{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "6387c9e1", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "ca9233f0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/kaggle/working'" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 17, "id": "3d2f98af", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[0m\u001b[01;34mtest\u001b[0m/ \u001b[01;32mtest_pairs.txt\u001b[0m* \u001b[01;34mtrain\u001b[0m/ \u001b[01;32mtrain_pairs.txt\u001b[0m*\n" ] } ], "source": [ "ls /kaggle/input/viton-hd-dataset" ] }, { "cell_type": "code", "execution_count": 18, "id": "dc0f36f4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cloning into 'stable-diffusion'...\n", "remote: Enumerating objects: 150, done.\u001b[K\n", "remote: Counting objects: 100% (150/150), done.\u001b[K\n", "remote: Compressing objects: 100% (124/124), done.\u001b[K\n", "remote: Total 150 (delta 36), reused 139 (delta 26), pack-reused 0 (from 0)\u001b[K\n", "Receiving objects: 100% (150/150), 9.11 MiB | 20.74 MiB/s, done.\n", "Resolving deltas: 100% (36/36), done.\n" ] } ], "source": [ "!git clone -b CatVTON https://github.com/Harsh-Kesharwani/stable-diffusion.git" ] }, { "cell_type": "code", "execution_count": 19, "id": "a0bf01ab", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/kaggle/working/stable-diffusion\n" ] } ], "source": [ "cd stable-diffusion/" ] }, { "cell_type": "code", "execution_count": 20, "id": "1401cd56", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2025-06-13 07:07:34-- https://huggingface.co/sd-legacy/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt\n", "Resolving huggingface.co (huggingface.co)... 18.67.93.22, 18.67.93.63, 18.67.93.58, ...\n", "Connecting to huggingface.co (huggingface.co)|18.67.93.22|:443... connected.\n", "HTTP request sent, awaiting response... 307 Temporary Redirect\n", "Location: /stable-diffusion-v1-5/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt [following]\n", "--2025-06-13 07:07:34-- https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt\n", "Reusing existing connection to huggingface.co:443.\n", "HTTP request sent, awaiting response... 302 Found\n", "Location: https://cdn-lfs.hf.co/repos/f6/56/f656f0fa3b8a40ac76d297fa2a4b00f981e8eb1261963460764e7dd3b35ec97f/c6bbc15e3224e6973459ba78de4998b80b50112b0ae5b5c67113d56b4e366b19?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27sd-v1-5-inpainting.ckpt%3B+filename%3D%22sd-v1-5-inpainting.ckpt%22%3B&Expires=1749802055&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTc0OTgwMjA1NX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5oZi5jby9yZXBvcy9mNi81Ni9mNjU2ZjBmYTNiOGE0MGFjNzZkMjk3ZmEyYTRiMDBmOTgxZThlYjEyNjE5NjM0NjA3NjRlN2RkM2IzNWVjOTdmL2M2YmJjMTVlMzIyNGU2OTczNDU5YmE3OGRlNDk5OGI4MGI1MDExMmIwYWU1YjVjNjcxMTNkNTZiNGUzNjZiMTk%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qIn1dfQ__&Signature=m4Xzc4SaPX28SXT9wK8qPXBWIr7uKmVt6iq2D3qMALrJWCfm1a4FHoshqkXLDrchchDIkAImr7l-yDlAv9x15JsX09FidLsSVU8UXS4a%7Em69hgWMTgloTObR3HlTwY9EQ7t%7ErneASRUS5r%7E2szyfyrlN-n4-U9QWCmyOikaumCc0PbAHE6lRNcy7FSCTxQGM48h%7EQBZ37iQArWW2JC%7E-apwm1knzGt422ywPlQws2qREoUeCPoXFWKl-iX1%7EqDimjSepdm2ZGt-COfekmJddQWXuCQAj7uY5YKcE3qEt7IBcaj96MNbF8b2qxTNbLrzgXioIzl0SIw8Ws-YUOu5I3A__&Key-Pair-Id=K3RPWS32NSSJCE 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3.163.189.90, 3.163.189.114, ...\n", "Connecting to huggingface.co (huggingface.co)|3.163.189.37|:443... connected.\n", "HTTP request sent, awaiting response... 302 Found\n", "Location: 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[following]\n", "--2025-06-11 10:33:19-- 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"Resolving cdn-lfs-us-1.hf.co (cdn-lfs-us-1.hf.co)... 18.238.238.75, 18.238.238.106, 18.238.238.119, ...\n", "Connecting to cdn-lfs-us-1.hf.co (cdn-lfs-us-1.hf.co)|18.238.238.75|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 198303368 (189M) [binary/octet-stream]\n", "Saving to: ‘model.safetensors?download=true’\n", "\n", "model.safetensors?d 100%[===================>] 189.12M 298MB/s in 0.6s \n", "\n", "2025-06-11 10:33:20 (298 MB/s) - ‘model.safetensors?download=true’ saved [198303368/198303368]\n", "\n" ] } ], "source": [ "# !wget https://huggingface.co/zhengchong/CatVTON/resolve/main/vitonhd-16k-512/attention/model.safetensors?download=true " ] }, { "cell_type": "code", "execution_count": 11, "id": "ca20c487", "metadata": {}, "outputs": [], "source": [ "mv 'model.safetensors?download=true' model.safetensors" ] }, { "cell_type": "code", "execution_count": 12, "id": "6d0a1287", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "attention.py encoder.py\t model.safetensors sd-v1-5-inpainting.ckpt\n", "clip.py interface.py\t pipeline.py\t test.ipynb\n", "ddpm.py merges.txt\t README.md\t vocab.json\n", "decoder.py model_converter.py requirements.txt\n", "diffusion.py model.py\t\t sample_dataset\n" ] } ], "source": [ "!ls" ] }, { "cell_type": "code", "execution_count": 14, "id": "8f11470e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/kaggle/working/stable-diffusion/CatVTON\n" ] } ], "source": [ "cd .." ] }, { "cell_type": "code", "execution_count": 15, "id": "cb794cb3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "app_flux.py eval.py preprocess_agnostic_mask.py \u001b[0m\u001b[01;34mstable-diffusion\u001b[0m/\n", "app_p2p.py index.html \u001b[01;34m__pycache__\u001b[0m/ utils.py\n", "app.py inference.py README.md\n", "\u001b[01;34mdensepose\u001b[0m/ LICENSE requirements.txt\n", "\u001b[01;34mdetectron2\u001b[0m/ \u001b[01;34mmodel\u001b[0m/ \u001b[01;34mresource\u001b[0m/\n" ] } ], "source": [ "ls" ] }, { "cell_type": "code", "execution_count": 16, "id": "b6af145b", "metadata": {}, "outputs": [], "source": [ "import os\n", "import shutil\n", "\n", "src_dir = \"./stable-diffusion\"\n", "dst_dir = \".\"\n", "\n", "for filename in os.listdir(src_dir):\n", " src_path = os.path.join(src_dir, filename)\n", " dst_path = os.path.join(dst_dir, filename)\n", " if os.path.isfile(src_path):\n", " shutil.move(src_path, dst_path)\n", " elif os.path.isdir(src_path):\n", " shutil.move(src_path, dst_path)" ] }, { "cell_type": "code", "execution_count": null, "id": "63ee438c", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "60598bd3", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 229, "id": "192a649c", "metadata": {}, "outputs": [], "source": [ "import torch\n", "import gc\n", "\n", "# Clear CUDA cache and collect garbage\n", "torch.cuda.empty_cache()\n", "gc.collect()\n", "\n", "# Delete all user-defined variables except for built-ins and modules\n", "for var in list(globals()):\n", " if not var.startswith(\"__\") and var not in [\"torch\", \"gc\"]:\n", " del globals()[var]\n", "\n", "gc.collect()\n", "torch.cuda.empty_cache()" ] }, { "cell_type": "code", "execution_count": 245, "id": "a3a4a5dc", "metadata": {}, "outputs": [], "source": [ "import torch\n", "import gc\n", "\n", "# Clear CUDA cache and collect garbage\n", "torch.cuda.empty_cache()\n", "gc.collect()\n", "\n", "# Delete all user-defined variables except for built-ins and modules\n", "for var_name in list(globals()):\n", " if not var_name.startswith(\"__\") and var_name not in [\"torch\", \"gc\"]:\n", " del globals()[var_name]\n", "\n", "gc.collect()\n", "torch.cuda.empty_cache()\n", "\n", "import tensorflow as tf\n", "tf.keras.backend.clear_session()" ] }, { "cell_type": "code", "execution_count": 4, "id": "91ef7a4e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import torch\n", "import gc\n", "\n", "torch.cuda.empty_cache() # Release unused GPU memory\n", "gc.collect() # Run Python garbage collector" ] }, { "cell_type": "code", "execution_count": 9, "id": "08f29055", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GPU memory used: 0.00 MB / 16269.25 MB\n" ] } ], "source": [ "import torch\n", "\n", "if torch.cuda.is_available():\n", " used = torch.cuda.memory_allocated() / 1024 ** 2 # in MB\n", " total = torch.cuda.get_device_properties(0).total_memory / 1024 ** 2 # in MB\n", " print(f\"GPU memory used: {used:.2f} MB / {total:.2f} MB\")\n", "else:\n", " print(\"CUDA is not available.\")" ] }, { "cell_type": "code", "execution_count": 197, "id": "6fbde810", "metadata": {}, "outputs": [], "source": [ "# rm -rf output" ] }, { "cell_type": "code", "execution_count": null, "id": "37335c1e", "metadata": {}, "outputs": [], "source": [ "def compute_vae_encodings(image_tensor, encoder, device=\"cuda\"):\n", " \"\"\"Encode image using VAE encoder\"\"\"\n", " # Generate random noise for encoding\n", " encoder_noise = torch.randn(\n", " (image_tensor.shape[0], 4, image_tensor.shape[2] // 8, image_tensor.shape[3] // 8),\n", " device=device,\n", " )\n", " \n", " # Encode using your custom encoder\n", " latent = encoder(image_tensor, encoder_noise)\n", " return latent" ] }, { "cell_type": "code", "execution_count": null, "id": "35d98b83", "metadata": {}, "outputs": [], "source": [ "def get_trainable_module(unet, trainable_module_name):\n", " if trainable_module_name == \"unet\":\n", " return unet\n", " elif trainable_module_name == \"transformer\":\n", " trainable_modules = torch.nn.ModuleList()\n", " for blocks in [unet.encoders, unet.bottleneck, unet.decoders]:\n", " if hasattr(blocks, \"attentions\"):\n", " trainable_modules.append(blocks.attentions)\n", " else:\n", " for block in blocks:\n", " if hasattr(block, \"attentions\"):\n", " trainable_modules.append(block.attentions)\n", " return trainable_modules\n", " elif trainable_module_name == \"attention\":\n", " attn_blocks = torch.nn.ModuleList()\n", " for name, param in unet.named_modules():\n", " if \"attention_1\" in name:\n", " attn_blocks.append(param)\n", " return attn_blocks\n", " else:\n", " raise ValueError(f\"Unknown trainable_module_name: {trainable_module_name}\")" ] }, { "cell_type": "code", "execution_count": null, "id": "d7ff094a", "metadata": {}, "outputs": [], "source": [ "from torch.nn import functional as F\n", "import torch\n", "# from flash_attn import flash_attn_func\n", "\n", "class SkipAttnProcessor(torch.nn.Module):\n", " def __init__(self, *args, **kwargs) -> None:\n", " super().__init__()\n", "\n", " def __call__(\n", " self,\n", " attn,\n", " hidden_states,\n", " encoder_hidden_states=None,\n", " attention_mask=None,\n", " temb=None,\n", " ):\n", " return hidden_states\n", "\n", "class AttnProcessor2_0(torch.nn.Module):\n", " r\"\"\"\n", " Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).\n", " \"\"\"\n", "\n", " def __init__(\n", " self,\n", " hidden_size=None,\n", " cross_attention_dim=None,\n", " **kwargs\n", " ):\n", " super().__init__()\n", " if not hasattr(F, \"scaled_dot_product_attention\"):\n", " raise ImportError(\"AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.\")\n", "\n", " def __call__(\n", " self,\n", " attn,\n", " hidden_states,\n", " encoder_hidden_states=None,\n", " attention_mask=None,\n", " temb=None,\n", " *args,\n", " **kwargs,\n", " ):\n", " residual = hidden_states\n", "\n", " if attn.spatial_norm is not None:\n", " hidden_states = attn.spatial_norm(hidden_states, temb)\n", "\n", " input_ndim = hidden_states.ndim\n", "\n", " if input_ndim == 4:\n", " batch_size, channel, height, width = hidden_states.shape\n", " hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n", "\n", " batch_size, sequence_length, _ = (\n", " hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n", " )\n", "\n", " if attention_mask is not None:\n", " attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n", " # scaled_dot_product_attention expects attention_mask shape to be\n", " # (batch, heads, source_length, target_length)\n", " attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])\n", "\n", " if attn.group_norm is not None:\n", " hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)\n", "\n", " query = attn.to_q(hidden_states)\n", "\n", " if encoder_hidden_states is None:\n", " encoder_hidden_states = hidden_states\n", " elif attn.norm_cross:\n", " encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n", "\n", " key = attn.to_k(encoder_hidden_states)\n", " value = attn.to_v(encoder_hidden_states)\n", "\n", " inner_dim = key.shape[-1]\n", " head_dim = inner_dim // attn.heads\n", "\n", " query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n", "\n", " key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n", " value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n", "\n", " # the output of sdp = (batch, num_heads, seq_len, head_dim)\n", " # TODO: add support for attn.scale when we move to Torch 2.1\n", " \n", " hidden_states = F.scaled_dot_product_attention(\n", " query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False\n", " )\n", " # hidden_states = flash_attn_func(\n", " # query, key, value, dropout_p=0.0, causal=False\n", " # )\n", "\n", " hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)\n", " hidden_states = hidden_states.to(query.dtype)\n", "\n", " # linear proj\n", " hidden_states = attn.to_out[0](hidden_states)\n", " # dropout\n", " hidden_states = attn.to_out[1](hidden_states)\n", "\n", " if input_ndim == 4:\n", " hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n", "\n", " if attn.residual_connection:\n", " hidden_states = hidden_states + residual\n", "\n", " hidden_states = hidden_states / attn.rescale_output_factor\n", "\n", " return hidden_states\n", " " ] }, { "cell_type": "code", "execution_count": null, "id": "84a7fa87", "metadata": {}, "outputs": [], "source": [ "import os\n", "import json\n", "import torch\n", "\n", "def init_adapter(unet, \n", " cross_attn_cls=SkipAttnProcessor,\n", " self_attn_cls=None,\n", " cross_attn_dim=None, \n", " **kwargs):\n", " if cross_attn_dim is None:\n", " cross_attn_dim = unet.config.cross_attention_dim\n", " attn_procs = {}\n", " for name in unet.attn_processors.keys():\n", " cross_attention_dim = None if name.endswith(\"attn1.processor\") else cross_attn_dim\n", " if name.startswith(\"mid_block\"):\n", " hidden_size = unet.config.block_out_channels[-1]\n", " elif name.startswith(\"up_blocks\"):\n", " block_id = int(name[len(\"up_blocks.\")])\n", " hidden_size = list(reversed(unet.config.block_out_channels))[block_id]\n", " elif name.startswith(\"down_blocks\"):\n", " block_id = int(name[len(\"down_blocks.\")])\n", " hidden_size = unet.config.block_out_channels[block_id]\n", " if cross_attention_dim is None:\n", " if self_attn_cls is not None:\n", " attn_procs[name] = self_attn_cls(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, **kwargs)\n", " else:\n", " # retain the original attn processor\n", " attn_procs[name] = AttnProcessor2_0(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, **kwargs)\n", " else:\n", " attn_procs[name] = cross_attn_cls(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, **kwargs)\n", " \n", " unet.set_attn_processor(attn_procs)\n", " adapter_modules = torch.nn.ModuleList(unet.attn_processors.values())\n", " return adapter_modules\n", "\n", "def init_diffusion_model(diffusion_model_name_or_path, unet_class=None):\n", " from diffusers import AutoencoderKL\n", " from transformers import CLIPTextModel, CLIPTokenizer\n", "\n", " text_encoder = CLIPTextModel.from_pretrained(diffusion_model_name_or_path, subfolder=\"text_encoder\")\n", " vae = AutoencoderKL.from_pretrained(diffusion_model_name_or_path, subfolder=\"vae\")\n", " tokenizer = CLIPTokenizer.from_pretrained(diffusion_model_name_or_path, subfolder=\"tokenizer\")\n", " try:\n", " unet_folder = os.path.join(diffusion_model_name_or_path, \"unet\")\n", " unet_configs = json.load(open(os.path.join(unet_folder, \"config.json\"), \"r\"))\n", " unet = unet_class(**unet_configs)\n", " unet.load_state_dict(torch.load(os.path.join(unet_folder, \"diffusion_pytorch_model.bin\"), map_location=\"cpu\"), strict=True)\n", " except:\n", " unet = None\n", " return text_encoder, vae, tokenizer, unet\n", "\n", "def attn_of_unet(unet):\n", " attn_blocks = torch.nn.ModuleList()\n", " for name, param in unet.named_modules():\n", " if \"attn1\" in name:\n", " attn_blocks.append(param)\n", " return attn_blocks\n", "\n", "def get_trainable_module(unet, trainable_module_name):\n", " if trainable_module_name == \"unet\":\n", " return unet\n", " elif trainable_module_name == \"transformer\":\n", " trainable_modules = torch.nn.ModuleList()\n", " for blocks in [unet.down_blocks, unet.mid_block, unet.up_blocks]:\n", " if hasattr(blocks, \"attentions\"):\n", " trainable_modules.append(blocks.attentions)\n", " else:\n", " for block in blocks:\n", " if hasattr(block, \"attentions\"):\n", " trainable_modules.append(block.attentions)\n", " return trainable_modules\n", " elif trainable_module_name == \"attention\":\n", " attn_blocks = torch.nn.ModuleList()\n", " for name, param in unet.named_modules():\n", " if \"attn1\" in name:\n", " attn_blocks.append(param)\n", " return attn_blocks\n", " else:\n", " raise ValueError(f\"Unknown trainable_module_name: {trainable_module_name}\")\n", "\n", " \n", " " ] }, { "cell_type": "code", "execution_count": null, "id": "6028381d", "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'model'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_662/1349749640.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtransformers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCLIPImageProcessor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattn_processor\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSkipAttnProcessor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 18\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_trainable_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minit_adapter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m from utils import (check_inputs, get_time_embedding, numpy_to_pil, prepare_image,\n", "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'model'" ] } ], "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 accelerate import load_checkpoint_in_model\n", "from diffusers import AutoencoderKL, DDIMScheduler, UNet2DConditionModel\n", "from diffusers.pipelines.stable_diffusion.safety_checker import \\\n", " StableDiffusionSafetyChecker\n", "from diffusers.utils.torch_utils import randn_tensor\n", "from huggingface_hub import snapshot_download\n", "from transformers import CLIPImageProcessor\n", "\n", "from utils import (check_inputs, get_time_embedding, numpy_to_pil, prepare_image,\n", " prepare_mask_image, resize_and_crop, resize_and_padding)\n", "from ddpm import DDPMSampler\n", "\n", "class CatVTONPipeline:\n", " def __init__(\n", " self, \n", " base_ckpt, \n", " attn_ckpt, \n", " attn_ckpt_version=\"mix\",\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", " if not skip_safety_check:\n", " self.feature_extractor = CLIPImageProcessor.from_pretrained(base_ckpt, subfolder=\"feature_extractor\")\n", " self.safety_checker = StableDiffusionSafetyChecker.from_pretrained(base_ckpt, subfolder=\"safety_checker\").to(device, dtype=weight_dtype)\n", " self.unet = UNet2DConditionModel.from_pretrained(base_ckpt, subfolder=\"unet\").to(device, dtype=weight_dtype)\n", " # self.unet=models.get('diffusion', None)\n", " init_adapter(self.unet, cross_attn_cls=SkipAttnProcessor) # Skip Cross-Attention\n", " self.attn_modules = get_trainable_module(self.unet, \"attention\")\n", " self.auto_attn_ckpt_load(attn_ckpt, attn_ckpt_version)\n", " # Pytorch 2.0 Compile\n", " # if compile:\n", " # self.unet = torch.compile(self.unet)\n", " # self.vae = torch.compile(self.vae, mode=\"reduce-overhead\")\n", " \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", " def auto_attn_ckpt_load(self, attn_ckpt, version):\n", " sub_folder = {\n", " \"mix\": \"mix-48k-1024\",\n", " \"vitonhd\": \"vitonhd-16k-512\",\n", " \"dresscode\": \"dresscode-16k-512\",\n", " }[version]\n", " if os.path.exists(attn_ckpt):\n", " load_checkpoint_in_model(self.attn_modules, os.path.join(attn_ckpt, sub_folder, 'attention'))\n", " else:\n", " repo_path = snapshot_download(repo_id=attn_ckpt)\n", " print(f\"Downloaded {attn_ckpt} to {repo_path}\")\n", " load_checkpoint_in_model(self.attn_modules, os.path.join(repo_path, sub_folder, 'attention'))\n", " \n", " def run_safety_checker(self, image):\n", " if self.safety_checker is None:\n", " has_nsfw_concept = None\n", " else:\n", " safety_checker_input = self.feature_extractor(image, return_tensors=\"pt\").to(self.device)\n", " image, has_nsfw_concept = self.safety_checker(\n", " images=image, clip_input=safety_checker_input.pixel_values.to(self.weight_dtype)\n", " )\n", " return image, has_nsfw_concept\n", " \n", " def prepare_extra_step_kwargs(self, generator, eta):\n", " # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n", " # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n", " # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n", " # and should be between [0, 1]\n", "\n", " accepts_eta = \"eta\" in set(\n", " inspect.signature(self.noise_scheduler.step).parameters.keys()\n", " )\n", " extra_step_kwargs = {}\n", " if accepts_eta:\n", " extra_step_kwargs[\"eta\"] = eta\n", "\n", " # check if the scheduler accepts generator\n", " accepts_generator = \"generator\" in set(\n", " inspect.signature(self.noise_scheduler.step).parameters.keys()\n", " )\n", " if accepts_generator:\n", " extra_step_kwargs[\"generator\"] = generator\n", " return extra_step_kwargs\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", " # 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", " # Denoising loop\n", " # extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n", " # num_warmup_steps = (len(timesteps) - num_inference_steps * self.noise_scheduler.order)\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", " # time_embedding = get_time_embedding(t.item())\n", " # time_embedding = time_embedding.repeat(inpainting_latent_model_input.shape[0], 1).to(self.device, dtype=self.weight_dtype)\n", " noise_pred= self.unet(\n", " inpainting_latent_model_input,\n", " # time_embedding\n", " t.to(self.device),\n", " encoder_hidden_states=None, # FIXME\n", " return_dict=False,\n", " )[0]\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", " # Safety Check\n", " if not self.skip_safety_check:\n", " current_script_directory = os.path.dirname(os.path.realpath(__file__))\n", " nsfw_image = os.path.join(os.path.dirname(current_script_directory), 'resource', 'img', 'NSFW.jpg')\n", " nsfw_image = PIL.Image.open(nsfw_image).resize(image[0].size)\n", " image_np = np.array(image)\n", " _, has_nsfw_concept = self.run_safety_checker(image=image_np)\n", " for i, not_safe in enumerate(has_nsfw_concept):\n", " if not_safe:\n", " image[i] = nsfw_image\n", " return image\n" ] }, { "cell_type": "code", "execution_count": null, "id": "94e19198", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "An error occurred while trying to fetch booksforcharlie/stable-diffusion-inpainting: booksforcharlie/stable-diffusion-inpainting does not appear to have a file named diffusion_pytorch_model.safetensors.\n", "Defaulting to unsafe serialization. Pass `allow_pickle=False` to raise an error instead.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "912125b29fef4b31aff0e4433b03b876", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Fetching 12 files: 0%| | 0/12 [00:00\u001b[0;34m()\u001b[0m\n\u001b[1;32m 352\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"__main__\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32mdef\u001b[0m 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"\u001b[0;32m/tmp/ipykernel_72/4184774867.py\u001b[0m in \u001b[0;36mmain\u001b[0;34m()\u001b[0m\n\u001b[1;32m 303\u001b[0m \u001b[0mmasks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mask'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 304\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 305\u001b[0;31m results = pipeline(\n\u001b[0m\u001b[1;32m 306\u001b[0m \u001b[0mperson_images\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 307\u001b[0m \u001b[0mcloth_images\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32mdef\u001b[0m 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"\u001b[0;32m/tmp/ipykernel_72/4282996458.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 184\u001b[0m \u001b[0minpainting_latent_model_input\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;31m# time_embedding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 186\u001b[0;31m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 187\u001b[0m \u001b[0mencoder_hidden_states\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# FIXME\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 188\u001b[0m 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This can be a local path or a model identifier from the Model Hub.\"\n", " ),\n", " )\n", " parser.add_argument(\n", " \"--resume_path\",\n", " type=str,\n", " default=\"zhengchong/CatVTON\",\n", " help=(\n", " \"The Path to the checkpoint of trained tryon model.\"\n", " ),\n", " )\n", " parser.add_argument(\n", " \"--dataset_name\",\n", " type=str,\n", " required=True,\n", " help=\"The datasets to use for evaluation.\",\n", " )\n", " parser.add_argument(\n", " \"--data_root_path\", \n", " type=str, \n", " required=True,\n", " help=\"Path to the dataset to evaluate.\"\n", " )\n", " parser.add_argument(\n", " \"--output_dir\",\n", " type=str,\n", " default=\"output\",\n", " help=\"The output directory where the model predictions will be written.\",\n", " )\n", "\n", " parser.add_argument(\n", " \"--seed\", type=int, default=555, help=\"A seed for reproducible evaluation.\"\n", " )\n", " parser.add_argument(\n", " \"--batch_size\", type=int, default=8, help=\"The batch size for evaluation.\"\n", " )\n", " \n", " parser.add_argument(\n", " \"--num_inference_steps\",\n", " type=int,\n", " default=50,\n", " help=\"Number of inference steps to perform.\",\n", " )\n", " parser.add_argument(\n", " \"--guidance_scale\",\n", " type=float,\n", " default=2.5,\n", " help=\"The scale of classifier-free guidance for inference.\",\n", " )\n", "\n", " parser.add_argument(\n", " \"--width\",\n", " type=int,\n", " default=384,\n", " help=(\n", " \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n", " \" resolution\"\n", " ),\n", " )\n", " parser.add_argument(\n", " \"--height\",\n", " type=int,\n", " default=512,\n", " help=(\n", " \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n", " \" resolution\"\n", " ),\n", " )\n", " parser.add_argument(\n", " \"--repaint\", \n", " action=\"store_true\", \n", " help=\"Whether to repaint the result image with the original background.\"\n", " )\n", " parser.add_argument(\n", " \"--eval_pair\",\n", " action=\"store_true\",\n", " help=\"Whether or not to evaluate the pair.\",\n", " )\n", " parser.add_argument(\n", " \"--concat_eval_results\",\n", " action=\"store_true\",\n", " help=\"Whether or not to concatenate the all conditions into one image.\",\n", " )\n", " parser.add_argument(\n", " \"--allow_tf32\",\n", " action=\"store_true\",\n", " default=True,\n", " help=(\n", " \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n", " \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n", " ),\n", " )\n", " parser.add_argument(\n", " \"--dataloader_num_workers\",\n", " type=int,\n", " default=8,\n", " help=(\n", " \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n", " ),\n", " )\n", " parser.add_argument(\n", " \"--mixed_precision\",\n", " type=str,\n", " default=\"bf16\",\n", " choices=[\"no\", \"fp16\", \"bf16\"],\n", " help=(\n", " \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n", " \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n", " \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n", " ),\n", " )\n", "\n", " parser.add_argument(\n", " \"--concat_axis\",\n", " type=str,\n", " choices=[\"x\", \"y\", 'random'],\n", " default=\"y\",\n", " help=\"The axis to concat the cloth feature, select from ['x', 'y', 'random'].\",\n", " )\n", " parser.add_argument(\n", " \"--enable_condition_noise\",\n", " action=\"store_true\",\n", " default=True,\n", " help=\"Whether or not to enable condition noise.\",\n", " )\n", " \n", " args = parser.parse_args()\n", " env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n", " if env_local_rank != -1 and env_local_rank != args.local_rank:\n", " args.local_rank = env_local_rank\n", "\n", " return args\n", "\n", "@torch.no_grad()\n", "def main():\n", " # args = parse_args()\n", "\n", " # Replace with your actual data root and output directory paths\n", " # !CUDA_VISIBLE_DEVICES=0 python inference.py \\\n", " # --dataset vitonhd \\\n", " # --data_root_path /kaggle/input/viton-hd-dataset \\\n", " # --output_dir ./output \\\n", " # --dataloader_num_workers 8 \\\n", " # --batch_size 8 \\\n", " # --seed 555 \\\n", " # --mixed_precision no \\\n", " # --allow_tf32 \\\n", " # --repaint \\\n", " # --eval_pair\n", " \n", " args=argparse.Namespace()\n", " args.__dict__= {\n", " \"base_model_path\": \"booksforcharlie/stable-diffusion-inpainting\",\n", " \"resume_path\": \"zhengchong/CatVTON\",\n", " \"dataset_name\": \"vitonhd\",\n", " # \"data_root_path\": \"/kaggle/input/viton-hd-dataset\",\n", " \"data_root_path\": \"/kaggle/working/stable-diffusion/sample_dataset\",\n", " \"output_dir\": \"./output\",\n", " \"seed\": 555,\n", " \"batch_size\": 2,\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", " models=load_model.preload_models_from_standard_weights(ckpt_path=\"sd-v1-5-inpainting.ckpt\", device=\"cuda\", finetune_weights_path=\"/kaggle/working/stable-diffusion/checkpoints/checkpoint_epoch_10.pth\")\n", "\n", " # Pipeline\n", " pipeline = CatVTONPipeline(\n", " attn_ckpt_version=args.dataset_name,\n", " attn_ckpt=args.resume_path,\n", " base_ckpt=args.base_model_path,\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", " # 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": "5c2d9f98", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "143d0ef9", "metadata": {}, "outputs": [], "source": [ "# rm -rf output" ] }, { "cell_type": "code", "execution_count": 37, "id": "77c56140", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "05006_00.jpg 11001_00.jpg\n" ] } ], "source": [ "import sys\n", "f='/kaggle/input/viton-hd-dataset/test_pairs.txt'\n", "with open(f, 'r') as file:\n", " lines = file.readlines()\n", "person_img, cloth_img = lines[0].strip().split(\" \")\n", "mask_img = person_img\n", "\n", "print(person_img, cloth_img)" ] }, { "cell_type": "code", "execution_count": 38, "id": "0fdf30ae", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "app_flux.py eval.py preprocess_agnostic_mask.py\n", "app_p2p.py index.html \u001b[0m\u001b[01;34m__pycache__\u001b[0m/\n", "app.py inference.py README.md\n", "attention.py interface.py requirements.txt\n", "clip.py LICENSE \u001b[01;34mresource\u001b[0m/\n", "ddpm.py merges.txt \u001b[01;34msample_dataset\u001b[0m/\n", "decoder.py \u001b[01;34mmodel\u001b[0m/ sd-v1-5-inpainting.ckpt\n", "\u001b[01;34mdensepose\u001b[0m/ model_converter.py \u001b[01;34mstable-diffusion\u001b[0m/\n", "\u001b[01;34mdetectron2\u001b[0m/ model.safetensors test.ipynb\n", "diffusion.py \u001b[01;34moutput\u001b[0m/ utils.py\n", "encoder.py pipeline.py vocab.json\n" ] } ], "source": [ "ls" ] }, { "cell_type": "code", "execution_count": null, "id": "d4063d0b", "metadata": {}, "outputs": [], "source": [ "# rm -rf output" ] }, { "cell_type": "code", "execution_count": 97, "id": "52e3cd56", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "02532_00.jpg 03921_00.jpg 08088_00.jpg 12419_00.jpg\n", "03191_00.jpg 05006_00.jpg 08650_00.jpg 12562_00.jpg\n" ] } ], "source": [ "ls ./output/vitonhd-512/unpaired" ] }, { "cell_type": "code", "execution_count": 98, "id": "ac7340f8", "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'_oh'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_71/1176057974.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/IPython/core/displayhook.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, result)\u001b[0m\n\u001b[1;32m 261\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_output_prompt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 262\u001b[0m \u001b[0mformat_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmd_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_format_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 263\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_user_ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 264\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcull_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: '_oh'" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from PIL import Image\n", "img=Image.open('./output/vitonhd-512/unpaired/12562_00.jpg')\n", "\n", "import matplotlib.pyplot as plt\n", "plt.imshow(img)" ] }, { "cell_type": "code", "execution_count": 25, "id": "86b70586", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-0.5, 767.5, 1023.5, -0.5)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from PIL import Image\n", "\n", "person_image = Image.open(f\"/kaggle/input/viton-hd-dataset/test/image/{person_img}\").convert(\"RGB\")\n", "cloth_image = Image.open(f\"/kaggle/input/viton-hd-dataset/test/cloth/{cloth_img}\").convert(\"RGB\")\n", "mask_image = Image.open(f\"/kaggle/input/viton-hd-dataset/test/agnostic-mask/{mask_img.replace('.jpg', '_mask.png')}\").convert(\"L\")\n", "\n", "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(12, 4))\n", "plt.subplot(1, 3, 1)\n", "plt.imshow(person_image)\n", "plt.title(\"Person Image\")\n", "plt.axis('off')\n", "\n", "plt.subplot(1, 3, 2)\n", "plt.imshow(cloth_image)\n", "plt.title(\"Cloth Image\")\n", "plt.axis('off')\n", "plt.subplot(1, 3, 3)\n", "plt.imshow(mask_image, cmap='gray')\n", "plt.title(\"Mask Image\")\n", "plt.axis('off')" ] }, { "cell_type": "code", "execution_count": null, "id": "826427b6", "metadata": { "vscode": { "languageId": "shellscript" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2025-06-11 03:13:29.903172: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "E0000 00:00:1749611610.125212 127 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "E0000 00:00:1749611610.187151 127 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", "scheduler_config.json: 100%|███████████████████| 313/313 [00:00<00:00, 1.89MB/s]\n", "config.json: 100%|█████████████████████████████| 547/547 [00:00<00:00, 4.50MB/s]\n", "diffusion_pytorch_model.safetensors: 100%|████| 335M/335M [00:01<00:00, 250MB/s]\n", "config.json: 100%|█████████████████████████████| 748/748 [00:00<00:00, 4.77MB/s]\n", "An error occurred while trying to fetch booksforcharlie/stable-diffusion-inpainting: booksforcharlie/stable-diffusion-inpainting does not appear to have a file named diffusion_pytorch_model.safetensors.\n", "Defaulting to unsafe serialization. Pass `allow_pickle=False` to raise an error instead.\n", "diffusion_pytorch_model.bin: 100%|██████████| 3.44G/3.44G [00:13<00:00, 251MB/s]\n", "Fetching 12 files: 0%| | 0/12 [00:00\n", " main()\n", " File \"/usr/local/lib/python3.11/dist-packages/torch/utils/_contextlib.py\", line 116, in decorate_context\n", " return func(*args, **kwargs)\n", " ^^^^^^^^^^^^^^^^^^^^^\n", " File \"/kaggle/working/CatVTON/inference.py\", line 269, in main\n", " dataset = VITONHDTestDataset(args)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/kaggle/working/CatVTON/inference.py\", line 18, in __init__\n", " self.data = self.load_data()\n", " ^^^^^^^^^^^^^^^^\n", " File \"/kaggle/working/CatVTON/inference.py\", line 39, in load_data\n", " assert os.path.exists(pair_txt:=os.path.join(self.args.data_root_path, 'test_pairs_unpaired.txt')), f\"File {pair_txt} does not exist.\"\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", "AssertionError: File /kaggle/input/viton-hd-dataset/test_pairs_unpaired.txt does not exist.\n" ] } ], "source": [ "# # Replace with your actual data root and output directory paths\n", "# !CUDA_VISIBLE_DEVICES=0 python inference.py \\\n", "# --dataset vitonhd \\\n", "# --data_root_path /kaggle/input/viton-hd-dataset \\\n", "# --output_dir ./output \\\n", "# --dataloader_num_workers 8 \\\n", "# --batch_size 8 \\\n", "# --seed 555 \\\n", "# --mixed_precision no \\\n", "# --allow_tf32 \\\n", "# --repaint \\\n", "# --eval_pair" ] }, { "cell_type": "code", "execution_count": null, "id": "e417edb7", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "1c86c58d", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 5 }