Spaces:
Running
Running
File size: 18,428 Bytes
02ca424 f6fbe2a 76c374a 02ca424 76c374a 02ca424 76c374a 02ca424 76c374a 02ca424 f6fbe2a 02ca424 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "237f5cbf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model already downloaded.\n"
]
}
],
"source": [
"# !wget https://huggingface.co/sd-legacy/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt\n",
"\n",
"# check if the model is downloaded, if not download it\n",
"import os\n",
"if not os.path.exists(\"sd-v1-5-inpainting.ckpt\"):\n",
" !wget https://huggingface.co/sd-legacy/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt\n",
"else:\n",
" print(\"Model already downloaded.\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "24bd99d5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loaded finetuned weights from finetuned_weights.safetensors\n",
"Loading 0.in_proj.weight\n",
"Loading 0.out_proj.weight\n",
"Loading 0.out_proj.bias\n",
"Loading 8.in_proj.weight\n",
"Loading 8.out_proj.weight\n",
"Loading 8.out_proj.bias\n",
"Loading 16.in_proj.weight\n",
"Loading 16.out_proj.weight\n",
"Loading 16.out_proj.bias\n",
"Loading 24.in_proj.weight\n",
"Loading 24.out_proj.weight\n",
"Loading 24.out_proj.bias\n",
"Loading 32.in_proj.weight\n",
"Loading 32.out_proj.weight\n",
"Loading 32.out_proj.bias\n",
"Loading 40.in_proj.weight\n",
"Loading 40.out_proj.weight\n",
"Loading 40.out_proj.bias\n",
"Loading 48.in_proj.weight\n",
"Loading 48.out_proj.weight\n",
"Loading 48.out_proj.bias\n",
"Loading 56.in_proj.weight\n",
"Loading 56.out_proj.weight\n",
"Loading 56.out_proj.bias\n",
"Loading 64.in_proj.weight\n",
"Loading 64.out_proj.weight\n",
"Loading 64.out_proj.bias\n",
"Loading 72.in_proj.weight\n",
"Loading 72.out_proj.weight\n",
"Loading 72.out_proj.bias\n",
"Loading 80.in_proj.weight\n",
"Loading 80.out_proj.weight\n",
"Loading 80.out_proj.bias\n",
"Loading 88.in_proj.weight\n",
"Loading 88.out_proj.weight\n",
"Loading 88.out_proj.bias\n",
"Loading 96.in_proj.weight\n",
"Loading 96.out_proj.weight\n",
"Loading 96.out_proj.bias\n",
"Loading 104.in_proj.weight\n",
"Loading 104.out_proj.weight\n",
"Loading 104.out_proj.bias\n",
"Loading 112.in_proj.weight\n",
"Loading 112.out_proj.weight\n",
"Loading 112.out_proj.bias\n",
"Loading 120.in_proj.weight\n",
"Loading 120.out_proj.weight\n",
"Loading 120.out_proj.bias\n",
"\n",
"Attention module weights loaded from {finetune_weights_path} successfully.\n"
]
}
],
"source": [
"import load_model\n",
"\n",
"models=load_model.preload_models_from_standard_weights(ckpt_path=\"sd-v1-5-inpainting.ckpt\", device=\"cuda\", finetune_weights_path=\"finetuned_weights.safetensors\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "bab24c29",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/mahesh/miniconda3/envs/harsh/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"import inspect\n",
"import os\n",
"from typing import Union\n",
"\n",
"import PIL\n",
"import numpy as np\n",
"import torch\n",
"import tqdm\n",
"from diffusers.utils.torch_utils import randn_tensor\n",
"\n",
"from utils import (check_inputs, get_time_embedding, numpy_to_pil, prepare_image,\n",
" prepare_mask_image, compute_vae_encodings)\n",
"from ddpm import DDPMSampler\n",
"\n",
"class CatVTONPipeline:\n",
" def __init__(\n",
" self, \n",
" weight_dtype=torch.float32,\n",
" device='cuda',\n",
" compile=False,\n",
" skip_safety_check=True,\n",
" use_tf32=True,\n",
" models={},\n",
" ):\n",
" self.device = device\n",
" self.weight_dtype = weight_dtype\n",
" self.skip_safety_check = skip_safety_check\n",
" self.models = models\n",
"\n",
" self.generator = torch.Generator(device=device)\n",
" self.noise_scheduler = DDPMSampler(generator=self.generator)\n",
" # self.vae = AutoencoderKL.from_pretrained(\"stabilityai/sd-vae-ft-mse\").to(device, dtype=weight_dtype)\n",
" self.encoder= models.get('encoder', None)\n",
" self.decoder= models.get('decoder', None)\n",
" \n",
" self.unet=models.get('diffusion', None) \n",
" # # Enable TF32 for faster training on Ampere GPUs (A100 and RTX 30 series).\n",
" if use_tf32:\n",
" torch.set_float32_matmul_precision(\"high\")\n",
" torch.backends.cuda.matmul.allow_tf32 = True\n",
"\n",
" @torch.no_grad()\n",
" def __call__(\n",
" self, \n",
" image: Union[PIL.Image.Image, torch.Tensor],\n",
" condition_image: Union[PIL.Image.Image, torch.Tensor],\n",
" mask: Union[PIL.Image.Image, torch.Tensor],\n",
" num_inference_steps: int = 50,\n",
" guidance_scale: float = 2.5,\n",
" height: int = 1024,\n",
" width: int = 768,\n",
" generator=None,\n",
" eta=1.0,\n",
" **kwargs\n",
" ):\n",
" concat_dim = -2 # FIXME: y axis concat\n",
" # Prepare inputs to Tensor\n",
" image, condition_image, mask = check_inputs(image, condition_image, mask, width, height)\n",
" image = prepare_image(image).to(self.device, dtype=self.weight_dtype)\n",
" condition_image = prepare_image(condition_image).to(self.device, dtype=self.weight_dtype)\n",
" mask = prepare_mask_image(mask).to(self.device, dtype=self.weight_dtype)\n",
" # Mask image\n",
" masked_image = image * (mask < 0.5)\n",
" # VAE encoding\n",
" masked_latent = compute_vae_encodings(masked_image, self.encoder)\n",
" condition_latent = compute_vae_encodings(condition_image, self.encoder)\n",
" mask_latent = torch.nn.functional.interpolate(mask, size=masked_latent.shape[-2:], mode=\"nearest\")\n",
" del image, mask, condition_image\n",
" # Concatenate latents\n",
" masked_latent_concat = torch.cat([masked_latent, condition_latent], dim=concat_dim)\n",
" mask_latent_concat = torch.cat([mask_latent, torch.zeros_like(mask_latent)], dim=concat_dim)\n",
" # Prepare noise\n",
" latents = randn_tensor(\n",
" masked_latent_concat.shape,\n",
" generator=generator,\n",
" device=masked_latent_concat.device,\n",
" dtype=self.weight_dtype,\n",
" )\n",
" # Prepare timesteps\n",
" self.noise_scheduler.set_inference_timesteps(num_inference_steps)\n",
" timesteps = self.noise_scheduler.timesteps\n",
" # latents = latents * self.noise_scheduler.init_noise_sigma\n",
" latents = self.noise_scheduler.add_noise(latents, timesteps[0])\n",
" \n",
" # Classifier-Free Guidance\n",
" if do_classifier_free_guidance := (guidance_scale > 1.0):\n",
" masked_latent_concat = torch.cat(\n",
" [\n",
" torch.cat([masked_latent, torch.zeros_like(condition_latent)], dim=concat_dim),\n",
" masked_latent_concat,\n",
" ]\n",
" )\n",
" mask_latent_concat = torch.cat([mask_latent_concat] * 2)\n",
"\n",
" num_warmup_steps = 0 # For simple DDPM, no warmup needed\n",
" with tqdm(total=num_inference_steps) as progress_bar:\n",
" for i, t in enumerate(timesteps):\n",
" # expand the latents if we are doing classifier free guidance\n",
" non_inpainting_latent_model_input = (torch.cat([latents] * 2) if do_classifier_free_guidance else latents)\n",
" # non_inpainting_latent_model_input = self.noise_scheduler.scale_model_input(non_inpainting_latent_model_input, t)\n",
" # prepare the input for the inpainting model\n",
" inpainting_latent_model_input = torch.cat([non_inpainting_latent_model_input, mask_latent_concat, masked_latent_concat], dim=1).to(self.device, dtype=self.weight_dtype)\n",
" # predict the noise residual\n",
" \n",
" timestep = t.repeat(inpainting_latent_model_input.shape[0])\n",
" time_embedding = get_time_embedding(timestep).to(self.device, dtype=self.weight_dtype)\n",
"\n",
" noise_pred = self.unet(\n",
" inpainting_latent_model_input,\n",
" time_embedding\n",
" )\n",
" # perform guidance\n",
" if do_classifier_free_guidance:\n",
" noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n",
" noise_pred = noise_pred_uncond + guidance_scale * (\n",
" noise_pred_text - noise_pred_uncond\n",
" )\n",
" # compute the previous noisy sample x_t -> x_t-1\n",
" latents = self.noise_scheduler.step(\n",
" t, latents, noise_pred\n",
" )\n",
" # call the callback, if provided\n",
" if i == len(timesteps) - 1 or (\n",
" (i + 1) > num_warmup_steps\n",
" ):\n",
" progress_bar.update()\n",
"\n",
" # Decode the final latents\n",
" latents = latents.split(latents.shape[concat_dim] // 2, dim=concat_dim)[0]\n",
" # latents = 1 / self.vae.config.scaling_factor * latents\n",
" # image = self.vae.decode(latents.to(self.device, dtype=self.weight_dtype)).sample\n",
" image = self.decoder(latents.to(self.device, dtype=self.weight_dtype))\n",
" image = (image / 2 + 0.5).clamp(0, 1)\n",
" # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n",
" image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n",
" image = numpy_to_pil(image)\n",
" \n",
" return image\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a729bf46",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset vitonhd loaded, total 20 pairs.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 50/50 [00:07<00:00, 7.04it/s]\n",
"100%|██████████| 50/50 [00:06<00:00, 7.32it/s]\n",
"100%|██████████| 50/50 [00:07<00:00, 7.01it/s]\n",
"100%|██████████| 50/50 [00:07<00:00, 6.82it/s]\n",
"100%|██████████| 50/50 [00:07<00:00, 6.86it/s]\n",
"100%|██████████| 50/50 [00:06<00:00, 7.25it/s]\n",
"100%|██████████| 50/50 [00:06<00:00, 7.24it/s]\n",
"100%|██████████| 50/50 [00:07<00:00, 6.89it/s]\n",
"100%|██████████| 50/50 [00:07<00:00, 6.90it/s]\n",
"100%|██████████| 50/50 [00:07<00:00, 7.02it/s]\n",
"100%|██████████| 50/50 [00:06<00:00, 7.40it/s]\n",
"100%|██████████| 50/50 [00:06<00:00, 7.15it/s]\n",
"100%|██████████| 50/50 [00:07<00:00, 6.79it/s]\n",
"100%|██████████| 50/50 [00:07<00:00, 7.07it/s]\n",
"100%|██████████| 50/50 [00:07<00:00, 7.14it/s]\n",
"100%|██████████| 50/50 [00:06<00:00, 7.32it/s]\n",
"100%|██████████| 50/50 [00:07<00:00, 7.13it/s]\n",
"100%|██████████| 50/50 [00:07<00:00, 7.05it/s]\n",
"100%|██████████| 50/50 [00:07<00:00, 7.06it/s]\n",
"100%|██████████| 50/50 [00:07<00:00, 7.09it/s]\n",
"100%|██████████| 20/20 [02:28<00:00, 7.40s/it]\n"
]
}
],
"source": [
"import os\n",
"import numpy as np\n",
"import torch\n",
"import argparse\n",
"from torch.utils.data import Dataset, DataLoader\n",
"from VITON_Dataset import VITONHDTestDataset\n",
"from diffusers.image_processor import VaeImageProcessor\n",
"from tqdm import tqdm\n",
"from PIL import Image, ImageFilter\n",
"\n",
"from utils import repaint, to_pil_image\n",
"\n",
"@torch.no_grad()\n",
"def main():\n",
" args=argparse.Namespace()\n",
" args.__dict__= {\n",
" \"dataset_name\": \"vitonhd\",\n",
" \"data_root_path\": \"./sample_dataset\",\n",
" \"output_dir\": \"./mask-based-output\",\n",
" \"seed\": 555,\n",
" \"batch_size\": 1,\n",
" \"num_inference_steps\": 50,\n",
" \"guidance_scale\": 2.5,\n",
" \"width\": 384,\n",
" \"height\": 512,\n",
" \"repaint\": True,\n",
" \"eval_pair\": False,\n",
" \"concat_eval_results\": True,\n",
" \"allow_tf32\": True,\n",
" \"dataloader_num_workers\": 4,\n",
" \"mixed_precision\": 'no',\n",
" \"concat_axis\": 'y',\n",
" \"enable_condition_noise\": True,\n",
" \"is_train\": False\n",
" }\n",
"\n",
" # Pipeline\n",
" pipeline = CatVTONPipeline(\n",
" weight_dtype={\n",
" \"no\": torch.float32,\n",
" \"fp16\": torch.float16,\n",
" \"bf16\": torch.bfloat16,\n",
" }[args.mixed_precision],\n",
" device=\"cuda\",\n",
" skip_safety_check=True,\n",
" models=models,\n",
" )\n",
" # Dataset\n",
" if args.dataset_name == \"vitonhd\":\n",
" dataset = VITONHDTestDataset(args)\n",
" else:\n",
" raise ValueError(f\"Invalid dataset name {args.dataset}.\")\n",
" print(f\"Dataset {args.dataset_name} loaded, total {len(dataset)} pairs.\")\n",
" dataloader = DataLoader(\n",
" dataset,\n",
" batch_size=args.batch_size,\n",
" shuffle=False,\n",
" num_workers=args.dataloader_num_workers\n",
" )\n",
" \n",
" # Inference\n",
" generator = torch.Generator(device='cuda').manual_seed(args.seed)\n",
" args.output_dir = os.path.join(args.output_dir, f\"{args.dataset_name}-{args.height}\", \"paired\" if args.eval_pair else \"unpaired\")\n",
" if not os.path.exists(args.output_dir):\n",
" os.makedirs(args.output_dir)\n",
" \n",
" for batch in tqdm(dataloader):\n",
" person_images = batch['person']\n",
" cloth_images = batch['cloth']\n",
" masks = batch['mask']\n",
"\n",
" results = pipeline(\n",
" person_images,\n",
" cloth_images,\n",
" masks,\n",
" num_inference_steps=args.num_inference_steps,\n",
" guidance_scale=args.guidance_scale,\n",
" height=args.height,\n",
" width=args.width,\n",
" generator=generator,\n",
" )\n",
" \n",
" if args.concat_eval_results or args.repaint:\n",
" person_images = to_pil_image(person_images)\n",
" cloth_images = to_pil_image(cloth_images)\n",
" masks = to_pil_image(masks)\n",
" for i, result in enumerate(results):\n",
" person_name = batch['person_name'][i]\n",
" output_path = os.path.join(args.output_dir, person_name)\n",
" if not os.path.exists(os.path.dirname(output_path)):\n",
" os.makedirs(os.path.dirname(output_path))\n",
" if args.repaint:\n",
" person_path, mask_path = dataset.data[batch['index'][i]]['person'], dataset.data[batch['index'][i]]['mask']\n",
" person_image= Image.open(person_path).resize(result.size, Image.LANCZOS)\n",
" mask = Image.open(mask_path).resize(result.size, Image.NEAREST)\n",
" result = repaint(person_image, mask, result)\n",
" if args.concat_eval_results:\n",
" w, h = result.size\n",
" concated_result = Image.new('RGB', (w*3, h))\n",
" concated_result.paste(person_images[i], (0, 0))\n",
" concated_result.paste(cloth_images[i], (w, 0)) \n",
" concated_result.paste(result, (w*2, 0))\n",
" result = concated_result\n",
" result.save(output_path)\n",
"\n",
"if __name__ == \"__main__\":\n",
" main()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "55d88911",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "harsh",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.18"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|