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{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"id": "32b7d029-64ce-4361-acde-dc72d67637b7",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import copy\n",
"import torch\n",
"import torch.nn as nn\n",
"import clip\n",
"from transformers import CLIPProcessor\n",
"from huggingface_hub import PyTorchModelHubMixin\n",
"from transformers import PretrainedConfig\n",
"\n",
"class CSDCLIPConfig(PretrainedConfig):\n",
" model_type = \"csd_clip\"\n",
"\n",
" def __init__(\n",
" self,\n",
" name=\"csd_large\",\n",
" embedding_dim=1024,\n",
" feature_dim=1024,\n",
" content_dim=768,\n",
" style_dim=768,\n",
" content_proj_head=\"default\",\n",
" **kwargs\n",
" ):\n",
" super().__init__(**kwargs)\n",
" self.name = name\n",
" self.embedding_dim = embedding_dim\n",
" self.content_proj_head = content_proj_head\n",
" self.task_specific_params = None # Add this line\n",
"\n",
"class CSD_CLIP(nn.Module, PyTorchModelHubMixin):\n",
" \"\"\"backbone + projection head\"\"\"\n",
" def __init__(self, name='vit_large',content_proj_head='default'):\n",
" super(CSD_CLIP, self).__init__()\n",
" self.content_proj_head = content_proj_head\n",
" if name == 'vit_large':\n",
" clipmodel, _ = clip.load(\"ViT-L/14\")\n",
" self.backbone = clipmodel.visual\n",
" self.embedding_dim = 1024\n",
" self.feature_dim = 1024\n",
" self.content_dim = 768\n",
" self.style_dim = 768\n",
" self.name = \"csd_large\"\n",
" elif name == 'vit_base':\n",
" clipmodel, _ = clip.load(\"ViT-B/16\")\n",
" self.backbone = clipmodel.visual\n",
" self.embedding_dim = 768 \n",
" self.feature_dim = 512\n",
" self.content_dim = 512\n",
" self.style_dim = 512\n",
" self.name = \"csd_base\"\n",
" else:\n",
" raise Exception('This model is not implemented')\n",
"\n",
" self.last_layer_style = copy.deepcopy(self.backbone.proj)\n",
" self.last_layer_content = copy.deepcopy(self.backbone.proj)\n",
"\n",
" self.backbone.proj = None\n",
" \n",
" self.config = CSDCLIPConfig(\n",
" name=self.name,\n",
" embedding_dim=self.embedding_dim,\n",
" feature_dim=self.feature_dim,\n",
" content_dim=self.content_dim,\n",
" style_dim=self.style_dim,\n",
" content_proj_head=self.content_proj_head\n",
" )\n",
"\n",
" def get_config(self):\n",
" return self.config.to_dict()\n",
"\n",
" @property\n",
" def dtype(self):\n",
" return self.backbone.conv1.weight.dtype\n",
" \n",
" @property\n",
" def device(self):\n",
" return next(self.parameters()).device\n",
"\n",
" def forward(self, input_data):\n",
" \n",
" feature = self.backbone(input_data)\n",
"\n",
" style_output = feature @ self.last_layer_style\n",
" style_output = nn.functional.normalize(style_output, dim=1, p=2)\n",
"\n",
" content_output = feature @ self.last_layer_content\n",
" content_output = nn.functional.normalize(content_output, dim=1, p=2)\n",
" \n",
" return feature, content_output, style_output\n",
"\n",
"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
"model = CSD_CLIP.from_pretrained(\"yuxi-liu-wired/CSD\")\n",
"model.to(device);"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bbd750f6-fde9-48ed-a7d8-42ee5d31429d",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import torch\n",
"from transformers import Pipeline\n",
"from typing import Union, List\n",
"from PIL import Image\n",
"\n",
"class CSDCLIPPipeline(Pipeline):\n",
" def __init__(self, model, processor, device=None):\n",
" if device is None:\n",
" device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
" super().__init__(model=model, tokenizer=None, device=device)\n",
" self.processor = processor\n",
"\n",
" def _sanitize_parameters(self, **kwargs):\n",
" return {}, {}, {}\n",
"\n",
" def preprocess(self, images):\n",
" if isinstance(images, (str, Image.Image)):\n",
" images = [images]\n",
" \n",
" processed = self.processor(images=images, return_tensors=\"pt\", padding=True, truncation=True)\n",
" return {k: v.to(self.device) for k, v in processed.items()}\n",
"\n",
" def _forward(self, model_inputs):\n",
" pixel_values = model_inputs['pixel_values'].to(self.model.dtype)\n",
" with torch.no_grad():\n",
" features, content_output, style_output = self.model(pixel_values)\n",
" return {\"features\": features, \"content_output\": content_output, \"style_output\": style_output}\n",
"\n",
" def postprocess(self, model_outputs):\n",
" return {\n",
" \"features\": model_outputs[\"features\"].cpu().numpy(),\n",
" \"content_output\": model_outputs[\"content_output\"].cpu().numpy(),\n",
" \"style_output\": model_outputs[\"style_output\"].cpu().numpy()\n",
" }\n",
"\n",
" def __call__(self, images: Union[str, List[str], Image.Image, List[Image.Image]]):\n",
" return super().__call__(images)\n",
"\n",
"processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-large-patch14\")\n",
"pipeline = CSDCLIPPipeline(model=model, processor=processor, device=device)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4107999a-c48c-4cb4-9247-9836dfb27e98",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Processing images: 100%|█████████████████████████████████████████████████████████████| 900/900 [01:09<00:00, 12.86it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing complete. Results saved to 'processed_dataset.parquet'\n"
]
}
],
"source": [
"import io\n",
"from PIL import Image\n",
"from datasets import load_dataset\n",
"import pandas as pd\n",
"from tqdm import tqdm\n",
"\n",
"def to_jpeg(image):\n",
" buffered = io.BytesIO()\n",
" if image.mode not in (\"RGB\"):\n",
" image = image.convert(\"RGB\")\n",
" image.save(buffered, format='JPEG')\n",
" return buffered.getvalue() \n",
"\n",
"def scale_image(image, max_resolution):\n",
" if max(image.width, image.height) > max_resolution:\n",
" image = image.resize((max_resolution, int(image.height * max_resolution / image.width)))\n",
" return image\n",
"\n",
"def process_dataset(pipeline, dataset_name, dataset_size=900, max_resolution=192):\n",
" dataset = load_dataset(dataset_name, split='train')\n",
" dataset = dataset.select(range(dataset_size))\n",
" \n",
" # Print the column names\n",
" print(\"Dataset columns:\", dataset.column_names)\n",
" \n",
" # Initialize lists to store results\n",
" embeddings = []\n",
" jpeg_images = []\n",
" \n",
" # Process each item in the dataset\n",
" for item in tqdm(dataset, desc=\"Processing images\"):\n",
" try:\n",
" img = item['image']\n",
" \n",
" # If img is a string (file path), load the image\n",
" if isinstance(img, str):\n",
" img = Image.open(img)\n",
"\n",
"\n",
" output = pipeline(img)\n",
" style_output = output[\"style_output\"].squeeze(0)\n",
" \n",
" img = scale_image(img, max_resolution)\n",
" jpeg_img = to_jpeg(img)\n",
" \n",
" # Append results to lists\n",
" embeddings.append(style_output)\n",
" jpeg_images.append(jpeg_img)\n",
" except Exception as e:\n",
" print(f\"Error processing item: {e}\")\n",
" \n",
" # Create a DataFrame with the results\n",
" df = pd.DataFrame({\n",
" 'embedding': embeddings,\n",
" 'image': jpeg_images\n",
" })\n",
" \n",
" df.to_parquet('processed_dataset.parquet')\n",
" print(\"Processing complete. Results saved to 'processed_dataset.parquet'\")\n",
"\n",
"process_dataset(pipeline, \"yuxi-liu-wired/style-content-grid-SDXL\", \n",
" dataset_size=900, max_resolution=192)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "066ec067-edb1-4110-a0fe-8d7c97311790",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:diffgan]",
"language": "python",
"name": "conda-env-diffgan-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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