{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Encrypt images before editing"
      ],
      "metadata": {
        "id": "-CUcwUSG3nfO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "import os\n",
        "import shutil\n",
        "import zipfile\n",
        "import glob\n",
        "from PIL import Image\n",
        "import io\n",
        "import hashlib\n",
        "\n",
        "!pip install -q pynacl\n",
        "\n",
        "from nacl.secret import SecretBox\n",
        "from nacl.utils import random\n",
        "\n",
        "# Mount Drive\n",
        "drive.mount('/content/drive', force_remount=True)\n",
        "\n",
        "# =============================================================================\n",
        "#@markdown ### 🔧 Settings\n",
        "# =============================================================================\n",
        "foreground_images_zip_path = '/content/drive/MyDrive/foregrounds.zip' #@param {type:\"string\"}\n",
        "\n",
        "background_images_zip_path = '/content/drive/MyDrive/backgrounds.zip' #@param {type:\"string\"}\n",
        "\n",
        "encryption_password = \"banana\" #@param {type:\"string\"}\n",
        "\n",
        "output_drive_base_path = \"/content/drive/MyDrive/\" #@param {type:\"string\"}\n",
        "\n",
        "output_folder_name = \"my_flux_dataset\" #@param {type:\"string\"}\n",
        "\n",
        "# Additional config values\n",
        "MODEL_ID = \"codeShare/FLUX.2-klein-9b-SDNQ-4bit\" #@param {type:\"string\"}\n",
        "edit_prompt = \"remove the text and put the characters in the real photo\" #@param {type:\"string\"}\n",
        "resolution = \"1024 x 1024 (Square)\" #@param {type:\"string\"}\n",
        "\n",
        "# =============================================================================\n",
        "# ===== KEY DERIVATION =====\n",
        "# =============================================================================\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",
        "# =============================================================================\n",
        "# ===== IMAGE → BYTES =====\n",
        "# =============================================================================\n",
        "def pil_to_bytes(img):\n",
        "    buf = io.BytesIO()\n",
        "    img.save(buf, format=\"JPEG\", quality=95)\n",
        "    return buf.getvalue()\n",
        "\n",
        "# =============================================================================\n",
        "# ===== ENCRYPT =====\n",
        "# =============================================================================\n",
        "def encrypt_bytes(data):\n",
        "    nonce = random(SecretBox.NONCE_SIZE)\n",
        "    return box.encrypt(data, nonce)\n",
        "\n",
        "# =============================================================================\n",
        "# ====================== PROCESS AND ENCRYPT ZIP ======================\n",
        "# =============================================================================\n",
        "def process_and_encrypt_zip(input_zip_filepath, output_encrypted_zip_name, output_base_path, secret_box_instance):\n",
        "    print(f\"\\n📦 Extracting images from {os.path.basename(input_zip_filepath)}...\")\n",
        "    temp_extract_dir = f'/content/temp_extract_{os.path.basename(input_zip_filepath).replace(\".zip\", \"\")}'\n",
        "\n",
        "    if os.path.exists(temp_extract_dir):\n",
        "        shutil.rmtree(temp_extract_dir)\n",
        "    os.makedirs(temp_extract_dir, exist_ok=True)\n",
        "\n",
        "    try:\n",
        "        with zipfile.ZipFile(input_zip_filepath, 'r') as z:\n",
        "            z.extractall(temp_extract_dir)\n",
        "    except FileNotFoundError:\n",
        "        print(f\"⚠️ Warning: Input zip file not found: {input_zip_filepath}. Skipping this zip.\")\n",
        "        return\n",
        "    except Exception as e:\n",
        "        print(f\"❌ Error extracting {input_zip_filepath}: {e}. Skipping this zip.\")\n",
        "        return\n",
        "\n",
        "    image_files = sorted(glob.glob(os.path.join(temp_extract_dir, '*.*')))\n",
        "    image_files = [f for f in image_files if f.lower().endswith(('.png','.jpg','.jpeg','.webp','.gif'))]\n",
        "\n",
        "    print(f\"✅ Found {len(image_files)} images to encrypt in {os.path.basename(input_zip_filepath)}.\")\n",
        "\n",
        "    encrypted_image_data_for_zip = []\n",
        "    for img_path in image_files:\n",
        "        try:\n",
        "            img = Image.open(img_path).convert(\"RGB\")\n",
        "            img_bytes = pil_to_bytes(img)\n",
        "            enc_msg = encrypt_bytes(img_bytes)\n",
        "            encrypted_image_data_for_zip.append((os.path.basename(img_path), enc_msg))\n",
        "        except Exception as e:\n",
        "            print(f\"❌ Error processing image {os.path.basename(img_path)}: {e}\")\n",
        "\n",
        "    print(f\"🔐 Encrypted {len(encrypted_image_data_for_zip)} images from {os.path.basename(input_zip_filepath)}.\")\n",
        "\n",
        "    # Create encrypted zip\n",
        "    temp_encrypted_zip_path = os.path.join('/content', f'temp_{output_encrypted_zip_name}')\n",
        "\n",
        "    with zipfile.ZipFile(temp_encrypted_zip_path, 'w') as ez:\n",
        "        for original_fname, enc_msg in encrypted_image_data_for_zip:\n",
        "            encrypted_filename = f\"{os.path.splitext(original_fname)[0]}_encrypted.bin\"\n",
        "            combined_bytes = enc_msg.nonce + enc_msg.ciphertext\n",
        "            ez.writestr(encrypted_filename, combined_bytes)\n",
        "\n",
        "    final_zip_path = os.path.join(output_base_path, output_encrypted_zip_name)\n",
        "    shutil.move(temp_encrypted_zip_path, final_zip_path)\n",
        "\n",
        "    print(f\"✅ Saved: {final_zip_path}\")\n",
        "\n",
        "    # Clean up temporary extraction directory\n",
        "    if os.path.exists(temp_extract_dir):\n",
        "        shutil.rmtree(temp_extract_dir)\n",
        "\n",
        "\n",
        "# =============================================================================\n",
        "# ====================== CREATE OUTPUT FOLDER ======================-\n",
        "# =============================================================================\n",
        "output_drive_full_path = os.path.join(output_drive_base_path, output_folder_name)\n",
        "os.makedirs(output_drive_full_path, exist_ok=True)\n",
        "\n",
        "print(f\"📁 Output folder created: {output_drive_full_path}\")\n",
        "\n",
        "# =============================================================================\n",
        "# ====================== PROCESS BOTH ZIPS ======================-\n",
        "# =============================================================================\n",
        "process_and_encrypt_zip(foreground_images_zip_path, \"foregrounds.zip\", output_drive_full_path, box)\n",
        "process_and_encrypt_zip(background_images_zip_path, \"backgrounds.zip\", output_drive_full_path, box)\n",
        "\n",
        "\n",
        "# =============================================================================\n",
        "# ====================== SAVE CONFIG FILES ======================-\n",
        "# =============================================================================\n",
        "print(\"\\n📝 Saving config files...\")\n",
        "\n",
        "password_txt_path = os.path.join(output_drive_full_path, \"password.txt\")\n",
        "model_id_path = os.path.join(output_drive_full_path, \"model_id.txt\")\n",
        "resolution_path = os.path.join(output_drive_full_path, \"resolution.txt\")\n",
        "edit_prompt_path = os.path.join(output_drive_full_path, \"edit_prompt.txt\")\n",
        "\n",
        "# Write files\n",
        "with open(password_txt_path, 'w', encoding='utf-8') as f:\n",
        "    f.write(encryption_password)\n",
        "\n",
        "with open(model_id_path, 'w', encoding='utf-8') as f:\n",
        "    f.write(MODEL_ID)\n",
        "\n",
        "with open(resolution_path, 'w', encoding='utf-8') as f:\n",
        "    f.write(resolution)\n",
        "\n",
        "with open(edit_prompt_path, 'w', encoding='utf-8') as f:\n",
        "    f.write(edit_prompt)\n",
        "\n",
        "print(f\"🔑 Saved: {password_txt_path}\")\n",
        "print(f\"🧠 Saved: {model_id_path}\")\n",
        "print(f\"📐 Saved: {resolution_path}\")\n",
        "print(f\" Saved: {edit_prompt}\")\n",
        "\n",
        "# =============================================================================\n",
        "# ====================== DONE ======================-\n",
        "# =============================================================================\n",
        "print(\"\\n🎉 DONE! Files saved to Google Drive:\")\n",
        "print(f\"   📂 {output_drive_full_path}/\")\n",
        "print(f\"      • foregrounds.zip\")\n",
        "print(f\"      • backgrounds.zip\")\n",
        "print(f\"      • password.txt\")\n",
        "print(f\"      • model_id.txt\")\n",
        "print(f\"      • edit_prompt.txt\")\n",
        "print(f\"      • resolution.txt\")\n"
      ],
      "metadata": {
        "id": "9XzM6NLc6BWo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Run Klein 9B on T4"
      ],
      "metadata": {
        "id": "RuoZGdZGyAll"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "313e3f19"
      },
      "source": [
        "### Load SDNQ model from Hugging Face Hub\n",
        "\n",
        "Now, let's load the model from the Hugging Face repository we just created and ensure SDNQ is properly applied."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "367e4a6f"
      },
      "source": [
        "#@markdown =============================================================================\n",
        "#@markdown CELL 1: Google Colab Setup + Dependencies\n",
        "#@markdown =============================================================================\n",
        "\n",
        "# --- Mount Google Drive ---\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# --- HF Login ---\n",
        "from google.colab import userdata\n",
        "from huggingface_hub import login\n",
        "\n",
        "try:\n",
        "    hf_token = userdata.get('HF_TOKEN')\n",
        "    login(token=hf_token)\n",
        "    print(\"✅ HF login successful\")\n",
        "except Exception as e:\n",
        "    print(f\"⚠️ HF login failed: {e}\")\n",
        "\n",
        "# --- Cleanup old diffusers ---\n",
        "import os\n",
        "import gc\n",
        "import shutil\n",
        "import torch\n",
        "\n",
        "print(\"🧹 Removing old diffusers...\")\n",
        "!pip uninstall -y diffusers > /dev/null 2>&1\n",
        "!rm -rf /usr/local/lib/python3.11/dist-packages/diffusers*\n",
        "!rm -rf ~/.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",
        "\n",
        "print(\"🔐 Installing dependencies...\")\n",
        "!pip install -q pynacl datasets accelerate transformers sentencepiece safetensors sdnq\n",
        "\n",
        "print(\"🧹 Purging pip cache...\")\n",
        "!python -m pip cache purge\n",
        "\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()\n",
        "\n",
        "print(\"✅ Cell 1 complete!\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown =============================================================================\n",
        "#@markdown CELL 2: User Settings + Google Drive Dataset Paths\n",
        "#@markdown =============================================================================\n",
        "\n",
        "import os , torch\n",
        "from pathlib import Path\n",
        "\n",
        "# =============================================================================\n",
        "# GOOGLE DRIVE DATASET ROOT\n",
        "# =============================================================================\n",
        "\n",
        "# Example:\n",
        "# /content/drive/MyDrive/my_flux_dataset/\n",
        "#\n",
        "# ├── password.txt\n",
        "# ├── model_id.txt\n",
        "# ├── edit_prompt.txt\n",
        "# ├── resolution.txt\n",
        "# ├── foregrounds/\n",
        "# └── backgrounds/\n",
        "\n",
        "google_drive_dataset_path = \"/content/drive/MyDrive/my_flux_dataset\"\n",
        "\n",
        "# =============================================================================\n",
        "# CONFIG FILES\n",
        "# =============================================================================\n",
        "\n",
        "password_txt_file = \"password.txt\"\n",
        "\n",
        "# Defaults\n",
        "edit_prompt = \"improve this illustration\"\n",
        "resolution = \"1024 x 1024 (Square)\"\n",
        "\n",
        "# Hardcoded dimensions\n",
        "target_height = 1024\n",
        "target_width = 1024\n",
        "\n",
        "print(\"🔑 Reading config files from Google Drive...\")\n",
        "\n",
        "dataset_path = Path(google_drive_dataset_path)\n",
        "\n",
        "encryption_password = (dataset_path / \"password.txt\").read_text(encoding=\"utf-8\").strip()\n",
        "\n",
        "MODEL_ID = \"codeShare/FLUX.2-klein-9b-SDNQ-2bit\"  # Hardcoded\n",
        "#(dataset_path / \"model_id.txt\").read_text(encoding=\"utf-8\").strip()\n",
        "\n",
        "edit_prompt = (dataset_path / \"edit_prompt.txt\").read_text(encoding=\"utf-8\").strip()\n",
        "resolution = (dataset_path / \"resolution.txt\").read_text(encoding=\"utf-8\").strip()\n",
        "\n",
        "print(\"✅ Config loaded successfully\")\n",
        "print(f\"   Model      : {MODEL_ID}\")\n",
        "print(f\"   Resolution : {resolution}\")\n",
        "\n",
        "# =============================================================================\n",
        "# DATASET PATHS\n",
        "# =============================================================================\n",
        "\n",
        "encrypted_input_dataset_path = os.path.join(\n",
        "    google_drive_dataset_path,\n",
        "    \"foregrounds.zip\"\n",
        ")\n",
        "\n",
        "backgrounds_input_dataset_path = os.path.join(\n",
        "    google_drive_dataset_path,\n",
        "    \"backgrounds.zip\"\n",
        ")\n",
        "\n",
        "print(f\"   Foregrounds : {encrypted_input_dataset_path}\")\n",
        "print(f\"   Backgrounds : {backgrounds_input_dataset_path}\")\n",
        "\n",
        "# =============================================================================\n",
        "# SETTINGS\n",
        "# =============================================================================\n",
        "\n",
        "debug = True\n",
        "save_checkpoint_every_n = False\n",
        "\n",
        "# =============================================================================\n",
        "# MULTI GPU SUPPORT\n",
        "# =============================================================================\n",
        "\n",
        "NUM_GPUS = torch.cuda.device_count()\n",
        "\n",
        "if NUM_GPUS == 0:\n",
        "    raise RuntimeError(\"❌ No CUDA GPU detected\")\n",
        "\n",
        "print(f\"\\n🚀 Detected {NUM_GPUS} GPU(s)\")\n",
        "\n",
        "for i in range(NUM_GPUS):\n",
        "    print(f\"   GPU {i}: {torch.cuda.get_device_name(i)}\")\n",
        "\n",
        "# Pipes per GPU\n",
        "PIPES_PER_GPU = 1\n",
        "\n",
        "TOTAL_PIPES = NUM_GPUS * PIPES_PER_GPU\n",
        "\n",
        "print(f\"\\n🧠 Total Pipes Configured: {TOTAL_PIPES}\")"
      ],
      "metadata": {
        "id": "BFn9u0Gcu9f3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown =============================================================================\n",
        "#@markdown CELL 3: Load Flux Klein Pipes\n",
        "#@markdown =============================================================================\n",
        "\n",
        "import gc , torch\n",
        "from functools import partial\n",
        "\n",
        "from diffusers import Flux2KleinPipeline\n",
        "from sdnq.loader import apply_sdnq_options_to_model\n",
        "\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()\n",
        "\n",
        "# Re-declare NUM_GPUS and PIPES_PER_GPU for robustness in case kernel state is lost\n",
        "# These values are expected to be set in CELL 2.\n",
        "NUM_GPUS = torch.cuda.device_count()\n",
        "\n",
        "# =============================================================================\n",
        "# PATCH PROMPT ENCODING\n",
        "# =============================================================================\n",
        "\n",
        "def patch_encode_prompt(\n",
        "    pipe,\n",
        "    cached_prompt_embeds,\n",
        "    cached_text_ids,\n",
        "    gpu_id\n",
        "):\n",
        "\n",
        "    def patched(\n",
        "        self,\n",
        "        prompt=None,\n",
        "        device=None,\n",
        "        num_images_per_prompt=1,\n",
        "        prompt_embeds=None,\n",
        "        **kwargs\n",
        "    ):\n",
        "        device = device or torch.device(f\"cuda:{gpu_id}\")\n",
        "\n",
        "        if prompt_embeds is None:\n",
        "            emb = cached_prompt_embeds.to(device)\n",
        "\n",
        "            emb = emb.repeat(1, num_images_per_prompt, 1)\n",
        "\n",
        "            emb = emb.view(\n",
        "                emb.shape[0] * num_images_per_prompt,\n",
        "                emb.shape[1],\n",
        "                -1\n",
        "            )\n",
        "\n",
        "            tids = cached_text_ids.to(device)\n",
        "\n",
        "            return emb, tids\n",
        "\n",
        "        return self._original_encode_prompt(\n",
        "            prompt,\n",
        "            device,\n",
        "            num_images_per_prompt,\n",
        "            prompt_embeds,\n",
        "            **kwargs\n",
        "        )\n",
        "\n",
        "    if not hasattr(pipe, \"_original_encode_prompt\"):\n",
        "        pipe._original_encode_prompt = pipe.encode_prompt\n",
        "\n",
        "    pipe.encode_prompt = partial(patched, pipe)\n",
        "\n",
        "    return pipe\n",
        "\n",
        "# =============================================================================\n",
        "# LOAD PIPE\n",
        "# =============================================================================\n",
        "\n",
        "#def load_klein_pipe(gpu_id: int, pipe_id: int):\n",
        "\n",
        "\n",
        "\n",
        "gpu_id = 0\n",
        "pipe_id = 0\n",
        "torch.cuda.set_device(gpu_id)\n",
        "\n",
        "print(f\"\\n🚀 Loading Pipe {pipe_id} on GPU {gpu_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(f\"🔧 Applying SDNQ to Pipe {pipe_id}\")\n",
        "\n",
        "pipe.transformer = apply_sdnq_options_to_model(\n",
        "    pipe.transformer,\n",
        "    use_quantized_matmul=False\n",
        ")\n",
        "\n",
        "# Apply SDNQ to text_encoder and vae as well\n",
        "pipe.text_encoder = apply_sdnq_options_to_model(\n",
        "    pipe.text_encoder,\n",
        "    use_quantized_matmul=False\n",
        ")\n",
        "pipe.vae = apply_sdnq_options_to_model(\n",
        "    pipe.vae,\n",
        "    use_quantized_matmul=False\n",
        ")\n",
        "\n",
        "# -------------------------------------------------------------------------\n",
        "# Prompt Embeddings\n",
        "# -------------------------------------------------------------------------\n",
        "\n",
        "print(f\"🧠 Computing prompt embeddings for Pipe {pipe_id}\")\n",
        "\n",
        "pipe.text_encoder = pipe.text_encoder.to(f\"cuda:{gpu_id}\")\n",
        "\n",
        "with torch.inference_mode():\n",
        "\n",
        "    prompt_embeds, text_ids = pipe.encode_prompt(\n",
        "        prompt=edit_prompt,\n",
        "        device=torch.device(f\"cuda:{gpu_id}\"),\n",
        "        num_images_per_prompt=1,\n",
        "    )\n",
        "\n",
        "pipe.edit_prompt_embeds = prompt_embeds.cpu()\n",
        "pipe.edit_text_ids = text_ids.cpu()\n",
        "\n",
        "pipe = patch_encode_prompt(\n",
        "    pipe,\n",
        "    pipe.edit_prompt_embeds,\n",
        "    pipe.edit_text_ids,\n",
        "    gpu_id\n",
        ")\n",
        "\n",
        "# -------------------------------------------------------------------------\n",
        "# Remove text encoder after caching embeddings\n",
        "# -------------------------------------------------------------------------\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214,
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            "02f09d3aa9ca4b1982b06a1e3a6f43d4",
            "b6499e76139042b29e4e8bfc14c334c2",
            "0becaceb56804276bc1182d252478647",
            "72fa9ff87a7645eb8a3fbfda7991b36d",
            "eb2cefd2387e43fe82e8aedf70ca2468",
            "4cf48b613fb94ce59570ac4f69d286ba",
            "3c0ea4c424ae4811860d6a0f4aa03beb"
          ]
        },
        "id": "_Sk8M-gRvI47",
        "outputId": "efbab1c1-57d5-456a-b666-f59e7bb79860"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "🚀 Loading Pipe 0 on GPU 0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (incomplete total...): 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e8a8bfe13850486bab5bfec8b155084a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Fetching 11 files:   0%|          | 0/11 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "847277f6f3cb4ead8a054c9b182c546a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading pipeline components...:   0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7708a24ca7f040f68d792053ff74b759"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading weights:   0%|          | 0/899 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "713810f4d11c4162abc172de9312a6ad"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🔧 Applying SDNQ to Pipe 0\n",
            "🧠 Computing prompt embeddings for Pipe 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"🧹 Unloading text encoder for Pipe {pipe_id}\")\n",
        "\n",
        "if hasattr(pipe, \"text_encoder\"):\n",
        "    del pipe.text_encoder\n",
        "\n",
        "if hasattr(pipe, \"tokenizer\"):\n",
        "    del pipe.tokenizer\n",
        "\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JUcTYF2W05fp",
        "outputId": "58b13a78-bca4-4ab9-9433-eb50c8cc3509"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🧹 Unloading text encoder for Pipe 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# -------------------------------------------------------------------------\n",
        "# CPU Offload\n",
        "# -------------------------------------------------------------------------\n",
        "\n",
        "pipe.enable_model_cpu_offload(gpu_id=gpu_id)\n",
        "pipe.vae.enable_slicing()\n",
        "pipe.vae.enable_tiling()\n",
        "pipe.enable_attention_slicing(1)\n",
        "\n",
        "print(\n",
        "    f\"✅ Pipe {pipe_id} Ready | \"\n",
        "    f\"GPU {gpu_id} VRAM: \"\n",
        "    f\"{torch.cuda.memory_allocated(gpu_id)/1e9:.2f} GB\"\n",
        ")"
      ],
      "metadata": {
        "id": "LXy05Y8a0bF8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown =============================================================================\n",
        "#@markdown CELL 4: Parallel Image Editing Workers (ZIP Dataset Version)\n",
        "#@markdown =============================================================================\n",
        "\n",
        "import os\n",
        "import io\n",
        "import gc\n",
        "import zipfile\n",
        "import torch\n",
        "import shutil\n",
        "import hashlib\n",
        "import traceback\n",
        "import random as pyrandom\n",
        "\n",
        "from pathlib import Path\n",
        "from PIL import Image\n",
        "from concurrent.futures import ThreadPoolExecutor, as_completed\n",
        "\n",
        "from nacl.secret import SecretBox\n",
        "\n",
        "# =============================================================================\n",
        "# OUTPUT DIRECTORIES\n",
        "# =============================================================================\n",
        "\n",
        "# Using the single global pipe_id and gpu_id\n",
        "output_folders = [\n",
        "    f\"/content/edited_gpu{gpu_id}_pipe{pipe_id}\"\n",
        "]\n",
        "\n",
        "final_output_folder = \"/content/final_encrypted\"\n",
        "\n",
        "for folder in output_folders + [final_output_folder]:\n",
        "    os.makedirs(folder, exist_ok=True)\n",
        "\n",
        "# =============================================================================\n",
        "# ENCRYPTION SETUP\n",
        "# =============================================================================\n",
        "\n",
        "password = Path(\n",
        "    google_drive_dataset_path,\n",
        "    \"password.txt\"\n",
        ").read_text(encoding=\"utf-8\").strip()\n",
        "\n",
        "key = hashlib.sha256(password.encode()).digest()\n",
        "\n",
        "# =============================================================================\n",
        "# OPEN ZIP FILES\n",
        "# =============================================================================\n",
        "\n",
        "print(\"📦 Opening ZIP datasets...\")\n",
        "\n",
        "foreground_zip = zipfile.ZipFile(\n",
        "    encrypted_input_dataset_path,\n",
        "    \"r\"\n",
        ")\n",
        "\n",
        "background_zip = zipfile.ZipFile(\n",
        "    backgrounds_input_dataset_path,\n",
        "    \"r\"\n",
        ")\n",
        "\n",
        "# =============================================================================\n",
        "# LOAD FILE LISTS\n",
        "# =============================================================================\n",
        "\n",
        "input_files = sorted([\n",
        "    x for x in foreground_zip.namelist()\n",
        "    if x.endswith(\"_encrypted.bin\")\n",
        "])\n",
        "\n",
        "bg_files = sorted([\n",
        "    x for x in background_zip.namelist()\n",
        "    if x.endswith(\"_encrypted.bin\")\n",
        "])\n",
        "\n",
        "print(f\"📊 Found {len(input_files)} foreground images\")\n",
        "print(f\"🌄 Found {len(bg_files)} background images\")\n",
        "\n",
        "if len(input_files) == 0:\n",
        "    raise RuntimeError(\"❌ No foreground encrypted files found\")\n",
        "\n",
        "if len(bg_files) == 0:\n",
        "    raise RuntimeError(\"❌ No background encrypted files found\")\n",
        "\n",
        "# =============================================================================\n",
        "# WORKER FUNCTION\n",
        "# =============================================================================\n",
        "\n",
        "def worker_thread(\n",
        "    pipe_instance,\n",
        "    thread_gpu_id,\n",
        "    start_idx,\n",
        "    num_images,\n",
        "    thread_worker_id\n",
        "):\n",
        "\n",
        "    import torch._dynamo\n",
        "\n",
        "    torch._dynamo.reset()\n",
        "    torch._dynamo.config.suppress_errors = True\n",
        "    torch._dynamo.config.disable = True\n",
        "\n",
        "    os.environ[\"TORCHDYNAMO_DISABLE\"] = \"1\"\n",
        "\n",
        "    torch.cuda.set_device(thread_gpu_id)\n",
        "\n",
        "    box = SecretBox(key)\n",
        "\n",
        "    # -------------------------------------------------------------------------\n",
        "    # Reopen ZIPs inside thread\n",
        "    # -------------------------------------------------------------------------\n",
        "\n",
        "    local_foreground_zip = zipfile.ZipFile(\n",
        "        encrypted_input_dataset_path,\n",
        "        \"r\"\n",
        "    )\n",
        "\n",
        "    local_background_zip = zipfile.ZipFile(\n",
        "        backgrounds_input_dataset_path,\n",
        "        \"r\"\n",
        "    )\n",
        "\n",
        "    # -------------------------------------------------------------------------\n",
        "    # Encryption Helpers\n",
        "    # -------------------------------------------------------------------------\n",
        "\n",
        "    def decrypt(data):\n",
        "        return box.decrypt(data)\n",
        "\n",
        "    def encrypt(data):\n",
        "        return box.encrypt(data)\n",
        "\n",
        "    def bytes_to_pil(b):\n",
        "        return Image.open(io.BytesIO(b)).convert(\"RGB\")\n",
        "\n",
        "    def pil_to_bytes(img):\n",
        "\n",
        "        buf = io.BytesIO()\n",
        "\n",
        "        img.save(\n",
        "            buf,\n",
        "            format=\"JPEG\",\n",
        "            quality=90\n",
        "        )\n",
        "\n",
        "        return buf.getvalue()\n",
        "\n",
        "    # -------------------------------------------------------------------------\n",
        "    # Processing Loop\n",
        "    # -------------------------------------------------------------------------\n",
        "\n",
        "    for i in range(num_images):\n",
        "\n",
        "        global_idx = start_idx + i\n",
        "\n",
        "        if global_idx >= len(input_files):\n",
        "            break\n",
        "\n",
        "        try:\n",
        "\n",
        "            gc.collect()\n",
        "            torch.cuda.empty_cache()\n",
        "\n",
        "            # -------------------------------------------------------------\n",
        "            # Load encrypted foreground from ZIP\n",
        "            # -------------------------------------------------------------\n",
        "\n",
        "            fg_name = input_files[global_idx]\n",
        "\n",
        "            with local_foreground_zip.open(fg_name) as f:\n",
        "                input_enc = f.read()\n",
        "\n",
        "            input_image = bytes_to_pil(\n",
        "                decrypt(input_enc)\n",
        "            )\n",
        "\n",
        "            # -------------------------------------------------------------\n",
        "            # Load random encrypted background from ZIP\n",
        "            # -------------------------------------------------------------\n",
        "\n",
        "            bg_name = pyrandom.choice(bg_files)\n",
        "\n",
        "            with local_background_zip.open(bg_name) as f:\n",
        "                bg_enc = f.read()\n",
        "\n",
        "            bg_image = bytes_to_pil(\n",
        "                decrypt(bg_enc)\n",
        "            )\n",
        "\n",
        "            # -------------------------------------------------------------\n",
        "            # Inference\n",
        "            # -------------------------------------------------------------\n",
        "\n",
        "            with torch.inference_mode(), torch.no_grad():\n",
        "\n",
        "                result = pipe_instance(\n",
        "                    prompt=edit_prompt,\n",
        "                    image=[input_image, bg_image],\n",
        "                    height=target_height,\n",
        "                    width=target_width,\n",
        "                    guidance_scale=1.0,\n",
        "                    num_inference_steps=4,\n",
        "                    generator=torch.Generator(\n",
        "                        f\"cuda:{thread_gpu_id}\"\n",
        "                    ).manual_seed(42),\n",
        "                    output_type=\"pil\",\n",
        "                ).images[0]\n",
        "\n",
        "            # -------------------------------------------------------------\n",
        "            # Encrypt result\n",
        "            # -------------------------------------------------------------\n",
        "\n",
        "            encrypted_result = encrypt(\n",
        "                pil_to_bytes(result)\n",
        "            )\n",
        "\n",
        "            out_path = os.path.join(\n",
        "                output_folders[thread_worker_id],\n",
        "                f\"edited_{global_idx:06d}.enc\"\n",
        "            )\n",
        "\n",
        "            with open(out_path, \"wb\") as f:\n",
        "                f.write(encrypted_result)\n",
        "\n",
        "            print(\n",
        "                f\"✅ Worker {thread_worker_id} \"\n",
        "                f\"(GPU {thread_gpu_id}) \"\n",
        "                f\"→ {global_idx+1}/{len(input_files)}\"\n",
        "            )\n",
        "\n",
        "            del result\n",
        "            del input_image\n",
        "            del bg_image\n",
        "\n",
        "        except Exception:\n",
        "\n",
        "            print(\n",
        "                f\"❌ Worker {thread_worker_id} \"\n",
        "                f\"failed at index {global_idx}\"\n",
        "            )\n",
        "\n",
        "            print(traceback.format_exc())\n",
        "\n",
        "    # -------------------------------------------------------------------------\n",
        "    # Cleanup ZIP handles\n",
        "    # -------------------------------------------------------------------------\n",
        "\n",
        "    local_foreground_zip.close()\n",
        "    local_background_zip.close()\n",
        "\n",
        "    return True\n",
        "\n",
        "# =============================================================================\n",
        "# RUN WORKER (SINGLE PIPE VERSION)\n",
        "# =============================================================================\n",
        "\n",
        "print(\"\\n🚀 Starting single worker...\")\n",
        "\n",
        "# Direct call to worker_thread for a single pipe\n",
        "# Pass the globally available `pipe`, `gpu_id`, `0` (start_idx), `len(input_files)` (num_images), `0` (worker_id)\n",
        "worker_thread(\n",
        "    pipe,\n",
        "    gpu_id,\n",
        "    0,\n",
        "    len(input_files),\n",
        "    0\n",
        ")\n",
        "\n",
        "# =============================================================================\n",
        "# CLOSE MAIN ZIPS\n",
        "# =============================================================================\n",
        "\n",
        "foreground_zip.close()\n",
        "background_zip.close()\n",
        "\n",
        "print(\"\\n🎉 All workers complete!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237,
          "referenced_widgets": [
            "8d7e444391584c0299c4f732e90dcddf",
            "f272862a96274a5dac5e706076827e00",
            "0095187f4f9b411b8bf87136f31277de",
            "c15792c4b8754d00801c1047be0eb92e",
            "fcd94d17b095407b9a415e7b8d2599ff",
            "badf0f4e6ce7445ba897d8d34102e3b0",
            "6a7867fc78a94d9f8e1e9ecb8a0f9be9",
            "2ae0c1610e5048b088e325c430a48283",
            "93979a32b99d437cb09a52c97d009c04",
            "29882e3dce79431a9700d7e6b553866a",
            "826593794d2d48a8be1abe236062b402",
            "c2bd91ae13e54fde808909adc3ba3bad",
            "f034124e8709493eb4928a4eac80ac1e",
            "8fe94f0caf7f448c8fd86ad8ea3fe1e7",
            "2b2308e4c05844ae8e36b75fafba829e",
            "8d304de0596240529e163e62e719005a",
            "e128a079052f4909beafb89dc83d36c4",
            "bba89375435e4e40a5ced555db5ac9e3",
            "314a0acb209c4e038c819ca14e9b2387",
            "d762b4ca3b254e99809637aa07eb3dc7",
            "5ab98272ee75419cb79d6ec7f012ca84",
            "13c3dae3b101407592b6c00b6af1d70e"
          ]
        },
        "id": "3iHN3iICvPxh",
        "outputId": "c901fc6f-107d-4df2-910b-2a0c1ca07d8b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "📦 Opening ZIP datasets...\n",
            "📊 Found 2 foreground images\n",
            "🌄 Found 2 background images\n",
            "\n",
            "🚀 Starting single worker...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8d7e444391584c0299c4f732e90dcddf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Worker 0 (GPU 0) → 1/2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c2bd91ae13e54fde808909adc3ba3bad"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Worker 0 (GPU 0) → 2/2\n",
            "\n",
            "🎉 All workers complete!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown CELL 5: Combine Encrypted Outputs into a Single ZIP\n",
        "import os\n",
        "import zipfile\n",
        "from pathlib import Path\n",
        "\n",
        "# Define the output zip file path\n",
        "combined_zip_path = \"/content/final_encrypted/results.zip\"\n",
        "\n",
        "# List the directories containing the encrypted output files\n",
        "# Updated to use the single output folder structure\n",
        "output_dirs = [\n",
        "    f\"/content/edited_gpu{gpu_id}_pipe{pipe_id}\"\n",
        "]\n",
        "\n",
        "all_encrypted_files = []\n",
        "for output_dir in output_dirs:\n",
        "    if os.path.exists(output_dir):\n",
        "        for f in os.listdir(output_dir):\n",
        "            if f.endswith(\".enc\"):\n",
        "                all_encrypted_files.append(os.path.join(output_dir, f))\n",
        "\n",
        "# Sort the files to ensure consistent enumeration (optional, but good practice)\n",
        "all_encrypted_files.sort()\n",
        "\n",
        "print(f\"Found {len(all_encrypted_files)} encrypted files to combine.\")\n",
        "\n",
        "if not all_encrypted_files:\n",
        "    print(\"⚠️ No encrypted files found to combine. Skipping zip creation.\")\n",
        "else:\n",
        "    # Create a new zip file and add the encrypted files with new names\n",
        "    with zipfile.ZipFile(combined_zip_path, 'w', zipfile.ZIP_DEFLATED) as zf:\n",
        "        for i, file_path in enumerate(all_encrypted_files):\n",
        "            # New filename format: edited_000001.enc, edited_000002.enc, etc.\n",
        "            new_file_name = f\"edited_{i+1:06d}.enc\"\n",
        "            zf.write(file_path, arcname=new_file_name)\n",
        "            print(f\"  Added {os.path.basename(file_path)} as {new_file_name} to zip.\")\n",
        "\n",
        "    print(f\"✅ Successfully created combined zip file: {combined_zip_path}\")\n",
        "    print(f\"   Total files in zip: {len(all_encrypted_files)}\")"
      ],
      "metadata": {
        "id": "VrkbSBEPvee2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Decrypt edited results"
      ],
      "metadata": {
        "id": "yoSGdP68321C"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GlgvVJA7NXeP"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import shutil\n",
        "import io\n",
        "import hashlib\n",
        "from pathlib import Path\n",
        "from PIL import Image\n",
        "!pip install pynacl\n",
        "from nacl.secret import SecretBox\n",
        "from google.colab import files\n",
        "import zipfile\n",
        "from google.colab import drive\n",
        "#@markdown 🔑 Decrypt Results (either by widget uploading zip or by manual filepath to saved zip on drive)\n",
        "print(\"🔓 Decryption Notebook Ready\")\n",
        "\n",
        "# ------------------- INPUTS -------------------\n",
        "password = 'banana' # @param {type:\"string\"}\n",
        "use_manual_filepath = False  # @param {type:\"boolean\"}\n",
        "manual_filepath = '/content/drive/MyDrive/encrypted.zip' # @param {type:\"string\"}\n",
        "\n",
        "# ------------------- FILE INPUT LOGIC -------------------\n",
        "zip_filename = None\n",
        "\n",
        "if use_manual_filepath:\n",
        "    if not os.path.exists('/content/drive'):\n",
        "        print(\"🔄 Mounting Google Drive...\")\n",
        "        drive.mount('/content/drive')\n",
        "\n",
        "    if not manual_filepath:\n",
        "        raise ValueError(\"❌ Manual filepath cannot be empty when 'Use manual filepath' is enabled!\")\n",
        "\n",
        "    if not os.path.exists(manual_filepath):\n",
        "        raise FileNotFoundError(f\"❌ File not found at manual filepath: {manual_filepath}\")\n",
        "\n",
        "    zip_filename = manual_filepath\n",
        "    print(f\"✅ Using manual filepath: {zip_filename}\")\n",
        "else:\n",
        "    print(\"\\n📤 Upload your final_encrypted_outputs.zip file:\")\n",
        "    uploaded = files.upload()\n",
        "\n",
        "    for fn in uploaded.keys():\n",
        "        if fn.endswith('.zip'):\n",
        "            zip_filename = fn\n",
        "            break\n",
        "\n",
        "    if not zip_filename:\n",
        "        raise ValueError(\"❌ No .zip file was uploaded!\")\n",
        "\n",
        "    print(f\"✅ Uploaded: {zip_filename}\")\n",
        "\n",
        "# ------------------- DECRYPTION FUNCTION -------------------\n",
        "def decrypt_data(encrypted_data: bytes, box: SecretBox) -> bytes:\n",
        "    try:\n",
        "        return box.decrypt(encrypted_data)\n",
        "    except Exception as e:\n",
        "        raise ValueError(f\"Decryption failed (wrong password?): {e})\")\n",
        "\n",
        "# ------------------- MAIN DECRYPTION -------------------\n",
        "\n",
        "if not password:\n",
        "    raise ValueError(\"❌ Password cannot be empty!\")\n",
        "\n",
        "# Derive the same key as in the encryption code\n",
        "key = hashlib.sha256(password.encode('utf-8')).digest()\n",
        "box = SecretBox(key)\n",
        "\n",
        "# Create output folder\n",
        "output_dir = \"/content/decrypted_images\"\n",
        "os.makedirs(output_dir, exist_ok=True)\n",
        "\n",
        "print(f\"\\n📂 Extracting and decrypting to: {output_dir}\")\n",
        "\n",
        "# Extract zip\n",
        "with zipfile.ZipFile(zip_filename, 'r') as zip_ref:\n",
        "    zip_ref.extractall(\"/content/temp_extracted\")\n",
        "\n",
        "# Find all .enc files\n",
        "enc_files = list(Path(\"/content/temp_extracted\").rglob(\"*.enc\"))\n",
        "\n",
        "print(f\"🔍 Found {len(enc_files)} encrypted files\")\n",
        "\n",
        "success_count = 0\n",
        "\n",
        "for enc_path in enc_files:\n",
        "    try:\n",
        "        with open(enc_path, \"rb\") as f:\n",
        "            encrypted_bytes = f.read()\n",
        "\n",
        "        # Decrypt\n",
        "        decrypted_bytes = decrypt_data(encrypted_bytes, box)\n",
        "\n",
        "        # Convert to PIL Image and save as JPG\n",
        "        img = Image.open(io.BytesIO(decrypted_bytes)).convert(\"RGB\")\n",
        "\n",
        "        output_path = os.path.join(output_dir, enc_path.stem + \".jpg\")\n",
        "        img.save(output_path, format=\"JPEG\", quality=95)\n",
        "\n",
        "        success_count += 1\n",
        "        print(f\"✅ Decrypted: {enc_path.name} → {Path(output_path).name}\")\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"❌ Failed {enc_path.name}: {e}\")\n",
        "\n",
        "# Cleanup temp folder\n",
        "shutil.rmtree(\"/content/temp_extracted\", ignore_errors=True)\n",
        "\n",
        "print(f\"\\n🎉 Decryption complete! {success_count}/{len(enc_files)} files decrypted successfully.\")\n",
        "print(f\"📁 Decrypted images saved to: {output_dir}\")\n",
        "\n",
        "# Optional: Download all decrypted images as a new zip\n",
        "download_zip = True #input(\"\\nDo you want to download all decrypted images as a zip? (y/n): \").lower().strip() == 'y'\n",
        "\n",
        "if download_zip:\n",
        "    download_zip_path = \"/content/decrypted_images.zip\"\n",
        "    shutil.make_archive(\"/content/decrypted_images\", 'zip', output_dir)\n",
        "    files.download(download_zip_path)\n",
        "    print(\"📥 Download started...\")"
      ]
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