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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "6d50f66c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model already downloaded.\n"
]
}
],
"source": [
"# check if the model is downloaded, if not download it\n",
"import os\n",
"if not os.path.exists(\"instruct-pix2pix-00-22000.ckpt\"):\n",
" !wget https://huggingface.co/timbrooks/instruct-pix2pix/resolve/main/instruct-pix2pix-00-22000.ckpt\n",
"else:\n",
" print(\"Model already downloaded.\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3598a305",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loaded finetuned weights from maskfree_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",
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"Loading 24.out_proj.bias\n",
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"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",
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"Loading 48.out_proj.bias\n",
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"Loading 56.out_proj.bias\n",
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"Loading 64.out_proj.bias\n",
"Loading 72.in_proj.weight\n",
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"Loading 80.in_proj.weight\n",
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"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=\"instruct-pix2pix-00-22000.ckpt\", device=\"cuda\", finetune_weights_path=\"maskfree_finetuned_weights.safetensors\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "78e3d8b9",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5627b2d2",
"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"
]
},
{
"ename": "AssertionError",
"evalue": "File ./sample_dataset/samples_pairs.txt does not exist.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[3], line 97\u001b[0m\n\u001b[1;32m 94\u001b[0m result\u001b[38;5;241m.\u001b[39msave(output_path)\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__main__\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m---> 97\u001b[0m \u001b[43mmain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/miniconda3/envs/harsh/lib/python3.10/site-packages/torch/utils/_contextlib.py:116\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 115\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 116\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"Cell \u001b[0;32mIn[3], line 48\u001b[0m, in \u001b[0;36mmain\u001b[0;34m()\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;66;03m# Dataset\u001b[39;00m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m args\u001b[38;5;241m.\u001b[39mdataset_name \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvitonhd\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m---> 48\u001b[0m dataset \u001b[38;5;241m=\u001b[39m \u001b[43mVITONHDTestDataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid dataset name \u001b[39m\u001b[38;5;132;01m{\u001b[39;00margs\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[0;32m~/harsh/stable-diffusion/VITON_Dataset.py:14\u001b[0m, in \u001b[0;36mInferenceDataset.__init__\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvae_processor \u001b[38;5;241m=\u001b[39m VaeImageProcessor(vae_scale_factor\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m8\u001b[39m) \n\u001b[1;32m 13\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmask_processor \u001b[38;5;241m=\u001b[39m VaeImageProcessor(vae_scale_factor\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m8\u001b[39m, do_normalize\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, do_binarize\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, do_convert_grayscale\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m) \n\u001b[0;32m---> 14\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/harsh/stable-diffusion/VITON_Dataset.py:36\u001b[0m, in \u001b[0;36mVITONHDTestDataset.load_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mload_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 35\u001b[0m name\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mis_train \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msamples\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 36\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(pair_txt\u001b[38;5;241m:=\u001b[39mos\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mdata_root_path, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_pairs.txt\u001b[39m\u001b[38;5;124m'\u001b[39m)), \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFile \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpair_txt\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not exist.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(pair_txt, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m 38\u001b[0m lines \u001b[38;5;241m=\u001b[39m f\u001b[38;5;241m.\u001b[39mreadlines()\n",
"\u001b[0;31mAssertionError\u001b[0m: File ./sample_dataset/samples_pairs.txt does not exist."
]
}
],
"source": [
"import os\n",
"import torch\n",
"import argparse\n",
"from torch.utils.data import DataLoader\n",
"from VITON_Dataset import VITONHDTestDataset\n",
"from tqdm import tqdm\n",
"from PIL import Image\n",
"from CatVTON_model import CatVTONPix2PixPipeline\n",
"\n",
"from utils import 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-free-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",
" \"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 = CatVTONPix2PixPipeline(\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",
"\n",
" results = pipeline(\n",
" person_images,\n",
" cloth_images,\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:\n",
" person_images = to_pil_image(person_images)\n",
" cloth_images = to_pil_image(cloth_images)\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.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": "39537851",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "22fb6113",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "1c374cc6",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "bddce5df",
"metadata": {
"vscode": {
"languageId": "markdown"
}
},
"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",
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|