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{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"machine_shape":"hm","gpuType":"T4","provenance":[{"file_id":"https://huggingface.co/SmilingWolf/wd-vit-tagger-v3.ipynb","timestamp":1754135740338}]},"accelerator":"GPU","kaggle":{"accelerator":"gpu"},"language_info":{"name":"python"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","source":["## Local Inference on GPU\n","Model page: https://huggingface.co/SmilingWolf/wd-vit-tagger-v3\n","\n","⚠️ If the generated code snippets do not work, please open an issue on either the [model repo](https://huggingface.co/SmilingWolf/wd-vit-tagger-v3)\n","\t\t\tand/or on [huggingface.js](https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/src/model-libraries-snippets.ts) 🙏"],"metadata":{"id":"6g088Uu0kg21"}},{"cell_type":"code","source":["\n","%cd /content/\n","!unzip training_data.zip\n","\n","\n"],"metadata":{"id":"c60a6jW-YwsN"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mhccTDyzirVn"},"outputs":[],"source":["# @markdown Split the image into 20 parts prior to running\n","no_parts = 1 # @param {type:'slider', min:1,max:30,step:1}\n","print(f'Splitting all images found under /content/... \\n into {no_parts} along x-axis')\n","import os,math,random\n","from PIL import Image\n","home_directory = '/content/'\n","using_Kaggle = os.environ.get('KAGGLE_URL_BASE','')\n","if using_Kaggle : home_directory = '/kaggle/working/'\n","%cd {home_directory}\n","\n","def my_mkdirs(folder):\n"," if os.path.exists(folder)==False:\n"," os.makedirs(folder)\n","\n","\n","tgt_folder = f'/content/tmp/'\n","split_folder = f'/content/input_images/'\n","my_mkdirs(f'{split_folder}')\n","\n","\n","src_folder = '/content/'\n","suffixes = ['.gif','.png', '.jpeg' , '.webp' , '.jpg']\n","num = 1\n","for filename in os.listdir(src_folder):\n"," for suffix in suffixes:\n"," if not filename.find(suffix)>-1: continue\n"," #while os.path.exists(f'{tgt_folder}{num}.txt'):num = num+1\n"," print(filename)\n"," %cd {src_folder}\n"," textpath = filename.replace(suffix,'.txt')\n"," #os.remove(f'{filename}')\n"," #continue\n"," image = Image.open(f\"{filename}\").convert('RGB')\n"," w,h=image.size\n"," #grid = product(range(0, h-h%d, d), range(0, w-w%d, d))\n"," divs=no_parts\n"," step=math.floor(w/divs)\n"," %cd {split_folder}\n"," for index in range(divs):\n"," box = (step*index, 0 ,step*(index+1),math.floor(1.0*h))\n"," image.crop(box).save(f'{num}_{index}.jpeg','JPEG')\n"," %cd /content/\n"," if os.path.exists(textpath):\n"," with open(f'{textpath}', 'r') as file:\n"," _tags = file.read()\n","\n"," print(_tags)\n"," if not _tags:continue\n"," tags=''\n"," _tags = [item.strip() for item in f'{_tags}'.split(',')]\n"," random.shuffle(_tags)\n"," for tag in _tags:\n"," tags = tags + tag + ' , '\n"," #----#\n"," tags = (tags + 'AAAA').replace(' , AAAA','')\n"," prompt_str = f' {tags}'\n"," %cd {split_folder}\n"," f = open(f'{num}_{index}.txt','w')\n"," f.write(f'{prompt_str}')\n"," f.close()\n"," #---#\n"," #-----#\n"," #----#\n"," num = num+1\n"," #caption = stream_chat(input_image, \"descriptive\", \"formal\", \"any\")\n"," #print(f\"...\\n\\n...caption for {filename}\\n\\n...\")\n"," #print(caption)\n"," #---------#\n"," #f = open(f\"{num}.txt\", \"w\")\n"," #f.write(f'{caption}')\n"," #f.close()\n"," #input_image.save(f'{num}.jpeg', \"JPEG\")\n"," os.remove(f\"{src_folder}{filename}\")\n"," #os.remove(f'{src_folder}{textpath}')"]},{"cell_type":"code","source":["# Install required packages\n","!pip install timm pillow pandas requests\n","\n","import os\n","import timm\n","import torch\n","from PIL import Image\n","import torchvision.transforms as transforms\n","import numpy as np\n","import pandas as pd\n","import requests\n","from io import StringIO\n","\n","# Download the selected_tags.csv file\n","tags_url = \"https://huggingface.co/SmilingWolf/wd-vit-tagger-v3/resolve/main/selected_tags.csv\"\n","response = requests.get(tags_url)\n","if response.status_code != 200:\n"," raise Exception(f\"Failed to download selected_tags.csv: Status code {response.status_code}\")\n","tags_df = pd.read_csv(StringIO(response.text))\n","tags = tags_df['name'].tolist() # Extract tag names from the CSV\n","print(f\"Loaded {len(tags)} tags from CSV: {tags[:10]}...\") # Print first 10 tags for verification\n","\n","# Set up the model\n","model = timm.create_model(\"hf_hub:SmilingWolf/wd-vit-tagger-v3\", pretrained=True)\n","model.eval()\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","model = model.to(device)\n","print(f\"Model loaded on device: {device}\")\n","\n","# Verify model output size\n","dummy_input = torch.zeros(1, 3, 448, 448).to(device)\n","with torch.no_grad():\n"," dummy_output = model(dummy_input)\n","model_output_size = dummy_output.shape[1]\n","print(f\"Model output size: {model_output_size}\")\n","\n","# Check if tags length matches model output\n","if len(tags) != model_output_size:\n"," raise ValueError(f\"Tag list length ({len(tags)}) does not match model output size ({model_output_size})\")\n","\n","# Define image preprocessing\n","preprocess = transforms.Compose([\n"," transforms.Resize((448, 448)),\n"," transforms.ToTensor(),\n"," transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n","])\n","\n","# Set paths\n","input_folder = \"/content/input_images\" # Change to your input folder path\n","output_folder = \"/content/tmp\" # Change to your output folder path\n","\n","# Create output folder if it doesn't exist\n","os.makedirs(output_folder, exist_ok=True)\n","\n","# Function to process an image and generate tags\n","def process_image(image_path, tags_list):\n"," try:\n"," # Load and preprocess image\n"," image = Image.open(image_path).convert(\"RGB\")\n"," input_tensor = preprocess(image).unsqueeze(0).to(device)\n","\n"," # Get model predictions\n"," with torch.no_grad():\n"," logits = model(input_tensor)\n","\n"," # Apply sigmoid to get probabilities\n"," probs = torch.sigmoid(logits).cpu().numpy()[0]\n","\n"," # Ensure probs length matches tags length\n"," if len(probs) != len(tags_list):\n"," raise ValueError(f\"Model output ({len(probs)}) does not match tags length ({len(tags_list)}) for {image_path}\")\n","\n"," # Get tags with probability > 0.5 (adjust threshold as needed)\n"," selected_tags = [tags_list[i] for i, prob in enumerate(probs) if prob > 0.5]\n","\n"," return selected_tags, image\n"," except Exception as e:\n"," raise Exception(f\"Error in process_image for {image_path}: {str(e)}\")\n","\n","# Process all images in the input folder\n","for filename in sorted(os.listdir(input_folder)): # Sort for reproducibility\n"," if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.gif')):\n"," image_path = os.path.join(input_folder, filename)\n","\n"," # Generate tags and get processed image\n"," try:\n"," selected_tags, image = process_image(image_path, tags) # Pass tags explicitly\n","\n"," # Define output paths\n"," base_name = os.path.splitext(filename)[0]\n"," output_image_path = os.path.join(output_folder, f\"{base_name}.jpg\")\n"," output_txt_path = os.path.join(output_folder, f\"{base_name}.txt\")\n","\n"," # Save image as JPG\n"," image.save(output_image_path, \"JPEG\", quality=95)\n","\n"," # Save tags to text file\n"," with open(output_txt_path, \"w\") as f:\n"," f.write(\", \".join(selected_tags))\n","\n"," print(f\"Processed {filename}: {selected_tags}\")\n"," except Exception as e:\n"," print(f\"Error processing {filename}: {str(e)}\")\n"," continue\n","\n","print(\"Processing complete!\")"],"metadata":{"id":"_oFljDxysGR4"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5EztLCjkPq4U"},"outputs":[],"source":["import shutil\n","%cd /content/\n","shutil.make_archive('/content/tmp', 'zip', '/content/tmp')"]}]} |