Spaces:
Runtime error
Runtime error
File size: 10,400 Bytes
b3478e4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 |
from enum import Enum, unique
import cv2
import torch
from basicsr.utils import img2tensor
from ldm.util import resize_numpy_image
from PIL import Image
from torch import autocast
@unique
class ExtraCondition(Enum):
sketch = 0
keypose = 1
seg = 2
depth = 3
canny = 4
style = 5
color = 6
openpose = 7
def get_cond_model(opt, cond_type: ExtraCondition):
if cond_type == ExtraCondition.sketch:
from ldm.modules.extra_condition.model_edge import pidinet
model = pidinet()
ckp = torch.load('models/table5_pidinet.pth', map_location='cpu')['state_dict']
model.load_state_dict({k.replace('module.', ''): v for k, v in ckp.items()}, strict=True)
model.to(opt.device)
return model
elif cond_type == ExtraCondition.seg:
raise NotImplementedError
elif cond_type == ExtraCondition.keypose:
import mmcv
from mmdet.apis import init_detector
from mmpose.apis import init_pose_model
det_config = 'configs/mm/faster_rcnn_r50_fpn_coco.py'
det_checkpoint = 'models/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
pose_config = 'configs/mm/hrnet_w48_coco_256x192.py'
pose_checkpoint = 'models/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth'
det_config_mmcv = mmcv.Config.fromfile(det_config)
det_model = init_detector(det_config_mmcv, det_checkpoint, device=opt.device)
pose_config_mmcv = mmcv.Config.fromfile(pose_config)
pose_model = init_pose_model(pose_config_mmcv, pose_checkpoint, device=opt.device)
return {'pose_model': pose_model, 'det_model': det_model}
elif cond_type == ExtraCondition.depth:
from ldm.modules.extra_condition.midas.api import MiDaSInference
model = MiDaSInference(model_type='dpt_hybrid').to(opt.device)
return model
elif cond_type == ExtraCondition.canny:
return None
elif cond_type == ExtraCondition.style:
from transformers import CLIPProcessor, CLIPVisionModel
version = 'openai/clip-vit-large-patch14'
processor = CLIPProcessor.from_pretrained(version)
clip_vision_model = CLIPVisionModel.from_pretrained(version).to(opt.device)
return {'processor': processor, 'clip_vision_model': clip_vision_model}
elif cond_type == ExtraCondition.color:
return None
elif cond_type == ExtraCondition.openpose:
from ldm.modules.extra_condition.openpose.api import OpenposeInference
model = OpenposeInference().to(opt.device)
return model
else:
raise NotImplementedError
def get_cond_sketch(opt, cond_image, cond_inp_type, cond_model=None):
if isinstance(cond_image, str):
edge = cv2.imread(cond_image)
else:
# for gradio input, pay attention, it's rgb numpy
edge = cv2.cvtColor(cond_image, cv2.COLOR_RGB2BGR)
edge = resize_numpy_image(edge, max_resolution=opt.max_resolution, resize_short_edge=opt.resize_short_edge)
opt.H, opt.W = edge.shape[:2]
if cond_inp_type == 'sketch':
edge = img2tensor(edge)[0].unsqueeze(0).unsqueeze(0) / 255.
edge = edge.to(opt.device)
elif cond_inp_type == 'image':
edge = img2tensor(edge).unsqueeze(0) / 255.
edge = cond_model(edge.to(opt.device))[-1]
else:
raise NotImplementedError
# edge = 1-edge # for white background
edge = edge > 0.5
edge = edge.float()
return edge
def get_cond_seg(opt, cond_image, cond_inp_type='image', cond_model=None):
if isinstance(cond_image, str):
seg = cv2.imread(cond_image)
else:
seg = cv2.cvtColor(cond_image, cv2.COLOR_RGB2BGR)
seg = resize_numpy_image(seg, max_resolution=opt.max_resolution, resize_short_edge=opt.resize_short_edge)
opt.H, opt.W = seg.shape[:2]
if cond_inp_type == 'seg':
seg = img2tensor(seg).unsqueeze(0) / 255.
seg = seg.to(opt.device)
else:
raise NotImplementedError
return seg
def get_cond_keypose(opt, cond_image, cond_inp_type='image', cond_model=None):
if isinstance(cond_image, str):
pose = cv2.imread(cond_image)
else:
pose = cv2.cvtColor(cond_image, cv2.COLOR_RGB2BGR)
pose = resize_numpy_image(pose, max_resolution=opt.max_resolution, resize_short_edge=opt.resize_short_edge)
opt.H, opt.W = pose.shape[:2]
if cond_inp_type == 'keypose':
pose = img2tensor(pose).unsqueeze(0) / 255.
pose = pose.to(opt.device)
elif cond_inp_type == 'image':
from ldm.modules.extra_condition.utils import imshow_keypoints
from mmdet.apis import inference_detector
from mmpose.apis import (inference_top_down_pose_model, process_mmdet_results)
# mmpose seems not compatible with autocast fp16
with autocast("cuda", dtype=torch.float32):
mmdet_results = inference_detector(cond_model['det_model'], pose)
# keep the person class bounding boxes.
person_results = process_mmdet_results(mmdet_results, 1)
# optional
return_heatmap = False
dataset = cond_model['pose_model'].cfg.data['test']['type']
# e.g. use ('backbone', ) to return backbone feature
output_layer_names = None
pose_results, returned_outputs = inference_top_down_pose_model(
cond_model['pose_model'],
pose,
person_results,
bbox_thr=0.2,
format='xyxy',
dataset=dataset,
dataset_info=None,
return_heatmap=return_heatmap,
outputs=output_layer_names)
# show the results
pose = imshow_keypoints(pose, pose_results, radius=2, thickness=2)
pose = img2tensor(pose).unsqueeze(0) / 255.
pose = pose.to(opt.device)
else:
raise NotImplementedError
return pose
def get_cond_depth(opt, cond_image, cond_inp_type='image', cond_model=None):
if isinstance(cond_image, str):
depth = cv2.imread(cond_image)
else:
depth = cv2.cvtColor(cond_image, cv2.COLOR_RGB2BGR)
depth = resize_numpy_image(depth, max_resolution=opt.max_resolution, resize_short_edge=opt.resize_short_edge)
opt.H, opt.W = depth.shape[:2]
if cond_inp_type == 'depth':
depth = img2tensor(depth).unsqueeze(0) / 255.
depth = depth.to(opt.device)
elif cond_inp_type == 'image':
depth = img2tensor(depth).unsqueeze(0) / 127.5 - 1.0
depth = cond_model(depth.to(opt.device)).repeat(1, 3, 1, 1)
depth -= torch.min(depth)
depth /= torch.max(depth)
else:
raise NotImplementedError
return depth
def get_cond_canny(opt, cond_image, cond_inp_type='image', cond_model=None):
if isinstance(cond_image, str):
canny = cv2.imread(cond_image)
else:
canny = cv2.cvtColor(cond_image, cv2.COLOR_RGB2BGR)
canny = resize_numpy_image(canny, max_resolution=opt.max_resolution, resize_short_edge=opt.resize_short_edge)
opt.H, opt.W = canny.shape[:2]
if cond_inp_type == 'canny':
canny = img2tensor(canny)[0:1].unsqueeze(0) / 255.
canny = canny.to(opt.device)
elif cond_inp_type == 'image':
canny = cv2.Canny(canny, 100, 200)[..., None]
canny = img2tensor(canny).unsqueeze(0) / 255.
canny = canny.to(opt.device)
else:
raise NotImplementedError
return canny
def get_cond_style(opt, cond_image, cond_inp_type='image', cond_model=None):
assert cond_inp_type == 'image'
if isinstance(cond_image, str):
style = Image.open(cond_image)
else:
# numpy image to PIL image
style = Image.fromarray(cond_image)
style_for_clip = cond_model['processor'](images=style, return_tensors="pt")['pixel_values']
style_feat = cond_model['clip_vision_model'](style_for_clip.to(opt.device))['last_hidden_state']
return style_feat
def get_cond_color(opt, cond_image, cond_inp_type='image', cond_model=None):
if isinstance(cond_image, str):
color = cv2.imread(cond_image)
else:
color = cv2.cvtColor(cond_image, cv2.COLOR_RGB2BGR)
color = resize_numpy_image(color, max_resolution=opt.max_resolution, resize_short_edge=opt.resize_short_edge)
opt.H, opt.W = color.shape[:2]
if cond_inp_type == 'image':
color = cv2.resize(color, (opt.W//64, opt.H//64), interpolation=cv2.INTER_CUBIC)
color = cv2.resize(color, (opt.W, opt.H), interpolation=cv2.INTER_NEAREST)
color = img2tensor(color).unsqueeze(0) / 255.
color = color.to(opt.device)
return color
def get_cond_openpose(opt, cond_image, cond_inp_type='image', cond_model=None):
if isinstance(cond_image, str):
openpose_keypose = cv2.imread(cond_image)
else:
openpose_keypose = cv2.cvtColor(cond_image, cv2.COLOR_RGB2BGR)
openpose_keypose = resize_numpy_image(
openpose_keypose, max_resolution=opt.max_resolution, resize_short_edge=opt.resize_short_edge)
opt.H, opt.W = openpose_keypose.shape[:2]
if cond_inp_type == 'openpose':
openpose_keypose = img2tensor(openpose_keypose).unsqueeze(0) / 255.
openpose_keypose = openpose_keypose.to(opt.device)
elif cond_inp_type == 'image':
with autocast('cuda', dtype=torch.float32):
openpose_keypose = cond_model(openpose_keypose)
openpose_keypose = img2tensor(openpose_keypose).unsqueeze(0) / 255.
openpose_keypose = openpose_keypose.to(opt.device)
else:
raise NotImplementedError
return openpose_keypose
def get_adapter_feature(inputs, adapters):
ret_feat_map = None
ret_feat_seq = None
if not isinstance(inputs, list):
inputs = [inputs]
adapters = [adapters]
for input, adapter in zip(inputs, adapters):
cur_feature = adapter['model'](input)
if isinstance(cur_feature, list):
if ret_feat_map is None:
ret_feat_map = list(map(lambda x: x * adapter['cond_weight'], cur_feature))
else:
ret_feat_map = list(map(lambda x, y: x + y * adapter['cond_weight'], ret_feat_map, cur_feature))
else:
if ret_feat_seq is None:
ret_feat_seq = cur_feature
else:
ret_feat_seq = torch.cat([ret_feat_seq, cur_feature], dim=1)
return ret_feat_map, ret_feat_seq
|