Upload klein_edit.ipynb

klein_edit.ipynb

@@ -1 +1 @@
-{"cells":[{"cell_type":"code","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! RESTART RUNTIME now, then run Cell 2 → Cell 3 → Cell 4\")"],"metadata":{"cellView":"form","id":"OXGhsHQwWue_"},"execution_count":null,"outputs":[]},{"cell_type":"code","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 = True #@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)\")"],"metadata":{"cellView":"form","id":"a1wZv8PrXAe2"},"execution_count":null,"outputs":[]},{"cell_type":"code","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)\")"],"metadata":{"cellView":"form","id":"WdNfo33NXNEa"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# =============================================================================\n","#@markdown # **CELL 4**: INFERENCE\n","# • Edit prompt and zip_path below\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 dark gray.' #@param {type:\"string\"}\n","zip_path = '/content/drive/MyDrive/lora_test.zip' #@param {type:\"string\"}\n","\n","import shutil\n","import datetime\n","\n","output_folder = '/content/edited_images'   # ← TEMP folder only (never saved to Drive directly)\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","# ====================== FULL BATCH INFERENCE ======================\n","print(f\"\\n🚀 Starting batch edit on {len(image_files)} images...\")\n","\n","for i, img_path in enumerate(image_files):\n","    filename = os.path.basename(img_path)\n","    print(f\"[{i+1}/{len(image_files)}] {filename}\")\n","\n","    gc.collect()\n","    torch.cuda.empty_cache()\n","\n","    input_image = Image.open(img_path).convert(\"RGB\")\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=input_image,\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\"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}\")\n","\n","print(\"\\n🎉 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/edited_images_{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)\n","print(\"\\nImages are NOT saved directly to Drive – only the zip file.\")"],"metadata":{"cellView":"form","id":"bY-m1KJJXdia"},"execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[{"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}],"gpuType":"T4"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0}
+{"cells":[{"cell_type":"code","source":["# =============================================================================\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! RESTART RUNTIME now, then run Cell 2 → Cell 3 → Cell 4\")"],"metadata":{"cellView":"form","id":"OXGhsHQwWue_"},"execution_count":null,"outputs":[]},{"cell_type":"code","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)\")"],"metadata":{"cellView":"form","id":"a1wZv8PrXAe2"},"execution_count":null,"outputs":[]},{"cell_type":"code","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)\")"],"metadata":{"cellView":"form","id":"WdNfo33NXNEa"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# =============================================================================\n","#@markdown # **CELL 4**: INFERENCE\n","# • Edit prompt and zip_path below\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 dark gray.' #@param {type:\"string\"}\n","zip_path = '/content/drive/MyDrive/images.zip' #@param {type:\"string\"}\n","\n","import shutil\n","import datetime\n","\n","output_folder = '/content/edited_images'   # ← TEMP folder only (never saved to Drive directly)\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","# ====================== FULL BATCH INFERENCE ======================\n","print(f\"\\n🚀 Starting batch edit on {len(image_files)} images...\")\n","\n","for i, img_path in enumerate(image_files):\n","    filename = os.path.basename(img_path)\n","    print(f\"[{i+1}/{len(image_files)}] {filename}\")\n","\n","    gc.collect()\n","    torch.cuda.empty_cache()\n","\n","    input_image = Image.open(img_path).convert(\"RGB\")\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=input_image,\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\"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}\")\n","\n","print(\"\\n🎉 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/edited_images_{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)\n","print(\"\\nImages are NOT saved directly to Drive – only the zip file.\")"],"metadata":{"cellView":"form","id":"bY-m1KJJXdia"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# =============================================================================\n","#@markdown # **CELL 5**: MULTI-IMAGE INFERENCE (Klein + 1024×1024 #181818 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 = 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. replace it with the exact solid dark gray background from the second reference image.' #@param {type:\"string\"}\n","zip_path = '/content/drive/MyDrive/images.zipi' #@param {type:\"string\"}\n","\n","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\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 the 1024x1024 gray reference image (once) ======================\n","print(\"🟩 Creating 1024×1024 #181818 solid gray reference image...\")\n","gray_image = Image.new(\"RGB\", (1024, 1024), \"#181818\")\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","    print(f\"[{i+1}/{len(image_files)}] {filename}  →  + gray #181818 reference\")\n","\n","    gc.collect()\n","    torch.cuda.empty_cache()\n","\n","    input_image = Image.open(img_path).convert(\"RGB\")\n","\n","    # === MULTI-IMAGE LIST: base image first, gray reference second ===\n","    reference_images = [input_image, gray_image]\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 gray #181818)\")\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/multi_edited_images_{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)"],"metadata":{"cellView":"form","id":"q9I9wxghuUGP"},"execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[{"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}],"gpuType":"T4"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0}