{"cells":[{"cell_type":"markdown","source":["# Session A , encrypt dataset"],"metadata":{"id":"ljPFdGvYyD-6"}},{"cell_type":"code","source":["# =============================================================================\n","#@markdown # **CELL 1A**: Mount Drive + HF auth + Install dependencies\n","# =============================================================================\n","# This cell mounts Google Drive, logs into Hugging Face, removes old diffusers,\n","# and installs the latest diffusers + encryption libraries (pynacl + datasets).\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(\"🔐 Installing encryption + datasets...\")\n","!pip install -q pynacl datasets\n","\n","print(\"✅ Cell 1A complete!\")"],"metadata":{"cellView":"form","id":"XwwB-W8CCXof"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# =============================================================================\n","#@markdown # **CELL 2A**: Set input ZIP path + optional upload widget\n","# =============================================================================\n","# This cell defines where the source ZIP is (Drive or upload) and sets zip_path\n","# for Cell 3A. You can enable the widget to upload images or a ZIP directly.\n","upload_from_widget = False #@param {type:'boolean'}\n","input_zip_path = '/content/drive/MyDrive/gow.zip' #@param {type:'string'}\n","\n","zip_path = None\n","\n","if not upload_from_widget:\n","    zip_path = input_zip_path\n","    print(f\"✅ Using ZIP from Drive: {zip_path}\")\n","else:\n","    print(\"📤 Widget upload enabled – run the upload section below.\")\n","\n","#---#\n","#@title **Upload files via widget (only run if upload_from_widget = True)**\n","import os\n","from google.colab import files\n","import zipfile\n","import shutil\n","\n","if upload_from_widget:\n","    print(\"Please upload your ZIP file or individual image files now.\")\n","    uploaded = files.upload()\n","\n","    if not uploaded:\n","        raise ValueError(\"No files uploaded.\")\n","\n","    if len(uploaded) == 1 and list(uploaded.keys())[0].endswith('.zip'):\n","        uploaded_filename = list(uploaded.keys())[0]\n","        shutil.move(uploaded_filename, '/content/' + uploaded_filename)\n","        zip_path = '/content/' + uploaded_filename\n","        print(f\"✅ Using uploaded ZIP: {zip_path}\")\n","    else:\n","        temp_img_dir = '/content/uploaded_images_temp'\n","        os.makedirs(temp_img_dir, exist_ok=True)\n","        image_count = 0\n","        for fname, content in uploaded.items():\n","            if fname.lower().endswith(('.png', '.jpg', '.jpeg', '.webp', '.gif')):\n","                with open(os.path.join(temp_img_dir, fname), 'wb') as f:\n","                    f.write(content)\n","                image_count += 1\n","            else:\n","                print(f\"Skipping non-image: {fname}\")\n","\n","        if image_count == 0:\n","            raise ValueError(\"No valid images uploaded.\")\n","\n","        zip_path = '/content/uploaded_images.zip'\n","        with zipfile.ZipFile(zip_path, 'w') as zf:\n","            for root, _, files_in_dir in os.walk(temp_img_dir):\n","                for file_in_dir in files_in_dir:\n","                    zf.write(os.path.join(root, file_in_dir), os.path.basename(file_in_dir))\n","        shutil.rmtree(temp_img_dir)\n","        print(f\"✅ Created ZIP from {image_count} images: {zip_path}\")\n","else:\n","    print(\"Widget upload disabled – using Drive path from above.\")\n","\n","print(f\"Final zip_path ready for Cell 3A: {zip_path}\")"],"metadata":{"cellView":"form","id":"F-RG42ycCifw"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# =============================================================================\n","#@markdown # **CELL 3A**: Build ENCRYPTED INPUT DATASET\n","# =============================================================================\n","# This cell extracts the ZIP, encrypts every image using your password,\n","# creates a Hugging Face Dataset, and saves it to disk.\n","import hashlib\n","import io\n","import glob\n","from PIL import Image\n","from datasets import Dataset\n","from nacl.secret import SecretBox\n","from nacl.utils import random\n","import zipfile\n","\n","debug = False #@param {type:\"boolean\"}\n","\n","# 🔑 USER KEY\n","encryption_password = \"banana\" #@param {type:\"string\"}\n","\n","# ===== KEY DERIVATION =====\n","def derive_key(password):\n","    return hashlib.sha256(password.encode()).digest()\n","\n","SECRET_KEY = derive_key(encryption_password)\n","box = SecretBox(SECRET_KEY)\n","\n","# ===== IMAGE SERIALIZATION =====\n","def pil_to_bytes(img):\n","    buf = io.BytesIO()\n","    img.save(buf, format=\"PNG\")\n","    return buf.getvalue()\n","\n","# ===== ENCRYPT =====\n","def encrypt_bytes(data):\n","    nonce = random(SecretBox.NONCE_SIZE)\n","    return box.encrypt(data, nonce)\n","\n","# ====================== LOAD ZIP ======================\n","if debug:\n","    print(f\"📦 Building encrypted dataset from: {zip_path}\")\n","\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","\n","if debug:\n","    print(f\"Found {len(image_files)} images to encrypt.\")\n","\n","records = []\n","\n","for img_path in image_files:\n","    img = Image.open(img_path).convert(\"RGB\")\n","    img_bytes = pil_to_bytes(img)\n","    enc = encrypt_bytes(img_bytes)\n","    records.append({\"image_encrypted\": enc})\n","\n","print(f\"✅ Encrypted {len(records)} images\")\n","\n","# ====================== SAVE DATASET ======================\n","ds = Dataset.from_list(records)\n","dataset_path = \"/content/drive/MyDrive/encrypted_input_dataset\"\n","ds.save_to_disk(dataset_path)\n","\n","if debug:\n","    print(f\"📦 Dataset saved to: {dataset_path}\")\n","\n","print(f\"✅ Encrypted input dataset ready at {dataset_path}\")\n","print(\"👉 You can now run Session B.\")"],"metadata":{"cellView":"form","id":"w_brUz7ZClFq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# ================================================\n","#@title 🔌 End Session A. Auto Disconnect Colab Session (optional)\n","# ================================================\n","enable = False #@param {type:'boolean'}\n","if enable:\n","    print(\"🔌 Disconnecting Colab session in 3 seconds...\")\n","    import time\n","    time.sleep(3)\n","    from google.colab import runtime\n","    runtime.unassign()\n","    print(\"Session disconnected.\")"],"metadata":{"cellView":"form","id":"qYSlTQfsCuf2"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Session B : Inference on Encrypted dataset"],"metadata":{"id":"Cs5ftnHQyapT"}},{"cell_type":"code","source":["# Change session B to work on kaggle\n","# The HF_TOKEN can be found in kaggle secrets\n","# The kaggle session uses two GPUs\n","# Load split the encoded dataset in half ,\n","# and load a klein model on each GPU , write\n","# their outputs to two separate folders in workspace.\n","# If possible , see if you can use less harddrive memory when\n","# storing the encoder images.\n","# Write @param type string input on path to\n","# kaggle repository where to fetch the encoded images\n","# Write another @param type string to the txt file in the repo\n","# containing the encryption_password\n","# upload the checkpoint zips to the kaggle repository.\n","# once a zip is uploaded , delete the encoded images from the workspace.\n","# once completing the script , delete the uploaded checkpoint zips\n","# from the kaggle repository , and upload the zip with the full set of images.\n","\n","# =============================================================================\n","#@markdown # **CELL 1B**: Mount Drive + HF auth + Install dependencies\n","# =============================================================================\n","# Same setup as Session A.\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(\"🔐 Installing encryption + datasets...\")\n","!pip install -q pynacl datasets\n","\n","print(\"✅ Cell 1B complete!\")"],"metadata":{"id":"xIxwtjOsC0_I"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# =============================================================================\n","#@markdown # **CELL 2B**: Set inference parameters (model, prompt, resolution, etc.)\n","# =============================================================================\n","# This cell contains ALL user-adjustable settings for the editing run.\n","MODEL_ID = \"codeShare/FLUX.2-klein-AIO-SDNQ-4bit-dynamic\" #@param ['codeShare/Flux-Klein-SDNQ-4bit','codeShare/FLUX.2-klein-AIO-SDNQ-4bit-dynamic', 'codeShare/unstableRevolution_SDNQ']\n","encrypted_input_dataset_path = \"/content/drive/MyDrive/encrypted_input_dataset\" #@param {type:\"string\"}\n","edit_prompt = 'improve this illustration.  fine art color contrast with pleasant quality. the background is dark gray.' #@param {type:'string'}\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)\"]\n","\n","max_image_dimension = 2048 #@param {type:\"slider\", min:512, max:4096, step:256}\n","save_to_drive_every_n = True #@param {type:\"boolean\"}\n","save_every_n = 10 #@param {type:\"slider\", min:1, max:100, step:1}\n","\n","debug = False #@param {type:\"boolean\"}\n","\n","print(\"✅ All parameters set for Session B.\")\n","if debug:\n","    print(f\"   Model           : {MODEL_ID}\")\n","    print(f\"   Dataset path    : {encrypted_input_dataset_path}\")\n","    print(f\"   Edit prompt     : {edit_prompt}\")\n","    print(f\"   Resolution      : {resolution}\")"],"metadata":{"cellView":"form","id":"tr9qLR-DC29J"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# =============================================================================\n","#@markdown # **CELL 3B**: Load SDNQ-optimized FLUX.2-Klein model\n","# =============================================================================\n","# Loads the selected model from HF and applies SDNQ + memory optimizations.\n","import torch\n","import gc\n","import os\n","from diffusers import Flux2KleinPipeline\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(f\"🔄 Loading model: {MODEL_ID}\")\n","\n","pipe = Flux2KleinPipeline.from_pretrained(\n","    MODEL_ID,\n","    torch_dtype=torch.float16,\n","    low_cpu_mem_usage=True,\n","    device_map=\"cpu\"\n",")\n","\n","print(\"✅ Base pipeline loaded (CPU-safe)\")\n","\n","print(\"🔥 Applying SDNQ optimizations...\")\n","os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n","\n","if torch.cuda.is_available():\n","    if triton_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","        if debug:\n","            print(\"   ✅ Quantized 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(\"✅ Model ready for inference\")\n","print(f\"VRAM usage: {torch.cuda.memory_allocated() / 1e9:.2f} GB\")\n","print(\"👉 Run Cell 4B now\")"],"metadata":{"cellView":"form","id":"NZXoIzK2DCBw"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# =============================================================================\n","#@markdown # **CELL 4B**: MULTI-IMAGE INFERENCE on encrypted dataset\n","# =============================================================================\n","# Decrypts → edits with prompt → re-encrypts every image.\n","# Saves checkpoints to Drive every N images + final encrypted ZIP.\n","import os\n","import shutil\n","import torch\n","import gc\n","import datetime\n","import io\n","import hashlib\n","\n","from PIL import Image\n","from nacl.secret import SecretBox\n","from nacl.utils import random\n","from datasets import load_from_disk, Dataset\n","\n","# ================= SETTINGS (from Cell 2B) =================\n","output_folder = '/content/edited_images_multi'\n","checkpoint_folder = '/content/drive/MyDrive/klein_checkpoints'\n","\n","# ================= CLEAN WORKSPACE =================\n","print(\"🧹 Clearing old folders...\")\n","for p in [output_folder]:\n","    if os.path.exists(p):\n","        shutil.rmtree(p)\n","os.makedirs(output_folder, exist_ok=True)\n","os.makedirs(checkpoint_folder, exist_ok=True)\n","\n","# ================= 🔑 KEY =================\n","def derive_key(password):\n","    return hashlib.sha256(password.encode()).digest()\n","\n","encryption_password = \"banana\" #@param {type:'string'}\n","SECRET_KEY = derive_key(encryption_password)   # password from Cell 2B\n","box = SecretBox(SECRET_KEY)\n","\n","# ================= CRYPTO =================\n","def decrypt_bytes(enc):\n","    return box.decrypt(enc)\n","\n","def encrypt_bytes(data):\n","    nonce = random(SecretBox.NONCE_SIZE)\n","    return box.encrypt(data, nonce)\n","\n","# ================= IMAGE UTILS =================\n","def bytes_to_pil(b):\n","    return Image.open(io.BytesIO(b)).convert(\"RGB\")\n","\n","def pil_to_bytes(img):\n","    buf = io.BytesIO()\n","    img.save(buf, format=\"PNG\")\n","    return buf.getvalue()\n","\n","# Parse resolution\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","reference_image_to_pair = Image.new(\"RGB\", (target_width, target_height), \"#181818\")\n","\n","# ================= LOAD DATASET =================\n","print(f\"📦 Loading encrypted dataset: {encrypted_input_dataset_path}\")\n","input_dataset = load_from_disk(encrypted_input_dataset_path)\n","print(f\"✅ Loaded {len(input_dataset)} encrypted samples\")\n","\n","# ================= CHECKPOINT HELPER =================\n","def save_checkpoint(batch_files, checkpoint_id):\n","    if not save_to_drive_every_n:\n","        return\n","    zip_path = f\"{checkpoint_folder}/checkpoint_{checkpoint_id}.zip\"\n","    tmp = f\"{checkpoint_folder}/tmp_{checkpoint_id}\"\n","    os.makedirs(tmp, exist_ok=True)\n","    for f in batch_files:\n","        shutil.copy(f, tmp)\n","    shutil.make_archive(zip_path.replace('.zip',''), 'zip', tmp)\n","    shutil.rmtree(tmp)\n","    print(f\"💾 Checkpoint saved: {zip_path}\")\n","\n","# ================= INFERENCE LOOP =================\n","print(\"\\n🚀 Starting encrypted multi-image inference...\")\n","\n","output_dataset_records = []\n","batch_outputs = []\n","checkpoint_id = 0\n","\n","for i, item in enumerate(input_dataset):\n","    gc.collect()\n","    torch.cuda.empty_cache()\n","\n","    enc_bytes = item[\"image_encrypted\"]\n","    dec_bytes = decrypt_bytes(enc_bytes)\n","    input_image = bytes_to_pil(dec_bytes)\n","\n","    if debug:\n","        print(f\"   [{i+1}/{len(input_dataset)}] Input size: {input_image.size}\")\n","\n","    # Resize if needed\n","    if max(input_image.width, input_image.height) > max_image_dimension:\n","        aspect = input_image.width / input_image.height\n","        if input_image.width > input_image.height:\n","            new_w = max_image_dimension\n","            new_h = int(max_image_dimension / aspect)\n","        else:\n","            new_h = max_image_dimension\n","            new_w = int(max_image_dimension * aspect)\n","        input_image = input_image.resize((new_w, new_h), Image.LANCZOS)\n","\n","    reference_images = [input_image, reference_image_to_pair]\n","\n","    result = pipe(\n","        prompt=edit_prompt,\n","        image=reference_images,\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","    out_bytes = pil_to_bytes(result)\n","    enc_out = encrypt_bytes(out_bytes)\n","\n","    out_path = os.path.join(output_folder, f\"edited_{i}.enc\")\n","    with open(out_path, \"wb\") as f:\n","        f.write(enc_out)\n","\n","    output_dataset_records.append({\"image_encrypted\": enc_out})\n","    batch_outputs.append(out_path)\n","\n","    print(f\"[{i+1}/{len(input_dataset)}] processed\")\n","\n","    if save_to_drive_every_n and len(batch_outputs) >= save_every_n:\n","        checkpoint_id += 1\n","        save_checkpoint(batch_outputs, checkpoint_id)\n","        batch_outputs = []\n","\n","if save_to_drive_every_n and len(batch_outputs) > 0:\n","    checkpoint_id += 1\n","    save_checkpoint(batch_outputs, checkpoint_id)\n","\n","print(\"\\n✅ BATCH COMPLETE!\")\n","\n","output_dataset = Dataset.from_list(output_dataset_records)\n","output_dataset_path = \"/content/encrypted_output_dataset\"\n","output_dataset.save_to_disk(output_dataset_path)\n","print(f\"📦 Encrypted output dataset saved: {output_dataset_path}\")\n","\n","timestamp = datetime.datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n","final_zip = f\"/content/drive/MyDrive/klein_encrypted_outputs_{timestamp}.zip\"\n","shutil.make_archive(final_zip.replace('.zip',''), 'zip', output_folder)\n","print(f\"📦 Final encrypted outputs ZIP → {final_zip}\")\n","print(\"🎯 Session B finished – ready for Session C\")"],"metadata":{"cellView":"form","id":"P1dwJTGSDIA-"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Cell 5B\n","# Clean unassigned junk files from VRAM  , run this if interrupting the cell script\n","# This cell is useful incase I interrupt cell 4\n","import torch , gc\n","torch.cuda.empty_cache()\n","gc.collect()"],"metadata":{"id":"e46WUV7jMInu","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1777213657366,"user_tz":-120,"elapsed":994,"user":{"displayName":"fukU Google","userId":"02763165356193834046"}},"outputId":"b534b8c5-c09a-4b42-aa6c-1efcf685490a"},"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1457"]},"metadata":{},"execution_count":9}]},{"cell_type":"code","source":["# ================================================\n","#@title 🔌 End Session B. Auto Disconnect Colab Session (optional)\n","# ================================================\n","enable = False #@param {type:'boolean'}\n","if enable:\n","    print(\"🔌 Disconnecting Colab session in 3 seconds...\")\n","    import time\n","    time.sleep(3)\n","    from google.colab import runtime\n","    runtime.unassign()\n","    print(\"Session disconnected.\")"],"metadata":{"cellView":"form","id":"kN2h-2bkDO8g"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Session C , Decrypt a dataset"],"metadata":{"id":"dTwnU7KYzY2J"}},{"cell_type":"code","source":["# =============================================================================\n","#@markdown # **CELL 1C**: Mount Drive + HF auth + Install dependencies\n","# =============================================================================\n","# Same setup as previous sessions.\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(\"🔐 Installing encryption + datasets...\")\n","!pip install -q pynacl datasets\n","\n","print(\"✅ Cell 1C complete!\")"],"metadata":{"cellView":"form","id":"pqeZgcSeDWUm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1777213778608,"user_tz":-120,"elapsed":75494,"user":{"displayName":"fukU Google","userId":"02763165356193834046"}},"outputId":"962b2da4-0f1c-4ebd-fa3f-3851cc441d2f"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n","🧹 Removing old diffusers...\n","🔄 Installing latest diffusers...\n","  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n","  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n","  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","  Building wheel for diffusers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","Files removed: 6\n","🔐 Installing encryption + datasets...\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.4/1.4 MB\u001b[0m \u001b[31m24.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h✅ Cell 1C complete!\n"]}]},{"cell_type":"code","source":["# =============================================================================\n","#@markdown # **CELL 2C**: Decrypt dataset / checkpoints / ZIP → final images ZIP\n","# =============================================================================\n","# Choose one mode with the checkboxes. Outputs a clean ZIP of decrypted images.\n","# Supports all checkpoints (default), dataset, or single ZIP.\n","import os\n","import zipfile\n","import io\n","import hashlib\n","from PIL import Image\n","from nacl.secret import SecretBox\n","from datasets import load_from_disk\n","from google.colab import files\n","\n","# ================= MODE CONTROLS =================\n","process_all_checkpoints = True #@param {type:\"boolean\"}\n","decrypt_from_dataset = False #@param {type:\"boolean\"}\n","decrypt_from_zip = False #@param {type:\"boolean\"}\n","upload_zip_manually = False #@param {type:\"boolean\"}\n","download_output_zip = True #@param {type:\"boolean\"}\n","\n","dataset_to_decrypt = \"/content/encrypted_output_dataset\" #@param {type:\"string\"}\n","encrypted_zip_path = \"/content/drive/MyDrive/klein_checkpoints/checkpoint_2.zip\" #@param {type:\"string\"}\n","output_zip_name = \"decrypted_results.zip\" #@param {type:\"string\"}\n","\n","image_format = \"JPG\" #@param [\"PNG\", \"JPG\"]\n","\n","output_base_folder = \"/content/zip_outputs\"\n","os.makedirs(output_base_folder, exist_ok=True)\n","checkpoint_dir = \"/content/drive/MyDrive/klein_checkpoints\"\n","\n","debug = False #@param {type:\"boolean\"}\n","\n","# ================= 🔑 KEY =================\n","decryption_password = \"banana\" #@param {type:\"string\"}\n","def derive_key(password):\n","    return hashlib.sha256(password.encode()).digest()\n","\n","SECRET_KEY = derive_key(decryption_password)\n","box = SecretBox(SECRET_KEY)\n","\n","def decrypt_bytes(enc):\n","    return box.decrypt(enc)\n","\n","# ================= IMAGE IO =================\n","def bytes_to_pil(b):\n","    return Image.open(io.BytesIO(b)).convert(\"RGB\")\n","\n","# ================= DECRYPT HELPERS =================\n","def decrypt_stream(enc_bytes_list):\n","    out = []\n","    for enc in enc_bytes_list:\n","        dec = decrypt_bytes(enc)\n","        out.append(bytes_to_pil(dec))\n","    return out\n","\n","def decrypt_zip(zip_path):\n","    with zipfile.ZipFile(zip_path, \"r\") as z:\n","        enc_files = [z.read(n) for n in z.namelist() if n.endswith(\".enc\")]\n","    return decrypt_stream(enc_files)\n","\n","def decrypt_dataset(ds):\n","    return decrypt_stream([x[\"image_encrypted\"] for x in ds])\n","\n","# ================= ZIP BUILDER =================\n","def write_images_to_zip(image_pil_list, out_zip_path, fmt):\n","    with zipfile.ZipFile(out_zip_path, \"w\", compression=zipfile.ZIP_DEFLATED) as zf:\n","        ext = \"png\" if fmt == \"PNG\" else \"jpg\"\n","        for i, img in enumerate(image_pil_list):\n","            buf = io.BytesIO()\n","            if fmt == \"JPG\":\n","                img.save(buf, format=\"JPEG\", quality=95, optimize=True)\n","            else:\n","                img.save(buf, format=fmt)\n","            zf.writestr(f\"{i:05d}.{ext}\", buf.getvalue())\n","\n","# ================= OUTPUT BUFFER =================\n","all_image_pil = []\n","\n","# =============================================================================\n","# 🔥 MODE 1: ALL CHECKPOINTS (recommended)\n","# =============================================================================\n","if process_all_checkpoints:\n","    print(f\"📂 Scanning checkpoints in: {checkpoint_dir}\")\n","    checkpoint_zips = sorted([\n","        os.path.join(checkpoint_dir, f)\n","        for f in os.listdir(checkpoint_dir)\n","        if f.endswith(\".zip\") and \"checkpoint\" in f\n","    ])\n","    print(f\"🔍 Found {len(checkpoint_zips)} checkpoint ZIPs\")\n","    for idx, zpath in enumerate(checkpoint_zips):\n","        if debug:\n","            print(f\"   Processing {zpath}\")\n","        imgs = decrypt_zip(zpath)\n","        all_image_pil.extend(imgs)\n","\n","# =============================================================================\n","# 🔓 MODE 2: ENCRYPTED DATASET\n","# =============================================================================\n","elif decrypt_from_dataset:\n","    print(f\"📦 Loading dataset: {dataset_to_decrypt}\")\n","    ds = load_from_disk(dataset_to_decrypt)\n","    imgs = decrypt_dataset(ds)\n","    all_image_pil.extend(imgs)\n","\n","# =============================================================================\n","# 🔓 MODE 3: SINGLE ZIP\n","# =============================================================================\n","elif decrypt_from_zip:\n","    if upload_zip_manually:\n","        print(\"📤 Please upload your encrypted ZIP now...\")\n","        uploaded = files.upload()\n","        zip_name = list(uploaded.keys())[0]\n","        zip_path = \"/content/\" + zip_name\n","        with open(zip_path, \"wb\") as f:\n","            f.write(uploaded[zip_name])\n","    else:\n","        zip_path = encrypted_zip_path\n","    print(f\"📦 Decrypting single ZIP: {zip_path}\")\n","    imgs = decrypt_zip(zip_path)\n","    all_image_pil.extend(imgs)\n","\n","# =============================================================================\n","# 📦 FINAL PACKAGING\n","# =============================================================================\n","if download_output_zip and all_image_pil:\n","    final_zip = os.path.join(output_base_folder, output_zip_name)\n","    print(f\"📦 Building final {image_format} ZIP with {len(all_image_pil)} images → {final_zip}\")\n","    write_images_to_zip(all_image_pil, final_zip, image_format)\n","    files.download(final_zip)\n","    print(\"⬇️ Download started via google.colab.files.download\")\n","else:\n","    print(\"✅ Decryption complete – no download requested.\")\n","\n","print(\"🎯 Session C finished!\")"],"metadata":{"cellView":"form","id":"puoG9y2nDZn3"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"a7rdpSwVz0ZX"},"outputs":[],"source":["# ================================================\n","#@title 🔌 End session C. Auto Disconnect Colab Session\n","# ================================================\n","\n","enable = False #@param {type:'boolean'}\n","if enable:\n","  print(\"🔌 Disconnecting Colab session in 3 seconds...\")\n","  import time\n","  time.sleep(3)\n","\n","  from google.colab import runtime\n","  runtime.unassign()\n","\n","  print(\"Session disconnected.\")"]},{"cell_type":"markdown","source":["# Select Klein processed file to use next"],"metadata":{"id":"s7R3l3-pY5W7"}},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"id":"ijzwEoboHVPR"},"execution_count":null,"outputs":[]},{"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.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"},"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 = 60 #@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":"markdown","source":["# ⚙️ Create composites"],"metadata":{"id":"kVAe29swHo4I"}},{"cell_type":"code","source":["#@markdown Set zip file input path for making composites\n","input_zip_path = \"/content/drive/MyDrive/PROCESSED_klein_processed.zip\"   #@param {type:'string'}\n","zip_file_path = input_zip_path"],"metadata":{"id":"IXQKiBVrNPcE"},"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 = 100  #@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","# ────────────────────────────────────────────────\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 100 image composites to choose for lora training\n","\n","//-----//"],"metadata":{"id":"tJlvhf9xDmA7"}}],"metadata":{"colab":{"provenance":[{"file_id":"https://huggingface.co/codeShare/FLUX.2-klein-AIO-SDNQ-4bit-dynamic/blob/main/run_klein_edit_aio.ipynb","timestamp":1777129973676},{"file_id":"https://huggingface.co/codeShare/FLUX.2-klein-AIO-SDNQ-4bit-dynamic/blob/main/notebooks/klein_edit_aio.ipynb","timestamp":1777127335701},{"file_id":"https://huggingface.co/codeShare/FLUX.2-klein-AIO-SDNQ-4bit-dynamic/blob/main/notebooks/klein_edit_aio.ipynb","timestamp":1776991571771},{"file_id":"https://huggingface.co/codeShare/FLUX.2-klein-AIO-SDNQ-4bit-dynamic/blob/main/notebooks/klein_edit_aio.ipynb","timestamp":1776952244463},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/klein_prepare_lora_set.ipynb","timestamp":1776905776793},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/klein_prepare_lora_set.ipynb","timestamp":1776904822796},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/klein_prepare_lora_set.ipynb","timestamp":1776896778295},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/klein_prepare_lora_set.ipynb","timestamp":1776814708184},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/klein_prepare_lora_set.ipynb","timestamp":1776796861814},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/klein_prepare_lora_set.ipynb","timestamp":1776795061697},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/klein_prepare_lora_set.ipynb","timestamp":1776794307247},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/klein_prepare_lora_set.ipynb","timestamp":1776790556621},{"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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