Upload dino_tagger.ipynb

dino_tagger.ipynb

@@ -1 +1 @@
-{"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"id":"pb9bxY0OE81H"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#@markdown ✅ Huggingface download (fast if you have added HF_TOKEN to Colab Secrets in this instance)\n","# =============================================\n","# Cell 1: Setup - Dependencies, HF Hub Download & Tagger (GPU Optimized)\n","# =============================================\n","\n","# Install dependencies\n","!pip install -q torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118\n","!pip install -q huggingface_hub safetensors pillow\n","\n","import torch\n","from PIL import Image\n","import os\n","import zipfile\n","from pathlib import Path\n","from google.colab import files\n","from huggingface_hub import hf_hub_download\n","\n","# Check GPU availability\n","if torch.cuda.is_available():\n","    print(f\"✅ GPU detected: {torch.cuda.get_device_name(0)}\")\n","    device = \"cuda\"\n","else:\n","    print(\"⚠️ No GPU found. Running on CPU (will be slower).\")\n","    device = \"cpu\"\n","\n","print(\"Downloading model files via Hugging Face Hub (faster & resumable)...\")\n","\n","# Download using hf_hub_download (recommended way)\n","hf_hub_download(\n","    repo_id=\"lodestones/tagger-experiment\",\n","    filename=\"tagger_proto.safetensors\",\n","    local_dir=\"/content\",\n","    local_dir_use_symlinks=False\n",")\n","\n","hf_hub_download(\n","    repo_id=\"lodestones/tagger-experiment\",\n","    filename=\"tagger_vocab_with_categories.json\",\n","    local_dir=\"/content\",\n","    local_dir_use_symlinks=False\n",")\n","\n","hf_hub_download(\n","    repo_id=\"lodestones/tagger-experiment\",\n","    filename=\"inference_tagger_standalone.py\",\n","    local_dir=\"/content\",\n","    local_dir_use_symlinks=False\n",")\n","\n","print(\"✅ Model files downloaded successfully via Hugging Face Hub.\")\n","\n","# Load the Tagger\n","from inference_tagger_standalone import Tagger\n","\n","tagger = Tagger(\n","    checkpoint_path=\"/content/tagger_proto.safetensors\",\n","    vocab_path=\"/content/tagger_vocab_with_categories.json\",\n","    device=device,\n","    dtype=torch.bfloat16 if device == \"cuda\" else torch.float32,\n","    max_size=1024\n",")\n","\n","print(f\"✅ Tagger loaded successfully on {tagger.device}\")"],"metadata":{"id":"880QrkhOZLM3","cellView":"form"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# =============================================\n","# Cell 2: Process Images from Upload or ZIP + Custom Output\n","# =============================================\n","\n","# @title ⚙️ Run the tagger (Manual upload)\n","\n","# === Input Mode ===\n","use_manual_upload = True        #param {type:\"boolean\"}\n","zip_file_path = \"\"                # param {type:\"string\"}\n","\n","# === Tagging Settings ===\n","threshold_percent = 90            # param {type:\"slider\", min:1, max:95, step:1}\n","max_tags = 500                     # param {type:\"slider\", min:5, max:150, step:5}\n","use_max_tags = False               # param {type:\"boolean\"}\n","add_commas = True                # param {type:\"boolean\"}\n","\n","# === Output Settings ===\n","output_zip_path = \"/content/dino_tagger_output.zip\"   # @param {type:\"string\"}\n","auto_download = True             #param {type:\"boolean\"}\n","\n","print(f\"Input  → Manual Upload: {use_manual_upload} | ZIP: '{zip_file_path}'\")\n","print(f\"Output → Save to: '{output_zip_path}' | Auto-download: {auto_download}\")\n","print(f\"Tagging → Threshold: {threshold_percent}% | Max tags: {max_tags} | Force max: {use_max_tags} | Commas: {add_commas}\\n\")\n","\n","# ------------------- Prepare images -------------------\n","output_dir = Path(\"/content/output\")\n","output_dir.mkdir(exist_ok=True)\n","\n","image_files = []\n","\n","if use_manual_upload:\n","    print(\"Please upload your images (multiple files supported)...\")\n","    uploaded = files.upload()\n","    image_files = [f for f in uploaded.keys()\n","                   if f.lower().endswith(('.png', '.jpg', '.jpeg', '.webp', '.bmp'))]\n","    print(f\"✅ Uploaded {len(image_files)} image(s) manually.\")\n","\n","else:\n","    if not zip_file_path or not Path(zip_file_path).exists():\n","        print(f\"❌ ZIP file not found at: {zip_file_path}\")\n","    else:\n","        print(f\"Extracting images from ZIP: {zip_file_path}\")\n","        extract_dir = Path(\"/content/extracted_images\")\n","        extract_dir.mkdir(exist_ok=True)\n","\n","        with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:\n","            zip_ref.extractall(extract_dir)\n","\n","        for ext in ['.png', '.jpg', '.jpeg', '.webp', '.bmp']:\n","            image_files.extend(list(extract_dir.rglob(f\"*{ext}\")))\n","            image_files.extend(list(extract_dir.rglob(f\"*{ext.upper()}\")))\n","\n","        image_files = sorted(set(str(p) for p in image_files))\n","        print(f\"✅ Found {len(image_files)} image(s) in the ZIP file.\")\n","\n","# ------------------- Process images -------------------\n","if not image_files:\n","    print(\"❌ No images found to process.\")\n","else:\n","    print(f\"\\nStarting tagging for {len(image_files)} image(s) on {tagger.device}...\\n\")\n","\n","    threshold = threshold_percent / 100.0\n","\n","    for i, img_path in enumerate(image_files, start=1):\n","        img_name = Path(img_path).name\n","        print(f\"[{i}/{len(image_files)}] Processing: {img_name}\")\n","\n","        # Use GPU-accelerated prediction\n","        if use_max_tags:\n","            tags_list = tagger.predict(img_path, topk=max_tags, threshold=None)\n","        else:\n","            tags_list = tagger.predict(img_path, topk=None, threshold=threshold)\n","            if len(tags_list) > max_tags:\n","                tags_list = tags_list[:max_tags]\n","\n","        # Save image as numbered JPG\n","        img = Image.open(img_path).convert(\"RGB\")\n","        new_img_path = output_dir / f\"{i}.jpg\"\n","        img.save(new_img_path, \"JPEG\", quality=95)\n","\n","        # Prepare single-line tag string\n","        tags_only = [tag for tag, prob in tags_list]\n","        tag_text = \" , \".join(tags_only) if add_commas else \" \".join(tags_only)\n","\n","        # Save tags\n","        tag_file = output_dir / f\"{i}.txt\"\n","        with open(tag_file, \"w\", encoding=\"utf-8\") as f:\n","            f.write(tag_text)\n","\n","        print(f\"   → Saved {i}.jpg + {i}.txt  ({len(tags_list)} tags)\")\n","\n","    # Create output zip at custom path\n","    final_zip_path = Path(output_zip_path)\n","    final_zip_path.parent.mkdir(parents=True, exist_ok=True)\n","\n","    with zipfile.ZipFile(final_zip_path, \"w\") as zipf:\n","        for file in sorted(output_dir.iterdir()):\n","            zipf.write(file, file.name)\n","\n","    print(f\"\\n✅ Processing complete! Output ZIP saved to: {final_zip_path}\")\n","\n","    # Auto-download if enabled\n","    if auto_download:\n","        files.download(str(final_zip_path))\n","        print(\"📥 Auto-download triggered.\")\n","    else:\n","        print(\"✅ Auto-download disabled. You can manually download the file from the path above.\")"],"metadata":{"id":"aR-oik8TTpNU"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# =============================================\n","# Cell 3: Unload previous tagger + Run WD-VIT Tagger + Prepend Tags\n","# =============================================\n","\n","#@markdown 🧠 WD-VIT Tagger v3 - Prepend Tags to Existing Files\n","\n","import torch\n","import gc\n","import os\n","from pathlib import Path\n","import zipfile\n","from google.colab import files\n","import timm\n","import torchvision.transforms as transforms\n","from PIL import Image\n","import pandas as pd\n","import requests\n","from io import StringIO\n","\n","print(\"🧹 Unloading previous tagger from VRAM...\")\n","\n","# Unload previous model to free VRAM\n","if 'tagger' in globals():\n","    del tagger\n","    print(\"   → Deleted tagger object\")\n","\n","if torch.cuda.is_available():\n","    torch.cuda.empty_cache()\n","    torch.cuda.synchronize()\n","    gc.collect()\n","    print(f\"   → VRAM cleared. Current memory: {torch.cuda.memory_allocated()/1024**2:.1f} MB\")\n","\n","print(\"\\n📥 Loading WD-VIT Tagger v3...\")\n","\n","# ==================== Setup WD Tagger ====================\n","\n","# Download tags\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: {response.status_code}\")\n","\n","tags_df = pd.read_csv(StringIO(response.text))\n","tags_list = tags_df['name'].tolist()\n","print(f\"✅ Loaded {len(tags_list)} tags from selected_tags.csv\")\n","\n","# Load 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\"✅ WD-VIT Tagger loaded on {device}\")\n","\n","# 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","# ==================== Settings ====================\n","\n","threshold = 0.7                    # @param {type:\"slider\", min:0.1, max:0.95, step:0.01}\n","add_commas = True                  # @param {type:\"boolean\"}\n","output_zip_path = \"/content/dino_wd_combined_output.zip\"  # @param {type:\"string\"}\n","auto_download = True               # @param {type:\"boolean\"}\n","\n","print(f\"\\nSettings → WD Threshold: {threshold} | Add commas: {add_commas}\")\n","print(f\"Output → {output_zip_path} | Auto-download: {auto_download}\\n\")\n","\n","# ==================== Process Images ====================\n","\n","input_dir = Path(\"/content/output\")   # From Cell 2\n","output_dir = Path(\"/content/wd_combined_output\")\n","output_dir.mkdir(exist_ok=True)\n","\n","image_files = sorted(list(input_dir.glob(\"*.jpg\")))\n","\n","if not image_files:\n","    print(\"❌ No images found in /content/output/\")\n","else:\n","    print(f\"Starting WD tagging for {len(image_files)} images...\\n\")\n","\n","    for i, img_path in enumerate(image_files, start=1):\n","        img_name = img_path.name\n","        txt_path = input_dir / f\"{i}.txt\"   # Original tags from Cell 2\n","\n","        print(f\"[{i}/{len(image_files)}] Processing: {img_name}\")\n","\n","        # Load and preprocess image\n","        image = Image.open(img_path).convert(\"RGB\")\n","        input_tensor = preprocess(image).unsqueeze(0).to(device)\n","\n","        # Inference\n","        with torch.no_grad():\n","            logits = model(input_tensor)\n","            probs = torch.sigmoid(logits).cpu().numpy()[0]\n","\n","        # Get WD tags above threshold\n","        wd_tags = [tags_list[j] for j, prob in enumerate(probs) if prob > threshold]\n","\n","        # Read existing tags from Cell 2\n","        if txt_path.exists():\n","            with open(txt_path, \"r\", encoding=\"utf-8\") as f:\n","                existing_text = f.read().strip()\n","            existing_tags = [t.strip() for t in existing_text.replace(\",\", \" \").split() if t.strip()]\n","        else:\n","            existing_tags = []\n","\n","        # Combine: WD tags first, then existing tags\n","        combined_tags = wd_tags + existing_tags\n","\n","        # Remove possible duplicates while preserving order\n","        seen = set()\n","        final_tags = []\n","        for tag in combined_tags:\n","            if tag not in seen:\n","                seen.add(tag)\n","                final_tags.append(tag)\n","\n","        # Create tag string\n","        tag_text = \" , \".join(final_tags) if add_commas else \" \".join(final_tags)\n","\n","        # Save updated files\n","        new_img_path = output_dir / f\"{i}.jpg\"\n","        new_txt_path = output_dir / f\"{i}.txt\"\n","\n","        image.save(new_img_path, \"JPEG\", quality=95)\n","\n","        with open(new_txt_path, \"w\", encoding=\"utf-8\") as f:\n","            f.write(tag_text)\n","\n","        print(f\"   → Saved {i}.jpg + {i}.txt  (WD: {len(wd_tags)} | Total: {len(final_tags)})\")\n","\n","    # ==================== Create Final ZIP ====================\n","\n","    final_zip = Path(output_zip_path)\n","    final_zip.parent.mkdir(parents=True, exist_ok=True)\n","\n","    with zipfile.ZipFile(final_zip, \"w\") as zipf:\n","        for file in sorted(output_dir.iterdir()):\n","            zipf.write(file, file.name)\n","\n","    print(f\"\\n✅ All done! Combined output saved to: {final_zip}\")\n","\n","    if auto_download:\n","        files.download(str(final_zip))\n","        print(\"📥 Auto-download triggered.\")\n","    else:\n","        print(\"✅ Auto-download disabled.\")"],"metadata":{"cellView":"form","id":"58THbptXvXIZ"},"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"cellView":"form","id":"d4c348b4"},"source":["# =============================================\n","# Cell 2: Process Images from Upload or ZIP + Custom Output\n","# =============================================\n","\n","# @title 📦 ⚙️ Run the tagger using .zip file in Drive + custom options\n","\n","# === Input Mode ===\n","use_manual_upload = False        #param {type:\"boolean\"}\n","zip_file_path = \"/content/drive/MyDrive/fetch.zip\"                # @param {type:\"string\"}\n","\n","# === Tagging Settings ===\n","threshold_percent = 65            # @param {type:\"slider\", min:1, max:95, step:1}\n","max_tags = 300                     # @param {type:\"slider\", min:5, max:500, step:5}\n","use_max_tags = False               # @param {type:\"boolean\"}\n","add_commas = True                # @param {type:\"boolean\"}\n","\n","# === Output Settings ===\n","output_zip_path = \"/content/drive/MyDrive/dino_tagger_output.zip\"   # @param {type:\"string\"}\n","auto_download = False             #param {type:\"boolean\"}\n","\n","print(f\"Input  → Manual Upload: {use_manual_upload} | ZIP: '{zip_file_path}'\")\n","print(f\"Output → Save to: '{output_zip_path}' | Auto-download: {auto_download}\")\n","print(f\"Tagging → Threshold: {threshold_percent}% | Max tags: {max_tags} | Force max: {use_max_tags} | Commas: {add_commas}\\n\")\n","\n","# ------------------- Prepare images -------------------\n","output_dir = Path(\"/content/output\")\n","output_dir.mkdir(exist_ok=True)\n","\n","image_files = []\n","\n","if use_manual_upload:\n","    print(\"Please upload your images (multiple files supported)...\")\n","    uploaded = files.upload()\n","    image_files = [f for f in uploaded.keys()\n","                   if f.lower().endswith(('.png', '.jpg', '.jpeg', '.webp', '.bmp'))]\n","    print(f\"✅ Uploaded {len(image_files)} image(s) manually.\")\n","\n","else:\n","    if not zip_file_path or not Path(zip_file_path).exists():\n","        print(f\"❌ ZIP file not found at: {zip_file_path}\")\n","    else:\n","        print(f\"Extracting images from ZIP: {zip_file_path}\")\n","        extract_dir = Path(\"/content/extracted_images\")\n","        extract_dir.mkdir(exist_ok=True)\n","\n","        with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:\n","            zip_ref.extractall(extract_dir)\n","\n","        temp_image_files = []\n","        for ext in ['.png', '.jpg', '.jpeg', '.webp', '.bmp']:\n","            temp_image_files.extend(list(extract_dir.rglob(f\"*{ext}\")))\n","            temp_image_files.extend(list(extract_dir.rglob(f\"*{ext.upper()}\")))\n","\n","        # Filter out files from __MACOSX directories that cause issues\n","        image_files = sorted(set(str(p) for p in temp_image_files if \"__MACOSX\" not in str(p)))\n","        print(f\"✅ Found {len(image_files)} image(s) in the ZIP file (excluding macOS metadata files).\")\n","\n","# ------------------- Process images -------------------\n","if not image_files:\n","    print(\"❌ No images found to process.\")\n","else:\n","    print(f\"\\nStarting tagging for {len(image_files)} image(s) on {tagger.device}...\\n\")\n","\n","    threshold = threshold_percent / 100.0\n","\n","    for i, img_path in enumerate(image_files, start=1):\n","        img_name = Path(img_path).name\n","        print(f\"[{i}/{len(image_files)}] Processing: {img_name}\")\n","\n","        # Use GPU-accelerated prediction\n","        if use_max_tags:\n","            tags_list = tagger.predict(img_path, topk=max_tags, threshold=None)\n","        else:\n","            tags_list = tagger.predict(img_path, topk=None, threshold=threshold)\n","            if len(tags_list) > max_tags:\n","                tags_list = tags_list[:max_tags]\n","\n","        # Save image as numbered JPG\n","        img = Image.open(img_path).convert(\"RGB\")\n","        new_img_path = output_dir / f\"{i}.jpg\"\n","        img.save(new_img_path, \"JPEG\", quality=95)\n","\n","        # Prepare single-line tag string\n","        tags_only = [tag for tag, prob in tags_list]\n","        tag_text = \" , \".join(tags_only) if add_commas else \" \".join(tags_only)\n","\n","        # Save tags\n","        tag_file = output_dir / f\"{i}.txt\"\n","        with open(tag_file, \"w\", encoding=\"utf-8\") as f:\n","            f.write(tag_text)\n","\n","        print(f\"   → Saved {i}.jpg + {i}.txt  ({len(tags_list)} tags)\")\n","\n","    # Create output zip at custom path\n","    final_zip_path = Path(output_zip_path)\n","    final_zip_path.parent.mkdir(parents=True, exist_ok=True)\n","\n","    with zipfile.ZipFile(final_zip_path, \"w\") as zipf:\n","        for file in sorted(output_dir.iterdir()):\n","            zipf.write(file, file.name)\n","\n","    print(f\"\\n✅ Processing complete! Output ZIP saved to: {final_zip_path}\")\n","\n","    # Auto-download if enabled\n","    if auto_download:\n","        files.download(str(final_zip_path))\n","        print(\"📥 Auto-download triggered.\")\n","    else:\n","        print(\"✅ Auto-download disabled. You can manually download the file from the path above.\")"],"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# =============================================\n","# Cell 3: Disconnect Runtime After Processing (Optional)\n","# =============================================\n","\n","# @title Disconnect from Session After Completion\n","\n","disconnect_after_run = True  # @param {type:\"boolean\"}\n","\n","if disconnect_after_run:\n","    print(\"🔌 Disconnecting Colab runtime in 10 seconds...\")\n","    print(\"This will stop the current session and free up GPU resources.\")\n","\n","    import time\n","    time.sleep(10)\n","\n","    from google.colab import runtime\n","    runtime.unassign()\n","\n","    print(\"✅ Runtime disconnected.\")\n","else:\n","    print(\"✅ Disconnect option is disabled.\")\n","    print(\"You can manually disconnect via Runtime → Disconnect and delete runtime if needed.\")"],"metadata":{"id":"byGffTyyPgEY","cellView":"form"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Image Tagger using lodestones/tagger-experiment\n","\n","This Colab notebook lets you automatically generate **tags** for a batch of images using the [lodestones/tagger-experiment](https://huggingface.co/lodestones/tagger-experiment) model.\n","\n","### Features:\n","- Process images either by **manual upload** or by providing a **ZIP file** containing your images\n","- Adjustable **probability threshold** (only tags above this % are kept when not forcing max tags)\n","- Control the **maximum number of tags** per image\n","- Option to **force the top N tags** (ignores threshold) or strictly respect the probability threshold\n","- Output format: Images saved as `1.jpg`, `2.jpg`, ... and corresponding tags as `1.txt`, `2.txt`, ...\n","- Tags in each `.txt` file are saved as a **single line** — either space-separated or comma-separated\n","- Final result is packaged into a downloadable `output.zip` file\n","\n","### How to use:\n","1. Run **Cell 1** first (installs dependencies and loads the model)\n","2. In **Cell 2**, configure your settings:\n","   - Choose between manual upload or ZIP file input\n","   - Adjust threshold, max tags, and output style using the sliders and checkboxes\n","3. Run **Cell 2** — upload images or point to your ZIP file\n","4. Wait for processing and download the resulting `output.zip`\n","\n","The notebook preserves the original image quality (saved as high-quality JPG) and organizes everything with simple numbered filenames for easy use in training pipelines."],"metadata":{"id":"q-fVFdZqMxRA"}},{"cell_type":"code","source":["#@markdown Download method using wget (backup method)\n","# =============================================\n","# Cell 1: Setup - Dependencies, Model & Tagger (GPU Optimized)\n","# =============================================\n","\n","# Install dependencies\n","!pip install -q torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118\n","!pip install -q safetensors pillow\n","\n","import torch\n","from PIL import Image\n","import os\n","import zipfile\n","from pathlib import Path\n","from google.colab import files\n","\n","# Check GPU availability\n","if torch.cuda.is_available():\n","    print(f\"✅ GPU detected: {torch.cuda.get_device_name(0)}\")\n","    device = \"cuda\"\n","else:\n","    print(\"⚠️ No GPU found. Running on CPU (will be slower).\")\n","    device = \"cpu\"\n","\n","print(\"Downloading model files...\")\n","\n","!wget -q https://huggingface.co/lodestones/tagger-experiment/resolve/main/tagger_proto.safetensors -O tagger_proto.safetensors\n","!wget -q https://huggingface.co/lodestones/tagger-experiment/resolve/main/tagger_vocab_with_categories.json -O tagger_vocab_with_categories.json\n","!wget -q https://huggingface.co/lodestones/tagger-experiment/resolve/main/inference_tagger_standalone.py -O inference_tagger_standalone.py\n","\n","print(\"✅ Model files downloaded.\")\n","\n","# Load the Tagger with GPU preference\n","from inference_tagger_standalone import Tagger\n","\n","tagger = Tagger(\n","    checkpoint_path=\"tagger_proto.safetensors\",\n","    vocab_path=\"tagger_vocab_with_categories.json\",\n","    device=device,                                   # Explicit GPU if available\n","    dtype=torch.bfloat16 if device == \"cuda\" else torch.float32,\n","    max_size=1024\n",")\n","\n","print(f\"✅ Tagger loaded successfully on {tagger.device}\")"],"metadata":{"id":"qEQZt_7nPffG","cellView":"form"},"execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/dino_tagger.ipynb","timestamp":1775233735620},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/dino_tagger.ipynb","timestamp":1775159289229},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/dino_tagger.ipynb","timestamp":1774964848181},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/dino_tagger.ipynb","timestamp":1774906863352},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/dino_tagger.ipynb","timestamp":1774901938471},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/dino_tagger.ipynb","timestamp":1774884683620},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/dino_tagger.ipynb","timestamp":1774883563998},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/dino_tagger.ipynb","timestamp":1774882033159},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/dino_tagger.ipynb","timestamp":1774881268156},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Drive to WebP.ipynb","timestamp":1774879795776},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Drive to WebP.ipynb","timestamp":1763646205520},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Drive to WebP.ipynb","timestamp":1760993725927},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1760450712160},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1756712618300},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1747490904984},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1740037333374},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1736477078136},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1725365086834}],"gpuType":"T4"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0}
+{"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"id":"pb9bxY0OE81H"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#@markdown ✅ Huggingface download (fast if you have added HF_TOKEN to Colab Secrets in this instance)\n","# =============================================\n","# Cell 1: Setup - Dependencies, HF Hub Download & Tagger (GPU Optimized)\n","# =============================================\n","\n","# Install dependencies\n","!pip install -q torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118\n","!pip install -q huggingface_hub safetensors pillow\n","\n","import torch\n","from PIL import Image\n","import os\n","import zipfile\n","from pathlib import Path\n","from google.colab import files\n","from huggingface_hub import hf_hub_download\n","\n","# Check GPU availability\n","if torch.cuda.is_available():\n","    print(f\"✅ GPU detected: {torch.cuda.get_device_name(0)}\")\n","    device = \"cuda\"\n","else:\n","    print(\"⚠️ No GPU found. Running on CPU (will be slower).\")\n","    device = \"cpu\"\n","\n","print(\"Downloading model files via Hugging Face Hub (faster & resumable)...\")\n","\n","# Download using hf_hub_download (recommended way)\n","hf_hub_download(\n","    repo_id=\"lodestones/tagger-experiment\",\n","    filename=\"tagger_proto.safetensors\",\n","    local_dir=\"/content\",\n","    local_dir_use_symlinks=False\n",")\n","\n","hf_hub_download(\n","    repo_id=\"lodestones/tagger-experiment\",\n","    filename=\"tagger_vocab_with_categories.json\",\n","    local_dir=\"/content\",\n","    local_dir_use_symlinks=False\n",")\n","\n","hf_hub_download(\n","    repo_id=\"lodestones/tagger-experiment\",\n","    filename=\"inference_tagger_standalone.py\",\n","    local_dir=\"/content\",\n","    local_dir_use_symlinks=False\n",")\n","\n","print(\"✅ Model files downloaded successfully via Hugging Face Hub.\")\n","\n","# Load the Tagger\n","from inference_tagger_standalone import Tagger\n","\n","tagger = Tagger(\n","    checkpoint_path=\"/content/tagger_proto.safetensors\",\n","    vocab_path=\"/content/tagger_vocab_with_categories.json\",\n","    device=device,\n","    dtype=torch.bfloat16 if device == \"cuda\" else torch.float32,\n","    max_size=1024\n",")\n","\n","print(f\"✅ Tagger loaded successfully on {tagger.device}\")"],"metadata":{"id":"880QrkhOZLM3","cellView":"form"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# =============================================\n","# Cell 2: Process Images from Upload or ZIP + Custom Output\n","# =============================================\n","\n","# @title ⚙️ Run the tagger (Manual upload)\n","\n","# === Input Mode ===\n","use_manual_upload = True        #param {type:\"boolean\"}\n","zip_file_path = \"\"                # param {type:\"string\"}\n","\n","# === Tagging Settings ===\n","threshold_percent = 90            # param {type:\"slider\", min:1, max:95, step:1}\n","max_tags = 500                     # param {type:\"slider\", min:5, max:150, step:5}\n","use_max_tags = False               # param {type:\"boolean\"}\n","add_commas = True                # param {type:\"boolean\"}\n","\n","# === Output Settings ===\n","output_zip_path = \"/content/dino_tagger_output.zip\"   # @param {type:\"string\"}\n","auto_download = True             #param {type:\"boolean\"}\n","\n","print(f\"Input  → Manual Upload: {use_manual_upload} | ZIP: '{zip_file_path}'\")\n","print(f\"Output → Save to: '{output_zip_path}' | Auto-download: {auto_download}\")\n","print(f\"Tagging → Threshold: {threshold_percent}% | Max tags: {max_tags} | Force max: {use_max_tags} | Commas: {add_commas}\\n\")\n","\n","# ------------------- Prepare images -------------------\n","output_dir = Path(\"/content/output\")\n","output_dir.mkdir(exist_ok=True)\n","\n","image_files = []\n","\n","if use_manual_upload:\n","    print(\"Please upload your images (multiple files supported)...\")\n","    uploaded = files.upload()\n","    image_files = [f for f in uploaded.keys()\n","                   if f.lower().endswith(('.png', '.jpg', '.jpeg', '.webp', '.bmp'))]\n","    print(f\"✅ Uploaded {len(image_files)} image(s) manually.\")\n","\n","else:\n","    if not zip_file_path or not Path(zip_file_path).exists():\n","        print(f\"❌ ZIP file not found at: {zip_file_path}\")\n","    else:\n","        print(f\"Extracting images from ZIP: {zip_file_path}\")\n","        extract_dir = Path(\"/content/extracted_images\")\n","        extract_dir.mkdir(exist_ok=True)\n","\n","        with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:\n","            zip_ref.extractall(extract_dir)\n","\n","        for ext in ['.png', '.jpg', '.jpeg', '.webp', '.bmp']:\n","            image_files.extend(list(extract_dir.rglob(f\"*{ext}\")))\n","            image_files.extend(list(extract_dir.rglob(f\"*{ext.upper()}\")))\n","\n","        image_files = sorted(set(str(p) for p in image_files))\n","        print(f\"✅ Found {len(image_files)} image(s) in the ZIP file.\")\n","\n","# ------------------- Process images -------------------\n","if not image_files:\n","    print(\"❌ No images found to process.\")\n","else:\n","    print(f\"\\nStarting tagging for {len(image_files)} image(s) on {tagger.device}...\\n\")\n","\n","    threshold = threshold_percent / 100.0\n","\n","    for i, img_path in enumerate(image_files, start=1):\n","        img_name = Path(img_path).name\n","        print(f\"[{i}/{len(image_files)}] Processing: {img_name}\")\n","\n","        # Use GPU-accelerated prediction\n","        if use_max_tags:\n","            tags_list = tagger.predict(img_path, topk=max_tags, threshold=None)\n","        else:\n","            tags_list = tagger.predict(img_path, topk=None, threshold=threshold)\n","            if len(tags_list) > max_tags:\n","                tags_list = tags_list[:max_tags]\n","\n","        # Save image as numbered JPG\n","        img = Image.open(img_path).convert(\"RGB\")\n","        new_img_path = output_dir / f\"{i}.jpg\"\n","        img.save(new_img_path, \"JPEG\", quality=95)\n","\n","        # Prepare single-line tag string\n","        tags_only = [tag for tag, prob in tags_list]\n","        tag_text = \" , \".join(tags_only) if add_commas else \" \".join(tags_only)\n","\n","        # Save tags\n","        tag_file = output_dir / f\"{i}.txt\"\n","        with open(tag_file, \"w\", encoding=\"utf-8\") as f:\n","            f.write(tag_text)\n","\n","        print(f\"   → Saved {i}.jpg + {i}.txt  ({len(tags_list)} tags)\")\n","\n","    # Create output zip at custom path\n","    final_zip_path = Path(output_zip_path)\n","    final_zip_path.parent.mkdir(parents=True, exist_ok=True)\n","\n","    with zipfile.ZipFile(final_zip_path, \"w\") as zipf:\n","        for file in sorted(output_dir.iterdir()):\n","            zipf.write(file, file.name)\n","\n","    print(f\"\\n✅ Processing complete! Output ZIP saved to: {final_zip_path}\")\n","\n","    # Auto-download if enabled\n","    if auto_download:\n","        files.download(str(final_zip_path))\n","        print(\"📥 Auto-download triggered.\")\n","    else:\n","        print(\"✅ Auto-download disabled. You can manually download the file from the path above.\")"],"metadata":{"id":"aR-oik8TTpNU","cellView":"form"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# =============================================\n","# Cell 3: Unload previous tagger + Run WD-VIT Tagger + Prepend Tags\n","# =============================================\n","\n","#@markdown 🧠 WD-VIT Tagger v3 - Prepend Tags to Existing Files\n","\n","import torch\n","import gc\n","import os\n","from pathlib import Path\n","import zipfile\n","from google.colab import files\n","import timm\n","import torchvision.transforms as transforms\n","from PIL import Image\n","import pandas as pd\n","import requests\n","from io import StringIO\n","\n","print(\"🧹 Unloading previous tagger from VRAM...\")\n","\n","# Unload previous model to free VRAM\n","if 'tagger' in globals():\n","    del tagger\n","    print(\"   → Deleted tagger object\")\n","\n","if torch.cuda.is_available():\n","    torch.cuda.empty_cache()\n","    torch.cuda.synchronize()\n","    gc.collect()\n","    print(f\"   → VRAM cleared. Current memory: {torch.cuda.memory_allocated()/1024**2:.1f} MB\")\n","\n","print(\"\\n📥 Loading WD-VIT Tagger v3...\")\n","\n","# ==================== Setup WD Tagger ====================\n","\n","# Download tags\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: {response.status_code}\")\n","\n","tags_df = pd.read_csv(StringIO(response.text))\n","tags_list = tags_df['name'].tolist()\n","print(f\"✅ Loaded {len(tags_list)} tags from selected_tags.csv\")\n","\n","# Load 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\"✅ WD-VIT Tagger loaded on {device}\")\n","\n","# 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","# ==================== Settings ====================\n","\n","threshold = 0.7                    # @param {type:\"slider\", min:0.1, max:0.95, step:0.01}\n","add_commas = True                  # @param {type:\"boolean\"}\n","output_zip_path = \"/content/dino_wd_combined_output.zip\"  # @param {type:\"string\"}\n","auto_download = True               # @param {type:\"boolean\"}\n","\n","print(f\"\\nSettings → WD Threshold: {threshold} | Add commas: {add_commas}\")\n","print(f\"Output → {output_zip_path} | Auto-download: {auto_download}\\n\")\n","\n","# ==================== Process Images ====================\n","\n","input_dir = Path(\"/content/output\")   # From Cell 2\n","output_dir = Path(\"/content/wd_combined_output\")\n","output_dir.mkdir(exist_ok=True)\n","\n","image_files = sorted(list(input_dir.glob(\"*.jpg\")))\n","\n","if not image_files:\n","    print(\"❌ No images found in /content/output/\")\n","else:\n","    print(f\"Starting WD tagging for {len(image_files)} images...\\n\")\n","\n","    for i, img_path in enumerate(image_files, start=1):\n","        img_name = img_path.name\n","        txt_path = input_dir / f\"{i}.txt\"   # Original tags from Cell 2\n","\n","        print(f\"[{i}/{len(image_files)}] Processing: {img_name}\")\n","\n","        # Load and preprocess image\n","        image = Image.open(img_path).convert(\"RGB\")\n","        input_tensor = preprocess(image).unsqueeze(0).to(device)\n","\n","        # Inference\n","        with torch.no_grad():\n","            logits = model(input_tensor)\n","            probs = torch.sigmoid(logits).cpu().numpy()[0]\n","\n","        # Get WD tags above threshold\n","        wd_tags = [tags_list[j] for j, prob in enumerate(probs) if prob > threshold]\n","\n","        # Read existing tags from Cell 2\n","        if txt_path.exists():\n","            with open(txt_path, \"r\", encoding=\"utf-8\") as f:\n","                existing_text = f.read().strip()\n","            existing_tags = [t.strip() for t in existing_text.replace(\",\", \" \").split() if t.strip()]\n","        else:\n","            existing_tags = []\n","\n","        # Combine: WD tags first, then existing tags\n","        combined_tags = wd_tags + existing_tags\n","\n","        # Remove possible duplicates while preserving order\n","        seen = set()\n","        final_tags = []\n","        for tag in combined_tags:\n","            if tag not in seen:\n","                seen.add(tag)\n","                final_tags.append(tag)\n","\n","        # Create tag string\n","        tag_text = \" , \".join(final_tags) if add_commas else \" \".join(final_tags)\n","\n","        # Save updated files\n","        new_img_path = output_dir / f\"{i}.jpg\"\n","        new_txt_path = output_dir / f\"{i}.txt\"\n","\n","        image.save(new_img_path, \"JPEG\", quality=95)\n","\n","        with open(new_txt_path, \"w\", encoding=\"utf-8\") as f:\n","            f.write(tag_text)\n","\n","        print(f\"   → Saved {i}.jpg + {i}.txt  (WD: {len(wd_tags)} | Total: {len(final_tags)})\")\n","\n","    # ==================== Create Final ZIP ====================\n","\n","    final_zip = Path(output_zip_path)\n","    final_zip.parent.mkdir(parents=True, exist_ok=True)\n","\n","    with zipfile.ZipFile(final_zip, \"w\") as zipf:\n","        for file in sorted(output_dir.iterdir()):\n","            zipf.write(file, file.name)\n","\n","    print(f\"\\n✅ All done! Combined output saved to: {final_zip}\")\n","\n","    if auto_download:\n","        files.download(str(final_zip))\n","        print(\"📥 Auto-download triggered.\")\n","    else:\n","        print(\"✅ Auto-download disabled.\")"],"metadata":{"cellView":"form","id":"58THbptXvXIZ"},"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"cellView":"form","id":"d4c348b4"},"source":["# =============================================\n","# Cell 2: Process Images from Upload or ZIP + Custom Output\n","# =============================================\n","\n","# @title 📦 ⚙️ Run the tagger using .zip file in Drive + custom options\n","\n","# === Input Mode ===\n","use_manual_upload = False        #param {type:\"boolean\"}\n","zip_file_path = \"/content/drive/MyDrive/fetch.zip\"                # @param {type:\"string\"}\n","\n","# === Tagging Settings ===\n","threshold_percent = 65            # @param {type:\"slider\", min:1, max:95, step:1}\n","max_tags = 300                     # @param {type:\"slider\", min:5, max:500, step:5}\n","use_max_tags = False               # @param {type:\"boolean\"}\n","add_commas = True                # @param {type:\"boolean\"}\n","\n","# === Output Settings ===\n","output_zip_path = \"/content/drive/MyDrive/dino_tagger_output.zip\"   # @param {type:\"string\"}\n","auto_download = False             #param {type:\"boolean\"}\n","\n","print(f\"Input  → Manual Upload: {use_manual_upload} | ZIP: '{zip_file_path}'\")\n","print(f\"Output → Save to: '{output_zip_path}' | Auto-download: {auto_download}\")\n","print(f\"Tagging → Threshold: {threshold_percent}% | Max tags: {max_tags} | Force max: {use_max_tags} | Commas: {add_commas}\\n\")\n","\n","# ------------------- Prepare images -------------------\n","output_dir = Path(\"/content/output\")\n","output_dir.mkdir(exist_ok=True)\n","\n","image_files = []\n","\n","if use_manual_upload:\n","    print(\"Please upload your images (multiple files supported)...\")\n","    uploaded = files.upload()\n","    image_files = [f for f in uploaded.keys()\n","                   if f.lower().endswith(('.png', '.jpg', '.jpeg', '.webp', '.bmp'))]\n","    print(f\"✅ Uploaded {len(image_files)} image(s) manually.\")\n","\n","else:\n","    if not zip_file_path or not Path(zip_file_path).exists():\n","        print(f\"❌ ZIP file not found at: {zip_file_path}\")\n","    else:\n","        print(f\"Extracting images from ZIP: {zip_file_path}\")\n","        extract_dir = Path(\"/content/extracted_images\")\n","        extract_dir.mkdir(exist_ok=True)\n","\n","        with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:\n","            zip_ref.extractall(extract_dir)\n","\n","        temp_image_files = []\n","        for ext in ['.png', '.jpg', '.jpeg', '.webp', '.bmp']:\n","            temp_image_files.extend(list(extract_dir.rglob(f\"*{ext}\")))\n","            temp_image_files.extend(list(extract_dir.rglob(f\"*{ext.upper()}\")))\n","\n","        # Filter out files from __MACOSX directories that cause issues\n","        image_files = sorted(set(str(p) for p in temp_image_files if \"__MACOSX\" not in str(p)))\n","        print(f\"✅ Found {len(image_files)} image(s) in the ZIP file (excluding macOS metadata files).\")\n","\n","# ------------------- Process images -------------------\n","if not image_files:\n","    print(\"❌ No images found to process.\")\n","else:\n","    print(f\"\\nStarting tagging for {len(image_files)} image(s) on {tagger.device}...\\n\")\n","\n","    threshold = threshold_percent / 100.0\n","\n","    for i, img_path in enumerate(image_files, start=1):\n","        img_name = Path(img_path).name\n","        print(f\"[{i}/{len(image_files)}] Processing: {img_name}\")\n","\n","        # Use GPU-accelerated prediction\n","        if use_max_tags:\n","            tags_list = tagger.predict(img_path, topk=max_tags, threshold=None)\n","        else:\n","            tags_list = tagger.predict(img_path, topk=None, threshold=threshold)\n","            if len(tags_list) > max_tags:\n","                tags_list = tags_list[:max_tags]\n","\n","        # Save image as numbered JPG\n","        img = Image.open(img_path).convert(\"RGB\")\n","        new_img_path = output_dir / f\"{i}.jpg\"\n","        img.save(new_img_path, \"JPEG\", quality=95)\n","\n","        # Prepare single-line tag string\n","        tags_only = [tag for tag, prob in tags_list]\n","        tag_text = \" , \".join(tags_only) if add_commas else \" \".join(tags_only)\n","\n","        # Save tags\n","        tag_file = output_dir / f\"{i}.txt\"\n","        with open(tag_file, \"w\", encoding=\"utf-8\") as f:\n","            f.write(tag_text)\n","\n","        print(f\"   → Saved {i}.jpg + {i}.txt  ({len(tags_list)} tags)\")\n","\n","    # Create output zip at custom path\n","    final_zip_path = Path(output_zip_path)\n","    final_zip_path.parent.mkdir(parents=True, exist_ok=True)\n","\n","    with zipfile.ZipFile(final_zip_path, \"w\") as zipf:\n","        for file in sorted(output_dir.iterdir()):\n","            zipf.write(file, file.name)\n","\n","    print(f\"\\n✅ Processing complete! Output ZIP saved to: {final_zip_path}\")\n","\n","    # Auto-download if enabled\n","    if auto_download:\n","        files.download(str(final_zip_path))\n","        print(\"📥 Auto-download triggered.\")\n","    else:\n","        print(\"✅ Auto-download disabled. You can manually download the file from the path above.\")"],"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# =============================================\n","# Cell 3: Disconnect Runtime After Processing (Optional)\n","# =============================================\n","\n","# @title Disconnect from Session After Completion\n","\n","disconnect_after_run = True  # @param {type:\"boolean\"}\n","\n","if disconnect_after_run:\n","    print(\"🔌 Disconnecting Colab runtime in 10 seconds...\")\n","    print(\"This will stop the current session and free up GPU resources.\")\n","\n","    import time\n","    time.sleep(10)\n","\n","    from google.colab import runtime\n","    runtime.unassign()\n","\n","    print(\"✅ Runtime disconnected.\")\n","else:\n","    print(\"✅ Disconnect option is disabled.\")\n","    print(\"You can manually disconnect via Runtime → Disconnect and delete runtime if needed.\")"],"metadata":{"id":"byGffTyyPgEY","cellView":"form"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Image Tagger using lodestones/tagger-experiment\n","\n","This Colab notebook lets you automatically generate **tags** for a batch of images using the [lodestones/tagger-experiment](https://huggingface.co/lodestones/tagger-experiment) model.\n","\n","### Features:\n","- Process images either by **manual upload** or by providing a **ZIP file** containing your images\n","- Adjustable **probability threshold** (only tags above this % are kept when not forcing max tags)\n","- Control the **maximum number of tags** per image\n","- Option to **force the top N tags** (ignores threshold) or strictly respect the probability threshold\n","- Output format: Images saved as `1.jpg`, `2.jpg`, ... and corresponding tags as `1.txt`, `2.txt`, ...\n","- Tags in each `.txt` file are saved as a **single line** — either space-separated or comma-separated\n","- Final result is packaged into a downloadable `output.zip` file\n","\n","### How to use:\n","1. Run **Cell 1** first (installs dependencies and loads the model)\n","2. In **Cell 2**, configure your settings:\n","   - Choose between manual upload or ZIP file input\n","   - Adjust threshold, max tags, and output style using the sliders and checkboxes\n","3. Run **Cell 2** — upload images or point to your ZIP file\n","4. Wait for processing and download the resulting `output.zip`\n","\n","The notebook preserves the original image quality (saved as high-quality JPG) and organizes everything with simple numbered filenames for easy use in training pipelines."],"metadata":{"id":"q-fVFdZqMxRA"}},{"cell_type":"code","source":["#@markdown Download method using wget (backup method)\n","# =============================================\n","# Cell 1: Setup - Dependencies, Model & Tagger (GPU Optimized)\n","# =============================================\n","\n","# Install dependencies\n","!pip install -q torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118\n","!pip install -q safetensors pillow\n","\n","import torch\n","from PIL import Image\n","import os\n","import zipfile\n","from pathlib import Path\n","from google.colab import files\n","\n","# Check GPU availability\n","if torch.cuda.is_available():\n","    print(f\"✅ GPU detected: {torch.cuda.get_device_name(0)}\")\n","    device = \"cuda\"\n","else:\n","    print(\"⚠️ No GPU found. Running on CPU (will be slower).\")\n","    device = \"cpu\"\n","\n","print(\"Downloading model files...\")\n","\n","!wget -q https://huggingface.co/lodestones/tagger-experiment/resolve/main/tagger_proto.safetensors -O tagger_proto.safetensors\n","!wget -q https://huggingface.co/lodestones/tagger-experiment/resolve/main/tagger_vocab_with_categories.json -O tagger_vocab_with_categories.json\n","!wget -q https://huggingface.co/lodestones/tagger-experiment/resolve/main/inference_tagger_standalone.py -O inference_tagger_standalone.py\n","\n","print(\"✅ Model files downloaded.\")\n","\n","# Load the Tagger with GPU preference\n","from inference_tagger_standalone import Tagger\n","\n","tagger = Tagger(\n","    checkpoint_path=\"tagger_proto.safetensors\",\n","    vocab_path=\"tagger_vocab_with_categories.json\",\n","    device=device,                                   # Explicit GPU if available\n","    dtype=torch.bfloat16 if device == \"cuda\" else torch.float32,\n","    max_size=1024\n",")\n","\n","print(f\"✅ Tagger loaded successfully on {tagger.device}\")"],"metadata":{"id":"qEQZt_7nPffG","cellView":"form"},"execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/dino_tagger.ipynb","timestamp":1775233835241},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/dino_tagger.ipynb","timestamp":1775233735620},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/dino_tagger.ipynb","timestamp":1775159289229},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/dino_tagger.ipynb","timestamp":1774964848181},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/dino_tagger.ipynb","timestamp":1774906863352},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/dino_tagger.ipynb","timestamp":1774901938471},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/dino_tagger.ipynb","timestamp":1774884683620},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/dino_tagger.ipynb","timestamp":1774883563998},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/dino_tagger.ipynb","timestamp":1774882033159},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/dino_tagger.ipynb","timestamp":1774881268156},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Drive to WebP.ipynb","timestamp":1774879795776},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Drive to WebP.ipynb","timestamp":1763646205520},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Drive to WebP.ipynb","timestamp":1760993725927},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1760450712160},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1756712618300},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1747490904984},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1740037333374},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1736477078136},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1725365086834}],"gpuType":"T4"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0}