import torch from basicsr.utils import img2tensor, tensor2img from pytorch_lightning import seed_everything from ldm.models.diffusion.plms import PLMSSampler from ldm.modules.encoders.adapter import Adapter, Adapter_light, StyleAdapter from ldm.util import instantiate_from_config from ldm.modules.structure_condition.model_edge import pidinet from ldm.modules.structure_condition.model_seg import seger, Colorize from ldm.modules.structure_condition.midas.api import MiDaSInference import gradio as gr from omegaconf import OmegaConf import mmcv from mmdet.apis import inference_detector, init_detector from mmpose.apis import (inference_top_down_pose_model, init_pose_model, process_mmdet_results, vis_pose_result) import os import cv2 import numpy as np import torch.nn.functional as F from transformers import CLIPProcessor, CLIPVisionModel from PIL import Image def preprocessing(image, device): # Resize scale = 640 / max(image.shape[:2]) image = cv2.resize(image, dsize=None, fx=scale, fy=scale) raw_image = image.astype(np.uint8) # Subtract mean values image = image.astype(np.float32) image -= np.array( [ float(104.008), float(116.669), float(122.675), ] ) # Convert to torch.Tensor and add "batch" axis image = torch.from_numpy(image.transpose(2, 0, 1)).float().unsqueeze(0) image = image.to(device) return image, raw_image def imshow_keypoints(img, pose_result, skeleton=None, kpt_score_thr=0.1, pose_kpt_color=None, pose_link_color=None, radius=4, thickness=1): """Draw keypoints and links on an image. Args: img (ndarry): The image to draw poses on. pose_result (list[kpts]): The poses to draw. Each element kpts is a set of K keypoints as an Kx3 numpy.ndarray, where each keypoint is represented as x, y, score. kpt_score_thr (float, optional): Minimum score of keypoints to be shown. Default: 0.3. pose_kpt_color (np.array[Nx3]`): Color of N keypoints. If None, the keypoint will not be drawn. pose_link_color (np.array[Mx3]): Color of M links. If None, the links will not be drawn. thickness (int): Thickness of lines. """ img_h, img_w, _ = img.shape img = np.zeros(img.shape) for idx, kpts in enumerate(pose_result): if idx > 1: continue kpts = kpts['keypoints'] # print(kpts) kpts = np.array(kpts, copy=False) # draw each point on image if pose_kpt_color is not None: assert len(pose_kpt_color) == len(kpts) for kid, kpt in enumerate(kpts): x_coord, y_coord, kpt_score = int(kpt[0]), int(kpt[1]), kpt[2] if kpt_score < kpt_score_thr or pose_kpt_color[kid] is None: # skip the point that should not be drawn continue color = tuple(int(c) for c in pose_kpt_color[kid]) cv2.circle(img, (int(x_coord), int(y_coord)), radius, color, -1) # draw links if skeleton is not None and pose_link_color is not None: assert len(pose_link_color) == len(skeleton) for sk_id, sk in enumerate(skeleton): pos1 = (int(kpts[sk[0], 0]), int(kpts[sk[0], 1])) pos2 = (int(kpts[sk[1], 0]), int(kpts[sk[1], 1])) if (pos1[0] <= 0 or pos1[0] >= img_w or pos1[1] <= 0 or pos1[1] >= img_h or pos2[0] <= 0 or pos2[0] >= img_w or pos2[1] <= 0 or pos2[1] >= img_h or kpts[sk[0], 2] < kpt_score_thr or kpts[sk[1], 2] < kpt_score_thr or pose_link_color[sk_id] is None): # skip the link that should not be drawn continue color = tuple(int(c) for c in pose_link_color[sk_id]) cv2.line(img, pos1, pos2, color, thickness=thickness) return img def load_model_from_config(config, ckpt, verbose=False): print(f"Loading model from {ckpt}") pl_sd = torch.load(ckpt, map_location="cpu") if "global_step" in pl_sd: print(f"Global Step: {pl_sd['global_step']}") if "state_dict" in pl_sd: sd = pl_sd["state_dict"] else: sd = pl_sd model = instantiate_from_config(config.model) _, _ = model.load_state_dict(sd, strict=False) model.cuda() model.eval() return model class Model_all: def __init__(self, device='cpu'): # common part self.device = device self.config = OmegaConf.load("configs/stable-diffusion/app.yaml") self.config.model.params.cond_stage_config.params.device = device self.base_model = load_model_from_config(self.config, "models/sd-v1-4.ckpt").to(device) self.current_base = 'sd-v1-4.ckpt' self.sampler = PLMSSampler(self.base_model) # sketch part self.model_sketch = Adapter(channels=[320, 640, 1280, 1280][:4], nums_rb=2, ksize=1, sk=True, use_conv=False).to(device) self.model_sketch.load_state_dict(torch.load("models/t2iadapter_sketch_sd14v1.pth", map_location=device)) self.model_edge = pidinet().to(device) self.model_edge.load_state_dict({k.replace('module.', ''): v for k, v in torch.load('models/table5_pidinet.pth', map_location=device)[ 'state_dict'].items()}) # segmentation part self.model_seger = seger().to(device) self.model_seger.eval() self.coler = Colorize(n=182) self.model_seg = Adapter(cin=int(3 * 64), channels=[320, 640, 1280, 1280][:4], nums_rb=2, ksize=1, sk=True, use_conv=False).to(device) self.model_seg.load_state_dict(torch.load("models/t2iadapter_seg_sd14v1.pth", map_location=device)) # depth part self.depth_model = MiDaSInference(model_type='dpt_hybrid').to(device) self.model_depth = Adapter(cin=3 * 64, channels=[320, 640, 1280, 1280][:4], nums_rb=2, ksize=1, sk=True, use_conv=False).to(device) self.model_depth.load_state_dict(torch.load("models/t2iadapter_depth_sd14v1.pth", map_location=device)) # keypose part self.model_pose = Adapter(cin=int(3 * 64), channels=[320, 640, 1280, 1280][:4], nums_rb=2, ksize=1, sk=True, use_conv=False).to(device) self.model_pose.load_state_dict(torch.load("models/t2iadapter_keypose_sd14v1.pth", map_location=device)) # openpose part self.model_openpose = Adapter(cin=int(3 * 64), channels=[320, 640, 1280, 1280][:4], nums_rb=2, ksize=1, sk=True, use_conv=False).to(device) self.model_openpose.load_state_dict(torch.load("models/t2iadapter_openpose_sd14v1.pth", map_location=device)) # color part self.model_color = Adapter_light(cin=int(3 * 64), channels=[320, 640, 1280, 1280], nums_rb=4).to(device) self.model_color.load_state_dict(torch.load("models/t2iadapter_color_sd14v1_small.pth", map_location=device)) # style part self.model_style = StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8).to(device) self.model_style.load_state_dict(torch.load("models/t2iadapter_style_sd14v1.pth", map_location=device)) self.clip_processor = CLIPProcessor.from_pretrained('openai/clip-vit-large-patch14') self.clip_vision_model = CLIPVisionModel.from_pretrained('openai/clip-vit-large-patch14').to(device) device = 'cpu' ## mmpose det_config = 'models/faster_rcnn_r50_fpn_coco.py' det_checkpoint = 'models/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth' pose_config = 'models/hrnet_w48_coco_256x192.py' pose_checkpoint = 'models/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth' self.det_cat_id = 1 self.bbox_thr = 0.2 ## detector det_config_mmcv = mmcv.Config.fromfile(det_config) self.det_model = init_detector(det_config_mmcv, det_checkpoint, device=device) pose_config_mmcv = mmcv.Config.fromfile(pose_config) self.pose_model = init_pose_model(pose_config_mmcv, pose_checkpoint, device=device) ## color self.skeleton = [[15, 13], [13, 11], [16, 14], [14, 12], [11, 12], [5, 11], [6, 12], [5, 6], [5, 7], [6, 8], [7, 9], [8, 10], [1, 2], [0, 1], [0, 2], [1, 3], [2, 4], [3, 5], [4, 6]] self.pose_kpt_color = [[51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255], [0, 255, 0], [255, 128, 0], [0, 255, 0], [255, 128, 0], [0, 255, 0], [255, 128, 0], [0, 255, 0], [255, 128, 0], [0, 255, 0], [255, 128, 0], [0, 255, 0], [255, 128, 0]] self.pose_link_color = [[0, 255, 0], [0, 255, 0], [255, 128, 0], [255, 128, 0], [51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255], [0, 255, 0], [255, 128, 0], [0, 255, 0], [255, 128, 0], [51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255]] def load_vae(self): vae_sd = torch.load(os.path.join('models', 'anything-v4.0.vae.pt'), map_location="cuda") sd = vae_sd["state_dict"] self.base_model.first_stage_model.load_state_dict(sd, strict=False) @torch.no_grad() def process_sketch(self, input_img, type_in, color_back, prompt, neg_prompt, pos_prompt, fix_sample, scale, con_strength, base_model): if self.current_base != base_model: ckpt = os.path.join("models", base_model) pl_sd = torch.load(ckpt, map_location="cuda") if "state_dict" in pl_sd: sd = pl_sd["state_dict"] else: sd = pl_sd self.base_model.load_state_dict(sd, strict=False) self.current_base = base_model if 'anything' in base_model.lower(): self.load_vae() con_strength = int((1 - con_strength) * 50) if fix_sample == 'True': seed_everything(42) im = cv2.resize(input_img, (512, 512)) if type_in == 'Sketch': if color_back == 'White': im = 255 - im im_edge = im.copy() im = img2tensor(im)[0].unsqueeze(0).unsqueeze(0) / 255. im = im > 0.5 im = im.float() elif type_in == 'Image': im = img2tensor(im).unsqueeze(0) / 255. im = self.model_edge(im.to(self.device))[-1] im = im > 0.5 im = im.float() im_edge = tensor2img(im) # extract condition features c = self.base_model.get_learned_conditioning([prompt + ', ' + pos_prompt]) nc = self.base_model.get_learned_conditioning([neg_prompt]) features_adapter = self.model_sketch(im.to(self.device)) shape = [4, 64, 64] # sampling samples_ddim, _ = self.sampler.sample(S=50, conditioning=c, batch_size=1, shape=shape, verbose=False, unconditional_guidance_scale=scale, unconditional_conditioning=nc, eta=0.0, x_T=None, features_adapter1=features_adapter, mode='sketch', con_strength=con_strength) x_samples_ddim = self.base_model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) x_samples_ddim = x_samples_ddim.to('cpu') x_samples_ddim = x_samples_ddim.permute(0, 2, 3, 1).numpy()[0] x_samples_ddim = 255. * x_samples_ddim x_samples_ddim = x_samples_ddim.astype(np.uint8) return [im_edge, x_samples_ddim] @torch.no_grad() def process_color_sketch(self, input_img_sketch, input_img_color, type_in, type_in_color, w_sketch, w_color, color_back, prompt, neg_prompt, pos_prompt, fix_sample, scale, con_strength, base_model): if self.current_base != base_model: ckpt = os.path.join("models", base_model) pl_sd = torch.load(ckpt, map_location="cuda") if "state_dict" in pl_sd: sd = pl_sd["state_dict"] else: sd = pl_sd self.base_model.load_state_dict(sd, strict=False) self.current_base = base_model if 'anything' in base_model.lower(): self.load_vae() con_strength = int((1 - con_strength) * 50) if fix_sample == 'True': seed_everything(42) im = cv2.resize(input_img_sketch, (512, 512)) if type_in == 'Sketch': if color_back == 'White': im = 255 - im im_edge = im.copy() im = img2tensor(im)[0].unsqueeze(0).unsqueeze(0) / 255. im = im > 0.5 im = im.float() elif type_in == 'Image': im = img2tensor(im).unsqueeze(0) / 255. im = self.model_edge(im.to(self.device))[-1]#.cuda() im = im > 0.5 im = im.float() im_edge = tensor2img(im) if type_in_color == 'Image': input_img_color = cv2.resize(input_img_color,(512//64, 512//64), interpolation=cv2.INTER_CUBIC) input_img_color = cv2.resize(input_img_color,(512,512), interpolation=cv2.INTER_NEAREST) else: input_img_color = cv2.resize(input_img_color, (512, 512)) im_color = input_img_color.copy() im_color_tensor = img2tensor(input_img_color, bgr2rgb=False).unsqueeze(0) / 255. # extract condition features c = self.base_model.get_learned_conditioning([prompt + ', ' + pos_prompt]) nc = self.base_model.get_learned_conditioning([neg_prompt]) features_adapter_sketch = self.model_sketch(im.to(self.device)) features_adapter_color = self.model_color(im_color_tensor.to(self.device)) features_adapter = [fs*w_sketch+fc*w_color for fs, fc in zip(features_adapter_sketch,features_adapter_color)] shape = [4, 64, 64] # sampling samples_ddim, _ = self.sampler.sample(S=50, conditioning=c, batch_size=1, shape=shape, verbose=False, unconditional_guidance_scale=scale, unconditional_conditioning=nc, eta=0.0, x_T=None, features_adapter1=features_adapter, mode='sketch', con_strength=con_strength) x_samples_ddim = self.base_model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) x_samples_ddim = x_samples_ddim.to('cpu') x_samples_ddim = x_samples_ddim.permute(0, 2, 3, 1).numpy()[0] x_samples_ddim = 255. * x_samples_ddim x_samples_ddim = x_samples_ddim.astype(np.uint8) return [im_edge, im_color, x_samples_ddim] @torch.no_grad() def process_style_sketch(self, input_img_sketch, input_img_style, type_in, color_back, prompt, neg_prompt, pos_prompt, fix_sample, scale, con_strength, base_model): if self.current_base != base_model: ckpt = os.path.join("models", base_model) pl_sd = torch.load(ckpt, map_location="cuda") if "state_dict" in pl_sd: sd = pl_sd["state_dict"] else: sd = pl_sd self.base_model.load_state_dict(sd, strict=False) self.current_base = base_model if 'anything' in base_model.lower(): self.load_vae() con_strength = int((1 - con_strength) * 50) if fix_sample == 'True': seed_everything(42) im = cv2.resize(input_img_sketch, (512, 512)) if type_in == 'Sketch': if color_back == 'White': im = 255 - im im_edge = im.copy() im = img2tensor(im)[0].unsqueeze(0).unsqueeze(0) / 255. im = im > 0.5 im = im.float() elif type_in == 'Image': im = img2tensor(im).unsqueeze(0) / 255. im = self.model_edge(im.to(self.device))[-1]#.cuda() im = im > 0.5 im = im.float() im_edge = tensor2img(im) style = Image.fromarray(input_img_style) style_for_clip = self.clip_processor(images=style, return_tensors="pt")['pixel_values'] style_feat = self.clip_vision_model(style_for_clip.to(self.device))['last_hidden_state'] style_feat = self.model_style(style_feat) # extract condition features c = self.base_model.get_learned_conditioning([prompt + ', ' + pos_prompt]) nc = self.base_model.get_learned_conditioning([neg_prompt]) features_adapter = self.model_sketch(im.to(self.device)) shape = [4, 64, 64] # sampling samples_ddim, _ = self.sampler.sample(S=50, conditioning=c, batch_size=1, shape=shape, verbose=False, unconditional_guidance_scale=scale, unconditional_conditioning=nc, eta=0.0, x_T=None, features_adapter1=features_adapter, mode='style', con_strength=con_strength, style_feature=style_feat) x_samples_ddim = self.base_model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) x_samples_ddim = x_samples_ddim.to('cpu') x_samples_ddim = x_samples_ddim.permute(0, 2, 3, 1).numpy()[0] x_samples_ddim = 255. * x_samples_ddim x_samples_ddim = x_samples_ddim.astype(np.uint8) return [im_edge, x_samples_ddim] @torch.no_grad() def process_color(self, input_img, prompt, neg_prompt, pos_prompt, w_color, type_in_color, fix_sample, scale, con_strength, base_model): if self.current_base != base_model: ckpt = os.path.join("models", base_model) pl_sd = torch.load(ckpt, map_location="cuda") if "state_dict" in pl_sd: sd = pl_sd["state_dict"] else: sd = pl_sd self.base_model.load_state_dict(sd, strict=False) self.current_base = base_model if 'anything' in base_model.lower(): self.load_vae() con_strength = int((1 - con_strength) * 50) if fix_sample == 'True': seed_everything(42) if type_in_color == 'Image': input_img = cv2.resize(input_img,(512//64, 512//64), interpolation=cv2.INTER_CUBIC) input_img = cv2.resize(input_img,(512,512), interpolation=cv2.INTER_NEAREST) else: input_img = cv2.resize(input_img, (512, 512)) im_color = input_img.copy() im = img2tensor(input_img, bgr2rgb=False).unsqueeze(0) / 255. # extract condition features c = self.base_model.get_learned_conditioning([prompt + ', ' + pos_prompt]) nc = self.base_model.get_learned_conditioning([neg_prompt]) features_adapter = self.model_color(im.to(self.device)) features_adapter = [fi*w_color for fi in features_adapter] shape = [4, 64, 64] # sampling samples_ddim, _ = self.sampler.sample(S=50, conditioning=c, batch_size=1, shape=shape, verbose=False, unconditional_guidance_scale=scale, unconditional_conditioning=nc, eta=0.0, x_T=None, features_adapter1=features_adapter, mode='sketch', con_strength=con_strength) x_samples_ddim = self.base_model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) x_samples_ddim = x_samples_ddim.to('cpu') x_samples_ddim = x_samples_ddim.permute(0, 2, 3, 1).numpy()[0] x_samples_ddim = 255. * x_samples_ddim x_samples_ddim = x_samples_ddim.astype(np.uint8) return [im_color, x_samples_ddim] @torch.no_grad() def process_depth(self, input_img, type_in, prompt, neg_prompt, pos_prompt, fix_sample, scale, con_strength, base_model): if self.current_base != base_model: ckpt = os.path.join("models", base_model) pl_sd = torch.load(ckpt, map_location="cuda") if "state_dict" in pl_sd: sd = pl_sd["state_dict"] else: sd = pl_sd self.base_model.load_state_dict(sd, strict=False) self.current_base = base_model if 'anything' in base_model.lower(): self.load_vae() con_strength = int((1 - con_strength) * 50) if fix_sample == 'True': seed_everything(42) im = cv2.resize(input_img, (512, 512)) if type_in == 'Depth': im_depth = im.copy() depth = img2tensor(im).unsqueeze(0) / 255. elif type_in == 'Image': im = img2tensor(im).unsqueeze(0) / 127.5 - 1.0 depth = self.depth_model(im.to(self.device)).repeat(1, 3, 1, 1) depth -= torch.min(depth) depth /= torch.max(depth) im_depth = tensor2img(depth) # extract condition features c = self.base_model.get_learned_conditioning([prompt + ', ' + pos_prompt]) nc = self.base_model.get_learned_conditioning([neg_prompt]) features_adapter = self.model_depth(depth.to(self.device)) shape = [4, 64, 64] # sampling samples_ddim, _ = self.sampler.sample(S=50, conditioning=c, batch_size=1, shape=shape, verbose=False, unconditional_guidance_scale=scale, unconditional_conditioning=nc, eta=0.0, x_T=None, features_adapter1=features_adapter, mode='sketch', con_strength=con_strength) x_samples_ddim = self.base_model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) x_samples_ddim = x_samples_ddim.to('cpu') x_samples_ddim = x_samples_ddim.permute(0, 2, 3, 1).numpy()[0] x_samples_ddim = 255. * x_samples_ddim x_samples_ddim = x_samples_ddim.astype(np.uint8) return [im_depth, x_samples_ddim] @torch.no_grad() def process_depth_keypose(self, input_img_depth, input_img_keypose, type_in_depth, type_in_keypose, w_depth, w_keypose, prompt, neg_prompt, pos_prompt, fix_sample, scale, con_strength, base_model): if self.current_base != base_model: ckpt = os.path.join("models", base_model) pl_sd = torch.load(ckpt, map_location="cuda") if "state_dict" in pl_sd: sd = pl_sd["state_dict"] else: sd = pl_sd self.base_model.load_state_dict(sd, strict=False) self.current_base = base_model if 'anything' in base_model.lower(): self.load_vae() if fix_sample == 'True': seed_everything(42) im_depth = cv2.resize(input_img_depth, (512, 512)) im_keypose = cv2.resize(input_img_keypose, (512, 512)) # get depth if type_in_depth == 'Depth': im_depth_out = im_depth.copy() depth = img2tensor(im_depth).unsqueeze(0) / 255. elif type_in_depth == 'Image': im_depth = img2tensor(im_depth).unsqueeze(0) / 127.5 - 1.0 depth = self.depth_model(im_depth.to(self.device)).repeat(1, 3, 1, 1) depth -= torch.min(depth) depth /= torch.max(depth) im_depth_out = tensor2img(depth) # get keypose if type_in_keypose == 'Keypose': im_keypose_out = im_keypose.copy()[:,:,::-1] elif type_in_keypose == 'Image': image = im_keypose.copy() im_keypose = img2tensor(im_keypose).unsqueeze(0) / 255. mmdet_results = inference_detector(self.det_model, image) # keep the person class bounding boxes. person_results = process_mmdet_results(mmdet_results, self.det_cat_id) # optional return_heatmap = False dataset = self.pose_model.cfg.data['test']['type'] # e.g. use ('backbone', ) to return backbone feature output_layer_names = None pose_results, _ = inference_top_down_pose_model( self.pose_model, image, person_results, bbox_thr=self.bbox_thr, format='xyxy', dataset=dataset, dataset_info=None, return_heatmap=return_heatmap, outputs=output_layer_names) # show the results im_keypose_out = imshow_keypoints( image, pose_results, skeleton=self.skeleton, pose_kpt_color=self.pose_kpt_color, pose_link_color=self.pose_link_color, radius=2, thickness=2) im_keypose_out = im_keypose_out.astype(np.uint8) # extract condition features c = self.base_model.get_learned_conditioning([prompt + ', ' + pos_prompt]) nc = self.base_model.get_learned_conditioning([neg_prompt]) features_adapter_depth = self.model_depth(depth.to(self.device)) pose = img2tensor(im_keypose_out, bgr2rgb=True, float32=True) / 255. pose = pose.unsqueeze(0) features_adapter_keypose = self.model_pose(pose.to(self.device)) features_adapter = [f_d * w_depth + f_k * w_keypose for f_d, f_k in zip(features_adapter_depth, features_adapter_keypose)] shape = [4, 64, 64] # sampling con_strength = int((1 - con_strength) * 50) samples_ddim, _ = self.sampler.sample(S=50, conditioning=c, batch_size=1, shape=shape, verbose=False, unconditional_guidance_scale=scale, unconditional_conditioning=nc, eta=0.0, x_T=None, features_adapter1=features_adapter, mode='sketch', con_strength=con_strength) x_samples_ddim = self.base_model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) x_samples_ddim = x_samples_ddim.to('cpu') x_samples_ddim = x_samples_ddim.permute(0, 2, 3, 1).numpy()[0] x_samples_ddim = 255. * x_samples_ddim x_samples_ddim = x_samples_ddim.astype(np.uint8) return [im_depth_out, im_keypose_out[:, :, ::-1], x_samples_ddim] @torch.no_grad() def process_seg(self, input_img, type_in, prompt, neg_prompt, pos_prompt, fix_sample, scale, con_strength, base_model): if self.current_base != base_model: ckpt = os.path.join("models", base_model) pl_sd = torch.load(ckpt, map_location="cuda") if "state_dict" in pl_sd: sd = pl_sd["state_dict"] else: sd = pl_sd self.base_model.load_state_dict(sd, strict=False) self.current_base = base_model if 'anything' in base_model.lower(): self.load_vae() con_strength = int((1 - con_strength) * 50) if fix_sample == 'True': seed_everything(42) im = cv2.resize(input_img, (512, 512)) if type_in == 'Segmentation': im_seg = im.copy() im = img2tensor(im).unsqueeze(0) / 255. labelmap = im.float() elif type_in == 'Image': im, _ = preprocessing(im, self.device) _, _, H, W = im.shape # Image -> Probability map logits = self.model_seger(im) logits = F.interpolate(logits, size=(H, W), mode="bilinear", align_corners=False) probs = F.softmax(logits, dim=1)[0] probs = probs.cpu().data.numpy() labelmap = np.argmax(probs, axis=0) labelmap = self.coler(labelmap) labelmap = np.transpose(labelmap, (1, 2, 0)) labelmap = cv2.resize(labelmap, (512, 512)) labelmap = img2tensor(labelmap, bgr2rgb=False, float32=True) / 255. im_seg = tensor2img(labelmap)[:, :, ::-1] labelmap = labelmap.unsqueeze(0) # extract condition features c = self.base_model.get_learned_conditioning([prompt + ', ' + pos_prompt]) nc = self.base_model.get_learned_conditioning([neg_prompt]) features_adapter = self.model_seg(labelmap.to(self.device)) shape = [4, 64, 64] # sampling samples_ddim, _ = self.sampler.sample(S=50, conditioning=c, batch_size=1, shape=shape, verbose=False, unconditional_guidance_scale=scale, unconditional_conditioning=nc, eta=0.0, x_T=None, features_adapter1=features_adapter, mode='sketch', con_strength=con_strength) x_samples_ddim = self.base_model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) x_samples_ddim = x_samples_ddim.to('cpu') x_samples_ddim = x_samples_ddim.permute(0, 2, 3, 1).numpy()[0] x_samples_ddim = 255. * x_samples_ddim x_samples_ddim = x_samples_ddim.astype(np.uint8) return [im_seg, x_samples_ddim] @torch.no_grad() def process_draw(self, input_img, prompt, neg_prompt, pos_prompt, fix_sample, scale, con_strength, base_model): if self.current_base != base_model: ckpt = os.path.join("models", base_model) pl_sd = torch.load(ckpt, map_location="cuda") if "state_dict" in pl_sd: sd = pl_sd["state_dict"] else: sd = pl_sd self.base_model.load_state_dict(sd, strict=False) self.current_base = base_model if 'anything' in base_model.lower(): self.load_vae() con_strength = int((1 - con_strength) * 50) if fix_sample == 'True': seed_everything(42) input_img = input_img['mask'] c = input_img[:, :, 0:3].astype(np.float32) a = input_img[:, :, 3:4].astype(np.float32) / 255.0 im = c * a + 255.0 * (1.0 - a) im = im.clip(0, 255).astype(np.uint8) im = cv2.resize(im, (512, 512)) im_edge = im.copy() im = img2tensor(im)[0].unsqueeze(0).unsqueeze(0) / 255. im = im > 0.5 im = im.float() # extract condition features c = self.base_model.get_learned_conditioning([prompt + ', ' + pos_prompt]) nc = self.base_model.get_learned_conditioning([neg_prompt]) features_adapter = self.model_sketch(im.to(self.device)) shape = [4, 64, 64] # sampling samples_ddim, _ = self.sampler.sample(S=50, conditioning=c, batch_size=1, shape=shape, verbose=False, unconditional_guidance_scale=scale, unconditional_conditioning=nc, eta=0.0, x_T=None, features_adapter1=features_adapter, mode='sketch', con_strength=con_strength) x_samples_ddim = self.base_model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) x_samples_ddim = x_samples_ddim.to('cpu') x_samples_ddim = x_samples_ddim.permute(0, 2, 3, 1).numpy()[0] x_samples_ddim = 255. * x_samples_ddim x_samples_ddim = x_samples_ddim.astype(np.uint8) return [im_edge, x_samples_ddim] @torch.no_grad() def process_keypose(self, input_img, type_in, prompt, neg_prompt, pos_prompt, fix_sample, scale, con_strength, base_model): if self.current_base != base_model: ckpt = os.path.join("models", base_model) pl_sd = torch.load(ckpt, map_location="cuda") if "state_dict" in pl_sd: sd = pl_sd["state_dict"] else: sd = pl_sd self.base_model.load_state_dict(sd, strict=False) self.current_base = base_model if 'anything' in base_model.lower(): self.load_vae() con_strength = int((1 - con_strength) * 50) if fix_sample == 'True': seed_everything(42) im = cv2.resize(input_img, (512, 512)) if type_in == 'Keypose': im_pose = im.copy()[:,:,::-1] elif type_in == 'Image': image = im.copy() im = img2tensor(im).unsqueeze(0) / 255. mmdet_results = inference_detector(self.det_model, image) # keep the person class bounding boxes. person_results = process_mmdet_results(mmdet_results, self.det_cat_id) # optional return_heatmap = False dataset = self.pose_model.cfg.data['test']['type'] # e.g. use ('backbone', ) to return backbone feature output_layer_names = None pose_results, _ = inference_top_down_pose_model( self.pose_model, image, person_results, bbox_thr=self.bbox_thr, format='xyxy', dataset=dataset, dataset_info=None, return_heatmap=return_heatmap, outputs=output_layer_names) # show the results im_pose = imshow_keypoints( image, pose_results, skeleton=self.skeleton, pose_kpt_color=self.pose_kpt_color, pose_link_color=self.pose_link_color, radius=2, thickness=2) # im_pose = cv2.resize(im_pose, (512, 512)) # extract condition features c = self.base_model.get_learned_conditioning([prompt + ', ' + pos_prompt]) nc = self.base_model.get_learned_conditioning([neg_prompt]) pose = img2tensor(im_pose, bgr2rgb=True, float32=True) / 255. pose = pose.unsqueeze(0) features_adapter = self.model_pose(pose.to(self.device)) shape = [4, 64, 64] # sampling samples_ddim, _ = self.sampler.sample(S=50, conditioning=c, batch_size=1, shape=shape, verbose=False, unconditional_guidance_scale=scale, unconditional_conditioning=nc, eta=0.0, x_T=None, features_adapter1=features_adapter, mode='sketch', con_strength=con_strength) x_samples_ddim = self.base_model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) x_samples_ddim = x_samples_ddim.to('cpu') x_samples_ddim = x_samples_ddim.permute(0, 2, 3, 1).numpy()[0] x_samples_ddim = 255. * x_samples_ddim x_samples_ddim = x_samples_ddim.astype(np.uint8) return [im_pose[:, :, ::-1].astype(np.uint8), x_samples_ddim] @torch.no_grad() def process_openpose(self, input_img, type_in, prompt, neg_prompt, pos_prompt, fix_sample, scale, con_strength, base_model): if self.current_base != base_model: ckpt = os.path.join("models", base_model) pl_sd = torch.load(ckpt, map_location="cuda") if "state_dict" in pl_sd: sd = pl_sd["state_dict"] else: sd = pl_sd self.base_model.load_state_dict(sd, strict=False) self.current_base = base_model if 'anything' in base_model.lower(): self.load_vae() con_strength = int((1 - con_strength) * 50) if fix_sample == 'True': seed_everything(42) im = cv2.resize(input_img, (512, 512)) if type_in == 'Openpose': im_pose = im.copy()[:,:,::-1] elif type_in == 'Image': from ldm.modules.structure_condition.openpose.api import OpenposeInference model = OpenposeInference() keypose = model(im) im_pose = keypose.copy()[:,:,::-1] # keypose = img2tensor(keypose).unsqueeze(0) / 255. # extract condition features c = self.base_model.get_learned_conditioning([prompt + ', ' + pos_prompt]) nc = self.base_model.get_learned_conditioning([neg_prompt]) pose = img2tensor(im_pose, bgr2rgb=True, float32=True) / 255. pose = pose.unsqueeze(0) features_adapter = self.model_openpose(pose.to(self.device)) shape = [4, 64, 64] # sampling samples_ddim, _ = self.sampler.sample(S=50, conditioning=c, batch_size=1, shape=shape, verbose=False, unconditional_guidance_scale=scale, unconditional_conditioning=nc, eta=0.0, x_T=None, features_adapter1=features_adapter, mode='sketch', con_strength=con_strength) x_samples_ddim = self.base_model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) x_samples_ddim = x_samples_ddim.to('cpu') x_samples_ddim = x_samples_ddim.permute(0, 2, 3, 1).numpy()[0] x_samples_ddim = 255. * x_samples_ddim x_samples_ddim = x_samples_ddim.astype(np.uint8) return [im_pose[:, :, ::-1].astype(np.uint8), x_samples_ddim] if __name__ == '__main__': model = Model_all('cpu')