diff --git "a/klein_prepare_lora_set.ipynb" "b/klein_prepare_lora_set.ipynb"
--- "a/klein_prepare_lora_set.ipynb"
+++ "b/klein_prepare_lora_set.ipynb"
@@ -1 +1 @@
-{"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"id":"r6q_m-6Pmm5w"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#@title Set path to zip file on your drive , and set klein edit prompt\n","input_zip_path = '/content/drive/MyDrive/my_images.zip' #@param {type:'string'}\n","zip_path = input_zip_path\n","edit_prompt = 'using the dark color found at the corners as the background color. remove the four corners. remove the background.  make sure the bare skin against the solid color background looks like a real photo. ' #@param }¥{type:'string'}"],"metadata":{"id":"K13pRro0lxAw","cellView":"form"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"HX3I-YqowwO9"},"source":["# ⚙️ Run Klein Edit (GPU)"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"OXGhsHQwWue_"},"outputs":[],"source":["# =============================================================================\n","#@markdown # **CELL 1**: Mount Drive + HF auth\n","# =============================================================================\n","\n","from google.colab import drive, userdata\n","from huggingface_hub import login\n","import torch\n","import os\n","import gc\n","import shutil\n","\n","drive.mount('/content/drive')\n","\n","hf_token = userdata.get('HF_TOKEN')\n","if hf_token:\n","    login(token=hf_token)\n","else:\n","    print(\"⚠️ No HF_TOKEN found in secrets.\")\n","\n","print(\"🧹 Removing old diffusers...\")\n","!pip uninstall -y diffusers > /dev/null 2>&1\n","!rm -rf /usr/local/lib/python3.12/dist-packages/diffusers* ~/.cache/pip/*diffusers*\n","\n","print(\"🔄 Installing latest diffusers...\")\n","!pip install -q git+https://github.com/huggingface/diffusers.git --force-reinstall --no-deps\n","!python -m pip cache purge\n","\n","print(\"✅ Cell 1 complete!\")"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"a1wZv8PrXAe2"},"outputs":[],"source":["# =============================================================================\n","#@markdown # **CELL 2**: Fixed settings (resolution + options)\n","# =============================================================================\n","\n","resolution = '1024 x 1024 (Square)' #@param [\"1024 x 1024 (Square)\", \"512 x 1024 (Portrait)\", \"768 x 1024 (Slight Portrait)\", \"1536 x 1024 (Landscape)\", \"2048 x 1024 (Wide Landscape)\"] {type:\"string\"}\n","use_txt_prompts = True #@param {type:\"boolean\"}\n","debug = False #@param {type:\"boolean\"}\n","\n","res_map = {\n","    \"1024 x 1024 (Square)\": (1024, 1024),\n","    \"512 x 1024 (Portrait)\": (512, 1024),\n","    \"768 x 1024 (Slight Portrait)\": (768, 1024),\n","    \"1536 x 1024 (Landscape)\": (1536, 1024),\n","    \"2048 x 1024 (Wide Landscape)\": (2048, 1024)\n","}\n","target_width, target_height = res_map[resolution]\n","\n","print(\"✅ Cell 2 settings loaded\")\n","print(f\"   Resolution: {target_width}×{target_height}\")\n","print(f\"   Use .txt prompts: {use_txt_prompts}\")\n","print(f\"   Debug mode: {debug}\")\n","print(\"\\nNow run Cell 3 (model load), then Cell 4 (inference)\")"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"WdNfo33NXNEa"},"outputs":[],"source":["# =============================================================================\n","#@markdown # **CELL 3**: Load QUANTIZED Flux2KleinPipeline + SDNQ optimizations\n","#(model stays in memory)\n","# =============================================================================\n","\n","import torch\n","import gc\n","from diffusers import Flux2KleinPipeline\n","import zipfile\n","import glob\n","import os\n","\n","print(\"📦 Installing sdnq...\")\n","!pip install -q sdnq\n","\n","from sdnq.common import use_torch_compile as triton_is_available\n","from sdnq.loader import apply_sdnq_options_to_model\n","\n","gc.collect()\n","torch.cuda.empty_cache()\n","\n","print(\"🔄 Loading QUANTIZED Flux2KleinPipeline (4-bit SDNQ + float16 for T4)...\")\n","pipe = Flux2KleinPipeline.from_pretrained(\n","    \"Disty0/FLUX.2-klein-4B-SDNQ-4bit-dynamic\",\n","    torch_dtype=torch.float16\n",")\n","\n","print(f\"✅ Pipeline loaded – VRAM: {torch.cuda.memory_allocated() / 1e9:.2f} GB\")\n","\n","# ====================== Optimizations for T4 ======================\n","print(\"🔥 Applying SDNQ + T4 optimizations...\")\n","os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n","\n","if triton_is_available and torch.cuda.is_available():\n","    pipe.transformer = apply_sdnq_options_to_model(pipe.transformer, use_quantized_matmul=True)\n","    pipe.text_encoder = apply_sdnq_options_to_model(pipe.text_encoder, use_quantized_matmul=True)\n","    print(\"   ✅ INT8 MatMul enabled\")\n","\n","pipe.enable_model_cpu_offload()\n","pipe.vae.enable_slicing()\n","pipe.vae.enable_tiling()\n","\n","gc.collect()\n","torch.cuda.empty_cache()\n","torch.cuda.reset_peak_memory_stats()\n","\n","print(\"✅ Cell 3 complete – model is ready!\")\n","print(\"   Now go to Cell 4 and run it (you can change zip/prompt in Cell 4 and re-run Cell 4 only)\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"q9I9wxghuUGP","cellView":"form"},"outputs":[],"source":["import shutil\n","import datetime\n","import os\n","import glob\n","import zipfile\n","import gc\n","import torch\n","from PIL import Image, ImageDraw # Added ImageDraw\n","from google.colab import files\n","\n","# =============================================================================\n","#@markdown # **CELL 4**: MULTI-IMAGE INFERENCE (Klein + custom/gray reference)\n","# • Uses the **same zip** as Cell 4 but now does true multi-image editing\n","# • First image = your original (the one being edited)\n","# • Second image = your custom uploaded image OR a solid #181818 gray square (perfect for background replacement)\n","# • Re-run this cell only with new values\n","# • Auto-clears old data → processes → saves **only ZIP** to Drive\n","# =============================================================================\n","\n","#edit_prompt = 'remove the background. the background is gray. ' #@param {type:\"string\"}\n","#zip_path = '/content/drive/MyDrive/input_images.zip' #@param {type:\"string\"}\n","upload_custom_reference = False #@param {type:\"boolean\"}\n","max_image_dimension = 2048 #@param {type:\"slider\", min:512, max:4096, step:256}\n","add_gray_corners = True #@param {type:\"boolean\"}\n","gray_square_size = 50 #@param {type:\"slider\", min:10, max:200, step:10}\n","\n","output_folder = '/content/edited_images_multi'   # ← separate temp folder so it doesn't clash with Cell 4\n","\n","print(\"🧹 Clearing old temporary folders so you can reuse with new zip/prompt...\")\n","if os.path.exists('/content/input_images'):\n","    shutil.rmtree('/content/input_images')\n","if os.path.exists(output_folder):\n","    shutil.rmtree(output_folder)\n","os.makedirs(output_folder, exist_ok=True)\n","\n","# ====================== Unzip + build prompt map ======================\n","print(f\"📦 Unzipping: {zip_path}...\")\n","with zipfile.ZipFile(zip_path, 'r') as z:\n","    z.extractall('/content/input_images')\n","\n","image_files = sorted(glob.glob('/content/input_images/*.*'))\n","image_files = [f for f in image_files if f.lower().endswith(('.png', '.jpg', '.jpeg', '.webp'))]\n","print(f\"Found {len(image_files)} images.\")\n","\n","prompt_map = {}\n","txt_count = 0\n","for img_path in image_files:\n","    base = os.path.splitext(os.path.basename(img_path))[0]\n","    txt_path = os.path.join('/content/input_images', f\"{base}.txt\")\n","    if use_txt_prompts and os.path.exists(txt_path):\n","        with open(txt_path, 'r', encoding='utf-8') as f:\n","            prompt_map[img_path] = f.read().strip()\n","        txt_count += 1\n","    else:\n","        prompt_map[img_path] = None\n","\n","print(f\"Found {txt_count} matching .txt files\")\n","\n","# ====================== Create or load the second reference image (once) ======================\n","uploaded_reference_filepath = None\n","reference_image_to_pair = None # Initialize to None\n","\n","if upload_custom_reference: # Only attempt to get a reference if the checkbox is enabled\n","    print(\"Please upload your reference image now.\")\n","    uploaded_files = files.upload()\n","\n","    if uploaded_files:\n","        uploaded_reference_filepath = list(uploaded_files.keys())[0]\n","        print(f\"✅ Uploaded reference image: {uploaded_reference_filepath}\")\n","        # Save the uploaded file to a temporary location to be opened by PIL\n","        with open(uploaded_reference_filepath, 'wb') as f:\n","            f.write(uploaded_files[uploaded_reference_filepath])\n","\n","    if uploaded_reference_filepath: # Check if a file was actually uploaded\n","        print(\"📁 Using uploaded reference image...\")\n","        try:\n","            # Open the temporary file\n","            reference_image_to_pair = Image.open(uploaded_reference_filepath).convert(\"RGB\")\n","            #if reference_image_to_pair.size != (target_width, target_height):\n","             #   print(f\"   ⚠️ Resizing uploaded reference image from {reference_image_to_pair.size} to {target_width}x{target_height}\")\n","             #   reference_image_to_pair = reference_image_to_pair.resize((target_width, target_height), Image.LANCZOS)\n","        except Exception as e:\n","            print(f\"   ❌ Error loading uploaded image: {e}. Falling back to gray reference.\")\n","            reference_image_to_pair = Image.new(\"RGB\", (target_width, target_height), \"#181818\")\n","    else: # If upload_custom_reference is True but no file was uploaded\n","        print(f\"🟩 No custom reference image uploaded. Creating {target_width}x{target_height} #181818 solid gray reference image...\")\n","        reference_image_to_pair = Image.new(\"RGB\", (target_width, target_height), \"#181818\")\n","else:\n","    print(\"🚫 Custom reference image upload disabled. Will process images without a second reference.\")\n","    # reference_image_to_pair remains None, which is the desired behavior for the pipe call.\n","\n","# Clean up the temporarily saved file if it was uploadedi\n","if uploaded_reference_filepath and os.path.exists(uploaded_reference_filepath):\n","    os.remove(uploaded_reference_filepath)\n","\n","# ==================== Helper function for adding gray squares ====================\n","def add_corner_squares(image: Image.Image, square_size: int, color=(24, 24, 24)) -> Image.Image:\n","    \"\"\"Adds four gray squares to the corners of the input image.\"\"\"\n","    img_copy = image.copy()\n","    draw = ImageDraw.Draw(img_copy)\n","    width, height = img_copy.size\n","\n","    # Top-left\n","    draw.rectangle([(0, 0), (square_size, square_size)], fill=color)\n","    # Top-right\n","    draw.rectangle([(width - square_size, 0), (width, square_size)], fill=color)\n","    # Bottom-left\n","    draw.rectangle([(0, height - square_size), (square_size, height)], fill=color)\n","    # Bottom-right\n","    draw.rectangle([(width - square_size, height - square_size), (width, height)], fill=color)\n","    return img_copy\n","\n","# ====================== FULL BATCH MULTI-IMAGE INFERENCE ======================\n","print(f\"\\n🚀 Starting batch MULTI-IMAGE edit on {len(image_files)} images...\")\n","\n","for i, img_path in enumerate(image_files):\n","    filename = os.path.basename(img_path)\n","\n","    gc.collect()\n","    torch.cuda.empty_cache()\n","\n","    input_image = Image.open(img_path).convert(\"RGB\")\n","\n","    # Resize input_image if its largest dimension exceeds max_image_dimension\n","    if max(input_image.width, input_image.height) > max_image_dimension:\n","        print(f\"   ⚠️ Resizing {filename} from {input_image.size} to max dimension {max_image_dimension}\")\n","        aspect_ratio = input_image.width / input_image.height\n","        if input_image.width > input_image.height:\n","            new_width = max_image_dimension\n","            new_height = int(max_image_dimension / aspect_ratio)\n","        else:\n","            new_height = max_image_dimension\n","            new_width = int(max_image_dimension * aspect_ratio)\n","        input_image = input_image.resize((new_width, new_height), Image.LANCZOS)\n","\n","    # Apply gray corners if enabled\n","    if add_gray_corners:\n","        print(f\"   Adding {gray_square_size}x{gray_square_size} gray squares to corners of {filename}\")\n","        input_image = add_corner_squares(input_image, gray_square_size)\n","\n","    # === MULTI-IMAGE LIST: base image first, the chosen reference second ===\n","    if reference_image_to_pair is not None:\n","        reference_images = [input_image, reference_image_to_pair]\n","        print(f\"[{i+1}/{len(image_files)}] {filename}  →  + custom reference\")\n","    else:\n","        reference_images = [input_image]\n","        print(f\"[{i+1}/{len(image_files)}] {filename}  →  (no custom reference)\")\n","\n","    current_prompt = prompt_map[img_path] if use_txt_prompts and prompt_map[img_path] is not None else edit_prompt\n","\n","    result = pipe(\n","        prompt=current_prompt,\n","        image=reference_images,           # ← KEY: list of images for Klein multi-reference\n","        height=target_height,\n","        width=target_width,\n","        guidance_scale=1.0,\n","        num_inference_steps=4,\n","        generator=torch.Generator(\"cuda\").manual_seed(42),\n","        output_type=\"pil\",\n","    ).images[0]\n","\n","    output_filename = f\"multi_edited_{filename}\"\n","    output_path = os.path.join(output_folder, output_filename)\n","    result.save(output_path)\n","\n","    if debug:\n","        print(f\"   → Saved {output_filename} (multi-ref with custom/gray image)\") # Updated message\n","\n","print(\"\\n→ MULTI-IMAGE BATCH COMPLETE!\")\n","\n","# ====================== AUTO ZIP TO DRIVE (unique name) ======================\n","timestamp = datetime.datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n","drive_zip_path = f\"/content/drive/MyDrive/klein_processed_{timestamp}.zip\"\n","\n","print(f\"📦 Creating zip → {drive_zip_path}\")\n","shutil.make_archive(drive_zip_path.replace('.zip', ''), 'zip', output_folder)\n","\n","print(\"✅ ALL DONE!\")\n","print(f\"Zip file saved to your Google Drive:\")\n","print(drive_zip_path)"]},{"cell_type":"markdown","source":["# Select klein edited zip file to process next\n","Results from earlier cell have been saved to your drive as <br> 'content/drive/MyDrive/klein_processed_(date_number).zip'\n","\n","You can disconnect from the session  , and reconnect to new runtime to prevent conflict in installed libraries. you can run this code on CPU"],"metadata":{"id":"F5NDcZ6rZw6Q"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"OdcEXFs4oBt_","cellView":"form"},"outputs":[],"source":["import os\n","\n","klein_edited_zip_path = '/content/drive/MyDrive/klein_processed_20260421_123232.zip' #@param {type:'string'}\n","zip_path = klein_edited_zip_path\n","\n","if not os.path.exists(zip_path):\n","    raise FileNotFoundError(f\"❌ Klein edited zip file not found:\\n{zip_path}\\n\\nPlease ensure the path is correct and the file exists in your Drive.\")"]},{"cell_type":"markdown","source":["# ⚙️ Create 1024x1024 frames from selected klein edited set"],"metadata":{"id":"4PjK9a8Sni3A"}},{"cell_type":"code","source":["#@title 🖼️ **Image Background Processor (v1)**\n","#@markdown ---\n","#@markdown ## Enable Processor V1\n","enable_processor_v1 = False #@param {type:\"boolean\"}\n","\n","#@title 🖼️ **Image Background Processor**\n","#(v2 - Solid #181818 Flood-Fill + Auto-Clear)\n","#**Crop left/right empty space + Convert *any* gray background to exact #181818**\n","#@markdown ---\n","#**New**: Automatically clears all previous processed images on re-run\n","#(no more mixing old files when you run again)\n","\n","#Run this cell (Shift + Enter)\n","#Fill the form below\n","#Click **Run**\n","\n","# ==================== FORM PARAMETERS ====================\n","\n","#input_zip_path = \"/content/drive/MyDrive/klein_processed.zip\" #@param {type:\"string\", placeholder:\"Paste full path to your ZIP file here\"}\n","input_zip_path = zip_path\n","\n","tolerance_crop = 30 #@param {type:\"slider\", min:0, max:100, step:1, title:\"🟦 Crop Tolerance (higher = more aggressive cropping)\"}\n","\n","bg_replace_threshold = 20 #@param {type:\"slider\", min:10, max:100, step:1}\n","\n","force_remount = False #@param {type:\"boolean\", title:\"🔄 Force remount Drive (recommended)\"}\n","\n","#@markdown ---\n","\n","\n","if enable_processor_v1:\n","    from google.colab import drive\n","    import zipfile\n","    import os\n","    import shutil                          # ← NEW for clearing folders\n","    from PIL import Image\n","    from collections import deque\n","\n","    # ========================= CONFIG =========================\n","    TARGET_GRAY = (24, 24, 24)   # Exact #181818 - do not change\n","    # =======================================================\n","\n","    print(\"🚀 Starting image processor (Solid Background v2 + Auto-Clear)...\")\n","\n","    # 1. Mount Google Drive\n","    drive.mount('/content/drive', force_remount=force_remount)\n","\n","    # 2. Validate ZIP path\n","    if not input_zip_path or not os.path.exists(input_zip_path):\n","        raise FileNotFoundError(f\"❌ ZIP file not found:\\n{input_zip_path}\\n\\nPlease paste the correct full path (e.g. /content/drive/MyDrive/my_photos.zip)\")\n","\n","    print(f\"✅ ZIP found: {input_zip_path}\")\n","\n","    # 3. Define working folders\n","    extract_dir = '/content/extracted_images'\n","    processed_dir = '/content/processed_images'\n","\n","    # === NEW: Clear previous processed & extracted images on every run ===\n","    for directory in [extract_dir, processed_dir]:\n","        if os.path.exists(directory):\n","            shutil.rmtree(directory)\n","            print(f\"🧹 Cleared old folder: {directory}\")\n","    os.makedirs(extract_dir, exist_ok=True)\n","    os.makedirs(processed_dir, exist_ok=True)\n","    print(\"✅ Fresh folders ready\")\n","\n","    # 4. Unzip\n","    print(\"📦 Unzipping...\")\n","    with zipfile.ZipFile(input_zip_path, 'r') as zip_ref:\n","        zip_ref.extractall(extract_dir)\n","    print(f\"✅ Unzipped to {extract_dir}\")\n","\n","    # ==================== HELPER 1: Crop only left & right ====================\n","    def crop_left_right(img):\n","        if img.mode != 'RGB':\n","            img = img.convert('RGB')\n","\n","        width, height = img.size\n","        bg_color = img.getpixel((0, 0))\n","\n","        left = width\n","        right = 0\n","\n","        for x in range(width):\n","            column_is_empty = True\n","            for y in range(height):\n","                px = img.getpixel((x, y))\n","                if any(abs(a - b) > tolerance_crop for a, b in zip(px, bg_color)):\n","                    column_is_empty = False\n","                    break\n","            if not column_is_empty:\n","                left = min(left, x)\n","                right = max(right, x)\n","\n","        if left >= right:\n","            return img\n","\n","        return img.crop((left, 0, right + 1, height))\n","\n","    # ==================== HELPER 2: FLOOD-FILL BACKGROUND FROM EDGES ====================\n","    def adjust_background(img, original_bg_color):\n","        if img.mode != 'RGB':\n","            img = img.convert('RGB')\n","\n","        width, height = img.size\n","        pixels = img.load()\n","        threshold = bg_replace_threshold\n","\n","        visited = set()\n","        queue = deque()\n","        directions = [(dx, dy) for dx in [-1, 0, 1] for dy in [-1, 0, 1] if not (dx == 0 and dy == 0)]\n","\n","        # Seed from borders\n","        for x in range(width):\n","            for y in (0, height - 1):\n","                if (x, y) not in visited:\n","                    px = pixels[x, y]\n","                    dist = ((px[0] - original_bg_color[0])**2 + (px[1] - original_bg_color[1])**2 + (px[2] - original_bg_color[2])**2) ** 0.5\n","                    if dist <= threshold:\n","                        queue.append((x, y))\n","                        visited.add((x, y))\n","                        pixels[x, y] = TARGET_GRAY\n","\n","        for y in range(height):\n","            for x in (0, width - 1):\n","                if (x, y) not in visited:\n","                    px = pixels[x, y]\n","                    dist = ((px[0] - original_bg_color[0])**2 + (px[1] - original_bg_color[1])**2 + (px[2] - original_bg_color[2])**2) ** 0.5\n","                    if dist <= threshold:\n","                        queue.append((x, y))\n","                        visited.add((x, y))\n","                        pixels[x, y] = TARGET_GRAY\n","\n","        # Flood fill\n","        while queue:\n","            x, y = queue.popleft()\n","            for dx, dy in directions:\n","                nx, ny = x + dx, y + dy\n","                if 0 <= nx < width and 0 <= ny < height and (nx, ny) not in visited:\n","                    px = pixels[nx, ny]\n","                    dist = ((px[0] - original_bg_color[0])**2 + (px[1] - original_bg_color[1])**2 + (px[2] - original_bg_color[2])**2) ** 0.5\n","                    if dist <= threshold:\n","                        visited.add((nx, ny))\n","                        pixels[nx, ny] = TARGET_GRAY\n","                        queue.append((nx, ny))\n","\n","        return img\n","\n","    # 5. Process every image\n","    print(\"🖼️ Processing images...\")\n","    image_extensions = ('.png', '.jpg', '.jpeg', '.bmp', '.tiff', '.webp')\n","\n","    processed_count = 0\n","    for filename in os.listdir(extract_dir):\n","        if filename.lower().endswith(image_extensions):\n","            img_path = os.path.join(extract_dir, filename)\n","            try:\n","                with Image.open(img_path) as original_img:\n","                    cropped = crop_left_right(original_img)\n","                    orig_bg = original_img.getpixel((0, 0))\n","                    processed = adjust_background(cropped, orig_bg)\n","\n","                    out_path = os.path.join(processed_dir, filename)\n","                    processed.save(out_path, quality=95 if filename.lower().endswith(('.jpg', '.jpeg')) else None)\n","\n","                    processed_count += 1\n","                    print(f\"   ✅ {filename}\")\n","            except Exception as e:\n","                print(f\"   ❌ Skipped {filename}: {e}\")\n","\n","    print(f\"\\n🎉 Processed {processed_count} images!\")\n","\n","    # 6. Create final ZIP in Drive\n","    output_zip_name = \"PROCESSED_\" + os.path.basename(zip_path)\n","    output_zip_path = os.path.join(os.path.dirname(zip_path), output_zip_name)\n","    zip_path = output_zip_path\n","\n","    print(f\"📦 Creating final ZIP: {output_zip_name}\")\n","    with zipfile.ZipFile(output_zip_path, 'w', zipfile.ZIP_DEFLATED) as zip_out:\n","        for root, _, files in os.walk(processed_dir):\n","            for file in files:\n","                file_path = os.path.join(root, file)\n","                arcname = os.path.relpath(file_path, processed_dir)\n","                zip_out.write(file_path, arcname)\n","\n","    print(\"\\n✅ ALL DONE! Solid #181818 background achieved.\")\n","    print(f\"📍 Saved to your Drive at:\\n   {output_zip_path}\")\n"],"metadata":{"id":"HN4kta-OpZ4W","cellView":"form"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#@title **🖼️ Image Background Processor V2 (WIP)**\n","#@markdown ---\n","#@markdown ## Enable Processor V2\n","enable_processor_v2 = True #@param {type:\"boolean\"}\n","\n","#@title **🖼️ Image Background Processor V2 (WIP)**\n","#(v2.7 - **Inverse speck removal** + Border-protected forward speck + % Margin + Solid #181818)\n","\n","#**Crop left/right empty space + Convert *any* gray background to exact #181818**\n","#**New in v2.7**:\n","#   • **Inverse speck removal**: finds tiny foreground (subject-colored) specks/islands inside the background and replaces them with solid #181818\n","#   • Forward speck removal (previous) still protects any #181818 connected to the outer image edge\n","#   • Anti-aliasing is guaranteed to be the **very last step** (after both forward + inverse speck removal)\n","#   • All previous features (4-corner BG, % margin, checkboxes, distance-based enclosed speck removal) unchanged\n","\n","# ==================== FORM PARAMETERS ====================\n","\n","input_zip_path = zip_path\n","\n","tolerance_crop = 30 #@param {type:\"slider\", min:0, max:100, step:1, title:\"🔵 Crop Tolerance (higher = more aggressive cropping)\"}\n","\n","bg_replace_threshold = 10 #@param {type:\"slider\", min:10, max:100, step:1, title:\"🎨 Legacy absolute threshold (kept for compatibility)\"}\n","\n","# ── % Margin for BG color estimation ──\n","bg_margin_percent = 6 #@param {type:\"slider\", min:0, max:50, step:1, title:\"% Margin for BG color estimation (wider = more tolerant to inner-loop gray variations)\"}\n","\n","# ── Feature Checkboxes ──\n","enable_robust_bg_detection = True #@param {type:\"boolean\", title:\"✅ Use 4-corner robust BG color (best for inner loops)\"}\n","enable_floodfill_background_adjustment = True #@param {type:\"boolean\", title:\"✅ Enable solid #181818 background replacement\"}\n","enable_small_speck_removal = True #@param {type:\"boolean\"}\n","#title:\"✅ Forward speck removal: remove tiny #181818 specks on subject\"}\n","enable_inverse_speck_removal = True #@param {type:\"boolean\"}\n","#title:\"✅ Inverse speck removal: fill tiny subject specks inside background with #181818\"}\n","enable_anti_aliasing = True #@param {type:\"boolean\", title:\"✅ Smooth edges (Gaussian anti-aliasing)\"}\n","\n","anti_aliasing_strength = 6 #@param {type:\"slider\", min:1, max:40, step:1, title:\"   ↳ Anti-aliasing Strength (pixels to blend)\"}\n","\n","# ── Forward speck removal (enclosed only) ──\n","min_floodfill_steps = 8 #@param {type:\"slider\", min:1, max:20, step:1, title:\"   ↳ Min floodfill steps to foreground (N) for ENCLOSED BG only (outer-edge-connected BG always protected)\"}\n","\n","# ── Inverse speck removal ──\n","max_inverse_speck_size = 10 #@param {type:\"slider\", min:1, max:100, step:1, title:\"   ↳ Max size of foreground specks to fill with #181818\"}\n","\n","force_remount = False #@param {type:\"boolean\", title:\"🔄 Force remount Drive\"}\n","\n","#@markdown ---\n","\n","\n","if enable_processor_v2:\n","    from google.colab import drive\n","    import zipfile\n","    import os\n","    import shutil\n","    from PIL import Image, ImageFilter\n","    from collections import deque\n","\n","    # ========================= CONFIG =========================\n","    TARGET_GRAY = (24, 24, 24)   # Exact #181818\n","    # =======================================================\n","\n","    print(\"🚀 Starting Image Background Processor v2.7 (Forward + Inverse speck removal)...\")\n","\n","    # 1. Mount Google Drive\n","    drive.mount('/content/drive', force_remount=force_remount)\n","\n","    # 2. Validate ZIP path\n","    if not input_zip_path or not os.path.exists(input_zip_path):\n","        raise FileNotFoundError(f\"❌ ZIP file not found:\\n{input_zip_path}\\n\\nPlease paste the correct full path\")\n","\n","    print(f\"✅ ZIP found: {input_zip_path}\")\n","\n","    # 3. Define working folders\n","    extract_dir = '/content/extracted_images'\n","    processed_dir = '/content/processed_images'\n","\n","    # Clear previous runs\n","    for directory in [extract_dir, processed_dir]:\n","        if os.path.exists(directory):\n","            shutil.rmtree(directory)\n","            print(f\"﹅ Cleared old folder: {directory}\")\n","    os.makedirs(extract_dir, exist_ok=True)\n","    os.makedirs(processed_dir, exist_ok=True)\n","    print(\"✅ Fresh folders ready\")\n","\n","    # 4. Unzip\n","    print(\"🚀 Unzipping...\")\n","    with zipfile.ZipFile(input_zip_path, 'r') as zip_ref:\n","        zip_ref.extractall(extract_dir)\n","    print(f\"✅ Unzipped to {extract_dir}\")\n","\n","    # ==================== HELPER 1: Crop only left & right ====================\n","    def crop_left_right(img):\n","        if img.mode != 'RGB':\n","            img = img.convert('RGB')\n","        width, height = img.size\n","        bg_color = img.getpixel((0, 0))\n","        left = width\n","        right = 0\n","        for x in range(width):\n","            column_is_empty = True\n","            for y in range(height):\n","                px = img.getpixel((x, y))\n","                if any(abs(a - b) > tolerance_crop for a, b in zip(px, bg_color)):\n","                    column_is_empty = False\n","                    break\n","            if not column_is_empty:\n","                left = min(left, x)\n","                right = max(right, x)\n","        if left >= right:\n","            return img\n","        return img.crop((left, 0, right + 1, height))\n","\n","    # ==================== HELPER 2: FULL BACKGROUND PROCESSOR (v2.7) ====================\n","    def adjust_background(img, original_bg_color,\n","                          enable_floodfill_background_adjustment,\n","                          enable_small_speck_removal,\n","                          enable_inverse_speck_removal,\n","                          enable_anti_aliasing,\n","                          anti_aliasing_strength,\n","                          min_floodfill_steps,\n","                          max_inverse_speck_size,\n","                          bg_margin_percent):\n","        if img.mode != 'RGB':\n","            img = img.convert('RGB')\n","\n","        width, height = img.size\n","        pixels_access = img.load()\n","        target_gray = TARGET_GRAY\n","        directions = [(dx, dy) for dx in [-1, 0, 1] for dy in [-1, 0, 1] if not (dx == 0 and dy == 0)]\n","\n","        # 1. Identify foreground using % margin\n","        per_channel_margin = int(255 * (bg_margin_percent / 100.0))\n","        is_foreground = [[False for _ in range(height)] for _ in range(width)]\n","        for y in range(height):\n","            for x in range(width):\n","                px = pixels_access[(x, y)]\n","                is_bg = all(abs(px[i] - original_bg_color[i]) <= per_channel_margin for i in range(3))\n","                if not is_bg:\n","                    is_foreground[x][y] = True\n","\n","        # 2. Base image: solid #181818 + foreground\n","        base_processed_img = Image.new(\"RGB\", (width, height), target_gray)\n","        base_processed_pixels = base_processed_img.load()\n","        for y in range(height):\n","            for x in range(width):\n","                if is_foreground[x][y]:\n","                    base_processed_pixels[(x, y)] = pixels_access[(x, y)]\n","\n","        # 3. Forward speck removal (border-protected, enclosed only)\n","        if enable_floodfill_background_adjustment and enable_small_speck_removal:\n","            print(\"   🔍 Running forward speck removal (border-protected)...\")\n","            refined_img = base_processed_img.copy()\n","            refined_pixels = refined_img.load()\n","\n","            # Mark border-connected #181818\n","            connected_to_border = [[False] * height for _ in range(width)]\n","            queue = deque()\n","            for x in range(width):\n","                for y_border in [0, height-1]:\n","                    if refined_pixels[(x, y_border)] == target_gray and not connected_to_border[x][y_border]:\n","                        connected_to_border[x][y_border] = True\n","                        queue.append((x, y_border))\n","            for y in range(height):\n","                for x_border in [0, width-1]:\n","                    if refined_pixels[(x_border, y)] == target_gray and not connected_to_border[x_border][y]:\n","                        connected_to_border[x_border][y] = True\n","                        queue.append((x_border, y))\n","            while queue:\n","                cx, cy = queue.popleft()\n","                for dx, dy in directions:\n","                    nx, ny = cx + dx, cy + dy\n","                    if 0 <= nx < width and 0 <= ny < height and not connected_to_border[nx][ny] and refined_pixels[(nx, ny)] == target_gray:\n","                        connected_to_border[nx][ny] = True\n","                        queue.append((nx, ny))\n","\n","            # Distance to foreground (for enclosed BG only)\n","            distance = [[-1] * height for _ in range(width)]\n","            queue = deque()\n","            for y in range(height):\n","                for x in range(width):\n","                    if is_foreground[x][y]:\n","                        for dx, dy in directions:\n","                            nx, ny = x + dx, y + dy\n","                            if 0 <= nx < width and 0 <= ny < height and not is_foreground[nx][ny] and distance[nx][ny] == -1:\n","                                distance[nx][ny] = 1\n","                                queue.append((nx, ny))\n","                                break\n","            while queue:\n","                cx, cy = queue.popleft()\n","                for dx, dy in directions:\n","                    nx, ny = cx + dx, cy + dy\n","                    if 0 <= nx < width and 0 <= ny < height and not is_foreground[nx][ny] and distance[nx][ny] == -1:\n","                        distance[nx][ny] = distance[cx][cy] + 1\n","                        queue.append((nx, ny))\n","\n","            # Revert only enclosed + too-close BG pixels\n","            for y in range(height):\n","                for x in range(width):\n","                    if refined_pixels[(x, y)] == target_gray:\n","                        if not connected_to_border[x][y] and (distance[x][y] == -1 or distance[x][y] < min_floodfill_steps):\n","                            refined_pixels[(x, y)] = pixels_access[(x, y)]\n","\n","            base_processed_img = refined_img\n","            base_processed_pixels = base_processed_img.load()\n","\n","        # 4. INVERSE speck removal (tiny foreground specks inside background → #181818)\n","        if enable_floodfill_background_adjustment and enable_inverse_speck_removal:\n","            print(\"   🔍 Running inverse speck removal (small FG specks → #181818)...\")\n","            refined_img = base_processed_img.copy()\n","            refined_pixels = refined_img.load()\n","            visited = [[False] * height for _ in range(width)]\n","\n","            for y in range(height):\n","                for x in range(width):\n","                    if refined_pixels[(x, y)] != target_gray and not visited[x][y]:\n","                        component = []\n","                        queue = deque([(x, y)])\n","                        visited[x][y] = True\n","                        while queue:\n","                            cx, cy = queue.popleft()\n","                            component.append((cx, cy))\n","                            for dx, dy in directions:\n","                                nx, ny = cx + dx, cy + dy\n","                                if 0 <= nx < width and 0 <= ny < height and not visited[nx][ny] and refined_pixels[(nx, ny)] != target_gray:\n","                                    visited[nx][ny] = True\n","                                    queue.append((nx, ny))\n","                        # Fill small foreground specks with solid #181818\n","                        if len(component) < max_inverse_speck_size:\n","                            for px, py in component:\n","                                refined_pixels[(px, py)] = target_gray\n","\n","            base_processed_img = refined_img\n","            base_processed_pixels = base_processed_img.load()\n","\n","        # 5. Anti-aliasing – ALWAYS LAST (as requested)\n","        if not enable_anti_aliasing:\n","            return base_processed_img\n","\n","        mask = Image.new(\"L\", (width, height), 0)\n","        mask_pixels = mask.load()\n","        for y in range(height):\n","            for x in range(width):\n","                if base_processed_pixels[(x, y)] != target_gray:\n","                    mask_pixels[(x, y)] = 255\n","\n","        blur_radius = anti_aliasing_strength / 2.0\n","        blurred_mask = mask.filter(ImageFilter.GaussianBlur(radius=blur_radius))\n","\n","        solid_bg = Image.new(\"RGB\", (width, height), target_gray)\n","        final_img_with_aa = Image.composite(base_processed_img, solid_bg, blurred_mask)\n","\n","        return final_img_with_aa\n","\n","    # ==================== MAIN PROCESSING LOOP ====================\n","    print(\"🚀 Processing images...\")\n","    image_extensions = ('.png', '.jpg', '.jpeg', '.bmp', '.tiff', '.webp')\n","\n","    processed_count = 0\n","    for filename in os.listdir(extract_dir):\n","        if filename.lower().endswith(image_extensions):\n","            img_path = os.path.join(extract_dir, filename)\n","            try:\n","                with Image.open(img_path) as original_img:\n","                    # === ROBUST BACKGROUND COLOR DETECTION ===\n","                    cropped = crop_left_right(original_img)\n","                    if cropped.mode != 'RGB':\n","                        cropped = cropped.convert('RGB')\n","\n","                    if enable_robust_bg_detection:\n","                        w, h = cropped.size\n","                        samples = [\n","                            cropped.getpixel((0, 0)),\n","                            cropped.getpixel((w-1, 0)),\n","                            cropped.getpixel((0, h-1)),\n","                            cropped.getpixel((w-1, h-1))\n","                        ]\n","                        orig_bg = tuple(sum(c) // len(samples) for c in zip(*samples))\n","                        print(f\"   📍 Using robust 4-corner BG color for {filename}\")\n","                    else:\n","                        orig_bg = cropped.getpixel((0, 0))\n","\n","                    # === PROCESS IMAGE ===\n","                    if enable_floodfill_background_adjustment:\n","                        processed = adjust_background(\n","                            cropped,\n","                            orig_bg,\n","                            enable_floodfill_background_adjustment,\n","                            enable_small_speck_removal,\n","                            enable_inverse_speck_removal,\n","                            enable_anti_aliasing,\n","                            anti_aliasing_strength,\n","                            min_floodfill_steps,\n","                            max_inverse_speck_size,\n","                            bg_margin_percent\n","                        )\n","                    else:\n","                        processed = cropped\n","\n","                    out_path = os.path.join(processed_dir, filename)\n","                    processed.save(out_path, quality=95 if filename.lower().endswith(('.jpg', '.jpeg')) else None)\n","\n","                    processed_count += 1\n","                    print(f\"   ✅ {filename}\")\n","\n","            except Exception as e:\n","                print(f\"   ❌ Skipped {filename}: {e}\")\n","\n","    print(f\"\\n🎉 Processed {processed_count} images!\")\n","\n","    # 6. Create final ZIP\n","    output_zip_name = \"PROCESSED_\" + os.path.basename(input_zip_path)\n","    output_zip_path = os.path.join(os.path.dirname(input_zip_path), output_zip_name)\n","\n","    print(f\"🚀 Creating final ZIP: {output_zip_name}\")\n","    with zipfile.ZipFile(output_zip_path, 'w', zipfile.ZIP_DEFLATED) as zip_out:\n","        for root, _, files in os.walk(processed_dir):\n","            for file in files:\n","                file_path = os.path.join(root, file)\n","                arcname = os.path.relpath(file_path, processed_dir)\n","                zip_out.write(file_path, arcname)\n","\n","    print(\"\\n✅ ALL DONE! Solid #181818 everywhere (forward + inverse specks cleaned) + smooth AA last\")\n","    print(f\"📍 Saved to your Drive at:\\n   {output_zip_path}\")\n"],"metadata":{"cellView":"form","id":"tFGWg7jNv4mo"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"93JpoQJ77NGu","cellView":"form"},"outputs":[],"source":["# ────────────────────────────────────────────────\n","# 🎨 CLEAN ROW → PURE 1024x1024 FRAMES (NO DISTORTION)\n","# ────────────────────────────────────────────────\n","\n","#@markdown ## ⚙️ Main Settings\n","num_frames = 500  #@param {type:\"slider\", min:10, max:2000, step:10}\n","overlap_percent = 0  #@param {type:\"slider\", min:0, max:50, step:1}\n","min_step_ratio = 0.8  #@param {type:\"slider\", min:0.5, max:1.0, step:0.05}\n","max_step_ratio = 1.0  #@param {type:\"slider\", min:0.8, max:1.5, step:0.05}\n","\n","#@markdown ## 🧱 Border\n","border_width = 8  #@param {type:\"slider\", min:0, max:100}\n","\n","#@markdown ## 💾 Input / Output\n","save_to_google_drive = True  #@param {type:\"boolean\"}\n","drive_folder_name = \"vertical_slices_output\"  #@param {type:\"string\"}\n","#input_zip_path = \"/content/drive/MyDrive/PROCESSED_klein_processed.zip\"  #@param {type:\"string\"}\n","zip_file_path = zip_path\n","# ────────────────────────────────────────────────\n","\n","FRAME_SIZE = 1024\n","INNER_SIZE = FRAME_SIZE - 2 * border_width\n","BORDER_COLOR = (24, 24, 24)\n","\n","import os, random, zipfile\n","import numpy as np\n","from PIL import Image\n","from google.colab import drive\n","\n","# ────────────────────────────────────────────────\n","# Mount Drive\n","# ────────────────────────────────────────────────\n","\n","drive_mounted = False\n","if save_to_google_drive:\n","    try:\n","        drive.mount('/content/drive', force_remount=False)\n","        drive_mounted = True\n","        drive_output_dir = os.path.join(\"/content/drive/MyDrive\", drive_folder_name)\n","        os.makedirs(drive_output_dir, exist_ok=True)\n","        print(\"Drive mounted\")\n","    except:\n","        save_to_google_drive = False\n","\n","# ────────────────────────────────────────────────\n","# Extract ZIP\n","# ────────────────────────────────────────────────\n","\n","extract_root = \"/content/extracted_images\"\n","output_dir = \"/content/frames\"\n","\n","os.makedirs(extract_root, exist_ok=True)\n","os.makedirs(output_dir, exist_ok=True)\n","\n","with zipfile.ZipFile(zip_file_path, 'r') as zf:\n","    zf.extractall(extract_root)\n","\n","# ────────────────────────────────────────────────\n","# Load images\n","# ────────────────────────────────────────────────\n","\n","valid_exts = ('.jpg','.jpeg','.png','.webp')\n","all_sources = []\n","\n","for root, _, files in os.walk(extract_root):\n","    for f in files:\n","        if f.lower().endswith(valid_exts):\n","            all_sources.append(os.path.join(root, f))\n","\n","print(\"Images found:\", len(all_sources))\n","\n","# ────────────────────────────────────────────────\n","# Build row UNTIL enough width\n","# ────────────────────────────────────────────────\n","\n","def build_row_until_width(all_sources, target_h, overlap_ratio, required_width):\n","    canvas = Image.new(\"RGB\", (required_width, target_h))\n","    x = 0\n","\n","    while x < required_width:\n","        src = random.choice(all_sources)\n","\n","        try:\n","            im = Image.open(src).convert(\"RGB\")\n","            w, h = im.size\n","\n","            scale = target_h / h\n","            new_w = int(w * scale)\n","\n","            im = im.resize((new_w, target_h), Image.LANCZOS)\n","\n","            # overlap\n","            if x > 0:\n","                x -= int(new_w * overlap_ratio)\n","\n","            canvas.paste(im, (x, 0))\n","            x += new_w\n","\n","        except:\n","            continue\n","\n","    return canvas\n","\n","# ────────────────────────────────────────────────\n","# Calculate required width\n","# ────────────────────────────────────────────────\n","\n","overlap_ratio = overlap_percent / 100.0\n","\n","avg_step = (min_step_ratio + max_step_ratio) / 2\n","estimated_width = int(num_frames * INNER_SIZE * avg_step * 1.2)\n","\n","print(\"Building row width:\", estimated_width)\n","\n","row_img = build_row_until_width(\n","    all_sources,\n","    INNER_SIZE,\n","    overlap_ratio,\n","    estimated_width\n",")\n","\n","row_np = np.array(row_img)\n","row_h, row_w = row_np.shape[:2]\n","\n","# ────────────────────────────────────────────────\n","# Generate frames (NO distortion)\n","# ────────────────────────────────────────────────\n","\n","frame_idx = 0\n","x_cursor = 0\n","\n","while x_cursor + INNER_SIZE <= row_w and frame_idx < num_frames:\n","\n","    step = random.randint(\n","        int(INNER_SIZE * min_step_ratio),\n","        int(INNER_SIZE * max_step_ratio)\n","    )\n","\n","    crop = row_np[:, x_cursor:x_cursor + INNER_SIZE]\n","\n","    final = Image.new(\"RGB\", (FRAME_SIZE, FRAME_SIZE), BORDER_COLOR)\n","    final.paste(Image.fromarray(crop), (border_width, border_width))\n","\n","    fname = f\"frame_{frame_idx:04d}.jpg\"\n","    final.save(os.path.join(output_dir, fname), \"JPEG\", quality=90)\n","\n","    frame_idx += 1\n","    x_cursor += step\n","\n","    if frame_idx % 50 == 0:\n","        print(frame_idx, \"frames done\")\n","\n","print(\"Finished:\", frame_idx)\n","\n","# ────────────────────────────────────────────────\n","# Save ZIP\n","# ────────────────────────────────────────────────\n","\n","if save_to_google_drive and drive_mounted:\n","\n","    zip_path = \"/content/output.zip\"\n","    drive_path = os.path.join(drive_output_dir, \"vertical_slices.zip\")\n","\n","    with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zf:\n","        for f in os.listdir(output_dir):\n","            if f.endswith(\".jpg\"):\n","                zf.write(os.path.join(output_dir, f), arcname=f)\n","\n","    !cp -f \"{zip_path}\" \"{drive_path}\"\n","\n","    print(\"Saved to Drive:\", drive_path)"]},{"cell_type":"markdown","source":["# Done!\n","\n","In the zip file /content/drive/MyDrive/vertical_slices_output/vertical_slices.zip  ,  there you will find 500 image composites to choose for lora training"],"metadata":{"id":"tJlvhf9xDmA7"}}],"metadata":{"colab":{"provenance":[{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/klein_prepare_lora_set.ipynb","timestamp":1776789720072},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/klein_prepare_lora_set.ipynb","timestamp":1776787634021},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/klein_prepare_lora_set.ipynb","timestamp":1776773678064},{"file_id":"151XI3nRFxLRbwRc8V6OfWyqp-zTnfySv","timestamp":1776764241991},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/klein_edit.ipynb","timestamp":1776702729526},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/vertical_slice_prepper.ipynb","timestamp":1776687886662},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/vertical_slice_prepper.ipynb","timestamp":1776366149549},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/lora_vertical_slice_dataset_creator.ipynb","timestamp":1776287741995},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/lora_vertical_slice_dataset_creator.ipynb","timestamp":1776178739426},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/lora_vertical_slice_dataset_creator.ipynb","timestamp":1776027716448},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/lora_vertical_slice_dataset_creator.ipynb","timestamp":1773663661932},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_caption_prepper.ipynb","timestamp":1773663290922},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_caption_prepper.ipynb","timestamp":1773264797996},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_caption_prepper.ipynb","timestamp":1773163850245},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_caption_prepper.ipynb","timestamp":1773090196076},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_caption_prepper.ipynb","timestamp":1773089575687},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_caption_prepper.ipynb","timestamp":1773080355474},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Drive to WebP.ipynb","timestamp":1772998638620},{"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}],"collapsed_sections":["HX3I-YqowwO9","4PjK9a8Sni3A"],"gpuType":"T4"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
+{"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"id":"r6q_m-6Pmm5w"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#@title Set path to zip file on your drive , and set klein edit prompt\n","input_zip_path = '/content/drive/MyDrive/my_images.zip' #@param {type:'string'}\n","zip_path = input_zip_path\n","edit_prompt = 'the background is a single color at the corners.  remove the background.  make sure the bare skin against the single color background looks like a real photo. ' #@param }¥{type:'string'}"],"metadata":{"id":"K13pRro0lxAw","cellView":"form"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"HX3I-YqowwO9"},"source":["# ⚙️ Run Klein Edit (GPU)"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"OXGhsHQwWue_"},"outputs":[],"source":["# =============================================================================\n","#@markdown # **CELL 1**: Mount Drive + HF auth\n","# =============================================================================\n","\n","from google.colab import drive, userdata\n","from huggingface_hub import login\n","import torch\n","import os\n","import gc\n","import shutil\n","\n","drive.mount('/content/drive')\n","\n","hf_token = userdata.get('HF_TOKEN')\n","if hf_token:\n","    login(token=hf_token)\n","else:\n","    print(\"⚠️ No HF_TOKEN found in secrets.\")\n","\n","print(\"🧹 Removing old diffusers...\")\n","!pip uninstall -y diffusers > /dev/null 2>&1\n","!rm -rf /usr/local/lib/python3.12/dist-packages/diffusers* ~/.cache/pip/*diffusers*\n","\n","print(\"🔄 Installing latest diffusers...\")\n","!pip install -q git+https://github.com/huggingface/diffusers.git --force-reinstall --no-deps\n","!python -m pip cache purge\n","\n","print(\"✅ Cell 1 complete!\")"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"a1wZv8PrXAe2"},"outputs":[],"source":["# =============================================================================\n","#@markdown # **CELL 2**: Fixed settings (resolution + options)\n","# =============================================================================\n","\n","resolution = '1024 x 1024 (Square)' #@param [\"1024 x 1024 (Square)\", \"512 x 1024 (Portrait)\", \"768 x 1024 (Slight Portrait)\", \"1536 x 1024 (Landscape)\", \"2048 x 1024 (Wide Landscape)\"] {type:\"string\"}\n","use_txt_prompts = True #@param {type:\"boolean\"}\n","debug = False #@param {type:\"boolean\"}\n","\n","res_map = {\n","    \"1024 x 1024 (Square)\": (1024, 1024),\n","    \"512 x 1024 (Portrait)\": (512, 1024),\n","    \"768 x 1024 (Slight Portrait)\": (768, 1024),\n","    \"1536 x 1024 (Landscape)\": (1536, 1024),\n","    \"2048 x 1024 (Wide Landscape)\": (2048, 1024)\n","}\n","target_width, target_height = res_map[resolution]\n","\n","print(\"✅ Cell 2 settings loaded\")\n","print(f\"   Resolution: {target_width}×{target_height}\")\n","print(f\"   Use .txt prompts: {use_txt_prompts}\")\n","print(f\"   Debug mode: {debug}\")\n","print(\"\\nNow run Cell 3 (model load), then Cell 4 (inference)\")"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"WdNfo33NXNEa"},"outputs":[],"source":["# =============================================================================\n","#@markdown # **CELL 3**: Load QUANTIZED Flux2KleinPipeline + SDNQ optimizations\n","#(model stays in memory)\n","# =============================================================================\n","\n","import torch\n","import gc\n","from diffusers import Flux2KleinPipeline\n","import zipfile\n","import glob\n","import os\n","\n","print(\"📦 Installing sdnq...\")\n","!pip install -q sdnq\n","\n","from sdnq.common import use_torch_compile as triton_is_available\n","from sdnq.loader import apply_sdnq_options_to_model\n","\n","gc.collect()\n","torch.cuda.empty_cache()\n","\n","print(\"🔄 Loading QUANTIZED Flux2KleinPipeline (4-bit SDNQ + float16 for T4)...\")\n","pipe = Flux2KleinPipeline.from_pretrained(\n","    \"Disty0/FLUX.2-klein-4B-SDNQ-4bit-dynamic\",\n","    torch_dtype=torch.float16\n",")\n","\n","print(f\"✅ Pipeline loaded – VRAM: {torch.cuda.memory_allocated() / 1e9:.2f} GB\")\n","\n","# ====================== Optimizations for T4 ======================\n","print(\"🔥 Applying SDNQ + T4 optimizations...\")\n","os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n","\n","if triton_is_available and torch.cuda.is_available():\n","    pipe.transformer = apply_sdnq_options_to_model(pipe.transformer, use_quantized_matmul=True)\n","    pipe.text_encoder = apply_sdnq_options_to_model(pipe.text_encoder, use_quantized_matmul=True)\n","    print(\"   ✅ INT8 MatMul enabled\")\n","\n","pipe.enable_model_cpu_offload()\n","pipe.vae.enable_slicing()\n","pipe.vae.enable_tiling()\n","\n","gc.collect()\n","torch.cuda.empty_cache()\n","torch.cuda.reset_peak_memory_stats()\n","\n","print(\"✅ Cell 3 complete – model is ready!\")\n","print(\"   Now go to Cell 4 and run it (you can change zip/prompt in Cell 4 and re-run Cell 4 only)\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"q9I9wxghuUGP","cellView":"form"},"outputs":[],"source":["import shutil\n","import datetime\n","import os\n","import glob\n","import zipfile\n","import gc\n","import torch\n","from PIL import Image, ImageDraw # Added ImageDraw\n","from google.colab import files\n","\n","# =============================================================================\n","#@markdown # **CELL 4**: MULTI-IMAGE INFERENCE (Klein + custom/gray reference)\n","# • Uses the **same zip** as Cell 4 but now does true multi-image editing\n","# • First image = your original (the one being edited)\n","# • Second image = your custom uploaded image OR a solid #181818 gray square (perfect for background replacement)\n","# • Re-run this cell only with new values\n","# • Auto-clears old data → processes → saves **only ZIP** to Drive\n","# =============================================================================\n","\n","#edit_prompt = 'remove the background. the background is gray. ' #@param {type:\"string\"}\n","#zip_path = '/content/drive/MyDrive/input_images.zip' #@param {type:\"string\"}\n","upload_custom_reference = False #@param {type:\"boolean\"}\n","max_image_dimension = 2048 #@param {type:\"slider\", min:512, max:4096, step:256}\n","add_gray_corners = True #@param {type:\"boolean\"}\n","gray_square_size = 50 #@param {type:\"slider\", min:10, max:200, step:10}\n","\n","output_folder = '/content/edited_images_multi'   # ← separate temp folder so it doesn't clash with Cell 4\n","\n","print(\"🧹 Clearing old temporary folders so you can reuse with new zip/prompt...\")\n","if os.path.exists('/content/input_images'):\n","    shutil.rmtree('/content/input_images')\n","if os.path.exists(output_folder):\n","    shutil.rmtree(output_folder)\n","os.makedirs(output_folder, exist_ok=True)\n","\n","# ====================== Unzip + build prompt map ======================\n","print(f\"📦 Unzipping: {zip_path}...\")\n","with zipfile.ZipFile(zip_path, 'r') as z:\n","    z.extractall('/content/input_images')\n","\n","image_files = sorted(glob.glob('/content/input_images/*.*'))\n","image_files = [f for f in image_files if f.lower().endswith(('.png', '.jpg', '.jpeg', '.webp'))]\n","print(f\"Found {len(image_files)} images.\")\n","\n","prompt_map = {}\n","txt_count = 0\n","for img_path in image_files:\n","    base = os.path.splitext(os.path.basename(img_path))[0]\n","    txt_path = os.path.join('/content/input_images', f\"{base}.txt\")\n","    if use_txt_prompts and os.path.exists(txt_path):\n","        with open(txt_path, 'r', encoding='utf-8') as f:\n","            prompt_map[img_path] = f.read().strip()\n","        txt_count += 1\n","    else:\n","        prompt_map[img_path] = None\n","\n","print(f\"Found {txt_count} matching .txt files\")\n","\n","# ====================== Create or load the second reference image (once) ======================\n","uploaded_reference_filepath = None\n","reference_image_to_pair = None # Initialize to None\n","\n","if upload_custom_reference: # Only attempt to get a reference if the checkbox is enabled\n","    print(\"Please upload your reference image now.\")\n","    uploaded_files = files.upload()\n","\n","    if uploaded_files:\n","        uploaded_reference_filepath = list(uploaded_files.keys())[0]\n","        print(f\"✅ Uploaded reference image: {uploaded_reference_filepath}\")\n","        # Save the uploaded file to a temporary location to be opened by PIL\n","        with open(uploaded_reference_filepath, 'wb') as f:\n","            f.write(uploaded_files[uploaded_reference_filepath])\n","\n","    if uploaded_reference_filepath: # Check if a file was actually uploaded\n","        print(\"📁 Using uploaded reference image...\")\n","        try:\n","            # Open the temporary file\n","            reference_image_to_pair = Image.open(uploaded_reference_filepath).convert(\"RGB\")\n","            #if reference_image_to_pair.size != (target_width, target_height):\n","             #   print(f\"   ⚠️ Resizing uploaded reference image from {reference_image_to_pair.size} to {target_width}x{target_height}\")\n","             #   reference_image_to_pair = reference_image_to_pair.resize((target_width, target_height), Image.LANCZOS)\n","        except Exception as e:\n","            print(f\"   ❌ Error loading uploaded image: {e}. Falling back to gray reference.\")\n","            reference_image_to_pair = Image.new(\"RGB\", (target_width, target_height), \"#181818\")\n","    else: # If upload_custom_reference is True but no file was uploaded\n","        print(f\"🟩 No custom reference image uploaded. Creating {target_width}x{target_height} #181818 solid gray reference image...\")\n","        reference_image_to_pair = Image.new(\"RGB\", (target_width, target_height), \"#181818\")\n","else:\n","    print(\"🚫 Custom reference image upload disabled. Will process images without a second reference.\")\n","    # reference_image_to_pair remains None, which is the desired behavior for the pipe call.\n","\n","# Clean up the temporarily saved file if it was uploadedi\n","if uploaded_reference_filepath and os.path.exists(uploaded_reference_filepath):\n","    os.remove(uploaded_reference_filepath)\n","\n","# ==================== Helper function for adding gray squares ====================\n","def add_corner_squares(image: Image.Image, square_size: int, color=(24, 24, 24)) -> Image.Image:\n","    \"\"\"Adds four gray squares to the corners of the input image.\"\"\"\n","    img_copy = image.copy()\n","    draw = ImageDraw.Draw(img_copy)\n","    width, height = img_copy.size\n","\n","    # Top-left\n","    draw.rectangle([(0, 0), (square_size, square_size)], fill=color)\n","    # Top-right\n","    draw.rectangle([(width - square_size, 0), (width, square_size)], fill=color)\n","    # Bottom-left\n","    draw.rectangle([(0, height - square_size), (square_size, height)], fill=color)\n","    # Bottom-right\n","    draw.rectangle([(width - square_size, height - square_size), (width, height)], fill=color)\n","    return img_copy\n","\n","# ====================== FULL BATCH MULTI-IMAGE INFERENCE ======================\n","print(f\"\\n🚀 Starting batch MULTI-IMAGE edit on {len(image_files)} images...\")\n","\n","for i, img_path in enumerate(image_files):\n","    filename = os.path.basename(img_path)\n","\n","    gc.collect()\n","    torch.cuda.empty_cache()\n","\n","    input_image = Image.open(img_path).convert(\"RGB\")\n","\n","    # Resize input_image if its largest dimension exceeds max_image_dimension\n","    if max(input_image.width, input_image.height) > max_image_dimension:\n","        print(f\"   ⚠️ Resizing {filename} from {input_image.size} to max dimension {max_image_dimension}\")\n","        aspect_ratio = input_image.width / input_image.height\n","        if input_image.width > input_image.height:\n","            new_width = max_image_dimension\n","            new_height = int(max_image_dimension / aspect_ratio)\n","        else:\n","            new_height = max_image_dimension\n","            new_width = int(max_image_dimension * aspect_ratio)\n","        input_image = input_image.resize((new_width, new_height), Image.LANCZOS)\n","\n","    # Apply gray corners if enabled\n","    if add_gray_corners:\n","        print(f\"   Adding {gray_square_size}x{gray_square_size} gray squares to corners of {filename}\")\n","        input_image = add_corner_squares(input_image, gray_square_size)\n","\n","    # === MULTI-IMAGE LIST: base image first, the chosen reference second ===\n","    if reference_image_to_pair is not None:\n","        reference_images = [input_image, reference_image_to_pair]\n","        print(f\"[{i+1}/{len(image_files)}] {filename}  →  + custom reference\")\n","    else:\n","        reference_images = [input_image]\n","        print(f\"[{i+1}/{len(image_files)}] {filename}  →  (no custom reference)\")\n","\n","    current_prompt = prompt_map[img_path] if use_txt_prompts and prompt_map[img_path] is not None else edit_prompt\n","\n","    result = pipe(\n","        prompt=current_prompt,\n","        image=reference_images,           # ← KEY: list of images for Klein multi-reference\n","        height=target_height,\n","        width=target_width,\n","        guidance_scale=1.0,\n","        num_inference_steps=4,\n","        generator=torch.Generator(\"cuda\").manual_seed(42),\n","        output_type=\"pil\",\n","    ).images[0]\n","\n","    output_filename = f\"multi_edited_{filename}\"\n","    output_path = os.path.join(output_folder, output_filename)\n","    result.save(output_path)\n","\n","    if debug:\n","        print(f\"   → Saved {output_filename} (multi-ref with custom/gray image)\") # Updated message\n","\n","print(\"\\n→ MULTI-IMAGE BATCH COMPLETE!\")\n","\n","# ====================== AUTO ZIP TO DRIVE (unique name) ======================\n","timestamp = datetime.datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n","drive_zip_path = f\"/content/drive/MyDrive/klein_processed_{timestamp}.zip\"\n","\n","print(f\"📦 Creating zip → {drive_zip_path}\")\n","shutil.make_archive(drive_zip_path.replace('.zip', ''), 'zip', output_folder)\n","\n","print(\"✅ ALL DONE!\")\n","print(f\"Zip file saved to your Google Drive:\")\n","print(drive_zip_path)"]},{"cell_type":"markdown","source":["# Select klein edited zip file to process next\n","Results from earlier cell have been saved to your drive as <br> 'content/drive/MyDrive/klein_processed_(date_number).zip'\n","\n","You can disconnect from the session  , and reconnect to new runtime to prevent conflict in installed libraries. you can run this code on CPU"],"metadata":{"id":"F5NDcZ6rZw6Q"}},{"cell_type":"code","execution_count":2,"metadata":{"id":"OdcEXFs4oBt_","cellView":"form","executionInfo":{"status":"ok","timestamp":1776790055074,"user_tz":-120,"elapsed":267,"user":{"displayName":"No Name","userId":"10578412414437288386"}}},"outputs":[],"source":["import os\n","\n","klein_edited_zip_path = '/content/drive/MyDrive/klein_processed_20260421_164108.zip' #@param {type:'string'}\n","zip_path = klein_edited_zip_path\n","\n","if not os.path.exists(zip_path):\n","    raise FileNotFoundError(f\"❌ Klein edited zip file not found:\\n{zip_path}\\n\\nPlease ensure the path is correct and the file exists in your Drive.\")"]},{"cell_type":"markdown","source":["# ⚙️ Create 1024x1024 frames from selected klein edited set"],"metadata":{"id":"4PjK9a8Sni3A"}},{"cell_type":"code","source":["#@title 🖼️ **Image Background Processor (v1)**\n","#@markdown ---\n","#@markdown ## Enable Processor V1\n","enable_processor_v1 = False #@param {type:\"boolean\"}\n","\n","#@title 🖼️ **Image Background Processor**\n","#(v2 - Solid #181818 Flood-Fill + Auto-Clear)\n","#**Crop left/right empty space + Convert *any* gray background to exact #181818**\n","#@markdown ---\n","#**New**: Automatically clears all previous processed images on re-run\n","#(no more mixing old files when you run again)\n","\n","#Run this cell (Shift + Enter)\n","#Fill the form below\n","#Click **Run**\n","\n","# ==================== FORM PARAMETERS ====================\n","\n","#input_zip_path = \"/content/drive/MyDrive/klein_processed.zip\" #@param {type:\"string\", placeholder:\"Paste full path to your ZIP file here\"}\n","input_zip_path = zip_path\n","\n","tolerance_crop = 50 #@param {type:\"slider\", min:0, max:100, step:1, title:\"🟦 Crop Tolerance (higher = more aggressive cropping)\"}\n","\n","bg_replace_threshold = 20 #@param {type:\"slider\", min:10, max:100, step:1}\n","\n","force_remount = False #@param {type:\"boolean\", title:\"🔄 Force remount Drive (recommended)\"}\n","\n","#@markdown ---\n","\n","\n","if enable_processor_v1:\n","    from google.colab import drive\n","    import zipfile\n","    import os\n","    import shutil                          # ← NEW for clearing folders\n","    from PIL import Image\n","    from collections import deque\n","\n","    # ========================= CONFIG =========================\n","    TARGET_GRAY = (24, 24, 24)   # Exact #181818 - do not change\n","    # =======================================================\n","\n","    print(\"🚀 Starting image processor (Solid Background v2 + Auto-Clear)...\")\n","\n","    # 1. Mount Google Drive\n","    drive.mount('/content/drive', force_remount=force_remount)\n","\n","    # 2. Validate ZIP path\n","    if not input_zip_path or not os.path.exists(input_zip_path):\n","        raise FileNotFoundError(f\"❌ ZIP file not found:\\n{input_zip_path}\\n\\nPlease paste the correct full path (e.g. /content/drive/MyDrive/my_photos.zip)\")\n","\n","    print(f\"✅ ZIP found: {input_zip_path}\")\n","\n","    # 3. Define working folders\n","    extract_dir = '/content/extracted_images'\n","    processed_dir = '/content/processed_images'\n","\n","    # === NEW: Clear previous processed & extracted images on every run ===\n","    for directory in [extract_dir, processed_dir]:\n","        if os.path.exists(directory):\n","            shutil.rmtree(directory)\n","            print(f\"🧹 Cleared old folder: {directory}\")\n","    os.makedirs(extract_dir, exist_ok=True)\n","    os.makedirs(processed_dir, exist_ok=True)\n","    print(\"✅ Fresh folders ready\")\n","\n","    # 4. Unzip\n","    print(\"📦 Unzipping...\")\n","    with zipfile.ZipFile(input_zip_path, 'r') as zip_ref:\n","        zip_ref.extractall(extract_dir)\n","    print(f\"✅ Unzipped to {extract_dir}\")\n","\n","    # ==================== HELPER 1: Crop only left & right ====================\n","    def crop_left_right(img):\n","        if img.mode != 'RGB':\n","            img = img.convert('RGB')\n","\n","        width, height = img.size\n","        bg_color = img.getpixel((0, 0))\n","\n","        left = width\n","        right = 0\n","\n","        for x in range(width):\n","            column_is_empty = True\n","            for y in range(height):\n","                px = img.getpixel((x, y))\n","                if any(abs(a - b) > tolerance_crop for a, b in zip(px, bg_color)):\n","                    column_is_empty = False\n","                    break\n","            if not column_is_empty:\n","                left = min(left, x)\n","                right = max(right, x)\n","\n","        if left >= right:\n","            return img\n","\n","        return img.crop((left, 0, right + 1, height))\n","\n","    # ==================== HELPER 2: FLOOD-FILL BACKGROUND FROM EDGES ====================\n","    def adjust_background(img, original_bg_color):\n","        if img.mode != 'RGB':\n","            img = img.convert('RGB')\n","\n","        width, height = img.size\n","        pixels = img.load()\n","        threshold = bg_replace_threshold\n","\n","        visited = set()\n","        queue = deque()\n","        directions = [(dx, dy) for dx in [-1, 0, 1] for dy in [-1, 0, 1] if not (dx == 0 and dy == 0)]\n","\n","        # Seed from borders\n","        for x in range(width):\n","            for y in (0, height - 1):\n","                if (x, y) not in visited:\n","                    px = pixels[x, y]\n","                    dist = ((px[0] - original_bg_color[0])**2 + (px[1] - original_bg_color[1])**2 + (px[2] - original_bg_color[2])**2) ** 0.5\n","                    if dist <= threshold:\n","                        queue.append((x, y))\n","                        visited.add((x, y))\n","                        pixels[x, y] = TARGET_GRAY\n","\n","        for y in range(height):\n","            for x in (0, width - 1):\n","                if (x, y) not in visited:\n","                    px = pixels[x, y]\n","                    dist = ((px[0] - original_bg_color[0])**2 + (px[1] - original_bg_color[1])**2 + (px[2] - original_bg_color[2])**2) ** 0.5\n","                    if dist <= threshold:\n","                        queue.append((x, y))\n","                        visited.add((x, y))\n","                        pixels[x, y] = TARGET_GRAY\n","\n","        # Flood fill\n","        while queue:\n","            x, y = queue.popleft()\n","            for dx, dy in directions:\n","                nx, ny = x + dx, y + dy\n","                if 0 <= nx < width and 0 <= ny < height and (nx, ny) not in visited:\n","                    px = pixels[nx, ny]\n","                    dist = ((px[0] - original_bg_color[0])**2 + (px[1] - original_bg_color[1])**2 + (px[2] - original_bg_color[2])**2) ** 0.5\n","                    if dist <= threshold:\n","                        visited.add((nx, ny))\n","                        pixels[nx, ny] = TARGET_GRAY\n","                        queue.append((nx, ny))\n","\n","        return img\n","\n","    # 5. Process every image\n","    print(\"🖼️ Processing images...\")\n","    image_extensions = ('.png', '.jpg', '.jpeg', '.bmp', '.tiff', '.webp')\n","\n","    processed_count = 0\n","    for filename in os.listdir(extract_dir):\n","        if filename.lower().endswith(image_extensions):\n","            img_path = os.path.join(extract_dir, filename)\n","            try:\n","                with Image.open(img_path) as original_img:\n","                    cropped = crop_left_right(original_img)\n","                    orig_bg = original_img.getpixel((0, 0))\n","                    processed = adjust_background(cropped, orig_bg)\n","\n","                    out_path = os.path.join(processed_dir, filename)\n","                    processed.save(out_path, quality=95 if filename.lower().endswith(('.jpg', '.jpeg')) else None)\n","\n","                    processed_count += 1\n","                    print(f\"   ✅ {filename}\")\n","            except Exception as e:\n","                print(f\"   ❌ Skipped {filename}: {e}\")\n","\n","    print(f\"\\n🎉 Processed {processed_count} images!\")\n","\n","    # 6. Create final ZIP in Drive\n","    output_zip_name = \"PROCESSED_\" + os.path.basename(zip_path)\n","    output_zip_path = os.path.join(os.path.dirname(zip_path), output_zip_name)\n","    zip_path = output_zip_path\n","\n","    print(f\"📦 Creating final ZIP: {output_zip_name}\")\n","    with zipfile.ZipFile(output_zip_path, 'w', zipfile.ZIP_DEFLATED) as zip_out:\n","        for root, _, files in os.walk(processed_dir):\n","            for file in files:\n","                file_path = os.path.join(root, file)\n","                arcname = os.path.relpath(file_path, processed_dir)\n","                zip_out.write(file_path, arcname)\n","\n","    print(\"\\n✅ ALL DONE! Solid #181818 background achieved.\")\n","    print(f\"📍 Saved to your Drive at:\\n   {output_zip_path}\")\n"],"metadata":{"id":"HN4kta-OpZ4W","cellView":"form","executionInfo":{"status":"ok","timestamp":1776790207061,"user_tz":-120,"elapsed":132,"user":{"displayName":"No Name","userId":"10578412414437288386"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["#@title **🖼️ Image Background Processor V2 (WIP)**\n","#@markdown ---\n","#@markdown ## Enable Processor V2\n","enable_processor_v2 = True #@param {type:\"boolean\"}\n","\n","#@title **🖼️ Image Background Processor V2 (WIP)**\n","#(v2.7 - **Inverse speck removal** + Border-protected forward speck + % Margin + Solid #181818)\n","\n","#**Crop left/right empty space + Convert *any* gray background to exact #181818**\n","#**New in v2.7**:\n","#   • **Inverse speck removal**: finds tiny foreground (subject-colored) specks/islands inside the background and replaces them with solid #181818\n","#   • Forward speck removal (previous) still protects any #181818 connected to the outer image edge\n","#   • Anti-aliasing is guaranteed to be the **very last step** (after both forward + inverse speck removal)\n","#   • All previous features (4-corner BG, % margin, checkboxes, distance-based enclosed speck removal) unchanged\n","\n","# ==================== FORM PARAMETERS ====================\n","\n","input_zip_path = zip_path\n","\n","tolerance_crop = 30 #@param {type:\"slider\", min:0, max:100, step:1, title:\"🔵 Crop Tolerance (higher = more aggressive cropping)\"}\n","\n","bg_replace_threshold = 10 #@param {type:\"slider\", min:10, max:100, step:1, title:\"🎨 Legacy absolute threshold (kept for compatibility)\"}\n","\n","# ── % Margin for BG color estimation ──\n","bg_margin_percent = 6 #@param {type:\"slider\", min:0, max:50, step:1, title:\"% Margin for BG color estimation (wider = more tolerant to inner-loop gray variations)\"}\n","\n","# ── Feature Checkboxes ──\n","enable_robust_bg_detection = True #@param {type:\"boolean\", title:\"✅ Use 4-corner robust BG color (best for inner loops)\"}\n","enable_floodfill_background_adjustment = True #@param {type:\"boolean\", title:\"✅ Enable solid #181818 background replacement\"}\n","enable_small_speck_removal = True #@param {type:\"boolean\"}\n","#title:\"✅ Forward speck removal: remove tiny #181818 specks on subject\"}\n","enable_inverse_speck_removal = True #@param {type:\"boolean\"}\n","#title:\"✅ Inverse speck removal: fill tiny subject specks inside background with #181818\"}\n","enable_anti_aliasing = True #@param {type:\"boolean\", title:\"✅ Smooth edges (Gaussian anti-aliasing)\"}\n","\n","anti_aliasing_strength = 6 #@param {type:\"slider\", min:1, max:40, step:1, title:\"   ↳ Anti-aliasing Strength (pixels to blend)\"}\n","\n","# ── Forward speck removal (enclosed only) ──\n","min_floodfill_steps = 8 #@param {type:\"slider\", min:1, max:20, step:1, title:\"   ↳ Min floodfill steps to foreground (N) for ENCLOSED BG only (outer-edge-connected BG always protected)\"}\n","\n","# ── Inverse speck removal ──\n","max_inverse_speck_size = 10 #@param {type:\"slider\", min:1, max:100, step:1, title:\"   ↳ Max size of foreground specks to fill with #181818\"}\n","\n","force_remount = False #@param {type:\"boolean\", title:\"🔄 Force remount Drive\"}\n","\n","#@markdown ---\n","\n","\n","if enable_processor_v2:\n","    from google.colab import drive\n","    import zipfile\n","    import os\n","    import shutil\n","    from PIL import Image, ImageFilter\n","    from collections import deque\n","\n","    # ========================= CONFIG =========================\n","    TARGET_GRAY = (24, 24, 24)   # Exact #181818\n","    # =======================================================\n","\n","    print(\"🚀 Starting Image Background Processor v2.7 (Forward + Inverse speck removal)...\")\n","\n","    # 1. Mount Google Drive\n","    drive.mount('/content/drive', force_remount=force_remount)\n","\n","    # 2. Validate ZIP path\n","    if not input_zip_path or not os.path.exists(input_zip_path):\n","        raise FileNotFoundError(f\"❌ ZIP file not found:\\n{input_zip_path}\\n\\nPlease paste the correct full path\")\n","\n","    print(f\"✅ ZIP found: {input_zip_path}\")\n","\n","    # 3. Define working folders\n","    extract_dir = '/content/extracted_images'\n","    processed_dir = '/content/processed_images'\n","\n","    # Clear previous runs\n","    for directory in [extract_dir, processed_dir]:\n","        if os.path.exists(directory):\n","            shutil.rmtree(directory)\n","            print(f\"﹅ Cleared old folder: {directory}\")\n","    os.makedirs(extract_dir, exist_ok=True)\n","    os.makedirs(processed_dir, exist_ok=True)\n","    print(\"✅ Fresh folders ready\")\n","\n","    # 4. Unzip\n","    print(\"🚀 Unzipping...\")\n","    with zipfile.ZipFile(input_zip_path, 'r') as zip_ref:\n","        zip_ref.extractall(extract_dir)\n","    print(f\"✅ Unzipped to {extract_dir}\")\n","\n","    # ==================== HELPER 1: Crop only left & right ====================\n","    def crop_left_right(img):\n","        if img.mode != 'RGB':\n","            img = img.convert('RGB')\n","        width, height = img.size\n","        bg_color = img.getpixel((0, 0))\n","        left = width\n","        right = 0\n","        for x in range(width):\n","            column_is_empty = True\n","            for y in range(height):\n","                px = img.getpixel((x, y))\n","                if any(abs(a - b) > tolerance_crop for a, b in zip(px, bg_color)):\n","                    column_is_empty = False\n","                    break\n","            if not column_is_empty:\n","                left = min(left, x)\n","                right = max(right, x)\n","        if left >= right:\n","            return img\n","        return img.crop((left, 0, right + 1, height))\n","\n","    # ==================== HELPER 2: FULL BACKGROUND PROCESSOR (v2.7) ====================\n","    def adjust_background(img, original_bg_color,\n","                          enable_floodfill_background_adjustment,\n","                          enable_small_speck_removal,\n","                          enable_inverse_speck_removal,\n","                          enable_anti_aliasing,\n","                          anti_aliasing_strength,\n","                          min_floodfill_steps,\n","                          max_inverse_speck_size,\n","                          bg_margin_percent):\n","        if img.mode != 'RGB':\n","            img = img.convert('RGB')\n","\n","        width, height = img.size\n","        pixels_access = img.load()\n","        target_gray = TARGET_GRAY\n","        directions = [(dx, dy) for dx in [-1, 0, 1] for dy in [-1, 0, 1] if not (dx == 0 and dy == 0)]\n","\n","        # 1. Identify foreground using % margin\n","        per_channel_margin = int(255 * (bg_margin_percent / 100.0))\n","        is_foreground = [[False for _ in range(height)] for _ in range(width)]\n","        for y in range(height):\n","            for x in range(width):\n","                px = pixels_access[(x, y)]\n","                is_bg = all(abs(px[i] - original_bg_color[i]) <= per_channel_margin for i in range(3))\n","                if not is_bg:\n","                    is_foreground[x][y] = True\n","\n","        # 2. Base image: solid #181818 + foreground\n","        base_processed_img = Image.new(\"RGB\", (width, height), target_gray)\n","        base_processed_pixels = base_processed_img.load()\n","        for y in range(height):\n","            for x in range(width):\n","                if is_foreground[x][y]:\n","                    base_processed_pixels[(x, y)] = pixels_access[(x, y)]\n","\n","        # 3. Forward speck removal (border-protected, enclosed only)\n","        if enable_floodfill_background_adjustment and enable_small_speck_removal:\n","            print(\"   🔍 Running forward speck removal (border-protected)...\")\n","            refined_img = base_processed_img.copy()\n","            refined_pixels = refined_img.load()\n","\n","            # Mark border-connected #181818\n","            connected_to_border = [[False] * height for _ in range(width)]\n","            queue = deque()\n","            for x in range(width):\n","                for y_border in [0, height-1]:\n","                    if refined_pixels[(x, y_border)] == target_gray and not connected_to_border[x][y_border]:\n","                        connected_to_border[x][y_border] = True\n","                        queue.append((x, y_border))\n","            for y in range(height):\n","                for x_border in [0, width-1]:\n","                    if refined_pixels[(x_border, y)] == target_gray and not connected_to_border[x_border][y]:\n","                        connected_to_border[x_border][y] = True\n","                        queue.append((x_border, y))\n","            while queue:\n","                cx, cy = queue.popleft()\n","                for dx, dy in directions:\n","                    nx, ny = cx + dx, cy + dy\n","                    if 0 <= nx < width and 0 <= ny < height and not connected_to_border[nx][ny] and refined_pixels[(nx, ny)] == target_gray:\n","                        connected_to_border[nx][ny] = True\n","                        queue.append((nx, ny))\n","\n","            # Distance to foreground (for enclosed BG only)\n","            distance = [[-1] * height for _ in range(width)]\n","            queue = deque()\n","            for y in range(height):\n","                for x in range(width):\n","                    if is_foreground[x][y]:\n","                        for dx, dy in directions:\n","                            nx, ny = x + dx, y + dy\n","                            if 0 <= nx < width and 0 <= ny < height and not is_foreground[nx][ny] and distance[nx][ny] == -1:\n","                                distance[nx][ny] = 1\n","                                queue.append((nx, ny))\n","                                break\n","            while queue:\n","                cx, cy = queue.popleft()\n","                for dx, dy in directions:\n","                    nx, ny = cx + dx, cy + dy\n","                    if 0 <= nx < width and 0 <= ny < height and not is_foreground[nx][ny] and distance[nx][ny] == -1:\n","                        distance[nx][ny] = distance[cx][cy] + 1\n","                        queue.append((nx, ny))\n","\n","            # Revert only enclosed + too-close BG pixels\n","            for y in range(height):\n","                for x in range(width):\n","                    if refined_pixels[(x, y)] == target_gray:\n","                        if not connected_to_border[x][y] and (distance[x][y] == -1 or distance[x][y] < min_floodfill_steps):\n","                            refined_pixels[(x, y)] = pixels_access[(x, y)]\n","\n","            base_processed_img = refined_img\n","            base_processed_pixels = base_processed_img.load()\n","\n","        # 4. INVERSE speck removal (tiny foreground specks inside background → #181818)\n","        if enable_floodfill_background_adjustment and enable_inverse_speck_removal:\n","            print(\"   🔍 Running inverse speck removal (small FG specks → #181818)...\")\n","            refined_img = base_processed_img.copy()\n","            refined_pixels = refined_img.load()\n","            visited = [[False] * height for _ in range(width)]\n","\n","            for y in range(height):\n","                for x in range(width):\n","                    if refined_pixels[(x, y)] != target_gray and not visited[x][y]:\n","                        component = []\n","                        queue = deque([(x, y)])\n","                        visited[x][y] = True\n","                        while queue:\n","                            cx, cy = queue.popleft()\n","                            component.append((cx, cy))\n","                            for dx, dy in directions:\n","                                nx, ny = cx + dx, cy + dy\n","                                if 0 <= nx < width and 0 <= ny < height and not visited[nx][ny] and refined_pixels[(nx, ny)] != target_gray:\n","                                    visited[nx][ny] = True\n","                                    queue.append((nx, ny))\n","                        # Fill small foreground specks with solid #181818\n","                        if len(component) < max_inverse_speck_size:\n","                            for px, py in component:\n","                                refined_pixels[(px, py)] = target_gray\n","\n","            base_processed_img = refined_img\n","            base_processed_pixels = base_processed_img.load()\n","\n","        # 5. Anti-aliasing – ALWAYS LAST (as requested)\n","        if not enable_anti_aliasing:\n","            return base_processed_img\n","\n","        mask = Image.new(\"L\", (width, height), 0)\n","        mask_pixels = mask.load()\n","        for y in range(height):\n","            for x in range(width):\n","                if base_processed_pixels[(x, y)] != target_gray:\n","                    mask_pixels[(x, y)] = 255\n","\n","        blur_radius = anti_aliasing_strength / 2.0\n","        blurred_mask = mask.filter(ImageFilter.GaussianBlur(radius=blur_radius))\n","\n","        solid_bg = Image.new(\"RGB\", (width, height), target_gray)\n","        final_img_with_aa = Image.composite(base_processed_img, solid_bg, blurred_mask)\n","\n","        return final_img_with_aa\n","\n","    # ==================== MAIN PROCESSING LOOP ====================\n","    print(\"🚀 Processing images...\")\n","    image_extensions = ('.png', '.jpg', '.jpeg', '.bmp', '.tiff', '.webp')\n","\n","    processed_count = 0\n","    for filename in os.listdir(extract_dir):\n","        if filename.lower().endswith(image_extensions):\n","            img_path = os.path.join(extract_dir, filename)\n","            try:\n","                with Image.open(img_path) as original_img:\n","                    # === ROBUST BACKGROUND COLOR DETECTION ===\n","                    cropped = crop_left_right(original_img)\n","                    if cropped.mode != 'RGB':\n","                        cropped = cropped.convert('RGB')\n","\n","                    if enable_robust_bg_detection:\n","                        w, h = cropped.size\n","                        samples = [\n","                            cropped.getpixel((0, 0)),\n","                            cropped.getpixel((w-1, 0)),\n","                            cropped.getpixel((0, h-1)),\n","                            cropped.getpixel((w-1, h-1))\n","                        ]\n","                        orig_bg = tuple(sum(c) // len(samples) for c in zip(*samples))\n","                        print(f\"   📍 Using robust 4-corner BG color for {filename}\")\n","                    else:\n","                        orig_bg = cropped.getpixel((0, 0))\n","\n","                    # === PROCESS IMAGE ===\n","                    if enable_floodfill_background_adjustment:\n","                        processed = adjust_background(\n","                            cropped,\n","                            orig_bg,\n","                            enable_floodfill_background_adjustment,\n","                            enable_small_speck_removal,\n","                            enable_inverse_speck_removal,\n","                            enable_anti_aliasing,\n","                            anti_aliasing_strength,\n","                            min_floodfill_steps,\n","                            max_inverse_speck_size,\n","                            bg_margin_percent\n","                        )\n","                    else:\n","                        processed = cropped\n","\n","                    out_path = os.path.join(processed_dir, filename)\n","                    processed.save(out_path, quality=95 if filename.lower().endswith(('.jpg', '.jpeg')) else None)\n","\n","                    processed_count += 1\n","                    print(f\"   ✅ {filename}\")\n","\n","            except Exception as e:\n","                print(f\"   ❌ Skipped {filename}: {e}\")\n","\n","    print(f\"\\n🎉 Processed {processed_count} images!\")\n","\n","    # 6. Create final ZIP\n","    output_zip_name = \"PROCESSED_\" + os.path.basename(input_zip_path)\n","    output_zip_path = os.path.join(os.path.dirname(input_zip_path), output_zip_name)\n","\n","    print(f\"🚀 Creating final ZIP: {output_zip_name}\")\n","    with zipfile.ZipFile(output_zip_path, 'w', zipfile.ZIP_DEFLATED) as zip_out:\n","        for root, _, files in os.walk(processed_dir):\n","            for file in files:\n","                file_path = os.path.join(root, file)\n","                arcname = os.path.relpath(file_path, processed_dir)\n","                zip_out.write(file_path, arcname)\n","\n","    print(\"\\n✅ ALL DONE! Solid #181818 everywhere (forward + inverse specks cleaned) + smooth AA last\")\n","    print(f\"📍 Saved to your Drive at:\\n   {output_zip_path}\")\n"],"metadata":{"cellView":"form","id":"tFGWg7jNv4mo"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"93JpoQJ77NGu","cellView":"form"},"outputs":[],"source":["# ────────────────────────────────────────────────\n","# 🎨 CLEAN ROW → PURE 1024x1024 FRAMES (NO DISTORTION)\n","# ────────────────────────────────────────────────\n","\n","#@markdown ## ⚙️ Main Settings\n","num_frames = 500  #@param {type:\"slider\", min:10, max:2000, step:10}\n","overlap_percent = 0  #@param {type:\"slider\", min:0, max:50, step:1}\n","min_step_ratio = 0.8  #@param {type:\"slider\", min:0.5, max:1.0, step:0.05}\n","max_step_ratio = 1.0  #@param {type:\"slider\", min:0.8, max:1.5, step:0.05}\n","\n","#@markdown ## 🧱 Border\n","border_width = 8  #@param {type:\"slider\", min:0, max:100}\n","\n","#@markdown ## 💾 Input / Output\n","save_to_google_drive = True  #@param {type:\"boolean\"}\n","drive_folder_name = \"vertical_slices_output\"  #@param {type:\"string\"}\n","#input_zip_path = \"/content/drive/MyDrive/PROCESSED_klein_processed.zip\"  #@param {type:\"string\"}\n","zip_file_path = zip_path\n","# ────────────────────────────────────────────────\n","\n","FRAME_SIZE = 1024\n","INNER_SIZE = FRAME_SIZE - 2 * border_width\n","BORDER_COLOR = (24, 24, 24)\n","\n","import os, random, zipfile\n","import numpy as np\n","from PIL import Image\n","from google.colab import drive\n","\n","# ────────────────────────────────────────────────\n","# Mount Drive\n","# ────────────────────────────────────────────────\n","\n","drive_mounted = False\n","if save_to_google_drive:\n","    try:\n","        drive.mount('/content/drive', force_remount=False)\n","        drive_mounted = True\n","        drive_output_dir = os.path.join(\"/content/drive/MyDrive\", drive_folder_name)\n","        os.makedirs(drive_output_dir, exist_ok=True)\n","        print(\"Drive mounted\")\n","    except:\n","        save_to_google_drive = False\n","\n","# ────────────────────────────────────────────────\n","# Extract ZIP\n","# ────────────────────────────────────────────────\n","\n","extract_root = \"/content/extracted_images\"\n","output_dir = \"/content/frames\"\n","\n","os.makedirs(extract_root, exist_ok=True)\n","os.makedirs(output_dir, exist_ok=True)\n","\n","with zipfile.ZipFile(zip_file_path, 'r') as zf:\n","    zf.extractall(extract_root)\n","\n","# ────────────────────────────────────────────────\n","# Load images\n","# ────────────────────────────────────────────────\n","\n","valid_exts = ('.jpg','.jpeg','.png','.webp')\n","all_sources = []\n","\n","for root, _, files in os.walk(extract_root):\n","    for f in files:\n","        if f.lower().endswith(valid_exts):\n","            all_sources.append(os.path.join(root, f))\n","\n","print(\"Images found:\", len(all_sources))\n","\n","# ────────────────────────────────────────────────\n","# Build row UNTIL enough width\n","# ────────────────────────────────────────────────\n","\n","def build_row_until_width(all_sources, target_h, overlap_ratio, required_width):\n","    canvas = Image.new(\"RGB\", (required_width, target_h))\n","    x = 0\n","\n","    while x < required_width:\n","        src = random.choice(all_sources)\n","\n","        try:\n","            im = Image.open(src).convert(\"RGB\")\n","            w, h = im.size\n","\n","            scale = target_h / h\n","            new_w = int(w * scale)\n","\n","            im = im.resize((new_w, target_h), Image.LANCZOS)\n","\n","            # overlap\n","            if x > 0:\n","                x -= int(new_w * overlap_ratio)\n","\n","            canvas.paste(im, (x, 0))\n","            x += new_w\n","\n","        except:\n","            continue\n","\n","    return canvas\n","\n","# ────────────────────────────────────────────────\n","# Calculate required width\n","# ────────────────────────────────────────────────\n","\n","overlap_ratio = overlap_percent / 100.0\n","\n","avg_step = (min_step_ratio + max_step_ratio) / 2\n","estimated_width = int(num_frames * INNER_SIZE * avg_step * 1.2)\n","\n","print(\"Building row width:\", estimated_width)\n","\n","row_img = build_row_until_width(\n","    all_sources,\n","    INNER_SIZE,\n","    overlap_ratio,\n","    estimated_width\n",")\n","\n","row_np = np.array(row_img)\n","row_h, row_w = row_np.shape[:2]\n","\n","# ────────────────────────────────────────────────\n","# Generate frames (NO distortion)\n","# ────────────────────────────────────────────────\n","\n","frame_idx = 0\n","x_cursor = 0\n","\n","while x_cursor + INNER_SIZE <= row_w and frame_idx < num_frames:\n","\n","    step = random.randint(\n","        int(INNER_SIZE * min_step_ratio),\n","        int(INNER_SIZE * max_step_ratio)\n","    )\n","\n","    crop = row_np[:, x_cursor:x_cursor + INNER_SIZE]\n","\n","    final = Image.new(\"RGB\", (FRAME_SIZE, FRAME_SIZE), BORDER_COLOR)\n","    final.paste(Image.fromarray(crop), (border_width, border_width))\n","\n","    fname = f\"frame_{frame_idx:04d}.jpg\"\n","    final.save(os.path.join(output_dir, fname), \"JPEG\", quality=90)\n","\n","    frame_idx += 1\n","    x_cursor += step\n","\n","    if frame_idx % 50 == 0:\n","        print(frame_idx, \"frames done\")\n","\n","print(\"Finished:\", frame_idx)\n","\n","# ────────────────────────────────────────────────\n","# Save ZIP\n","# ────────────────────────────────────────────────\n","\n","if save_to_google_drive and drive_mounted:\n","\n","    zip_path = \"/content/output.zip\"\n","    drive_path = os.path.join(drive_output_dir, \"vertical_slices.zip\")\n","\n","    with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zf:\n","        for f in os.listdir(output_dir):\n","            if f.endswith(\".jpg\"):\n","                zf.write(os.path.join(output_dir, f), arcname=f)\n","\n","    !cp -f \"{zip_path}\" \"{drive_path}\"\n","\n","    print(\"Saved to Drive:\", drive_path)"]},{"cell_type":"markdown","source":["# Done!\n","\n","In the zip file /content/drive/MyDrive/vertical_slices_output/vertical_slices.zip  ,  there you will find 500 image composites to choose for lora training"],"metadata":{"id":"tJlvhf9xDmA7"}}],"metadata":{"colab":{"provenance":[{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/klein_prepare_lora_set.ipynb","timestamp":1776789720072},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/klein_prepare_lora_set.ipynb","timestamp":1776787634021},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/klein_prepare_lora_set.ipynb","timestamp":1776773678064},{"file_id":"151XI3nRFxLRbwRc8V6OfWyqp-zTnfySv","timestamp":1776764241991},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/klein_edit.ipynb","timestamp":1776702729526},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/vertical_slice_prepper.ipynb","timestamp":1776687886662},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/vertical_slice_prepper.ipynb","timestamp":1776366149549},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/lora_vertical_slice_dataset_creator.ipynb","timestamp":1776287741995},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/lora_vertical_slice_dataset_creator.ipynb","timestamp":1776178739426},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/lora_vertical_slice_dataset_creator.ipynb","timestamp":1776027716448},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/lora_vertical_slice_dataset_creator.ipynb","timestamp":1773663661932},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_caption_prepper.ipynb","timestamp":1773663290922},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_caption_prepper.ipynb","timestamp":1773264797996},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_caption_prepper.ipynb","timestamp":1773163850245},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_caption_prepper.ipynb","timestamp":1773090196076},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_caption_prepper.ipynb","timestamp":1773089575687},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_caption_prepper.ipynb","timestamp":1773080355474},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Drive 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