA
+from einops import rearrange
+import cv2
+# utils
+from utils.utils import set_seed, count_param, print_peak_memory
+# model
+import imageio
+from model_lib.ControlNet.cldm.model import create_model
+import copy
+import glob
+import imageio
+from skimage.transform import resize
+from skimage import img_as_ubyte
+import face_alignment
+import sys
+from decord import VideoReader
+from decord import cpu, gpu
+
+TORCH_VERSION = torch.__version__.split(".")[0]
+FP16_DTYPE = torch.float16
+print(f"TORCH_VERSION={TORCH_VERSION} FP16_DTYPE={FP16_DTYPE}")
+
+def extract_local_feature_from_single_img(img, fa, remove_local=False, real_tocrop=None, target_res = 512):
+ device = img.device
+ pred = img.permute([1, 2, 0]).detach().cpu().numpy()
+
+ pred_lmks = img_as_ubyte(resize(pred, (256, 256)))
+
+ try:
+ lmks = fa.get_landmarks_from_image(pred_lmks, return_landmark_score=False)[0]
+ except:
+ print ('undetected faces!!')
+ if real_tocrop is None:
+ return torch.zeros_like(img) * 2 - 1., [196,196,320,320]
+ return torch.zeros_like(img), [196,196,320,320]
+
+ halfedge = 32
+ left_eye_center = (np.clip(np.round(np.mean(lmks[43:48], axis=0)), halfedge, 255-halfedge) * (target_res / 256)).astype(np.int32)
+ right_eye_center = (np.clip(np.round(np.mean(lmks[37:42], axis=0)), halfedge, 255-halfedge) * (target_res / 256)).astype(np.int32)
+ mouth_center = (np.clip(np.round(np.mean(lmks[49:68], axis=0)), halfedge, 255-halfedge) * (target_res / 256)).astype(np.int32)
+
+ if real_tocrop is not None:
+ pred = real_tocrop.permute([1, 2, 0]).detach().cpu().numpy()
+
+ half_size = target_res // 8 #64
+ if remove_local:
+ local_viz = pred
+ local_viz[left_eye_center[1] - half_size : left_eye_center[1] + half_size, left_eye_center[0] - half_size : left_eye_center[0] + half_size] = 0
+ local_viz[right_eye_center[1] - half_size : right_eye_center[1] + half_size, right_eye_center[0] - half_size : right_eye_center[0] + half_size] = 0
+ local_viz[mouth_center[1] - half_size : mouth_center[1] + half_size, mouth_center[0] - half_size : mouth_center[0] + half_size] = 0
+ else:
+ local_viz = np.zeros_like(pred)
+ local_viz[left_eye_center[1] - half_size : left_eye_center[1] + half_size, left_eye_center[0] - half_size : left_eye_center[0] + half_size] = pred[left_eye_center[1] - half_size : left_eye_center[1] + half_size, left_eye_center[0] - half_size : left_eye_center[0] + half_size]
+ local_viz[right_eye_center[1] - half_size : right_eye_center[1] + half_size, right_eye_center[0] - half_size : right_eye_center[0] + half_size] = pred[right_eye_center[1] - half_size : right_eye_center[1] + half_size, right_eye_center[0] - half_size : right_eye_center[0] + half_size]
+ local_viz[mouth_center[1] - half_size : mouth_center[1] + half_size, mouth_center[0] - half_size : mouth_center[0] + half_size] = pred[mouth_center[1] - half_size : mouth_center[1] + half_size, mouth_center[0] - half_size : mouth_center[0] + half_size]
+
+ local_viz = torch.from_numpy(local_viz).to(device)
+ local_viz = local_viz.permute([2, 0, 1])
+ if real_tocrop is None:
+ local_viz = local_viz * 2 - 1.
+ return local_viz
+
+def find_best_frame_byheadpose_fa(source_image, driving_video, fa):
+ input = img_as_ubyte(resize(source_image, (256, 256)))
+ try:
+ src_pose_array = fa.get_landmarks_from_image(input, return_landmark_score=False)[0]
+ except:
+ print ('undetected faces in the source image!!')
+ src_pose_array = np.zeros((68,2))
+ if len(src_pose_array) == 0:
+ return 0
+ min_diff = 1e8
+ best_frame = 0
+
+ for i in range(len(driving_video)):
+ frame = img_as_ubyte(resize(driving_video[i], (256, 256)))
+ try:
+ drv_pose_array = fa.get_landmarks_from_image(frame, return_landmark_score=False)[0]
+ except:
+ print ('undetected faces in the %d-th driving image!!'%i)
+ drv_pose_array = np.zeros((68,2))
+ diff = np.sum(np.abs(np.array(src_pose_array)-np.array(drv_pose_array)))
+ if diff < min_diff:
+ best_frame = i
+ min_diff = diff
+
+ return best_frame
+
+def adjust_driving_video_to_src_image(source_image, driving_video, fa, nm_res, nmd_res, best_frame=-1):
+ if best_frame == -2:
+ return [resize(frame, (nm_res, nm_res)) for frame in driving_video], [resize(frame, (nmd_res, nmd_res)) for frame in driving_video]
+ src = img_as_ubyte(resize(source_image[..., :3], (256, 256)))
+ if best_frame >= len(source_image):
+ raise ValueError(
+ f"please specify one frame in driving video of which the pose match best with the pose of source image"
+ )
+
+ if best_frame < 0:
+ best_frame = find_best_frame_byheadpose_fa(src, driving_video, fa)
+
+ print ('Best Frame: %d' % best_frame)
+ driving = img_as_ubyte(resize(driving_video[best_frame], (256, 256)))
+
+ src_lmks = fa.get_landmarks_from_image(src, return_landmark_score=False)
+ drv_lmks = fa.get_landmarks_from_image(driving, return_landmark_score=False)
+
+ if (src_lmks is None) or (drv_lmks is None):
+ return [resize(frame, (nm_res, nm_res)) for frame in driving_video], [resize(frame, (nmd_res, nmd_res)) for frame in driving_video]
+ src_lmks = src_lmks[0]
+ drv_lmks = drv_lmks[0]
+ src_centers = np.mean(src_lmks, axis=0)
+ drv_centers = np.mean(drv_lmks, axis=0)
+ edge_src = (np.max(src_lmks, axis=0) - np.min(src_lmks, axis=0))*0.5
+ edge_drv = (np.max(drv_lmks, axis=0) - np.min(drv_lmks, axis=0))*0.5
+
+ #matching three points
+ src_point=np.array([[src_centers[0]-edge_src[0],src_centers[1]-edge_src[1]],[src_centers[0]+edge_src[0],src_centers[1]-edge_src[1]],[src_centers[0]-edge_src[0],src_centers[1]+edge_src[1]],[src_centers[0]+edge_src[0],src_centers[1]+edge_src[1]]]).astype(np.float32)
+ dst_point=np.array([[drv_centers[0]-edge_drv[0],drv_centers[1]-edge_drv[1]],[drv_centers[0]+edge_drv[0],drv_centers[1]-edge_drv[1]],[drv_centers[0]-edge_drv[0],drv_centers[1]+edge_drv[1]],[drv_centers[0]+edge_drv[0],drv_centers[1]+edge_drv[1]]]).astype(np.float32)
+
+ adjusted_driving_video = []
+ adjusted_driving_video_hd = []
+
+ for frame in driving_video:
+ frame_ld = resize(frame, (nm_res, nm_res))
+ frame_hd = resize(frame, (nmd_res, nmd_res))
+ zoomed=cv2.warpAffine(frame_ld, cv2.getAffineTransform(dst_point[:3], src_point[:3]), (nm_res, nm_res))
+ zoomed_hd=cv2.warpAffine(frame_hd, cv2.getAffineTransform(dst_point[:3] * 2, src_point[:3] * 2), (nmd_res, nmd_res))
+ adjusted_driving_video.append(zoomed)
+ adjusted_driving_video_hd.append(zoomed_hd)
+
+ return adjusted_driving_video, adjusted_driving_video_hd
+
+def x_portrait_data_prep(source_image_path, driving_video_path, device, best_frame_id=0, start_idx = 0, num_frames=0, skip=1, output_local=False, more_source_image_pattern="", target_resolution = 512):
+ source_image = imageio.imread(source_image_path)
+ if '.mp4' in driving_video_path:
+ reader = imageio.get_reader(driving_video_path)
+ fps = reader.get_meta_data()['fps']
+ driving_video = []
+ try:
+ for im in reader:
+ driving_video.append(im)
+ except RuntimeError:
+ pass
+ reader.close()
+ else:
+ driving_video = [imageio.imread(driving_video_path)[...,:3]]
+ fps = 1
+
+ nmd_res = target_resolution
+ nm_res = 256
+ source_image_hd = resize(source_image, (nmd_res, nmd_res))[..., :3]
+
+ if more_source_image_pattern:
+ more_source_paths = glob.glob(more_source_image_pattern)
+ more_sources_hd = []
+ for more_source_path in more_source_paths:
+ more_source_image = imageio.imread(more_source_path)
+ more_source_image_hd = resize(more_source_image, (nmd_res, nmd_res))[..., :3]
+ more_source_hd = torch.tensor(more_source_image_hd[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2)
+ more_source_hd = more_source_hd.to(device)
+ more_sources_hd.append(more_source_hd)
+ more_sources_hd = torch.stack(more_sources_hd, dim = 1)
+ else:
+ more_sources_hd = None
+
+ fa = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D, flip_input=True, device='cuda')
+
+ driving_video, driving_video_hd = adjust_driving_video_to_src_image(source_image, driving_video, fa, nm_res, nmd_res, best_frame_id)
+
+ if num_frames == 0:
+ end_idx = len(driving_video)
+ else:
+ num_frames = min(len(driving_video), num_frames)
+ end_idx = start_idx + num_frames * skip
+
+ driving_video = driving_video[start_idx:end_idx][::skip]
+ driving_video_hd = driving_video_hd[start_idx:end_idx][::skip]
+ num_frames = len(driving_video)
+
+ with torch.no_grad():
+ real_source_hd = torch.tensor(source_image_hd[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2)
+ real_source_hd = real_source_hd.to(device)
+
+ driving_hd = torch.tensor(np.array(driving_video_hd).astype(np.float32)).permute(0, 3, 1, 2).to(device)
+
+ local_features = []
+ raw_drivings=[]
+
+ for frame_idx in range(0, num_frames):
+ raw_drivings.append(driving_hd[frame_idx:frame_idx+1] * 2 - 1.)
+ if output_local:
+ local_feature_img = extract_local_feature_from_single_img(driving_hd[frame_idx], fa,target_res=nmd_res)
+ local_features.append(local_feature_img)
+
+
+ batch_data = {}
+ batch_data['fps'] = fps
+ real_source_hd = real_source_hd * 2 - 1
+ batch_data['sources'] = real_source_hd[:, None, :, :, :].repeat([num_frames, 1, 1, 1, 1])
+ if more_sources_hd is not None:
+ more_sources_hd = more_sources_hd * 2 - 1
+ batch_data['more_sources'] = more_sources_hd.repeat([num_frames, 1, 1, 1, 1])
+
+ raw_drivings = torch.stack(raw_drivings, dim = 0)
+ batch_data['conditions'] = raw_drivings
+ if output_local:
+ batch_data['local'] = torch.stack(local_features, dim = 0)
+
+ return batch_data
+
+# You can now use the modified state_dict without the deleted keys
+def load_state_dict(model, ckpt_path, reinit_hint_block=False, strict=True, map_location="cpu"):
+ print(f"Loading model state dict from {ckpt_path} ...")
+ state_dict = torch.load(ckpt_path, map_location=map_location)
+ state_dict = state_dict.get('state_dict', state_dict)
+ if reinit_hint_block:
+ print("Ignoring hint block parameters from checkpoint!")
+ for k in list(state_dict.keys()):
+ if k.startswith("control_model.input_hint_block"):
+ state_dict.pop(k)
+ model.load_state_dict(state_dict, strict=strict)
+ del state_dict
+
+def get_cond_control(args, batch_data, control_type, device, start, end, model=None, batch_size=None, train=True, key=0):
+
+ control_type = copy.deepcopy(control_type)
+ vae_bs = 16
+ if control_type == "appearance_pose_local_mm":
+ src = batch_data['sources'][start:end, key].cuda()
+ c_cat_list = batch_data['conditions'][start:end].cuda()
+ cond_image = []
+ for k in range(0, end-start, vae_bs):
+ cond_image.append(model.get_first_stage_encoding(model.encode_first_stage(src[k:k+vae_bs])))
+ cond_image = torch.concat(cond_image, dim=0)
+ cond_img_cat = cond_image
+ p_local = batch_data['local'][start:end].cuda()
+ print ('Total frames:{}'.format(cond_img_cat.shape))
+ more_cond_imgs = []
+ if 'more_sources' in batch_data:
+ num_additional_cond_imgs = batch_data['more_sources'].shape[1]
+ for i in range(num_additional_cond_imgs):
+ m_cond_img = batch_data['more_sources'][start:end, i]
+ m_cond_img = model.get_first_stage_encoding(model.encode_first_stage(m_cond_img))
+ more_cond_imgs.append([m_cond_img.to(device)])
+
+ return [cond_img_cat.to(device), c_cat_list, p_local, more_cond_imgs]
+ else:
+ raise NotImplementedError(f"cond_type={control_type} not supported!")
+
+def visualize_mm(args, name, batch_data, infer_model, nSample, local_image_dir, num_mix=4, preset_output_name=''):
+ driving_video_name = os.path.basename(batch_data['video_name']).split('.')[0]
+ source_name = os.path.basename(batch_data['source_name']).split('.')[0]
+
+ if not os.path.exists(local_image_dir):
+ os.mkdir(local_image_dir)
+
+ uc_scale = args.uc_scale
+ if preset_output_name:
+ preset_output_name = preset_output_name.split('.')[0]+'.mp4'
+ output_path = f"{local_image_dir}/{preset_output_name}"
+ else:
+ output_path = f"{local_image_dir}/{name}_{args.control_type}_uc{uc_scale}_{source_name}_by_{driving_video_name}_mix{num_mix}.mp4"
+
+ infer_model.eval()
+
+ gene_img_list = []
+
+ _, _, ch, h, w = batch_data['sources'].shape
+
+ vae_bs = 16
+
+ if args.initial_facevid2vid_results:
+ facevid2vid = []
+ facevid2vid_results = VideoReader(args.initial_facevid2vid_results, ctx=cpu(0))
+ for frame_id in range(len(facevid2vid_results)):
+ frame = cv2.resize(facevid2vid_results[frame_id].asnumpy(),(512,512)) / 255
+ facevid2vid.append(torch.from_numpy(frame * 2 - 1).permute(2,0,1))
+ cond = torch.stack(facevid2vid)[:nSample].float().to(args.device)
+ pre_noise=[]
+ for i in range(0, nSample, vae_bs):
+ pre_noise.append(infer_model.get_first_stage_encoding(infer_model.encode_first_stage(cond[i:i+vae_bs])))
+ pre_noise = torch.cat(pre_noise, dim=0)
+ pre_noise = infer_model.q_sample(x_start = pre_noise, t = torch.tensor([999]).to(pre_noise.device))
+ else:
+ cond = batch_data['sources'][:nSample].reshape([-1, ch, h, w])
+ pre_noise=[]
+ for i in range(0, nSample, vae_bs):
+ pre_noise.append(infer_model.get_first_stage_encoding(infer_model.encode_first_stage(cond[i:i+vae_bs])))
+ pre_noise = torch.cat(pre_noise, dim=0)
+ pre_noise = infer_model.q_sample(x_start = pre_noise, t = torch.tensor([999]).to(pre_noise.device))
+
+ text = ["" for _ in range(nSample)]
+
+ all_c_cat = get_cond_control(args, batch_data, args.control_type, args.device, start=0, end=nSample, model=infer_model, train=False)
+ cond_img_cat = [all_c_cat[0]]
+ pose_cond_list = [rearrange(all_c_cat[1], "b f c h w -> (b f) c h w")]
+ local_pose_cond_list = [all_c_cat[2]]
+
+ c_cross = infer_model.get_learned_conditioning(text)[:nSample]
+ uc_cross = infer_model.get_unconditional_conditioning(nSample)
+
+ c = {"c_crossattn": [c_cross], "image_control": cond_img_cat}
+ if "appearance_pose" in args.control_type:
+ c['c_concat'] = pose_cond_list
+ if "appearance_pose_local" in args.control_type:
+ c["local_c_concat"] = local_pose_cond_list
+
+ if len(all_c_cat) > 3 and len(all_c_cat[3]) > 0:
+ c['more_image_control'] = all_c_cat[3]
+
+ if args.control_mode == "controlnet_important":
+ uc = {"c_crossattn": [uc_cross]}
+ else:
+ uc = {"c_crossattn": [uc_cross], "image_control":cond_img_cat}
+
+ if "appearance_pose" in args.control_type:
+ uc['c_concat'] = [torch.zeros_like(pose_cond_list[0])]
+
+ if "appearance_pose_local" in args.control_type:
+ uc["local_c_concat"] = [torch.zeros_like(local_pose_cond_list[0])]
+
+ if len(all_c_cat) > 3 and len(all_c_cat[3]) > 0:
+ uc['more_image_control'] = all_c_cat[3]
+
+ if args.wonoise:
+ c['wonoise'] = True
+ uc['wonoise'] = True
+ else:
+ c['wonoise'] = False
+ uc['wonoise'] = False
+
+ noise = pre_noise.to(c_cross.device)
+
+ with torch.cuda.amp.autocast(enabled=args.use_fp16, dtype=FP16_DTYPE):
+ infer_model.to(args.device)
+ infer_model.eval()
+
+ gene_img, _ = infer_model.sample_log(cond=c,
+ batch_size=args.num_drivings, ddim=True,
+ ddim_steps=args.ddim_steps, eta=args.eta,
+ unconditional_guidance_scale=uc_scale,
+ unconditional_conditioning=uc,
+ inpaint=None,
+ x_T=noise,
+ num_overlap=num_mix,
+ )
+
+ for i in range(0, nSample, vae_bs):
+ gene_img_part = infer_model.decode_first_stage( gene_img[i:i+vae_bs] )
+ gene_img_list.append(gene_img_part.float().clamp(-1, 1).cpu())
+
+ _, c, h, w = gene_img_list[0].shape
+
+ cond_image = batch_data["conditions"].reshape([-1,c,h,w])[:nSample].cpu()
+ l_cond_image = batch_data["local"].reshape([-1,c,h,w])[:nSample].cpu()
+ orig_image = batch_data["sources"][:nSample, 0].cpu()
+
+ output_img = torch.cat(gene_img_list + [cond_image.cpu()]+[l_cond_image.cpu()]+[orig_image.cpu()]).float().clamp(-1,1).add(1).mul(0.5)
+
+ num_cols = 4
+ output_img = output_img.reshape([num_cols, 1, nSample, c, h, w]).permute([1, 0, 2, 3, 4,5])
+
+ output_img = output_img.permute([2, 3, 0, 4, 1, 5]).reshape([-1, c, h, num_cols * w])
+ output_img = torch.permute(output_img, [0, 2, 3, 1])
+
+ output_img = output_img.data.cpu().numpy()
+ output_img = img_as_ubyte(output_img)
+ imageio.mimsave(output_path, output_img[:,:,:512], fps=batch_data['fps'], quality=10, pixelformat='yuv420p', codec='libx264')
+
+def main(args):
+
+ # ******************************
+ # initialize training
+ # ******************************
+ args.world_size = 1
+ args.local_rank = 0
+ args.rank = 0
+ args.device = torch.device("cuda", args.local_rank)
+
+ # set seed for reproducibility
+ set_seed(args.seed)
+
+ # ******************************
+ # create model
+ # ******************************
+ model = create_model(args.model_config).cpu()
+ model.sd_locked = args.sd_locked
+ model.only_mid_control = args.only_mid_control
+ model.to(args.local_rank)
+ if not os.path.exists(args.output_dir):
+ os.makedirs(args.output_dir)
+ if args.local_rank == 0:
+ print('Total base parameters {:.02f}M'.format(count_param([model])))
+ if args.ema_rate is not None and args.ema_rate > 0 and args.rank == 0:
+ print(f"Creating EMA model at ema_rate={args.ema_rate}")
+ model_ema = EMA(model, beta=args.ema_rate, update_after_step=0, update_every=1)
+ else:
+ model_ema = None
+
+ # ******************************
+ # load pre-trained models
+ # ******************************
+ if args.resume_dir is not None:
+ if args.local_rank == 0:
+ load_state_dict(model, args.resume_dir, strict=False)
+ else:
+ print('please privide the correct resume_dir!')
+ exit()
+
+ # ******************************
+ # create DDP model
+ # ******************************
+ if args.compile and TORCH_VERSION == "2":
+ model = torch.compile(model)
+
+ torch.cuda.set_device(args.local_rank)
+ print_peak_memory("Max memory allocated after creating DDP", args.local_rank)
+ infer_model = model.module if hasattr(model, "module") else model
+
+ with torch.no_grad():
+ driving_videos = glob.glob(args.driving_video)
+ for driving_video in driving_videos:
+ print ('working on {}'.format(os.path.basename(driving_video)))
+ infer_batch_data = x_portrait_data_prep(args.source_image, driving_video, args.device, args.best_frame, start_idx = args.start_idx, num_frames = args.out_frames, skip=args.skip, output_local=True)
+ infer_batch_data['video_name'] = os.path.basename(driving_video)
+ infer_batch_data['source_name'] = args.source_image
+ nSample = infer_batch_data['sources'].shape[0]
+ visualize_mm(args, "inference", infer_batch_data, infer_model, nSample=nSample, local_image_dir=args.output_dir, num_mix=args.num_mix)
+
+
+if __name__ == "__main__":
+
+ str2bool = lambda arg: bool(int(arg))
+ parser = argparse.ArgumentParser(description='Control Net training')
+ ## Model
+ parser.add_argument('--model_config', type=str, default="model_lib/ControlNet/models/cldm_v15_video_appearance.yaml",
+ help="The path of model config file")
+ parser.add_argument('--reinit_hint_block', action='store_true', default=False,
+ help="Re-initialize hint blocks for channel mis-match")
+ parser.add_argument('--sd_locked', type =str2bool, default=True,
+ help='Freeze parameters in original stable-diffusion decoder')
+ parser.add_argument('--only_mid_control', type =str2bool, default=False,
+ help='Only control middle blocks')
+ parser.add_argument('--control_type', type=str, default="appearance_pose_local_mm",
+ help='The type of conditioning')
+ parser.add_argument("--control_mode", type=str, default="controlnet_important",
+ help="Set controlnet is more important or balance.")
+ parser.add_argument('--wonoise', action='store_false', default=True,
+ help='Use with referenceonly, remove adding noise on reference image')
+
+ ## Training
+ parser.add_argument("--local_rank", type=int, default=0)
+ parser.add_argument("--world_size", type=int, default=1)
+ parser.add_argument('--seed', type=int, default=42,
+ help='random seed for initialization')
+ parser.add_argument('--use_fp16', action='store_false', default=True,
+ help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit')
+ parser.add_argument('--compile', type=str2bool, default=False,
+ help='compile model (for torch 2)')
+ parser.add_argument('--eta', type = float, default = 0.0,
+ help='eta during DDIM Sampling')
+ parser.add_argument('--ema_rate', type = float, default = 0,
+ help='rate for ema')
+ ## inference
+ parser.add_argument("--initial_facevid2vid_results", type=str, default=None,
+ help="facevid2vid results for noise initialization")
+ parser.add_argument('--ddim_steps', type = int, default = 1,
+ help='denoising steps')
+ parser.add_argument('--uc_scale', type = int, default = 5,
+ help='cfg')
+ parser.add_argument("--num_drivings", type = int, default = 16,
+ help="Number of driving images in a single sequence of video.")
+ parser.add_argument("--output_dir", type=str, default=None, required=True,
+ help="The output directory where the model predictions and checkpoints will be written.")
+ parser.add_argument("--resume_dir", type=str, default=None,
+ help="The resume directory where the model checkpoints will be loaded.")
+ parser.add_argument("--source_image", type=str, default="",
+ help="The source image for neural motion.")
+ parser.add_argument("--more_source_image_pattern", type=str, default="",
+ help="The source image for neural motion.")
+ parser.add_argument("--driving_video", type=str, default="",
+ help="The source image mask for neural motion.")
+ parser.add_argument('--best_frame', type=int, default=0,
+ help='best matching frame index')
+ parser.add_argument('--start_idx', type=int, default=0,
+ help='starting frame index')
+ parser.add_argument('--skip', type=int, default=1,
+ help='skip frame')
+ parser.add_argument('--num_mix', type=int, default=4,
+ help='num overlapping frames')
+ parser.add_argument('--out_frames', type=int, default=0,
+ help='num frames')
+ args = parser.parse_args()
+
+ main(args)
+
\ No newline at end of file
diff --git a/env_install.sh b/env_install.sh
new file mode 100644
index 0000000000000000000000000000000000000000..2d039ffe92bf396edd814a617605554a6643fc64
--- /dev/null
+++ b/env_install.sh
@@ -0,0 +1,2 @@
+pip install -r requirements.txt
+sudo apt install python3-tk
\ No newline at end of file
diff --git a/model_lib/ControlNet/cldm/__pycache__/cldm.cpython-39.pyc b/model_lib/ControlNet/cldm/__pycache__/cldm.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..2f54f837d9e586d3f32211c5f7bf2582e9a4deef
Binary files /dev/null and b/model_lib/ControlNet/cldm/__pycache__/cldm.cpython-39.pyc differ
diff --git a/model_lib/ControlNet/cldm/__pycache__/model.cpython-39.pyc b/model_lib/ControlNet/cldm/__pycache__/model.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b4665b7b02d2e7f9fed88477d837f941a72e7f34
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diff --git a/model_lib/ControlNet/cldm/cldm.py b/model_lib/ControlNet/cldm/cldm.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b2b13aee78bbc8fd3523c589785ce35d902bade
--- /dev/null
+++ b/model_lib/ControlNet/cldm/cldm.py
@@ -0,0 +1,715 @@
+from re import I
+import torch
+import torch as th
+import torch.nn as nn
+from model_lib.ControlNet.ldm.modules.diffusionmodules.util import (
+ conv_nd,
+ linear,
+ zero_module,
+ timestep_embedding,
+)
+
+from model_lib.ControlNet.ldm.modules.attention import SpatialTransformer
+from model_lib.ControlNet.ldm.modules.diffusionmodules.openaimodel import TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock,Upsample, UNetModel_Temporal
+from model_lib.ControlNet.ldm.models.diffusion.ddpm import LatentDiffusionReferenceOnly
+from model_lib.ControlNet.ldm.util import exists, instantiate_from_config
+
+## TODO: here UNet
+class ControlledUnetModelAttn_Temporal_Pose_Local(UNetModel_Temporal):
+ def forward(self, x, timesteps=None, context=None, control=None, pose_control=None,local_pose_control=None,only_mid_control=False, attention_mode=None,uc=False, **kwargs):
+ hs = []
+ bank_attn = control
+ attn_index = 0
+
+ t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+ emb = self.time_embed(t_emb)
+ h = x.type(self.dtype)
+ num_input_motion_module = 0
+ if uc:
+ for i, module in enumerate(self.input_blocks):
+ if i in [1,2,4,5,7,8,10,11]:
+ motion_module = self.input_blocks_motion_module[num_input_motion_module]
+ h = module(h, emb, context,uc=uc) # Attn here
+ h = motion_module(h, emb, context)
+ num_input_motion_module += 1
+ else:
+ h = module(h, emb, context,uc=uc) # Attn here
+ hs.append(h)
+
+ h = self.middle_block(h, emb, context,uc=uc) # Attn here
+
+ for i, module in enumerate(self.output_blocks):
+ output_block_motion_module = self.output_blocks_motion_module[i]
+ if only_mid_control:
+ h = torch.cat([h, hs.pop()], dim=1)
+ h = module(h, emb, context,uc=uc)
+ else:
+ h = torch.cat([h, hs.pop()], dim=1)
+ h = module(h, emb, context,uc=uc) # Attn here
+ h = output_block_motion_module(h, emb, context)
+
+ else:
+ num_input_motion_module = 0
+ for i, module in enumerate(self.input_blocks):
+ if i in [1,2,4,5,7,8,10,11]:
+ motion_module = self.input_blocks_motion_module[num_input_motion_module]
+ h, attn_index = module(h, emb, context, bank_attn, attention_mode, attn_index)
+ h = motion_module(h, emb, context)
+ num_input_motion_module += 1
+ else:
+ h, attn_index = module(h, emb, context, bank_attn, attention_mode, attn_index) # Attn here
+ hs.append(h)
+
+ h, attn_index = self.middle_block(h, emb, context, bank_attn, attention_mode, attn_index) # Attn here
+
+ amplify_f = 1.
+
+ if pose_control is not None:
+ h += pose_control.pop() * amplify_f
+
+ if local_pose_control is not None:
+ h += local_pose_control.pop() * amplify_f
+
+ for i, module in enumerate(self.output_blocks):
+ output_block_motion_module = self.output_blocks_motion_module[i]
+ if only_mid_control or (bank_attn is None):
+ h = torch.cat([h, hs.pop()], dim=1)
+ h = module(h, emb, context)
+ else:
+ if pose_control is not None and local_pose_control is not None:
+ h = torch.cat([h, hs.pop() + pose_control.pop() * amplify_f + local_pose_control.pop() * amplify_f], dim=1)
+ elif pose_control is not None:
+ h = torch.cat([h, hs.pop() + pose_control.pop() * amplify_f], dim=1)
+ elif local_pose_control is not None:
+ h = torch.cat([h, hs.pop() + local_pose_control.pop() * amplify_f], dim=1)
+ else:
+ h = torch.cat([h, hs.pop()], dim=1)
+
+ h, attn_index = module(h, emb, context, bank_attn, attention_mode, attn_index) # Attn here
+ h = output_block_motion_module(h, emb, context)
+
+ h = h.type(x.dtype)
+ return self.out(h)
+
+
+## ControlNet Reference Only-Like Attention
+class ControlNetReferenceOnly(nn.Module):
+ def __init__(
+ self,
+ image_size,
+ in_channels,
+ model_channels,
+ hint_channels,
+ out_channels,
+ num_res_blocks,
+ attention_resolutions,
+ dropout=0,
+ channel_mult=(1, 2, 4, 8),
+ conv_resample=True,
+ dims=2,
+ use_checkpoint=False,
+ use_fp16=False,
+ num_heads=-1,
+ num_head_channels=-1,
+ num_heads_upsample=-1,
+ use_scale_shift_norm=False,
+ resblock_updown=False,
+ use_new_attention_order=False,
+ use_spatial_transformer=False, # custom transformer support
+ transformer_depth=1, # custom transformer support
+ context_dim=None, # custom transformer support
+ n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
+ legacy=True,
+ disable_self_attentions=None,
+ num_attention_blocks=None,
+ disable_middle_self_attn=False,
+ use_linear_in_transformer=False,
+ ):
+ super().__init__()
+ if use_spatial_transformer:
+ assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
+
+ if context_dim is not None:
+ assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
+ from omegaconf.listconfig import ListConfig
+ if type(context_dim) == ListConfig:
+ context_dim = list(context_dim)
+
+ if num_heads_upsample == -1:
+ num_heads_upsample = num_heads
+
+ if num_heads == -1:
+ assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
+
+ if num_head_channels == -1:
+ assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
+
+ self.dims = dims
+ self.image_size = image_size
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.model_channels = model_channels
+ if isinstance(num_res_blocks, int):
+ self.num_res_blocks = len(channel_mult) * [num_res_blocks]
+ else:
+ if len(num_res_blocks) != len(channel_mult):
+ raise ValueError("provide num_res_blocks either as an int (globally constant) or "
+ "as a list/tuple (per-level) with the same length as channel_mult")
+ self.num_res_blocks = num_res_blocks
+ if disable_self_attentions is not None:
+ # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
+ assert len(disable_self_attentions) == len(channel_mult)
+ if num_attention_blocks is not None:
+ assert len(num_attention_blocks) == len(self.num_res_blocks)
+ assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
+ print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
+ f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
+ f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
+ f"attention will still not be set.")
+
+ self.attention_resolutions = attention_resolutions
+ self.dropout = dropout
+ self.channel_mult = channel_mult
+ self.conv_resample = conv_resample
+ self.use_checkpoint = use_checkpoint
+ self.dtype = th.float16 if use_fp16 else th.float32
+ self.num_heads = num_heads
+ self.num_head_channels = num_head_channels
+ self.num_heads_upsample = num_heads_upsample
+ self.predict_codebook_ids = n_embed is not None
+
+ time_embed_dim = model_channels * 4
+ self.time_embed = nn.Sequential(
+ linear(model_channels, time_embed_dim),
+ nn.SiLU(),
+ linear(time_embed_dim, time_embed_dim),
+ )
+
+ self.input_blocks = nn.ModuleList(
+ [
+ TimestepEmbedSequential(
+ conv_nd(dims, in_channels, model_channels, 3, padding=1)
+ )
+ ]
+ )
+
+ self.input_hint_block = TimestepEmbedSequential(
+ conv_nd(dims, hint_channels, 16, 3, padding=1),
+ nn.SiLU(),
+ conv_nd(dims, 16, 16, 3, padding=1),
+ nn.SiLU(),
+ conv_nd(dims, 16, 32, 3, padding=1, stride=2),
+ nn.SiLU(),
+ conv_nd(dims, 32, 32, 3, padding=1),
+ nn.SiLU(),
+ conv_nd(dims, 32, 96, 3, padding=1, stride=2),
+ nn.SiLU(),
+ conv_nd(dims, 96, 96, 3, padding=1),
+ nn.SiLU(),
+ conv_nd(dims, 96, 256, 3, padding=1, stride=2),
+ nn.SiLU(),
+ zero_module(conv_nd(dims, 256, model_channels, 3, padding=1))
+ )
+
+ self._feature_size = model_channels
+ input_block_chans = [model_channels]
+ ch = model_channels
+ ds = 1
+ for level, mult in enumerate(channel_mult):
+ for nr in range(self.num_res_blocks[level]):
+ layers = [
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels=mult * model_channels,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ )
+ ]
+ ch = mult * model_channels
+ if ds in attention_resolutions:
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ if exists(disable_self_attentions):
+ disabled_sa = disable_self_attentions[level]
+ else:
+ disabled_sa = False
+
+ if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
+ layers.append(
+ AttentionBlock(
+ ch,
+ use_checkpoint=use_checkpoint,
+ num_heads=num_heads,
+ num_head_channels=dim_head,
+ use_new_attention_order=use_new_attention_order,
+ ) if not use_spatial_transformer else SpatialTransformer(
+ ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+ disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
+ use_checkpoint=use_checkpoint
+ )
+ )
+ self.input_blocks.append(TimestepEmbedSequential(*layers))
+ self._feature_size += ch
+ input_block_chans.append(ch)
+ if level != len(channel_mult) - 1:
+ out_ch = ch
+ self.input_blocks.append(
+ TimestepEmbedSequential(
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels=out_ch,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ down=True,
+ )
+ if resblock_updown
+ else Downsample(
+ ch, conv_resample, dims=dims, out_channels=out_ch
+ )
+ )
+ )
+ ch = out_ch
+ input_block_chans.append(ch)
+ ds *= 2
+ self._feature_size += ch
+
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ self.middle_block = TimestepEmbedSequential(
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ ),
+ AttentionBlock(
+ ch,
+ use_checkpoint=use_checkpoint,
+ num_heads=num_heads,
+ num_head_channels=dim_head,
+ use_new_attention_order=use_new_attention_order,
+ ) if not use_spatial_transformer else SpatialTransformer( # always uses a self-attn
+ ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+ disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
+ use_checkpoint=use_checkpoint
+ ),
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ ),
+ )
+ self._feature_size += ch
+
+
+ self.output_blocks = nn.ModuleList([])
+ for level, mult in list(enumerate(channel_mult))[::-1]:
+ for i in range(self.num_res_blocks[level] + 1):
+ ich = input_block_chans.pop()
+ layers = [
+ ResBlock(
+ ch + ich,
+ time_embed_dim,
+ dropout,
+ out_channels=model_channels * mult,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ )
+ ]
+ ch = model_channels * mult
+ if ds in attention_resolutions:
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ if exists(disable_self_attentions):
+ disabled_sa = disable_self_attentions[level]
+ else:
+ disabled_sa = False
+
+ if not exists(num_attention_blocks) or i < num_attention_blocks[level]:
+ layers.append(
+ AttentionBlock(
+ ch,
+ use_checkpoint=use_checkpoint,
+ num_heads=num_heads_upsample,
+ num_head_channels=dim_head,
+ use_new_attention_order=use_new_attention_order,
+ ) if not use_spatial_transformer else SpatialTransformer(
+ ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+ disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
+ use_checkpoint=use_checkpoint
+ )
+ )
+ if level and i == self.num_res_blocks[level]:
+ out_ch = ch
+ layers.append(
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels=out_ch,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ up=True,
+ )
+ if resblock_updown
+ else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
+ )
+ ds //= 2
+ self.output_blocks.append(TimestepEmbedSequential(*layers))
+ self._feature_size += ch
+
+
+ def forward(self, x, hint, timesteps, context, attention_bank=None, attention_mode=None,uc=False, **kwargs):
+ hs = []
+ t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+ emb = self.time_embed(t_emb)
+ banks = attention_bank
+ outs = []
+ h = x.type(self.dtype)
+ for module in self.input_blocks:
+ h = module(h, emb, context, banks, attention_mode,uc)
+ hs.append(h)
+
+ h = self.middle_block(h, emb, context, banks, attention_mode,uc)
+
+ for module in self.output_blocks:
+ h = th.cat([h, hs.pop()], dim=1)
+ h = module(h, emb, context, banks, attention_mode,uc)
+
+ return outs
+
+### ControlNet Origin
+class ControlNet(nn.Module):
+ def __init__(
+ self,
+ image_size,
+ in_channels,
+ model_channels,
+ hint_channels,
+ num_res_blocks,
+ attention_resolutions,
+ dropout=0,
+ channel_mult=(1, 2, 4, 8),
+ conv_resample=True,
+ dims=2,
+ use_checkpoint=False,
+ use_fp16=False,
+ num_heads=-1,
+ num_head_channels=-1,
+ num_heads_upsample=-1,
+ use_scale_shift_norm=False,
+ resblock_updown=False,
+ use_new_attention_order=False,
+ use_spatial_transformer=False, # custom transformer support
+ transformer_depth=1, # custom transformer support
+ context_dim=None, # custom transformer support
+ n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
+ legacy=True,
+ disable_self_attentions=None,
+ num_attention_blocks=None,
+ disable_middle_self_attn=False,
+ use_linear_in_transformer=False,
+ ):
+ super().__init__()
+ if use_spatial_transformer:
+ assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
+
+ if context_dim is not None:
+ assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
+ from omegaconf.listconfig import ListConfig
+ if type(context_dim) == ListConfig:
+ context_dim = list(context_dim)
+
+ if num_heads_upsample == -1:
+ num_heads_upsample = num_heads
+
+ if num_heads == -1:
+ assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
+
+ if num_head_channels == -1:
+ assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
+
+ self.dims = dims
+ self.image_size = image_size
+ self.in_channels = in_channels
+ self.model_channels = model_channels
+ if isinstance(num_res_blocks, int):
+ self.num_res_blocks = len(channel_mult) * [num_res_blocks]
+ else:
+ if len(num_res_blocks) != len(channel_mult):
+ raise ValueError("provide num_res_blocks either as an int (globally constant) or "
+ "as a list/tuple (per-level) with the same length as channel_mult")
+ self.num_res_blocks = num_res_blocks
+ if disable_self_attentions is not None:
+ # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
+ assert len(disable_self_attentions) == len(channel_mult)
+ if num_attention_blocks is not None:
+ assert len(num_attention_blocks) == len(self.num_res_blocks)
+ assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
+ print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
+ f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
+ f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
+ f"attention will still not be set.")
+
+ self.attention_resolutions = attention_resolutions
+ self.dropout = dropout
+ self.channel_mult = channel_mult
+ self.conv_resample = conv_resample
+ self.use_checkpoint = use_checkpoint
+ self.dtype = th.float16 if use_fp16 else th.float32
+ self.num_heads = num_heads
+ self.num_head_channels = num_head_channels
+ self.num_heads_upsample = num_heads_upsample
+ self.predict_codebook_ids = n_embed is not None
+
+ time_embed_dim = model_channels * 4
+ self.time_embed = nn.Sequential(
+ linear(model_channels, time_embed_dim),
+ nn.SiLU(),
+ linear(time_embed_dim, time_embed_dim),
+ )
+
+ self.input_blocks = nn.ModuleList(
+ [
+ TimestepEmbedSequential(
+ conv_nd(dims, in_channels, model_channels, 3, padding=1)
+ )
+ ]
+ )
+ self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels)])
+
+ self.input_hint_block = TimestepEmbedSequential(
+ conv_nd(dims, hint_channels, 16, 3, padding=1),
+ nn.SiLU(),
+ conv_nd(dims, 16, 16, 3, padding=1),
+ nn.SiLU(),
+ conv_nd(dims, 16, 32, 3, padding=1, stride=2),
+ nn.SiLU(),
+ conv_nd(dims, 32, 32, 3, padding=1),
+ nn.SiLU(),
+ conv_nd(dims, 32, 96, 3, padding=1, stride=2),
+ nn.SiLU(),
+ conv_nd(dims, 96, 96, 3, padding=1),
+ nn.SiLU(),
+ conv_nd(dims, 96, 256, 3, padding=1, stride=2),
+ nn.SiLU(),
+ zero_module(conv_nd(dims, 256, model_channels, 3, padding=1))
+ )
+
+ self._feature_size = model_channels
+ input_block_chans = [model_channels]
+ ch = model_channels
+ ds = 1
+ for level, mult in enumerate(channel_mult):
+ for nr in range(self.num_res_blocks[level]):
+ layers = [
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels=mult * model_channels,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ )
+ ]
+ ch = mult * model_channels
+ if ds in attention_resolutions:
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ if exists(disable_self_attentions):
+ disabled_sa = disable_self_attentions[level]
+ else:
+ disabled_sa = False
+
+ if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
+ layers.append(
+ AttentionBlock(
+ ch,
+ use_checkpoint=use_checkpoint,
+ num_heads=num_heads,
+ num_head_channels=dim_head,
+ use_new_attention_order=use_new_attention_order,
+ ) if not use_spatial_transformer else SpatialTransformer(
+ ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+ disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
+ use_checkpoint=use_checkpoint
+ )
+ )
+ self.input_blocks.append(TimestepEmbedSequential(*layers))
+ self.zero_convs.append(self.make_zero_conv(ch))
+ self._feature_size += ch
+ input_block_chans.append(ch)
+ if level != len(channel_mult) - 1:
+ out_ch = ch
+ self.input_blocks.append(
+ TimestepEmbedSequential(
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels=out_ch,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ down=True,
+ )
+ if resblock_updown
+ else Downsample(
+ ch, conv_resample, dims=dims, out_channels=out_ch
+ )
+ )
+ )
+ ch = out_ch
+ input_block_chans.append(ch)
+ self.zero_convs.append(self.make_zero_conv(ch))
+ ds *= 2
+ self._feature_size += ch
+
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ self.middle_block = TimestepEmbedSequential(
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ ),
+ AttentionBlock(
+ ch,
+ use_checkpoint=use_checkpoint,
+ num_heads=num_heads,
+ num_head_channels=dim_head,
+ use_new_attention_order=use_new_attention_order,
+ ) if not use_spatial_transformer else SpatialTransformer( # always uses a self-attn
+ ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+ disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
+ use_checkpoint=use_checkpoint
+ ),
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ ),
+ )
+ self.middle_block_out = self.make_zero_conv(ch)
+ self._feature_size += ch
+
+ def make_zero_conv(self, channels):
+ return TimestepEmbedSequential(zero_module(conv_nd(self.dims, channels, channels, 1, padding=0)))
+
+ def forward(self, x, hint, timesteps, context, **kwargs):
+ t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+ emb = self.time_embed(t_emb)
+
+ guided_hint = self.input_hint_block(hint, emb, context)
+
+ outs = []
+ h = x.type(self.dtype)
+
+ for module, zero_conv in zip(self.input_blocks, self.zero_convs):
+ if guided_hint is not None:
+ h = module(h, emb, context)
+ h += guided_hint
+ guided_hint = None
+ else:
+ h = module(h, emb, context)
+ outs.append(zero_conv(h, emb, context))
+
+ h = self.middle_block(h, emb, context)
+ outs.append(self.middle_block_out(h, emb, context))
+
+ return outs
+
+class ControlLDMReferenceOnly_Temporal_Pose_Local(LatentDiffusionReferenceOnly):
+
+ def __init__(self, control_key, only_mid_control,appearance_control_stage_config, pose_control_stage_config, local_pose_control_stage_config, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ print(args)
+ print(kwargs)
+ self.control_key = control_key
+ self.only_mid_control = only_mid_control
+ self.control_enabled = True
+ self.appearance_control_model = instantiate_from_config(appearance_control_stage_config)
+ self.pose_control_model = instantiate_from_config(pose_control_stage_config)
+ self.local_pose_control_model = instantiate_from_config(local_pose_control_stage_config)
+
+ def apply_model(self, x_noisy, t, cond, reference_image_noisy, more_reference_image_noisy=[], uc=False,*args, **kwargs):
+ assert isinstance(cond, dict)
+ diffusion_model = self.model.diffusion_model
+ cond_txt = torch.cat(cond['c_crossattn'], 1)
+ if self.control_enabled and 'c_crossattn_void' in cond and cond['c_crossattn_void'] is not None:
+ cond_txt_void = torch.cat(cond['c_crossattn_void'], 1)
+ else:
+ cond_txt_void = cond_txt
+ attention_bank = []
+
+ if reference_image_noisy is not None:
+ empty_outs = self.appearance_control_model(x=reference_image_noisy, hint=None, timesteps=t, context=cond_txt_void, attention_bank=attention_bank, attention_mode='write',uc=uc)
+ for m_reference_image_noisy in more_reference_image_noisy:
+ l_attention_bank = []
+ empty_outs = self.appearance_control_model(x=m_reference_image_noisy, hint=None, timesteps=t, context=cond_txt_void, attention_bank=l_attention_bank, attention_mode='write',uc=uc)
+ for j in range(len(attention_bank)):
+ for k in range(len(attention_bank[j])):
+ attention_bank[j][k] = torch.concat([attention_bank[j][k], l_attention_bank[j][k]], dim=1)
+
+ if not uc:
+ if self.control_enabled and 'c_concat' in cond and cond['c_concat'] is not None:
+ cond_hint = torch.cat(cond['c_concat'], 1)
+ pose_control = self.pose_control_model(x=x_noisy, hint=cond_hint, timesteps=t, context=cond_txt_void)
+
+ if self.control_enabled and 'local_c_concat' in cond and cond['local_c_concat'] is not None:
+ cond_hint = torch.cat(cond['local_c_concat'], 1)
+ local_pose_control = self.local_pose_control_model(x=x_noisy, hint=cond_hint, timesteps=t, context=cond_txt_void)
+ else:
+ pose_control = None
+ local_pose_control = None
+ eps = diffusion_model(x=x_noisy, timesteps=t, context=cond_txt, control=attention_bank, pose_control=pose_control, local_pose_control=local_pose_control, only_mid_control=self.only_mid_control, attention_mode='read',uc=uc)
+ return eps
+
+ @torch.no_grad()
+ def get_unconditional_conditioning(self, N):
+ return self.get_learned_conditioning([""] * N)
diff --git a/model_lib/ControlNet/cldm/model.py b/model_lib/ControlNet/cldm/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1ea9c6e80c10a2b7c04c5d244dd44b0fd5c1d94
--- /dev/null
+++ b/model_lib/ControlNet/cldm/model.py
@@ -0,0 +1,28 @@
+import os
+import torch
+
+from omegaconf import OmegaConf
+from model_lib.ControlNet.ldm.util import instantiate_from_config
+
+
+def get_state_dict(d):
+ return d.get('state_dict', d)
+
+
+def load_state_dict(ckpt_path, location='cpu'):
+ _, extension = os.path.splitext(ckpt_path)
+ if extension.lower() == ".safetensors":
+ import safetensors.torch
+ state_dict = safetensors.torch.load_file(ckpt_path, device=location)
+ else:
+ state_dict = get_state_dict(torch.load(ckpt_path, map_location=torch.device(location)))
+ state_dict = get_state_dict(state_dict)
+ print(f'Loaded state_dict from [{ckpt_path}]')
+ return state_dict
+
+
+def create_model(config_path):
+ config = OmegaConf.load(config_path)
+ model = instantiate_from_config(config.model).cpu()
+ print(f'Loaded model config from [{config_path}]')
+ return model
diff --git a/model_lib/ControlNet/ldm/__pycache__/util.cpython-39.pyc b/model_lib/ControlNet/ldm/__pycache__/util.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8610ab88d0112efa78a0006cb82664bf81009559
Binary files /dev/null and b/model_lib/ControlNet/ldm/__pycache__/util.cpython-39.pyc differ
diff --git a/model_lib/ControlNet/ldm/data/__init__.py b/model_lib/ControlNet/ldm/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/model_lib/ControlNet/ldm/data/util.py b/model_lib/ControlNet/ldm/data/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..d121f8fa530233fcaa4c3967c6c6e4b30abcfd46
--- /dev/null
+++ b/model_lib/ControlNet/ldm/data/util.py
@@ -0,0 +1,24 @@
+import torch
+
+from model_lib.ControlNet.ldm.modules.midas.api import load_midas_transform
+
+
+class AddMiDaS(object):
+ def __init__(self, model_type):
+ super().__init__()
+ self.transform = load_midas_transform(model_type)
+
+ def pt2np(self, x):
+ x = ((x + 1.0) * .5).detach().cpu().numpy()
+ return x
+
+ def np2pt(self, x):
+ x = torch.from_numpy(x) * 2 - 1.
+ return x
+
+ def __call__(self, sample):
+ # sample['jpg'] is tensor hwc in [-1, 1] at this point
+ x = self.pt2np(sample['jpg'])
+ x = self.transform({"image": x})["image"]
+ sample['midas_in'] = x
+ return sample
\ No newline at end of file
diff --git a/model_lib/ControlNet/ldm/models/__pycache__/autoencoder.cpython-39.pyc b/model_lib/ControlNet/ldm/models/__pycache__/autoencoder.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5bb2e707dbb6ab94e19073af17f01956ef3b8924
Binary files /dev/null and b/model_lib/ControlNet/ldm/models/__pycache__/autoencoder.cpython-39.pyc differ
diff --git a/model_lib/ControlNet/ldm/models/autoencoder.py b/model_lib/ControlNet/ldm/models/autoencoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..b14b864f746620fbab071aebe9ee75150bcc9b64
--- /dev/null
+++ b/model_lib/ControlNet/ldm/models/autoencoder.py
@@ -0,0 +1,219 @@
+import torch
+import pytorch_lightning as pl
+import torch.nn.functional as F
+from contextlib import contextmanager
+
+from model_lib.ControlNet.ldm.modules.diffusionmodules.model import Encoder, Decoder
+from model_lib.ControlNet.ldm.modules.distributions.distributions import DiagonalGaussianDistribution
+
+from model_lib.ControlNet.ldm.util import instantiate_from_config
+from model_lib.ControlNet.ldm.modules.ema import LitEma
+
+
+class AutoencoderKL(pl.LightningModule):
+ def __init__(self,
+ ddconfig,
+ lossconfig,
+ embed_dim,
+ ckpt_path=None,
+ ignore_keys=[],
+ image_key="image",
+ colorize_nlabels=None,
+ monitor=None,
+ ema_decay=None,
+ learn_logvar=False
+ ):
+ super().__init__()
+ self.learn_logvar = learn_logvar
+ self.image_key = image_key
+ self.encoder = Encoder(**ddconfig)
+ self.decoder = Decoder(**ddconfig)
+ self.loss = instantiate_from_config(lossconfig)
+ assert ddconfig["double_z"]
+ self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
+ self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+ self.embed_dim = embed_dim
+ if colorize_nlabels is not None:
+ assert type(colorize_nlabels)==int
+ self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
+ if monitor is not None:
+ self.monitor = monitor
+
+ self.use_ema = ema_decay is not None
+ if self.use_ema:
+ self.ema_decay = ema_decay
+ assert 0. < ema_decay < 1.
+ self.model_ema = LitEma(self, decay=ema_decay)
+ print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+ if ckpt_path is not None:
+ self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+
+ def init_from_ckpt(self, path, ignore_keys=list()):
+ sd = torch.load(path, map_location="cpu")["state_dict"]
+ keys = list(sd.keys())
+ for k in keys:
+ for ik in ignore_keys:
+ if k.startswith(ik):
+ print("Deleting key {} from state_dict.".format(k))
+ del sd[k]
+ self.load_state_dict(sd, strict=False)
+ print(f"Restored from {path}")
+
+ @contextmanager
+ def ema_scope(self, context=None):
+ if self.use_ema:
+ self.model_ema.store(self.parameters())
+ self.model_ema.copy_to(self)
+ if context is not None:
+ print(f"{context}: Switched to EMA weights")
+ try:
+ yield None
+ finally:
+ if self.use_ema:
+ self.model_ema.restore(self.parameters())
+ if context is not None:
+ print(f"{context}: Restored training weights")
+
+ def on_train_batch_end(self, *args, **kwargs):
+ if self.use_ema:
+ self.model_ema(self)
+
+ def encode(self, x):
+ h = self.encoder(x)
+ moments = self.quant_conv(h)
+ posterior = DiagonalGaussianDistribution(moments)
+ return posterior
+
+ def decode(self, z):
+ z = self.post_quant_conv(z)
+ dec = self.decoder(z)
+ return dec
+
+ def forward(self, input, sample_posterior=True):
+ posterior = self.encode(input)
+ if sample_posterior:
+ z = posterior.sample()
+ else:
+ z = posterior.mode()
+ dec = self.decode(z)
+ return dec, posterior
+
+ def get_input(self, batch, k):
+ x = batch[k]
+ if len(x.shape) == 3:
+ x = x[..., None]
+ x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
+ return x
+
+ def training_step(self, batch, batch_idx, optimizer_idx):
+ inputs = self.get_input(batch, self.image_key)
+ reconstructions, posterior = self(inputs)
+
+ if optimizer_idx == 0:
+ # train encoder+decoder+logvar
+ aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
+ last_layer=self.get_last_layer(), split="train")
+ self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+ self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
+ return aeloss
+
+ if optimizer_idx == 1:
+ # train the discriminator
+ discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
+ last_layer=self.get_last_layer(), split="train")
+
+ self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+ self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
+ return discloss
+
+ def validation_step(self, batch, batch_idx):
+ log_dict = self._validation_step(batch, batch_idx)
+ with self.ema_scope():
+ log_dict_ema = self._validation_step(batch, batch_idx, postfix="_ema")
+ return log_dict
+
+ def _validation_step(self, batch, batch_idx, postfix=""):
+ inputs = self.get_input(batch, self.image_key)
+ reconstructions, posterior = self(inputs)
+ aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
+ last_layer=self.get_last_layer(), split="val"+postfix)
+
+ discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
+ last_layer=self.get_last_layer(), split="val"+postfix)
+
+ self.log(f"val{postfix}/rec_loss", log_dict_ae[f"val{postfix}/rec_loss"])
+ self.log_dict(log_dict_ae)
+ self.log_dict(log_dict_disc)
+ return self.log_dict
+
+ def configure_optimizers(self):
+ lr = self.learning_rate
+ ae_params_list = list(self.encoder.parameters()) + list(self.decoder.parameters()) + list(
+ self.quant_conv.parameters()) + list(self.post_quant_conv.parameters())
+ if self.learn_logvar:
+ print(f"{self.__class__.__name__}: Learning logvar")
+ ae_params_list.append(self.loss.logvar)
+ opt_ae = torch.optim.Adam(ae_params_list,
+ lr=lr, betas=(0.5, 0.9))
+ opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
+ lr=lr, betas=(0.5, 0.9))
+ return [opt_ae, opt_disc], []
+
+ def get_last_layer(self):
+ return self.decoder.conv_out.weight
+
+ @torch.no_grad()
+ def log_images(self, batch, only_inputs=False, log_ema=False, **kwargs):
+ log = dict()
+ x = self.get_input(batch, self.image_key)
+ x = x.to(self.device)
+ if not only_inputs:
+ xrec, posterior = self(x)
+ if x.shape[1] > 3:
+ # colorize with random projection
+ assert xrec.shape[1] > 3
+ x = self.to_rgb(x)
+ xrec = self.to_rgb(xrec)
+ log["samples"] = self.decode(torch.randn_like(posterior.sample()))
+ log["reconstructions"] = xrec
+ if log_ema or self.use_ema:
+ with self.ema_scope():
+ xrec_ema, posterior_ema = self(x)
+ if x.shape[1] > 3:
+ # colorize with random projection
+ assert xrec_ema.shape[1] > 3
+ xrec_ema = self.to_rgb(xrec_ema)
+ log["samples_ema"] = self.decode(torch.randn_like(posterior_ema.sample()))
+ log["reconstructions_ema"] = xrec_ema
+ log["inputs"] = x
+ return log
+
+ def to_rgb(self, x):
+ assert self.image_key == "segmentation"
+ if not hasattr(self, "colorize"):
+ self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
+ x = F.conv2d(x, weight=self.colorize)
+ x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
+ return x
+
+
+class IdentityFirstStage(torch.nn.Module):
+ def __init__(self, *args, vq_interface=False, **kwargs):
+ self.vq_interface = vq_interface
+ super().__init__()
+
+ def encode(self, x, *args, **kwargs):
+ return x
+
+ def decode(self, x, *args, **kwargs):
+ return x
+
+ def quantize(self, x, *args, **kwargs):
+ if self.vq_interface:
+ return x, None, [None, None, None]
+ return x
+
+ def forward(self, x, *args, **kwargs):
+ return x
+
diff --git a/model_lib/ControlNet/ldm/models/diffusion/__init__.py b/model_lib/ControlNet/ldm/models/diffusion/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/model_lib/ControlNet/ldm/models/diffusion/__pycache__/__init__.cpython-39.pyc b/model_lib/ControlNet/ldm/models/diffusion/__pycache__/__init__.cpython-39.pyc
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diff --git a/model_lib/ControlNet/ldm/models/diffusion/__pycache__/ddim.cpython-39.pyc b/model_lib/ControlNet/ldm/models/diffusion/__pycache__/ddim.cpython-39.pyc
new file mode 100644
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diff --git a/model_lib/ControlNet/ldm/models/diffusion/__pycache__/ddpm.cpython-39.pyc b/model_lib/ControlNet/ldm/models/diffusion/__pycache__/ddpm.cpython-39.pyc
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diff --git a/model_lib/ControlNet/ldm/models/diffusion/ddim.py b/model_lib/ControlNet/ldm/models/diffusion/ddim.py
new file mode 100644
index 0000000000000000000000000000000000000000..b45f5e247bc9005ac02c3ba8466b2eae8ea2a0d6
--- /dev/null
+++ b/model_lib/ControlNet/ldm/models/diffusion/ddim.py
@@ -0,0 +1,763 @@
+"""SAMPLING ONLY."""
+
+import pdb
+import random
+
+import numpy as np
+import torch
+from model_lib.ControlNet.ldm.modules.diffusionmodules.util import (
+ extract_into_tensor, make_ddim_sampling_parameters, make_ddim_timesteps,
+ noise_like)
+from model_lib.ControlNet.ldm.util import default
+from tqdm import tqdm
+
+
+class DDIMSampler(object):
+ def __init__(self, model, schedule="linear", **kwargs):
+ super().__init__()
+ self.model = model
+ self.ddpm_num_timesteps = model.num_timesteps
+ self.schedule = schedule
+
+ def register_buffer(self, name, attr):
+ if type(attr) == torch.Tensor:
+ if attr.device != torch.device("cuda"):
+ attr = attr.to(torch.device("cuda"))
+ setattr(self, name, attr)
+
+ def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
+ self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
+ num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
+ alphas_cumprod = self.model.alphas_cumprod
+ assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
+ to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
+
+ self.register_buffer('betas', to_torch(self.model.betas))
+ self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+ self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
+
+ # calculations for diffusion q(x_t | x_{t-1}) and others
+ self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
+ self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
+
+ # ddim sampling parameters
+ ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
+ ddim_timesteps=self.ddim_timesteps,
+ eta=ddim_eta,verbose=verbose)
+ self.register_buffer('ddim_sigmas', ddim_sigmas)
+ self.register_buffer('ddim_alphas', ddim_alphas)
+ self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
+ self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
+ sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
+ (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
+ 1 - self.alphas_cumprod / self.alphas_cumprod_prev))
+ self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
+
+ @torch.no_grad()
+ def sample(self,
+ S,
+ batch_size,
+ shape,
+ conditioning=None,
+ callback=None,
+ normals_sequence=None,
+ img_callback=None,
+ quantize_x0=False,
+ eta=0.,
+ mask=None,
+ x0=None,
+ temperature=1.,
+ noise_dropout=0.,
+ score_corrector=None,
+ corrector_kwargs=None,
+ verbose=True,
+ x_T=None,
+ log_every_t=100,
+ unconditional_guidance_scale=1.,
+ unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+ dynamic_threshold=None,
+ ucg_schedule=None,
+ inpaint=None,
+ **kwargs
+ ):
+ if conditioning is not None:
+ if isinstance(conditioning, dict):
+ ctmp = conditioning[list(conditioning.keys())[0]]
+ while isinstance(ctmp, list): ctmp = ctmp[0]
+ cbs = ctmp.shape[0]
+ if cbs != batch_size:
+ print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+
+ elif isinstance(conditioning, list):
+ for ctmp in conditioning:
+ if ctmp.shape[0] != batch_size:
+ print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+
+ else:
+ if conditioning.shape[0] != batch_size:
+ print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+ self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
+ # sampling
+ C, H, W = shape
+ size = (batch_size, C, H, W)
+ print(f'Data shape for DDIM sampling is {C, H, W}')
+
+ samples, intermediates = self.ddim_sampling(conditioning, size,
+ callback=callback,
+ img_callback=img_callback,
+ quantize_denoised=quantize_x0,
+ mask=mask, x0=x0,
+ ddim_use_original_steps=False,
+ noise_dropout=noise_dropout,
+ temperature=temperature,
+ score_corrector=score_corrector,
+ corrector_kwargs=corrector_kwargs,
+ x_T=x_T,
+ log_every_t=log_every_t,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning,
+ dynamic_threshold=dynamic_threshold,
+ ucg_schedule=ucg_schedule,
+ inpaint=inpaint
+ )
+ return samples, intermediates
+
+ @torch.no_grad()
+ def ddim_sampling(self, cond, shape,
+ x_T=None, ddim_use_original_steps=False,
+ callback=None, timesteps=None, quantize_denoised=False,
+ mask=None, x0=None, img_callback=None, log_every_t=100,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+ unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None,
+ ucg_schedule=None,inpaint=None):
+ device = self.model.betas.device
+ b = shape[0]
+ if x_T is None:
+ img = torch.randn(shape, device=device)
+ else:
+ img = x_T
+
+ if timesteps is None:
+ timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
+ elif timesteps is not None and not ddim_use_original_steps:
+ subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
+ timesteps = self.ddim_timesteps[:subset_end]
+
+ intermediates = {'x_inter': [img], 'pred_x0': [img]}
+ time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
+ total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
+ print(f"Running DDIM Sampling with {total_steps} timesteps")
+
+ iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
+
+ for i, step in enumerate(iterator):
+ index = total_steps - i - 1
+ ts = torch.full((b,), step, device=device, dtype=torch.long)
+
+ if mask is not None:
+ assert x0 is not None
+ img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?
+ img = img_orig * mask + (1. - mask) * img
+
+ if ucg_schedule is not None:
+ assert len(ucg_schedule) == len(time_range)
+ unconditional_guidance_scale = ucg_schedule[i]
+
+ model_output = self.p_sample_ddim(img, cond, ts,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning,
+ inpaint=inpaint)
+ outs = self.pred_x_prev_from_eps(img, cond, ts, model_output, index=index, use_original_steps=ddim_use_original_steps,
+ quantize_denoised=quantize_denoised, temperature=temperature,
+ noise_dropout=noise_dropout, score_corrector=score_corrector,
+ corrector_kwargs=corrector_kwargs,
+ dynamic_threshold=dynamic_threshold)
+ img, pred_x0 = outs
+ if callback: callback(i)
+ if img_callback: img_callback(pred_x0, i)
+
+ if index % log_every_t == 0 or index == total_steps - 1:
+ intermediates['x_inter'].append(img)
+ intermediates['pred_x0'].append(pred_x0)
+
+ return img, intermediates
+
+ @torch.no_grad()
+ def p_sample_ddim(self, x, c, t, unconditional_guidance_scale=1., unconditional_conditioning=None, inpaint=None):
+
+ if inpaint is None:
+ x_In = x
+ else:
+ x_In = torch.cat([x,inpaint],dim=1)
+
+ if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+ model_output = self.model.apply_model(x_In, t, c)
+ else:
+ x_in = torch.cat([x_In] * 2)
+ t_in = torch.cat([t] * 2)
+ if isinstance(c, dict):
+ assert isinstance(unconditional_conditioning, dict)
+ c_in = dict()
+ for k in c:
+ if isinstance(c[k], list):
+ c_in[k] = [torch.cat([
+ unconditional_conditioning[k][i],
+ c[k][i]]) for i in range(len(c[k]))]
+ else:
+ c_in[k] = torch.cat([
+ unconditional_conditioning[k],
+ c[k]])
+ elif isinstance(c, list):
+ c_in = list()
+ assert isinstance(unconditional_conditioning, list)
+ for i in range(len(c)):
+ c_in.append(torch.cat([unconditional_conditioning[i], c[i]]))
+ else:
+ c_in = torch.cat([unconditional_conditioning, c])
+ model_uncond, model_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) # , reference_image_noisy
+ model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond)
+
+ return model_output
+
+ @torch.no_grad()
+ def pred_x_prev_from_eps(self, x, c, t, model_output, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+ dynamic_threshold=None):
+ b, *_, device = *x.shape, x.device
+ if self.model.parameterization == "v":
+ e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)
+ else:
+ e_t = model_output
+
+ if score_corrector is not None:
+ assert self.model.parameterization == "eps", 'not implemented'
+ e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+ alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+ alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+ sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+ sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+ # select parameters corresponding to the currently considered timestep
+ a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+ a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+ sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+ sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+ # current prediction for x_0
+ if self.model.parameterization != "v":
+ pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+ else:
+ pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)
+
+ if quantize_denoised:
+ pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+
+ if dynamic_threshold is not None:
+ raise NotImplementedError()
+
+ # direction pointing to x_t
+ dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+ noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+ if noise_dropout > 0.:
+ noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+ x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+ return x_prev, pred_x0
+
+ @torch.no_grad()
+ def encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None,
+ unconditional_guidance_scale=1.0, unconditional_conditioning=None, callback=None):
+ num_reference_steps = self.ddpm_num_timesteps if use_original_steps else self.ddim_timesteps.shape[0]
+
+ assert t_enc <= num_reference_steps
+ num_steps = t_enc
+
+ if use_original_steps:
+ alphas_next = self.alphas_cumprod[:num_steps]
+ alphas = self.alphas_cumprod_prev[:num_steps]
+ else:
+ alphas_next = self.ddim_alphas[:num_steps]
+ alphas = torch.tensor(self.ddim_alphas_prev[:num_steps])
+
+ x_next = x0
+ intermediates = []
+ inter_steps = []
+ for i in tqdm(range(num_steps), desc='Encoding Image'):
+ t = torch.full((x0.shape[0],), i, device=self.model.device, dtype=torch.long)
+ if unconditional_guidance_scale == 1.:
+ noise_pred = self.model.apply_model(x_next, t, c)
+ else:
+ assert unconditional_conditioning is not None
+ e_t_uncond, noise_pred = torch.chunk(
+ self.model.apply_model(torch.cat((x_next, x_next)), torch.cat((t, t)),
+ torch.cat((unconditional_conditioning, c))), 2)
+ noise_pred = e_t_uncond + unconditional_guidance_scale * (noise_pred - e_t_uncond)
+
+ xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next
+ weighted_noise_pred = alphas_next[i].sqrt() * (
+ (1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) * noise_pred
+ x_next = xt_weighted + weighted_noise_pred
+ if return_intermediates and i % (
+ num_steps // return_intermediates) == 0 and i < num_steps - 1:
+ intermediates.append(x_next)
+ inter_steps.append(i)
+ elif return_intermediates and i >= num_steps - 2:
+ intermediates.append(x_next)
+ inter_steps.append(i)
+ if callback: callback(i)
+
+ out = {'x_encoded': x_next, 'intermediate_steps': inter_steps}
+ if return_intermediates:
+ out.update({'intermediates': intermediates})
+ return x_next, out
+
+ @torch.no_grad()
+ def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
+ # fast, but does not allow for exact reconstruction
+ # t serves as an index to gather the correct alphas
+ if use_original_steps:
+ sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
+ sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
+ else:
+ sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
+ sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
+
+ if noise is None:
+ noise = torch.randn_like(x0)
+ return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
+ extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
+
+ @torch.no_grad()
+ def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
+ use_original_steps=False, callback=None, inpaint=None):
+
+ timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
+ timesteps = timesteps[:t_start]
+
+ time_range = np.flip(timesteps)
+ total_steps = timesteps.shape[0]
+ print(f"Running DDIM Sampling with {total_steps} timesteps")
+
+ iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
+ x_dec = x_latent
+ for i, step in enumerate(iterator):
+ index = total_steps - i - 1
+ ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
+ model_output = self.p_sample_ddim(x_dec, cond, ts,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning, inpaint=inpaint)
+ x_dec, _ = self.pred_x_prev_from_eps(x_dec, cond, ts, model_output, index=index, use_original_steps=use_original_steps)
+
+ if callback: callback(i)
+ return x_dec
+
+
+class DDIMSampler_ReferenceOnly(object):
+ def __init__(self, model, schedule="linear", **kwargs):
+ super().__init__()
+ self.model = model
+ self.ddpm_num_timesteps = model.num_timesteps
+ self.schedule = schedule
+
+ def register_buffer(self, name, attr):
+ if type(attr) == torch.Tensor:
+ if attr.device != torch.device("cuda"):
+ attr = attr.to(torch.device("cuda"))
+ setattr(self, name, attr)
+
+ def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
+ self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
+ num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
+ alphas_cumprod = self.model.alphas_cumprod
+ assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
+ to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
+
+ self.register_buffer('betas', to_torch(self.model.betas))
+ self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+ self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
+
+ # calculations for diffusion q(x_t | x_{t-1}) and others
+ self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
+ self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
+
+ # ddim sampling parameters
+ ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
+ ddim_timesteps=self.ddim_timesteps,
+ eta=ddim_eta,verbose=verbose)
+ self.register_buffer('ddim_sigmas', ddim_sigmas)
+ self.register_buffer('ddim_alphas', ddim_alphas)
+ self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
+ self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
+ sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
+ (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
+ 1 - self.alphas_cumprod / self.alphas_cumprod_prev))
+ self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
+
+ @torch.no_grad()
+ def sample(self,
+ S,
+ batch_size,
+ shape,
+ conditioning=None,
+ callback=None,
+ normals_sequence=None,
+ img_callback=None,
+ quantize_x0=False,
+ eta=0.,
+ mask=None,
+ x0=None,
+ temperature=1.,
+ noise_dropout=0.,
+ score_corrector=None,
+ corrector_kwargs=None,
+ verbose=True,
+ x_T=None,
+ log_every_t=100,
+ unconditional_guidance_scale=1.,
+ unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+ dynamic_threshold=None,
+ ucg_schedule=None,
+ inpaint=None,
+ num_overlap=0,
+ **kwargs
+ ):
+ if conditioning is not None:
+ if isinstance(conditioning, dict):
+ ctmp = conditioning[list(conditioning.keys())[0]]
+ while isinstance(ctmp, list): ctmp = ctmp[0]
+ cbs = ctmp.shape[0]
+ if cbs != batch_size:
+ print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+
+ elif isinstance(conditioning, list):
+ for ctmp in conditioning:
+ if ctmp.shape[0] != batch_size:
+ print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+
+ else:
+ if conditioning.shape[0] != batch_size:
+ print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+ self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
+ # sampling
+ C, H, W = shape
+ size = (batch_size, C, H, W)
+ print(f'Data shape for DDIM sampling is {C, H, W}')
+
+ samples, intermediates = self.ddim_sampling(conditioning, size,
+ callback=callback,
+ img_callback=img_callback,
+ quantize_denoised=quantize_x0,
+ mask=mask, x0=x0,
+ ddim_use_original_steps=False,
+ noise_dropout=noise_dropout,
+ temperature=temperature,
+ score_corrector=score_corrector,
+ corrector_kwargs=corrector_kwargs,
+ x_T=x_T,
+ log_every_t=log_every_t,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning,
+ dynamic_threshold=dynamic_threshold,
+ ucg_schedule=ucg_schedule,
+ inpaint=inpaint,
+ num_overlap=num_overlap
+ )
+ return samples, intermediates
+
+ @torch.no_grad()
+ def ddim_sampling(self, cond, shape,
+ x_T=None, ddim_use_original_steps=False,
+ callback=None, timesteps=None, quantize_denoised=False,
+ mask=None, x0=None, img_callback=None, log_every_t=100,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+ unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None,
+ ucg_schedule=None,inpaint=None,num_overlap=0):
+ device = self.model.betas.device
+ b = shape[0]
+ if x_T is None:
+ img = torch.randn(shape, device=device)
+ else:
+ img = x_T
+
+ if timesteps is None:
+ timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
+ elif timesteps is not None and not ddim_use_original_steps:
+ subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
+ timesteps = self.ddim_timesteps[:subset_end]
+
+ intermediates = {'x_inter': [img], 'pred_x0': [img]}
+ time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
+ total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
+
+
+ iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
+
+ num_frames = img.shape[0]
+
+ for i, step in enumerate(iterator):
+ index = total_steps - i - 1
+ ts = torch.full((b,), step, device=device, dtype=torch.long)
+
+ if mask is not None:
+ assert x0 is not None
+ img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?
+ img = img_orig * mask + (1. - mask) * img
+ if ucg_schedule is not None:
+ assert len(ucg_schedule) == len(time_range)
+ unconditional_guidance_scale = ucg_schedule[i]
+ if num_overlap == 0:
+ model_output = self.p_sample_ddim(img, cond, ts, unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning, inpaint=inpaint)
+ else:
+ model_output_all = torch.zeros_like(img)
+ counts = torch.zeros(num_frames).cuda()
+ offset = random.randint(0, num_frames-1)
+ skip = b - num_overlap
+ for start_idx in range(offset, offset+num_frames-num_overlap, skip):
+ indices = torch.arange(start_idx, start_idx + b) % num_frames
+ sel_cond = {}
+ for k, v in cond.items():
+ if isinstance(v, list) and k != 'more_image_control':
+ sel_cond[k] = [c[indices] for c in v]
+ elif k == 'more_image_control':
+ num_more_refs = len(v)
+ sel_cond[k] = []
+ for i in range(num_more_refs):
+ sel_cond[k].append([c[indices] for c in v[i]])
+ else:
+ sel_cond[k] = v
+ sel_uncond = {}
+ for k, v in unconditional_conditioning.items():
+ if isinstance(v, list) and k != 'more_image_control':
+ sel_uncond[k] = [c[indices] for c in v]
+ elif k == 'more_image_control':
+ num_more_refs = len(v)
+ sel_uncond[k] = []
+ for i in range(num_more_refs):
+ sel_uncond[k].append([c[indices] for c in v[i]])
+ else:
+ sel_uncond[k] = v
+ model_output = self.p_sample_ddim(img[indices], sel_cond, ts, unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=sel_uncond, inpaint=inpaint)
+ model_output_all[indices] += model_output
+ counts[indices] += 1
+ model_output = model_output_all / counts.reshape(-1, 1, 1, 1)
+
+ outs = self.pred_x_prev_from_eps(img, cond, ts, model_output, index=index, temperature=temperature,
+ noise_dropout=noise_dropout, score_corrector=score_corrector,
+ corrector_kwargs=corrector_kwargs, dynamic_threshold=dynamic_threshold)
+
+ img, pred_x0 = outs
+ if callback: callback(i)
+ if img_callback: img_callback(pred_x0, i)
+
+ if index % log_every_t == 0 or index == total_steps - 1:
+ intermediates['x_inter'].append(img)
+ intermediates['pred_x0'].append(pred_x0)
+
+ return img, intermediates
+
+ @torch.no_grad()
+ def p_sample_ddim(self, x, c, t, unconditional_guidance_scale=1., unconditional_conditioning=None, inpaint=None):
+ if inpaint is None:
+ x_In = x
+ else:
+ x_In = torch.cat([x,inpaint],dim=1)
+
+ if 'image_control' in c and c['image_control'] is not None:
+ cond_image_start = torch.cat(c['image_control'], 1)
+ if c['wonoise']:
+ reference_image_noisy = cond_image_start
+ else:
+ reference_image_noisy = self.model.q_sample(cond_image_start,t)
+
+ more_reference_image_noisy = []
+ if 'more_image_control' in c and c['more_image_control'] is not None:
+ num_additional_ref_imgs = len(c['more_image_control'])
+ for i in range(num_additional_ref_imgs):
+ m_ref_img_noisy = torch.cat(c['more_image_control'][i], 1)
+ if not c['wonoise']:
+ m_ref_img_noisy = self.model.q_sample(m_ref_img_noisy, t)
+ more_reference_image_noisy.append(m_ref_img_noisy)
+
+ if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+ model_output = self.model.apply_model(x_In, t, c)
+ else:
+ if 'image_control' in unconditional_conditioning and unconditional_conditioning['image_control'] is not None:
+ x_in = torch.cat([x_In] * 2)
+ t_in = torch.cat([t] * 2)
+ reference_image_noisy_in = torch.cat([reference_image_noisy] * 2)
+ more_reference_image_noisy = [torch.cat([m_ref_img] * 2) for m_ref_img in more_reference_image_noisy]
+ if isinstance(c, dict):
+ assert isinstance(unconditional_conditioning, dict)
+ c_in = dict()
+ for k in c:
+ if isinstance(c[k], list):
+ c_in[k] = [torch.cat([
+ unconditional_conditioning[k][i],
+ c[k][i]]) for i in range(len(c[k]))]
+ else:
+ try:
+ c_in[k] = torch.cat([
+ unconditional_conditioning[k],
+ c[k]])
+ except:
+ c_in[k] = unconditional_conditioning[k]
+ elif isinstance(c, list):
+ c_in = list()
+ assert isinstance(unconditional_conditioning, list)
+ for i in range(len(c)):
+ c_in.append(torch.cat([unconditional_conditioning[i], c[i]]))
+ else:
+ c_in = torch.cat([unconditional_conditioning, c])
+ # pdb.set_trace()
+
+ model_uncond, model_t = self.model.apply_model(x_in, t_in, c_in, reference_image_noisy_in, more_reference_image_noisy = more_reference_image_noisy).chunk(2) # , reference_image_noisy
+ else:
+ x_in = x_In
+ t_in = t
+ c_in = c
+ reference_image_noisy_in = reference_image_noisy
+ model_t = self.model.apply_model(x_in, t_in, c_in, reference_image_noisy_in, more_reference_image_noisy = more_reference_image_noisy)
+ model_uncond = self.model.apply_model(x_in, t_in, unconditional_conditioning, None,uc=True)
+ # pdb.set_trace()
+ model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond)
+
+ return model_output
+
+ @torch.no_grad()
+ def pred_x_prev_from_eps(self, x, c, t, model_output, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+ dynamic_threshold=None):
+ b, *_, device = *x.shape, x.device
+ if self.model.parameterization == "v":
+ e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)
+ else:
+ e_t = model_output
+ if score_corrector is not None:
+ assert self.model.parameterization == "eps", 'not implemented'
+ e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+ alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+ alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+ sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+ sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+
+ # select parameters corresponding to the currently considered timestep
+ a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+ a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+ sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+ # print ('sigma_t: {}'.format(sigma_t[0, 0, 0, 0]))
+ sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+ # current prediction for x_0
+ if self.model.parameterization != "v":
+ pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+ else:
+ pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)
+ if quantize_denoised:
+ pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+
+ if dynamic_threshold is not None:
+ raise NotImplementedError()
+ # direction pointing to x_t
+ dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+ noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+ if noise_dropout > 0.:
+ noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+
+ x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+
+ return x_prev, pred_x0
+
+ @torch.no_grad()
+ def encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None,
+ unconditional_guidance_scale=1.0, unconditional_conditioning=None, callback=None):
+ num_reference_steps = self.ddpm_num_timesteps if use_original_steps else self.ddim_timesteps.shape[0]
+
+ assert t_enc <= num_reference_steps
+ num_steps = t_enc
+
+ if use_original_steps:
+ alphas_next = self.alphas_cumprod[:num_steps]
+ alphas = self.alphas_cumprod_prev[:num_steps]
+ else:
+ alphas_next = self.ddim_alphas[:num_steps]
+ alphas = torch.tensor(self.ddim_alphas_prev[:num_steps])
+
+ x_next = x0
+ intermediates = []
+ inter_steps = []
+ for i in tqdm(range(num_steps), desc='Encoding Image'):
+ t = torch.full((x0.shape[0],), i, device=self.model.device, dtype=torch.long)
+ if unconditional_guidance_scale == 1.:
+ noise_pred = self.model.apply_model(x_next, t, c)
+ else:
+ assert unconditional_conditioning is not None
+ e_t_uncond, noise_pred = torch.chunk(
+ self.model.apply_model(torch.cat((x_next, x_next)), torch.cat((t, t)),
+ torch.cat((unconditional_conditioning, c))), 2)
+ noise_pred = e_t_uncond + unconditional_guidance_scale * (noise_pred - e_t_uncond)
+
+ xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next
+ weighted_noise_pred = alphas_next[i].sqrt() * (
+ (1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) * noise_pred
+ x_next = xt_weighted + weighted_noise_pred
+ if return_intermediates and i % (
+ num_steps // return_intermediates) == 0 and i < num_steps - 1:
+ intermediates.append(x_next)
+ inter_steps.append(i)
+ elif return_intermediates and i >= num_steps - 2:
+ intermediates.append(x_next)
+ inter_steps.append(i)
+ if callback: callback(i)
+
+ out = {'x_encoded': x_next, 'intermediate_steps': inter_steps}
+ if return_intermediates:
+ out.update({'intermediates': intermediates})
+ return x_next, out
+
+ @torch.no_grad()
+ def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
+ # fast, but does not allow for exact reconstruction
+ # t serves as an index to gather the correct alphas
+ if use_original_steps:
+ sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
+ sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
+ else:
+ sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
+ sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
+
+ if noise is None:
+ noise = torch.randn_like(x0)
+ return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
+ extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
+
+ @torch.no_grad()
+ def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
+ use_original_steps=False, callback=None, inpaint=None):
+
+ timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
+ timesteps = timesteps[:t_start]
+
+ time_range = np.flip(timesteps)
+ total_steps = timesteps.shape[0]
+ print(f"Running DDIM Sampling with {total_steps} timesteps")
+
+ iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
+ x_dec = x_latent
+ for i, step in enumerate(iterator):
+ index = total_steps - i - 1
+ ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
+
+ model_output = self.p_sample_ddim(x_dec, cond, ts,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning, inpaint=inpaint)
+
+ x_dec, _ = self.pred_x_prev_from_eps(x_dec, cond, ts, model_output, index)
+ if callback: callback(i)
+ return x_dec
\ No newline at end of file
diff --git a/model_lib/ControlNet/ldm/models/diffusion/ddpm.py b/model_lib/ControlNet/ldm/models/diffusion/ddpm.py
new file mode 100644
index 0000000000000000000000000000000000000000..307569f2ad7cd06b44dd48dfc6871922278ddb97
--- /dev/null
+++ b/model_lib/ControlNet/ldm/models/diffusion/ddpm.py
@@ -0,0 +1,2602 @@
+"""
+wild mixture of
+https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
+https://github.com/CompVis/taming-transformers
+-- merci
+"""
+
+import torch
+import torch.nn as nn
+import numpy as np
+import pytorch_lightning as pl
+from torch.optim.lr_scheduler import LambdaLR
+from einops import rearrange, repeat
+from contextlib import contextmanager, nullcontext
+from functools import partial
+import itertools
+from tqdm import tqdm
+from torchvision.utils import make_grid
+from pytorch_lightning.utilities.distributed import rank_zero_only
+from omegaconf import ListConfig
+import pdb
+from model_lib.ControlNet.ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
+from model_lib.ControlNet.ldm.modules.ema import LitEma
+from model_lib.ControlNet.ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
+from model_lib.ControlNet.ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL
+from model_lib.ControlNet.ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
+from model_lib.ControlNet.ldm.models.diffusion.ddim import DDIMSampler, DDIMSampler_ReferenceOnly
+
+
+__conditioning_keys__ = {'concat': 'c_concat',
+ 'crossattn': 'c_crossattn',
+ 'adm': 'y'}
+
+
+def disabled_train(self, mode=True):
+ """Overwrite model.train with this function to make sure train/eval mode
+ does not change anymore."""
+ return self
+
+
+def uniform_on_device(r1, r2, shape, device):
+ return (r1 - r2) * torch.rand(*shape, device=device) + r2
+
+
+class DDPM(pl.LightningModule):
+ # classic DDPM with Gaussian diffusion, in image space
+ def __init__(self,
+ unet_config,
+ timesteps=1000,
+ beta_schedule="linear",
+ loss_type="l2",
+ ckpt_path=None,
+ ignore_keys=[],
+ load_only_unet=False,
+ monitor="val/loss",
+ use_ema=True,
+ first_stage_key="image",
+ image_size=256,
+ channels=3,
+ log_every_t=100,
+ clip_denoised=True,
+ linear_start=1e-4,
+ linear_end=2e-2,
+ cosine_s=8e-3,
+ given_betas=None,
+ original_elbo_weight=0.,
+ v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
+ l_simple_weight=1.,
+ conditioning_key=None,
+ parameterization="eps", # all assuming fixed variance schedules
+ scheduler_config=None,
+ use_positional_encodings=False,
+ learn_logvar=False,
+ logvar_init=0.,
+ make_it_fit=False,
+ ucg_training=None,
+ reset_ema=False,
+ reset_num_ema_updates=False,
+ ):
+ super().__init__()
+ assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"'
+ self.parameterization = parameterization
+ print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
+ self.cond_stage_model = None
+ self.clip_denoised = clip_denoised
+ self.log_every_t = log_every_t
+ self.first_stage_key = first_stage_key
+ self.image_size = image_size # try conv?
+ self.channels = channels
+ self.use_positional_encodings = use_positional_encodings
+ self.model = DiffusionWrapper(unet_config, conditioning_key)
+ count_params(self.model, verbose=True)
+ self.use_ema = use_ema
+ if self.use_ema:
+ self.model_ema = LitEma(self.model)
+ print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+ self.use_scheduler = scheduler_config is not None
+ if self.use_scheduler:
+ self.scheduler_config = scheduler_config
+
+ self.v_posterior = v_posterior
+ self.original_elbo_weight = original_elbo_weight
+ self.l_simple_weight = l_simple_weight
+
+ if monitor is not None:
+ self.monitor = monitor
+ self.make_it_fit = make_it_fit
+ if reset_ema: assert exists(ckpt_path)
+ if ckpt_path is not None:
+ self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
+ if reset_ema:
+ assert self.use_ema
+ print(f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
+ self.model_ema = LitEma(self.model)
+ if reset_num_ema_updates:
+ print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
+ assert self.use_ema
+ self.model_ema.reset_num_updates()
+
+ self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
+ linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
+
+ self.loss_type = loss_type
+
+ self.learn_logvar = learn_logvar
+ logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
+ if self.learn_logvar:
+ self.logvar = nn.Parameter(self.logvar, requires_grad=True)
+ else:
+ self.register_buffer('logvar', logvar)
+
+ self.ucg_training = ucg_training or dict()
+ if self.ucg_training:
+ self.ucg_prng = np.random.RandomState()
+
+ def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
+ linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ if exists(given_betas):
+ betas = given_betas
+ else:
+ betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
+ cosine_s=cosine_s)
+ alphas = 1. - betas
+ alphas_cumprod = np.cumprod(alphas, axis=0)
+ alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+
+ timesteps, = betas.shape
+ self.num_timesteps = int(timesteps)
+ self.linear_start = linear_start
+ self.linear_end = linear_end
+ assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
+
+ to_torch = partial(torch.tensor, dtype=torch.float32)
+
+ self.register_buffer('betas', to_torch(betas))
+ self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+ self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
+
+ # calculations for diffusion q(x_t | x_{t-1}) and others
+ self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+ self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+ self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
+ self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
+ self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
+
+ # calculations for posterior q(x_{t-1} | x_t, x_0)
+ posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
+ 1. - alphas_cumprod) + self.v_posterior * betas
+ # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+ self.register_buffer('posterior_variance', to_torch(posterior_variance))
+ # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+ self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
+ self.register_buffer('posterior_mean_coef1', to_torch(
+ betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
+ self.register_buffer('posterior_mean_coef2', to_torch(
+ (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
+
+ if self.parameterization == "eps":
+ lvlb_weights = self.betas ** 2 / (
+ 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
+ elif self.parameterization == "x0":
+ lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
+ elif self.parameterization == "v":
+ lvlb_weights = torch.ones_like(self.betas ** 2 / (
+ 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)))
+ else:
+ raise NotImplementedError("mu not supported")
+ lvlb_weights[0] = lvlb_weights[1]
+ self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
+ assert not torch.isnan(self.lvlb_weights).all()
+
+ @contextmanager
+ def ema_scope(self, context=None):
+ if self.use_ema:
+ self.model_ema.store(self.model.parameters())
+ self.model_ema.copy_to(self.model)
+ if context is not None:
+ print(f"{context}: Switched to EMA weights")
+ try:
+ yield None
+ finally:
+ if self.use_ema:
+ self.model_ema.restore(self.model.parameters())
+ if context is not None:
+ print(f"{context}: Restored training weights")
+
+ @torch.no_grad()
+ def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
+ sd = torch.load(path, map_location="cpu")
+ if "state_dict" in list(sd.keys()):
+ sd = sd["state_dict"]
+ keys = list(sd.keys())
+ for k in keys:
+ for ik in ignore_keys:
+ if k.startswith(ik):
+ print("Deleting key {} from state_dict.".format(k))
+ del sd[k]
+ if self.make_it_fit:
+ n_params = len([name for name, _ in
+ itertools.chain(self.named_parameters(),
+ self.named_buffers())])
+ for name, param in tqdm(
+ itertools.chain(self.named_parameters(),
+ self.named_buffers()),
+ desc="Fitting old weights to new weights",
+ total=n_params
+ ):
+ if not name in sd:
+ continue
+ old_shape = sd[name].shape
+ new_shape = param.shape
+ assert len(old_shape) == len(new_shape)
+ if len(new_shape) > 2:
+ # we only modify first two axes
+ assert new_shape[2:] == old_shape[2:]
+ # assumes first axis corresponds to output dim
+ if not new_shape == old_shape:
+ new_param = param.clone()
+ old_param = sd[name]
+ if len(new_shape) == 1:
+ for i in range(new_param.shape[0]):
+ new_param[i] = old_param[i % old_shape[0]]
+ elif len(new_shape) >= 2:
+ for i in range(new_param.shape[0]):
+ for j in range(new_param.shape[1]):
+ new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]]
+
+ n_used_old = torch.ones(old_shape[1])
+ for j in range(new_param.shape[1]):
+ n_used_old[j % old_shape[1]] += 1
+ n_used_new = torch.zeros(new_shape[1])
+ for j in range(new_param.shape[1]):
+ n_used_new[j] = n_used_old[j % old_shape[1]]
+
+ n_used_new = n_used_new[None, :]
+ while len(n_used_new.shape) < len(new_shape):
+ n_used_new = n_used_new.unsqueeze(-1)
+ new_param /= n_used_new
+
+ sd[name] = new_param
+
+ missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
+ sd, strict=False)
+ print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+ if len(missing) > 0:
+ print(f"Missing Keys:\n {missing}")
+ if len(unexpected) > 0:
+ print(f"\nUnexpected Keys:\n {unexpected}")
+
+ def q_mean_variance(self, x_start, t):
+ """
+ Get the distribution q(x_t | x_0).
+ :param x_start: the [N x C x ...] tensor of noiseless inputs.
+ :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+ :return: A tuple (mean, variance, log_variance), all of x_start's shape.
+ """
+ mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
+ variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
+ log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
+ return mean, variance, log_variance
+
+ def predict_start_from_noise(self, x_t, t, noise):
+ return (
+ extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
+ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
+ )
+
+ def predict_start_from_z_and_v(self, x_t, t, v):
+ # self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+ # self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+ return (
+ extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
+ )
+
+ def predict_eps_from_z_and_v(self, x_t, t, v):
+ return (
+ extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v +
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t
+ )
+
+ def q_posterior(self, x_start, x_t, t):
+ posterior_mean = (
+ extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
+ extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
+ )
+ posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
+ posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
+ return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+ def p_mean_variance(self, x, t, clip_denoised: bool):
+ model_out = self.model(x, t)
+ if self.parameterization == "eps":
+ x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+ elif self.parameterization == "x0":
+ x_recon = model_out
+ if clip_denoised:
+ x_recon.clamp_(-1., 1.)
+
+ model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+ return model_mean, posterior_variance, posterior_log_variance
+
+ @torch.no_grad()
+ def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
+ b, *_, device = *x.shape, x.device
+ model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
+ noise = noise_like(x.shape, device, repeat_noise)
+ # no noise when t == 0
+ nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+ @torch.no_grad()
+ def p_sample_loop(self, shape, return_intermediates=False):
+ device = self.betas.device
+ b = shape[0]
+ img = torch.randn(shape, device=device)
+ intermediates = [img]
+ for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
+ img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
+ clip_denoised=self.clip_denoised)
+ if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
+ intermediates.append(img)
+ if return_intermediates:
+ return img, intermediates
+ return img
+
+ @torch.no_grad()
+ def sample(self, batch_size=16, return_intermediates=False):
+ image_size = self.image_size
+ channels = self.channels
+ return self.p_sample_loop((batch_size, channels, image_size, image_size),
+ return_intermediates=return_intermediates)
+
+ def q_sample(self, x_start, t, noise=None):
+ noise = default(noise, lambda: torch.randn_like(x_start))
+ return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
+
+ def get_v(self, x, noise, t):
+ return (
+ extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise -
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x
+ )
+
+ def get_loss(self, pred, target, mean=True):
+ if self.loss_type == 'l1':
+ loss = (target - pred).abs()
+ if mean:
+ loss = loss.mean()
+ elif self.loss_type == 'l2':
+ if mean:
+ loss = torch.nn.functional.mse_loss(target, pred)
+ else:
+ loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
+ else:
+ raise NotImplementedError("unknown loss type '{loss_type}'")
+
+ return loss
+
+ def p_losses(self, x_start, t, noise=None):
+ noise = default(noise, lambda: torch.randn_like(x_start))
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+ model_out = self.model(x_noisy, t)
+
+ loss_dict = {}
+ if self.parameterization == "eps":
+ target = noise
+ elif self.parameterization == "x0":
+ target = x_start
+ elif self.parameterization == "v":
+ target = self.get_v(x_start, noise, t)
+ else:
+ raise NotImplementedError(f"Parameterization {self.parameterization} not yet supported")
+
+ loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
+
+ log_prefix = 'train' if self.training else 'val'
+
+ loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
+ loss_simple = loss.mean() * self.l_simple_weight
+
+ loss_vlb = (self.lvlb_weights[t] * loss).mean()
+ loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
+
+ loss = loss_simple + self.original_elbo_weight * loss_vlb
+
+ loss_dict.update({f'{log_prefix}/loss': loss})
+
+ return loss, loss_dict
+
+ def forward(self, x, *args, **kwargs):
+ # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
+ # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
+ t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+ return self.p_losses(x, t, *args, **kwargs)
+
+ def get_input(self, batch, k):
+ x = batch[k]
+ if len(x.shape) == 3:
+ x = x[..., None]
+ x = rearrange(x, 'b h w c -> b c h w')
+ x = x.to(memory_format=torch.contiguous_format).float()
+ return x
+
+ def shared_step(self, batch):
+ x = self.get_input(batch, self.first_stage_key)
+ loss, loss_dict = self(x)
+ return loss, loss_dict
+
+ def training_step(self, batch, batch_idx):
+ for k in self.ucg_training:
+ p = self.ucg_training[k]["p"]
+ val = self.ucg_training[k]["val"]
+ if val is None:
+ val = ""
+ for i in range(len(batch[k])):
+ if self.ucg_prng.choice(2, p=[1 - p, p]):
+ batch[k][i] = val
+
+ loss, loss_dict = self.shared_step(batch)
+
+ self.log_dict(loss_dict, prog_bar=True,
+ logger=True, on_step=True, on_epoch=True)
+
+ self.log("global_step", self.global_step,
+ prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+ if self.use_scheduler:
+ lr = self.optimizers().param_groups[0]['lr']
+ self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+ return loss
+
+ @torch.no_grad()
+ def validation_step(self, batch, batch_idx):
+ _, loss_dict_no_ema = self.shared_step(batch)
+ with self.ema_scope():
+ _, loss_dict_ema = self.shared_step(batch)
+ loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
+ self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+ self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+
+ def on_train_batch_end(self, *args, **kwargs):
+ if self.use_ema:
+ self.model_ema(self.model)
+
+ def _get_rows_from_list(self, samples):
+ n_imgs_per_row = len(samples)
+ denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
+ denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+ denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+ return denoise_grid
+
+ @torch.no_grad()
+ def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
+ log = dict()
+ x = self.get_input(batch, self.first_stage_key)
+ N = min(x.shape[0], N)
+ n_row = min(x.shape[0], n_row)
+ x = x.to(self.device)[:N]
+ log["inputs"] = x
+
+ # get diffusion row
+ diffusion_row = list()
+ x_start = x[:n_row]
+
+ for t in range(self.num_timesteps):
+ if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+ t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+ t = t.to(self.device).long()
+ noise = torch.randn_like(x_start)
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+ diffusion_row.append(x_noisy)
+
+ log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
+
+ if sample:
+ # get denoise row
+ with self.ema_scope("Plotting"):
+ samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
+
+ log["samples"] = samples
+ log["denoise_row"] = self._get_rows_from_list(denoise_row)
+
+ if return_keys:
+ if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+ return log
+ else:
+ return {key: log[key] for key in return_keys}
+ return log
+
+ def configure_optimizers(self):
+ lr = self.learning_rate
+ params = list(self.model.parameters())
+ if self.learn_logvar:
+ params = params + [self.logvar]
+ opt = torch.optim.AdamW(params, lr=lr)
+ return opt
+
+
+class LatentDiffusion(DDPM):
+ """main class"""
+
+ def __init__(self,
+ first_stage_config,
+ cond_stage_config,
+ num_timesteps_cond=None,
+ cond_stage_key="image",
+ cond_stage_trainable=False,
+ concat_mode=True,
+ cond_stage_forward=None,
+ conditioning_key=None,
+ scale_factor=1.0,
+ scale_by_std=False,
+ force_null_conditioning=False,
+ *args, **kwargs):
+ self.force_null_conditioning = force_null_conditioning
+ self.num_timesteps_cond = default(num_timesteps_cond, 1)
+ self.scale_by_std = scale_by_std
+ assert self.num_timesteps_cond <= kwargs['timesteps']
+ # for backwards compatibility after implementation of DiffusionWrapper
+ if conditioning_key is None:
+ conditioning_key = 'concat' if concat_mode else 'crossattn'
+ if cond_stage_config == '__is_unconditional__' and not self.force_null_conditioning:
+ conditioning_key = None
+ ckpt_path = kwargs.pop("ckpt_path", None)
+ reset_ema = kwargs.pop("reset_ema", False)
+ reset_num_ema_updates = kwargs.pop("reset_num_ema_updates", False)
+ ignore_keys = kwargs.pop("ignore_keys", [])
+ super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
+ self.concat_mode = concat_mode
+ self.cond_stage_trainable = cond_stage_trainable
+ self.cond_stage_key = cond_stage_key
+ try:
+ self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
+ except:
+ self.num_downs = 0
+ if not scale_by_std:
+ self.scale_factor = scale_factor
+ else:
+ self.register_buffer('scale_factor', torch.tensor(scale_factor))
+ self.instantiate_first_stage(first_stage_config)
+ self.instantiate_cond_stage(cond_stage_config)
+ self.cond_stage_forward = cond_stage_forward
+ self.clip_denoised = False
+ self.bbox_tokenizer = None
+
+ self.restarted_from_ckpt = False
+ if ckpt_path is not None:
+ self.init_from_ckpt(ckpt_path, ignore_keys)
+ self.restarted_from_ckpt = True
+ if reset_ema:
+ assert self.use_ema
+ print(
+ f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
+ self.model_ema = LitEma(self.model)
+ if reset_num_ema_updates:
+ print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
+ assert self.use_ema
+ self.model_ema.reset_num_updates()
+
+ def make_cond_schedule(self, ):
+ self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
+ ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
+ self.cond_ids[:self.num_timesteps_cond] = ids
+
+ @rank_zero_only
+ @torch.no_grad()
+ def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
+ # only for very first batch
+ if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
+ assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
+ # set rescale weight to 1./std of encodings
+ print("### USING STD-RESCALING ###")
+ x = super().get_input(batch, self.first_stage_key)
+ x = x.to(self.device)
+ encoder_posterior = self.encode_first_stage(x)
+ z = self.get_first_stage_encoding(encoder_posterior).detach()
+ del self.scale_factor
+ self.register_buffer('scale_factor', 1. / z.flatten().std())
+ print(f"setting self.scale_factor to {self.scale_factor}")
+ print("### USING STD-RESCALING ###")
+
+ def register_schedule(self,
+ given_betas=None, beta_schedule="linear", timesteps=1000,
+ linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
+
+ self.shorten_cond_schedule = self.num_timesteps_cond > 1
+ if self.shorten_cond_schedule:
+ self.make_cond_schedule()
+
+ def instantiate_first_stage(self, config):
+ model = instantiate_from_config(config)
+ self.first_stage_model = model.eval()
+ self.first_stage_model.train = disabled_train
+ for param in self.first_stage_model.parameters():
+ param.requires_grad = False
+
+ def instantiate_cond_stage(self, config):
+ if not self.cond_stage_trainable:
+ if config == "__is_first_stage__":
+ print("Using first stage also as cond stage.")
+ self.cond_stage_model = self.first_stage_model
+ elif config == "__is_unconditional__":
+ print(f"Training {self.__class__.__name__} as an unconditional model.")
+ self.cond_stage_model = None
+ # self.be_unconditional = True
+ else:
+ model = instantiate_from_config(config)
+ self.cond_stage_model = model.eval()
+ self.cond_stage_model.train = disabled_train
+ for param in self.cond_stage_model.parameters():
+ param.requires_grad = False
+ else:
+ assert config != '__is_first_stage__'
+ assert config != '__is_unconditional__'
+ model = instantiate_from_config(config)
+ self.cond_stage_model = model
+
+ def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
+ denoise_row = []
+ for zd in tqdm(samples, desc=desc):
+ denoise_row.append(self.decode_first_stage(zd.to(self.device),
+ force_not_quantize=force_no_decoder_quantization))
+ n_imgs_per_row = len(denoise_row)
+ denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
+ denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
+ denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+ denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+ return denoise_grid
+
+ def get_first_stage_encoding(self, encoder_posterior):
+ if isinstance(encoder_posterior, DiagonalGaussianDistribution):
+ z = encoder_posterior.sample()
+ elif isinstance(encoder_posterior, torch.Tensor):
+ z = encoder_posterior
+ else:
+ raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
+ return self.scale_factor * z
+
+ def get_learned_conditioning(self, c):
+ if self.cond_stage_forward is None:
+ if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
+ c = self.cond_stage_model.encode(c)
+ if isinstance(c, DiagonalGaussianDistribution):
+ c = c.mode()
+ else:
+ c = self.cond_stage_model(c)
+ else:
+ assert hasattr(self.cond_stage_model, self.cond_stage_forward)
+ c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
+ return c
+
+ def meshgrid(self, h, w):
+ y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
+ x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
+
+ arr = torch.cat([y, x], dim=-1)
+ return arr
+
+ def delta_border(self, h, w):
+ """
+ :param h: height
+ :param w: width
+ :return: normalized distance to image border,
+ wtith min distance = 0 at border and max dist = 0.5 at image center
+ """
+ lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
+ arr = self.meshgrid(h, w) / lower_right_corner
+ dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
+ dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
+ edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
+ return edge_dist
+
+ def get_weighting(self, h, w, Ly, Lx, device):
+ weighting = self.delta_border(h, w)
+ weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
+ self.split_input_params["clip_max_weight"], )
+ weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
+
+ if self.split_input_params["tie_braker"]:
+ L_weighting = self.delta_border(Ly, Lx)
+ L_weighting = torch.clip(L_weighting,
+ self.split_input_params["clip_min_tie_weight"],
+ self.split_input_params["clip_max_tie_weight"])
+
+ L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
+ weighting = weighting * L_weighting
+ return weighting
+
+ def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
+ """
+ :param x: img of size (bs, c, h, w)
+ :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
+ """
+ bs, nc, h, w = x.shape
+
+ # number of crops in image
+ Ly = (h - kernel_size[0]) // stride[0] + 1
+ Lx = (w - kernel_size[1]) // stride[1] + 1
+
+ if uf == 1 and df == 1:
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+ unfold = torch.nn.Unfold(**fold_params)
+
+ fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
+
+ weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
+ normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
+ weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
+
+ elif uf > 1 and df == 1:
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+ unfold = torch.nn.Unfold(**fold_params)
+
+ fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
+ dilation=1, padding=0,
+ stride=(stride[0] * uf, stride[1] * uf))
+ fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
+
+ weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
+ normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap
+ weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
+
+ elif df > 1 and uf == 1:
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+ unfold = torch.nn.Unfold(**fold_params)
+
+ fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
+ dilation=1, padding=0,
+ stride=(stride[0] // df, stride[1] // df))
+ fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
+
+ weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
+ normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap
+ weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
+
+ else:
+ raise NotImplementedError
+
+ return fold, unfold, normalization, weighting
+
+ @torch.no_grad()
+ def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
+ cond_key=None, return_original_cond=False, bs=None, return_x=False):
+ x = super().get_input(batch, k)
+ if bs is not None:
+ x = x[:bs]
+ x = x.to(self.device)
+ encoder_posterior = self.encode_first_stage(x)
+ z = self.get_first_stage_encoding(encoder_posterior).detach()
+
+ if self.model.conditioning_key is not None and not self.force_null_conditioning:
+ if cond_key is None:
+ cond_key = self.cond_stage_key
+ if cond_key != self.first_stage_key:
+ if cond_key in ['caption', 'coordinates_bbox', "txt"]:
+ xc = batch[cond_key]
+ elif cond_key in ['class_label', 'cls']:
+ xc = batch
+ else:
+ xc = super().get_input(batch, cond_key).to(self.device)
+ else:
+ xc = x
+ if not self.cond_stage_trainable or force_c_encode:
+ if isinstance(xc, dict) or isinstance(xc, list):
+ c = self.get_learned_conditioning(xc)
+ else:
+ c = self.get_learned_conditioning(xc.to(self.device))
+ else:
+ c = xc
+ if bs is not None:
+ c = c[:bs]
+
+ if self.use_positional_encodings:
+ pos_x, pos_y = self.compute_latent_shifts(batch)
+ ckey = __conditioning_keys__[self.model.conditioning_key]
+ c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
+
+ else:
+ c = None
+ xc = None
+ if self.use_positional_encodings:
+ pos_x, pos_y = self.compute_latent_shifts(batch)
+ c = {'pos_x': pos_x, 'pos_y': pos_y}
+ out = [z, c]
+ if return_first_stage_outputs:
+ xrec = self.decode_first_stage(z)
+ out.extend([x, xrec])
+ if return_x:
+ out.extend([x])
+ if return_original_cond:
+ out.append(xc)
+ return out
+
+ @torch.no_grad()
+ def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
+ if predict_cids:
+ if z.dim() == 4:
+ z = torch.argmax(z.exp(), dim=1).long()
+ z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
+ z = rearrange(z, 'b h w c -> b c h w').contiguous()
+
+ z = 1. / self.scale_factor * z
+ return self.first_stage_model.decode(z)
+
+ @torch.no_grad()
+ def encode_first_stage(self, x):
+ return self.first_stage_model.encode(x)
+
+ def shared_step(self, batch, **kwargs):
+ x, c = self.get_input(batch, self.first_stage_key)
+ loss = self(x, c)
+ return loss
+
+ def forward(self, x, c, *args, **kwargs):
+ t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+ if self.model.conditioning_key is not None:
+ assert c is not None
+ if self.cond_stage_trainable:
+ c = self.get_learned_conditioning(c)
+ if self.shorten_cond_schedule: # TODO: drop this option
+ tc = self.cond_ids[t].to(self.device)
+ c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
+ return self.p_losses(x, c, t, *args, **kwargs)
+
+ def apply_model(self, x_noisy, t, cond, return_ids=False):
+ if isinstance(cond, dict):
+ # hybrid case, cond is expected to be a dict
+ pass
+ else:
+ if not isinstance(cond, list):
+ cond = [cond]
+ key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
+ cond = {key: cond}
+
+ x_recon = self.model(x_noisy, t, **cond)
+
+ if isinstance(x_recon, tuple) and not return_ids:
+ return x_recon[0]
+ else:
+ return x_recon
+
+ def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
+ return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
+ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+
+ def _prior_bpd(self, x_start):
+ """
+ Get the prior KL term for the variational lower-bound, measured in
+ bits-per-dim.
+ This term can't be optimized, as it only depends on the encoder.
+ :param x_start: the [N x C x ...] tensor of inputs.
+ :return: a batch of [N] KL values (in bits), one per batch element.
+ """
+ batch_size = x_start.shape[0]
+ t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
+ qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
+ kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
+ return mean_flat(kl_prior) / np.log(2.0)
+
+ def p_losses(self, x_start, cond, t, noise=None):
+ noise = default(noise, lambda: torch.randn_like(x_start))
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+ model_output = self.apply_model(x_noisy, t, cond)
+
+ loss_dict = {}
+ prefix = 'train' if self.training else 'val'
+
+ if self.parameterization == "x0":
+ target = x_start
+ elif self.parameterization == "eps":
+ target = noise
+ elif self.parameterization == "v":
+ target = self.get_v(x_start, noise, t)
+ else:
+ raise NotImplementedError()
+
+ loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
+ loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
+
+ logvar_t = self.logvar[t].to(self.device)
+ loss = loss_simple / torch.exp(logvar_t) + logvar_t
+ # loss = loss_simple / torch.exp(self.logvar) + self.logvar
+ if self.learn_logvar:
+ loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
+ loss_dict.update({'logvar': self.logvar.data.mean()})
+
+ loss = self.l_simple_weight * loss.mean()
+
+ loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
+ loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
+ loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
+ loss += (self.original_elbo_weight * loss_vlb)
+ loss_dict.update({f'{prefix}/loss': loss})
+
+ return loss, loss_dict
+
+ def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
+ return_x0=False, score_corrector=None, corrector_kwargs=None):
+ t_in = t
+ model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
+
+ if score_corrector is not None:
+ assert self.parameterization == "eps"
+ model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
+
+ if return_codebook_ids:
+ model_out, logits = model_out
+
+ if self.parameterization == "eps":
+ x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+ elif self.parameterization == "x0":
+ x_recon = model_out
+ else:
+ raise NotImplementedError()
+
+ if clip_denoised:
+ x_recon.clamp_(-1., 1.)
+ if quantize_denoised:
+ x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
+ model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+ if return_codebook_ids:
+ return model_mean, posterior_variance, posterior_log_variance, logits
+ elif return_x0:
+ return model_mean, posterior_variance, posterior_log_variance, x_recon
+ else:
+ return model_mean, posterior_variance, posterior_log_variance
+
+ @torch.no_grad()
+ def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
+ return_codebook_ids=False, quantize_denoised=False, return_x0=False,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
+ b, *_, device = *x.shape, x.device
+ outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
+ return_codebook_ids=return_codebook_ids,
+ quantize_denoised=quantize_denoised,
+ return_x0=return_x0,
+ score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+ if return_codebook_ids:
+ raise DeprecationWarning("Support dropped.")
+ model_mean, _, model_log_variance, logits = outputs
+ elif return_x0:
+ model_mean, _, model_log_variance, x0 = outputs
+ else:
+ model_mean, _, model_log_variance = outputs
+
+ noise = noise_like(x.shape, device, repeat_noise) * temperature
+ if noise_dropout > 0.:
+ noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+ # no noise when t == 0
+ nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+
+ if return_codebook_ids:
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
+ if return_x0:
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
+ else:
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+ @torch.no_grad()
+ def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
+ img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
+ score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
+ log_every_t=None):
+ if not log_every_t:
+ log_every_t = self.log_every_t
+ timesteps = self.num_timesteps
+ if batch_size is not None:
+ b = batch_size if batch_size is not None else shape[0]
+ shape = [batch_size] + list(shape)
+ else:
+ b = batch_size = shape[0]
+ if x_T is None:
+ img = torch.randn(shape, device=self.device)
+ else:
+ img = x_T
+ intermediates = []
+ if cond is not None:
+ if isinstance(cond, dict):
+ cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+ list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+ else:
+ cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+
+ if start_T is not None:
+ timesteps = min(timesteps, start_T)
+ iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
+ total=timesteps) if verbose else reversed(
+ range(0, timesteps))
+ if type(temperature) == float:
+ temperature = [temperature] * timesteps
+
+ for i in iterator:
+ ts = torch.full((b,), i, device=self.device, dtype=torch.long)
+ if self.shorten_cond_schedule:
+ assert self.model.conditioning_key != 'hybrid'
+ tc = self.cond_ids[ts].to(cond.device)
+ cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+ img, x0_partial = self.p_sample(img, cond, ts,
+ clip_denoised=self.clip_denoised,
+ quantize_denoised=quantize_denoised, return_x0=True,
+ temperature=temperature[i], noise_dropout=noise_dropout,
+ score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+ if mask is not None:
+ assert x0 is not None
+ img_orig = self.q_sample(x0, ts)
+ img = img_orig * mask + (1. - mask) * img
+
+ if i % log_every_t == 0 or i == timesteps - 1:
+ intermediates.append(x0_partial)
+ if callback: callback(i)
+ if img_callback: img_callback(img, i)
+ return img, intermediates
+
+ @torch.no_grad()
+ def p_sample_loop(self, cond, shape, return_intermediates=False,
+ x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
+ mask=None, x0=None, img_callback=None, start_T=None,
+ log_every_t=None):
+
+ if not log_every_t:
+ log_every_t = self.log_every_t
+ device = self.betas.device
+ b = shape[0]
+ if x_T is None:
+ img = torch.randn(shape, device=device)
+ else:
+ img = x_T
+
+ intermediates = [img]
+ if timesteps is None:
+ timesteps = self.num_timesteps
+
+ if start_T is not None:
+ timesteps = min(timesteps, start_T)
+ iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
+ range(0, timesteps))
+
+ if mask is not None:
+ assert x0 is not None
+ assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
+
+ for i in iterator:
+ ts = torch.full((b,), i, device=device, dtype=torch.long)
+ if self.shorten_cond_schedule:
+ assert self.model.conditioning_key != 'hybrid'
+ tc = self.cond_ids[ts].to(cond.device)
+ cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+ img = self.p_sample(img, cond, ts,
+ clip_denoised=self.clip_denoised,
+ quantize_denoised=quantize_denoised)
+ if mask is not None:
+ img_orig = self.q_sample(x0, ts)
+ img = img_orig * mask + (1. - mask) * img
+
+ if i % log_every_t == 0 or i == timesteps - 1:
+ intermediates.append(img)
+ if callback: callback(i)
+ if img_callback: img_callback(img, i)
+
+ if return_intermediates:
+ return img, intermediates
+ return img
+
+ @torch.no_grad()
+ def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
+ verbose=True, timesteps=None, quantize_denoised=False,
+ mask=None, x0=None, shape=None, **kwargs):
+ if shape is None:
+ shape = (batch_size, self.channels, self.image_size, self.image_size)
+ if cond is not None:
+ if isinstance(cond, dict):
+ cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+ list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+ else:
+ cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+ return self.p_sample_loop(cond,
+ shape,
+ return_intermediates=return_intermediates, x_T=x_T,
+ verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
+ mask=mask, x0=x0)
+
+ @torch.no_grad()
+ def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
+ if ddim:
+ ddim_sampler = DDIMSampler(self)
+ shape = (self.channels, self.image_size, self.image_size)
+
+ samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size,
+ shape, cond, verbose=False, **kwargs)
+
+ else:
+ samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
+ return_intermediates=True, **kwargs)
+
+ return samples, intermediates
+
+ @torch.no_grad()
+ def get_unconditional_conditioning(self, batch_size, null_label=None):
+ if null_label is not None:
+ xc = null_label
+ if isinstance(xc, ListConfig):
+ xc = list(xc)
+ if isinstance(xc, dict) or isinstance(xc, list):
+ c = self.get_learned_conditioning(xc)
+ else:
+ if hasattr(xc, "to"):
+ xc = xc.to(self.device)
+ c = self.get_learned_conditioning(xc)
+ else:
+ if self.cond_stage_key in ["class_label", "cls"]:
+ xc = self.cond_stage_model.get_unconditional_conditioning(batch_size, device=self.device)
+ return self.get_learned_conditioning(xc)
+ else:
+ raise NotImplementedError("todo")
+ if isinstance(c, list): # in case the encoder gives us a list
+ for i in range(len(c)):
+ c[i] = repeat(c[i], '1 ... -> b ...', b=batch_size).to(self.device)
+ else:
+ c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device)
+ return c
+
+ @torch.no_grad()
+ def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=50, ddim_eta=0., return_keys=None,
+ quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
+ plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,
+ use_ema_scope=True,
+ **kwargs):
+ ema_scope = self.ema_scope if use_ema_scope else nullcontext
+ use_ddim = ddim_steps is not None
+
+ log = dict()
+ z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
+ return_first_stage_outputs=True,
+ force_c_encode=True,
+ return_original_cond=True,
+ bs=N)
+ N = min(x.shape[0], N)
+ n_row = min(x.shape[0], n_row)
+ log["inputs"] = x
+ log["reconstruction"] = xrec
+ if self.model.conditioning_key is not None:
+ if hasattr(self.cond_stage_model, "decode"):
+ xc = self.cond_stage_model.decode(c)
+ log["conditioning"] = xc
+ elif self.cond_stage_key in ["caption", "txt"]:
+ xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
+ log["conditioning"] = xc
+ elif self.cond_stage_key in ['class_label', "cls"]:
+ try:
+ xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
+ log['conditioning'] = xc
+ except KeyError:
+ # probably no "human_label" in batch
+ pass
+ elif isimage(xc):
+ log["conditioning"] = xc
+ if ismap(xc):
+ log["original_conditioning"] = self.to_rgb(xc)
+
+ if plot_diffusion_rows:
+ # get diffusion row
+ diffusion_row = list()
+ z_start = z[:n_row]
+ for t in range(self.num_timesteps):
+ if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+ t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+ t = t.to(self.device).long()
+ noise = torch.randn_like(z_start)
+ z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+ diffusion_row.append(self.decode_first_stage(z_noisy))
+
+ diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
+ diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+ diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+ diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+ log["diffusion_row"] = diffusion_grid
+
+ if sample:
+ # get denoise row
+ with ema_scope("Sampling"):
+ samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta)
+ # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
+ x_samples = self.decode_first_stage(samples)
+ log["samples"] = x_samples
+ if plot_denoise_rows:
+ denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+ log["denoise_row"] = denoise_grid
+
+ if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
+ self.first_stage_model, IdentityFirstStage):
+ # also display when quantizing x0 while sampling
+ with ema_scope("Plotting Quantized Denoised"):
+ samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta,
+ quantize_denoised=True)
+ # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
+ # quantize_denoised=True)
+ x_samples = self.decode_first_stage(samples.to(self.device))
+ log["samples_x0_quantized"] = x_samples
+
+ if unconditional_guidance_scale > 1.0:
+ uc = self.get_unconditional_conditioning(N, unconditional_guidance_label)
+ if self.model.conditioning_key == "crossattn-adm":
+ uc = {"c_crossattn": [uc], "c_adm": c["c_adm"]}
+ with ema_scope("Sampling with classifier-free guidance"):
+ samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=uc,
+ )
+ x_samples_cfg = self.decode_first_stage(samples_cfg)
+ log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
+
+ if inpaint:
+ # make a simple center square
+ b, h, w = z.shape[0], z.shape[2], z.shape[3]
+ mask = torch.ones(N, h, w).to(self.device)
+ # zeros will be filled in
+ mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
+ mask = mask[:, None, ...]
+ with ema_scope("Plotting Inpaint"):
+ samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,
+ ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+ x_samples = self.decode_first_stage(samples.to(self.device))
+ log["samples_inpainting"] = x_samples
+ log["mask"] = mask
+
+ # outpaint
+ mask = 1. - mask
+ with ema_scope("Plotting Outpaint"):
+ samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,
+ ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+ x_samples = self.decode_first_stage(samples.to(self.device))
+ log["samples_outpainting"] = x_samples
+
+ if plot_progressive_rows:
+ with ema_scope("Plotting Progressives"):
+ img, progressives = self.progressive_denoising(c,
+ shape=(self.channels, self.image_size, self.image_size),
+ batch_size=N)
+ prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
+ log["progressive_row"] = prog_row
+
+ if return_keys:
+ if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+ return log
+ else:
+ return {key: log[key] for key in return_keys}
+ return log
+
+ def configure_optimizers(self):
+ lr = self.learning_rate
+ params = list(self.model.parameters())
+ if self.cond_stage_trainable:
+ print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
+ params = params + list(self.cond_stage_model.parameters())
+ if self.learn_logvar:
+ print('Diffusion model optimizing logvar')
+ params.append(self.logvar)
+ opt = torch.optim.AdamW(params, lr=lr)
+ if self.use_scheduler:
+ assert 'target' in self.scheduler_config
+ scheduler = instantiate_from_config(self.scheduler_config)
+
+ print("Setting up LambdaLR scheduler...")
+ scheduler = [
+ {
+ 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
+ 'interval': 'step',
+ 'frequency': 1
+ }]
+ return [opt], scheduler
+ return opt
+
+ @torch.no_grad()
+ def to_rgb(self, x):
+ x = x.float()
+ if not hasattr(self, "colorize"):
+ self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
+ x = nn.functional.conv2d(x, weight=self.colorize)
+ x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
+ return x
+
+
+class DiffusionWrapper(pl.LightningModule):
+ def __init__(self, diff_model_config, conditioning_key):
+ super().__init__()
+ self.sequential_cross_attn = diff_model_config.pop("sequential_crossattn", False)
+ self.diffusion_model = instantiate_from_config(diff_model_config)
+ self.conditioning_key = conditioning_key
+ assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm', 'hybrid-adm', 'crossattn-adm']
+
+ def forward(self, x, t, c_concat: list = None, c_crossattn: list = None, c_adm=None):
+ if self.conditioning_key is None:
+ out = self.diffusion_model(x, t)
+ elif self.conditioning_key == 'concat':
+ xc = torch.cat([x] + c_concat, dim=1)
+ out = self.diffusion_model(xc, t)
+ elif self.conditioning_key == 'crossattn':
+ if not self.sequential_cross_attn:
+ cc = torch.cat(c_crossattn, 1)
+ else:
+ cc = c_crossattn
+ out = self.diffusion_model(x, t, context=cc)
+ elif self.conditioning_key == 'hybrid':
+ xc = torch.cat([x] + c_concat, dim=1)
+ cc = torch.cat(c_crossattn, 1)
+ out = self.diffusion_model(xc, t, context=cc)
+ elif self.conditioning_key == 'hybrid-adm':
+ assert c_adm is not None
+ xc = torch.cat([x] + c_concat, dim=1)
+ cc = torch.cat(c_crossattn, 1)
+ out = self.diffusion_model(xc, t, context=cc, y=c_adm)
+ elif self.conditioning_key == 'crossattn-adm':
+ assert c_adm is not None
+ cc = torch.cat(c_crossattn, 1)
+ out = self.diffusion_model(x, t, context=cc, y=c_adm)
+ elif self.conditioning_key == 'adm':
+ cc = c_crossattn[0]
+ out = self.diffusion_model(x, t, y=cc)
+ else:
+ raise NotImplementedError()
+
+ return out
+
+
+class LatentUpscaleDiffusion(LatentDiffusion):
+ def __init__(self, *args, low_scale_config, low_scale_key="LR", noise_level_key=None, **kwargs):
+ super().__init__(*args, **kwargs)
+ # assumes that neither the cond_stage nor the low_scale_model contain trainable params
+ assert not self.cond_stage_trainable
+ self.instantiate_low_stage(low_scale_config)
+ self.low_scale_key = low_scale_key
+ self.noise_level_key = noise_level_key
+
+ def instantiate_low_stage(self, config):
+ model = instantiate_from_config(config)
+ self.low_scale_model = model.eval()
+ self.low_scale_model.train = disabled_train
+ for param in self.low_scale_model.parameters():
+ param.requires_grad = False
+
+ @torch.no_grad()
+ def get_input(self, batch, k, cond_key=None, bs=None, log_mode=False):
+ if not log_mode:
+ z, c = super().get_input(batch, k, force_c_encode=True, bs=bs)
+ else:
+ z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
+ force_c_encode=True, return_original_cond=True, bs=bs)
+ x_low = batch[self.low_scale_key][:bs]
+ x_low = rearrange(x_low, 'b h w c -> b c h w')
+ x_low = x_low.to(memory_format=torch.contiguous_format).float()
+ zx, noise_level = self.low_scale_model(x_low)
+ if self.noise_level_key is not None:
+ # get noise level from batch instead, e.g. when extracting a custom noise level for bsr
+ raise NotImplementedError('TODO')
+
+ all_conds = {"c_concat": [zx], "c_crossattn": [c], "c_adm": noise_level}
+ if log_mode:
+ # TODO: maybe disable if too expensive
+ x_low_rec = self.low_scale_model.decode(zx)
+ return z, all_conds, x, xrec, xc, x_low, x_low_rec, noise_level
+ return z, all_conds
+
+ @torch.no_grad()
+ def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
+ plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True,
+ unconditional_guidance_scale=1., unconditional_guidance_label=None, use_ema_scope=True,
+ **kwargs):
+ ema_scope = self.ema_scope if use_ema_scope else nullcontext
+ use_ddim = ddim_steps is not None
+
+ log = dict()
+ z, c, x, xrec, xc, x_low, x_low_rec, noise_level = self.get_input(batch, self.first_stage_key, bs=N,
+ log_mode=True)
+ N = min(x.shape[0], N)
+ n_row = min(x.shape[0], n_row)
+ log["inputs"] = x
+ log["reconstruction"] = xrec
+ log["x_lr"] = x_low
+ log[f"x_lr_rec_@noise_levels{'-'.join(map(lambda x: str(x), list(noise_level.cpu().numpy())))}"] = x_low_rec
+ if self.model.conditioning_key is not None:
+ if hasattr(self.cond_stage_model, "decode"):
+ xc = self.cond_stage_model.decode(c)
+ log["conditioning"] = xc
+ elif self.cond_stage_key in ["caption", "txt"]:
+ xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
+ log["conditioning"] = xc
+ elif self.cond_stage_key in ['class_label', 'cls']:
+ xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
+ log['conditioning'] = xc
+ elif isimage(xc):
+ log["conditioning"] = xc
+ if ismap(xc):
+ log["original_conditioning"] = self.to_rgb(xc)
+
+ if plot_diffusion_rows:
+ # get diffusion row
+ diffusion_row = list()
+ z_start = z[:n_row]
+ for t in range(self.num_timesteps):
+ if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+ t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+ t = t.to(self.device).long()
+ noise = torch.randn_like(z_start)
+ z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+ diffusion_row.append(self.decode_first_stage(z_noisy))
+
+ diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
+ diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+ diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+ diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+ log["diffusion_row"] = diffusion_grid
+
+ if sample:
+ # get denoise row
+ with ema_scope("Sampling"):
+ samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta)
+ # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
+ x_samples = self.decode_first_stage(samples)
+ log["samples"] = x_samples
+ if plot_denoise_rows:
+ denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+ log["denoise_row"] = denoise_grid
+
+ if unconditional_guidance_scale > 1.0:
+ uc_tmp = self.get_unconditional_conditioning(N, unconditional_guidance_label)
+ # TODO explore better "unconditional" choices for the other keys
+ # maybe guide away from empty text label and highest noise level and maximally degraded zx?
+ uc = dict()
+ for k in c:
+ if k == "c_crossattn":
+ assert isinstance(c[k], list) and len(c[k]) == 1
+ uc[k] = [uc_tmp]
+ elif k == "c_adm": # todo: only run with text-based guidance?
+ assert isinstance(c[k], torch.Tensor)
+ #uc[k] = torch.ones_like(c[k]) * self.low_scale_model.max_noise_level
+ uc[k] = c[k]
+ elif isinstance(c[k], list):
+ uc[k] = [c[k][i] for i in range(len(c[k]))]
+ else:
+ uc[k] = c[k]
+
+ with ema_scope("Sampling with classifier-free guidance"):
+ samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=uc,
+ )
+ x_samples_cfg = self.decode_first_stage(samples_cfg)
+ log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
+
+ if plot_progressive_rows:
+ with ema_scope("Plotting Progressives"):
+ img, progressives = self.progressive_denoising(c,
+ shape=(self.channels, self.image_size, self.image_size),
+ batch_size=N)
+ prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
+ log["progressive_row"] = prog_row
+
+ return log
+
+
+class LatentFinetuneDiffusion(LatentDiffusion):
+ """
+ Basis for different finetunas, such as inpainting or depth2image
+ To disable finetuning mode, set finetune_keys to None
+ """
+
+ def __init__(self,
+ concat_keys: tuple,
+ finetune_keys=("model.diffusion_model.input_blocks.0.0.weight",
+ "model_ema.diffusion_modelinput_blocks00weight"
+ ),
+ keep_finetune_dims=4,
+ # if model was trained without concat mode before and we would like to keep these channels
+ c_concat_log_start=None, # to log reconstruction of c_concat codes
+ c_concat_log_end=None,
+ *args, **kwargs
+ ):
+ ckpt_path = kwargs.pop("ckpt_path", None)
+ ignore_keys = kwargs.pop("ignore_keys", list())
+ super().__init__(*args, **kwargs)
+ self.finetune_keys = finetune_keys
+ self.concat_keys = concat_keys
+ self.keep_dims = keep_finetune_dims
+ self.c_concat_log_start = c_concat_log_start
+ self.c_concat_log_end = c_concat_log_end
+ if exists(self.finetune_keys): assert exists(ckpt_path), 'can only finetune from a given checkpoint'
+ if exists(ckpt_path):
+ self.init_from_ckpt(ckpt_path, ignore_keys)
+
+ def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
+ sd = torch.load(path, map_location="cpu")
+ if "state_dict" in list(sd.keys()):
+ sd = sd["state_dict"]
+ keys = list(sd.keys())
+ for k in keys:
+ for ik in ignore_keys:
+ if k.startswith(ik):
+ print("Deleting key {} from state_dict.".format(k))
+ del sd[k]
+
+ # make it explicit, finetune by including extra input channels
+ if exists(self.finetune_keys) and k in self.finetune_keys:
+ new_entry = None
+ for name, param in self.named_parameters():
+ if name in self.finetune_keys:
+ print(
+ f"modifying key '{name}' and keeping its original {self.keep_dims} (channels) dimensions only")
+ new_entry = torch.zeros_like(param) # zero init
+ assert exists(new_entry), 'did not find matching parameter to modify'
+ new_entry[:, :self.keep_dims, ...] = sd[k]
+ sd[k] = new_entry
+
+ missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
+ sd, strict=False)
+ print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+ if len(missing) > 0:
+ print(f"Missing Keys: {missing}")
+ if len(unexpected) > 0:
+ print(f"Unexpected Keys: {unexpected}")
+
+ @torch.no_grad()
+ def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
+ quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
+ plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,
+ use_ema_scope=True,
+ **kwargs):
+ ema_scope = self.ema_scope if use_ema_scope else nullcontext
+ use_ddim = ddim_steps is not None
+
+ log = dict()
+ z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, bs=N, return_first_stage_outputs=True)
+ c_cat, c = c["c_concat"][0], c["c_crossattn"][0]
+ N = min(x.shape[0], N)
+ n_row = min(x.shape[0], n_row)
+ log["inputs"] = x
+ log["reconstruction"] = xrec
+ if self.model.conditioning_key is not None:
+ if hasattr(self.cond_stage_model, "decode"):
+ xc = self.cond_stage_model.decode(c)
+ log["conditioning"] = xc
+ elif self.cond_stage_key in ["caption", "txt"]:
+ xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
+ log["conditioning"] = xc
+ elif self.cond_stage_key in ['class_label', 'cls']:
+ xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
+ log['conditioning'] = xc
+ elif isimage(xc):
+ log["conditioning"] = xc
+ if ismap(xc):
+ log["original_conditioning"] = self.to_rgb(xc)
+
+ if not (self.c_concat_log_start is None and self.c_concat_log_end is None):
+ log["c_concat_decoded"] = self.decode_first_stage(c_cat[:, self.c_concat_log_start:self.c_concat_log_end])
+
+ if plot_diffusion_rows:
+ # get diffusion row
+ diffusion_row = list()
+ z_start = z[:n_row]
+ for t in range(self.num_timesteps):
+ if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+ t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+ t = t.to(self.device).long()
+ noise = torch.randn_like(z_start)
+ z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+ diffusion_row.append(self.decode_first_stage(z_noisy))
+
+ diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
+ diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+ diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+ diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+ log["diffusion_row"] = diffusion_grid
+
+ if sample:
+ # get denoise row
+ with ema_scope("Sampling"):
+ samples, z_denoise_row = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
+ batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta)
+ # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
+ x_samples = self.decode_first_stage(samples)
+ log["samples"] = x_samples
+ if plot_denoise_rows:
+ denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+ log["denoise_row"] = denoise_grid
+
+ if unconditional_guidance_scale > 1.0:
+ uc_cross = self.get_unconditional_conditioning(N, unconditional_guidance_label)
+ uc_cat = c_cat
+ uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]}
+ with ema_scope("Sampling with classifier-free guidance"):
+ samples_cfg, _ = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
+ batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=uc_full,
+ )
+ x_samples_cfg = self.decode_first_stage(samples_cfg)
+ log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
+
+ return log
+
+
+class LatentInpaintDiffusion(LatentFinetuneDiffusion):
+ """
+ can either run as pure inpainting model (only concat mode) or with mixed conditionings,
+ e.g. mask as concat and text via cross-attn.
+ To disable finetuning mode, set finetune_keys to None
+ """
+
+ def __init__(self,
+ concat_keys=("mask", "masked_image"),
+ masked_image_key="masked_image",
+ *args, **kwargs
+ ):
+ super().__init__(concat_keys, *args, **kwargs)
+ self.masked_image_key = masked_image_key
+ assert self.masked_image_key in concat_keys
+
+ @torch.no_grad()
+ def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False):
+ # note: restricted to non-trainable encoders currently
+ assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for inpainting'
+ z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
+ force_c_encode=True, return_original_cond=True, bs=bs)
+
+ assert exists(self.concat_keys)
+ c_cat = list()
+ for ck in self.concat_keys:
+ cc = rearrange(batch[ck], 'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float()
+ if bs is not None:
+ cc = cc[:bs]
+ cc = cc.to(self.device)
+ bchw = z.shape
+ if ck != self.masked_image_key:
+ cc = torch.nn.functional.interpolate(cc, size=bchw[-2:])
+ else:
+ cc = self.get_first_stage_encoding(self.encode_first_stage(cc))
+ c_cat.append(cc)
+ c_cat = torch.cat(c_cat, dim=1)
+ all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
+ if return_first_stage_outputs:
+ return z, all_conds, x, xrec, xc
+ return z, all_conds
+
+ @torch.no_grad()
+ def log_images(self, *args, **kwargs):
+ log = super(LatentInpaintDiffusion, self).log_images(*args, **kwargs)
+ log["masked_image"] = rearrange(args[0]["masked_image"],
+ 'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float()
+ return log
+
+
+class LatentDepth2ImageDiffusion(LatentFinetuneDiffusion):
+ """
+ condition on monocular depth estimation
+ """
+
+ def __init__(self, depth_stage_config, concat_keys=("midas_in",), *args, **kwargs):
+ super().__init__(concat_keys=concat_keys, *args, **kwargs)
+ self.depth_model = instantiate_from_config(depth_stage_config)
+ self.depth_stage_key = concat_keys[0]
+
+ @torch.no_grad()
+ def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False):
+ # note: restricted to non-trainable encoders currently
+ assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for depth2img'
+ z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
+ force_c_encode=True, return_original_cond=True, bs=bs)
+
+ assert exists(self.concat_keys)
+ assert len(self.concat_keys) == 1
+ c_cat = list()
+ for ck in self.concat_keys:
+ cc = batch[ck]
+ if bs is not None:
+ cc = cc[:bs]
+ cc = cc.to(self.device)
+ cc = self.depth_model(cc)
+ cc = torch.nn.functional.interpolate(
+ cc,
+ size=z.shape[2:],
+ mode="bicubic",
+ align_corners=False,
+ )
+
+ depth_min, depth_max = torch.amin(cc, dim=[1, 2, 3], keepdim=True), torch.amax(cc, dim=[1, 2, 3],
+ keepdim=True)
+ cc = 2. * (cc - depth_min) / (depth_max - depth_min + 0.001) - 1.
+ c_cat.append(cc)
+ c_cat = torch.cat(c_cat, dim=1)
+ all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
+ if return_first_stage_outputs:
+ return z, all_conds, x, xrec, xc
+ return z, all_conds
+
+ @torch.no_grad()
+ def log_images(self, *args, **kwargs):
+ log = super().log_images(*args, **kwargs)
+ depth = self.depth_model(args[0][self.depth_stage_key])
+ depth_min, depth_max = torch.amin(depth, dim=[1, 2, 3], keepdim=True), \
+ torch.amax(depth, dim=[1, 2, 3], keepdim=True)
+ log["depth"] = 2. * (depth - depth_min) / (depth_max - depth_min) - 1.
+ return log
+
+
+class LatentUpscaleFinetuneDiffusion(LatentFinetuneDiffusion):
+ """
+ condition on low-res image (and optionally on some spatial noise augmentation)
+ """
+ def __init__(self, concat_keys=("lr",), reshuffle_patch_size=None,
+ low_scale_config=None, low_scale_key=None, *args, **kwargs):
+ super().__init__(concat_keys=concat_keys, *args, **kwargs)
+ self.reshuffle_patch_size = reshuffle_patch_size
+ self.low_scale_model = None
+ if low_scale_config is not None:
+ print("Initializing a low-scale model")
+ assert exists(low_scale_key)
+ self.instantiate_low_stage(low_scale_config)
+ self.low_scale_key = low_scale_key
+
+ def instantiate_low_stage(self, config):
+ model = instantiate_from_config(config)
+ self.low_scale_model = model.eval()
+ self.low_scale_model.train = disabled_train
+ for param in self.low_scale_model.parameters():
+ param.requires_grad = False
+
+ @torch.no_grad()
+ def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False):
+ # note: restricted to non-trainable encoders currently
+ assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for upscaling-ft'
+ z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
+ force_c_encode=True, return_original_cond=True, bs=bs)
+
+ assert exists(self.concat_keys)
+ assert len(self.concat_keys) == 1
+ # optionally make spatial noise_level here
+ c_cat = list()
+ noise_level = None
+ for ck in self.concat_keys:
+ cc = batch[ck]
+ cc = rearrange(cc, 'b h w c -> b c h w')
+ if exists(self.reshuffle_patch_size):
+ assert isinstance(self.reshuffle_patch_size, int)
+ cc = rearrange(cc, 'b c (p1 h) (p2 w) -> b (p1 p2 c) h w',
+ p1=self.reshuffle_patch_size, p2=self.reshuffle_patch_size)
+ if bs is not None:
+ cc = cc[:bs]
+ cc = cc.to(self.device)
+ if exists(self.low_scale_model) and ck == self.low_scale_key:
+ cc, noise_level = self.low_scale_model(cc)
+ c_cat.append(cc)
+ c_cat = torch.cat(c_cat, dim=1)
+ if exists(noise_level):
+ all_conds = {"c_concat": [c_cat], "c_crossattn": [c], "c_adm": noise_level}
+ else:
+ all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
+ if return_first_stage_outputs:
+ return z, all_conds, x, xrec, xc
+ return z, all_conds
+
+ @torch.no_grad()
+ def log_images(self, *args, **kwargs):
+ log = super().log_images(*args, **kwargs)
+ log["lr"] = rearrange(args[0]["lr"], 'b h w c -> b c h w')
+ return log
+
+
+
+
+class LatentDiffusionReferenceOnly(DDPM):
+ """main class"""
+
+ def __init__(self,
+ first_stage_config,
+ cond_stage_config=None,
+ num_timesteps_cond=None,
+ cond_stage_key="image",
+ cond_stage_trainable=False,
+ concat_mode=True,
+ cond_stage_forward=None,
+ conditioning_key=None,
+ scale_factor=1.0,
+ scale_by_std=False,
+ force_null_conditioning=False,
+ *args, **kwargs):
+ self.force_null_conditioning = force_null_conditioning
+ self.num_timesteps_cond = default(num_timesteps_cond, 1)
+ self.scale_by_std = scale_by_std
+ assert self.num_timesteps_cond <= kwargs['timesteps']
+ # for backwards compatibility after implementation of DiffusionWrapper
+ if conditioning_key is None:
+ conditioning_key = 'concat' if concat_mode else 'crossattn'
+ if cond_stage_config == '__is_unconditional__' and not self.force_null_conditioning:
+ conditioning_key = None
+ ckpt_path = kwargs.pop("ckpt_path", None)
+ reset_ema = kwargs.pop("reset_ema", False)
+ reset_num_ema_updates = kwargs.pop("reset_num_ema_updates", False)
+ ignore_keys = kwargs.pop("ignore_keys", [])
+ super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
+ self.concat_mode = concat_mode
+ self.cond_stage_trainable = cond_stage_trainable
+ self.cond_stage_key = cond_stage_key
+ try:
+ self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
+ except:
+ self.num_downs = 0
+ if not scale_by_std:
+ self.scale_factor = scale_factor
+ else:
+ self.register_buffer('scale_factor', torch.tensor(scale_factor))
+ self.instantiate_first_stage(first_stage_config)
+ if cond_stage_config is not None:
+ self.instantiate_cond_stage(cond_stage_config)
+ self.cond_stage_forward = cond_stage_forward
+ self.clip_denoised = False
+ self.bbox_tokenizer = None
+
+ self.restarted_from_ckpt = False
+ if ckpt_path is not None:
+ self.init_from_ckpt(ckpt_path, ignore_keys)
+ self.restarted_from_ckpt = True
+ if reset_ema:
+ assert self.use_ema
+ print(
+ f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
+ self.model_ema = LitEma(self.model)
+ if reset_num_ema_updates:
+ print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
+ assert self.use_ema
+ self.model_ema.reset_num_updates()
+
+ def make_cond_schedule(self, ):
+ self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
+ ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
+ self.cond_ids[:self.num_timesteps_cond] = ids
+
+ @rank_zero_only
+ @torch.no_grad()
+ def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
+ # only for very first batch
+ if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
+ assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
+ # set rescale weight to 1./std of encodings
+ print("### USING STD-RESCALING ###")
+ x = super().get_input(batch, self.first_stage_key)
+ x = x.to(self.device)
+ encoder_posterior = self.encode_first_stage(x)
+ z = self.get_first_stage_encoding(encoder_posterior).detach()
+ del self.scale_factor
+ self.register_buffer('scale_factor', 1. / z.flatten().std())
+ print(f"setting self.scale_factor to {self.scale_factor}")
+ print("### USING STD-RESCALING ###")
+
+ def register_schedule(self,
+ given_betas=None, beta_schedule="linear", timesteps=1000,
+ linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
+
+ self.shorten_cond_schedule = self.num_timesteps_cond > 1
+ if self.shorten_cond_schedule:
+ self.make_cond_schedule()
+
+ def instantiate_first_stage(self, config):
+ model = instantiate_from_config(config)
+ self.first_stage_model = model.eval()
+ self.first_stage_model.train = disabled_train
+ for param in self.first_stage_model.parameters():
+ param.requires_grad = False
+
+ def instantiate_cond_stage(self, config):
+ if not self.cond_stage_trainable:
+ if config == "__is_first_stage__":
+ print("Using first stage also as cond stage.")
+ self.cond_stage_model = self.first_stage_model
+ elif config == "__is_unconditional__":
+ print(f"Training {self.__class__.__name__} as an unconditional model.")
+ self.cond_stage_model = None
+ # self.be_unconditional = True
+ else:
+ model = instantiate_from_config(config)
+ self.cond_stage_model = model.eval()
+ self.cond_stage_model.train = disabled_train
+ for param in self.cond_stage_model.parameters():
+ param.requires_grad = False
+ else:
+ assert config != '__is_first_stage__'
+ assert config != '__is_unconditional__'
+ model = instantiate_from_config(config)
+ self.cond_stage_model = model
+
+ def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
+ denoise_row = []
+ for zd in tqdm(samples, desc=desc):
+ denoise_row.append(self.decode_first_stage(zd.to(self.device),
+ force_not_quantize=force_no_decoder_quantization))
+ n_imgs_per_row = len(denoise_row)
+ denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
+ denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
+ denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+ denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+ return denoise_grid
+
+ def get_first_stage_encoding(self, encoder_posterior):
+ if isinstance(encoder_posterior, DiagonalGaussianDistribution):
+ z = encoder_posterior.sample()
+ elif isinstance(encoder_posterior, torch.Tensor):
+ z = encoder_posterior
+ else:
+ raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
+ return self.scale_factor * z
+
+ def get_learned_conditioning(self, c):
+ if self.cond_stage_forward is None:
+ if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
+ c = self.cond_stage_model.encode(c)
+ if isinstance(c, DiagonalGaussianDistribution):
+ c = c.mode()
+ else:
+ c = self.cond_stage_model(c)
+ else:
+ assert hasattr(self.cond_stage_model, self.cond_stage_forward)
+ c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
+ return c
+
+ def meshgrid(self, h, w):
+ y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
+ x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
+
+ arr = torch.cat([y, x], dim=-1)
+ return arr
+
+ def delta_border(self, h, w):
+ """
+ :param h: height
+ :param w: width
+ :return: normalized distance to image border,
+ wtith min distance = 0 at border and max dist = 0.5 at image center
+ """
+ lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
+ arr = self.meshgrid(h, w) / lower_right_corner
+ dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
+ dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
+ edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
+ return edge_dist
+
+ def get_weighting(self, h, w, Ly, Lx, device):
+ weighting = self.delta_border(h, w)
+ weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
+ self.split_input_params["clip_max_weight"], )
+ weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
+
+ if self.split_input_params["tie_braker"]:
+ L_weighting = self.delta_border(Ly, Lx)
+ L_weighting = torch.clip(L_weighting,
+ self.split_input_params["clip_min_tie_weight"],
+ self.split_input_params["clip_max_tie_weight"])
+
+ L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
+ weighting = weighting * L_weighting
+ return weighting
+
+ def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
+ """
+ :param x: img of size (bs, c, h, w)
+ :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
+ """
+ bs, nc, h, w = x.shape
+
+ # number of crops in image
+ Ly = (h - kernel_size[0]) // stride[0] + 1
+ Lx = (w - kernel_size[1]) // stride[1] + 1
+
+ if uf == 1 and df == 1:
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+ unfold = torch.nn.Unfold(**fold_params)
+
+ fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
+
+ weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
+ normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
+ weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
+
+ elif uf > 1 and df == 1:
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+ unfold = torch.nn.Unfold(**fold_params)
+
+ fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
+ dilation=1, padding=0,
+ stride=(stride[0] * uf, stride[1] * uf))
+ fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
+
+ weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
+ normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap
+ weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
+
+ elif df > 1 and uf == 1:
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+ unfold = torch.nn.Unfold(**fold_params)
+
+ fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
+ dilation=1, padding=0,
+ stride=(stride[0] // df, stride[1] // df))
+ fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
+
+ weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
+ normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap
+ weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
+
+ else:
+ raise NotImplementedError
+
+ return fold, unfold, normalization, weighting
+
+ @torch.no_grad()
+ def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
+ cond_key=None, return_original_cond=False, bs=None, return_x=False):
+ x = super().get_input(batch, k)
+ if bs is not None:
+ x = x[:bs]
+ x = x.to(self.device)
+ encoder_posterior = self.encode_first_stage(x)
+ z = self.get_first_stage_encoding(encoder_posterior).detach()
+
+ if self.model.conditioning_key is not None and not self.force_null_conditioning:
+ if cond_key is None:
+ cond_key = self.cond_stage_key
+ if cond_key != self.first_stage_key:
+ if cond_key in ['caption', 'coordinates_bbox', "txt"]:
+ xc = batch[cond_key]
+ elif cond_key in ['class_label', 'cls']:
+ xc = batch
+ else:
+ xc = super().get_input(batch, cond_key).to(self.device)
+ else:
+ xc = x
+ if not self.cond_stage_trainable or force_c_encode:
+ if isinstance(xc, dict) or isinstance(xc, list):
+ c = self.get_learned_conditioning(xc)
+ else:
+ c = self.get_learned_conditioning(xc.to(self.device))
+ else:
+ c = xc
+ if bs is not None:
+ c = c[:bs]
+
+ if self.use_positional_encodings:
+ pos_x, pos_y = self.compute_latent_shifts(batch)
+ ckey = __conditioning_keys__[self.model.conditioning_key]
+ c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
+
+ else:
+ c = None
+ xc = None
+ if self.use_positional_encodings:
+ pos_x, pos_y = self.compute_latent_shifts(batch)
+ c = {'pos_x': pos_x, 'pos_y': pos_y}
+ out = [z, c]
+ if return_first_stage_outputs:
+ xrec = self.decode_first_stage(z)
+ out.extend([x, xrec])
+ if return_x:
+ out.extend([x])
+ if return_original_cond:
+ out.append(xc)
+ return out
+
+ @torch.no_grad()
+ def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
+ if predict_cids:
+ if z.dim() == 4:
+ z = torch.argmax(z.exp(), dim=1).long()
+ z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
+ z = rearrange(z, 'b h w c -> b c h w').contiguous()
+
+ z = 1. / self.scale_factor * z
+ return self.first_stage_model.decode(z)
+
+ @torch.no_grad()
+ def encode_first_stage(self, x):
+ return self.first_stage_model.encode(x)
+
+ def shared_step(self, batch, **kwargs):
+ x, c = self.get_input(batch, self.first_stage_key)
+ loss = self(x, c)
+ return loss
+
+ def forward(self, x, c, *args, **kwargs):
+ t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+ if self.model.conditioning_key is not None:
+ assert c is not None
+ if self.cond_stage_trainable:
+ c = self.get_learned_conditioning(c)
+ if self.shorten_cond_schedule: # TODO: drop this option
+ tc = self.cond_ids[t].to(self.device)
+ c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
+ return self.p_losses(x, c, t, *args, **kwargs)
+
+ def apply_model(self, x_noisy, t, cond, reference_image_noisy=None ,return_ids=False):
+ if isinstance(cond, dict):
+ # hybrid case, cond is expected to be a dict
+ pass
+ else:
+ if not isinstance(cond, list):
+ cond = [cond]
+ key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
+ cond = {key: cond}
+
+ x_recon = self.model(x_noisy, t, **cond)
+
+ if isinstance(x_recon, tuple) and not return_ids:
+ return x_recon[0]
+ else:
+ return x_recon
+
+ def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
+ return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
+ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+
+ def _prior_bpd(self, x_start):
+ """
+ Get the prior KL term for the variational lower-bound, measured in
+ bits-per-dim.
+ This term can't be optimized, as it only depends on the encoder.
+ :param x_start: the [N x C x ...] tensor of inputs.
+ :return: a batch of [N] KL values (in bits), one per batch element.
+ """
+ batch_size = x_start.shape[0]
+ t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
+ qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
+ kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
+ return mean_flat(kl_prior) / np.log(2.0)
+
+ def p_losses(self, x_start, cond, t, noise=None):
+ noise = default(noise, lambda: torch.randn_like(x_start))
+ # pdb.set_trace()
+ if 'image_control' in cond and cond['image_control'] is not None:
+ cond_image_start = torch.cat(cond['image_control'], 1)
+ # pdb.set_trace()
+
+ # pdb.set_trace()
+ if cond['wonoise']:
+ reference_image_noisy = cond_image_start
+ else:
+ reference_image_noisy = self.q_sample(x_start=cond_image_start, t=t, noise=noise)
+ # pdb.set_trace()
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+ model_output = self.apply_model(x_noisy, t, cond, reference_image_noisy)
+ else:
+ model_output = self.apply_model(x_noisy, t, cond, None)
+ loss_dict = {}
+ prefix = 'train' if self.training else 'val'
+
+ if self.parameterization == "x0":
+ target = x_start
+ elif self.parameterization == "eps":
+ target = noise
+ elif self.parameterization == "v":
+ target = self.get_v(x_start, noise, t)
+ else:
+ raise NotImplementedError()
+
+ loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
+ loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
+
+ logvar_t = self.logvar[t].to(self.device)
+ loss = loss_simple / torch.exp(logvar_t) + logvar_t
+ # loss = loss_simple / torch.exp(self.logvar) + self.logvar
+ if self.learn_logvar:
+ loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
+ loss_dict.update({'logvar': self.logvar.data.mean()})
+
+ loss = self.l_simple_weight * loss.mean()
+
+ loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
+ loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
+ loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
+ loss += (self.original_elbo_weight * loss_vlb)
+ loss_dict.update({f'{prefix}/loss': loss})
+
+ return loss, loss_dict
+
+ def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
+ return_x0=False, score_corrector=None, corrector_kwargs=None):
+ t_in = t
+ model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
+
+ if score_corrector is not None:
+ assert self.parameterization == "eps"
+ model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
+
+ if return_codebook_ids:
+ model_out, logits = model_out
+
+ if self.parameterization == "eps":
+ x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+ elif self.parameterization == "x0":
+ x_recon = model_out
+ else:
+ raise NotImplementedError()
+
+ if clip_denoised:
+ x_recon.clamp_(-1., 1.)
+ if quantize_denoised:
+ x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
+ model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+ if return_codebook_ids:
+ return model_mean, posterior_variance, posterior_log_variance, logits
+ elif return_x0:
+ return model_mean, posterior_variance, posterior_log_variance, x_recon
+ else:
+ return model_mean, posterior_variance, posterior_log_variance
+
+ @torch.no_grad()
+ def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
+ return_codebook_ids=False, quantize_denoised=False, return_x0=False,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
+ b, *_, device = *x.shape, x.device
+ outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
+ return_codebook_ids=return_codebook_ids,
+ quantize_denoised=quantize_denoised,
+ return_x0=return_x0,
+ score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+ if return_codebook_ids:
+ raise DeprecationWarning("Support dropped.")
+ model_mean, _, model_log_variance, logits = outputs
+ elif return_x0:
+ model_mean, _, model_log_variance, x0 = outputs
+ else:
+ model_mean, _, model_log_variance = outputs
+
+ noise = noise_like(x.shape, device, repeat_noise) * temperature
+ if noise_dropout > 0.:
+ noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+ # no noise when t == 0
+ nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+
+ if return_codebook_ids:
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
+ if return_x0:
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
+ else:
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+ @torch.no_grad()
+ def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
+ img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
+ score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
+ log_every_t=None):
+ if not log_every_t:
+ log_every_t = self.log_every_t
+ timesteps = self.num_timesteps
+ if batch_size is not None:
+ b = batch_size if batch_size is not None else shape[0]
+ shape = [batch_size] + list(shape)
+ else:
+ b = batch_size = shape[0]
+ if x_T is None:
+ img = torch.randn(shape, device=self.device)
+ else:
+ img = x_T
+ intermediates = []
+ if cond is not None:
+ if isinstance(cond, dict):
+ cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+ list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+ else:
+ cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+
+ if start_T is not None:
+ timesteps = min(timesteps, start_T)
+ iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
+ total=timesteps) if verbose else reversed(
+ range(0, timesteps))
+ if type(temperature) == float:
+ temperature = [temperature] * timesteps
+
+ for i in iterator:
+ ts = torch.full((b,), i, device=self.device, dtype=torch.long)
+ if self.shorten_cond_schedule:
+ assert self.model.conditioning_key != 'hybrid'
+ tc = self.cond_ids[ts].to(cond.device)
+ cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+ img, x0_partial = self.p_sample(img, cond, ts,
+ clip_denoised=self.clip_denoised,
+ quantize_denoised=quantize_denoised, return_x0=True,
+ temperature=temperature[i], noise_dropout=noise_dropout,
+ score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+ if mask is not None:
+ assert x0 is not None
+ img_orig = self.q_sample(x0, ts)
+ img = img_orig * mask + (1. - mask) * img
+
+ if i % log_every_t == 0 or i == timesteps - 1:
+ intermediates.append(x0_partial)
+ if callback: callback(i)
+ if img_callback: img_callback(img, i)
+ return img, intermediates
+
+ @torch.no_grad()
+ def p_sample_loop(self, cond, shape, return_intermediates=False,
+ x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
+ mask=None, x0=None, img_callback=None, start_T=None,
+ log_every_t=None):
+
+ if not log_every_t:
+ log_every_t = self.log_every_t
+ device = self.betas.device
+ b = shape[0]
+ if x_T is None:
+ img = torch.randn(shape, device=device)
+ else:
+ img = x_T
+
+ intermediates = [img]
+ if timesteps is None:
+ timesteps = self.num_timesteps
+
+ if start_T is not None:
+ timesteps = min(timesteps, start_T)
+ iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
+ range(0, timesteps))
+
+ if mask is not None:
+ assert x0 is not None
+ assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
+
+ for i in iterator:
+ ts = torch.full((b,), i, device=device, dtype=torch.long)
+ if self.shorten_cond_schedule:
+ assert self.model.conditioning_key != 'hybrid'
+ tc = self.cond_ids[ts].to(cond.device)
+ cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+ img = self.p_sample(img, cond, ts,
+ clip_denoised=self.clip_denoised,
+ quantize_denoised=quantize_denoised)
+ if mask is not None:
+ img_orig = self.q_sample(x0, ts)
+ img = img_orig * mask + (1. - mask) * img
+
+ if i % log_every_t == 0 or i == timesteps - 1:
+ intermediates.append(img)
+ if callback: callback(i)
+ if img_callback: img_callback(img, i)
+
+ if return_intermediates:
+ return img, intermediates
+ return img
+
+ @torch.no_grad()
+ def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
+ verbose=True, timesteps=None, quantize_denoised=False,
+ mask=None, x0=None, shape=None, **kwargs):
+ if shape is None:
+ shape = (batch_size, self.channels, self.image_size, self.image_size)
+ if cond is not None:
+ if isinstance(cond, dict):
+ cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+ list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+ else:
+ cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+ return self.p_sample_loop(cond,
+ shape,
+ return_intermediates=return_intermediates, x_T=x_T,
+ verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
+ mask=mask, x0=x0)
+
+ @torch.no_grad()
+ def sample_log(self, cond, batch_size, ddim, ddim_steps, num_overlap=0, lcm=False, **kwargs):
+ if ddim:
+ ddim_sampler = DDIMSampler_ReferenceOnly(self)
+ shape = (self.channels, self.image_size, self.image_size)
+ samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size,
+ shape, cond, verbose=False, num_overlap=num_overlap, **kwargs)
+
+ else:
+ samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
+ return_intermediates=True, **kwargs)
+
+ return samples, intermediates
+
+ @torch.no_grad()
+ def get_unconditional_conditioning(self, batch_size, null_label=None):
+ if null_label is not None:
+ xc = null_label
+ if isinstance(xc, ListConfig):
+ xc = list(xc)
+ if isinstance(xc, dict) or isinstance(xc, list):
+ c = self.get_learned_conditioning(xc)
+ else:
+ if hasattr(xc, "to"):
+ xc = xc.to(self.device)
+ c = self.get_learned_conditioning(xc)
+ else:
+ if self.cond_stage_key in ["class_label", "cls"]:
+ xc = self.cond_stage_model.get_unconditional_conditioning(batch_size, device=self.device)
+ return self.get_learned_conditioning(xc)
+ else:
+ raise NotImplementedError("todo")
+ if isinstance(c, list): # in case the encoder gives us a list
+ for i in range(len(c)):
+ c[i] = repeat(c[i], '1 ... -> b ...', b=batch_size).to(self.device)
+ else:
+ c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device)
+ return c
+
+ @torch.no_grad()
+ def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=50, ddim_eta=0., return_keys=None,
+ quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
+ plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,
+ use_ema_scope=True,
+ **kwargs):
+ ema_scope = self.ema_scope if use_ema_scope else nullcontext
+ use_ddim = ddim_steps is not None
+
+ log = dict()
+ z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
+ return_first_stage_outputs=True,
+ force_c_encode=True,
+ return_original_cond=True,
+ bs=N)
+ N = min(x.shape[0], N)
+ n_row = min(x.shape[0], n_row)
+ log["inputs"] = x
+ log["reconstruction"] = xrec
+ if self.model.conditioning_key is not None:
+ if hasattr(self.cond_stage_model, "decode"):
+ xc = self.cond_stage_model.decode(c)
+ log["conditioning"] = xc
+ elif self.cond_stage_key in ["caption", "txt"]:
+ xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
+ log["conditioning"] = xc
+ elif self.cond_stage_key in ['class_label', "cls"]:
+ try:
+ xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
+ log['conditioning'] = xc
+ except KeyError:
+ # probably no "human_label" in batch
+ pass
+ elif isimage(xc):
+ log["conditioning"] = xc
+ if ismap(xc):
+ log["original_conditioning"] = self.to_rgb(xc)
+
+ if plot_diffusion_rows:
+ # get diffusion row
+ diffusion_row = list()
+ z_start = z[:n_row]
+ for t in range(self.num_timesteps):
+ if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+ t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+ t = t.to(self.device).long()
+ noise = torch.randn_like(z_start)
+ z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+ diffusion_row.append(self.decode_first_stage(z_noisy))
+
+ diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
+ diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+ diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+ diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+ log["diffusion_row"] = diffusion_grid
+
+ if sample:
+ # get denoise row
+ with ema_scope("Sampling"):
+ samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta)
+ # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
+ x_samples = self.decode_first_stage(samples)
+ log["samples"] = x_samples
+ if plot_denoise_rows:
+ denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+ log["denoise_row"] = denoise_grid
+
+ if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
+ self.first_stage_model, IdentityFirstStage):
+ # also display when quantizing x0 while sampling
+ with ema_scope("Plotting Quantized Denoised"):
+ samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta,
+ quantize_denoised=True)
+ # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
+ # quantize_denoised=True)
+ x_samples = self.decode_first_stage(samples.to(self.device))
+ log["samples_x0_quantized"] = x_samples
+
+ if unconditional_guidance_scale > 1.0:
+ uc = self.get_unconditional_conditioning(N, unconditional_guidance_label)
+ if self.model.conditioning_key == "crossattn-adm":
+ uc = {"c_crossattn": [uc], "c_adm": c["c_adm"]}
+ with ema_scope("Sampling with classifier-free guidance"):
+ samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=uc,
+ )
+ x_samples_cfg = self.decode_first_stage(samples_cfg)
+ log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
+
+ if inpaint:
+ # make a simple center square
+ b, h, w = z.shape[0], z.shape[2], z.shape[3]
+ mask = torch.ones(N, h, w).to(self.device)
+ # zeros will be filled in
+ mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
+ mask = mask[:, None, ...]
+ with ema_scope("Plotting Inpaint"):
+ samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,
+ ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+ x_samples = self.decode_first_stage(samples.to(self.device))
+ log["samples_inpainting"] = x_samples
+ log["mask"] = mask
+
+ # outpaint
+ mask = 1. - mask
+ with ema_scope("Plotting Outpaint"):
+ samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,
+ ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+ x_samples = self.decode_first_stage(samples.to(self.device))
+ log["samples_outpainting"] = x_samples
+
+ if plot_progressive_rows:
+ with ema_scope("Plotting Progressives"):
+ img, progressives = self.progressive_denoising(c,
+ shape=(self.channels, self.image_size, self.image_size),
+ batch_size=N)
+ prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
+ log["progressive_row"] = prog_row
+
+ if return_keys:
+ if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+ return log
+ else:
+ return {key: log[key] for key in return_keys}
+ return log
+
+ def configure_optimizers(self):
+ lr = self.learning_rate
+ params = list(self.model.parameters())
+ if self.cond_stage_trainable:
+ print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
+ params = params + list(self.cond_stage_model.parameters())
+ if self.learn_logvar:
+ print('Diffusion model optimizing logvar')
+ params.append(self.logvar)
+ opt = torch.optim.AdamW(params, lr=lr)
+ if self.use_scheduler:
+ assert 'target' in self.scheduler_config
+ scheduler = instantiate_from_config(self.scheduler_config)
+
+ print("Setting up LambdaLR scheduler...")
+ scheduler = [
+ {
+ 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
+ 'interval': 'step',
+ 'frequency': 1
+ }]
+ return [opt], scheduler
+ return opt
+
+ @torch.no_grad()
+ def to_rgb(self, x):
+ x = x.float()
+ if not hasattr(self, "colorize"):
+ self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
+ x = nn.functional.conv2d(x, weight=self.colorize)
+ x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
+ return x
diff --git a/model_lib/ControlNet/ldm/models/diffusion/dpm_solver/__init__.py b/model_lib/ControlNet/ldm/models/diffusion/dpm_solver/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7427f38c07530afbab79154ea8aaf88c4bf70a08
--- /dev/null
+++ b/model_lib/ControlNet/ldm/models/diffusion/dpm_solver/__init__.py
@@ -0,0 +1 @@
+from .sampler import DPMSolverSampler
\ No newline at end of file
diff --git a/model_lib/ControlNet/ldm/models/diffusion/dpm_solver/dpm_solver.py b/model_lib/ControlNet/ldm/models/diffusion/dpm_solver/dpm_solver.py
new file mode 100644
index 0000000000000000000000000000000000000000..095e5ba3ce0b1aa7f4b3f1e2e5d8fff7cfe6dc8c
--- /dev/null
+++ b/model_lib/ControlNet/ldm/models/diffusion/dpm_solver/dpm_solver.py
@@ -0,0 +1,1154 @@
+import torch
+import torch.nn.functional as F
+import math
+from tqdm import tqdm
+
+
+class NoiseScheduleVP:
+ def __init__(
+ self,
+ schedule='discrete',
+ betas=None,
+ alphas_cumprod=None,
+ continuous_beta_0=0.1,
+ continuous_beta_1=20.,
+ ):
+ """Create a wrapper class for the forward SDE (VP type).
+ ***
+ Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
+ We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.
+ ***
+ The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).
+ We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).
+ Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:
+ log_alpha_t = self.marginal_log_mean_coeff(t)
+ sigma_t = self.marginal_std(t)
+ lambda_t = self.marginal_lambda(t)
+ Moreover, as lambda(t) is an invertible function, we also support its inverse function:
+ t = self.inverse_lambda(lambda_t)
+ ===============================================================
+ We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).
+ 1. For discrete-time DPMs:
+ For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:
+ t_i = (i + 1) / N
+ e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.
+ We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.
+ Args:
+ betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)
+ alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)
+ Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.
+ **Important**: Please pay special attention for the args for `alphas_cumprod`:
+ The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that
+ q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ).
+ Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have
+ alpha_{t_n} = \sqrt{\hat{alpha_n}},
+ and
+ log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}).
+ 2. For continuous-time DPMs:
+ We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise
+ schedule are the default settings in DDPM and improved-DDPM:
+ Args:
+ beta_min: A `float` number. The smallest beta for the linear schedule.
+ beta_max: A `float` number. The largest beta for the linear schedule.
+ cosine_s: A `float` number. The hyperparameter in the cosine schedule.
+ cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.
+ T: A `float` number. The ending time of the forward process.
+ ===============================================================
+ Args:
+ schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,
+ 'linear' or 'cosine' for continuous-time DPMs.
+ Returns:
+ A wrapper object of the forward SDE (VP type).
+
+ ===============================================================
+ Example:
+ # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):
+ >>> ns = NoiseScheduleVP('discrete', betas=betas)
+ # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1):
+ >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
+ # For continuous-time DPMs (VPSDE), linear schedule:
+ >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)
+ """
+
+ if schedule not in ['discrete', 'linear', 'cosine']:
+ raise ValueError(
+ "Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(
+ schedule))
+
+ self.schedule = schedule
+ if schedule == 'discrete':
+ if betas is not None:
+ log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)
+ else:
+ assert alphas_cumprod is not None
+ log_alphas = 0.5 * torch.log(alphas_cumprod)
+ self.total_N = len(log_alphas)
+ self.T = 1.
+ self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1))
+ self.log_alpha_array = log_alphas.reshape((1, -1,))
+ else:
+ self.total_N = 1000
+ self.beta_0 = continuous_beta_0
+ self.beta_1 = continuous_beta_1
+ self.cosine_s = 0.008
+ self.cosine_beta_max = 999.
+ self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (
+ 1. + self.cosine_s) / math.pi - self.cosine_s
+ self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.))
+ self.schedule = schedule
+ if schedule == 'cosine':
+ # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.
+ # Note that T = 0.9946 may be not the optimal setting. However, we find it works well.
+ self.T = 0.9946
+ else:
+ self.T = 1.
+
+ def marginal_log_mean_coeff(self, t):
+ """
+ Compute log(alpha_t) of a given continuous-time label t in [0, T].
+ """
+ if self.schedule == 'discrete':
+ return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device),
+ self.log_alpha_array.to(t.device)).reshape((-1))
+ elif self.schedule == 'linear':
+ return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
+ elif self.schedule == 'cosine':
+ log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))
+ log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0
+ return log_alpha_t
+
+ def marginal_alpha(self, t):
+ """
+ Compute alpha_t of a given continuous-time label t in [0, T].
+ """
+ return torch.exp(self.marginal_log_mean_coeff(t))
+
+ def marginal_std(self, t):
+ """
+ Compute sigma_t of a given continuous-time label t in [0, T].
+ """
+ return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
+
+ def marginal_lambda(self, t):
+ """
+ Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
+ """
+ log_mean_coeff = self.marginal_log_mean_coeff(t)
+ log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
+ return log_mean_coeff - log_std
+
+ def inverse_lambda(self, lamb):
+ """
+ Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
+ """
+ if self.schedule == 'linear':
+ tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
+ Delta = self.beta_0 ** 2 + tmp
+ return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)
+ elif self.schedule == 'discrete':
+ log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb)
+ t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]),
+ torch.flip(self.t_array.to(lamb.device), [1]))
+ return t.reshape((-1,))
+ else:
+ log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
+ t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (
+ 1. + self.cosine_s) / math.pi - self.cosine_s
+ t = t_fn(log_alpha)
+ return t
+
+
+def model_wrapper(
+ model,
+ noise_schedule,
+ model_type="noise",
+ model_kwargs={},
+ guidance_type="uncond",
+ condition=None,
+ unconditional_condition=None,
+ guidance_scale=1.,
+ classifier_fn=None,
+ classifier_kwargs={},
+):
+ """Create a wrapper function for the noise prediction model.
+ DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
+ firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
+ We support four types of the diffusion model by setting `model_type`:
+ 1. "noise": noise prediction model. (Trained by predicting noise).
+ 2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0).
+ 3. "v": velocity prediction model. (Trained by predicting the velocity).
+ The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].
+ [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models."
+ arXiv preprint arXiv:2202.00512 (2022).
+ [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
+ arXiv preprint arXiv:2210.02303 (2022).
+
+ 4. "score": marginal score function. (Trained by denoising score matching).
+ Note that the score function and the noise prediction model follows a simple relationship:
+ ```
+ noise(x_t, t) = -sigma_t * score(x_t, t)
+ ```
+ We support three types of guided sampling by DPMs by setting `guidance_type`:
+ 1. "uncond": unconditional sampling by DPMs.
+ The input `model` has the following format:
+ ``
+ model(x, t_input, **model_kwargs) -> noise | x_start | v | score
+ ``
+ 2. "classifier": classifier guidance sampling [3] by DPMs and another classifier.
+ The input `model` has the following format:
+ ``
+ model(x, t_input, **model_kwargs) -> noise | x_start | v | score
+ ``
+ The input `classifier_fn` has the following format:
+ ``
+ classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)
+ ``
+ [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis,"
+ in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.
+ 3. "classifier-free": classifier-free guidance sampling by conditional DPMs.
+ The input `model` has the following format:
+ ``
+ model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
+ ``
+ And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
+ [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
+ arXiv preprint arXiv:2207.12598 (2022).
+
+ The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
+ or continuous-time labels (i.e. epsilon to T).
+ We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:
+ ``
+ def model_fn(x, t_continuous) -> noise:
+ t_input = get_model_input_time(t_continuous)
+ return noise_pred(model, x, t_input, **model_kwargs)
+ ``
+ where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
+ ===============================================================
+ Args:
+ model: A diffusion model with the corresponding format described above.
+ noise_schedule: A noise schedule object, such as NoiseScheduleVP.
+ model_type: A `str`. The parameterization type of the diffusion model.
+ "noise" or "x_start" or "v" or "score".
+ model_kwargs: A `dict`. A dict for the other inputs of the model function.
+ guidance_type: A `str`. The type of the guidance for sampling.
+ "uncond" or "classifier" or "classifier-free".
+ condition: A pytorch tensor. The condition for the guided sampling.
+ Only used for "classifier" or "classifier-free" guidance type.
+ unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.
+ Only used for "classifier-free" guidance type.
+ guidance_scale: A `float`. The scale for the guided sampling.
+ classifier_fn: A classifier function. Only used for the classifier guidance.
+ classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.
+ Returns:
+ A noise prediction model that accepts the noised data and the continuous time as the inputs.
+ """
+
+ def get_model_input_time(t_continuous):
+ """
+ Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
+ For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
+ For continuous-time DPMs, we just use `t_continuous`.
+ """
+ if noise_schedule.schedule == 'discrete':
+ return (t_continuous - 1. / noise_schedule.total_N) * 1000.
+ else:
+ return t_continuous
+
+ def noise_pred_fn(x, t_continuous, cond=None):
+ if t_continuous.reshape((-1,)).shape[0] == 1:
+ t_continuous = t_continuous.expand((x.shape[0]))
+ t_input = get_model_input_time(t_continuous)
+ if cond is None:
+ output = model(x, t_input, **model_kwargs)
+ else:
+ output = model(x, t_input, cond, **model_kwargs)
+ if model_type == "noise":
+ return output
+ elif model_type == "x_start":
+ alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
+ dims = x.dim()
+ return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims)
+ elif model_type == "v":
+ alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
+ dims = x.dim()
+ return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
+ elif model_type == "score":
+ sigma_t = noise_schedule.marginal_std(t_continuous)
+ dims = x.dim()
+ return -expand_dims(sigma_t, dims) * output
+
+ def cond_grad_fn(x, t_input):
+ """
+ Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
+ """
+ with torch.enable_grad():
+ x_in = x.detach().requires_grad_(True)
+ log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)
+ return torch.autograd.grad(log_prob.sum(), x_in)[0]
+
+ def model_fn(x, t_continuous):
+ """
+ The noise predicition model function that is used for DPM-Solver.
+ """
+ if t_continuous.reshape((-1,)).shape[0] == 1:
+ t_continuous = t_continuous.expand((x.shape[0]))
+ if guidance_type == "uncond":
+ return noise_pred_fn(x, t_continuous)
+ elif guidance_type == "classifier":
+ assert classifier_fn is not None
+ t_input = get_model_input_time(t_continuous)
+ cond_grad = cond_grad_fn(x, t_input)
+ sigma_t = noise_schedule.marginal_std(t_continuous)
+ noise = noise_pred_fn(x, t_continuous)
+ return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad
+ elif guidance_type == "classifier-free":
+ if guidance_scale == 1. or unconditional_condition is None:
+ return noise_pred_fn(x, t_continuous, cond=condition)
+ else:
+ x_in = torch.cat([x] * 2)
+ t_in = torch.cat([t_continuous] * 2)
+ c_in = torch.cat([unconditional_condition, condition])
+ noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
+ return noise_uncond + guidance_scale * (noise - noise_uncond)
+
+ assert model_type in ["noise", "x_start", "v"]
+ assert guidance_type in ["uncond", "classifier", "classifier-free"]
+ return model_fn
+
+
+class DPM_Solver:
+ def __init__(self, model_fn, noise_schedule, predict_x0=False, thresholding=False, max_val=1.):
+ """Construct a DPM-Solver.
+ We support both the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0").
+ If `predict_x0` is False, we use the solver for the noise prediction model (DPM-Solver).
+ If `predict_x0` is True, we use the solver for the data prediction model (DPM-Solver++).
+ In such case, we further support the "dynamic thresholding" in [1] when `thresholding` is True.
+ The "dynamic thresholding" can greatly improve the sample quality for pixel-space DPMs with large guidance scales.
+ Args:
+ model_fn: A noise prediction model function which accepts the continuous-time input (t in [epsilon, T]):
+ ``
+ def model_fn(x, t_continuous):
+ return noise
+ ``
+ noise_schedule: A noise schedule object, such as NoiseScheduleVP.
+ predict_x0: A `bool`. If true, use the data prediction model; else, use the noise prediction model.
+ thresholding: A `bool`. Valid when `predict_x0` is True. Whether to use the "dynamic thresholding" in [1].
+ max_val: A `float`. Valid when both `predict_x0` and `thresholding` are True. The max value for thresholding.
+
+ [1] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022b.
+ """
+ self.model = model_fn
+ self.noise_schedule = noise_schedule
+ self.predict_x0 = predict_x0
+ self.thresholding = thresholding
+ self.max_val = max_val
+
+ def noise_prediction_fn(self, x, t):
+ """
+ Return the noise prediction model.
+ """
+ return self.model(x, t)
+
+ def data_prediction_fn(self, x, t):
+ """
+ Return the data prediction model (with thresholding).
+ """
+ noise = self.noise_prediction_fn(x, t)
+ dims = x.dim()
+ alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)
+ x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)
+ if self.thresholding:
+ p = 0.995 # A hyperparameter in the paper of "Imagen" [1].
+ s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
+ s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
+ x0 = torch.clamp(x0, -s, s) / s
+ return x0
+
+ def model_fn(self, x, t):
+ """
+ Convert the model to the noise prediction model or the data prediction model.
+ """
+ if self.predict_x0:
+ return self.data_prediction_fn(x, t)
+ else:
+ return self.noise_prediction_fn(x, t)
+
+ def get_time_steps(self, skip_type, t_T, t_0, N, device):
+ """Compute the intermediate time steps for sampling.
+ Args:
+ skip_type: A `str`. The type for the spacing of the time steps. We support three types:
+ - 'logSNR': uniform logSNR for the time steps.
+ - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
+ - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
+ t_T: A `float`. The starting time of the sampling (default is T).
+ t_0: A `float`. The ending time of the sampling (default is epsilon).
+ N: A `int`. The total number of the spacing of the time steps.
+ device: A torch device.
+ Returns:
+ A pytorch tensor of the time steps, with the shape (N + 1,).
+ """
+ if skip_type == 'logSNR':
+ lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))
+ lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))
+ logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device)
+ return self.noise_schedule.inverse_lambda(logSNR_steps)
+ elif skip_type == 'time_uniform':
+ return torch.linspace(t_T, t_0, N + 1).to(device)
+ elif skip_type == 'time_quadratic':
+ t_order = 2
+ t = torch.linspace(t_T ** (1. / t_order), t_0 ** (1. / t_order), N + 1).pow(t_order).to(device)
+ return t
+ else:
+ raise ValueError(
+ "Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
+
+ def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
+ """
+ Get the order of each step for sampling by the singlestep DPM-Solver.
+ We combine both DPM-Solver-1,2,3 to use all the function evaluations, which is named as "DPM-Solver-fast".
+ Given a fixed number of function evaluations by `steps`, the sampling procedure by DPM-Solver-fast is:
+ - If order == 1:
+ We take `steps` of DPM-Solver-1 (i.e. DDIM).
+ - If order == 2:
+ - Denote K = (steps // 2). We take K or (K + 1) intermediate time steps for sampling.
+ - If steps % 2 == 0, we use K steps of DPM-Solver-2.
+ - If steps % 2 == 1, we use K steps of DPM-Solver-2 and 1 step of DPM-Solver-1.
+ - If order == 3:
+ - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
+ - If steps % 3 == 0, we use (K - 2) steps of DPM-Solver-3, and 1 step of DPM-Solver-2 and 1 step of DPM-Solver-1.
+ - If steps % 3 == 1, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-1.
+ - If steps % 3 == 2, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-2.
+ ============================================
+ Args:
+ order: A `int`. The max order for the solver (2 or 3).
+ steps: A `int`. The total number of function evaluations (NFE).
+ skip_type: A `str`. The type for the spacing of the time steps. We support three types:
+ - 'logSNR': uniform logSNR for the time steps.
+ - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
+ - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
+ t_T: A `float`. The starting time of the sampling (default is T).
+ t_0: A `float`. The ending time of the sampling (default is epsilon).
+ device: A torch device.
+ Returns:
+ orders: A list of the solver order of each step.
+ """
+ if order == 3:
+ K = steps // 3 + 1
+ if steps % 3 == 0:
+ orders = [3, ] * (K - 2) + [2, 1]
+ elif steps % 3 == 1:
+ orders = [3, ] * (K - 1) + [1]
+ else:
+ orders = [3, ] * (K - 1) + [2]
+ elif order == 2:
+ if steps % 2 == 0:
+ K = steps // 2
+ orders = [2, ] * K
+ else:
+ K = steps // 2 + 1
+ orders = [2, ] * (K - 1) + [1]
+ elif order == 1:
+ K = 1
+ orders = [1, ] * steps
+ else:
+ raise ValueError("'order' must be '1' or '2' or '3'.")
+ if skip_type == 'logSNR':
+ # To reproduce the results in DPM-Solver paper
+ timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
+ else:
+ timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[
+ torch.cumsum(torch.tensor([0, ] + orders)).to(device)]
+ return timesteps_outer, orders
+
+ def denoise_to_zero_fn(self, x, s):
+ """
+ Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization.
+ """
+ return self.data_prediction_fn(x, s)
+
+ def dpm_solver_first_update(self, x, s, t, model_s=None, return_intermediate=False):
+ """
+ DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`.
+ Args:
+ x: A pytorch tensor. The initial value at time `s`.
+ s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
+ t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+ model_s: A pytorch tensor. The model function evaluated at time `s`.
+ If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
+ return_intermediate: A `bool`. If true, also return the model value at time `s`.
+ Returns:
+ x_t: A pytorch tensor. The approximated solution at time `t`.
+ """
+ ns = self.noise_schedule
+ dims = x.dim()
+ lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
+ h = lambda_t - lambda_s
+ log_alpha_s, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(t)
+ sigma_s, sigma_t = ns.marginal_std(s), ns.marginal_std(t)
+ alpha_t = torch.exp(log_alpha_t)
+
+ if self.predict_x0:
+ phi_1 = torch.expm1(-h)
+ if model_s is None:
+ model_s = self.model_fn(x, s)
+ x_t = (
+ expand_dims(sigma_t / sigma_s, dims) * x
+ - expand_dims(alpha_t * phi_1, dims) * model_s
+ )
+ if return_intermediate:
+ return x_t, {'model_s': model_s}
+ else:
+ return x_t
+ else:
+ phi_1 = torch.expm1(h)
+ if model_s is None:
+ model_s = self.model_fn(x, s)
+ x_t = (
+ expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
+ - expand_dims(sigma_t * phi_1, dims) * model_s
+ )
+ if return_intermediate:
+ return x_t, {'model_s': model_s}
+ else:
+ return x_t
+
+ def singlestep_dpm_solver_second_update(self, x, s, t, r1=0.5, model_s=None, return_intermediate=False,
+ solver_type='dpm_solver'):
+ """
+ Singlestep solver DPM-Solver-2 from time `s` to time `t`.
+ Args:
+ x: A pytorch tensor. The initial value at time `s`.
+ s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
+ t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+ r1: A `float`. The hyperparameter of the second-order solver.
+ model_s: A pytorch tensor. The model function evaluated at time `s`.
+ If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
+ return_intermediate: A `bool`. If true, also return the model value at time `s` and `s1` (the intermediate time).
+ solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+ The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+ Returns:
+ x_t: A pytorch tensor. The approximated solution at time `t`.
+ """
+ if solver_type not in ['dpm_solver', 'taylor']:
+ raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
+ if r1 is None:
+ r1 = 0.5
+ ns = self.noise_schedule
+ dims = x.dim()
+ lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
+ h = lambda_t - lambda_s
+ lambda_s1 = lambda_s + r1 * h
+ s1 = ns.inverse_lambda(lambda_s1)
+ log_alpha_s, log_alpha_s1, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(
+ s1), ns.marginal_log_mean_coeff(t)
+ sigma_s, sigma_s1, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(t)
+ alpha_s1, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_t)
+
+ if self.predict_x0:
+ phi_11 = torch.expm1(-r1 * h)
+ phi_1 = torch.expm1(-h)
+
+ if model_s is None:
+ model_s = self.model_fn(x, s)
+ x_s1 = (
+ expand_dims(sigma_s1 / sigma_s, dims) * x
+ - expand_dims(alpha_s1 * phi_11, dims) * model_s
+ )
+ model_s1 = self.model_fn(x_s1, s1)
+ if solver_type == 'dpm_solver':
+ x_t = (
+ expand_dims(sigma_t / sigma_s, dims) * x
+ - expand_dims(alpha_t * phi_1, dims) * model_s
+ - (0.5 / r1) * expand_dims(alpha_t * phi_1, dims) * (model_s1 - model_s)
+ )
+ elif solver_type == 'taylor':
+ x_t = (
+ expand_dims(sigma_t / sigma_s, dims) * x
+ - expand_dims(alpha_t * phi_1, dims) * model_s
+ + (1. / r1) * expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * (
+ model_s1 - model_s)
+ )
+ else:
+ phi_11 = torch.expm1(r1 * h)
+ phi_1 = torch.expm1(h)
+
+ if model_s is None:
+ model_s = self.model_fn(x, s)
+ x_s1 = (
+ expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
+ - expand_dims(sigma_s1 * phi_11, dims) * model_s
+ )
+ model_s1 = self.model_fn(x_s1, s1)
+ if solver_type == 'dpm_solver':
+ x_t = (
+ expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
+ - expand_dims(sigma_t * phi_1, dims) * model_s
+ - (0.5 / r1) * expand_dims(sigma_t * phi_1, dims) * (model_s1 - model_s)
+ )
+ elif solver_type == 'taylor':
+ x_t = (
+ expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
+ - expand_dims(sigma_t * phi_1, dims) * model_s
+ - (1. / r1) * expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * (model_s1 - model_s)
+ )
+ if return_intermediate:
+ return x_t, {'model_s': model_s, 'model_s1': model_s1}
+ else:
+ return x_t
+
+ def singlestep_dpm_solver_third_update(self, x, s, t, r1=1. / 3., r2=2. / 3., model_s=None, model_s1=None,
+ return_intermediate=False, solver_type='dpm_solver'):
+ """
+ Singlestep solver DPM-Solver-3 from time `s` to time `t`.
+ Args:
+ x: A pytorch tensor. The initial value at time `s`.
+ s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
+ t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+ r1: A `float`. The hyperparameter of the third-order solver.
+ r2: A `float`. The hyperparameter of the third-order solver.
+ model_s: A pytorch tensor. The model function evaluated at time `s`.
+ If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
+ model_s1: A pytorch tensor. The model function evaluated at time `s1` (the intermediate time given by `r1`).
+ If `model_s1` is None, we evaluate the model at `s1`; otherwise we directly use it.
+ return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
+ solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+ The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+ Returns:
+ x_t: A pytorch tensor. The approximated solution at time `t`.
+ """
+ if solver_type not in ['dpm_solver', 'taylor']:
+ raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
+ if r1 is None:
+ r1 = 1. / 3.
+ if r2 is None:
+ r2 = 2. / 3.
+ ns = self.noise_schedule
+ dims = x.dim()
+ lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
+ h = lambda_t - lambda_s
+ lambda_s1 = lambda_s + r1 * h
+ lambda_s2 = lambda_s + r2 * h
+ s1 = ns.inverse_lambda(lambda_s1)
+ s2 = ns.inverse_lambda(lambda_s2)
+ log_alpha_s, log_alpha_s1, log_alpha_s2, log_alpha_t = ns.marginal_log_mean_coeff(
+ s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(s2), ns.marginal_log_mean_coeff(t)
+ sigma_s, sigma_s1, sigma_s2, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(
+ s2), ns.marginal_std(t)
+ alpha_s1, alpha_s2, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_s2), torch.exp(log_alpha_t)
+
+ if self.predict_x0:
+ phi_11 = torch.expm1(-r1 * h)
+ phi_12 = torch.expm1(-r2 * h)
+ phi_1 = torch.expm1(-h)
+ phi_22 = torch.expm1(-r2 * h) / (r2 * h) + 1.
+ phi_2 = phi_1 / h + 1.
+ phi_3 = phi_2 / h - 0.5
+
+ if model_s is None:
+ model_s = self.model_fn(x, s)
+ if model_s1 is None:
+ x_s1 = (
+ expand_dims(sigma_s1 / sigma_s, dims) * x
+ - expand_dims(alpha_s1 * phi_11, dims) * model_s
+ )
+ model_s1 = self.model_fn(x_s1, s1)
+ x_s2 = (
+ expand_dims(sigma_s2 / sigma_s, dims) * x
+ - expand_dims(alpha_s2 * phi_12, dims) * model_s
+ + r2 / r1 * expand_dims(alpha_s2 * phi_22, dims) * (model_s1 - model_s)
+ )
+ model_s2 = self.model_fn(x_s2, s2)
+ if solver_type == 'dpm_solver':
+ x_t = (
+ expand_dims(sigma_t / sigma_s, dims) * x
+ - expand_dims(alpha_t * phi_1, dims) * model_s
+ + (1. / r2) * expand_dims(alpha_t * phi_2, dims) * (model_s2 - model_s)
+ )
+ elif solver_type == 'taylor':
+ D1_0 = (1. / r1) * (model_s1 - model_s)
+ D1_1 = (1. / r2) * (model_s2 - model_s)
+ D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
+ D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
+ x_t = (
+ expand_dims(sigma_t / sigma_s, dims) * x
+ - expand_dims(alpha_t * phi_1, dims) * model_s
+ + expand_dims(alpha_t * phi_2, dims) * D1
+ - expand_dims(alpha_t * phi_3, dims) * D2
+ )
+ else:
+ phi_11 = torch.expm1(r1 * h)
+ phi_12 = torch.expm1(r2 * h)
+ phi_1 = torch.expm1(h)
+ phi_22 = torch.expm1(r2 * h) / (r2 * h) - 1.
+ phi_2 = phi_1 / h - 1.
+ phi_3 = phi_2 / h - 0.5
+
+ if model_s is None:
+ model_s = self.model_fn(x, s)
+ if model_s1 is None:
+ x_s1 = (
+ expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
+ - expand_dims(sigma_s1 * phi_11, dims) * model_s
+ )
+ model_s1 = self.model_fn(x_s1, s1)
+ x_s2 = (
+ expand_dims(torch.exp(log_alpha_s2 - log_alpha_s), dims) * x
+ - expand_dims(sigma_s2 * phi_12, dims) * model_s
+ - r2 / r1 * expand_dims(sigma_s2 * phi_22, dims) * (model_s1 - model_s)
+ )
+ model_s2 = self.model_fn(x_s2, s2)
+ if solver_type == 'dpm_solver':
+ x_t = (
+ expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
+ - expand_dims(sigma_t * phi_1, dims) * model_s
+ - (1. / r2) * expand_dims(sigma_t * phi_2, dims) * (model_s2 - model_s)
+ )
+ elif solver_type == 'taylor':
+ D1_0 = (1. / r1) * (model_s1 - model_s)
+ D1_1 = (1. / r2) * (model_s2 - model_s)
+ D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
+ D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
+ x_t = (
+ expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
+ - expand_dims(sigma_t * phi_1, dims) * model_s
+ - expand_dims(sigma_t * phi_2, dims) * D1
+ - expand_dims(sigma_t * phi_3, dims) * D2
+ )
+
+ if return_intermediate:
+ return x_t, {'model_s': model_s, 'model_s1': model_s1, 'model_s2': model_s2}
+ else:
+ return x_t
+
+ def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"):
+ """
+ Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`.
+ Args:
+ x: A pytorch tensor. The initial value at time `s`.
+ model_prev_list: A list of pytorch tensor. The previous computed model values.
+ t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
+ t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+ solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+ The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+ Returns:
+ x_t: A pytorch tensor. The approximated solution at time `t`.
+ """
+ if solver_type not in ['dpm_solver', 'taylor']:
+ raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
+ ns = self.noise_schedule
+ dims = x.dim()
+ model_prev_1, model_prev_0 = model_prev_list
+ t_prev_1, t_prev_0 = t_prev_list
+ lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda(
+ t_prev_0), ns.marginal_lambda(t)
+ log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
+ sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
+ alpha_t = torch.exp(log_alpha_t)
+
+ h_0 = lambda_prev_0 - lambda_prev_1
+ h = lambda_t - lambda_prev_0
+ r0 = h_0 / h
+ D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
+ if self.predict_x0:
+ if solver_type == 'dpm_solver':
+ x_t = (
+ expand_dims(sigma_t / sigma_prev_0, dims) * x
+ - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
+ - 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * D1_0
+ )
+ elif solver_type == 'taylor':
+ x_t = (
+ expand_dims(sigma_t / sigma_prev_0, dims) * x
+ - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
+ + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1_0
+ )
+ else:
+ if solver_type == 'dpm_solver':
+ x_t = (
+ expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
+ - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
+ - 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * D1_0
+ )
+ elif solver_type == 'taylor':
+ x_t = (
+ expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
+ - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
+ - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1_0
+ )
+ return x_t
+
+ def multistep_dpm_solver_third_update(self, x, model_prev_list, t_prev_list, t, solver_type='dpm_solver'):
+ """
+ Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`.
+ Args:
+ x: A pytorch tensor. The initial value at time `s`.
+ model_prev_list: A list of pytorch tensor. The previous computed model values.
+ t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
+ t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+ solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+ The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+ Returns:
+ x_t: A pytorch tensor. The approximated solution at time `t`.
+ """
+ ns = self.noise_schedule
+ dims = x.dim()
+ model_prev_2, model_prev_1, model_prev_0 = model_prev_list
+ t_prev_2, t_prev_1, t_prev_0 = t_prev_list
+ lambda_prev_2, lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_2), ns.marginal_lambda(
+ t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t)
+ log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
+ sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
+ alpha_t = torch.exp(log_alpha_t)
+
+ h_1 = lambda_prev_1 - lambda_prev_2
+ h_0 = lambda_prev_0 - lambda_prev_1
+ h = lambda_t - lambda_prev_0
+ r0, r1 = h_0 / h, h_1 / h
+ D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
+ D1_1 = expand_dims(1. / r1, dims) * (model_prev_1 - model_prev_2)
+ D1 = D1_0 + expand_dims(r0 / (r0 + r1), dims) * (D1_0 - D1_1)
+ D2 = expand_dims(1. / (r0 + r1), dims) * (D1_0 - D1_1)
+ if self.predict_x0:
+ x_t = (
+ expand_dims(sigma_t / sigma_prev_0, dims) * x
+ - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
+ + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1
+ - expand_dims(alpha_t * ((torch.exp(-h) - 1. + h) / h ** 2 - 0.5), dims) * D2
+ )
+ else:
+ x_t = (
+ expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
+ - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
+ - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1
+ - expand_dims(sigma_t * ((torch.exp(h) - 1. - h) / h ** 2 - 0.5), dims) * D2
+ )
+ return x_t
+
+ def singlestep_dpm_solver_update(self, x, s, t, order, return_intermediate=False, solver_type='dpm_solver', r1=None,
+ r2=None):
+ """
+ Singlestep DPM-Solver with the order `order` from time `s` to time `t`.
+ Args:
+ x: A pytorch tensor. The initial value at time `s`.
+ s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
+ t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+ order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
+ return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
+ solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+ The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+ r1: A `float`. The hyperparameter of the second-order or third-order solver.
+ r2: A `float`. The hyperparameter of the third-order solver.
+ Returns:
+ x_t: A pytorch tensor. The approximated solution at time `t`.
+ """
+ if order == 1:
+ return self.dpm_solver_first_update(x, s, t, return_intermediate=return_intermediate)
+ elif order == 2:
+ return self.singlestep_dpm_solver_second_update(x, s, t, return_intermediate=return_intermediate,
+ solver_type=solver_type, r1=r1)
+ elif order == 3:
+ return self.singlestep_dpm_solver_third_update(x, s, t, return_intermediate=return_intermediate,
+ solver_type=solver_type, r1=r1, r2=r2)
+ else:
+ raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
+
+ def multistep_dpm_solver_update(self, x, model_prev_list, t_prev_list, t, order, solver_type='dpm_solver'):
+ """
+ Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`.
+ Args:
+ x: A pytorch tensor. The initial value at time `s`.
+ model_prev_list: A list of pytorch tensor. The previous computed model values.
+ t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
+ t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+ order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
+ solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+ The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+ Returns:
+ x_t: A pytorch tensor. The approximated solution at time `t`.
+ """
+ if order == 1:
+ return self.dpm_solver_first_update(x, t_prev_list[-1], t, model_s=model_prev_list[-1])
+ elif order == 2:
+ return self.multistep_dpm_solver_second_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
+ elif order == 3:
+ return self.multistep_dpm_solver_third_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
+ else:
+ raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
+
+ def dpm_solver_adaptive(self, x, order, t_T, t_0, h_init=0.05, atol=0.0078, rtol=0.05, theta=0.9, t_err=1e-5,
+ solver_type='dpm_solver'):
+ """
+ The adaptive step size solver based on singlestep DPM-Solver.
+ Args:
+ x: A pytorch tensor. The initial value at time `t_T`.
+ order: A `int`. The (higher) order of the solver. We only support order == 2 or 3.
+ t_T: A `float`. The starting time of the sampling (default is T).
+ t_0: A `float`. The ending time of the sampling (default is epsilon).
+ h_init: A `float`. The initial step size (for logSNR).
+ atol: A `float`. The absolute tolerance of the solver. For image data, the default setting is 0.0078, followed [1].
+ rtol: A `float`. The relative tolerance of the solver. The default setting is 0.05.
+ theta: A `float`. The safety hyperparameter for adapting the step size. The default setting is 0.9, followed [1].
+ t_err: A `float`. The tolerance for the time. We solve the diffusion ODE until the absolute error between the
+ current time and `t_0` is less than `t_err`. The default setting is 1e-5.
+ solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+ The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+ Returns:
+ x_0: A pytorch tensor. The approximated solution at time `t_0`.
+ [1] A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, and I. Mitliagkas, "Gotta go fast when generating data with score-based models," arXiv preprint arXiv:2105.14080, 2021.
+ """
+ ns = self.noise_schedule
+ s = t_T * torch.ones((x.shape[0],)).to(x)
+ lambda_s = ns.marginal_lambda(s)
+ lambda_0 = ns.marginal_lambda(t_0 * torch.ones_like(s).to(x))
+ h = h_init * torch.ones_like(s).to(x)
+ x_prev = x
+ nfe = 0
+ if order == 2:
+ r1 = 0.5
+ lower_update = lambda x, s, t: self.dpm_solver_first_update(x, s, t, return_intermediate=True)
+ higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1,
+ solver_type=solver_type,
+ **kwargs)
+ elif order == 3:
+ r1, r2 = 1. / 3., 2. / 3.
+ lower_update = lambda x, s, t: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1,
+ return_intermediate=True,
+ solver_type=solver_type)
+ higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_third_update(x, s, t, r1=r1, r2=r2,
+ solver_type=solver_type,
+ **kwargs)
+ else:
+ raise ValueError("For adaptive step size solver, order must be 2 or 3, got {}".format(order))
+ while torch.abs((s - t_0)).mean() > t_err:
+ t = ns.inverse_lambda(lambda_s + h)
+ x_lower, lower_noise_kwargs = lower_update(x, s, t)
+ x_higher = higher_update(x, s, t, **lower_noise_kwargs)
+ delta = torch.max(torch.ones_like(x).to(x) * atol, rtol * torch.max(torch.abs(x_lower), torch.abs(x_prev)))
+ norm_fn = lambda v: torch.sqrt(torch.square(v.reshape((v.shape[0], -1))).mean(dim=-1, keepdim=True))
+ E = norm_fn((x_higher - x_lower) / delta).max()
+ if torch.all(E <= 1.):
+ x = x_higher
+ s = t
+ x_prev = x_lower
+ lambda_s = ns.marginal_lambda(s)
+ h = torch.min(theta * h * torch.float_power(E, -1. / order).float(), lambda_0 - lambda_s)
+ nfe += order
+ print('adaptive solver nfe', nfe)
+ return x
+
+ def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform',
+ method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',
+ atol=0.0078, rtol=0.05,
+ ):
+ """
+ Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`.
+ =====================================================
+ We support the following algorithms for both noise prediction model and data prediction model:
+ - 'singlestep':
+ Singlestep DPM-Solver (i.e. "DPM-Solver-fast" in the paper), which combines different orders of singlestep DPM-Solver.
+ We combine all the singlestep solvers with order <= `order` to use up all the function evaluations (steps).
+ The total number of function evaluations (NFE) == `steps`.
+ Given a fixed NFE == `steps`, the sampling procedure is:
+ - If `order` == 1:
+ - Denote K = steps. We use K steps of DPM-Solver-1 (i.e. DDIM).
+ - If `order` == 2:
+ - Denote K = (steps // 2) + (steps % 2). We take K intermediate time steps for sampling.
+ - If steps % 2 == 0, we use K steps of singlestep DPM-Solver-2.
+ - If steps % 2 == 1, we use (K - 1) steps of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
+ - If `order` == 3:
+ - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
+ - If steps % 3 == 0, we use (K - 2) steps of singlestep DPM-Solver-3, and 1 step of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
+ - If steps % 3 == 1, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of DPM-Solver-1.
+ - If steps % 3 == 2, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of singlestep DPM-Solver-2.
+ - 'multistep':
+ Multistep DPM-Solver with the order of `order`. The total number of function evaluations (NFE) == `steps`.
+ We initialize the first `order` values by lower order multistep solvers.
+ Given a fixed NFE == `steps`, the sampling procedure is:
+ Denote K = steps.
+ - If `order` == 1:
+ - We use K steps of DPM-Solver-1 (i.e. DDIM).
+ - If `order` == 2:
+ - We firstly use 1 step of DPM-Solver-1, then use (K - 1) step of multistep DPM-Solver-2.
+ - If `order` == 3:
+ - We firstly use 1 step of DPM-Solver-1, then 1 step of multistep DPM-Solver-2, then (K - 2) step of multistep DPM-Solver-3.
+ - 'singlestep_fixed':
+ Fixed order singlestep DPM-Solver (i.e. DPM-Solver-1 or singlestep DPM-Solver-2 or singlestep DPM-Solver-3).
+ We use singlestep DPM-Solver-`order` for `order`=1 or 2 or 3, with total [`steps` // `order`] * `order` NFE.
+ - 'adaptive':
+ Adaptive step size DPM-Solver (i.e. "DPM-Solver-12" and "DPM-Solver-23" in the paper).
+ We ignore `steps` and use adaptive step size DPM-Solver with a higher order of `order`.
+ You can adjust the absolute tolerance `atol` and the relative tolerance `rtol` to balance the computatation costs
+ (NFE) and the sample quality.
+ - If `order` == 2, we use DPM-Solver-12 which combines DPM-Solver-1 and singlestep DPM-Solver-2.
+ - If `order` == 3, we use DPM-Solver-23 which combines singlestep DPM-Solver-2 and singlestep DPM-Solver-3.
+ =====================================================
+ Some advices for choosing the algorithm:
+ - For **unconditional sampling** or **guided sampling with small guidance scale** by DPMs:
+ Use singlestep DPM-Solver ("DPM-Solver-fast" in the paper) with `order = 3`.
+ e.g.
+ >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=False)
+ >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=3,
+ skip_type='time_uniform', method='singlestep')
+ - For **guided sampling with large guidance scale** by DPMs:
+ Use multistep DPM-Solver with `predict_x0 = True` and `order = 2`.
+ e.g.
+ >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=True)
+ >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=2,
+ skip_type='time_uniform', method='multistep')
+ We support three types of `skip_type`:
+ - 'logSNR': uniform logSNR for the time steps. **Recommended for low-resolutional images**
+ - 'time_uniform': uniform time for the time steps. **Recommended for high-resolutional images**.
+ - 'time_quadratic': quadratic time for the time steps.
+ =====================================================
+ Args:
+ x: A pytorch tensor. The initial value at time `t_start`
+ e.g. if `t_start` == T, then `x` is a sample from the standard normal distribution.
+ steps: A `int`. The total number of function evaluations (NFE).
+ t_start: A `float`. The starting time of the sampling.
+ If `T` is None, we use self.noise_schedule.T (default is 1.0).
+ t_end: A `float`. The ending time of the sampling.
+ If `t_end` is None, we use 1. / self.noise_schedule.total_N.
+ e.g. if total_N == 1000, we have `t_end` == 1e-3.
+ For discrete-time DPMs:
+ - We recommend `t_end` == 1. / self.noise_schedule.total_N.
+ For continuous-time DPMs:
+ - We recommend `t_end` == 1e-3 when `steps` <= 15; and `t_end` == 1e-4 when `steps` > 15.
+ order: A `int`. The order of DPM-Solver.
+ skip_type: A `str`. The type for the spacing of the time steps. 'time_uniform' or 'logSNR' or 'time_quadratic'.
+ method: A `str`. The method for sampling. 'singlestep' or 'multistep' or 'singlestep_fixed' or 'adaptive'.
+ denoise_to_zero: A `bool`. Whether to denoise to time 0 at the final step.
+ Default is `False`. If `denoise_to_zero` is `True`, the total NFE is (`steps` + 1).
+ This trick is firstly proposed by DDPM (https://arxiv.org/abs/2006.11239) and
+ score_sde (https://arxiv.org/abs/2011.13456). Such trick can improve the FID
+ for diffusion models sampling by diffusion SDEs for low-resolutional images
+ (such as CIFAR-10). However, we observed that such trick does not matter for
+ high-resolutional images. As it needs an additional NFE, we do not recommend
+ it for high-resolutional images.
+ lower_order_final: A `bool`. Whether to use lower order solvers at the final steps.
+ Only valid for `method=multistep` and `steps < 15`. We empirically find that
+ this trick is a key to stabilizing the sampling by DPM-Solver with very few steps
+ (especially for steps <= 10). So we recommend to set it to be `True`.
+ solver_type: A `str`. The taylor expansion type for the solver. `dpm_solver` or `taylor`. We recommend `dpm_solver`.
+ atol: A `float`. The absolute tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
+ rtol: A `float`. The relative tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
+ Returns:
+ x_end: A pytorch tensor. The approximated solution at time `t_end`.
+ """
+ t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end
+ t_T = self.noise_schedule.T if t_start is None else t_start
+ device = x.device
+ if method == 'adaptive':
+ with torch.no_grad():
+ x = self.dpm_solver_adaptive(x, order=order, t_T=t_T, t_0=t_0, atol=atol, rtol=rtol,
+ solver_type=solver_type)
+ elif method == 'multistep':
+ assert steps >= order
+ timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
+ assert timesteps.shape[0] - 1 == steps
+ with torch.no_grad():
+ vec_t = timesteps[0].expand((x.shape[0]))
+ model_prev_list = [self.model_fn(x, vec_t)]
+ t_prev_list = [vec_t]
+ # Init the first `order` values by lower order multistep DPM-Solver.
+ for init_order in tqdm(range(1, order), desc="DPM init order"):
+ vec_t = timesteps[init_order].expand(x.shape[0])
+ x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, init_order,
+ solver_type=solver_type)
+ model_prev_list.append(self.model_fn(x, vec_t))
+ t_prev_list.append(vec_t)
+ # Compute the remaining values by `order`-th order multistep DPM-Solver.
+ for step in tqdm(range(order, steps + 1), desc="DPM multistep"):
+ vec_t = timesteps[step].expand(x.shape[0])
+ if lower_order_final and steps < 15:
+ step_order = min(order, steps + 1 - step)
+ else:
+ step_order = order
+ x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, step_order,
+ solver_type=solver_type)
+ for i in range(order - 1):
+ t_prev_list[i] = t_prev_list[i + 1]
+ model_prev_list[i] = model_prev_list[i + 1]
+ t_prev_list[-1] = vec_t
+ # We do not need to evaluate the final model value.
+ if step < steps:
+ model_prev_list[-1] = self.model_fn(x, vec_t)
+ elif method in ['singlestep', 'singlestep_fixed']:
+ if method == 'singlestep':
+ timesteps_outer, orders = self.get_orders_and_timesteps_for_singlestep_solver(steps=steps, order=order,
+ skip_type=skip_type,
+ t_T=t_T, t_0=t_0,
+ device=device)
+ elif method == 'singlestep_fixed':
+ K = steps // order
+ orders = [order, ] * K
+ timesteps_outer = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=K, device=device)
+ for i, order in enumerate(orders):
+ t_T_inner, t_0_inner = timesteps_outer[i], timesteps_outer[i + 1]
+ timesteps_inner = self.get_time_steps(skip_type=skip_type, t_T=t_T_inner.item(), t_0=t_0_inner.item(),
+ N=order, device=device)
+ lambda_inner = self.noise_schedule.marginal_lambda(timesteps_inner)
+ vec_s, vec_t = t_T_inner.tile(x.shape[0]), t_0_inner.tile(x.shape[0])
+ h = lambda_inner[-1] - lambda_inner[0]
+ r1 = None if order <= 1 else (lambda_inner[1] - lambda_inner[0]) / h
+ r2 = None if order <= 2 else (lambda_inner[2] - lambda_inner[0]) / h
+ x = self.singlestep_dpm_solver_update(x, vec_s, vec_t, order, solver_type=solver_type, r1=r1, r2=r2)
+ if denoise_to_zero:
+ x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)
+ return x
+
+
+#############################################################
+# other utility functions
+#############################################################
+
+def interpolate_fn(x, xp, yp):
+ """
+ A piecewise linear function y = f(x), using xp and yp as keypoints.
+ We implement f(x) in a differentiable way (i.e. applicable for autograd).
+ The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)
+ Args:
+ x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).
+ xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.
+ yp: PyTorch tensor with shape [C, K].
+ Returns:
+ The function values f(x), with shape [N, C].
+ """
+ N, K = x.shape[0], xp.shape[1]
+ all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)
+ sorted_all_x, x_indices = torch.sort(all_x, dim=2)
+ x_idx = torch.argmin(x_indices, dim=2)
+ cand_start_idx = x_idx - 1
+ start_idx = torch.where(
+ torch.eq(x_idx, 0),
+ torch.tensor(1, device=x.device),
+ torch.where(
+ torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
+ ),
+ )
+ end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1)
+ start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)
+ end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)
+ start_idx2 = torch.where(
+ torch.eq(x_idx, 0),
+ torch.tensor(0, device=x.device),
+ torch.where(
+ torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
+ ),
+ )
+ y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)
+ start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2)
+ end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2)
+ cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)
+ return cand
+
+
+def expand_dims(v, dims):
+ """
+ Expand the tensor `v` to the dim `dims`.
+ Args:
+ `v`: a PyTorch tensor with shape [N].
+ `dim`: a `int`.
+ Returns:
+ a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.
+ """
+ return v[(...,) + (None,) * (dims - 1)]
\ No newline at end of file
diff --git a/model_lib/ControlNet/ldm/models/diffusion/dpm_solver/sampler.py b/model_lib/ControlNet/ldm/models/diffusion/dpm_solver/sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d137b8cf36718c1c58faa09f9dd919e5fb2977b
--- /dev/null
+++ b/model_lib/ControlNet/ldm/models/diffusion/dpm_solver/sampler.py
@@ -0,0 +1,87 @@
+"""SAMPLING ONLY."""
+import torch
+
+from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver
+
+
+MODEL_TYPES = {
+ "eps": "noise",
+ "v": "v"
+}
+
+
+class DPMSolverSampler(object):
+ def __init__(self, model, **kwargs):
+ super().__init__()
+ self.model = model
+ to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device)
+ self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))
+
+ def register_buffer(self, name, attr):
+ if type(attr) == torch.Tensor:
+ if attr.device != torch.device("cuda"):
+ attr = attr.to(torch.device("cuda"))
+ setattr(self, name, attr)
+
+ @torch.no_grad()
+ def sample(self,
+ S,
+ batch_size,
+ shape,
+ conditioning=None,
+ callback=None,
+ normals_sequence=None,
+ img_callback=None,
+ quantize_x0=False,
+ eta=0.,
+ mask=None,
+ x0=None,
+ temperature=1.,
+ noise_dropout=0.,
+ score_corrector=None,
+ corrector_kwargs=None,
+ verbose=True,
+ x_T=None,
+ log_every_t=100,
+ unconditional_guidance_scale=1.,
+ unconditional_conditioning=None,
+ # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+ **kwargs
+ ):
+ if conditioning is not None:
+ if isinstance(conditioning, dict):
+ cbs = conditioning[list(conditioning.keys())[0]].shape[0]
+ if cbs != batch_size:
+ print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+ else:
+ if conditioning.shape[0] != batch_size:
+ print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+ # sampling
+ C, H, W = shape
+ size = (batch_size, C, H, W)
+
+ print(f'Data shape for DPM-Solver sampling is {size}, sampling steps {S}')
+
+ device = self.model.betas.device
+ if x_T is None:
+ img = torch.randn(size, device=device)
+ else:
+ img = x_T
+
+ ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)
+
+ model_fn = model_wrapper(
+ lambda x, t, c: self.model.apply_model(x, t, c),
+ ns,
+ model_type=MODEL_TYPES[self.model.parameterization],
+ guidance_type="classifier-free",
+ condition=conditioning,
+ unconditional_condition=unconditional_conditioning,
+ guidance_scale=unconditional_guidance_scale,
+ )
+
+ dpm_solver = DPM_Solver(model_fn, ns, predict_x0=True, thresholding=False)
+ x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2, lower_order_final=True)
+
+ return x.to(device), None
\ No newline at end of file
diff --git a/model_lib/ControlNet/ldm/models/diffusion/plms.py b/model_lib/ControlNet/ldm/models/diffusion/plms.py
new file mode 100644
index 0000000000000000000000000000000000000000..7002a365d27168ced0a04e9a4d83e088f8284eae
--- /dev/null
+++ b/model_lib/ControlNet/ldm/models/diffusion/plms.py
@@ -0,0 +1,244 @@
+"""SAMPLING ONLY."""
+
+import torch
+import numpy as np
+from tqdm import tqdm
+from functools import partial
+
+from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
+from ldm.models.diffusion.sampling_util import norm_thresholding
+
+
+class PLMSSampler(object):
+ def __init__(self, model, schedule="linear", **kwargs):
+ super().__init__()
+ self.model = model
+ self.ddpm_num_timesteps = model.num_timesteps
+ self.schedule = schedule
+
+ def register_buffer(self, name, attr):
+ if type(attr) == torch.Tensor:
+ if attr.device != torch.device("cuda"):
+ attr = attr.to(torch.device("cuda"))
+ setattr(self, name, attr)
+
+ def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
+ if ddim_eta != 0:
+ raise ValueError('ddim_eta must be 0 for PLMS')
+ self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
+ num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
+ alphas_cumprod = self.model.alphas_cumprod
+ assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
+ to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
+
+ self.register_buffer('betas', to_torch(self.model.betas))
+ self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+ self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
+
+ # calculations for diffusion q(x_t | x_{t-1}) and others
+ self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
+ self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
+
+ # ddim sampling parameters
+ ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
+ ddim_timesteps=self.ddim_timesteps,
+ eta=ddim_eta,verbose=verbose)
+ self.register_buffer('ddim_sigmas', ddim_sigmas)
+ self.register_buffer('ddim_alphas', ddim_alphas)
+ self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
+ self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
+ sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
+ (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
+ 1 - self.alphas_cumprod / self.alphas_cumprod_prev))
+ self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
+
+ @torch.no_grad()
+ def sample(self,
+ S,
+ batch_size,
+ shape,
+ conditioning=None,
+ callback=None,
+ normals_sequence=None,
+ img_callback=None,
+ quantize_x0=False,
+ eta=0.,
+ mask=None,
+ x0=None,
+ temperature=1.,
+ noise_dropout=0.,
+ score_corrector=None,
+ corrector_kwargs=None,
+ verbose=True,
+ x_T=None,
+ log_every_t=100,
+ unconditional_guidance_scale=1.,
+ unconditional_conditioning=None,
+ # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+ dynamic_threshold=None,
+ **kwargs
+ ):
+ if conditioning is not None:
+ if isinstance(conditioning, dict):
+ cbs = conditioning[list(conditioning.keys())[0]].shape[0]
+ if cbs != batch_size:
+ print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+ else:
+ if conditioning.shape[0] != batch_size:
+ print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+ self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
+ # sampling
+ C, H, W = shape
+ size = (batch_size, C, H, W)
+ print(f'Data shape for PLMS sampling is {size}')
+
+ samples, intermediates = self.plms_sampling(conditioning, size,
+ callback=callback,
+ img_callback=img_callback,
+ quantize_denoised=quantize_x0,
+ mask=mask, x0=x0,
+ ddim_use_original_steps=False,
+ noise_dropout=noise_dropout,
+ temperature=temperature,
+ score_corrector=score_corrector,
+ corrector_kwargs=corrector_kwargs,
+ x_T=x_T,
+ log_every_t=log_every_t,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning,
+ dynamic_threshold=dynamic_threshold,
+ )
+ return samples, intermediates
+
+ @torch.no_grad()
+ def plms_sampling(self, cond, shape,
+ x_T=None, ddim_use_original_steps=False,
+ callback=None, timesteps=None, quantize_denoised=False,
+ mask=None, x0=None, img_callback=None, log_every_t=100,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+ unconditional_guidance_scale=1., unconditional_conditioning=None,
+ dynamic_threshold=None):
+ device = self.model.betas.device
+ b = shape[0]
+ if x_T is None:
+ img = torch.randn(shape, device=device)
+ else:
+ img = x_T
+
+ if timesteps is None:
+ timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
+ elif timesteps is not None and not ddim_use_original_steps:
+ subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
+ timesteps = self.ddim_timesteps[:subset_end]
+
+ intermediates = {'x_inter': [img], 'pred_x0': [img]}
+ time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps)
+ total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
+ print(f"Running PLMS Sampling with {total_steps} timesteps")
+
+ iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps)
+ old_eps = []
+
+ for i, step in enumerate(iterator):
+ index = total_steps - i - 1
+ ts = torch.full((b,), step, device=device, dtype=torch.long)
+ ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long)
+
+ if mask is not None:
+ assert x0 is not None
+ img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?
+ img = img_orig * mask + (1. - mask) * img
+
+ outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
+ quantize_denoised=quantize_denoised, temperature=temperature,
+ noise_dropout=noise_dropout, score_corrector=score_corrector,
+ corrector_kwargs=corrector_kwargs,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning,
+ old_eps=old_eps, t_next=ts_next,
+ dynamic_threshold=dynamic_threshold)
+ img, pred_x0, e_t = outs
+ old_eps.append(e_t)
+ if len(old_eps) >= 4:
+ old_eps.pop(0)
+ if callback: callback(i)
+ if img_callback: img_callback(pred_x0, i)
+
+ if index % log_every_t == 0 or index == total_steps - 1:
+ intermediates['x_inter'].append(img)
+ intermediates['pred_x0'].append(pred_x0)
+
+ return img, intermediates
+
+ @torch.no_grad()
+ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+ unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None,
+ dynamic_threshold=None):
+ b, *_, device = *x.shape, x.device
+
+ def get_model_output(x, t):
+ if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+ e_t = self.model.apply_model(x, t, c)
+ else:
+ x_in = torch.cat([x] * 2)
+ t_in = torch.cat([t] * 2)
+ c_in = torch.cat([unconditional_conditioning, c])
+ e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
+ e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
+
+ if score_corrector is not None:
+ assert self.model.parameterization == "eps"
+ e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+ return e_t
+
+ alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+ alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+ sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+ sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+
+ def get_x_prev_and_pred_x0(e_t, index):
+ # select parameters corresponding to the currently considered timestep
+ a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+ a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+ sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+ sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+
+ # current prediction for x_0
+ pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+ if quantize_denoised:
+ pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+ if dynamic_threshold is not None:
+ pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)
+ # direction pointing to x_t
+ dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+ noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+ if noise_dropout > 0.:
+ noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+ x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+ return x_prev, pred_x0
+
+ e_t = get_model_output(x, t)
+ if len(old_eps) == 0:
+ # Pseudo Improved Euler (2nd order)
+ x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
+ e_t_next = get_model_output(x_prev, t_next)
+ e_t_prime = (e_t + e_t_next) / 2
+ elif len(old_eps) == 1:
+ # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
+ e_t_prime = (3 * e_t - old_eps[-1]) / 2
+ elif len(old_eps) == 2:
+ # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
+ e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
+ elif len(old_eps) >= 3:
+ # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
+ e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
+
+ x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
+
+ return x_prev, pred_x0, e_t
diff --git a/model_lib/ControlNet/ldm/models/diffusion/sampling_util.py b/model_lib/ControlNet/ldm/models/diffusion/sampling_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..7eff02be6d7c54d43ee6680636ac0698dd3b3f33
--- /dev/null
+++ b/model_lib/ControlNet/ldm/models/diffusion/sampling_util.py
@@ -0,0 +1,22 @@
+import torch
+import numpy as np
+
+
+def append_dims(x, target_dims):
+ """Appends dimensions to the end of a tensor until it has target_dims dimensions.
+ From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py"""
+ dims_to_append = target_dims - x.ndim
+ if dims_to_append < 0:
+ raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
+ return x[(...,) + (None,) * dims_to_append]
+
+
+def norm_thresholding(x0, value):
+ s = append_dims(x0.pow(2).flatten(1).mean(1).sqrt().clamp(min=value), x0.ndim)
+ return x0 * (value / s)
+
+
+def spatial_norm_thresholding(x0, value):
+ # b c h w
+ s = x0.pow(2).mean(1, keepdim=True).sqrt().clamp(min=value)
+ return x0 * (value / s)
\ No newline at end of file
diff --git a/model_lib/ControlNet/ldm/modules/__pycache__/attention.cpython-39.pyc b/model_lib/ControlNet/ldm/modules/__pycache__/attention.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1263db5c78cbcfdc93aac447cf9947516893bc6e
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diff --git a/model_lib/ControlNet/ldm/modules/__pycache__/ema.cpython-39.pyc b/model_lib/ControlNet/ldm/modules/__pycache__/ema.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5e36a2fdf25c3e695bac7f84419aeb000bf22757
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diff --git a/model_lib/ControlNet/ldm/modules/__pycache__/motion_module.cpython-39.pyc b/model_lib/ControlNet/ldm/modules/__pycache__/motion_module.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d94dfec19e721dd91d36c0bc283b88866748bbdf
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diff --git a/model_lib/ControlNet/ldm/modules/attention.py b/model_lib/ControlNet/ldm/modules/attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e825fd9204bc7330d47e0a8730ec623b1b72878
--- /dev/null
+++ b/model_lib/ControlNet/ldm/modules/attention.py
@@ -0,0 +1,386 @@
+from inspect import isfunction
+import math
+# from turtle import forward
+import torch
+import torch.nn.functional as F
+from torch import nn, einsum
+from einops import rearrange, repeat
+from typing import Optional, Any
+import pdb
+from model_lib.ControlNet.ldm.modules.diffusionmodules.util import checkpoint
+
+
+try:
+ import xformers
+ import xformers.ops
+ XFORMERS_IS_AVAILBLE = True
+except:
+ XFORMERS_IS_AVAILBLE = False
+
+# CrossAttn precision handling
+import os
+_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32")
+
+def exists(val):
+ return val is not None
+
+
+def uniq(arr):
+ return{el: True for el in arr}.keys()
+
+
+def default(val, d):
+ if exists(val):
+ return val
+ return d() if isfunction(d) else d
+
+
+def max_neg_value(t):
+ return -torch.finfo(t.dtype).max
+
+
+def init_(tensor):
+ dim = tensor.shape[-1]
+ std = 1 / math.sqrt(dim)
+ tensor.uniform_(-std, std)
+ return tensor
+
+
+# feedforward
+class GEGLU(nn.Module):
+ def __init__(self, dim_in, dim_out):
+ super().__init__()
+ self.proj = nn.Linear(dim_in, dim_out * 2)
+
+ def forward(self, x):
+ x, gate = self.proj(x).chunk(2, dim=-1)
+ return x * F.gelu(gate)
+
+
+class FeedForward(nn.Module):
+ def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
+ super().__init__()
+ inner_dim = int(dim * mult)
+ dim_out = default(dim_out, dim)
+ project_in = nn.Sequential(
+ nn.Linear(dim, inner_dim),
+ nn.GELU()
+ ) if not glu else GEGLU(dim, inner_dim)
+
+ self.net = nn.Sequential(
+ project_in,
+ nn.Dropout(dropout),
+ nn.Linear(inner_dim, dim_out)
+ )
+
+ def forward(self, x):
+ return self.net(x)
+
+
+def zero_module(module):
+ """
+ Zero out the parameters of a module and return it.
+ """
+ for p in module.parameters():
+ p.detach().zero_()
+ return module
+
+
+def Normalize(in_channels):
+ return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+
+
+class SpatialSelfAttention(nn.Module):
+ def __init__(self, in_channels):
+ super().__init__()
+ self.in_channels = in_channels
+
+ self.norm = Normalize(in_channels)
+ self.q = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.k = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.v = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.proj_out = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+
+ def forward(self, x):
+ h_ = x
+ h_ = self.norm(h_)
+ q = self.q(h_)
+ k = self.k(h_)
+ v = self.v(h_)
+
+ # compute attention
+ b,c,h,w = q.shape
+ q = rearrange(q, 'b c h w -> b (h w) c')
+ k = rearrange(k, 'b c h w -> b c (h w)')
+ w_ = torch.einsum('bij,bjk->bik', q, k)
+
+ w_ = w_ * (int(c)**(-0.5))
+ w_ = torch.nn.functional.softmax(w_, dim=2)
+
+ # attend to values
+ v = rearrange(v, 'b c h w -> b c (h w)')
+ w_ = rearrange(w_, 'b i j -> b j i')
+ h_ = torch.einsum('bij,bjk->bik', v, w_)
+ h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
+ h_ = self.proj_out(h_)
+
+ return x+h_
+
+
+class CrossAttention(nn.Module):
+ def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., checkpoint=False):
+ super().__init__()
+ inner_dim = dim_head * heads
+ context_dim = default(context_dim, query_dim)
+
+ self.scale = dim_head ** -0.5
+ self.heads = heads
+
+ self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+ self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+ self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+
+ self.to_out = nn.Sequential(
+ nn.Linear(inner_dim, query_dim),
+ nn.Dropout(dropout)
+ )
+ self.checkpoint = checkpoint
+
+ def forward(self, x, context=None):
+ return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
+
+ def _forward(self, x, context=None):
+ h = self.heads
+
+ q = self.to_q(x)
+ context = default(context, x)
+ k = self.to_k(context)
+ v = self.to_v(context)
+
+ q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+
+ # force cast to fp32 to avoid overflowing
+ if _ATTN_PRECISION =="fp32":
+ with torch.autocast(enabled=False, device_type = 'cuda'):
+ q, k = q.float(), k.float()
+ sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
+ else:
+ sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
+
+ del q, k
+
+ # if exists(mask):
+ # mask = rearrange(mask, 'b ... -> b (...)')
+ # max_neg_value = -torch.finfo(sim.dtype).max
+ # mask = repeat(mask, 'b j -> (b h) () j', h=h)
+ # sim.masked_fill_(~mask, max_neg_value)
+
+ # attention, what we cannot get enough of
+ sim = sim.softmax(dim=-1)
+
+ out = einsum('b i j, b j d -> b i d', sim, v)
+ out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+ return self.to_out(out)
+
+
+class MemoryEfficientCrossAttention(nn.Module):
+ # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
+ def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, checkpoint=False):
+ super().__init__()
+ print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
+ f"{heads} heads.")
+ inner_dim = dim_head * heads
+ context_dim = default(context_dim, query_dim)
+
+ self.heads = heads
+ self.dim_head = dim_head
+
+ self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+ self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+ self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+
+ self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
+ self.attention_op: Optional[Any] = None
+ self.checkpoint = checkpoint
+
+ def forward(self, x, context=None):
+ return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
+
+ def _forward(self, x, context=None):
+ q = self.to_q(x)
+ context = default(context, x)
+ k = self.to_k(context)
+ v = self.to_v(context)
+
+ b, _, _ = q.shape
+ q, k, v = map(
+ lambda t: t.unsqueeze(3)
+ .reshape(b, t.shape[1], self.heads, self.dim_head)
+ .permute(0, 2, 1, 3)
+ .reshape(b * self.heads, t.shape[1], self.dim_head)
+ .contiguous(),
+ (q, k, v),
+ )
+
+ # actually compute the attention, what we cannot get enough of
+ out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
+
+ out = (
+ out.unsqueeze(0)
+ .reshape(b, self.heads, out.shape[1], self.dim_head)
+ .permute(0, 2, 1, 3)
+ .reshape(b, out.shape[1], self.heads * self.dim_head)
+ )
+ return self.to_out(out)
+
+
+class BasicTransformerBlock(nn.Module):
+ ATTENTION_MODES = {
+ "softmax": CrossAttention, # vanilla attention
+ "softmax-xformers": MemoryEfficientCrossAttention
+ }
+ def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
+ disable_self_attn=False):
+ super().__init__()
+ attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax"
+ assert attn_mode in self.ATTENTION_MODES
+ attn_cls = self.ATTENTION_MODES[attn_mode]
+ self.disable_self_attn = disable_self_attn
+ self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout, checkpoint=checkpoint,
+ context_dim=context_dim if self.disable_self_attn else None) # is a self-attention if not self.disable_self_attn
+ self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
+ self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim,
+ heads=n_heads, dim_head=d_head, dropout=dropout, checkpoint=checkpoint,) # is self-attn if context is none
+ self.norm1 = nn.LayerNorm(dim)
+ self.norm2 = nn.LayerNorm(dim)
+ self.norm3 = nn.LayerNorm(dim)
+ self.checkpoint = checkpoint
+
+ # def forward(self, x, context=None, banks=None, attention_mode=None, attn_index=None,uc=False):
+ # return checkpoint(self._forward, (x, context, banks, attention_mode, attn_index,uc), self.parameters(), self.checkpoint)
+
+ def forward(self, x, context=None, banks=None, attention_mode=None, attn_index=None,uc=False):
+
+ if uc or attention_mode is None:
+ x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x
+ else:
+
+ x_norm1 = self.norm1(x)
+ self_attn1 = None
+ self_attention_context = x_norm1
+ if attention_mode == 'write':
+ bank = []
+ bank.append(self_attention_context)
+ # bank.append(self_attention_context.detach().clone())
+
+ banks.append(bank)
+
+ if self_attn1 is None:
+
+ self_attn1 = self.attn1(x_norm1, context=self_attention_context)
+
+ x = self_attn1 + x
+
+
+ elif attention_mode == 'read':
+
+ current_bank = banks[attn_index]
+
+ if len(current_bank) > 0:
+ tmp = [self_attention_context] + current_bank
+ self_attn1 = self.attn1(x_norm1, context=torch.cat([self_attention_context] + current_bank, dim=1))
+
+ if self_attn1 is None:
+
+ self_attn1 = self.attn1(x_norm1, context=self_attention_context)
+
+ x = self_attn1 + x
+
+ else:
+ raise NotImplementedError
+
+ x = self.attn2(self.norm2(x), context=context) + x
+ x = self.ff(self.norm3(x)) + x
+ return x
+
+
+class SpatialTransformer(nn.Module):
+ """
+ Transformer block for image-like data.
+ First, project the input (aka embedding)
+ and reshape to b, t, d.
+ Then apply standard transformer action.
+ Finally, reshape to image
+ NEW: use_linear for more efficiency instead of the 1x1 convs
+ """
+ def __init__(self, in_channels, n_heads, d_head,
+ depth=1, dropout=0., context_dim=None,
+ disable_self_attn=False, use_linear=False,
+ use_checkpoint=True):
+ super().__init__()
+ if exists(context_dim) and not isinstance(context_dim, list):
+ context_dim = [context_dim]
+ self.in_channels = in_channels
+ inner_dim = n_heads * d_head
+ self.norm = Normalize(in_channels)
+ if not use_linear:
+ self.proj_in = nn.Conv2d(in_channels,
+ inner_dim,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ else:
+ self.proj_in = nn.Linear(in_channels, inner_dim)
+
+ self.transformer_blocks = nn.ModuleList(
+ [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
+ disable_self_attn=disable_self_attn, checkpoint=use_checkpoint)
+ for d in range(depth)]
+ )
+ if not use_linear:
+ self.proj_out = zero_module(nn.Conv2d(inner_dim,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0))
+ else:
+ self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
+ self.use_linear = use_linear
+
+ def forward(self, x, context=None, banks=None, attention_mode=None, attn_index=None,uc=False):
+ # note: if no context is given, cross-attention defaults to self-attention
+ if not isinstance(context, list):
+ context = [context]
+ b, c, h, w = x.shape
+ x_in = x
+ x = self.norm(x)
+ if not self.use_linear:
+ x = self.proj_in(x)
+ x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
+ if self.use_linear:
+ x = self.proj_in(x)
+ for i, block in enumerate(self.transformer_blocks):
+ x = block(x, context=context[i], banks=banks, attention_mode=attention_mode, attn_index=attn_index,uc=uc)
+ if self.use_linear:
+ x = self.proj_out(x)
+ x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
+ if not self.use_linear:
+ x = self.proj_out(x)
+ return x + x_in
+
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diff --git a/model_lib/ControlNet/ldm/modules/diffusionmodules/model.py b/model_lib/ControlNet/ldm/modules/diffusionmodules/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..47e26e8c2a1ae1098468efa255701b2a902a8e48
--- /dev/null
+++ b/model_lib/ControlNet/ldm/modules/diffusionmodules/model.py
@@ -0,0 +1,859 @@
+# pytorch_diffusion + derived encoder decoder
+import math
+import torch
+import torch.nn as nn
+import numpy as np
+from einops import rearrange
+from typing import Optional, Any
+
+from model_lib.ControlNet.ldm.modules.attention import MemoryEfficientCrossAttention
+
+# try:
+import xformers
+import xformers.ops
+XFORMERS_IS_AVAILBLE = True
+# except:
+# XFORMERS_IS_AVAILBLE = False
+# print("No module 'xformers'. Proceeding without it.")
+
+
+def get_timestep_embedding(timesteps, embedding_dim):
+ """
+ This matches the implementation in Denoising Diffusion Probabilistic Models:
+ From Fairseq.
+ Build sinusoidal embeddings.
+ This matches the implementation in tensor2tensor, but differs slightly
+ from the description in Section 3.5 of "Attention Is All You Need".
+ """
+ assert len(timesteps.shape) == 1
+
+ half_dim = embedding_dim // 2
+ emb = math.log(10000) / (half_dim - 1)
+ emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
+ emb = emb.to(device=timesteps.device)
+ emb = timesteps.float()[:, None] * emb[None, :]
+ emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+ if embedding_dim % 2 == 1: # zero pad
+ emb = torch.nn.functional.pad(emb, (0,1,0,0))
+ return emb
+
+
+def nonlinearity(x):
+ # swish
+ return x*torch.sigmoid(x)
+
+
+def Normalize(in_channels, num_groups=32):
+ return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+
+
+class Upsample(nn.Module):
+ def __init__(self, in_channels, with_conv):
+ super().__init__()
+ self.with_conv = with_conv
+ if self.with_conv:
+ self.conv = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def nearest_neighbor_upsample(self, x: torch.Tensor, scale_factor: int):
+ # Upsample {x} (NCHW) by scale factor {scale_factor} using nearest neighbor interpolation.
+ s = scale_factor
+ return x.reshape(*x.shape, 1, 1).expand(*x.shape, s, s).transpose(-2, -3).reshape(*x.shape[:2], *(s * hw for hw in x.shape[2:]))
+
+
+ def forward(self, x):
+ # x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
+ x = self.nearest_neighbor_upsample(x, scale_factor=2)
+ if self.with_conv:
+ x = self.conv(x)
+ return x
+
+
+class Downsample(nn.Module):
+ def __init__(self, in_channels, with_conv):
+ super().__init__()
+ self.with_conv = with_conv
+ if self.with_conv:
+ # no asymmetric padding in torch conv, must do it ourselves
+ self.conv = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=3,
+ stride=2,
+ padding=0)
+
+ def forward(self, x):
+ if self.with_conv:
+ pad = (0,1,0,1)
+ x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
+ x = self.conv(x)
+ else:
+ x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
+ return x
+
+
+class ResnetBlock(nn.Module):
+ def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
+ dropout, temb_channels=512):
+ super().__init__()
+ self.in_channels = in_channels
+ out_channels = in_channels if out_channels is None else out_channels
+ self.out_channels = out_channels
+ self.use_conv_shortcut = conv_shortcut
+
+ self.norm1 = Normalize(in_channels)
+ self.conv1 = torch.nn.Conv2d(in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+ if temb_channels > 0:
+ self.temb_proj = torch.nn.Linear(temb_channels,
+ out_channels)
+ self.norm2 = Normalize(out_channels)
+ self.dropout = torch.nn.Dropout(dropout)
+ self.conv2 = torch.nn.Conv2d(out_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+ if self.in_channels != self.out_channels:
+ if self.use_conv_shortcut:
+ self.conv_shortcut = torch.nn.Conv2d(in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+ else:
+ self.nin_shortcut = torch.nn.Conv2d(in_channels,
+ out_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+
+ def forward(self, x, temb):
+ h = x
+ h = self.norm1(h)
+ h = nonlinearity(h)
+ h = self.conv1(h)
+
+ if temb is not None:
+ h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
+
+ h = self.norm2(h)
+ h = nonlinearity(h)
+ h = self.dropout(h)
+ h = self.conv2(h)
+
+ if self.in_channels != self.out_channels:
+ if self.use_conv_shortcut:
+ x = self.conv_shortcut(x)
+ else:
+ x = self.nin_shortcut(x)
+
+ return x+h
+
+
+class AttnBlock(nn.Module):
+ def __init__(self, in_channels):
+ super().__init__()
+ self.in_channels = in_channels
+
+ self.norm = Normalize(in_channels)
+ self.q = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.k = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.v = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.proj_out = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+
+ def forward(self, x):
+ h_ = x
+ h_ = self.norm(h_)
+ q = self.q(h_)
+ k = self.k(h_)
+ v = self.v(h_)
+
+ # compute attention
+ b,c,h,w = q.shape
+ q = q.reshape(b,c,h*w)
+ q = q.permute(0,2,1) # b,hw,c
+ k = k.reshape(b,c,h*w) # b,c,hw
+ w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
+ w_ = w_ * (int(c)**(-0.5))
+ w_ = torch.nn.functional.softmax(w_, dim=2)
+
+ # attend to values
+ v = v.reshape(b,c,h*w)
+ w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q)
+ h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+ h_ = h_.reshape(b,c,h,w)
+
+ h_ = self.proj_out(h_)
+
+ return x+h_
+
+class MemoryEfficientAttnBlock(nn.Module):
+ """
+ Uses xformers efficient implementation,
+ see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
+ Note: this is a single-head self-attention operation
+ """
+ #
+ def __init__(self, in_channels):
+ super().__init__()
+ self.in_channels = in_channels
+
+ self.norm = Normalize(in_channels)
+ self.q = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.k = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.v = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.proj_out = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.attention_op: Optional[Any] = None
+
+ def forward(self, x):
+ h_ = x
+ h_ = self.norm(h_)
+ q = self.q(h_)
+ k = self.k(h_)
+ v = self.v(h_)
+
+ # compute attention
+ B, C, H, W = q.shape
+ q, k, v = map(lambda x: rearrange(x, 'b c h w -> b (h w) c'), (q, k, v))
+
+ q, k, v = map(
+ lambda t: t.unsqueeze(3)
+ .reshape(B, t.shape[1], 1, C)
+ .permute(0, 2, 1, 3)
+ .reshape(B * 1, t.shape[1], C)
+ .contiguous(),
+ (q, k, v),
+ )
+ out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
+
+ out = (
+ out.unsqueeze(0)
+ .reshape(B, 1, out.shape[1], C)
+ .permute(0, 2, 1, 3)
+ .reshape(B, out.shape[1], C)
+ )
+ out = rearrange(out, 'b (h w) c -> b c h w', b=B, h=H, w=W, c=C)
+ out = self.proj_out(out)
+ return x+out
+
+
+class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention):
+ def forward(self, x, context=None, mask=None):
+ b, c, h, w = x.shape
+ x = rearrange(x, 'b c h w -> b (h w) c')
+ out = super().forward(x, context=context, mask=mask)
+ out = rearrange(out, 'b (h w) c -> b c h w', h=h, w=w, c=c)
+ return x + out
+
+
+def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
+ assert attn_type in ["vanilla", "vanilla-xformers", "memory-efficient-cross-attn", "linear", "none"], f'attn_type {attn_type} unknown'
+ if XFORMERS_IS_AVAILBLE and attn_type == "vanilla":
+ attn_type = "vanilla-xformers"
+ print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
+ if attn_type == "vanilla":
+ assert attn_kwargs is None
+ return AttnBlock(in_channels)
+ elif attn_type == "vanilla-xformers":
+ print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...")
+ return MemoryEfficientAttnBlock(in_channels)
+ elif type == "memory-efficient-cross-attn":
+ attn_kwargs["query_dim"] = in_channels
+ return MemoryEfficientCrossAttentionWrapper(**attn_kwargs)
+ elif attn_type == "none":
+ return nn.Identity(in_channels)
+ else:
+ raise NotImplementedError()
+
+
+class Model(nn.Module):
+ def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+ attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+ resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
+ super().__init__()
+ if use_linear_attn: attn_type = "linear"
+ self.ch = ch
+ self.temb_ch = self.ch*4
+ self.num_resolutions = len(ch_mult)
+ self.num_res_blocks = num_res_blocks
+ self.resolution = resolution
+ self.in_channels = in_channels
+
+ self.use_timestep = use_timestep
+ if self.use_timestep:
+ # timestep embedding
+ self.temb = nn.Module()
+ self.temb.dense = nn.ModuleList([
+ torch.nn.Linear(self.ch,
+ self.temb_ch),
+ torch.nn.Linear(self.temb_ch,
+ self.temb_ch),
+ ])
+
+ # downsampling
+ self.conv_in = torch.nn.Conv2d(in_channels,
+ self.ch,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ curr_res = resolution
+ in_ch_mult = (1,)+tuple(ch_mult)
+ self.down = nn.ModuleList()
+ for i_level in range(self.num_resolutions):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+ block_in = ch*in_ch_mult[i_level]
+ block_out = ch*ch_mult[i_level]
+ for i_block in range(self.num_res_blocks):
+ block.append(ResnetBlock(in_channels=block_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout))
+ block_in = block_out
+ if curr_res in attn_resolutions:
+ attn.append(make_attn(block_in, attn_type=attn_type))
+ down = nn.Module()
+ down.block = block
+ down.attn = attn
+ if i_level != self.num_resolutions-1:
+ down.downsample = Downsample(block_in, resamp_with_conv)
+ curr_res = curr_res // 2
+ self.down.append(down)
+
+ # middle
+ self.mid = nn.Module()
+ self.mid.block_1 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+ self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
+ self.mid.block_2 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+
+ # upsampling
+ self.up = nn.ModuleList()
+ for i_level in reversed(range(self.num_resolutions)):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+ block_out = ch*ch_mult[i_level]
+ skip_in = ch*ch_mult[i_level]
+ for i_block in range(self.num_res_blocks+1):
+ if i_block == self.num_res_blocks:
+ skip_in = ch*in_ch_mult[i_level]
+ block.append(ResnetBlock(in_channels=block_in+skip_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout))
+ block_in = block_out
+ if curr_res in attn_resolutions:
+ attn.append(make_attn(block_in, attn_type=attn_type))
+ up = nn.Module()
+ up.block = block
+ up.attn = attn
+ if i_level != 0:
+ up.upsample = Upsample(block_in, resamp_with_conv)
+ curr_res = curr_res * 2
+ self.up.insert(0, up) # prepend to get consistent order
+
+ # end
+ self.norm_out = Normalize(block_in)
+ self.conv_out = torch.nn.Conv2d(block_in,
+ out_ch,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, x, t=None, context=None):
+ #assert x.shape[2] == x.shape[3] == self.resolution
+ if context is not None:
+ # assume aligned context, cat along channel axis
+ x = torch.cat((x, context), dim=1)
+ if self.use_timestep:
+ # timestep embedding
+ assert t is not None
+ temb = get_timestep_embedding(t, self.ch)
+ temb = self.temb.dense[0](temb)
+ temb = nonlinearity(temb)
+ temb = self.temb.dense[1](temb)
+ else:
+ temb = None
+
+ # downsampling
+ hs = [self.conv_in(x)]
+ for i_level in range(self.num_resolutions):
+ for i_block in range(self.num_res_blocks):
+ h = self.down[i_level].block[i_block](hs[-1], temb)
+ if len(self.down[i_level].attn) > 0:
+ h = self.down[i_level].attn[i_block](h)
+ hs.append(h)
+ if i_level != self.num_resolutions-1:
+ hs.append(self.down[i_level].downsample(hs[-1]))
+
+ # middle
+ h = hs[-1]
+ h = self.mid.block_1(h, temb)
+ h = self.mid.attn_1(h)
+ h = self.mid.block_2(h, temb)
+
+ # upsampling
+ for i_level in reversed(range(self.num_resolutions)):
+ for i_block in range(self.num_res_blocks+1):
+ h = self.up[i_level].block[i_block](
+ torch.cat([h, hs.pop()], dim=1), temb)
+ if len(self.up[i_level].attn) > 0:
+ h = self.up[i_level].attn[i_block](h)
+ if i_level != 0:
+ h = self.up[i_level].upsample(h)
+
+ # end
+ h = self.norm_out(h)
+ h = nonlinearity(h)
+ h = self.conv_out(h)
+ return h
+
+ def get_last_layer(self):
+ return self.conv_out.weight
+
+
+class Encoder(nn.Module):
+ def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+ attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+ resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
+ **ignore_kwargs):
+ super().__init__()
+ if use_linear_attn: attn_type = "linear"
+ self.ch = ch
+ self.temb_ch = 0
+ self.num_resolutions = len(ch_mult)
+ self.num_res_blocks = num_res_blocks
+ self.resolution = resolution
+ self.in_channels = in_channels
+
+ # downsampling
+ self.conv_in = torch.nn.Conv2d(in_channels,
+ self.ch,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ curr_res = resolution
+ in_ch_mult = (1,)+tuple(ch_mult)
+ self.in_ch_mult = in_ch_mult
+ self.down = nn.ModuleList()
+ for i_level in range(self.num_resolutions):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+ block_in = ch*in_ch_mult[i_level]
+ block_out = ch*ch_mult[i_level]
+ for i_block in range(self.num_res_blocks):
+ block.append(ResnetBlock(in_channels=block_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout))
+ block_in = block_out
+ if curr_res in attn_resolutions:
+ attn.append(make_attn(block_in, attn_type=attn_type))
+ down = nn.Module()
+ down.block = block
+ down.attn = attn
+ if i_level != self.num_resolutions-1:
+ down.downsample = Downsample(block_in, resamp_with_conv)
+ curr_res = curr_res // 2
+ self.down.append(down)
+
+ # middle
+ self.mid = nn.Module()
+ self.mid.block_1 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+ self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
+ self.mid.block_2 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+
+ # end
+ self.norm_out = Normalize(block_in)
+ self.conv_out = torch.nn.Conv2d(block_in,
+ 2*z_channels if double_z else z_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, x):
+ # timestep embedding
+ temb = None
+
+ # downsampling
+ hs = [self.conv_in(x)]
+ for i_level in range(self.num_resolutions):
+ for i_block in range(self.num_res_blocks):
+ h = self.down[i_level].block[i_block](hs[-1], temb)
+ if len(self.down[i_level].attn) > 0:
+ h = self.down[i_level].attn[i_block](h)
+ hs.append(h)
+ if i_level != self.num_resolutions-1:
+ hs.append(self.down[i_level].downsample(hs[-1]))
+
+ # middle
+ h = hs[-1]
+ h = self.mid.block_1(h, temb)
+ h = self.mid.attn_1(h)
+ h = self.mid.block_2(h, temb)
+
+ # end
+ h = self.norm_out(h)
+ h = nonlinearity(h)
+ h = self.conv_out(h)
+ return h
+
+
+class Decoder(nn.Module):
+ def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+ attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+ resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
+ attn_type="vanilla", **ignorekwargs):
+ super().__init__()
+ if use_linear_attn: attn_type = "linear"
+ self.ch = ch
+ self.temb_ch = 0
+ self.num_resolutions = len(ch_mult)
+ self.num_res_blocks = num_res_blocks
+ self.resolution = resolution
+ self.in_channels = in_channels
+ self.give_pre_end = give_pre_end
+ self.tanh_out = tanh_out
+
+ # compute in_ch_mult, block_in and curr_res at lowest res
+ in_ch_mult = (1,)+tuple(ch_mult)
+ block_in = ch*ch_mult[self.num_resolutions-1]
+ curr_res = resolution // 2**(self.num_resolutions-1)
+ self.z_shape = (1,z_channels,curr_res,curr_res)
+ print("Working with z of shape {} = {} dimensions.".format(
+ self.z_shape, np.prod(self.z_shape)))
+
+ # z to block_in
+ self.conv_in = torch.nn.Conv2d(z_channels,
+ block_in,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ # middle
+ self.mid = nn.Module()
+ self.mid.block_1 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+ self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
+ self.mid.block_2 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+
+ # upsampling
+ self.up = nn.ModuleList()
+ for i_level in reversed(range(self.num_resolutions)):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+ block_out = ch*ch_mult[i_level]
+ for i_block in range(self.num_res_blocks+1):
+ block.append(ResnetBlock(in_channels=block_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout))
+ block_in = block_out
+ if curr_res in attn_resolutions:
+ attn.append(make_attn(block_in, attn_type=attn_type))
+ up = nn.Module()
+ up.block = block
+ up.attn = attn
+ if i_level != 0:
+ up.upsample = Upsample(block_in, resamp_with_conv)
+ curr_res = curr_res * 2
+ self.up.insert(0, up) # prepend to get consistent order
+
+ # end
+ self.norm_out = Normalize(block_in)
+ self.conv_out = torch.nn.Conv2d(block_in,
+ out_ch,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, z):
+ #assert z.shape[1:] == self.z_shape[1:]
+ self.last_z_shape = z.shape
+
+ # timestep embedding
+ temb = None
+
+ # z to block_in
+ h = self.conv_in(z)
+
+ # middle
+ h = self.mid.block_1(h, temb)
+ h = self.mid.attn_1(h)
+ h = self.mid.block_2(h, temb)
+
+ # upsampling
+ for i_level in reversed(range(self.num_resolutions)):
+ for i_block in range(self.num_res_blocks+1):
+ h = self.up[i_level].block[i_block](h, temb)
+ if len(self.up[i_level].attn) > 0:
+ h = self.up[i_level].attn[i_block](h)
+ if i_level != 0:
+ h = self.up[i_level].upsample(h)
+
+ # end
+ if self.give_pre_end:
+ return h
+
+ h = self.norm_out(h)
+ h = nonlinearity(h)
+ h = self.conv_out(h)
+ if self.tanh_out:
+ h = torch.tanh(h)
+ return h
+
+
+class SimpleDecoder(nn.Module):
+ def __init__(self, in_channels, out_channels, *args, **kwargs):
+ super().__init__()
+ self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
+ ResnetBlock(in_channels=in_channels,
+ out_channels=2 * in_channels,
+ temb_channels=0, dropout=0.0),
+ ResnetBlock(in_channels=2 * in_channels,
+ out_channels=4 * in_channels,
+ temb_channels=0, dropout=0.0),
+ ResnetBlock(in_channels=4 * in_channels,
+ out_channels=2 * in_channels,
+ temb_channels=0, dropout=0.0),
+ nn.Conv2d(2*in_channels, in_channels, 1),
+ Upsample(in_channels, with_conv=True)])
+ # end
+ self.norm_out = Normalize(in_channels)
+ self.conv_out = torch.nn.Conv2d(in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, x):
+ for i, layer in enumerate(self.model):
+ if i in [1,2,3]:
+ x = layer(x, None)
+ else:
+ x = layer(x)
+
+ h = self.norm_out(x)
+ h = nonlinearity(h)
+ x = self.conv_out(h)
+ return x
+
+
+class UpsampleDecoder(nn.Module):
+ def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
+ ch_mult=(2,2), dropout=0.0):
+ super().__init__()
+ # upsampling
+ self.temb_ch = 0
+ self.num_resolutions = len(ch_mult)
+ self.num_res_blocks = num_res_blocks
+ block_in = in_channels
+ curr_res = resolution // 2 ** (self.num_resolutions - 1)
+ self.res_blocks = nn.ModuleList()
+ self.upsample_blocks = nn.ModuleList()
+ for i_level in range(self.num_resolutions):
+ res_block = []
+ block_out = ch * ch_mult[i_level]
+ for i_block in range(self.num_res_blocks + 1):
+ res_block.append(ResnetBlock(in_channels=block_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout))
+ block_in = block_out
+ self.res_blocks.append(nn.ModuleList(res_block))
+ if i_level != self.num_resolutions - 1:
+ self.upsample_blocks.append(Upsample(block_in, True))
+ curr_res = curr_res * 2
+
+ # end
+ self.norm_out = Normalize(block_in)
+ self.conv_out = torch.nn.Conv2d(block_in,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, x):
+ # upsampling
+ h = x
+ for k, i_level in enumerate(range(self.num_resolutions)):
+ for i_block in range(self.num_res_blocks + 1):
+ h = self.res_blocks[i_level][i_block](h, None)
+ if i_level != self.num_resolutions - 1:
+ h = self.upsample_blocks[k](h)
+ h = self.norm_out(h)
+ h = nonlinearity(h)
+ h = self.conv_out(h)
+ return h
+
+
+class LatentRescaler(nn.Module):
+ def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
+ super().__init__()
+ # residual block, interpolate, residual block
+ self.factor = factor
+ self.conv_in = nn.Conv2d(in_channels,
+ mid_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+ self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
+ out_channels=mid_channels,
+ temb_channels=0,
+ dropout=0.0) for _ in range(depth)])
+ self.attn = AttnBlock(mid_channels)
+ self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
+ out_channels=mid_channels,
+ temb_channels=0,
+ dropout=0.0) for _ in range(depth)])
+
+ self.conv_out = nn.Conv2d(mid_channels,
+ out_channels,
+ kernel_size=1,
+ )
+
+ def forward(self, x):
+ x = self.conv_in(x)
+ for block in self.res_block1:
+ x = block(x, None)
+ x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor))))
+ x = self.attn(x)
+ for block in self.res_block2:
+ x = block(x, None)
+ x = self.conv_out(x)
+ return x
+
+
+class MergedRescaleEncoder(nn.Module):
+ def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
+ attn_resolutions, dropout=0.0, resamp_with_conv=True,
+ ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1):
+ super().__init__()
+ intermediate_chn = ch * ch_mult[-1]
+ self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult,
+ z_channels=intermediate_chn, double_z=False, resolution=resolution,
+ attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv,
+ out_ch=None)
+ self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn,
+ mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth)
+
+ def forward(self, x):
+ x = self.encoder(x)
+ x = self.rescaler(x)
+ return x
+
+
+class MergedRescaleDecoder(nn.Module):
+ def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),
+ dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1):
+ super().__init__()
+ tmp_chn = z_channels*ch_mult[-1]
+ self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout,
+ resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks,
+ ch_mult=ch_mult, resolution=resolution, ch=ch)
+ self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn,
+ out_channels=tmp_chn, depth=rescale_module_depth)
+
+ def forward(self, x):
+ x = self.rescaler(x)
+ x = self.decoder(x)
+ return x
+
+
+class Upsampler(nn.Module):
+ def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
+ super().__init__()
+ assert out_size >= in_size
+ num_blocks = int(np.log2(out_size//in_size))+1
+ factor_up = 1.+ (out_size % in_size)
+ print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}")
+ self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels,
+ out_channels=in_channels)
+ self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2,
+ attn_resolutions=[], in_channels=None, ch=in_channels,
+ ch_mult=[ch_mult for _ in range(num_blocks)])
+
+ def forward(self, x):
+ x = self.rescaler(x)
+ x = self.decoder(x)
+ return x
+
+
+class Resize(nn.Module):
+ def __init__(self, in_channels=None, learned=False, mode="bilinear"):
+ super().__init__()
+ self.with_conv = learned
+ self.mode = mode
+ if self.with_conv:
+ print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode")
+ raise NotImplementedError()
+ assert in_channels is not None
+ # no asymmetric padding in torch conv, must do it ourselves
+ self.conv = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=4,
+ stride=2,
+ padding=1)
+
+ def forward(self, x, scale_factor=1.0):
+ if scale_factor==1.0:
+ return x
+ else:
+ x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)
+ return x
diff --git a/model_lib/ControlNet/ldm/modules/diffusionmodules/openaimodel.py b/model_lib/ControlNet/ldm/modules/diffusionmodules/openaimodel.py
new file mode 100644
index 0000000000000000000000000000000000000000..820f49db4076e721640aa95907a2fc47ab560673
--- /dev/null
+++ b/model_lib/ControlNet/ldm/modules/diffusionmodules/openaimodel.py
@@ -0,0 +1,1212 @@
+from abc import abstractmethod
+import math
+
+import numpy as np
+import torch as th
+import torch.nn as nn
+import torch.nn.functional as F
+import pdb
+from model_lib.ControlNet.ldm.modules.diffusionmodules.util import (
+ checkpoint,
+ conv_nd,
+ linear,
+ avg_pool_nd,
+ zero_module,
+ normalization,
+ timestep_embedding,
+)
+from model_lib.ControlNet.ldm.modules.attention import SpatialTransformer
+from model_lib.ControlNet.ldm.util import exists
+from model_lib.ControlNet.ldm.modules.motion_module import get_motion_module, VanillaTemporalModule, TemporalTransformer3DModel
+
+# dummy replace
+def convert_module_to_f16(x):
+ pass
+
+def convert_module_to_f32(x):
+ pass
+
+
+## go
+class AttentionPool2d(nn.Module):
+ """
+ Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
+ """
+
+ def __init__(
+ self,
+ spacial_dim: int,
+ embed_dim: int,
+ num_heads_channels: int,
+ output_dim: int = None,
+ ):
+ super().__init__()
+ self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5)
+ self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
+ self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
+ self.num_heads = embed_dim // num_heads_channels
+ self.attention = QKVAttention(self.num_heads)
+
+ def forward(self, x):
+ b, c, *_spatial = x.shape
+ x = x.reshape(b, c, -1) # NC(HW)
+ x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1)
+ x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1)
+ x = self.qkv_proj(x)
+ x = self.attention(x)
+ x = self.c_proj(x)
+ return x[:, :, 0]
+
+
+class TimestepBlock(nn.Module):
+ """
+ Any module where forward() takes timestep embeddings as a second argument.
+ """
+
+ @abstractmethod
+ def forward(self, x, emb):
+ """
+ Apply the module to `x` given `emb` timestep embeddings.
+ """
+
+
+class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
+ """
+ A sequential module that passes timestep embeddings to the children that
+ support it as an extra input.
+ """
+
+ def forward(self, x, emb, context=None, banks=None, attention_mode=None, attn_index=None, uc=False):
+ for layer in self:
+
+ if isinstance(layer, TimestepBlock):
+ # print("layer TimestepBlock")
+ x = layer(x, emb)
+ elif isinstance(layer, SpatialTransformer):
+ # print("layer SpatialTransformer")
+ if uc:
+ x = layer(x, context,uc=uc)
+ else:
+ # pdb.set_trace()
+ x = layer(x, context, banks, attention_mode, attn_index)
+ if attention_mode == 'read':
+ attn_index+=1
+ elif isinstance(layer, VanillaTemporalModule):
+ # print("layer Motion Module")
+ # pdb.set_trace()
+ x = layer(x, context)
+ else:
+ # print("layer others")
+ # pdb.set_trace()
+ x = layer(x)
+
+ if attention_mode == 'write':
+ return x
+ if attention_mode == 'read':
+ return x, attn_index
+ else:
+ return x
+
+
+class Upsample(nn.Module):
+ """
+ An upsampling layer with an optional convolution.
+ :param channels: channels in the inputs and outputs.
+ :param use_conv: a bool determining if a convolution is applied.
+ :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+ upsampling occurs in the inner-two dimensions.
+ """
+
+ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
+ super().__init__()
+ self.channels = channels
+ self.out_channels = out_channels or channels
+ self.use_conv = use_conv
+ self.dims = dims
+ if use_conv:
+ self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
+
+ def nearest_neighbor_upsample(self, x: th.Tensor, scale_factor: int):
+ # Upsample {x} (NCHW) by scale factor {scale_factor} using nearest neighbor interpolation.
+ s = scale_factor
+ return x.reshape(*x.shape, 1, 1).expand(*x.shape, s, s).transpose(-2, -3).reshape(*x.shape[:2], *(s * hw for hw in x.shape[2:]))
+
+
+ def forward(self, x):
+ assert x.shape[1] == self.channels
+ if self.dims == 3:
+ x = F.interpolate(
+ x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
+ )
+ else:
+ # x = F.interpolate(x, scale_factor=2, mode="nearest")
+ x = self.nearest_neighbor_upsample(x, scale_factor=2)
+
+ if self.use_conv:
+ x = self.conv(x)
+ return x
+
+class TransposedUpsample(nn.Module):
+ 'Learned 2x upsampling without padding'
+ def __init__(self, channels, out_channels=None, ks=5):
+ super().__init__()
+ self.channels = channels
+ self.out_channels = out_channels or channels
+
+ self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2)
+
+ def forward(self,x):
+ return self.up(x)
+
+
+class Downsample(nn.Module):
+ """
+ A downsampling layer with an optional convolution.
+ :param channels: channels in the inputs and outputs.
+ :param use_conv: a bool determining if a convolution is applied.
+ :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+ downsampling occurs in the inner-two dimensions.
+ """
+
+ def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
+ super().__init__()
+ self.channels = channels
+ self.out_channels = out_channels or channels
+ self.use_conv = use_conv
+ self.dims = dims
+ stride = 2 if dims != 3 else (1, 2, 2)
+ if use_conv:
+ self.op = conv_nd(
+ dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
+ )
+ else:
+ assert self.channels == self.out_channels
+ self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
+
+ def forward(self, x):
+ assert x.shape[1] == self.channels
+ return self.op(x)
+
+
+class ResBlock(TimestepBlock):
+ """
+ A residual block that can optionally change the number of channels.
+ :param channels: the number of input channels.
+ :param emb_channels: the number of timestep embedding channels.
+ :param dropout: the rate of dropout.
+ :param out_channels: if specified, the number of out channels.
+ :param use_conv: if True and out_channels is specified, use a spatial
+ convolution instead of a smaller 1x1 convolution to change the
+ channels in the skip connection.
+ :param dims: determines if the signal is 1D, 2D, or 3D.
+ :param use_checkpoint: if True, use gradient checkpointing on this module.
+ :param up: if True, use this block for upsampling.
+ :param down: if True, use this block for downsampling.
+ """
+
+ def __init__(
+ self,
+ channels,
+ emb_channels,
+ dropout,
+ out_channels=None,
+ use_conv=False,
+ use_scale_shift_norm=False,
+ dims=2,
+ use_checkpoint=False,
+ up=False,
+ down=False,
+ ):
+ super().__init__()
+ self.channels = channels
+ self.emb_channels = emb_channels
+ self.dropout = dropout
+ self.out_channels = out_channels or channels
+ self.use_conv = use_conv
+ self.use_checkpoint = use_checkpoint
+ self.use_scale_shift_norm = use_scale_shift_norm
+
+ self.in_layers = nn.Sequential(
+ normalization(channels),
+ nn.SiLU(),
+ conv_nd(dims, channels, self.out_channels, 3, padding=1),
+ )
+
+ self.updown = up or down
+
+ if up:
+ self.h_upd = Upsample(channels, False, dims)
+ self.x_upd = Upsample(channels, False, dims)
+ elif down:
+ self.h_upd = Downsample(channels, False, dims)
+ self.x_upd = Downsample(channels, False, dims)
+ else:
+ self.h_upd = self.x_upd = nn.Identity()
+
+ self.emb_layers = nn.Sequential(
+ nn.SiLU(),
+ linear(
+ emb_channels,
+ 2 * self.out_channels if use_scale_shift_norm else self.out_channels,
+ ),
+ )
+ self.out_layers = nn.Sequential(
+ normalization(self.out_channels),
+ nn.SiLU(),
+ nn.Dropout(p=dropout),
+ zero_module(
+ conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
+ ),
+ )
+
+ if self.out_channels == channels:
+ self.skip_connection = nn.Identity()
+ elif use_conv:
+ self.skip_connection = conv_nd(
+ dims, channels, self.out_channels, 3, padding=1
+ )
+ else:
+ self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
+
+ def forward(self, x, emb):
+ """
+ Apply the block to a Tensor, conditioned on a timestep embedding.
+ :param x: an [N x C x ...] Tensor of features.
+ :param emb: an [N x emb_channels] Tensor of timestep embeddings.
+ :return: an [N x C x ...] Tensor of outputs.
+ """
+ return checkpoint(
+ self._forward, (x, emb), self.parameters(), self.use_checkpoint
+ )
+
+
+ def _forward(self, x, emb):
+ if self.updown:
+ in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
+ h = in_rest(x)
+ h = self.h_upd(h)
+ x = self.x_upd(x)
+ h = in_conv(h)
+ else:
+ h = self.in_layers(x)
+ emb_out = self.emb_layers(emb).type(h.dtype)
+ while len(emb_out.shape) < len(h.shape):
+ emb_out = emb_out[..., None]
+ if self.use_scale_shift_norm:
+ out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+ scale, shift = th.chunk(emb_out, 2, dim=1)
+ h = out_norm(h) * (1 + scale) + shift
+ h = out_rest(h)
+ else:
+ h = h + emb_out
+ h = self.out_layers(h)
+ return self.skip_connection(x) + h
+
+class AttentionBlock(nn.Module):
+ """
+ An attention block that allows spatial positions to attend to each other.
+ Originally ported from here, but adapted to the N-d case.
+ https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
+ """
+
+ def __init__(
+ self,
+ channels,
+ num_heads=1,
+ num_head_channels=-1,
+ use_checkpoint=False,
+ use_new_attention_order=False,
+ ):
+ super().__init__()
+ self.channels = channels
+ if num_head_channels == -1:
+ self.num_heads = num_heads
+ else:
+ assert (
+ channels % num_head_channels == 0
+ ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
+ self.num_heads = channels // num_head_channels
+ self.use_checkpoint = use_checkpoint
+ self.norm = normalization(channels)
+ self.qkv = conv_nd(1, channels, channels * 3, 1)
+ if use_new_attention_order:
+ # split qkv before split heads
+ self.attention = QKVAttention(self.num_heads)
+ else:
+ # split heads before split qkv
+ self.attention = QKVAttentionLegacy(self.num_heads)
+
+ self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
+
+ def forward(self, x):
+ return checkpoint(self._forward, (x,), self.parameters(), True) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
+ #return pt_checkpoint(self._forward, x) # pytorch
+
+ def _forward(self, x):
+ b, c, *spatial = x.shape
+ x = x.reshape(b, c, -1)
+ qkv = self.qkv(self.norm(x))
+ h = self.attention(qkv)
+ h = self.proj_out(h)
+ return (x + h).reshape(b, c, *spatial)
+
+
+def count_flops_attn(model, _x, y):
+ """
+ A counter for the `thop` package to count the operations in an
+ attention operation.
+ Meant to be used like:
+ macs, params = thop.profile(
+ model,
+ inputs=(inputs, timestamps),
+ custom_ops={QKVAttention: QKVAttention.count_flops},
+ )
+ """
+ b, c, *spatial = y[0].shape
+ num_spatial = int(np.prod(spatial))
+ # We perform two matmuls with the same number of ops.
+ # The first computes the weight matrix, the second computes
+ # the combination of the value vectors.
+ matmul_ops = 2 * b * (num_spatial ** 2) * c
+ model.total_ops += th.DoubleTensor([matmul_ops])
+
+
+class QKVAttentionLegacy(nn.Module):
+ """
+ A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
+ """
+
+ def __init__(self, n_heads):
+ super().__init__()
+ self.n_heads = n_heads
+
+ def forward(self, qkv):
+ """
+ Apply QKV attention.
+ :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
+ :return: an [N x (H * C) x T] tensor after attention.
+ """
+ bs, width, length = qkv.shape
+ assert width % (3 * self.n_heads) == 0
+ ch = width // (3 * self.n_heads)
+ q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
+ scale = 1 / math.sqrt(math.sqrt(ch))
+ weight = th.einsum(
+ "bct,bcs->bts", q * scale, k * scale
+ ) # More stable with f16 than dividing afterwards
+ weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
+ a = th.einsum("bts,bcs->bct", weight, v)
+ return a.reshape(bs, -1, length)
+
+ @staticmethod
+ def count_flops(model, _x, y):
+ return count_flops_attn(model, _x, y)
+
+
+class QKVAttention(nn.Module):
+ """
+ A module which performs QKV attention and splits in a different order.
+ """
+
+ def __init__(self, n_heads):
+ super().__init__()
+ self.n_heads = n_heads
+
+ def forward(self, qkv):
+ """
+ Apply QKV attention.
+ :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
+ :return: an [N x (H * C) x T] tensor after attention.
+ """
+ bs, width, length = qkv.shape
+ assert width % (3 * self.n_heads) == 0
+ ch = width // (3 * self.n_heads)
+ q, k, v = qkv.chunk(3, dim=1)
+ scale = 1 / math.sqrt(math.sqrt(ch))
+ weight = th.einsum(
+ "bct,bcs->bts",
+ (q * scale).view(bs * self.n_heads, ch, length),
+ (k * scale).view(bs * self.n_heads, ch, length),
+ ) # More stable with f16 than dividing afterwards
+ weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
+ a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
+ return a.reshape(bs, -1, length)
+
+ @staticmethod
+ def count_flops(model, _x, y):
+ return count_flops_attn(model, _x, y)
+
+
+class UNetModel(nn.Module):
+ """
+ The full UNet model with attention and timestep embedding.
+ :param in_channels: channels in the input Tensor.
+ :param model_channels: base channel count for the model.
+ :param out_channels: channels in the output Tensor.
+ :param num_res_blocks: number of residual blocks per downsample.
+ :param attention_resolutions: a collection of downsample rates at which
+ attention will take place. May be a set, list, or tuple.
+ For example, if this contains 4, then at 4x downsampling, attention
+ will be used.
+ :param dropout: the dropout probability.
+ :param channel_mult: channel multiplier for each level of the UNet.
+ :param conv_resample: if True, use learned convolutions for upsampling and
+ downsampling.
+ :param dims: determines if the signal is 1D, 2D, or 3D.
+ :param num_classes: if specified (as an int), then this model will be
+ class-conditional with `num_classes` classes.
+ :param use_checkpoint: use gradient checkpointing to reduce memory usage.
+ :param num_heads: the number of attention heads in each attention layer.
+ :param num_heads_channels: if specified, ignore num_heads and instead use
+ a fixed channel width per attention head.
+ :param num_heads_upsample: works with num_heads to set a different number
+ of heads for upsampling. Deprecated.
+ :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
+ :param resblock_updown: use residual blocks for up/downsampling.
+ :param use_new_attention_order: use a different attention pattern for potentially
+ increased efficiency.
+ """
+
+ def __init__(
+ self,
+ image_size,
+ in_channels,
+ model_channels,
+ out_channels,
+ num_res_blocks,
+ attention_resolutions,
+ dropout=0,
+ channel_mult=(1, 2, 4, 8),
+ conv_resample=True,
+ dims=2,
+ num_classes=None,
+ use_checkpoint=False,
+ use_fp16=False,
+ num_heads=-1,
+ num_head_channels=-1,
+ num_heads_upsample=-1,
+ use_scale_shift_norm=False,
+ resblock_updown=False,
+ use_new_attention_order=False,
+ use_spatial_transformer=False, # custom transformer support
+ transformer_depth=1, # custom transformer support
+ context_dim=None, # custom transformer support
+ n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
+ legacy=True,
+ disable_self_attentions=None,
+ num_attention_blocks=None,
+ disable_middle_self_attn=False,
+ use_linear_in_transformer=False,
+ ):
+ super().__init__()
+ if use_spatial_transformer:
+ assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
+
+ if context_dim is not None:
+ assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
+ from omegaconf.listconfig import ListConfig
+ if type(context_dim) == ListConfig:
+ context_dim = list(context_dim)
+
+ if num_heads_upsample == -1:
+ num_heads_upsample = num_heads
+
+ if num_heads == -1:
+ assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
+
+ if num_head_channels == -1:
+ assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
+
+ self.image_size = image_size
+ self.in_channels = in_channels
+ self.model_channels = model_channels
+ self.out_channels = out_channels
+ if isinstance(num_res_blocks, int):
+ self.num_res_blocks = len(channel_mult) * [num_res_blocks]
+ else:
+ if len(num_res_blocks) != len(channel_mult):
+ raise ValueError("provide num_res_blocks either as an int (globally constant) or "
+ "as a list/tuple (per-level) with the same length as channel_mult")
+ self.num_res_blocks = num_res_blocks
+ if disable_self_attentions is not None:
+ # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
+ assert len(disable_self_attentions) == len(channel_mult)
+ if num_attention_blocks is not None:
+ assert len(num_attention_blocks) == len(self.num_res_blocks)
+ assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
+ print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
+ f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
+ f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
+ f"attention will still not be set.")
+
+ self.attention_resolutions = attention_resolutions
+ self.dropout = dropout
+ self.channel_mult = channel_mult
+ self.conv_resample = conv_resample
+ self.num_classes = num_classes
+ self.use_checkpoint = use_checkpoint
+ self.dtype = th.float16 if use_fp16 else th.float32
+ self.num_heads = num_heads
+ self.num_head_channels = num_head_channels
+ self.num_heads_upsample = num_heads_upsample
+ self.predict_codebook_ids = n_embed is not None
+
+ time_embed_dim = model_channels * 4
+ self.time_embed = nn.Sequential(
+ linear(model_channels, time_embed_dim),
+ nn.SiLU(),
+ linear(time_embed_dim, time_embed_dim),
+ )
+
+ if self.num_classes is not None:
+ if isinstance(self.num_classes, int):
+ self.label_emb = nn.Embedding(num_classes, time_embed_dim)
+ elif self.num_classes == "continuous":
+ print("setting up linear c_adm embedding layer")
+ self.label_emb = nn.Linear(1, time_embed_dim)
+ else:
+ raise ValueError()
+
+ self.input_blocks = nn.ModuleList(
+ [
+ TimestepEmbedSequential(
+ conv_nd(dims, in_channels, model_channels, 3, padding=1)
+ )
+ ]
+ )
+ self._feature_size = model_channels
+ input_block_chans = [model_channels]
+ ch = model_channels
+ ds = 1
+ for level, mult in enumerate(channel_mult):
+ for nr in range(self.num_res_blocks[level]):
+ layers = [
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels=mult * model_channels,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ )
+ ]
+ ch = mult * model_channels
+ if ds in attention_resolutions:
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ #num_heads = 1
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ if exists(disable_self_attentions):
+ disabled_sa = disable_self_attentions[level]
+ else:
+ disabled_sa = False
+
+ if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
+ layers.append(
+ AttentionBlock(
+ ch,
+ use_checkpoint=use_checkpoint,
+ num_heads=num_heads,
+ num_head_channels=dim_head,
+ use_new_attention_order=use_new_attention_order,
+ ) if not use_spatial_transformer else SpatialTransformer(
+ ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+ disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
+ use_checkpoint=use_checkpoint
+ )
+ )
+ self.input_blocks.append(TimestepEmbedSequential(*layers))
+ self._feature_size += ch
+ input_block_chans.append(ch)
+ if level != len(channel_mult) - 1:
+ out_ch = ch
+ self.input_blocks.append(
+ TimestepEmbedSequential(
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels=out_ch,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ down=True,
+ )
+ if resblock_updown
+ else Downsample(
+ ch, conv_resample, dims=dims, out_channels=out_ch
+ )
+ )
+ )
+ ch = out_ch
+ input_block_chans.append(ch)
+ ds *= 2
+ self._feature_size += ch
+
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ #num_heads = 1
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ self.middle_block = TimestepEmbedSequential(
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ ),
+ AttentionBlock(
+ ch,
+ use_checkpoint=use_checkpoint,
+ num_heads=num_heads,
+ num_head_channels=dim_head,
+ use_new_attention_order=use_new_attention_order,
+ ) if not use_spatial_transformer else SpatialTransformer( # always uses a self-attn
+ ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+ disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
+ use_checkpoint=use_checkpoint
+ ),
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ ),
+ )
+ self._feature_size += ch
+
+ self.output_blocks = nn.ModuleList([])
+ for level, mult in list(enumerate(channel_mult))[::-1]:
+ for i in range(self.num_res_blocks[level] + 1):
+ ich = input_block_chans.pop()
+ layers = [
+ ResBlock(
+ ch + ich,
+ time_embed_dim,
+ dropout,
+ out_channels=model_channels * mult,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ )
+ ]
+ ch = model_channels * mult
+ if ds in attention_resolutions:
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ #num_heads = 1
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ if exists(disable_self_attentions):
+ disabled_sa = disable_self_attentions[level]
+ else:
+ disabled_sa = False
+
+ if not exists(num_attention_blocks) or i < num_attention_blocks[level]:
+ layers.append(
+ AttentionBlock(
+ ch,
+ use_checkpoint=use_checkpoint,
+ num_heads=num_heads_upsample,
+ num_head_channels=dim_head,
+ use_new_attention_order=use_new_attention_order,
+ ) if not use_spatial_transformer else SpatialTransformer(
+ ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+ disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
+ use_checkpoint=use_checkpoint
+ )
+ )
+ if level and i == self.num_res_blocks[level]:
+ out_ch = ch
+ layers.append(
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels=out_ch,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ up=True,
+ )
+ if resblock_updown
+ else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
+ )
+ ds //= 2
+ self.output_blocks.append(TimestepEmbedSequential(*layers))
+ self._feature_size += ch
+
+ self.out = nn.Sequential(
+ normalization(ch),
+ nn.SiLU(),
+ zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
+ )
+ if self.predict_codebook_ids:
+ self.id_predictor = nn.Sequential(
+ normalization(ch),
+ conv_nd(dims, model_channels, n_embed, 1),
+ #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits
+ )
+
+ def convert_to_fp16(self):
+ """
+ Convert the torso of the model to float16.
+ """
+ self.input_blocks.apply(convert_module_to_f16)
+ self.middle_block.apply(convert_module_to_f16)
+ self.output_blocks.apply(convert_module_to_f16)
+
+ def convert_to_fp32(self):
+ """
+ Convert the torso of the model to float32.
+ """
+ self.input_blocks.apply(convert_module_to_f32)
+ self.middle_block.apply(convert_module_to_f32)
+ self.output_blocks.apply(convert_module_to_f32)
+
+ def forward(self, x, timesteps=None, context=None, y=None,**kwargs):
+ """
+ Apply the model to an input batch.
+ :param x: an [N x C x ...] Tensor of inputs.
+ :param timesteps: a 1-D batch of timesteps.
+ :param context: conditioning plugged in via crossattn
+ :param y: an [N] Tensor of labels, if class-conditional.
+ :return: an [N x C x ...] Tensor of outputs.
+ """
+ assert (y is not None) == (
+ self.num_classes is not None
+ ), "must specify y if and only if the model is class-conditional"
+ hs = []
+ t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+ emb = self.time_embed(t_emb)
+
+ if self.num_classes is not None:
+ assert y.shape[0] == x.shape[0]
+ emb = emb + self.label_emb(y)
+
+ h = x.type(self.dtype)
+ for module in self.input_blocks:
+ h = module(h, emb, context)
+ hs.append(h)
+ h = self.middle_block(h, emb, context)
+ for module in self.output_blocks:
+ h = th.cat([h, hs.pop()], dim=1)
+ h = module(h, emb, context)
+ h = h.type(x.dtype)
+ if self.predict_codebook_ids:
+ return self.id_predictor(h)
+ else:
+ return self.out(h)
+
+
+
+
+class UNetModel_Temporal(nn.Module):
+ """
+ The full UNet model with attention and timestep embedding.
+ :param in_channels: channels in the input Tensor.
+ :param model_channels: base channel count for the model.
+ :param out_channels: channels in the output Tensor.
+ :param num_res_blocks: number of residual blocks per downsample.
+ :param attention_resolutions: a collection of downsample rates at which
+ attention will take place. May be a set, list, or tuple.
+ For example, if this contains 4, then at 4x downsampling, attention
+ will be used.
+ :param dropout: the dropout probability.
+ :param channel_mult: channel multiplier for each level of the UNet.
+ :param conv_resample: if True, use learned convolutions for upsampling and
+ downsampling.
+ :param dims: determines if the signal is 1D, 2D, or 3D.
+ :param num_classes: if specified (as an int), then this model will be
+ class-conditional with `num_classes` classes.
+ :param use_checkpoint: use gradient checkpointing to reduce memory usage.
+ :param num_heads: the number of attention heads in each attention layer.
+ :param num_heads_channels: if specified, ignore num_heads and instead use
+ a fixed channel width per attention head.
+ :param num_heads_upsample: works with num_heads to set a different number
+ of heads for upsampling. Deprecated.
+ :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
+ :param resblock_updown: use residual blocks for up/downsampling.
+ :param use_new_attention_order: use a different attention pattern for potentially
+ increased efficiency.
+ """
+
+ def __init__(
+ self,
+ image_size,
+ in_channels,
+ model_channels,
+ out_channels,
+ num_res_blocks,
+ attention_resolutions,
+ dropout=0,
+ channel_mult=(1, 2, 4, 8),
+ conv_resample=True,
+ dims=2,
+ num_classes=None,
+ use_checkpoint=False,
+ use_fp16=False,
+ num_heads=-1,
+ num_head_channels=-1,
+ num_heads_upsample=-1,
+ use_scale_shift_norm=False,
+ resblock_updown=False,
+ use_new_attention_order=False,
+ use_spatial_transformer=False, # custom transformer support
+ transformer_depth=1, # custom transformer support
+ context_dim=None, # custom transformer support
+ n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
+ legacy=True,
+ disable_self_attentions=None,
+ num_attention_blocks=None,
+ disable_middle_self_attn=False,
+ use_linear_in_transformer=False,
+ unet_additional_kwargs=None,
+ ):
+ super().__init__()
+
+ ## Motion Module Kwagrs
+ self.unet_additional_kwargs = unet_additional_kwargs
+ self.use_motion_module = self.unet_additional_kwargs['use_motion_module']
+ self.motion_module_resolutions = self.unet_additional_kwargs['motion_module_resolutions']
+ self.unet_use_cross_frame_attention = self.unet_additional_kwargs['unet_use_cross_frame_attention']
+ self.unet_use_temporal_attention = self.unet_additional_kwargs['unet_use_temporal_attention']
+ self.motion_module_type = self.unet_additional_kwargs['motion_module_type']
+
+ self.motion_module_kwargs = self.unet_additional_kwargs['motion_module_kwargs']
+ self.num_attention_heads = self.motion_module_kwargs['num_attention_heads']
+ self.num_transformer_block = self.motion_module_kwargs['num_transformer_block']
+ self.attention_block_types = self.motion_module_kwargs['attention_block_types']
+ self.temporal_position_encoding = self.motion_module_kwargs['temporal_position_encoding']
+ self.temporal_position_encoding_max_len = self.motion_module_kwargs['temporal_position_encoding_max_len']
+ self.temporal_attention_dim_div = self.motion_module_kwargs['temporal_attention_dim_div']
+ self.zero_initialize = self.motion_module_kwargs['zero_initialize']
+
+
+ if use_spatial_transformer:
+ assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
+
+ if context_dim is not None:
+ assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
+ from omegaconf.listconfig import ListConfig
+ if type(context_dim) == ListConfig:
+ context_dim = list(context_dim)
+
+ if num_heads_upsample == -1:
+ num_heads_upsample = num_heads
+
+ if num_heads == -1:
+ assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
+
+ if num_head_channels == -1:
+ assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
+
+ self.image_size = image_size
+ self.in_channels = in_channels
+ self.model_channels = model_channels
+ self.out_channels = out_channels
+ if isinstance(num_res_blocks, int):
+ self.num_res_blocks = len(channel_mult) * [num_res_blocks] # 4 * [2]
+ else:
+ if len(num_res_blocks) != len(channel_mult):
+ raise ValueError("provide num_res_blocks either as an int (globally constant) or "
+ "as a list/tuple (per-level) with the same length as channel_mult")
+ self.num_res_blocks = num_res_blocks
+ if disable_self_attentions is not None:
+ # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
+ assert len(disable_self_attentions) == len(channel_mult)
+ if num_attention_blocks is not None:
+ assert len(num_attention_blocks) == len(self.num_res_blocks)
+ assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
+ print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
+ f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
+ f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
+ f"attention will still not be set.")
+
+ self.attention_resolutions = attention_resolutions
+ self.dropout = dropout
+ self.channel_mult = channel_mult
+ self.conv_resample = conv_resample
+ self.num_classes = num_classes
+ self.use_checkpoint = use_checkpoint
+ self.dtype = th.float16 if use_fp16 else th.float32
+ self.num_heads = num_heads
+ self.num_head_channels = num_head_channels
+ self.num_heads_upsample = num_heads_upsample
+ self.predict_codebook_ids = n_embed is not None
+
+ time_embed_dim = model_channels * 4
+ self.time_embed = nn.Sequential(
+ linear(model_channels, time_embed_dim),
+ nn.SiLU(),
+ linear(time_embed_dim, time_embed_dim),
+ )
+
+ if self.num_classes is not None:
+ if isinstance(self.num_classes, int):
+ self.label_emb = nn.Embedding(num_classes, time_embed_dim)
+ elif self.num_classes == "continuous":
+ print("setting up linear c_adm embedding layer")
+ self.label_emb = nn.Linear(1, time_embed_dim)
+ else:
+ raise ValueError()
+
+ self.input_blocks = nn.ModuleList(
+ [
+ TimestepEmbedSequential(
+ conv_nd(dims, in_channels, model_channels, 3, padding=1)
+ )
+ ]
+ )
+ self.input_blocks_motion_module = nn.ModuleList([])
+ self._feature_size = model_channels
+ input_block_chans = [model_channels]
+ ch = model_channels
+ ds = 1
+ for level, mult in enumerate(channel_mult):
+ for nr in range(self.num_res_blocks[level]):
+ layers = [
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels=mult * model_channels,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ )
+ ]
+ ch = mult * model_channels
+ if ds in attention_resolutions: # [1,2,4]
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ #num_heads = 1
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ if exists(disable_self_attentions):
+ disabled_sa = disable_self_attentions[level]
+ else:
+ disabled_sa = False
+
+ if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
+ layers.append(
+ AttentionBlock(
+ ch,
+ use_checkpoint=use_checkpoint,
+ num_heads=num_heads,
+ num_head_channels=dim_head,
+ use_new_attention_order=use_new_attention_order,
+ ) if not use_spatial_transformer else SpatialTransformer(
+ ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+ disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
+ use_checkpoint=use_checkpoint
+ )
+ )
+ if self.use_motion_module:
+ layers_motion_module=[
+ get_motion_module(
+ in_channels=ch,
+ motion_module_type=self.motion_module_type,
+ motion_module_kwargs=self.motion_module_kwargs,
+ )]
+ self.input_blocks.append(TimestepEmbedSequential(*layers))
+ if self.use_motion_module:
+ self.input_blocks_motion_module.append(TimestepEmbedSequential(*layers_motion_module))
+ self._feature_size += ch
+ input_block_chans.append(ch)
+ if level != len(channel_mult) - 1:
+ out_ch = ch
+ self.input_blocks.append(
+ TimestepEmbedSequential(
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels=out_ch,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ down=True,
+ )
+ if resblock_updown
+ else Downsample(
+ ch, conv_resample, dims=dims, out_channels=out_ch
+ )
+ )
+ )
+ ch = out_ch
+ input_block_chans.append(ch)
+ ds *= 2
+ self._feature_size += ch
+ # motion module [1,2,4,5,7,8,10,11] !!!! Conv RST RST Down RST RST Down RST RST Down RT RT
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ #num_heads = 1
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ self.middle_block = TimestepEmbedSequential(
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ ),
+ AttentionBlock(
+ ch,
+ use_checkpoint=use_checkpoint,
+ num_heads=num_heads,
+ num_head_channels=dim_head,
+ use_new_attention_order=use_new_attention_order,
+ ) if not use_spatial_transformer else SpatialTransformer( # always uses a self-attn
+ ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+ disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
+ use_checkpoint=use_checkpoint
+ ), # Follow by motion module
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ ),
+ )
+
+ self._feature_size += ch
+
+ self.output_blocks = nn.ModuleList([])
+ self.output_blocks_motion_module = nn.ModuleList([])
+ for level, mult in list(enumerate(channel_mult))[::-1]:
+ for i in range(self.num_res_blocks[level] + 1):
+ ich = input_block_chans.pop()
+ layers = [
+ ResBlock(
+ ch + ich,
+ time_embed_dim,
+ dropout,
+ out_channels=model_channels * mult,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ )
+ ]
+ ch = model_channels * mult
+ if ds in attention_resolutions:
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ #num_heads = 1
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ if exists(disable_self_attentions):
+ disabled_sa = disable_self_attentions[level]
+ else:
+ disabled_sa = False
+
+ if not exists(num_attention_blocks) or i < num_attention_blocks[level]:
+ layers.append(
+ AttentionBlock(
+ ch,
+ use_checkpoint=use_checkpoint,
+ num_heads=num_heads_upsample,
+ num_head_channels=dim_head,
+ use_new_attention_order=use_new_attention_order,
+ ) if not use_spatial_transformer else SpatialTransformer(
+ ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+ disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
+ use_checkpoint=use_checkpoint
+ )
+ )
+ if self.use_motion_module:
+ layers_motion_module=[
+ get_motion_module(
+ in_channels=ch,
+ motion_module_type=self.motion_module_type,
+ motion_module_kwargs=self.motion_module_kwargs,
+ )]
+ if level and i == self.num_res_blocks[level]:
+ out_ch = ch
+ layers.append(
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels=out_ch,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ up=True,
+ )
+ if resblock_updown
+ else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
+ )
+
+ # if self.use_motion_module:
+ # in_channel_mm_up = out_ch or ch
+ # layers_motion_module.append(
+ # get_motion_module(
+ # in_channels=in_channel_mm_up,
+ # motion_module_type=self.motion_module_type,
+ # motion_module_kwargs=self.motion_module_kwargs,
+ # )
+ # )
+ ds //= 2
+ self.output_blocks.append(TimestepEmbedSequential(*layers))
+ self.output_blocks_motion_module.append(TimestepEmbedSequential(*layers_motion_module))
+ self._feature_size += ch
+ # motion module [0,1,2,4,5,6,8,9,10,12,13,14] RT RT RT Up RST RST RST Up RST RST RST Up RST RST RST
+ self.out = nn.Sequential(
+ normalization(ch),
+ nn.SiLU(),
+ zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
+ )
+ if self.predict_codebook_ids:
+ self.id_predictor = nn.Sequential(
+ normalization(ch),
+ conv_nd(dims, model_channels, n_embed, 1),
+ #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits
+ )
+
+ def convert_to_fp16(self):
+ """
+ Convert the torso of the model to float16.
+ """
+ self.input_blocks.apply(convert_module_to_f16)
+ self.middle_block.apply(convert_module_to_f16)
+ self.output_blocks.apply(convert_module_to_f16)
+
+ def convert_to_fp32(self):
+ """
+ Convert the torso of the model to float32.
+ """
+ self.input_blocks.apply(convert_module_to_f32)
+ self.middle_block.apply(convert_module_to_f32)
+ self.output_blocks.apply(convert_module_to_f32)
+
+ def forward(self, x, timesteps=None, context=None, y=None,**kwargs):
+ pass
diff --git a/model_lib/ControlNet/ldm/modules/diffusionmodules/upscaling.py b/model_lib/ControlNet/ldm/modules/diffusionmodules/upscaling.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa47db44b6fdc8e0734ec217ae1f12bb7cfa5cbf
--- /dev/null
+++ b/model_lib/ControlNet/ldm/modules/diffusionmodules/upscaling.py
@@ -0,0 +1,81 @@
+import torch
+import torch.nn as nn
+import numpy as np
+from functools import partial
+
+from model_lib.ControlNet.ldm.modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule
+from model_lib.ControlNet.ldm.util import default
+
+
+class AbstractLowScaleModel(nn.Module):
+ # for concatenating a downsampled image to the latent representation
+ def __init__(self, noise_schedule_config=None):
+ super(AbstractLowScaleModel, self).__init__()
+ if noise_schedule_config is not None:
+ self.register_schedule(**noise_schedule_config)
+
+ def register_schedule(self, beta_schedule="linear", timesteps=1000,
+ linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
+ cosine_s=cosine_s)
+ alphas = 1. - betas
+ alphas_cumprod = np.cumprod(alphas, axis=0)
+ alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+
+ timesteps, = betas.shape
+ self.num_timesteps = int(timesteps)
+ self.linear_start = linear_start
+ self.linear_end = linear_end
+ assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
+
+ to_torch = partial(torch.tensor, dtype=torch.float32)
+
+ self.register_buffer('betas', to_torch(betas))
+ self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+ self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
+
+ # calculations for diffusion q(x_t | x_{t-1}) and others
+ self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+ self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+ self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
+ self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
+ self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
+
+ def q_sample(self, x_start, t, noise=None):
+ noise = default(noise, lambda: torch.randn_like(x_start))
+ return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
+
+ def forward(self, x):
+ return x, None
+
+ def decode(self, x):
+ return x
+
+
+class SimpleImageConcat(AbstractLowScaleModel):
+ # no noise level conditioning
+ def __init__(self):
+ super(SimpleImageConcat, self).__init__(noise_schedule_config=None)
+ self.max_noise_level = 0
+
+ def forward(self, x):
+ # fix to constant noise level
+ return x, torch.zeros(x.shape[0], device=x.device).long()
+
+
+class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel):
+ def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False):
+ super().__init__(noise_schedule_config=noise_schedule_config)
+ self.max_noise_level = max_noise_level
+
+ def forward(self, x, noise_level=None):
+ if noise_level is None:
+ noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
+ else:
+ assert isinstance(noise_level, torch.Tensor)
+ z = self.q_sample(x, noise_level)
+ return z, noise_level
+
+
+
diff --git a/model_lib/ControlNet/ldm/modules/diffusionmodules/util.py b/model_lib/ControlNet/ldm/modules/diffusionmodules/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..49bbec590a6a3ddfc4dbe385a796a1de86e86fce
--- /dev/null
+++ b/model_lib/ControlNet/ldm/modules/diffusionmodules/util.py
@@ -0,0 +1,305 @@
+# adopted from
+# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
+# and
+# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+# and
+# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
+#
+# thanks!
+
+
+import os
+import math
+import torch
+import torch.nn as nn
+import numpy as np
+from einops import repeat
+from model_lib.ControlNet.ldm.util import instantiate_from_config
+import pdb
+
+def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ if schedule == "linear":
+ betas = (
+ torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
+ )
+
+ elif schedule == "cosine":
+ timesteps = (
+ torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
+ )
+ alphas = timesteps / (1 + cosine_s) * np.pi / 2
+ alphas = torch.cos(alphas).pow(2)
+ alphas = alphas / alphas[0]
+ betas = 1 - alphas[1:] / alphas[:-1]
+ betas = np.clip(betas, a_min=0, a_max=0.999)
+
+ elif schedule == "sqrt_linear":
+ betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
+ elif schedule == "sqrt":
+ betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
+ else:
+ raise ValueError(f"schedule '{schedule}' unknown.")
+ return betas.numpy()
+
+
+def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
+ if ddim_discr_method == 'uniform':
+ c = num_ddpm_timesteps // num_ddim_timesteps
+ ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
+ elif ddim_discr_method == 'quad':
+ ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
+ else:
+ raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
+
+ # assert ddim_timesteps.shape[0] == num_ddim_timesteps
+ # add one to get the final alpha values right (the ones from first scale to data during sampling)
+ steps_out = ddim_timesteps + 1
+ if verbose:
+ print(f'Selected timesteps for ddim sampler: {steps_out}')
+ return steps_out
+
+
+def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
+ # select alphas for computing the variance schedule
+ alphas = alphacums[ddim_timesteps]
+ alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
+
+ # according the the formula provided in https://arxiv.org/abs/2010.02502
+ sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
+ if verbose:
+ print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
+ print(f'For the chosen value of eta, which is {eta}, '
+ f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
+ return sigmas, alphas, alphas_prev
+
+
+def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
+ """
+ Create a beta schedule that discretizes the given alpha_t_bar function,
+ which defines the cumulative product of (1-beta) over time from t = [0,1].
+ :param num_diffusion_timesteps: the number of betas to produce.
+ :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
+ produces the cumulative product of (1-beta) up to that
+ part of the diffusion process.
+ :param max_beta: the maximum beta to use; use values lower than 1 to
+ prevent singularities.
+ """
+ betas = []
+ for i in range(num_diffusion_timesteps):
+ t1 = i / num_diffusion_timesteps
+ t2 = (i + 1) / num_diffusion_timesteps
+ betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
+ return np.array(betas)
+
+
+def extract_into_tensor(a, t, x_shape):
+ b, *_ = t.shape
+ out = a.gather(-1, t)
+ return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+
+
+def checkpoint(func, inputs, params, flag):
+ """
+ Evaluate a function without caching intermediate activations, allowing for
+ reduced memory at the expense of extra compute in the backward pass.
+ :param func: the function to evaluate.
+ :param inputs: the argument sequence to pass to `func`.
+ :param params: a sequence of parameters `func` depends on but does not
+ explicitly take as arguments.
+ :param flag: if False, disable gradient checkpointing.
+ """
+ if flag:
+ args = tuple(inputs) + tuple(params)
+ return CheckpointFunction.apply(func, len(inputs), *args)
+ else:
+ return func(*inputs)
+
+
+class CheckpointFunction(torch.autograd.Function):
+ @staticmethod
+ def forward(ctx, run_function, length, *args):
+ ctx.run_function = run_function
+ ctx.input_tensors = list(args[:length])
+ ctx.input_params = list(args[length:])
+ ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(),
+ "dtype": torch.get_autocast_gpu_dtype(),
+ "cache_enabled": torch.is_autocast_cache_enabled()}
+ with torch.no_grad():
+ output_tensors = ctx.run_function(*ctx.input_tensors)
+ return output_tensors
+
+ @staticmethod
+ def backward(ctx, *output_grads):
+ input_tensors = []
+ input_tensor_index = []
+ for i, input_tensor in enumerate(ctx.input_tensors):
+ if isinstance(input_tensor, torch.Tensor):
+ input_tensors.append(input_tensor.detach().requires_grad_(True))
+ else:
+ input_tensors.append(input_tensor)
+ input_tensor_index.append(i)
+ ctx.input_tensors = input_tensors
+
+ length_input_tensors = len(input_tensors)
+ with torch.enable_grad(), \
+ torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs):
+ # Fixes a bug where the first op in run_function modifies the
+ # Tensor storage in place, which is not allowed for detach()'d
+ # Tensors.
+ shallow_copies = []
+ for input_tensor in ctx.input_tensors:
+ try:
+ shallow_copies.append(input_tensor.view_as(input_tensor))
+ except:
+ shallow_copies.append(input_tensor)
+ # shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
+ output_tensors = ctx.run_function(*shallow_copies)
+ # print(len(input_tensors))
+ # pdb.set_trace()
+ num_non_tensor = len(input_tensor_index)
+ for num in range(num_non_tensor):
+ index = input_tensor_index[num_non_tensor-1-num]
+ ctx.input_tensors.pop(index)
+
+ input_params = []
+ input_params_index = []
+ for i, input_param in enumerate(ctx.input_params):
+ if input_param.requires_grad == True:
+ input_params.append(input_param)
+ else:
+ input_params_index.append(i)
+ # pdb.set_trace()
+ input_grads = torch.autograd.grad(output_tensors,ctx.input_tensors + input_params,output_grads,allow_unused=True,)
+ # print(len(input_grads))
+ # pdb.set_trace()
+ input_grads = list(input_grads)
+ for index in input_tensor_index:
+ input_grads.insert(index, None)
+ if input_params_index == []:
+ pass
+ else:
+ for param_index in input_params_index:
+ input_grads.insert(length_input_tensors+param_index, None)
+ input_grads = tuple(input_grads)
+ del ctx.input_tensors
+ del ctx.input_params
+ del output_tensors
+ return (None, None) + input_grads
+
+def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
+ """
+ Create sinusoidal timestep embeddings.
+ :param timesteps: a 1-D Tensor of N indices, one per batch element.
+ These may be fractional.
+ :param dim: the dimension of the output.
+ :param max_period: controls the minimum frequency of the embeddings.
+ :return: an [N x dim] Tensor of positional embeddings.
+ """
+ if not repeat_only:
+ half = dim // 2
+ freqs = torch.exp(
+ -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
+ ).to(device=timesteps.device)
+ args = timesteps[:, None].float() * freqs[None]
+ embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+ if dim % 2:
+ embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+ else:
+ embedding = repeat(timesteps, 'b -> b d', d=dim)
+ return embedding
+
+
+def zero_module(module):
+ """
+ Zero out the parameters of a module and return it.
+ """
+ for p in module.parameters():
+ p.detach().zero_()
+ return module
+
+
+def scale_module(module, scale):
+ """
+ Scale the parameters of a module and return it.
+ """
+ for p in module.parameters():
+ p.detach().mul_(scale)
+ return module
+
+
+def mean_flat(tensor):
+ """
+ Take the mean over all non-batch dimensions.
+ """
+ return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+
+def normalization(channels):
+ """
+ Make a standard normalization layer.
+ :param channels: number of input channels.
+ :return: an nn.Module for normalization.
+ """
+ return GroupNorm32(32, channels)
+
+
+# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
+class SiLU(nn.Module):
+ def forward(self, x):
+ return x * torch.sigmoid(x)
+
+
+class GroupNorm32(nn.GroupNorm):
+ def forward(self, x):
+ return super().forward(x.float()).type(x.dtype)
+
+def conv_nd(dims, *args, **kwargs):
+ """
+ Create a 1D, 2D, or 3D convolution module.
+ """
+ if dims == 1:
+ return nn.Conv1d(*args, **kwargs)
+ elif dims == 2:
+ return nn.Conv2d(*args, **kwargs)
+ elif dims == 3:
+ return nn.Conv3d(*args, **kwargs)
+ raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def linear(*args, **kwargs):
+ """
+ Create a linear module.
+ """
+ return nn.Linear(*args, **kwargs)
+
+
+def avg_pool_nd(dims, *args, **kwargs):
+ """
+ Create a 1D, 2D, or 3D average pooling module.
+ """
+ if dims == 1:
+ return nn.AvgPool1d(*args, **kwargs)
+ elif dims == 2:
+ return nn.AvgPool2d(*args, **kwargs)
+ elif dims == 3:
+ return nn.AvgPool3d(*args, **kwargs)
+ raise ValueError(f"unsupported dimensions: {dims}")
+
+
+class HybridConditioner(nn.Module):
+
+ def __init__(self, c_concat_config, c_crossattn_config):
+ super().__init__()
+ self.concat_conditioner = instantiate_from_config(c_concat_config)
+ self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
+
+ def forward(self, c_concat, c_crossattn):
+ c_concat = self.concat_conditioner(c_concat)
+ c_crossattn = self.crossattn_conditioner(c_crossattn)
+ return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
+
+
+def noise_like(shape, device, repeat=False):
+ repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
+ noise = lambda: torch.randn(shape, device=device)
+ return repeat_noise() if repeat else noise()
\ No newline at end of file
diff --git a/model_lib/ControlNet/ldm/modules/distributions/__init__.py b/model_lib/ControlNet/ldm/modules/distributions/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/model_lib/ControlNet/ldm/modules/distributions/__pycache__/__init__.cpython-39.pyc b/model_lib/ControlNet/ldm/modules/distributions/__pycache__/__init__.cpython-39.pyc
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diff --git a/model_lib/ControlNet/ldm/modules/distributions/__pycache__/distributions.cpython-39.pyc b/model_lib/ControlNet/ldm/modules/distributions/__pycache__/distributions.cpython-39.pyc
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index 0000000000000000000000000000000000000000..550be27742cd0d8f537652ad9c8c1167ca131d60
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diff --git a/model_lib/ControlNet/ldm/modules/distributions/distributions.py b/model_lib/ControlNet/ldm/modules/distributions/distributions.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2b8ef901130efc171aa69742ca0244d94d3f2e9
--- /dev/null
+++ b/model_lib/ControlNet/ldm/modules/distributions/distributions.py
@@ -0,0 +1,92 @@
+import torch
+import numpy as np
+
+
+class AbstractDistribution:
+ def sample(self):
+ raise NotImplementedError()
+
+ def mode(self):
+ raise NotImplementedError()
+
+
+class DiracDistribution(AbstractDistribution):
+ def __init__(self, value):
+ self.value = value
+
+ def sample(self):
+ return self.value
+
+ def mode(self):
+ return self.value
+
+
+class DiagonalGaussianDistribution(object):
+ def __init__(self, parameters, deterministic=False):
+ self.parameters = parameters
+ self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
+ self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
+ self.deterministic = deterministic
+ self.std = torch.exp(0.5 * self.logvar)
+ self.var = torch.exp(self.logvar)
+ if self.deterministic:
+ self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
+
+ def sample(self):
+ x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
+ return x
+
+ def kl(self, other=None):
+ if self.deterministic:
+ return torch.Tensor([0.])
+ else:
+ if other is None:
+ return 0.5 * torch.sum(torch.pow(self.mean, 2)
+ + self.var - 1.0 - self.logvar,
+ dim=[1, 2, 3])
+ else:
+ return 0.5 * torch.sum(
+ torch.pow(self.mean - other.mean, 2) / other.var
+ + self.var / other.var - 1.0 - self.logvar + other.logvar,
+ dim=[1, 2, 3])
+
+ def nll(self, sample, dims=[1,2,3]):
+ if self.deterministic:
+ return torch.Tensor([0.])
+ logtwopi = np.log(2.0 * np.pi)
+ return 0.5 * torch.sum(
+ logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
+ dim=dims)
+
+ def mode(self):
+ return self.mean
+
+
+def normal_kl(mean1, logvar1, mean2, logvar2):
+ """
+ source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
+ Compute the KL divergence between two gaussians.
+ Shapes are automatically broadcasted, so batches can be compared to
+ scalars, among other use cases.
+ """
+ tensor = None
+ for obj in (mean1, logvar1, mean2, logvar2):
+ if isinstance(obj, torch.Tensor):
+ tensor = obj
+ break
+ assert tensor is not None, "at least one argument must be a Tensor"
+
+ # Force variances to be Tensors. Broadcasting helps convert scalars to
+ # Tensors, but it does not work for torch.exp().
+ logvar1, logvar2 = [
+ x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
+ for x in (logvar1, logvar2)
+ ]
+
+ return 0.5 * (
+ -1.0
+ + logvar2
+ - logvar1
+ + torch.exp(logvar1 - logvar2)
+ + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
+ )
diff --git a/model_lib/ControlNet/ldm/modules/ema.py b/model_lib/ControlNet/ldm/modules/ema.py
new file mode 100644
index 0000000000000000000000000000000000000000..bded25019b9bcbcd0260f0b8185f8c7859ca58c4
--- /dev/null
+++ b/model_lib/ControlNet/ldm/modules/ema.py
@@ -0,0 +1,80 @@
+import torch
+from torch import nn
+
+
+class LitEma(nn.Module):
+ def __init__(self, model, decay=0.9999, use_num_upates=True):
+ super().__init__()
+ if decay < 0.0 or decay > 1.0:
+ raise ValueError('Decay must be between 0 and 1')
+
+ self.m_name2s_name = {}
+ self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
+ self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates
+ else torch.tensor(-1, dtype=torch.int))
+
+ for name, p in model.named_parameters():
+ if p.requires_grad:
+ # remove as '.'-character is not allowed in buffers
+ s_name = name.replace('.', '')
+ self.m_name2s_name.update({name: s_name})
+ self.register_buffer(s_name, p.clone().detach().data)
+
+ self.collected_params = []
+
+ def reset_num_updates(self):
+ del self.num_updates
+ self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int))
+
+ def forward(self, model):
+ decay = self.decay
+
+ if self.num_updates >= 0:
+ self.num_updates += 1
+ decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))
+
+ one_minus_decay = 1.0 - decay
+
+ with torch.no_grad():
+ m_param = dict(model.named_parameters())
+ shadow_params = dict(self.named_buffers())
+
+ for key in m_param:
+ if m_param[key].requires_grad:
+ sname = self.m_name2s_name[key]
+ shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
+ shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
+ else:
+ assert not key in self.m_name2s_name
+
+ def copy_to(self, model):
+ m_param = dict(model.named_parameters())
+ shadow_params = dict(self.named_buffers())
+ for key in m_param:
+ if m_param[key].requires_grad:
+ m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
+ else:
+ assert not key in self.m_name2s_name
+
+ def store(self, parameters):
+ """
+ Save the current parameters for restoring later.
+ Args:
+ parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+ temporarily stored.
+ """
+ self.collected_params = [param.clone() for param in parameters]
+
+ def restore(self, parameters):
+ """
+ Restore the parameters stored with the `store` method.
+ Useful to validate the model with EMA parameters without affecting the
+ original optimization process. Store the parameters before the
+ `copy_to` method. After validation (or model saving), use this to
+ restore the former parameters.
+ Args:
+ parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+ updated with the stored parameters.
+ """
+ for c_param, param in zip(self.collected_params, parameters):
+ param.data.copy_(c_param.data)
diff --git a/model_lib/ControlNet/ldm/modules/encoders/__init__.py b/model_lib/ControlNet/ldm/modules/encoders/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/model_lib/ControlNet/ldm/modules/encoders/__pycache__/__init__.cpython-39.pyc b/model_lib/ControlNet/ldm/modules/encoders/__pycache__/__init__.cpython-39.pyc
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index 0000000000000000000000000000000000000000..7602c3179ecb782088d6d3b77817ba0d0a428bb3
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diff --git a/model_lib/ControlNet/ldm/modules/encoders/__pycache__/modules.cpython-39.pyc b/model_lib/ControlNet/ldm/modules/encoders/__pycache__/modules.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0291a12bb1fa9d484bef257343eddb46333c325b
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diff --git a/model_lib/ControlNet/ldm/modules/encoders/modules.py b/model_lib/ControlNet/ldm/modules/encoders/modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2ae9aec76d2c896c1796b1db1fe2e30df2891b8
--- /dev/null
+++ b/model_lib/ControlNet/ldm/modules/encoders/modules.py
@@ -0,0 +1,213 @@
+import torch
+import torch.nn as nn
+from torch.utils.checkpoint import checkpoint
+
+from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel
+
+import open_clip
+from model_lib.ControlNet.ldm.util import default, count_params
+
+
+class AbstractEncoder(nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ def encode(self, *args, **kwargs):
+ raise NotImplementedError
+
+
+class IdentityEncoder(AbstractEncoder):
+
+ def encode(self, x):
+ return x
+
+
+class ClassEmbedder(nn.Module):
+ def __init__(self, embed_dim, n_classes=1000, key='class', ucg_rate=0.1):
+ super().__init__()
+ self.key = key
+ self.embedding = nn.Embedding(n_classes, embed_dim)
+ self.n_classes = n_classes
+ self.ucg_rate = ucg_rate
+
+ def forward(self, batch, key=None, disable_dropout=False):
+ if key is None:
+ key = self.key
+ # this is for use in crossattn
+ c = batch[key][:, None]
+ if self.ucg_rate > 0. and not disable_dropout:
+ mask = 1. - torch.bernoulli(torch.ones_like(c) * self.ucg_rate)
+ c = mask * c + (1-mask) * torch.ones_like(c)*(self.n_classes-1)
+ c = c.long()
+ c = self.embedding(c)
+ return c
+
+ def get_unconditional_conditioning(self, bs, device="cuda"):
+ uc_class = self.n_classes - 1 # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000)
+ uc = torch.ones((bs,), device=device) * uc_class
+ uc = {self.key: uc}
+ return uc
+
+
+def disabled_train(self, mode=True):
+ """Overwrite model.train with this function to make sure train/eval mode
+ does not change anymore."""
+ return self
+
+
+class FrozenT5Embedder(AbstractEncoder):
+ """Uses the T5 transformer encoder for text"""
+ def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
+ super().__init__()
+ self.tokenizer = T5Tokenizer.from_pretrained(version)
+ self.transformer = T5EncoderModel.from_pretrained(version)
+ self.device = device
+ self.max_length = max_length # TODO: typical value?
+ if freeze:
+ self.freeze()
+
+ def freeze(self):
+ self.transformer = self.transformer.eval()
+ #self.train = disabled_train
+ for param in self.parameters():
+ param.requires_grad = False
+
+ def forward(self, text):
+ batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
+ return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+ tokens = batch_encoding["input_ids"].to(self.device)
+ outputs = self.transformer(input_ids=tokens)
+
+ z = outputs.last_hidden_state
+ return z
+
+ def encode(self, text):
+ return self(text)
+
+
+class FrozenCLIPEmbedder(AbstractEncoder):
+ """Uses the CLIP transformer encoder for text (from huggingface)"""
+ LAYERS = [
+ "last",
+ "pooled",
+ "hidden"
+ ]
+ def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77,
+ freeze=True, layer="last", layer_idx=None): # clip-vit-base-patch32
+ super().__init__()
+ assert layer in self.LAYERS
+ self.tokenizer = CLIPTokenizer.from_pretrained(version)
+ self.transformer = CLIPTextModel.from_pretrained(version)
+ self.device = device
+ self.max_length = max_length
+ if freeze:
+ self.freeze()
+ self.layer = layer
+ self.layer_idx = layer_idx
+ if layer == "hidden":
+ assert layer_idx is not None
+ assert 0 <= abs(layer_idx) <= 12
+
+ def freeze(self):
+ self.transformer = self.transformer.eval()
+ #self.train = disabled_train
+ for param in self.parameters():
+ param.requires_grad = False
+
+ def forward(self, text):
+ batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
+ return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+ tokens = batch_encoding["input_ids"].to(self.device)
+ outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer=="hidden")
+ if self.layer == "last":
+ z = outputs.last_hidden_state
+ elif self.layer == "pooled":
+ z = outputs.pooler_output[:, None, :]
+ else:
+ z = outputs.hidden_states[self.layer_idx]
+ return z
+
+ def encode(self, text):
+ return self(text)
+
+
+class FrozenOpenCLIPEmbedder(AbstractEncoder):
+ """
+ Uses the OpenCLIP transformer encoder for text
+ """
+ LAYERS = [
+ #"pooled",
+ "last",
+ "penultimate"
+ ]
+ def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77,
+ freeze=True, layer="last"):
+ super().__init__()
+ assert layer in self.LAYERS
+ model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version)
+ del model.visual
+ self.model = model
+
+ self.device = device
+ self.max_length = max_length
+ if freeze:
+ self.freeze()
+ self.layer = layer
+ if self.layer == "last":
+ self.layer_idx = 0
+ elif self.layer == "penultimate":
+ self.layer_idx = 1
+ else:
+ raise NotImplementedError()
+
+ def freeze(self):
+ self.model = self.model.eval()
+ for param in self.parameters():
+ param.requires_grad = False
+
+ def forward(self, text):
+ tokens = open_clip.tokenize(text)
+ z = self.encode_with_transformer(tokens.to(self.device))
+ return z
+
+ def encode_with_transformer(self, text):
+ x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model]
+ x = x + self.model.positional_embedding
+ x = x.permute(1, 0, 2) # NLD -> LND
+ x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
+ x = x.permute(1, 0, 2) # LND -> NLD
+ x = self.model.ln_final(x)
+ return x
+
+ def text_transformer_forward(self, x: torch.Tensor, attn_mask = None):
+ for i, r in enumerate(self.model.transformer.resblocks):
+ if i == len(self.model.transformer.resblocks) - self.layer_idx:
+ break
+ if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting():
+ x = checkpoint(r, x, attn_mask)
+ else:
+ x = r(x, attn_mask=attn_mask)
+ return x
+
+ def encode(self, text):
+ return self(text)
+
+
+class FrozenCLIPT5Encoder(AbstractEncoder):
+ def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_version="google/t5-v1_1-xl", device="cuda",
+ clip_max_length=77, t5_max_length=77):
+ super().__init__()
+ self.clip_encoder = FrozenCLIPEmbedder(clip_version, device, max_length=clip_max_length)
+ self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length)
+ print(f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder)*1.e-6:.2f} M parameters, "
+ f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder)*1.e-6:.2f} M params.")
+
+ def encode(self, text):
+ return self(text)
+
+ def forward(self, text):
+ clip_z = self.clip_encoder.encode(text)
+ t5_z = self.t5_encoder.encode(text)
+ return [clip_z, t5_z]
+
+
diff --git a/model_lib/ControlNet/ldm/modules/image_degradation/__init__.py b/model_lib/ControlNet/ldm/modules/image_degradation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7836cada81f90ded99c58d5942eea4c3477f58fc
--- /dev/null
+++ b/model_lib/ControlNet/ldm/modules/image_degradation/__init__.py
@@ -0,0 +1,2 @@
+from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr
+from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light
diff --git a/model_lib/ControlNet/ldm/modules/image_degradation/bsrgan.py b/model_lib/ControlNet/ldm/modules/image_degradation/bsrgan.py
new file mode 100644
index 0000000000000000000000000000000000000000..32ef56169978e550090261cddbcf5eb611a6173b
--- /dev/null
+++ b/model_lib/ControlNet/ldm/modules/image_degradation/bsrgan.py
@@ -0,0 +1,730 @@
+# -*- coding: utf-8 -*-
+"""
+# --------------------------------------------
+# Super-Resolution
+# --------------------------------------------
+#
+# Kai Zhang (cskaizhang@gmail.com)
+# https://github.com/cszn
+# From 2019/03--2021/08
+# --------------------------------------------
+"""
+
+import numpy as np
+import cv2
+import torch
+
+from functools import partial
+import random
+from scipy import ndimage
+import scipy
+import scipy.stats as ss
+from scipy.interpolate import interp2d
+from scipy.linalg import orth
+import albumentations
+
+import ldm.modules.image_degradation.utils_image as util
+
+
+def modcrop_np(img, sf):
+ '''
+ Args:
+ img: numpy image, WxH or WxHxC
+ sf: scale factor
+ Return:
+ cropped image
+ '''
+ w, h = img.shape[:2]
+ im = np.copy(img)
+ return im[:w - w % sf, :h - h % sf, ...]
+
+
+"""
+# --------------------------------------------
+# anisotropic Gaussian kernels
+# --------------------------------------------
+"""
+
+
+def analytic_kernel(k):
+ """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
+ k_size = k.shape[0]
+ # Calculate the big kernels size
+ big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
+ # Loop over the small kernel to fill the big one
+ for r in range(k_size):
+ for c in range(k_size):
+ big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
+ # Crop the edges of the big kernel to ignore very small values and increase run time of SR
+ crop = k_size // 2
+ cropped_big_k = big_k[crop:-crop, crop:-crop]
+ # Normalize to 1
+ return cropped_big_k / cropped_big_k.sum()
+
+
+def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
+ """ generate an anisotropic Gaussian kernel
+ Args:
+ ksize : e.g., 15, kernel size
+ theta : [0, pi], rotation angle range
+ l1 : [0.1,50], scaling of eigenvalues
+ l2 : [0.1,l1], scaling of eigenvalues
+ If l1 = l2, will get an isotropic Gaussian kernel.
+ Returns:
+ k : kernel
+ """
+
+ v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
+ V = np.array([[v[0], v[1]], [v[1], -v[0]]])
+ D = np.array([[l1, 0], [0, l2]])
+ Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
+ k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
+
+ return k
+
+
+def gm_blur_kernel(mean, cov, size=15):
+ center = size / 2.0 + 0.5
+ k = np.zeros([size, size])
+ for y in range(size):
+ for x in range(size):
+ cy = y - center + 1
+ cx = x - center + 1
+ k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
+
+ k = k / np.sum(k)
+ return k
+
+
+def shift_pixel(x, sf, upper_left=True):
+ """shift pixel for super-resolution with different scale factors
+ Args:
+ x: WxHxC or WxH
+ sf: scale factor
+ upper_left: shift direction
+ """
+ h, w = x.shape[:2]
+ shift = (sf - 1) * 0.5
+ xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
+ if upper_left:
+ x1 = xv + shift
+ y1 = yv + shift
+ else:
+ x1 = xv - shift
+ y1 = yv - shift
+
+ x1 = np.clip(x1, 0, w - 1)
+ y1 = np.clip(y1, 0, h - 1)
+
+ if x.ndim == 2:
+ x = interp2d(xv, yv, x)(x1, y1)
+ if x.ndim == 3:
+ for i in range(x.shape[-1]):
+ x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
+
+ return x
+
+
+def blur(x, k):
+ '''
+ x: image, NxcxHxW
+ k: kernel, Nx1xhxw
+ '''
+ n, c = x.shape[:2]
+ p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
+ x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
+ k = k.repeat(1, c, 1, 1)
+ k = k.view(-1, 1, k.shape[2], k.shape[3])
+ x = x.view(1, -1, x.shape[2], x.shape[3])
+ x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
+ x = x.view(n, c, x.shape[2], x.shape[3])
+
+ return x
+
+
+def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
+ """"
+ # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
+ # Kai Zhang
+ # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var
+ # max_var = 2.5 * sf
+ """
+ # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
+ lambda_1 = min_var + np.random.rand() * (max_var - min_var)
+ lambda_2 = min_var + np.random.rand() * (max_var - min_var)
+ theta = np.random.rand() * np.pi # random theta
+ noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
+
+ # Set COV matrix using Lambdas and Theta
+ LAMBDA = np.diag([lambda_1, lambda_2])
+ Q = np.array([[np.cos(theta), -np.sin(theta)],
+ [np.sin(theta), np.cos(theta)]])
+ SIGMA = Q @ LAMBDA @ Q.T
+ INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
+
+ # Set expectation position (shifting kernel for aligned image)
+ MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2)
+ MU = MU[None, None, :, None]
+
+ # Create meshgrid for Gaussian
+ [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
+ Z = np.stack([X, Y], 2)[:, :, :, None]
+
+ # Calcualte Gaussian for every pixel of the kernel
+ ZZ = Z - MU
+ ZZ_t = ZZ.transpose(0, 1, 3, 2)
+ raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
+
+ # shift the kernel so it will be centered
+ # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
+
+ # Normalize the kernel and return
+ # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
+ kernel = raw_kernel / np.sum(raw_kernel)
+ return kernel
+
+
+def fspecial_gaussian(hsize, sigma):
+ hsize = [hsize, hsize]
+ siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
+ std = sigma
+ [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
+ arg = -(x * x + y * y) / (2 * std * std)
+ h = np.exp(arg)
+ h[h < scipy.finfo(float).eps * h.max()] = 0
+ sumh = h.sum()
+ if sumh != 0:
+ h = h / sumh
+ return h
+
+
+def fspecial_laplacian(alpha):
+ alpha = max([0, min([alpha, 1])])
+ h1 = alpha / (alpha + 1)
+ h2 = (1 - alpha) / (alpha + 1)
+ h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
+ h = np.array(h)
+ return h
+
+
+def fspecial(filter_type, *args, **kwargs):
+ '''
+ python code from:
+ https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
+ '''
+ if filter_type == 'gaussian':
+ return fspecial_gaussian(*args, **kwargs)
+ if filter_type == 'laplacian':
+ return fspecial_laplacian(*args, **kwargs)
+
+
+"""
+# --------------------------------------------
+# degradation models
+# --------------------------------------------
+"""
+
+
+def bicubic_degradation(x, sf=3):
+ '''
+ Args:
+ x: HxWxC image, [0, 1]
+ sf: down-scale factor
+ Return:
+ bicubicly downsampled LR image
+ '''
+ x = util.imresize_np(x, scale=1 / sf)
+ return x
+
+
+def srmd_degradation(x, k, sf=3):
+ ''' blur + bicubic downsampling
+ Args:
+ x: HxWxC image, [0, 1]
+ k: hxw, double
+ sf: down-scale factor
+ Return:
+ downsampled LR image
+ Reference:
+ @inproceedings{zhang2018learning,
+ title={Learning a single convolutional super-resolution network for multiple degradations},
+ author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
+ booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
+ pages={3262--3271},
+ year={2018}
+ }
+ '''
+ x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror'
+ x = bicubic_degradation(x, sf=sf)
+ return x
+
+
+def dpsr_degradation(x, k, sf=3):
+ ''' bicubic downsampling + blur
+ Args:
+ x: HxWxC image, [0, 1]
+ k: hxw, double
+ sf: down-scale factor
+ Return:
+ downsampled LR image
+ Reference:
+ @inproceedings{zhang2019deep,
+ title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
+ author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
+ booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
+ pages={1671--1681},
+ year={2019}
+ }
+ '''
+ x = bicubic_degradation(x, sf=sf)
+ x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
+ return x
+
+
+def classical_degradation(x, k, sf=3):
+ ''' blur + downsampling
+ Args:
+ x: HxWxC image, [0, 1]/[0, 255]
+ k: hxw, double
+ sf: down-scale factor
+ Return:
+ downsampled LR image
+ '''
+ x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
+ # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
+ st = 0
+ return x[st::sf, st::sf, ...]
+
+
+def add_sharpening(img, weight=0.5, radius=50, threshold=10):
+ """USM sharpening. borrowed from real-ESRGAN
+ Input image: I; Blurry image: B.
+ 1. K = I + weight * (I - B)
+ 2. Mask = 1 if abs(I - B) > threshold, else: 0
+ 3. Blur mask:
+ 4. Out = Mask * K + (1 - Mask) * I
+ Args:
+ img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
+ weight (float): Sharp weight. Default: 1.
+ radius (float): Kernel size of Gaussian blur. Default: 50.
+ threshold (int):
+ """
+ if radius % 2 == 0:
+ radius += 1
+ blur = cv2.GaussianBlur(img, (radius, radius), 0)
+ residual = img - blur
+ mask = np.abs(residual) * 255 > threshold
+ mask = mask.astype('float32')
+ soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
+
+ K = img + weight * residual
+ K = np.clip(K, 0, 1)
+ return soft_mask * K + (1 - soft_mask) * img
+
+
+def add_blur(img, sf=4):
+ wd2 = 4.0 + sf
+ wd = 2.0 + 0.2 * sf
+ if random.random() < 0.5:
+ l1 = wd2 * random.random()
+ l2 = wd2 * random.random()
+ k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
+ else:
+ k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random())
+ img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
+
+ return img
+
+
+def add_resize(img, sf=4):
+ rnum = np.random.rand()
+ if rnum > 0.8: # up
+ sf1 = random.uniform(1, 2)
+ elif rnum < 0.7: # down
+ sf1 = random.uniform(0.5 / sf, 1)
+ else:
+ sf1 = 1.0
+ img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
+ img = np.clip(img, 0.0, 1.0)
+
+ return img
+
+
+# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
+# noise_level = random.randint(noise_level1, noise_level2)
+# rnum = np.random.rand()
+# if rnum > 0.6: # add color Gaussian noise
+# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+# elif rnum < 0.4: # add grayscale Gaussian noise
+# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+# else: # add noise
+# L = noise_level2 / 255.
+# D = np.diag(np.random.rand(3))
+# U = orth(np.random.rand(3, 3))
+# conv = np.dot(np.dot(np.transpose(U), D), U)
+# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+# img = np.clip(img, 0.0, 1.0)
+# return img
+
+def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
+ noise_level = random.randint(noise_level1, noise_level2)
+ rnum = np.random.rand()
+ if rnum > 0.6: # add color Gaussian noise
+ img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+ elif rnum < 0.4: # add grayscale Gaussian noise
+ img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+ else: # add noise
+ L = noise_level2 / 255.
+ D = np.diag(np.random.rand(3))
+ U = orth(np.random.rand(3, 3))
+ conv = np.dot(np.dot(np.transpose(U), D), U)
+ img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+ img = np.clip(img, 0.0, 1.0)
+ return img
+
+
+def add_speckle_noise(img, noise_level1=2, noise_level2=25):
+ noise_level = random.randint(noise_level1, noise_level2)
+ img = np.clip(img, 0.0, 1.0)
+ rnum = random.random()
+ if rnum > 0.6:
+ img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+ elif rnum < 0.4:
+ img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+ else:
+ L = noise_level2 / 255.
+ D = np.diag(np.random.rand(3))
+ U = orth(np.random.rand(3, 3))
+ conv = np.dot(np.dot(np.transpose(U), D), U)
+ img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+ img = np.clip(img, 0.0, 1.0)
+ return img
+
+
+def add_Poisson_noise(img):
+ img = np.clip((img * 255.0).round(), 0, 255) / 255.
+ vals = 10 ** (2 * random.random() + 2.0) # [2, 4]
+ if random.random() < 0.5:
+ img = np.random.poisson(img * vals).astype(np.float32) / vals
+ else:
+ img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
+ img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
+ noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
+ img += noise_gray[:, :, np.newaxis]
+ img = np.clip(img, 0.0, 1.0)
+ return img
+
+
+def add_JPEG_noise(img):
+ quality_factor = random.randint(30, 95)
+ img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
+ result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
+ img = cv2.imdecode(encimg, 1)
+ img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
+ return img
+
+
+def random_crop(lq, hq, sf=4, lq_patchsize=64):
+ h, w = lq.shape[:2]
+ rnd_h = random.randint(0, h - lq_patchsize)
+ rnd_w = random.randint(0, w - lq_patchsize)
+ lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
+
+ rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
+ hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
+ return lq, hq
+
+
+def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
+ """
+ This is the degradation model of BSRGAN from the paper
+ "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
+ ----------
+ img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
+ sf: scale factor
+ isp_model: camera ISP model
+ Returns
+ -------
+ img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
+ hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
+ """
+ isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
+ sf_ori = sf
+
+ h1, w1 = img.shape[:2]
+ img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
+ h, w = img.shape[:2]
+
+ if h < lq_patchsize * sf or w < lq_patchsize * sf:
+ raise ValueError(f'img size ({h1}X{w1}) is too small!')
+
+ hq = img.copy()
+
+ if sf == 4 and random.random() < scale2_prob: # downsample1
+ if np.random.rand() < 0.5:
+ img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
+ interpolation=random.choice([1, 2, 3]))
+ else:
+ img = util.imresize_np(img, 1 / 2, True)
+ img = np.clip(img, 0.0, 1.0)
+ sf = 2
+
+ shuffle_order = random.sample(range(7), 7)
+ idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
+ if idx1 > idx2: # keep downsample3 last
+ shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
+
+ for i in shuffle_order:
+
+ if i == 0:
+ img = add_blur(img, sf=sf)
+
+ elif i == 1:
+ img = add_blur(img, sf=sf)
+
+ elif i == 2:
+ a, b = img.shape[1], img.shape[0]
+ # downsample2
+ if random.random() < 0.75:
+ sf1 = random.uniform(1, 2 * sf)
+ img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
+ interpolation=random.choice([1, 2, 3]))
+ else:
+ k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
+ k_shifted = shift_pixel(k, sf)
+ k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
+ img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
+ img = img[0::sf, 0::sf, ...] # nearest downsampling
+ img = np.clip(img, 0.0, 1.0)
+
+ elif i == 3:
+ # downsample3
+ img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
+ img = np.clip(img, 0.0, 1.0)
+
+ elif i == 4:
+ # add Gaussian noise
+ img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
+
+ elif i == 5:
+ # add JPEG noise
+ if random.random() < jpeg_prob:
+ img = add_JPEG_noise(img)
+
+ elif i == 6:
+ # add processed camera sensor noise
+ if random.random() < isp_prob and isp_model is not None:
+ with torch.no_grad():
+ img, hq = isp_model.forward(img.copy(), hq)
+
+ # add final JPEG compression noise
+ img = add_JPEG_noise(img)
+
+ # random crop
+ img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
+
+ return img, hq
+
+
+# todo no isp_model?
+def degradation_bsrgan_variant(image, sf=4, isp_model=None):
+ """
+ This is the degradation model of BSRGAN from the paper
+ "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
+ ----------
+ sf: scale factor
+ isp_model: camera ISP model
+ Returns
+ -------
+ img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
+ hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
+ """
+ image = util.uint2single(image)
+ isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
+ sf_ori = sf
+
+ h1, w1 = image.shape[:2]
+ image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
+ h, w = image.shape[:2]
+
+ hq = image.copy()
+
+ if sf == 4 and random.random() < scale2_prob: # downsample1
+ if np.random.rand() < 0.5:
+ image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
+ interpolation=random.choice([1, 2, 3]))
+ else:
+ image = util.imresize_np(image, 1 / 2, True)
+ image = np.clip(image, 0.0, 1.0)
+ sf = 2
+
+ shuffle_order = random.sample(range(7), 7)
+ idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
+ if idx1 > idx2: # keep downsample3 last
+ shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
+
+ for i in shuffle_order:
+
+ if i == 0:
+ image = add_blur(image, sf=sf)
+
+ elif i == 1:
+ image = add_blur(image, sf=sf)
+
+ elif i == 2:
+ a, b = image.shape[1], image.shape[0]
+ # downsample2
+ if random.random() < 0.75:
+ sf1 = random.uniform(1, 2 * sf)
+ image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
+ interpolation=random.choice([1, 2, 3]))
+ else:
+ k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
+ k_shifted = shift_pixel(k, sf)
+ k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
+ image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
+ image = image[0::sf, 0::sf, ...] # nearest downsampling
+ image = np.clip(image, 0.0, 1.0)
+
+ elif i == 3:
+ # downsample3
+ image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
+ image = np.clip(image, 0.0, 1.0)
+
+ elif i == 4:
+ # add Gaussian noise
+ image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25)
+
+ elif i == 5:
+ # add JPEG noise
+ if random.random() < jpeg_prob:
+ image = add_JPEG_noise(image)
+
+ # elif i == 6:
+ # # add processed camera sensor noise
+ # if random.random() < isp_prob and isp_model is not None:
+ # with torch.no_grad():
+ # img, hq = isp_model.forward(img.copy(), hq)
+
+ # add final JPEG compression noise
+ image = add_JPEG_noise(image)
+ image = util.single2uint(image)
+ example = {"image":image}
+ return example
+
+
+# TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc...
+def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None):
+ """
+ This is an extended degradation model by combining
+ the degradation models of BSRGAN and Real-ESRGAN
+ ----------
+ img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
+ sf: scale factor
+ use_shuffle: the degradation shuffle
+ use_sharp: sharpening the img
+ Returns
+ -------
+ img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
+ hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
+ """
+
+ h1, w1 = img.shape[:2]
+ img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
+ h, w = img.shape[:2]
+
+ if h < lq_patchsize * sf or w < lq_patchsize * sf:
+ raise ValueError(f'img size ({h1}X{w1}) is too small!')
+
+ if use_sharp:
+ img = add_sharpening(img)
+ hq = img.copy()
+
+ if random.random() < shuffle_prob:
+ shuffle_order = random.sample(range(13), 13)
+ else:
+ shuffle_order = list(range(13))
+ # local shuffle for noise, JPEG is always the last one
+ shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6)))
+ shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13)))
+
+ poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1
+
+ for i in shuffle_order:
+ if i == 0:
+ img = add_blur(img, sf=sf)
+ elif i == 1:
+ img = add_resize(img, sf=sf)
+ elif i == 2:
+ img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
+ elif i == 3:
+ if random.random() < poisson_prob:
+ img = add_Poisson_noise(img)
+ elif i == 4:
+ if random.random() < speckle_prob:
+ img = add_speckle_noise(img)
+ elif i == 5:
+ if random.random() < isp_prob and isp_model is not None:
+ with torch.no_grad():
+ img, hq = isp_model.forward(img.copy(), hq)
+ elif i == 6:
+ img = add_JPEG_noise(img)
+ elif i == 7:
+ img = add_blur(img, sf=sf)
+ elif i == 8:
+ img = add_resize(img, sf=sf)
+ elif i == 9:
+ img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
+ elif i == 10:
+ if random.random() < poisson_prob:
+ img = add_Poisson_noise(img)
+ elif i == 11:
+ if random.random() < speckle_prob:
+ img = add_speckle_noise(img)
+ elif i == 12:
+ if random.random() < isp_prob and isp_model is not None:
+ with torch.no_grad():
+ img, hq = isp_model.forward(img.copy(), hq)
+ else:
+ print('check the shuffle!')
+
+ # resize to desired size
+ img = cv2.resize(img, (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])),
+ interpolation=random.choice([1, 2, 3]))
+
+ # add final JPEG compression noise
+ img = add_JPEG_noise(img)
+
+ # random crop
+ img, hq = random_crop(img, hq, sf, lq_patchsize)
+
+ return img, hq
+
+
+if __name__ == '__main__':
+ print("hey")
+ img = util.imread_uint('utils/test.png', 3)
+ print(img)
+ img = util.uint2single(img)
+ print(img)
+ img = img[:448, :448]
+ h = img.shape[0] // 4
+ print("resizing to", h)
+ sf = 4
+ deg_fn = partial(degradation_bsrgan_variant, sf=sf)
+ for i in range(20):
+ print(i)
+ img_lq = deg_fn(img)
+ print(img_lq)
+ img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"]
+ print(img_lq.shape)
+ print("bicubic", img_lq_bicubic.shape)
+ print(img_hq.shape)
+ lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
+ interpolation=0)
+ lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
+ interpolation=0)
+ img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
+ util.imsave(img_concat, str(i) + '.png')
+
+
diff --git a/model_lib/ControlNet/ldm/modules/image_degradation/bsrgan_light.py b/model_lib/ControlNet/ldm/modules/image_degradation/bsrgan_light.py
new file mode 100644
index 0000000000000000000000000000000000000000..808c7f882cb75e2ba2340d5b55881d11927351f0
--- /dev/null
+++ b/model_lib/ControlNet/ldm/modules/image_degradation/bsrgan_light.py
@@ -0,0 +1,651 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+import cv2
+import torch
+
+from functools import partial
+import random
+from scipy import ndimage
+import scipy
+import scipy.stats as ss
+from scipy.interpolate import interp2d
+from scipy.linalg import orth
+import albumentations
+
+import ldm.modules.image_degradation.utils_image as util
+
+"""
+# --------------------------------------------
+# Super-Resolution
+# --------------------------------------------
+#
+# Kai Zhang (cskaizhang@gmail.com)
+# https://github.com/cszn
+# From 2019/03--2021/08
+# --------------------------------------------
+"""
+
+def modcrop_np(img, sf):
+ '''
+ Args:
+ img: numpy image, WxH or WxHxC
+ sf: scale factor
+ Return:
+ cropped image
+ '''
+ w, h = img.shape[:2]
+ im = np.copy(img)
+ return im[:w - w % sf, :h - h % sf, ...]
+
+
+"""
+# --------------------------------------------
+# anisotropic Gaussian kernels
+# --------------------------------------------
+"""
+
+
+def analytic_kernel(k):
+ """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
+ k_size = k.shape[0]
+ # Calculate the big kernels size
+ big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
+ # Loop over the small kernel to fill the big one
+ for r in range(k_size):
+ for c in range(k_size):
+ big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
+ # Crop the edges of the big kernel to ignore very small values and increase run time of SR
+ crop = k_size // 2
+ cropped_big_k = big_k[crop:-crop, crop:-crop]
+ # Normalize to 1
+ return cropped_big_k / cropped_big_k.sum()
+
+
+def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
+ """ generate an anisotropic Gaussian kernel
+ Args:
+ ksize : e.g., 15, kernel size
+ theta : [0, pi], rotation angle range
+ l1 : [0.1,50], scaling of eigenvalues
+ l2 : [0.1,l1], scaling of eigenvalues
+ If l1 = l2, will get an isotropic Gaussian kernel.
+ Returns:
+ k : kernel
+ """
+
+ v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
+ V = np.array([[v[0], v[1]], [v[1], -v[0]]])
+ D = np.array([[l1, 0], [0, l2]])
+ Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
+ k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
+
+ return k
+
+
+def gm_blur_kernel(mean, cov, size=15):
+ center = size / 2.0 + 0.5
+ k = np.zeros([size, size])
+ for y in range(size):
+ for x in range(size):
+ cy = y - center + 1
+ cx = x - center + 1
+ k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
+
+ k = k / np.sum(k)
+ return k
+
+
+def shift_pixel(x, sf, upper_left=True):
+ """shift pixel for super-resolution with different scale factors
+ Args:
+ x: WxHxC or WxH
+ sf: scale factor
+ upper_left: shift direction
+ """
+ h, w = x.shape[:2]
+ shift = (sf - 1) * 0.5
+ xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
+ if upper_left:
+ x1 = xv + shift
+ y1 = yv + shift
+ else:
+ x1 = xv - shift
+ y1 = yv - shift
+
+ x1 = np.clip(x1, 0, w - 1)
+ y1 = np.clip(y1, 0, h - 1)
+
+ if x.ndim == 2:
+ x = interp2d(xv, yv, x)(x1, y1)
+ if x.ndim == 3:
+ for i in range(x.shape[-1]):
+ x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
+
+ return x
+
+
+def blur(x, k):
+ '''
+ x: image, NxcxHxW
+ k: kernel, Nx1xhxw
+ '''
+ n, c = x.shape[:2]
+ p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
+ x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
+ k = k.repeat(1, c, 1, 1)
+ k = k.view(-1, 1, k.shape[2], k.shape[3])
+ x = x.view(1, -1, x.shape[2], x.shape[3])
+ x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
+ x = x.view(n, c, x.shape[2], x.shape[3])
+
+ return x
+
+
+def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
+ """"
+ # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
+ # Kai Zhang
+ # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var
+ # max_var = 2.5 * sf
+ """
+ # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
+ lambda_1 = min_var + np.random.rand() * (max_var - min_var)
+ lambda_2 = min_var + np.random.rand() * (max_var - min_var)
+ theta = np.random.rand() * np.pi # random theta
+ noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
+
+ # Set COV matrix using Lambdas and Theta
+ LAMBDA = np.diag([lambda_1, lambda_2])
+ Q = np.array([[np.cos(theta), -np.sin(theta)],
+ [np.sin(theta), np.cos(theta)]])
+ SIGMA = Q @ LAMBDA @ Q.T
+ INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
+
+ # Set expectation position (shifting kernel for aligned image)
+ MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2)
+ MU = MU[None, None, :, None]
+
+ # Create meshgrid for Gaussian
+ [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
+ Z = np.stack([X, Y], 2)[:, :, :, None]
+
+ # Calcualte Gaussian for every pixel of the kernel
+ ZZ = Z - MU
+ ZZ_t = ZZ.transpose(0, 1, 3, 2)
+ raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
+
+ # shift the kernel so it will be centered
+ # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
+
+ # Normalize the kernel and return
+ # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
+ kernel = raw_kernel / np.sum(raw_kernel)
+ return kernel
+
+
+def fspecial_gaussian(hsize, sigma):
+ hsize = [hsize, hsize]
+ siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
+ std = sigma
+ [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
+ arg = -(x * x + y * y) / (2 * std * std)
+ h = np.exp(arg)
+ h[h < scipy.finfo(float).eps * h.max()] = 0
+ sumh = h.sum()
+ if sumh != 0:
+ h = h / sumh
+ return h
+
+
+def fspecial_laplacian(alpha):
+ alpha = max([0, min([alpha, 1])])
+ h1 = alpha / (alpha + 1)
+ h2 = (1 - alpha) / (alpha + 1)
+ h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
+ h = np.array(h)
+ return h
+
+
+def fspecial(filter_type, *args, **kwargs):
+ '''
+ python code from:
+ https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
+ '''
+ if filter_type == 'gaussian':
+ return fspecial_gaussian(*args, **kwargs)
+ if filter_type == 'laplacian':
+ return fspecial_laplacian(*args, **kwargs)
+
+
+"""
+# --------------------------------------------
+# degradation models
+# --------------------------------------------
+"""
+
+
+def bicubic_degradation(x, sf=3):
+ '''
+ Args:
+ x: HxWxC image, [0, 1]
+ sf: down-scale factor
+ Return:
+ bicubicly downsampled LR image
+ '''
+ x = util.imresize_np(x, scale=1 / sf)
+ return x
+
+
+def srmd_degradation(x, k, sf=3):
+ ''' blur + bicubic downsampling
+ Args:
+ x: HxWxC image, [0, 1]
+ k: hxw, double
+ sf: down-scale factor
+ Return:
+ downsampled LR image
+ Reference:
+ @inproceedings{zhang2018learning,
+ title={Learning a single convolutional super-resolution network for multiple degradations},
+ author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
+ booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
+ pages={3262--3271},
+ year={2018}
+ }
+ '''
+ x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror'
+ x = bicubic_degradation(x, sf=sf)
+ return x
+
+
+def dpsr_degradation(x, k, sf=3):
+ ''' bicubic downsampling + blur
+ Args:
+ x: HxWxC image, [0, 1]
+ k: hxw, double
+ sf: down-scale factor
+ Return:
+ downsampled LR image
+ Reference:
+ @inproceedings{zhang2019deep,
+ title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
+ author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
+ booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
+ pages={1671--1681},
+ year={2019}
+ }
+ '''
+ x = bicubic_degradation(x, sf=sf)
+ x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
+ return x
+
+
+def classical_degradation(x, k, sf=3):
+ ''' blur + downsampling
+ Args:
+ x: HxWxC image, [0, 1]/[0, 255]
+ k: hxw, double
+ sf: down-scale factor
+ Return:
+ downsampled LR image
+ '''
+ x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
+ # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
+ st = 0
+ return x[st::sf, st::sf, ...]
+
+
+def add_sharpening(img, weight=0.5, radius=50, threshold=10):
+ """USM sharpening. borrowed from real-ESRGAN
+ Input image: I; Blurry image: B.
+ 1. K = I + weight * (I - B)
+ 2. Mask = 1 if abs(I - B) > threshold, else: 0
+ 3. Blur mask:
+ 4. Out = Mask * K + (1 - Mask) * I
+ Args:
+ img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
+ weight (float): Sharp weight. Default: 1.
+ radius (float): Kernel size of Gaussian blur. Default: 50.
+ threshold (int):
+ """
+ if radius % 2 == 0:
+ radius += 1
+ blur = cv2.GaussianBlur(img, (radius, radius), 0)
+ residual = img - blur
+ mask = np.abs(residual) * 255 > threshold
+ mask = mask.astype('float32')
+ soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
+
+ K = img + weight * residual
+ K = np.clip(K, 0, 1)
+ return soft_mask * K + (1 - soft_mask) * img
+
+
+def add_blur(img, sf=4):
+ wd2 = 4.0 + sf
+ wd = 2.0 + 0.2 * sf
+
+ wd2 = wd2/4
+ wd = wd/4
+
+ if random.random() < 0.5:
+ l1 = wd2 * random.random()
+ l2 = wd2 * random.random()
+ k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
+ else:
+ k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random())
+ img = ndimage.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
+
+ return img
+
+
+def add_resize(img, sf=4):
+ rnum = np.random.rand()
+ if rnum > 0.8: # up
+ sf1 = random.uniform(1, 2)
+ elif rnum < 0.7: # down
+ sf1 = random.uniform(0.5 / sf, 1)
+ else:
+ sf1 = 1.0
+ img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
+ img = np.clip(img, 0.0, 1.0)
+
+ return img
+
+
+# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
+# noise_level = random.randint(noise_level1, noise_level2)
+# rnum = np.random.rand()
+# if rnum > 0.6: # add color Gaussian noise
+# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+# elif rnum < 0.4: # add grayscale Gaussian noise
+# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+# else: # add noise
+# L = noise_level2 / 255.
+# D = np.diag(np.random.rand(3))
+# U = orth(np.random.rand(3, 3))
+# conv = np.dot(np.dot(np.transpose(U), D), U)
+# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+# img = np.clip(img, 0.0, 1.0)
+# return img
+
+def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
+ noise_level = random.randint(noise_level1, noise_level2)
+ rnum = np.random.rand()
+ if rnum > 0.6: # add color Gaussian noise
+ img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+ elif rnum < 0.4: # add grayscale Gaussian noise
+ img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+ else: # add noise
+ L = noise_level2 / 255.
+ D = np.diag(np.random.rand(3))
+ U = orth(np.random.rand(3, 3))
+ conv = np.dot(np.dot(np.transpose(U), D), U)
+ img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+ img = np.clip(img, 0.0, 1.0)
+ return img
+
+
+def add_speckle_noise(img, noise_level1=2, noise_level2=25):
+ noise_level = random.randint(noise_level1, noise_level2)
+ img = np.clip(img, 0.0, 1.0)
+ rnum = random.random()
+ if rnum > 0.6:
+ img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+ elif rnum < 0.4:
+ img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+ else:
+ L = noise_level2 / 255.
+ D = np.diag(np.random.rand(3))
+ U = orth(np.random.rand(3, 3))
+ conv = np.dot(np.dot(np.transpose(U), D), U)
+ img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+ img = np.clip(img, 0.0, 1.0)
+ return img
+
+
+def add_Poisson_noise(img):
+ img = np.clip((img * 255.0).round(), 0, 255) / 255.
+ vals = 10 ** (2 * random.random() + 2.0) # [2, 4]
+ if random.random() < 0.5:
+ img = np.random.poisson(img * vals).astype(np.float32) / vals
+ else:
+ img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
+ img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
+ noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
+ img += noise_gray[:, :, np.newaxis]
+ img = np.clip(img, 0.0, 1.0)
+ return img
+
+
+def add_JPEG_noise(img):
+ quality_factor = random.randint(80, 95)
+ img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
+ result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
+ img = cv2.imdecode(encimg, 1)
+ img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
+ return img
+
+
+def random_crop(lq, hq, sf=4, lq_patchsize=64):
+ h, w = lq.shape[:2]
+ rnd_h = random.randint(0, h - lq_patchsize)
+ rnd_w = random.randint(0, w - lq_patchsize)
+ lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
+
+ rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
+ hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
+ return lq, hq
+
+
+def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
+ """
+ This is the degradation model of BSRGAN from the paper
+ "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
+ ----------
+ img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
+ sf: scale factor
+ isp_model: camera ISP model
+ Returns
+ -------
+ img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
+ hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
+ """
+ isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
+ sf_ori = sf
+
+ h1, w1 = img.shape[:2]
+ img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
+ h, w = img.shape[:2]
+
+ if h < lq_patchsize * sf or w < lq_patchsize * sf:
+ raise ValueError(f'img size ({h1}X{w1}) is too small!')
+
+ hq = img.copy()
+
+ if sf == 4 and random.random() < scale2_prob: # downsample1
+ if np.random.rand() < 0.5:
+ img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
+ interpolation=random.choice([1, 2, 3]))
+ else:
+ img = util.imresize_np(img, 1 / 2, True)
+ img = np.clip(img, 0.0, 1.0)
+ sf = 2
+
+ shuffle_order = random.sample(range(7), 7)
+ idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
+ if idx1 > idx2: # keep downsample3 last
+ shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
+
+ for i in shuffle_order:
+
+ if i == 0:
+ img = add_blur(img, sf=sf)
+
+ elif i == 1:
+ img = add_blur(img, sf=sf)
+
+ elif i == 2:
+ a, b = img.shape[1], img.shape[0]
+ # downsample2
+ if random.random() < 0.75:
+ sf1 = random.uniform(1, 2 * sf)
+ img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
+ interpolation=random.choice([1, 2, 3]))
+ else:
+ k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
+ k_shifted = shift_pixel(k, sf)
+ k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
+ img = ndimage.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
+ img = img[0::sf, 0::sf, ...] # nearest downsampling
+ img = np.clip(img, 0.0, 1.0)
+
+ elif i == 3:
+ # downsample3
+ img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
+ img = np.clip(img, 0.0, 1.0)
+
+ elif i == 4:
+ # add Gaussian noise
+ img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8)
+
+ elif i == 5:
+ # add JPEG noise
+ if random.random() < jpeg_prob:
+ img = add_JPEG_noise(img)
+
+ elif i == 6:
+ # add processed camera sensor noise
+ if random.random() < isp_prob and isp_model is not None:
+ with torch.no_grad():
+ img, hq = isp_model.forward(img.copy(), hq)
+
+ # add final JPEG compression noise
+ img = add_JPEG_noise(img)
+
+ # random crop
+ img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
+
+ return img, hq
+
+
+# todo no isp_model?
+def degradation_bsrgan_variant(image, sf=4, isp_model=None, up=False):
+ """
+ This is the degradation model of BSRGAN from the paper
+ "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
+ ----------
+ sf: scale factor
+ isp_model: camera ISP model
+ Returns
+ -------
+ img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
+ hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
+ """
+ image = util.uint2single(image)
+ isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
+ sf_ori = sf
+
+ h1, w1 = image.shape[:2]
+ image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
+ h, w = image.shape[:2]
+
+ hq = image.copy()
+
+ if sf == 4 and random.random() < scale2_prob: # downsample1
+ if np.random.rand() < 0.5:
+ image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
+ interpolation=random.choice([1, 2, 3]))
+ else:
+ image = util.imresize_np(image, 1 / 2, True)
+ image = np.clip(image, 0.0, 1.0)
+ sf = 2
+
+ shuffle_order = random.sample(range(7), 7)
+ idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
+ if idx1 > idx2: # keep downsample3 last
+ shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
+
+ for i in shuffle_order:
+
+ if i == 0:
+ image = add_blur(image, sf=sf)
+
+ # elif i == 1:
+ # image = add_blur(image, sf=sf)
+
+ if i == 0:
+ pass
+
+ elif i == 2:
+ a, b = image.shape[1], image.shape[0]
+ # downsample2
+ if random.random() < 0.8:
+ sf1 = random.uniform(1, 2 * sf)
+ image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
+ interpolation=random.choice([1, 2, 3]))
+ else:
+ k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
+ k_shifted = shift_pixel(k, sf)
+ k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
+ image = ndimage.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
+ image = image[0::sf, 0::sf, ...] # nearest downsampling
+
+ image = np.clip(image, 0.0, 1.0)
+
+ elif i == 3:
+ # downsample3
+ image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
+ image = np.clip(image, 0.0, 1.0)
+
+ elif i == 4:
+ # add Gaussian noise
+ image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2)
+
+ elif i == 5:
+ # add JPEG noise
+ if random.random() < jpeg_prob:
+ image = add_JPEG_noise(image)
+ #
+ # elif i == 6:
+ # # add processed camera sensor noise
+ # if random.random() < isp_prob and isp_model is not None:
+ # with torch.no_grad():
+ # img, hq = isp_model.forward(img.copy(), hq)
+
+ # add final JPEG compression noise
+ image = add_JPEG_noise(image)
+ image = util.single2uint(image)
+ if up:
+ image = cv2.resize(image, (w1, h1), interpolation=cv2.INTER_CUBIC) # todo: random, as above? want to condition on it then
+ example = {"image": image}
+ return example
+
+
+
+
+if __name__ == '__main__':
+ print("hey")
+ img = util.imread_uint('utils/test.png', 3)
+ img = img[:448, :448]
+ h = img.shape[0] // 4
+ print("resizing to", h)
+ sf = 4
+ deg_fn = partial(degradation_bsrgan_variant, sf=sf)
+ for i in range(20):
+ print(i)
+ img_hq = img
+ img_lq = deg_fn(img)["image"]
+ img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq)
+ print(img_lq)
+ img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"]
+ print(img_lq.shape)
+ print("bicubic", img_lq_bicubic.shape)
+ print(img_hq.shape)
+ lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
+ interpolation=0)
+ lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic),
+ (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
+ interpolation=0)
+ img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
+ util.imsave(img_concat, str(i) + '.png')
diff --git a/model_lib/ControlNet/ldm/modules/image_degradation/utils_image.py b/model_lib/ControlNet/ldm/modules/image_degradation/utils_image.py
new file mode 100644
index 0000000000000000000000000000000000000000..0175f155ad900ae33c3c46ed87f49b352e3faf98
--- /dev/null
+++ b/model_lib/ControlNet/ldm/modules/image_degradation/utils_image.py
@@ -0,0 +1,916 @@
+import os
+import math
+import random
+import numpy as np
+import torch
+import cv2
+from torchvision.utils import make_grid
+from datetime import datetime
+#import matplotlib.pyplot as plt # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
+
+
+os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
+
+
+'''
+# --------------------------------------------
+# Kai Zhang (github: https://github.com/cszn)
+# 03/Mar/2019
+# --------------------------------------------
+# https://github.com/twhui/SRGAN-pyTorch
+# https://github.com/xinntao/BasicSR
+# --------------------------------------------
+'''
+
+
+IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif']
+
+
+def is_image_file(filename):
+ return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
+
+
+def get_timestamp():
+ return datetime.now().strftime('%y%m%d-%H%M%S')
+
+
+def imshow(x, title=None, cbar=False, figsize=None):
+ plt.figure(figsize=figsize)
+ plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray')
+ if title:
+ plt.title(title)
+ if cbar:
+ plt.colorbar()
+ plt.show()
+
+
+def surf(Z, cmap='rainbow', figsize=None):
+ plt.figure(figsize=figsize)
+ ax3 = plt.axes(projection='3d')
+
+ w, h = Z.shape[:2]
+ xx = np.arange(0,w,1)
+ yy = np.arange(0,h,1)
+ X, Y = np.meshgrid(xx, yy)
+ ax3.plot_surface(X,Y,Z,cmap=cmap)
+ #ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap)
+ plt.show()
+
+
+'''
+# --------------------------------------------
+# get image pathes
+# --------------------------------------------
+'''
+
+
+def get_image_paths(dataroot):
+ paths = None # return None if dataroot is None
+ if dataroot is not None:
+ paths = sorted(_get_paths_from_images(dataroot))
+ return paths
+
+
+def _get_paths_from_images(path):
+ assert os.path.isdir(path), '{:s} is not a valid directory'.format(path)
+ images = []
+ for dirpath, _, fnames in sorted(os.walk(path)):
+ for fname in sorted(fnames):
+ if is_image_file(fname):
+ img_path = os.path.join(dirpath, fname)
+ images.append(img_path)
+ assert images, '{:s} has no valid image file'.format(path)
+ return images
+
+
+'''
+# --------------------------------------------
+# split large images into small images
+# --------------------------------------------
+'''
+
+
+def patches_from_image(img, p_size=512, p_overlap=64, p_max=800):
+ w, h = img.shape[:2]
+ patches = []
+ if w > p_max and h > p_max:
+ w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int))
+ h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int))
+ w1.append(w-p_size)
+ h1.append(h-p_size)
+# print(w1)
+# print(h1)
+ for i in w1:
+ for j in h1:
+ patches.append(img[i:i+p_size, j:j+p_size,:])
+ else:
+ patches.append(img)
+
+ return patches
+
+
+def imssave(imgs, img_path):
+ """
+ imgs: list, N images of size WxHxC
+ """
+ img_name, ext = os.path.splitext(os.path.basename(img_path))
+
+ for i, img in enumerate(imgs):
+ if img.ndim == 3:
+ img = img[:, :, [2, 1, 0]]
+ new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png')
+ cv2.imwrite(new_path, img)
+
+
+def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000):
+ """
+ split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size),
+ and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max)
+ will be splitted.
+ Args:
+ original_dataroot:
+ taget_dataroot:
+ p_size: size of small images
+ p_overlap: patch size in training is a good choice
+ p_max: images with smaller size than (p_max)x(p_max) keep unchanged.
+ """
+ paths = get_image_paths(original_dataroot)
+ for img_path in paths:
+ # img_name, ext = os.path.splitext(os.path.basename(img_path))
+ img = imread_uint(img_path, n_channels=n_channels)
+ patches = patches_from_image(img, p_size, p_overlap, p_max)
+ imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path)))
+ #if original_dataroot == taget_dataroot:
+ #del img_path
+
+'''
+# --------------------------------------------
+# makedir
+# --------------------------------------------
+'''
+
+
+def mkdir(path):
+ if not os.path.exists(path):
+ os.makedirs(path)
+
+
+def mkdirs(paths):
+ if isinstance(paths, str):
+ mkdir(paths)
+ else:
+ for path in paths:
+ mkdir(path)
+
+
+def mkdir_and_rename(path):
+ if os.path.exists(path):
+ new_name = path + '_archived_' + get_timestamp()
+ print('Path already exists. Rename it to [{:s}]'.format(new_name))
+ os.rename(path, new_name)
+ os.makedirs(path)
+
+
+'''
+# --------------------------------------------
+# read image from path
+# opencv is fast, but read BGR numpy image
+# --------------------------------------------
+'''
+
+
+# --------------------------------------------
+# get uint8 image of size HxWxn_channles (RGB)
+# --------------------------------------------
+def imread_uint(path, n_channels=3):
+ # input: path
+ # output: HxWx3(RGB or GGG), or HxWx1 (G)
+ if n_channels == 1:
+ img = cv2.imread(path, 0) # cv2.IMREAD_GRAYSCALE
+ img = np.expand_dims(img, axis=2) # HxWx1
+ elif n_channels == 3:
+ img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # BGR or G
+ if img.ndim == 2:
+ img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) # GGG
+ else:
+ img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB
+ return img
+
+
+# --------------------------------------------
+# matlab's imwrite
+# --------------------------------------------
+def imsave(img, img_path):
+ img = np.squeeze(img)
+ if img.ndim == 3:
+ img = img[:, :, [2, 1, 0]]
+ cv2.imwrite(img_path, img)
+
+def imwrite(img, img_path):
+ img = np.squeeze(img)
+ if img.ndim == 3:
+ img = img[:, :, [2, 1, 0]]
+ cv2.imwrite(img_path, img)
+
+
+
+# --------------------------------------------
+# get single image of size HxWxn_channles (BGR)
+# --------------------------------------------
+def read_img(path):
+ # read image by cv2
+ # return: Numpy float32, HWC, BGR, [0,1]
+ img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # cv2.IMREAD_GRAYSCALE
+ img = img.astype(np.float32) / 255.
+ if img.ndim == 2:
+ img = np.expand_dims(img, axis=2)
+ # some images have 4 channels
+ if img.shape[2] > 3:
+ img = img[:, :, :3]
+ return img
+
+
+'''
+# --------------------------------------------
+# image format conversion
+# --------------------------------------------
+# numpy(single) <---> numpy(unit)
+# numpy(single) <---> tensor
+# numpy(unit) <---> tensor
+# --------------------------------------------
+'''
+
+
+# --------------------------------------------
+# numpy(single) [0, 1] <---> numpy(unit)
+# --------------------------------------------
+
+
+def uint2single(img):
+
+ return np.float32(img/255.)
+
+
+def single2uint(img):
+
+ return np.uint8((img.clip(0, 1)*255.).round())
+
+
+def uint162single(img):
+
+ return np.float32(img/65535.)
+
+
+def single2uint16(img):
+
+ return np.uint16((img.clip(0, 1)*65535.).round())
+
+
+# --------------------------------------------
+# numpy(unit) (HxWxC or HxW) <---> tensor
+# --------------------------------------------
+
+
+# convert uint to 4-dimensional torch tensor
+def uint2tensor4(img):
+ if img.ndim == 2:
+ img = np.expand_dims(img, axis=2)
+ return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0)
+
+
+# convert uint to 3-dimensional torch tensor
+def uint2tensor3(img):
+ if img.ndim == 2:
+ img = np.expand_dims(img, axis=2)
+ return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.)
+
+
+# convert 2/3/4-dimensional torch tensor to uint
+def tensor2uint(img):
+ img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
+ if img.ndim == 3:
+ img = np.transpose(img, (1, 2, 0))
+ return np.uint8((img*255.0).round())
+
+
+# --------------------------------------------
+# numpy(single) (HxWxC) <---> tensor
+# --------------------------------------------
+
+
+# convert single (HxWxC) to 3-dimensional torch tensor
+def single2tensor3(img):
+ return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float()
+
+
+# convert single (HxWxC) to 4-dimensional torch tensor
+def single2tensor4(img):
+ return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0)
+
+
+# convert torch tensor to single
+def tensor2single(img):
+ img = img.data.squeeze().float().cpu().numpy()
+ if img.ndim == 3:
+ img = np.transpose(img, (1, 2, 0))
+
+ return img
+
+# convert torch tensor to single
+def tensor2single3(img):
+ img = img.data.squeeze().float().cpu().numpy()
+ if img.ndim == 3:
+ img = np.transpose(img, (1, 2, 0))
+ elif img.ndim == 2:
+ img = np.expand_dims(img, axis=2)
+ return img
+
+
+def single2tensor5(img):
+ return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0)
+
+
+def single32tensor5(img):
+ return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0)
+
+
+def single42tensor4(img):
+ return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()
+
+
+# from skimage.io import imread, imsave
+def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
+ '''
+ Converts a torch Tensor into an image Numpy array of BGR channel order
+ Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
+ Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
+ '''
+ tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # squeeze first, then clamp
+ tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1]
+ n_dim = tensor.dim()
+ if n_dim == 4:
+ n_img = len(tensor)
+ img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
+ img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
+ elif n_dim == 3:
+ img_np = tensor.numpy()
+ img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
+ elif n_dim == 2:
+ img_np = tensor.numpy()
+ else:
+ raise TypeError(
+ 'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
+ if out_type == np.uint8:
+ img_np = (img_np * 255.0).round()
+ # Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
+ return img_np.astype(out_type)
+
+
+'''
+# --------------------------------------------
+# Augmentation, flipe and/or rotate
+# --------------------------------------------
+# The following two are enough.
+# (1) augmet_img: numpy image of WxHxC or WxH
+# (2) augment_img_tensor4: tensor image 1xCxWxH
+# --------------------------------------------
+'''
+
+
+def augment_img(img, mode=0):
+ '''Kai Zhang (github: https://github.com/cszn)
+ '''
+ if mode == 0:
+ return img
+ elif mode == 1:
+ return np.flipud(np.rot90(img))
+ elif mode == 2:
+ return np.flipud(img)
+ elif mode == 3:
+ return np.rot90(img, k=3)
+ elif mode == 4:
+ return np.flipud(np.rot90(img, k=2))
+ elif mode == 5:
+ return np.rot90(img)
+ elif mode == 6:
+ return np.rot90(img, k=2)
+ elif mode == 7:
+ return np.flipud(np.rot90(img, k=3))
+
+
+def augment_img_tensor4(img, mode=0):
+ '''Kai Zhang (github: https://github.com/cszn)
+ '''
+ if mode == 0:
+ return img
+ elif mode == 1:
+ return img.rot90(1, [2, 3]).flip([2])
+ elif mode == 2:
+ return img.flip([2])
+ elif mode == 3:
+ return img.rot90(3, [2, 3])
+ elif mode == 4:
+ return img.rot90(2, [2, 3]).flip([2])
+ elif mode == 5:
+ return img.rot90(1, [2, 3])
+ elif mode == 6:
+ return img.rot90(2, [2, 3])
+ elif mode == 7:
+ return img.rot90(3, [2, 3]).flip([2])
+
+
+def augment_img_tensor(img, mode=0):
+ '''Kai Zhang (github: https://github.com/cszn)
+ '''
+ img_size = img.size()
+ img_np = img.data.cpu().numpy()
+ if len(img_size) == 3:
+ img_np = np.transpose(img_np, (1, 2, 0))
+ elif len(img_size) == 4:
+ img_np = np.transpose(img_np, (2, 3, 1, 0))
+ img_np = augment_img(img_np, mode=mode)
+ img_tensor = torch.from_numpy(np.ascontiguousarray(img_np))
+ if len(img_size) == 3:
+ img_tensor = img_tensor.permute(2, 0, 1)
+ elif len(img_size) == 4:
+ img_tensor = img_tensor.permute(3, 2, 0, 1)
+
+ return img_tensor.type_as(img)
+
+
+def augment_img_np3(img, mode=0):
+ if mode == 0:
+ return img
+ elif mode == 1:
+ return img.transpose(1, 0, 2)
+ elif mode == 2:
+ return img[::-1, :, :]
+ elif mode == 3:
+ img = img[::-1, :, :]
+ img = img.transpose(1, 0, 2)
+ return img
+ elif mode == 4:
+ return img[:, ::-1, :]
+ elif mode == 5:
+ img = img[:, ::-1, :]
+ img = img.transpose(1, 0, 2)
+ return img
+ elif mode == 6:
+ img = img[:, ::-1, :]
+ img = img[::-1, :, :]
+ return img
+ elif mode == 7:
+ img = img[:, ::-1, :]
+ img = img[::-1, :, :]
+ img = img.transpose(1, 0, 2)
+ return img
+
+
+def augment_imgs(img_list, hflip=True, rot=True):
+ # horizontal flip OR rotate
+ hflip = hflip and random.random() < 0.5
+ vflip = rot and random.random() < 0.5
+ rot90 = rot and random.random() < 0.5
+
+ def _augment(img):
+ if hflip:
+ img = img[:, ::-1, :]
+ if vflip:
+ img = img[::-1, :, :]
+ if rot90:
+ img = img.transpose(1, 0, 2)
+ return img
+
+ return [_augment(img) for img in img_list]
+
+
+'''
+# --------------------------------------------
+# modcrop and shave
+# --------------------------------------------
+'''
+
+
+def modcrop(img_in, scale):
+ # img_in: Numpy, HWC or HW
+ img = np.copy(img_in)
+ if img.ndim == 2:
+ H, W = img.shape
+ H_r, W_r = H % scale, W % scale
+ img = img[:H - H_r, :W - W_r]
+ elif img.ndim == 3:
+ H, W, C = img.shape
+ H_r, W_r = H % scale, W % scale
+ img = img[:H - H_r, :W - W_r, :]
+ else:
+ raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim))
+ return img
+
+
+def shave(img_in, border=0):
+ # img_in: Numpy, HWC or HW
+ img = np.copy(img_in)
+ h, w = img.shape[:2]
+ img = img[border:h-border, border:w-border]
+ return img
+
+
+'''
+# --------------------------------------------
+# image processing process on numpy image
+# channel_convert(in_c, tar_type, img_list):
+# rgb2ycbcr(img, only_y=True):
+# bgr2ycbcr(img, only_y=True):
+# ycbcr2rgb(img):
+# --------------------------------------------
+'''
+
+
+def rgb2ycbcr(img, only_y=True):
+ '''same as matlab rgb2ycbcr
+ only_y: only return Y channel
+ Input:
+ uint8, [0, 255]
+ float, [0, 1]
+ '''
+ in_img_type = img.dtype
+ img.astype(np.float32)
+ if in_img_type != np.uint8:
+ img *= 255.
+ # convert
+ if only_y:
+ rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
+ else:
+ rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
+ [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
+ if in_img_type == np.uint8:
+ rlt = rlt.round()
+ else:
+ rlt /= 255.
+ return rlt.astype(in_img_type)
+
+
+def ycbcr2rgb(img):
+ '''same as matlab ycbcr2rgb
+ Input:
+ uint8, [0, 255]
+ float, [0, 1]
+ '''
+ in_img_type = img.dtype
+ img.astype(np.float32)
+ if in_img_type != np.uint8:
+ img *= 255.
+ # convert
+ rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
+ [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]
+ if in_img_type == np.uint8:
+ rlt = rlt.round()
+ else:
+ rlt /= 255.
+ return rlt.astype(in_img_type)
+
+
+def bgr2ycbcr(img, only_y=True):
+ '''bgr version of rgb2ycbcr
+ only_y: only return Y channel
+ Input:
+ uint8, [0, 255]
+ float, [0, 1]
+ '''
+ in_img_type = img.dtype
+ img.astype(np.float32)
+ if in_img_type != np.uint8:
+ img *= 255.
+ # convert
+ if only_y:
+ rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
+ else:
+ rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
+ [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]
+ if in_img_type == np.uint8:
+ rlt = rlt.round()
+ else:
+ rlt /= 255.
+ return rlt.astype(in_img_type)
+
+
+def channel_convert(in_c, tar_type, img_list):
+ # conversion among BGR, gray and y
+ if in_c == 3 and tar_type == 'gray': # BGR to gray
+ gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list]
+ return [np.expand_dims(img, axis=2) for img in gray_list]
+ elif in_c == 3 and tar_type == 'y': # BGR to y
+ y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]
+ return [np.expand_dims(img, axis=2) for img in y_list]
+ elif in_c == 1 and tar_type == 'RGB': # gray/y to BGR
+ return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list]
+ else:
+ return img_list
+
+
+'''
+# --------------------------------------------
+# metric, PSNR and SSIM
+# --------------------------------------------
+'''
+
+
+# --------------------------------------------
+# PSNR
+# --------------------------------------------
+def calculate_psnr(img1, img2, border=0):
+ # img1 and img2 have range [0, 255]
+ #img1 = img1.squeeze()
+ #img2 = img2.squeeze()
+ if not img1.shape == img2.shape:
+ raise ValueError('Input images must have the same dimensions.')
+ h, w = img1.shape[:2]
+ img1 = img1[border:h-border, border:w-border]
+ img2 = img2[border:h-border, border:w-border]
+
+ img1 = img1.astype(np.float64)
+ img2 = img2.astype(np.float64)
+ mse = np.mean((img1 - img2)**2)
+ if mse == 0:
+ return float('inf')
+ return 20 * math.log10(255.0 / math.sqrt(mse))
+
+
+# --------------------------------------------
+# SSIM
+# --------------------------------------------
+def calculate_ssim(img1, img2, border=0):
+ '''calculate SSIM
+ the same outputs as MATLAB's
+ img1, img2: [0, 255]
+ '''
+ #img1 = img1.squeeze()
+ #img2 = img2.squeeze()
+ if not img1.shape == img2.shape:
+ raise ValueError('Input images must have the same dimensions.')
+ h, w = img1.shape[:2]
+ img1 = img1[border:h-border, border:w-border]
+ img2 = img2[border:h-border, border:w-border]
+
+ if img1.ndim == 2:
+ return ssim(img1, img2)
+ elif img1.ndim == 3:
+ if img1.shape[2] == 3:
+ ssims = []
+ for i in range(3):
+ ssims.append(ssim(img1[:,:,i], img2[:,:,i]))
+ return np.array(ssims).mean()
+ elif img1.shape[2] == 1:
+ return ssim(np.squeeze(img1), np.squeeze(img2))
+ else:
+ raise ValueError('Wrong input image dimensions.')
+
+
+def ssim(img1, img2):
+ C1 = (0.01 * 255)**2
+ C2 = (0.03 * 255)**2
+
+ img1 = img1.astype(np.float64)
+ img2 = img2.astype(np.float64)
+ kernel = cv2.getGaussianKernel(11, 1.5)
+ window = np.outer(kernel, kernel.transpose())
+
+ mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid
+ mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
+ mu1_sq = mu1**2
+ mu2_sq = mu2**2
+ mu1_mu2 = mu1 * mu2
+ sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
+ sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
+ sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
+
+ ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
+ (sigma1_sq + sigma2_sq + C2))
+ return ssim_map.mean()
+
+
+'''
+# --------------------------------------------
+# matlab's bicubic imresize (numpy and torch) [0, 1]
+# --------------------------------------------
+'''
+
+
+# matlab 'imresize' function, now only support 'bicubic'
+def cubic(x):
+ absx = torch.abs(x)
+ absx2 = absx**2
+ absx3 = absx**3
+ return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \
+ (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx))
+
+
+def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
+ if (scale < 1) and (antialiasing):
+ # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
+ kernel_width = kernel_width / scale
+
+ # Output-space coordinates
+ x = torch.linspace(1, out_length, out_length)
+
+ # Input-space coordinates. Calculate the inverse mapping such that 0.5
+ # in output space maps to 0.5 in input space, and 0.5+scale in output
+ # space maps to 1.5 in input space.
+ u = x / scale + 0.5 * (1 - 1 / scale)
+
+ # What is the left-most pixel that can be involved in the computation?
+ left = torch.floor(u - kernel_width / 2)
+
+ # What is the maximum number of pixels that can be involved in the
+ # computation? Note: it's OK to use an extra pixel here; if the
+ # corresponding weights are all zero, it will be eliminated at the end
+ # of this function.
+ P = math.ceil(kernel_width) + 2
+
+ # The indices of the input pixels involved in computing the k-th output
+ # pixel are in row k of the indices matrix.
+ indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(
+ 1, P).expand(out_length, P)
+
+ # The weights used to compute the k-th output pixel are in row k of the
+ # weights matrix.
+ distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices
+ # apply cubic kernel
+ if (scale < 1) and (antialiasing):
+ weights = scale * cubic(distance_to_center * scale)
+ else:
+ weights = cubic(distance_to_center)
+ # Normalize the weights matrix so that each row sums to 1.
+ weights_sum = torch.sum(weights, 1).view(out_length, 1)
+ weights = weights / weights_sum.expand(out_length, P)
+
+ # If a column in weights is all zero, get rid of it. only consider the first and last column.
+ weights_zero_tmp = torch.sum((weights == 0), 0)
+ if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
+ indices = indices.narrow(1, 1, P - 2)
+ weights = weights.narrow(1, 1, P - 2)
+ if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
+ indices = indices.narrow(1, 0, P - 2)
+ weights = weights.narrow(1, 0, P - 2)
+ weights = weights.contiguous()
+ indices = indices.contiguous()
+ sym_len_s = -indices.min() + 1
+ sym_len_e = indices.max() - in_length
+ indices = indices + sym_len_s - 1
+ return weights, indices, int(sym_len_s), int(sym_len_e)
+
+
+# --------------------------------------------
+# imresize for tensor image [0, 1]
+# --------------------------------------------
+def imresize(img, scale, antialiasing=True):
+ # Now the scale should be the same for H and W
+ # input: img: pytorch tensor, CHW or HW [0,1]
+ # output: CHW or HW [0,1] w/o round
+ need_squeeze = True if img.dim() == 2 else False
+ if need_squeeze:
+ img.unsqueeze_(0)
+ in_C, in_H, in_W = img.size()
+ out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
+ kernel_width = 4
+ kernel = 'cubic'
+
+ # Return the desired dimension order for performing the resize. The
+ # strategy is to perform the resize first along the dimension with the
+ # smallest scale factor.
+ # Now we do not support this.
+
+ # get weights and indices
+ weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
+ in_H, out_H, scale, kernel, kernel_width, antialiasing)
+ weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
+ in_W, out_W, scale, kernel, kernel_width, antialiasing)
+ # process H dimension
+ # symmetric copying
+ img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)
+ img_aug.narrow(1, sym_len_Hs, in_H).copy_(img)
+
+ sym_patch = img[:, :sym_len_Hs, :]
+ inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
+ sym_patch_inv = sym_patch.index_select(1, inv_idx)
+ img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv)
+
+ sym_patch = img[:, -sym_len_He:, :]
+ inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
+ sym_patch_inv = sym_patch.index_select(1, inv_idx)
+ img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
+
+ out_1 = torch.FloatTensor(in_C, out_H, in_W)
+ kernel_width = weights_H.size(1)
+ for i in range(out_H):
+ idx = int(indices_H[i][0])
+ for j in range(out_C):
+ out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
+
+ # process W dimension
+ # symmetric copying
+ out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We)
+ out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1)
+
+ sym_patch = out_1[:, :, :sym_len_Ws]
+ inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
+ sym_patch_inv = sym_patch.index_select(2, inv_idx)
+ out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv)
+
+ sym_patch = out_1[:, :, -sym_len_We:]
+ inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
+ sym_patch_inv = sym_patch.index_select(2, inv_idx)
+ out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
+
+ out_2 = torch.FloatTensor(in_C, out_H, out_W)
+ kernel_width = weights_W.size(1)
+ for i in range(out_W):
+ idx = int(indices_W[i][0])
+ for j in range(out_C):
+ out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i])
+ if need_squeeze:
+ out_2.squeeze_()
+ return out_2
+
+
+# --------------------------------------------
+# imresize for numpy image [0, 1]
+# --------------------------------------------
+def imresize_np(img, scale, antialiasing=True):
+ # Now the scale should be the same for H and W
+ # input: img: Numpy, HWC or HW [0,1]
+ # output: HWC or HW [0,1] w/o round
+ img = torch.from_numpy(img)
+ need_squeeze = True if img.dim() == 2 else False
+ if need_squeeze:
+ img.unsqueeze_(2)
+
+ in_H, in_W, in_C = img.size()
+ out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
+ kernel_width = 4
+ kernel = 'cubic'
+
+ # Return the desired dimension order for performing the resize. The
+ # strategy is to perform the resize first along the dimension with the
+ # smallest scale factor.
+ # Now we do not support this.
+
+ # get weights and indices
+ weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
+ in_H, out_H, scale, kernel, kernel_width, antialiasing)
+ weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
+ in_W, out_W, scale, kernel, kernel_width, antialiasing)
+ # process H dimension
+ # symmetric copying
+ img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C)
+ img_aug.narrow(0, sym_len_Hs, in_H).copy_(img)
+
+ sym_patch = img[:sym_len_Hs, :, :]
+ inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
+ sym_patch_inv = sym_patch.index_select(0, inv_idx)
+ img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv)
+
+ sym_patch = img[-sym_len_He:, :, :]
+ inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
+ sym_patch_inv = sym_patch.index_select(0, inv_idx)
+ img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
+
+ out_1 = torch.FloatTensor(out_H, in_W, in_C)
+ kernel_width = weights_H.size(1)
+ for i in range(out_H):
+ idx = int(indices_H[i][0])
+ for j in range(out_C):
+ out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])
+
+ # process W dimension
+ # symmetric copying
+ out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C)
+ out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1)
+
+ sym_patch = out_1[:, :sym_len_Ws, :]
+ inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
+ sym_patch_inv = sym_patch.index_select(1, inv_idx)
+ out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv)
+
+ sym_patch = out_1[:, -sym_len_We:, :]
+ inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
+ sym_patch_inv = sym_patch.index_select(1, inv_idx)
+ out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
+
+ out_2 = torch.FloatTensor(out_H, out_W, in_C)
+ kernel_width = weights_W.size(1)
+ for i in range(out_W):
+ idx = int(indices_W[i][0])
+ for j in range(out_C):
+ out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i])
+ if need_squeeze:
+ out_2.squeeze_()
+
+ return out_2.numpy()
+
+
+if __name__ == '__main__':
+ print('---')
+# img = imread_uint('test.bmp', 3)
+# img = uint2single(img)
+# img_bicubic = imresize_np(img, 1/4)
\ No newline at end of file
diff --git a/model_lib/ControlNet/ldm/modules/midas/__init__.py b/model_lib/ControlNet/ldm/modules/midas/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/model_lib/ControlNet/ldm/modules/midas/api.py b/model_lib/ControlNet/ldm/modules/midas/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..80112539cdc5a5f20c9eaecd07dd4f9289cdd8b0
--- /dev/null
+++ b/model_lib/ControlNet/ldm/modules/midas/api.py
@@ -0,0 +1,170 @@
+# based on https://github.com/isl-org/MiDaS
+
+import cv2
+import torch
+import torch.nn as nn
+from torchvision.transforms import Compose
+
+from model_lib.ControlNet.ldm.modules.midas.midas.dpt_depth import DPTDepthModel
+from model_lib.ControlNet.ldm.modules.midas.midas.midas_net import MidasNet
+from model_lib.ControlNet.ldm.modules.midas.midas.midas_net_custom import MidasNet_small
+from model_lib.ControlNet.ldm.modules.midas.midas.transforms import Resize, NormalizeImage, PrepareForNet
+
+
+ISL_PATHS = {
+ "dpt_large": "midas_models/dpt_large-midas-2f21e586.pt",
+ "dpt_hybrid": "midas_models/dpt_hybrid-midas-501f0c75.pt",
+ "midas_v21": "",
+ "midas_v21_small": "",
+}
+
+
+def disabled_train(self, mode=True):
+ """Overwrite model.train with this function to make sure train/eval mode
+ does not change anymore."""
+ return self
+
+
+def load_midas_transform(model_type):
+ # https://github.com/isl-org/MiDaS/blob/master/run.py
+ # load transform only
+ if model_type == "dpt_large": # DPT-Large
+ net_w, net_h = 384, 384
+ resize_mode = "minimal"
+ normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+ elif model_type == "dpt_hybrid": # DPT-Hybrid
+ net_w, net_h = 384, 384
+ resize_mode = "minimal"
+ normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+ elif model_type == "midas_v21":
+ net_w, net_h = 384, 384
+ resize_mode = "upper_bound"
+ normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+ elif model_type == "midas_v21_small":
+ net_w, net_h = 256, 256
+ resize_mode = "upper_bound"
+ normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+ else:
+ assert False, f"model_type '{model_type}' not implemented, use: --model_type large"
+
+ transform = Compose(
+ [
+ Resize(
+ net_w,
+ net_h,
+ resize_target=None,
+ keep_aspect_ratio=True,
+ ensure_multiple_of=32,
+ resize_method=resize_mode,
+ image_interpolation_method=cv2.INTER_CUBIC,
+ ),
+ normalization,
+ PrepareForNet(),
+ ]
+ )
+
+ return transform
+
+
+def load_model(model_type):
+ # https://github.com/isl-org/MiDaS/blob/master/run.py
+ # load network
+ model_path = ISL_PATHS[model_type]
+ if model_type == "dpt_large": # DPT-Large
+ model = DPTDepthModel(
+ path=model_path,
+ backbone="vitl16_384",
+ non_negative=True,
+ )
+ net_w, net_h = 384, 384
+ resize_mode = "minimal"
+ normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+ elif model_type == "dpt_hybrid": # DPT-Hybrid
+ model = DPTDepthModel(
+ path=model_path,
+ backbone="vitb_rn50_384",
+ non_negative=True,
+ )
+ net_w, net_h = 384, 384
+ resize_mode = "minimal"
+ normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+ elif model_type == "midas_v21":
+ model = MidasNet(model_path, non_negative=True)
+ net_w, net_h = 384, 384
+ resize_mode = "upper_bound"
+ normalization = NormalizeImage(
+ mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+ )
+
+ elif model_type == "midas_v21_small":
+ model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True,
+ non_negative=True, blocks={'expand': True})
+ net_w, net_h = 256, 256
+ resize_mode = "upper_bound"
+ normalization = NormalizeImage(
+ mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+ )
+
+ else:
+ print(f"model_type '{model_type}' not implemented, use: --model_type large")
+ assert False
+
+ transform = Compose(
+ [
+ Resize(
+ net_w,
+ net_h,
+ resize_target=None,
+ keep_aspect_ratio=True,
+ ensure_multiple_of=32,
+ resize_method=resize_mode,
+ image_interpolation_method=cv2.INTER_CUBIC,
+ ),
+ normalization,
+ PrepareForNet(),
+ ]
+ )
+
+ return model.eval(), transform
+
+
+class MiDaSInference(nn.Module):
+ MODEL_TYPES_TORCH_HUB = [
+ "DPT_Large",
+ "DPT_Hybrid",
+ "MiDaS_small"
+ ]
+ MODEL_TYPES_ISL = [
+ "dpt_large",
+ "dpt_hybrid",
+ "midas_v21",
+ "midas_v21_small",
+ ]
+
+ def __init__(self, model_type):
+ super().__init__()
+ assert (model_type in self.MODEL_TYPES_ISL)
+ model, _ = load_model(model_type)
+ self.model = model
+ self.model.train = disabled_train
+
+ def forward(self, x):
+ # x in 0..1 as produced by calling self.transform on a 0..1 float64 numpy array
+ # NOTE: we expect that the correct transform has been called during dataloading.
+ with torch.no_grad():
+ prediction = self.model(x)
+ prediction = torch.nn.functional.interpolate(
+ prediction.unsqueeze(1),
+ size=x.shape[2:],
+ mode="bicubic",
+ align_corners=False,
+ )
+ assert prediction.shape == (x.shape[0], 1, x.shape[2], x.shape[3])
+ return prediction
+
diff --git a/model_lib/ControlNet/ldm/modules/midas/midas/__init__.py b/model_lib/ControlNet/ldm/modules/midas/midas/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/model_lib/ControlNet/ldm/modules/midas/midas/base_model.py b/model_lib/ControlNet/ldm/modules/midas/midas/base_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..5cf430239b47ec5ec07531263f26f5c24a2311cd
--- /dev/null
+++ b/model_lib/ControlNet/ldm/modules/midas/midas/base_model.py
@@ -0,0 +1,16 @@
+import torch
+
+
+class BaseModel(torch.nn.Module):
+ def load(self, path):
+ """Load model from file.
+
+ Args:
+ path (str): file path
+ """
+ parameters = torch.load(path, map_location=torch.device('cpu'))
+
+ if "optimizer" in parameters:
+ parameters = parameters["model"]
+
+ self.load_state_dict(parameters)
diff --git a/model_lib/ControlNet/ldm/modules/midas/midas/blocks.py b/model_lib/ControlNet/ldm/modules/midas/midas/blocks.py
new file mode 100644
index 0000000000000000000000000000000000000000..2145d18fa98060a618536d9a64fe6589e9be4f78
--- /dev/null
+++ b/model_lib/ControlNet/ldm/modules/midas/midas/blocks.py
@@ -0,0 +1,342 @@
+import torch
+import torch.nn as nn
+
+from .vit import (
+ _make_pretrained_vitb_rn50_384,
+ _make_pretrained_vitl16_384,
+ _make_pretrained_vitb16_384,
+ forward_vit,
+)
+
+def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ignore",):
+ if backbone == "vitl16_384":
+ pretrained = _make_pretrained_vitl16_384(
+ use_pretrained, hooks=hooks, use_readout=use_readout
+ )
+ scratch = _make_scratch(
+ [256, 512, 1024, 1024], features, groups=groups, expand=expand
+ ) # ViT-L/16 - 85.0% Top1 (backbone)
+ elif backbone == "vitb_rn50_384":
+ pretrained = _make_pretrained_vitb_rn50_384(
+ use_pretrained,
+ hooks=hooks,
+ use_vit_only=use_vit_only,
+ use_readout=use_readout,
+ )
+ scratch = _make_scratch(
+ [256, 512, 768, 768], features, groups=groups, expand=expand
+ ) # ViT-H/16 - 85.0% Top1 (backbone)
+ elif backbone == "vitb16_384":
+ pretrained = _make_pretrained_vitb16_384(
+ use_pretrained, hooks=hooks, use_readout=use_readout
+ )
+ scratch = _make_scratch(
+ [96, 192, 384, 768], features, groups=groups, expand=expand
+ ) # ViT-B/16 - 84.6% Top1 (backbone)
+ elif backbone == "resnext101_wsl":
+ pretrained = _make_pretrained_resnext101_wsl(use_pretrained)
+ scratch = _make_scratch([256, 512, 1024, 2048], features, groups=groups, expand=expand) # efficientnet_lite3
+ elif backbone == "efficientnet_lite3":
+ pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, exportable=exportable)
+ scratch = _make_scratch([32, 48, 136, 384], features, groups=groups, expand=expand) # efficientnet_lite3
+ else:
+ print(f"Backbone '{backbone}' not implemented")
+ assert False
+
+ return pretrained, scratch
+
+
+def _make_scratch(in_shape, out_shape, groups=1, expand=False):
+ scratch = nn.Module()
+
+ out_shape1 = out_shape
+ out_shape2 = out_shape
+ out_shape3 = out_shape
+ out_shape4 = out_shape
+ if expand==True:
+ out_shape1 = out_shape
+ out_shape2 = out_shape*2
+ out_shape3 = out_shape*4
+ out_shape4 = out_shape*8
+
+ scratch.layer1_rn = nn.Conv2d(
+ in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+ )
+ scratch.layer2_rn = nn.Conv2d(
+ in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+ )
+ scratch.layer3_rn = nn.Conv2d(
+ in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+ )
+ scratch.layer4_rn = nn.Conv2d(
+ in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+ )
+
+ return scratch
+
+
+def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False):
+ efficientnet = torch.hub.load(
+ "rwightman/gen-efficientnet-pytorch",
+ "tf_efficientnet_lite3",
+ pretrained=use_pretrained,
+ exportable=exportable
+ )
+ return _make_efficientnet_backbone(efficientnet)
+
+
+def _make_efficientnet_backbone(effnet):
+ pretrained = nn.Module()
+
+ pretrained.layer1 = nn.Sequential(
+ effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2]
+ )
+ pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3])
+ pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5])
+ pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9])
+
+ return pretrained
+
+
+def _make_resnet_backbone(resnet):
+ pretrained = nn.Module()
+ pretrained.layer1 = nn.Sequential(
+ resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1
+ )
+
+ pretrained.layer2 = resnet.layer2
+ pretrained.layer3 = resnet.layer3
+ pretrained.layer4 = resnet.layer4
+
+ return pretrained
+
+
+def _make_pretrained_resnext101_wsl(use_pretrained):
+ resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl")
+ return _make_resnet_backbone(resnet)
+
+
+
+class Interpolate(nn.Module):
+ """Interpolation module.
+ """
+
+ def __init__(self, scale_factor, mode, align_corners=False):
+ """Init.
+
+ Args:
+ scale_factor (float): scaling
+ mode (str): interpolation mode
+ """
+ super(Interpolate, self).__init__()
+
+ self.interp = nn.functional.interpolate
+ self.scale_factor = scale_factor
+ self.mode = mode
+ self.align_corners = align_corners
+
+ def forward(self, x):
+ """Forward pass.
+
+ Args:
+ x (tensor): input
+
+ Returns:
+ tensor: interpolated data
+ """
+
+ x = self.interp(
+ x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners
+ )
+
+ return x
+
+
+class ResidualConvUnit(nn.Module):
+ """Residual convolution module.
+ """
+
+ def __init__(self, features):
+ """Init.
+
+ Args:
+ features (int): number of features
+ """
+ super().__init__()
+
+ self.conv1 = nn.Conv2d(
+ features, features, kernel_size=3, stride=1, padding=1, bias=True
+ )
+
+ self.conv2 = nn.Conv2d(
+ features, features, kernel_size=3, stride=1, padding=1, bias=True
+ )
+
+ self.relu = nn.ReLU(inplace=True)
+
+ def forward(self, x):
+ """Forward pass.
+
+ Args:
+ x (tensor): input
+
+ Returns:
+ tensor: output
+ """
+ out = self.relu(x)
+ out = self.conv1(out)
+ out = self.relu(out)
+ out = self.conv2(out)
+
+ return out + x
+
+
+class FeatureFusionBlock(nn.Module):
+ """Feature fusion block.
+ """
+
+ def __init__(self, features):
+ """Init.
+
+ Args:
+ features (int): number of features
+ """
+ super(FeatureFusionBlock, self).__init__()
+
+ self.resConfUnit1 = ResidualConvUnit(features)
+ self.resConfUnit2 = ResidualConvUnit(features)
+
+ def forward(self, *xs):
+ """Forward pass.
+
+ Returns:
+ tensor: output
+ """
+ output = xs[0]
+
+ if len(xs) == 2:
+ output += self.resConfUnit1(xs[1])
+
+ output = self.resConfUnit2(output)
+
+ output = nn.functional.interpolate(
+ output, scale_factor=2, mode="bilinear", align_corners=True
+ )
+
+ return output
+
+
+
+
+class ResidualConvUnit_custom(nn.Module):
+ """Residual convolution module.
+ """
+
+ def __init__(self, features, activation, bn):
+ """Init.
+
+ Args:
+ features (int): number of features
+ """
+ super().__init__()
+
+ self.bn = bn
+
+ self.groups=1
+
+ self.conv1 = nn.Conv2d(
+ features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
+ )
+
+ self.conv2 = nn.Conv2d(
+ features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
+ )
+
+ if self.bn==True:
+ self.bn1 = nn.BatchNorm2d(features)
+ self.bn2 = nn.BatchNorm2d(features)
+
+ self.activation = activation
+
+ self.skip_add = nn.quantized.FloatFunctional()
+
+ def forward(self, x):
+ """Forward pass.
+
+ Args:
+ x (tensor): input
+
+ Returns:
+ tensor: output
+ """
+
+ out = self.activation(x)
+ out = self.conv1(out)
+ if self.bn==True:
+ out = self.bn1(out)
+
+ out = self.activation(out)
+ out = self.conv2(out)
+ if self.bn==True:
+ out = self.bn2(out)
+
+ if self.groups > 1:
+ out = self.conv_merge(out)
+
+ return self.skip_add.add(out, x)
+
+ # return out + x
+
+
+class FeatureFusionBlock_custom(nn.Module):
+ """Feature fusion block.
+ """
+
+ def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True):
+ """Init.
+
+ Args:
+ features (int): number of features
+ """
+ super(FeatureFusionBlock_custom, self).__init__()
+
+ self.deconv = deconv
+ self.align_corners = align_corners
+
+ self.groups=1
+
+ self.expand = expand
+ out_features = features
+ if self.expand==True:
+ out_features = features//2
+
+ self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
+
+ self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn)
+ self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn)
+
+ self.skip_add = nn.quantized.FloatFunctional()
+
+ def forward(self, *xs):
+ """Forward pass.
+
+ Returns:
+ tensor: output
+ """
+ output = xs[0]
+
+ if len(xs) == 2:
+ res = self.resConfUnit1(xs[1])
+ output = self.skip_add.add(output, res)
+ # output += res
+
+ output = self.resConfUnit2(output)
+
+ output = nn.functional.interpolate(
+ output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
+ )
+
+ output = self.out_conv(output)
+
+ return output
+
diff --git a/model_lib/ControlNet/ldm/modules/midas/midas/dpt_depth.py b/model_lib/ControlNet/ldm/modules/midas/midas/dpt_depth.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e9aab5d2767dffea39da5b3f30e2798688216f1
--- /dev/null
+++ b/model_lib/ControlNet/ldm/modules/midas/midas/dpt_depth.py
@@ -0,0 +1,109 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .base_model import BaseModel
+from .blocks import (
+ FeatureFusionBlock,
+ FeatureFusionBlock_custom,
+ Interpolate,
+ _make_encoder,
+ forward_vit,
+)
+
+
+def _make_fusion_block(features, use_bn):
+ return FeatureFusionBlock_custom(
+ features,
+ nn.ReLU(False),
+ deconv=False,
+ bn=use_bn,
+ expand=False,
+ align_corners=True,
+ )
+
+
+class DPT(BaseModel):
+ def __init__(
+ self,
+ head,
+ features=256,
+ backbone="vitb_rn50_384",
+ readout="project",
+ channels_last=False,
+ use_bn=False,
+ ):
+
+ super(DPT, self).__init__()
+
+ self.channels_last = channels_last
+
+ hooks = {
+ "vitb_rn50_384": [0, 1, 8, 11],
+ "vitb16_384": [2, 5, 8, 11],
+ "vitl16_384": [5, 11, 17, 23],
+ }
+
+ # Instantiate backbone and reassemble blocks
+ self.pretrained, self.scratch = _make_encoder(
+ backbone,
+ features,
+ False, # Set to true of you want to train from scratch, uses ImageNet weights
+ groups=1,
+ expand=False,
+ exportable=False,
+ hooks=hooks[backbone],
+ use_readout=readout,
+ )
+
+ self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
+ self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
+ self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
+ self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
+
+ self.scratch.output_conv = head
+
+
+ def forward(self, x):
+ if self.channels_last == True:
+ x.contiguous(memory_format=torch.channels_last)
+
+ layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x)
+
+ layer_1_rn = self.scratch.layer1_rn(layer_1)
+ layer_2_rn = self.scratch.layer2_rn(layer_2)
+ layer_3_rn = self.scratch.layer3_rn(layer_3)
+ layer_4_rn = self.scratch.layer4_rn(layer_4)
+
+ path_4 = self.scratch.refinenet4(layer_4_rn)
+ path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
+ path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
+ path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
+
+ out = self.scratch.output_conv(path_1)
+
+ return out
+
+
+class DPTDepthModel(DPT):
+ def __init__(self, path=None, non_negative=True, **kwargs):
+ features = kwargs["features"] if "features" in kwargs else 256
+
+ head = nn.Sequential(
+ nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
+ Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
+ nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
+ nn.ReLU(True),
+ nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
+ nn.ReLU(True) if non_negative else nn.Identity(),
+ nn.Identity(),
+ )
+
+ super().__init__(head, **kwargs)
+
+ if path is not None:
+ self.load(path)
+
+ def forward(self, x):
+ return super().forward(x).squeeze(dim=1)
+
diff --git a/model_lib/ControlNet/ldm/modules/midas/midas/midas_net.py b/model_lib/ControlNet/ldm/modules/midas/midas/midas_net.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a954977800b0a0f48807e80fa63041910e33c1f
--- /dev/null
+++ b/model_lib/ControlNet/ldm/modules/midas/midas/midas_net.py
@@ -0,0 +1,76 @@
+"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
+This file contains code that is adapted from
+https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
+"""
+import torch
+import torch.nn as nn
+
+from .base_model import BaseModel
+from .blocks import FeatureFusionBlock, Interpolate, _make_encoder
+
+
+class MidasNet(BaseModel):
+ """Network for monocular depth estimation.
+ """
+
+ def __init__(self, path=None, features=256, non_negative=True):
+ """Init.
+
+ Args:
+ path (str, optional): Path to saved model. Defaults to None.
+ features (int, optional): Number of features. Defaults to 256.
+ backbone (str, optional): Backbone network for encoder. Defaults to resnet50
+ """
+ print("Loading weights: ", path)
+
+ super(MidasNet, self).__init__()
+
+ use_pretrained = False if path is None else True
+
+ self.pretrained, self.scratch = _make_encoder(backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained)
+
+ self.scratch.refinenet4 = FeatureFusionBlock(features)
+ self.scratch.refinenet3 = FeatureFusionBlock(features)
+ self.scratch.refinenet2 = FeatureFusionBlock(features)
+ self.scratch.refinenet1 = FeatureFusionBlock(features)
+
+ self.scratch.output_conv = nn.Sequential(
+ nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1),
+ Interpolate(scale_factor=2, mode="bilinear"),
+ nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1),
+ nn.ReLU(True),
+ nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
+ nn.ReLU(True) if non_negative else nn.Identity(),
+ )
+
+ if path:
+ self.load(path)
+
+ def forward(self, x):
+ """Forward pass.
+
+ Args:
+ x (tensor): input data (image)
+
+ Returns:
+ tensor: depth
+ """
+
+ layer_1 = self.pretrained.layer1(x)
+ layer_2 = self.pretrained.layer2(layer_1)
+ layer_3 = self.pretrained.layer3(layer_2)
+ layer_4 = self.pretrained.layer4(layer_3)
+
+ layer_1_rn = self.scratch.layer1_rn(layer_1)
+ layer_2_rn = self.scratch.layer2_rn(layer_2)
+ layer_3_rn = self.scratch.layer3_rn(layer_3)
+ layer_4_rn = self.scratch.layer4_rn(layer_4)
+
+ path_4 = self.scratch.refinenet4(layer_4_rn)
+ path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
+ path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
+ path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
+
+ out = self.scratch.output_conv(path_1)
+
+ return torch.squeeze(out, dim=1)
diff --git a/model_lib/ControlNet/ldm/modules/midas/midas/midas_net_custom.py b/model_lib/ControlNet/ldm/modules/midas/midas/midas_net_custom.py
new file mode 100644
index 0000000000000000000000000000000000000000..50e4acb5e53d5fabefe3dde16ab49c33c2b7797c
--- /dev/null
+++ b/model_lib/ControlNet/ldm/modules/midas/midas/midas_net_custom.py
@@ -0,0 +1,128 @@
+"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
+This file contains code that is adapted from
+https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
+"""
+import torch
+import torch.nn as nn
+
+from .base_model import BaseModel
+from .blocks import FeatureFusionBlock, FeatureFusionBlock_custom, Interpolate, _make_encoder
+
+
+class MidasNet_small(BaseModel):
+ """Network for monocular depth estimation.
+ """
+
+ def __init__(self, path=None, features=64, backbone="efficientnet_lite3", non_negative=True, exportable=True, channels_last=False, align_corners=True,
+ blocks={'expand': True}):
+ """Init.
+
+ Args:
+ path (str, optional): Path to saved model. Defaults to None.
+ features (int, optional): Number of features. Defaults to 256.
+ backbone (str, optional): Backbone network for encoder. Defaults to resnet50
+ """
+ print("Loading weights: ", path)
+
+ super(MidasNet_small, self).__init__()
+
+ use_pretrained = False if path else True
+
+ self.channels_last = channels_last
+ self.blocks = blocks
+ self.backbone = backbone
+
+ self.groups = 1
+
+ features1=features
+ features2=features
+ features3=features
+ features4=features
+ self.expand = False
+ if "expand" in self.blocks and self.blocks['expand'] == True:
+ self.expand = True
+ features1=features
+ features2=features*2
+ features3=features*4
+ features4=features*8
+
+ self.pretrained, self.scratch = _make_encoder(self.backbone, features, use_pretrained, groups=self.groups, expand=self.expand, exportable=exportable)
+
+ self.scratch.activation = nn.ReLU(False)
+
+ self.scratch.refinenet4 = FeatureFusionBlock_custom(features4, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
+ self.scratch.refinenet3 = FeatureFusionBlock_custom(features3, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
+ self.scratch.refinenet2 = FeatureFusionBlock_custom(features2, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
+ self.scratch.refinenet1 = FeatureFusionBlock_custom(features1, self.scratch.activation, deconv=False, bn=False, align_corners=align_corners)
+
+
+ self.scratch.output_conv = nn.Sequential(
+ nn.Conv2d(features, features//2, kernel_size=3, stride=1, padding=1, groups=self.groups),
+ Interpolate(scale_factor=2, mode="bilinear"),
+ nn.Conv2d(features//2, 32, kernel_size=3, stride=1, padding=1),
+ self.scratch.activation,
+ nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
+ nn.ReLU(True) if non_negative else nn.Identity(),
+ nn.Identity(),
+ )
+
+ if path:
+ self.load(path)
+
+
+ def forward(self, x):
+ """Forward pass.
+
+ Args:
+ x (tensor): input data (image)
+
+ Returns:
+ tensor: depth
+ """
+ if self.channels_last==True:
+ print("self.channels_last = ", self.channels_last)
+ x.contiguous(memory_format=torch.channels_last)
+
+
+ layer_1 = self.pretrained.layer1(x)
+ layer_2 = self.pretrained.layer2(layer_1)
+ layer_3 = self.pretrained.layer3(layer_2)
+ layer_4 = self.pretrained.layer4(layer_3)
+
+ layer_1_rn = self.scratch.layer1_rn(layer_1)
+ layer_2_rn = self.scratch.layer2_rn(layer_2)
+ layer_3_rn = self.scratch.layer3_rn(layer_3)
+ layer_4_rn = self.scratch.layer4_rn(layer_4)
+
+
+ path_4 = self.scratch.refinenet4(layer_4_rn)
+ path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
+ path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
+ path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
+
+ out = self.scratch.output_conv(path_1)
+
+ return torch.squeeze(out, dim=1)
+
+
+
+def fuse_model(m):
+ prev_previous_type = nn.Identity()
+ prev_previous_name = ''
+ previous_type = nn.Identity()
+ previous_name = ''
+ for name, module in m.named_modules():
+ if prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d and type(module) == nn.ReLU:
+ # print("FUSED ", prev_previous_name, previous_name, name)
+ torch.quantization.fuse_modules(m, [prev_previous_name, previous_name, name], inplace=True)
+ elif prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d:
+ # print("FUSED ", prev_previous_name, previous_name)
+ torch.quantization.fuse_modules(m, [prev_previous_name, previous_name], inplace=True)
+ # elif previous_type == nn.Conv2d and type(module) == nn.ReLU:
+ # print("FUSED ", previous_name, name)
+ # torch.quantization.fuse_modules(m, [previous_name, name], inplace=True)
+
+ prev_previous_type = previous_type
+ prev_previous_name = previous_name
+ previous_type = type(module)
+ previous_name = name
\ No newline at end of file
diff --git a/model_lib/ControlNet/ldm/modules/midas/midas/transforms.py b/model_lib/ControlNet/ldm/modules/midas/midas/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..350cbc11662633ad7f8968eb10be2e7de6e384e9
--- /dev/null
+++ b/model_lib/ControlNet/ldm/modules/midas/midas/transforms.py
@@ -0,0 +1,234 @@
+import numpy as np
+import cv2
+import math
+
+
+def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
+ """Rezise the sample to ensure the given size. Keeps aspect ratio.
+
+ Args:
+ sample (dict): sample
+ size (tuple): image size
+
+ Returns:
+ tuple: new size
+ """
+ shape = list(sample["disparity"].shape)
+
+ if shape[0] >= size[0] and shape[1] >= size[1]:
+ return sample
+
+ scale = [0, 0]
+ scale[0] = size[0] / shape[0]
+ scale[1] = size[1] / shape[1]
+
+ scale = max(scale)
+
+ shape[0] = math.ceil(scale * shape[0])
+ shape[1] = math.ceil(scale * shape[1])
+
+ # resize
+ sample["image"] = cv2.resize(
+ sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
+ )
+
+ sample["disparity"] = cv2.resize(
+ sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
+ )
+ sample["mask"] = cv2.resize(
+ sample["mask"].astype(np.float32),
+ tuple(shape[::-1]),
+ interpolation=cv2.INTER_NEAREST,
+ )
+ sample["mask"] = sample["mask"].astype(bool)
+
+ return tuple(shape)
+
+
+class Resize(object):
+ """Resize sample to given size (width, height).
+ """
+
+ def __init__(
+ self,
+ width,
+ height,
+ resize_target=True,
+ keep_aspect_ratio=False,
+ ensure_multiple_of=1,
+ resize_method="lower_bound",
+ image_interpolation_method=cv2.INTER_AREA,
+ ):
+ """Init.
+
+ Args:
+ width (int): desired output width
+ height (int): desired output height
+ resize_target (bool, optional):
+ True: Resize the full sample (image, mask, target).
+ False: Resize image only.
+ Defaults to True.
+ keep_aspect_ratio (bool, optional):
+ True: Keep the aspect ratio of the input sample.
+ Output sample might not have the given width and height, and
+ resize behaviour depends on the parameter 'resize_method'.
+ Defaults to False.
+ ensure_multiple_of (int, optional):
+ Output width and height is constrained to be multiple of this parameter.
+ Defaults to 1.
+ resize_method (str, optional):
+ "lower_bound": Output will be at least as large as the given size.
+ "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
+ "minimal": Scale as least as possible. (Output size might be smaller than given size.)
+ Defaults to "lower_bound".
+ """
+ self.__width = width
+ self.__height = height
+
+ self.__resize_target = resize_target
+ self.__keep_aspect_ratio = keep_aspect_ratio
+ self.__multiple_of = ensure_multiple_of
+ self.__resize_method = resize_method
+ self.__image_interpolation_method = image_interpolation_method
+
+ def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
+ y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+ if max_val is not None and y > max_val:
+ y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+ if y < min_val:
+ y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+ return y
+
+ def get_size(self, width, height):
+ # determine new height and width
+ scale_height = self.__height / height
+ scale_width = self.__width / width
+
+ if self.__keep_aspect_ratio:
+ if self.__resize_method == "lower_bound":
+ # scale such that output size is lower bound
+ if scale_width > scale_height:
+ # fit width
+ scale_height = scale_width
+ else:
+ # fit height
+ scale_width = scale_height
+ elif self.__resize_method == "upper_bound":
+ # scale such that output size is upper bound
+ if scale_width < scale_height:
+ # fit width
+ scale_height = scale_width
+ else:
+ # fit height
+ scale_width = scale_height
+ elif self.__resize_method == "minimal":
+ # scale as least as possbile
+ if abs(1 - scale_width) < abs(1 - scale_height):
+ # fit width
+ scale_height = scale_width
+ else:
+ # fit height
+ scale_width = scale_height
+ else:
+ raise ValueError(
+ f"resize_method {self.__resize_method} not implemented"
+ )
+
+ if self.__resize_method == "lower_bound":
+ new_height = self.constrain_to_multiple_of(
+ scale_height * height, min_val=self.__height
+ )
+ new_width = self.constrain_to_multiple_of(
+ scale_width * width, min_val=self.__width
+ )
+ elif self.__resize_method == "upper_bound":
+ new_height = self.constrain_to_multiple_of(
+ scale_height * height, max_val=self.__height
+ )
+ new_width = self.constrain_to_multiple_of(
+ scale_width * width, max_val=self.__width
+ )
+ elif self.__resize_method == "minimal":
+ new_height = self.constrain_to_multiple_of(scale_height * height)
+ new_width = self.constrain_to_multiple_of(scale_width * width)
+ else:
+ raise ValueError(f"resize_method {self.__resize_method} not implemented")
+
+ return (new_width, new_height)
+
+ def __call__(self, sample):
+ width, height = self.get_size(
+ sample["image"].shape[1], sample["image"].shape[0]
+ )
+
+ # resize sample
+ sample["image"] = cv2.resize(
+ sample["image"],
+ (width, height),
+ interpolation=self.__image_interpolation_method,
+ )
+
+ if self.__resize_target:
+ if "disparity" in sample:
+ sample["disparity"] = cv2.resize(
+ sample["disparity"],
+ (width, height),
+ interpolation=cv2.INTER_NEAREST,
+ )
+
+ if "depth" in sample:
+ sample["depth"] = cv2.resize(
+ sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
+ )
+
+ sample["mask"] = cv2.resize(
+ sample["mask"].astype(np.float32),
+ (width, height),
+ interpolation=cv2.INTER_NEAREST,
+ )
+ sample["mask"] = sample["mask"].astype(bool)
+
+ return sample
+
+
+class NormalizeImage(object):
+ """Normlize image by given mean and std.
+ """
+
+ def __init__(self, mean, std):
+ self.__mean = mean
+ self.__std = std
+
+ def __call__(self, sample):
+ sample["image"] = (sample["image"] - self.__mean) / self.__std
+
+ return sample
+
+
+class PrepareForNet(object):
+ """Prepare sample for usage as network input.
+ """
+
+ def __init__(self):
+ pass
+
+ def __call__(self, sample):
+ image = np.transpose(sample["image"], (2, 0, 1))
+ sample["image"] = np.ascontiguousarray(image).astype(np.float32)
+
+ if "mask" in sample:
+ sample["mask"] = sample["mask"].astype(np.float32)
+ sample["mask"] = np.ascontiguousarray(sample["mask"])
+
+ if "disparity" in sample:
+ disparity = sample["disparity"].astype(np.float32)
+ sample["disparity"] = np.ascontiguousarray(disparity)
+
+ if "depth" in sample:
+ depth = sample["depth"].astype(np.float32)
+ sample["depth"] = np.ascontiguousarray(depth)
+
+ return sample
diff --git a/model_lib/ControlNet/ldm/modules/midas/midas/vit.py b/model_lib/ControlNet/ldm/modules/midas/midas/vit.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea46b1be88b261b0dec04f3da0256f5f66f88a74
--- /dev/null
+++ b/model_lib/ControlNet/ldm/modules/midas/midas/vit.py
@@ -0,0 +1,491 @@
+import torch
+import torch.nn as nn
+import timm
+import types
+import math
+import torch.nn.functional as F
+
+
+class Slice(nn.Module):
+ def __init__(self, start_index=1):
+ super(Slice, self).__init__()
+ self.start_index = start_index
+
+ def forward(self, x):
+ return x[:, self.start_index :]
+
+
+class AddReadout(nn.Module):
+ def __init__(self, start_index=1):
+ super(AddReadout, self).__init__()
+ self.start_index = start_index
+
+ def forward(self, x):
+ if self.start_index == 2:
+ readout = (x[:, 0] + x[:, 1]) / 2
+ else:
+ readout = x[:, 0]
+ return x[:, self.start_index :] + readout.unsqueeze(1)
+
+
+class ProjectReadout(nn.Module):
+ def __init__(self, in_features, start_index=1):
+ super(ProjectReadout, self).__init__()
+ self.start_index = start_index
+
+ self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU())
+
+ def forward(self, x):
+ readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index :])
+ features = torch.cat((x[:, self.start_index :], readout), -1)
+
+ return self.project(features)
+
+
+class Transpose(nn.Module):
+ def __init__(self, dim0, dim1):
+ super(Transpose, self).__init__()
+ self.dim0 = dim0
+ self.dim1 = dim1
+
+ def forward(self, x):
+ x = x.transpose(self.dim0, self.dim1)
+ return x
+
+
+def forward_vit(pretrained, x):
+ b, c, h, w = x.shape
+
+ glob = pretrained.model.forward_flex(x)
+
+ layer_1 = pretrained.activations["1"]
+ layer_2 = pretrained.activations["2"]
+ layer_3 = pretrained.activations["3"]
+ layer_4 = pretrained.activations["4"]
+
+ layer_1 = pretrained.act_postprocess1[0:2](layer_1)
+ layer_2 = pretrained.act_postprocess2[0:2](layer_2)
+ layer_3 = pretrained.act_postprocess3[0:2](layer_3)
+ layer_4 = pretrained.act_postprocess4[0:2](layer_4)
+
+ unflatten = nn.Sequential(
+ nn.Unflatten(
+ 2,
+ torch.Size(
+ [
+ h // pretrained.model.patch_size[1],
+ w // pretrained.model.patch_size[0],
+ ]
+ ),
+ )
+ )
+
+ if layer_1.ndim == 3:
+ layer_1 = unflatten(layer_1)
+ if layer_2.ndim == 3:
+ layer_2 = unflatten(layer_2)
+ if layer_3.ndim == 3:
+ layer_3 = unflatten(layer_3)
+ if layer_4.ndim == 3:
+ layer_4 = unflatten(layer_4)
+
+ layer_1 = pretrained.act_postprocess1[3 : len(pretrained.act_postprocess1)](layer_1)
+ layer_2 = pretrained.act_postprocess2[3 : len(pretrained.act_postprocess2)](layer_2)
+ layer_3 = pretrained.act_postprocess3[3 : len(pretrained.act_postprocess3)](layer_3)
+ layer_4 = pretrained.act_postprocess4[3 : len(pretrained.act_postprocess4)](layer_4)
+
+ return layer_1, layer_2, layer_3, layer_4
+
+
+def _resize_pos_embed(self, posemb, gs_h, gs_w):
+ posemb_tok, posemb_grid = (
+ posemb[:, : self.start_index],
+ posemb[0, self.start_index :],
+ )
+
+ gs_old = int(math.sqrt(len(posemb_grid)))
+
+ posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
+ posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear")
+ posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1)
+
+ posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
+
+ return posemb
+
+
+def forward_flex(self, x):
+ b, c, h, w = x.shape
+
+ pos_embed = self._resize_pos_embed(
+ self.pos_embed, h // self.patch_size[1], w // self.patch_size[0]
+ )
+
+ B = x.shape[0]
+
+ if hasattr(self.patch_embed, "backbone"):
+ x = self.patch_embed.backbone(x)
+ if isinstance(x, (list, tuple)):
+ x = x[-1] # last feature if backbone outputs list/tuple of features
+
+ x = self.patch_embed.proj(x).flatten(2).transpose(1, 2)
+
+ if getattr(self, "dist_token", None) is not None:
+ cls_tokens = self.cls_token.expand(
+ B, -1, -1
+ ) # stole cls_tokens impl from Phil Wang, thanks
+ dist_token = self.dist_token.expand(B, -1, -1)
+ x = torch.cat((cls_tokens, dist_token, x), dim=1)
+ else:
+ cls_tokens = self.cls_token.expand(
+ B, -1, -1
+ ) # stole cls_tokens impl from Phil Wang, thanks
+ x = torch.cat((cls_tokens, x), dim=1)
+
+ x = x + pos_embed
+ x = self.pos_drop(x)
+
+ for blk in self.blocks:
+ x = blk(x)
+
+ x = self.norm(x)
+
+ return x
+
+
+activations = {}
+
+
+def get_activation(name):
+ def hook(model, input, output):
+ activations[name] = output
+
+ return hook
+
+
+def get_readout_oper(vit_features, features, use_readout, start_index=1):
+ if use_readout == "ignore":
+ readout_oper = [Slice(start_index)] * len(features)
+ elif use_readout == "add":
+ readout_oper = [AddReadout(start_index)] * len(features)
+ elif use_readout == "project":
+ readout_oper = [
+ ProjectReadout(vit_features, start_index) for out_feat in features
+ ]
+ else:
+ assert (
+ False
+ ), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'"
+
+ return readout_oper
+
+
+def _make_vit_b16_backbone(
+ model,
+ features=[96, 192, 384, 768],
+ size=[384, 384],
+ hooks=[2, 5, 8, 11],
+ vit_features=768,
+ use_readout="ignore",
+ start_index=1,
+):
+ pretrained = nn.Module()
+
+ pretrained.model = model
+ pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
+ pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
+ pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
+ pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
+
+ pretrained.activations = activations
+
+ readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
+
+ # 32, 48, 136, 384
+ pretrained.act_postprocess1 = nn.Sequential(
+ readout_oper[0],
+ Transpose(1, 2),
+ nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+ nn.Conv2d(
+ in_channels=vit_features,
+ out_channels=features[0],
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ ),
+ nn.ConvTranspose2d(
+ in_channels=features[0],
+ out_channels=features[0],
+ kernel_size=4,
+ stride=4,
+ padding=0,
+ bias=True,
+ dilation=1,
+ groups=1,
+ ),
+ )
+
+ pretrained.act_postprocess2 = nn.Sequential(
+ readout_oper[1],
+ Transpose(1, 2),
+ nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+ nn.Conv2d(
+ in_channels=vit_features,
+ out_channels=features[1],
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ ),
+ nn.ConvTranspose2d(
+ in_channels=features[1],
+ out_channels=features[1],
+ kernel_size=2,
+ stride=2,
+ padding=0,
+ bias=True,
+ dilation=1,
+ groups=1,
+ ),
+ )
+
+ pretrained.act_postprocess3 = nn.Sequential(
+ readout_oper[2],
+ Transpose(1, 2),
+ nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+ nn.Conv2d(
+ in_channels=vit_features,
+ out_channels=features[2],
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ ),
+ )
+
+ pretrained.act_postprocess4 = nn.Sequential(
+ readout_oper[3],
+ Transpose(1, 2),
+ nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+ nn.Conv2d(
+ in_channels=vit_features,
+ out_channels=features[3],
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ ),
+ nn.Conv2d(
+ in_channels=features[3],
+ out_channels=features[3],
+ kernel_size=3,
+ stride=2,
+ padding=1,
+ ),
+ )
+
+ pretrained.model.start_index = start_index
+ pretrained.model.patch_size = [16, 16]
+
+ # We inject this function into the VisionTransformer instances so that
+ # we can use it with interpolated position embeddings without modifying the library source.
+ pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
+ pretrained.model._resize_pos_embed = types.MethodType(
+ _resize_pos_embed, pretrained.model
+ )
+
+ return pretrained
+
+
+def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=None):
+ model = timm.create_model("vit_large_patch16_384", pretrained=pretrained)
+
+ hooks = [5, 11, 17, 23] if hooks == None else hooks
+ return _make_vit_b16_backbone(
+ model,
+ features=[256, 512, 1024, 1024],
+ hooks=hooks,
+ vit_features=1024,
+ use_readout=use_readout,
+ )
+
+
+def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=None):
+ model = timm.create_model("vit_base_patch16_384", pretrained=pretrained)
+
+ hooks = [2, 5, 8, 11] if hooks == None else hooks
+ return _make_vit_b16_backbone(
+ model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
+ )
+
+
+def _make_pretrained_deitb16_384(pretrained, use_readout="ignore", hooks=None):
+ model = timm.create_model("vit_deit_base_patch16_384", pretrained=pretrained)
+
+ hooks = [2, 5, 8, 11] if hooks == None else hooks
+ return _make_vit_b16_backbone(
+ model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
+ )
+
+
+def _make_pretrained_deitb16_distil_384(pretrained, use_readout="ignore", hooks=None):
+ model = timm.create_model(
+ "vit_deit_base_distilled_patch16_384", pretrained=pretrained
+ )
+
+ hooks = [2, 5, 8, 11] if hooks == None else hooks
+ return _make_vit_b16_backbone(
+ model,
+ features=[96, 192, 384, 768],
+ hooks=hooks,
+ use_readout=use_readout,
+ start_index=2,
+ )
+
+
+def _make_vit_b_rn50_backbone(
+ model,
+ features=[256, 512, 768, 768],
+ size=[384, 384],
+ hooks=[0, 1, 8, 11],
+ vit_features=768,
+ use_vit_only=False,
+ use_readout="ignore",
+ start_index=1,
+):
+ pretrained = nn.Module()
+
+ pretrained.model = model
+
+ if use_vit_only == True:
+ pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
+ pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
+ else:
+ pretrained.model.patch_embed.backbone.stages[0].register_forward_hook(
+ get_activation("1")
+ )
+ pretrained.model.patch_embed.backbone.stages[1].register_forward_hook(
+ get_activation("2")
+ )
+
+ pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
+ pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
+
+ pretrained.activations = activations
+
+ readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
+
+ if use_vit_only == True:
+ pretrained.act_postprocess1 = nn.Sequential(
+ readout_oper[0],
+ Transpose(1, 2),
+ nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+ nn.Conv2d(
+ in_channels=vit_features,
+ out_channels=features[0],
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ ),
+ nn.ConvTranspose2d(
+ in_channels=features[0],
+ out_channels=features[0],
+ kernel_size=4,
+ stride=4,
+ padding=0,
+ bias=True,
+ dilation=1,
+ groups=1,
+ ),
+ )
+
+ pretrained.act_postprocess2 = nn.Sequential(
+ readout_oper[1],
+ Transpose(1, 2),
+ nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+ nn.Conv2d(
+ in_channels=vit_features,
+ out_channels=features[1],
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ ),
+ nn.ConvTranspose2d(
+ in_channels=features[1],
+ out_channels=features[1],
+ kernel_size=2,
+ stride=2,
+ padding=0,
+ bias=True,
+ dilation=1,
+ groups=1,
+ ),
+ )
+ else:
+ pretrained.act_postprocess1 = nn.Sequential(
+ nn.Identity(), nn.Identity(), nn.Identity()
+ )
+ pretrained.act_postprocess2 = nn.Sequential(
+ nn.Identity(), nn.Identity(), nn.Identity()
+ )
+
+ pretrained.act_postprocess3 = nn.Sequential(
+ readout_oper[2],
+ Transpose(1, 2),
+ nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+ nn.Conv2d(
+ in_channels=vit_features,
+ out_channels=features[2],
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ ),
+ )
+
+ pretrained.act_postprocess4 = nn.Sequential(
+ readout_oper[3],
+ Transpose(1, 2),
+ nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+ nn.Conv2d(
+ in_channels=vit_features,
+ out_channels=features[3],
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ ),
+ nn.Conv2d(
+ in_channels=features[3],
+ out_channels=features[3],
+ kernel_size=3,
+ stride=2,
+ padding=1,
+ ),
+ )
+
+ pretrained.model.start_index = start_index
+ pretrained.model.patch_size = [16, 16]
+
+ # We inject this function into the VisionTransformer instances so that
+ # we can use it with interpolated position embeddings without modifying the library source.
+ pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
+
+ # We inject this function into the VisionTransformer instances so that
+ # we can use it with interpolated position embeddings without modifying the library source.
+ pretrained.model._resize_pos_embed = types.MethodType(
+ _resize_pos_embed, pretrained.model
+ )
+
+ return pretrained
+
+
+def _make_pretrained_vitb_rn50_384(
+ pretrained, use_readout="ignore", hooks=None, use_vit_only=False
+):
+ model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained)
+
+ hooks = [0, 1, 8, 11] if hooks == None else hooks
+ return _make_vit_b_rn50_backbone(
+ model,
+ features=[256, 512, 768, 768],
+ size=[384, 384],
+ hooks=hooks,
+ use_vit_only=use_vit_only,
+ use_readout=use_readout,
+ )
diff --git a/model_lib/ControlNet/ldm/modules/midas/utils.py b/model_lib/ControlNet/ldm/modules/midas/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a9d3b5b66370fa98da9e067ba53ead848ea9a59
--- /dev/null
+++ b/model_lib/ControlNet/ldm/modules/midas/utils.py
@@ -0,0 +1,189 @@
+"""Utils for monoDepth."""
+import sys
+import re
+import numpy as np
+import cv2
+import torch
+
+
+def read_pfm(path):
+ """Read pfm file.
+
+ Args:
+ path (str): path to file
+
+ Returns:
+ tuple: (data, scale)
+ """
+ with open(path, "rb") as file:
+
+ color = None
+ width = None
+ height = None
+ scale = None
+ endian = None
+
+ header = file.readline().rstrip()
+ if header.decode("ascii") == "PF":
+ color = True
+ elif header.decode("ascii") == "Pf":
+ color = False
+ else:
+ raise Exception("Not a PFM file: " + path)
+
+ dim_match = re.match(r"^(\d+)\s(\d+)\s$", file.readline().decode("ascii"))
+ if dim_match:
+ width, height = list(map(int, dim_match.groups()))
+ else:
+ raise Exception("Malformed PFM header.")
+
+ scale = float(file.readline().decode("ascii").rstrip())
+ if scale < 0:
+ # little-endian
+ endian = "<"
+ scale = -scale
+ else:
+ # big-endian
+ endian = ">"
+
+ data = np.fromfile(file, endian + "f")
+ shape = (height, width, 3) if color else (height, width)
+
+ data = np.reshape(data, shape)
+ data = np.flipud(data)
+
+ return data, scale
+
+
+def write_pfm(path, image, scale=1):
+ """Write pfm file.
+
+ Args:
+ path (str): pathto file
+ image (array): data
+ scale (int, optional): Scale. Defaults to 1.
+ """
+
+ with open(path, "wb") as file:
+ color = None
+
+ if image.dtype.name != "float32":
+ raise Exception("Image dtype must be float32.")
+
+ image = np.flipud(image)
+
+ if len(image.shape) == 3 and image.shape[2] == 3: # color image
+ color = True
+ elif (
+ len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1
+ ): # greyscale
+ color = False
+ else:
+ raise Exception("Image must have H x W x 3, H x W x 1 or H x W dimensions.")
+
+ file.write("PF\n" if color else "Pf\n".encode())
+ file.write("%d %d\n".encode() % (image.shape[1], image.shape[0]))
+
+ endian = image.dtype.byteorder
+
+ if endian == "<" or endian == "=" and sys.byteorder == "little":
+ scale = -scale
+
+ file.write("%f\n".encode() % scale)
+
+ image.tofile(file)
+
+
+def read_image(path):
+ """Read image and output RGB image (0-1).
+
+ Args:
+ path (str): path to file
+
+ Returns:
+ array: RGB image (0-1)
+ """
+ img = cv2.imread(path)
+
+ if img.ndim == 2:
+ img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+
+ img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0
+
+ return img
+
+
+def resize_image(img):
+ """Resize image and make it fit for network.
+
+ Args:
+ img (array): image
+
+ Returns:
+ tensor: data ready for network
+ """
+ height_orig = img.shape[0]
+ width_orig = img.shape[1]
+
+ if width_orig > height_orig:
+ scale = width_orig / 384
+ else:
+ scale = height_orig / 384
+
+ height = (np.ceil(height_orig / scale / 32) * 32).astype(int)
+ width = (np.ceil(width_orig / scale / 32) * 32).astype(int)
+
+ img_resized = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA)
+
+ img_resized = (
+ torch.from_numpy(np.transpose(img_resized, (2, 0, 1))).contiguous().float()
+ )
+ img_resized = img_resized.unsqueeze(0)
+
+ return img_resized
+
+
+def resize_depth(depth, width, height):
+ """Resize depth map and bring to CPU (numpy).
+
+ Args:
+ depth (tensor): depth
+ width (int): image width
+ height (int): image height
+
+ Returns:
+ array: processed depth
+ """
+ depth = torch.squeeze(depth[0, :, :, :]).to("cpu")
+
+ depth_resized = cv2.resize(
+ depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC
+ )
+
+ return depth_resized
+
+def write_depth(path, depth, bits=1):
+ """Write depth map to pfm and png file.
+
+ Args:
+ path (str): filepath without extension
+ depth (array): depth
+ """
+ write_pfm(path + ".pfm", depth.astype(np.float32))
+
+ depth_min = depth.min()
+ depth_max = depth.max()
+
+ max_val = (2**(8*bits))-1
+
+ if depth_max - depth_min > np.finfo("float").eps:
+ out = max_val * (depth - depth_min) / (depth_max - depth_min)
+ else:
+ out = np.zeros(depth.shape, dtype=depth.type)
+
+ if bits == 1:
+ cv2.imwrite(path + ".png", out.astype("uint8"))
+ elif bits == 2:
+ cv2.imwrite(path + ".png", out.astype("uint16"))
+
+ return
diff --git a/model_lib/ControlNet/ldm/modules/motion_module.py b/model_lib/ControlNet/ldm/modules/motion_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1812095a41fd3a4e52979490559a23afd46de7e
--- /dev/null
+++ b/model_lib/ControlNet/ldm/modules/motion_module.py
@@ -0,0 +1,351 @@
+from dataclasses import dataclass
+from typing import List, Optional, Tuple, Union
+
+import torch
+import numpy as np
+import torch.nn.functional as F
+from torch import nn
+import torchvision
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+# from diffusers.modeling_utils import ModelMixin
+from diffusers.utils import BaseOutput
+from diffusers.utils.import_utils import is_xformers_available
+# from diffusers.models.attention import CrossAttention, FeedForward
+from diffusers.models.attention import Attention as CrossAttention, FeedForward
+from einops import rearrange, repeat
+import math
+import pdb
+
+def zero_module(module):
+ # Zero out the parameters of a module and return it.
+ for p in module.parameters():
+ p.detach().zero_()
+ return module
+
+
+@dataclass
+class TemporalTransformer3DModelOutput(BaseOutput):
+ sample: torch.FloatTensor
+
+
+if is_xformers_available():
+ import xformers
+ import xformers.ops
+else:
+ xformers = None
+
+
+def get_motion_module(
+ in_channels,
+ motion_module_type: str,
+ motion_module_kwargs: dict
+):
+ if motion_module_type == "Vanilla":
+ return VanillaTemporalModule(in_channels=in_channels, **motion_module_kwargs,)
+ else:
+ raise ValueError
+
+
+class VanillaTemporalModule(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ num_attention_heads = 8,
+ num_transformer_block = 2,
+ attention_block_types =( "Temporal_Self", "Temporal_Self" ),
+ cross_frame_attention_mode = None,
+ temporal_position_encoding = False,
+ temporal_position_encoding_max_len = 24,
+ temporal_attention_dim_div = 1,
+ zero_initialize = True,
+ video_length = 16,
+ ):
+ super().__init__()
+
+ self.temporal_transformer = TemporalTransformer3DModel(
+ in_channels=in_channels,
+ num_attention_heads=num_attention_heads,
+ attention_head_dim=in_channels // num_attention_heads // temporal_attention_dim_div,
+ num_layers=num_transformer_block,
+ attention_block_types=attention_block_types,
+ cross_frame_attention_mode=cross_frame_attention_mode,
+ temporal_position_encoding=temporal_position_encoding,
+ temporal_position_encoding_max_len=temporal_position_encoding_max_len,
+ video_length=video_length
+ )
+
+ if zero_initialize:
+ self.temporal_transformer.proj_out = zero_module(self.temporal_transformer.proj_out)
+
+ def forward(self, input_tensor, encoder_hidden_states):
+ hidden_states = input_tensor
+ hidden_states = self.temporal_transformer(hidden_states, encoder_hidden_states)
+ output = hidden_states
+ return output
+
+
+class TemporalTransformer3DModel(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ num_attention_heads,
+ attention_head_dim,
+
+ num_layers,
+ attention_block_types = ( "Temporal_Self", "Temporal_Self", ),
+ dropout = 0.0,
+ norm_num_groups = 32,
+ cross_attention_dim = 768,
+ activation_fn = "geglu",
+ attention_bias = False,
+ upcast_attention = False,
+
+ cross_frame_attention_mode = None,
+ temporal_position_encoding = False,
+ temporal_position_encoding_max_len = 24,
+ video_length = 16,
+ ):
+ super().__init__()
+
+ inner_dim = num_attention_heads * attention_head_dim
+
+ self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+ self.proj_in = nn.Linear(in_channels, inner_dim)
+
+ self.transformer_blocks = nn.ModuleList(
+ [
+ TemporalTransformerBlock(
+ dim=inner_dim,
+ num_attention_heads=num_attention_heads,
+ attention_head_dim=attention_head_dim,
+ attention_block_types=attention_block_types,
+ dropout=dropout,
+ norm_num_groups=norm_num_groups,
+ cross_attention_dim=cross_attention_dim,
+ activation_fn=activation_fn,
+ attention_bias=attention_bias,
+ upcast_attention=upcast_attention,
+ cross_frame_attention_mode=cross_frame_attention_mode,
+ temporal_position_encoding=temporal_position_encoding,
+ temporal_position_encoding_max_len=temporal_position_encoding_max_len,
+ )
+ for d in range(num_layers)
+ ]
+ )
+ self.proj_out = nn.Linear(inner_dim, in_channels)
+ self.video_length = video_length
+
+ def forward(self, hidden_states, encoder_hidden_states=None):
+ assert hidden_states.dim() == 4, f"Expected hidden_states to have ndim=4, but got ndim={hidden_states.dim()}." # (b f) c h w
+ # video_length = 16 ### !!!!!
+ video_length = self.video_length
+ # print("warning: video_length is fixed as 16 in %s line 137, and received %d as argument"%(__file__, video_length))
+ # video_length = hidden_states.shape[2]
+ # hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w")
+
+ batch, channel, height, weight = hidden_states.shape
+ residual = hidden_states
+
+ hidden_states = self.norm(hidden_states)
+ inner_dim = hidden_states.shape[1]
+ hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
+ hidden_states = self.proj_in(hidden_states)
+
+ # Transformer Blocks
+ for block in self.transformer_blocks:
+ hidden_states = block(hidden_states, encoder_hidden_states=encoder_hidden_states, video_length=video_length)
+
+ # output
+ hidden_states = self.proj_out(hidden_states)
+ hidden_states = hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
+
+ output = hidden_states + residual
+ # output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length)
+
+ return output
+
+
+class TemporalTransformerBlock(nn.Module):
+ def __init__(
+ self,
+ dim,
+ num_attention_heads,
+ attention_head_dim,
+ attention_block_types = ( "Temporal_Self", "Temporal_Self", ),
+ dropout = 0.0,
+ norm_num_groups = 32,
+ cross_attention_dim = 768,
+ activation_fn = "geglu",
+ attention_bias = False,
+ upcast_attention = False,
+ cross_frame_attention_mode = None,
+ temporal_position_encoding = False,
+ temporal_position_encoding_max_len = 24,
+ ):
+ super().__init__()
+
+ attention_blocks = []
+ norms = []
+
+ for block_name in attention_block_types:
+ attention_blocks.append(
+ VersatileAttention(
+ attention_mode=block_name.split("_")[0],
+ cross_attention_dim=cross_attention_dim if block_name.endswith("_Cross") else None,
+
+ query_dim=dim,
+ heads=num_attention_heads,
+ dim_head=attention_head_dim,
+ dropout=dropout,
+ bias=attention_bias,
+ upcast_attention=upcast_attention,
+
+ cross_frame_attention_mode=cross_frame_attention_mode,
+ temporal_position_encoding=temporal_position_encoding,
+ temporal_position_encoding_max_len=temporal_position_encoding_max_len,
+ )
+ )
+ norms.append(nn.LayerNorm(dim))
+
+ self.attention_blocks = nn.ModuleList(attention_blocks)
+ self.norms = nn.ModuleList(norms)
+
+ self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn)
+ self.ff_norm = nn.LayerNorm(dim)
+
+
+ def forward(self, hidden_states, encoder_hidden_states=None, video_length=None):
+ for attention_block, norm in zip(self.attention_blocks, self.norms):
+ norm_hidden_states = norm(hidden_states)
+ hidden_states = attention_block(
+ norm_hidden_states,
+ encoder_hidden_states=encoder_hidden_states if attention_block.is_cross_attention else None,
+ video_length=video_length,
+ ) + hidden_states
+
+ hidden_states = self.ff(self.ff_norm(hidden_states)) + hidden_states
+
+ output = hidden_states
+ return output
+
+
+class PositionalEncoding(nn.Module):
+ def __init__(
+ self,
+ d_model,
+ dropout = 0.,
+ max_len = 24
+ ):
+ super().__init__()
+ self.dropout = nn.Dropout(p=dropout)
+ position = torch.arange(max_len).unsqueeze(1)
+ div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
+ pe = torch.zeros(1, max_len, d_model)
+ pe[0, :, 0::2] = torch.sin(position * div_term)
+ pe[0, :, 1::2] = torch.cos(position * div_term)
+ self.register_buffer('pe', pe)
+
+ def forward(self, x):
+ x = x + self.pe[:, :x.size(1)]
+ return self.dropout(x)
+
+
+class VersatileAttention(CrossAttention):
+ def __init__(
+ self,
+ attention_mode = None,
+ cross_frame_attention_mode = None,
+ temporal_position_encoding = False,
+ temporal_position_encoding_max_len = 24,
+ *args, **kwargs
+ ):
+ super().__init__(*args, **kwargs)
+ assert attention_mode == "Temporal"
+
+ self.attention_mode = attention_mode
+ self.is_cross_attention = kwargs["cross_attention_dim"] is not None
+
+ self.pos_encoder = PositionalEncoding(
+ kwargs["query_dim"],
+ dropout=0.,
+ max_len=temporal_position_encoding_max_len
+ ) if (temporal_position_encoding and attention_mode == "Temporal") else None
+
+ def extra_repr(self):
+ return f"(Module Info) Attention_Mode: {self.attention_mode}, Is_Cross_Attention: {self.is_cross_attention}"
+
+ def _memory_efficient_attention_xformers(self, query, key, value, attention_mask):
+ # TODO attention_mask
+ query = query.contiguous()
+ key = key.contiguous()
+ value = value.contiguous()
+ hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask)
+ # hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
+ hidden_states = self.batch_to_head_dim(hidden_states)
+ return hidden_states
+
+ def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None):
+ batch_size, sequence_length, _ = hidden_states.shape
+
+ if self.attention_mode == "Temporal":
+ d = hidden_states.shape[1]
+ hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length)
+
+ if self.pos_encoder is not None:
+ hidden_states = self.pos_encoder(hidden_states)
+
+ encoder_hidden_states = repeat(encoder_hidden_states, "b n c -> (b d) n c", d=d) if encoder_hidden_states is not None else encoder_hidden_states
+ else:
+ raise NotImplementedError
+
+ encoder_hidden_states = encoder_hidden_states
+
+ if self.group_norm is not None:
+ hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+ query = self.to_q(hidden_states)
+ dim = query.shape[-1]
+ # query = self.reshape_heads_to_batch_dim(query)
+ query = self.head_to_batch_dim(query)
+
+ if self.added_kv_proj_dim is not None:
+ raise NotImplementedError
+
+ encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
+ key = self.to_k(encoder_hidden_states)
+ value = self.to_v(encoder_hidden_states)
+
+ # key = self.reshape_heads_to_batch_dim(key)
+ # value = self.reshape_heads_to_batch_dim(value)
+ key = self.head_to_batch_dim(key)
+ value = self.head_to_batch_dim(value)
+
+ if attention_mask is not None:
+ if attention_mask.shape[-1] != query.shape[1]:
+ target_length = query.shape[1]
+ attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
+ attention_mask = attention_mask.repeat_interleave(self.heads, dim=0)
+
+ # attention, what we cannot get enough of
+ # if self._use_memory_efficient_attention_xformers:
+ if 1:
+ hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask)
+ # Some versions of xformers return output in fp32, cast it back to the dtype of the input
+ hidden_states = hidden_states.to(query.dtype)
+ else:
+ if self._slice_size is None or query.shape[0] // self._slice_size == 1:
+ hidden_states = self._attention(query, key, value, attention_mask)
+ else:
+ hidden_states = self._sliced_attention(query, key, value, sequence_length, dim, attention_mask)
+
+ # linear proj
+ hidden_states = self.to_out[0](hidden_states)
+
+ # dropout
+ hidden_states = self.to_out[1](hidden_states)
+
+ if self.attention_mode == "Temporal":
+ hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d)
+
+ return hidden_states
diff --git a/model_lib/ControlNet/ldm/util.py b/model_lib/ControlNet/ldm/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c09ca1c72f7ceb3f9d7f9546aae5561baf62b13
--- /dev/null
+++ b/model_lib/ControlNet/ldm/util.py
@@ -0,0 +1,197 @@
+import importlib
+
+import torch
+from torch import optim
+import numpy as np
+
+from inspect import isfunction
+from PIL import Image, ImageDraw, ImageFont
+
+
+def log_txt_as_img(wh, xc, size=10):
+ # wh a tuple of (width, height)
+ # xc a list of captions to plot
+ b = len(xc)
+ txts = list()
+ for bi in range(b):
+ txt = Image.new("RGB", wh, color="white")
+ draw = ImageDraw.Draw(txt)
+ font = ImageFont.truetype('data/DejaVuSans.ttf', size=size)
+ nc = int(40 * (wh[0] / 256))
+ lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))
+
+ try:
+ draw.text((0, 0), lines, fill="black", font=font)
+ except UnicodeEncodeError:
+ print("Cant encode string for logging. Skipping.")
+
+ txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
+ txts.append(txt)
+ txts = np.stack(txts)
+ txts = torch.tensor(txts)
+ return txts
+
+
+def ismap(x):
+ if not isinstance(x, torch.Tensor):
+ return False
+ return (len(x.shape) == 4) and (x.shape[1] > 3)
+
+
+def isimage(x):
+ if not isinstance(x,torch.Tensor):
+ return False
+ return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
+
+
+def exists(x):
+ return x is not None
+
+
+def default(val, d):
+ if exists(val):
+ return val
+ return d() if isfunction(d) else d
+
+
+def mean_flat(tensor):
+ """
+ https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
+ Take the mean over all non-batch dimensions.
+ """
+ return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+
+def count_params(model, verbose=False):
+ total_params = sum(p.numel() for p in model.parameters())
+ if verbose:
+ print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
+ return total_params
+
+
+def instantiate_from_config(config):
+ if not "target" in config:
+ if config == '__is_first_stage__':
+ return None
+ elif config == "__is_unconditional__":
+ return None
+ raise KeyError("Expected key `target` to instantiate.")
+ return get_obj_from_str(config["target"])(**config.get("params", dict()))
+
+
+def get_obj_from_str(string, reload=False):
+ module, cls = string.rsplit(".", 1)
+ if reload:
+ module_imp = importlib.import_module(module)
+ importlib.reload(module_imp)
+ return getattr(importlib.import_module(module, package=None), cls)
+
+
+class AdamWwithEMAandWings(optim.Optimizer):
+ # credit to https://gist.github.com/crowsonkb/65f7265353f403714fce3b2595e0b298
+ def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8, # TODO: check hyperparameters before using
+ weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999, # ema decay to match previous code
+ ema_power=1., param_names=()):
+ """AdamW that saves EMA versions of the parameters."""
+ if not 0.0 <= lr:
+ raise ValueError("Invalid learning rate: {}".format(lr))
+ if not 0.0 <= eps:
+ raise ValueError("Invalid epsilon value: {}".format(eps))
+ if not 0.0 <= betas[0] < 1.0:
+ raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
+ if not 0.0 <= betas[1] < 1.0:
+ raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
+ if not 0.0 <= weight_decay:
+ raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+ if not 0.0 <= ema_decay <= 1.0:
+ raise ValueError("Invalid ema_decay value: {}".format(ema_decay))
+ defaults = dict(lr=lr, betas=betas, eps=eps,
+ weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay,
+ ema_power=ema_power, param_names=param_names)
+ super().__init__(params, defaults)
+
+ def __setstate__(self, state):
+ super().__setstate__(state)
+ for group in self.param_groups:
+ group.setdefault('amsgrad', False)
+
+ @torch.no_grad()
+ def step(self, closure=None):
+ """Performs a single optimization step.
+ Args:
+ closure (callable, optional): A closure that reevaluates the model
+ and returns the loss.
+ """
+ loss = None
+ if closure is not None:
+ with torch.enable_grad():
+ loss = closure()
+
+ for group in self.param_groups:
+ params_with_grad = []
+ grads = []
+ exp_avgs = []
+ exp_avg_sqs = []
+ ema_params_with_grad = []
+ state_sums = []
+ max_exp_avg_sqs = []
+ state_steps = []
+ amsgrad = group['amsgrad']
+ beta1, beta2 = group['betas']
+ ema_decay = group['ema_decay']
+ ema_power = group['ema_power']
+
+ for p in group['params']:
+ if p.grad is None:
+ continue
+ params_with_grad.append(p)
+ if p.grad.is_sparse:
+ raise RuntimeError('AdamW does not support sparse gradients')
+ grads.append(p.grad)
+
+ state = self.state[p]
+
+ # State initialization
+ if len(state) == 0:
+ state['step'] = 0
+ # Exponential moving average of gradient values
+ state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
+ # Exponential moving average of squared gradient values
+ state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
+ if amsgrad:
+ # Maintains max of all exp. moving avg. of sq. grad. values
+ state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
+ # Exponential moving average of parameter values
+ state['param_exp_avg'] = p.detach().float().clone()
+
+ exp_avgs.append(state['exp_avg'])
+ exp_avg_sqs.append(state['exp_avg_sq'])
+ ema_params_with_grad.append(state['param_exp_avg'])
+
+ if amsgrad:
+ max_exp_avg_sqs.append(state['max_exp_avg_sq'])
+
+ # update the steps for each param group update
+ state['step'] += 1
+ # record the step after step update
+ state_steps.append(state['step'])
+
+ optim._functional.adamw(params_with_grad,
+ grads,
+ exp_avgs,
+ exp_avg_sqs,
+ max_exp_avg_sqs,
+ state_steps,
+ amsgrad=amsgrad,
+ beta1=beta1,
+ beta2=beta2,
+ lr=group['lr'],
+ weight_decay=group['weight_decay'],
+ eps=group['eps'],
+ maximize=False)
+
+ cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power)
+ for param, ema_param in zip(params_with_grad, ema_params_with_grad):
+ ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay)
+
+ return loss
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..98d99a58ad00b17d94ad22722d4c38c900119112
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,71 @@
+black==23.7.0
+chardet==5.1.0
+clip @ git+https://github.com/openai/CLIP.git
+einops>=0.6.1
+fairscale>=0.4.13
+fire>=0.5.0
+fsspec>=2023.6.0
+invisible-watermark>=0.2.0
+matplotlib>=3.7.2
+natsort>=8.4.0
+ninja>=1.11.1
+numpy==1.23.0
+omegaconf>=2.3.0
+open-clip-torch>=2.20.0
+pandas>=2.0.3
+pillow
+pudb>=2022.1.3
+pytorch-lightning==1.4.2
+pyyaml>=5.4.1
+scipy>=1.10.1
+streamlit>=0.73.1
+tensorboardx==2.6
+timm>=0.9.2
+tokenizers==0.12.1
+torch>=2.0.1
+torchaudio>=2.0.2
+torchdata==0.6.1
+torchmetrics==0.6.0
+torchvision>=0.15.2
+tqdm>=4.65.0
+transformers==4.30.0
+triton==2.0.0
+urllib3<1.27,>=1.25.4
+wandb>=0.15.6
+webdataset>=0.2.33
+wheel>=0.41.0
+xformers>=0.0.22
+streamlit-keyup==0.2.0
+
+# Basic
+scikit-image==0.19.3
+opencv-python==4.7.0.72
+imageio-ffmpeg==0.4.2
+imageio==2.9.0
+kornia==0.6.11
+
+# Training
+# byted-dataloader==0.3.7
+# byted-mloops==0.2.21
+accelerate==0.17.0
+ema-pytorch==0.2.1
+tensorboard==2.11.2
+
+# Language Model
+regex==2022.10.31
+ftfy==6.1.1
+# open_clip_torch==2.16.0
+
+# Model
+einops_exts==0.0.4
+rotary_embedding_torch==0.2.1
+entmax==1.1
+torchdiffeq==0.2.3
+diffusers==0.26.0
+# apt-get install ffmpeg libsm6 libxext6 -y
+
+# Face Keypoint
+thriftpy2
+
+decord
+face-alignment
\ No newline at end of file
diff --git a/scripts/test_xportrait.sh b/scripts/test_xportrait.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9eac7e2e80d573eaa6defbc6a9f96cba7e823e6b
--- /dev/null
+++ b/scripts/test_xportrait.sh
@@ -0,0 +1,12 @@
+python3 core/test_xportrait.py \
+--model_config config/cldm_v15_appearance_pose_local_mm.yaml \
+--output_dir outputs \
+--resume_dir checkpoint/model_state-415001.th \
+--seed 999 \
+--uc_scale 5 \
+--source_image assets/source_image.png \
+--driving_video assets/driving_video.mp4 \
+--best_frame 36 \
+--out_frames -1 \
+--num_mix 4 \
+--ddim_steps 30 \
\ No newline at end of file
diff --git a/utils/__init__.py b/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b32388a6440f72141282c712f52bf1b3d0a566f0
--- /dev/null
+++ b/utils/__init__.py
@@ -0,0 +1,6 @@
+from .io_utils import mkdirs, pickle_load, pickle_dump, walk_all_files_with_suffix, get_dirs, get_leave_dirs, \
+ merge_pkl_dict
+
+from .time_utils import time_since, Timer
+from .txt_utils import log_info, write_str_to_txt, write_namespace_to_txt, read_txt_to_str, read_txt_to_namespace, \
+ replace_txt_str
diff --git a/utils/__pycache__/__init__.cpython-39.pyc b/utils/__pycache__/__init__.cpython-39.pyc
new file mode 100644
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diff --git a/utils/__pycache__/io_utils.cpython-39.pyc b/utils/__pycache__/io_utils.cpython-39.pyc
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diff --git a/utils/__pycache__/time_utils.cpython-39.pyc b/utils/__pycache__/time_utils.cpython-39.pyc
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diff --git a/utils/__pycache__/txt_utils.cpython-39.pyc b/utils/__pycache__/txt_utils.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..cd6244d69c26ddb5927cd8af2ae4e3313a0bf815
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diff --git a/utils/__pycache__/utils.cpython-39.pyc b/utils/__pycache__/utils.cpython-39.pyc
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diff --git a/utils/io_utils.py b/utils/io_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ade4b0ed544f40e4d58dbb6e2cef3afcb850f4b5
--- /dev/null
+++ b/utils/io_utils.py
@@ -0,0 +1,82 @@
+# -*- coding: utf-8 -*-
+
+import os
+import errno
+import pickle
+from collections import deque
+import torch
+
+
+def mkdirs(path):
+ try:
+ os.makedirs(path)
+ except OSError as exc: # Python >2.5
+ if exc.errno == errno.EEXIST and os.path.isdir(path):
+ pass
+ else:
+ raise
+
+
+def pickle_load(file_path):
+ assert os.path.isfile(file_path), file_path
+ with open(file_path, 'rb') as f:
+ file = pickle.load(f)
+ return file
+
+
+def pickle_dump(file_path, file):
+ if os.path.isfile(file_path):
+ os.remove(file_path)
+ with open(file_path, 'wb') as f:
+ pickle.dump(file, f, protocol=2)
+
+
+def walk_all_files_with_suffix(dir,
+ suffixs=('.jpg', '.png', '.jpeg'),
+ sort=False):
+ paths = []
+ names = []
+ assert os.path.isdir(dir), '%s is not a valid directory' % dir
+ for root, _, fnames in sorted(os.walk(dir)):
+ for fname in fnames:
+ path = os.path.join(root, fname)
+ if os.path.splitext(fname)[1].lower() in suffixs:
+ paths.append(path)
+ if not sort:
+ names.append(fname)
+
+ if sort:
+ paths.sort()
+ for i in range(len(paths)):
+ names.append(os.path.basename(paths[i]))
+ return len(names), names, paths
+
+
+def get_dirs(root_dir):
+ dir_paths = []
+ dir_names = []
+ for lists in os.listdir(root_dir):
+ path = os.path.join(root_dir, lists)
+ if os.path.isdir(path):
+ dir_paths.append(path)
+ dir_names.append(os.path.basename(path))
+ dir_names.sort()
+ dir_paths.sort()
+ return len(dir_names), dir_names, dir_paths
+
+
+def get_leave_dirs(root_dir):
+ leave_dirs = []
+ for dirpath, dirnames, filenames in os.walk(root_dir):
+ if not dirnames:
+ leave_dirs.append(dirpath)
+ return leave_dirs
+
+
+def merge_pkl_dict(paths):
+ dicts = []
+ for i in range(len(paths)):
+ dicts.append(pickle_load(paths[i]))
+ for i in range(len(paths) - 1):
+ dicts[0].update(dicts[i + 1])
+ return dicts[0]
diff --git a/utils/time_utils.py b/utils/time_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b339791712d246e7c01af01b9f5b7fa3642f89e
--- /dev/null
+++ b/utils/time_utils.py
@@ -0,0 +1,24 @@
+# -*- coding: utf-8 -*-
+
+import time
+
+
+class Timer:
+ def __init__(self):
+ self.clock = {}
+
+ def start(self, key="default"):
+ self.clock[key] = time.time()
+
+ def end(self, key="default"):
+ if key not in self.clock:
+ raise Exception("{} is not in the clock.".format(key))
+ interval = time.time() - self.clock[key]
+ del self.clock[key]
+ return interval
+
+
+def time_since(last_time):
+ time_elapsed = time.time() - last_time
+ current_time = time.time()
+ return current_time, time_elapsed
diff --git a/utils/txt_utils.py b/utils/txt_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..3df6b5da54cef5b99d4aaa955cbfeb7cd9710a0c
--- /dev/null
+++ b/utils/txt_utils.py
@@ -0,0 +1,52 @@
+# -*- coding: utf-8 -*-
+
+import json
+import logging
+import argparse
+
+
+def log_info(s, use_log=True):
+ if use_log:
+ logging.info(s)
+ else:
+ print(s)
+
+
+def write_str_to_txt(file_path, str, mode='a'):
+ with open(file_path, mode) as f:
+ f.write(str)
+
+
+def write_namespace_to_txt(file_path, json_str, indent=4):
+ with open(file_path, 'a') as f:
+ f.write(json.dumps(vars(json_str), indent=indent))
+ f.write('\n')
+
+
+def read_txt_to_str(file_path):
+ with open(file_path, 'r') as f:
+ info_list = f.read().splitlines()
+ return info_list
+
+
+def read_txt_to_namespace(file_path):
+ with open(file_path, 'r') as f:
+ json_str = json.load(f)
+ args = argparse.Namespace(**json_str)
+ if type(args.loss_choice) is str:
+ # 向旧版本cfg兼容
+ args.loss_choice=[args.loss_choice]
+ args.loss_lambda=[1]
+ args.loss_return_dict=False
+ return args
+
+
+def replace_txt_str(txt_path, old_str, new_str):
+ file_data = ''
+ with open(txt_path, 'r') as f:
+ for idx, line in enumerate(f):
+ if old_str in line:
+ line = line.replace(old_str, new_str)
+ file_data += line
+ with open(txt_path, 'w') as f:
+ f.write(file_data)
diff --git a/utils/utils.py b/utils/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d4e44c9e21372a6db287964f734f82f5b77397f
--- /dev/null
+++ b/utils/utils.py
@@ -0,0 +1,78 @@
+import random
+import numpy as np
+import torch
+import matplotlib.pyplot as plt
+from PIL import Image
+
+def merge_lists_by_index(list1, list2):
+ # Check if both lists have the same number of elements
+ if len(list1) != len(list2):
+ raise ValueError("Both lists should have the same number of elements.")
+
+ # Merge the lists by concatenating strings at the same index
+ merged_list = [string1 + '. ' + string2 for string1, string2 in zip(list1, list2)]
+ return merged_list
+
+def plot_x_y(x, y, x_label, y_label, save_path, **kwargs):
+
+ plt.plot( x , y, **kwargs)
+ plt.xlabel(x_label)
+ plt.ylabel(y_label)
+ plt.legend()
+ plt.savefig(save_path)
+
+
+def set_seed(seed):
+ random.seed(seed)
+ np.random.seed(seed)
+ torch.manual_seed(seed)
+ torch.cuda.manual_seed_all(seed)
+
+# count # of param for a list of module
+def count_param(module_list):
+ return sum(x.numel() for module in module_list for x in module.parameters()) / 10**6
+
+# display the peak memory of cuda
+def print_peak_memory(prefix, device):
+ if device == 0:
+ print(f"{prefix}: {torch.cuda.max_memory_allocated(device) // 1e6}MB ")
+
+def anal_tensor(tensor, name):
+ sent = f" name: {name} mean: {tensor.mean().item()} std: {tensor.std().item()} min: {tensor.min().item()} max: {tensor.max().item()}"
+ print(sent)
+
+
+def split(lst, split_nbr):
+ div = len(lst) // split_nbr
+ rest = len(lst) % split_nbr
+ results = []
+ start, end = 0, div
+ while start < len(lst):
+ if rest >= 1:
+ end += 1
+ rest -= 1
+ results.append(lst[start:end])
+ start, end = end, end+div
+ return results
+
+def chunk(iterable, chunk_size):
+ ret = []
+ for record in iterable:
+ ret.append(record)
+ if len(ret) == chunk_size:
+ yield ret
+ ret = []
+ if ret:
+ yield ret
+
+def image_concat_h(im1, im2):
+ dst = Image.new('RGB', (im1.width + im2.width, im1.height))
+ dst.paste(im1, (0, 0))
+ dst.paste(im2, (im1.width, 0))
+ return dst
+
+def image_concat_v(im1, im2):
+ dst = Image.new('RGB', (im1.width, im1.height + im2.height))
+ dst.paste(im1, (0, 0))
+ dst.paste(im2, (0, im1.height))
+ return dst