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Refactor: Replace CLI wrapper with explicit OpenVINO API calls
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‎notebooks/biological-texture-generation-lcm/README.md‎

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# Biological Texture Generation (Iris) using LCM & OpenVINO™
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This notebook demonstrates how to generate high-fidelity biological textures, specifically human iris patterns, using **Latent Consistency Models (LCM)**.
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## Key Features
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- **Model:** [SimianLuo/LCM_Dreamshaper_v7](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7)
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- **Library:** [Optimum Intel](https://github.com/huggingface/optimum-intel) with OpenVINO™ backend.
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- **Performance:** Generates high-quality images in just 4-8 steps (vs 25-50 steps for standard Stable Diffusion).
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- **Hardware:** Supports Intel CPU, GPU (iGPU/dGPU), and NPU.
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## Usage
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1. Install dependencies.
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2. Select your target device (CPU/GPU) from the widget.
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3. Run the pipeline to generate synthetic iris data for biometric research.

‎notebooks/biological-texture-generation-lcm/biological-texture-generation-lcm.ipynb‎

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"id": "cXoFhlAHytJW",
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"outputId": "aa7ff889-4d2e-4ee9-ae63-385037be013a"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"🗑️ Old images deleted. Ready for fresh generation.\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"# Force delete old experiments to ensure next result is fresh\n",
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"import shutil\n",
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"import os\n",
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"\n",
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"if os.path.exists(\"/content/Ocular_Core_Lite/experiments\"):\n",
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" shutil.rmtree(\"/content/Ocular_Core_Lite/experiments\")\n",
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" print(\"🗑️ Old images deleted. Ready for fresh generation.\")\n",
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"\n",
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"<img referrerpolicy=\"no-referrer-when-downgrade\" src=\"https://static.scarf.sh/a.png?x-pxid=5b5a4db0-7875-4bfb-bdbd-01698b5b1a77&file=notebooks/biological-texture-generation-lcm/biological-texture-generation-lcm.ipynb\" />\n"
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"%pip install -q \"openvino>=2023.2.0\" \"git+https://github.com/huggingface/optimum-intel.git\" \"diffusers\" \"transformers\" \"gradio\" --extra-index-url https://download.pytorch.org/whl/cpu"
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]
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},
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{
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"id": "4hhdDBTlOHhv",
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"outputId": "850624c3-4a1e-42ea-b464-cb523b0927e9"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"⏳ Configuring environment... (This resolves Python 3.12 version conflicts)\n",
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"📦 Installing Ocular-Core-Lite library...\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"# @title 1. Initialize Ocular-Core Environment\n",
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"# @markdown This cell sets up the environment by resolving dependency conflicts between Colab's default Python 3.12 and our AI libraries.\n",
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"# @markdown **Note:** A \"Session Crashed\" message is expected at the end. This is a deliberate restart to load new libraries.\n",
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"\n",
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"import os\n",
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"import sys\n",
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"import subprocess\n",
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"\n",
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"print(\"⏳ Configuring environment... (This resolves Python 3.12 version conflicts)\")\n",
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"\n",
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"# 1. CLEANUP: Force uninstall libraries that often conflict in pre-installed Colab environments.\n",
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"# We remove 'transformers', 'optimum', etc., to start with a clean slate.\n",
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"subprocess.check_call(\n",
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" [\n",
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" sys.executable,\n",
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" \"-m\",\n",
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" \"pip\",\n",
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" \"uninstall\",\n",
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" \"-y\",\n",
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" \"transformers\",\n",
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" \"optimum\",\n",
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" \"optimum-intel\",\n",
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" \"openvino\",\n",
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" \"peft\",\n",
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" \"huggingface-hub\",\n",
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" \"tokenizers\",\n",
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" \"diffusers\",\n",
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" ]\n",
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")\n",
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"import ipywidgets as widgets\n",
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"import openvino as ov\n",
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"from optimum.intel.openvino import OVLatentConsistencyModelPipeline\n",
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"import gc\n",
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"\n",
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"# 2. INSTALL STABLE STACK: Install a specific set of versions known to work together (\"The Goldilocks Stack\").\n",
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"# - huggingface-hub==0.23.5: Pinned to keep the 'LocalEntryNotFoundError' class that peft needs.\n",
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"# - openvino>=2024.1.0: Required for Python 3.12 compatibility.\n",
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"# - optimum-intel: The bridge between Transformers and OpenVINO optimization.\n",
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"subprocess.check_call(\n",
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" [\n",
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" sys.executable,\n",
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" \"-m\",\n",
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" \"pip\",\n",
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" \"install\",\n",
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" \"huggingface-hub==0.23.5\", # Fixes 'LocalEntryNotFoundError' error\n",
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" \"peft==0.11.1\", # Adapter support for LoRA/LCM\n",
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" \"transformers==4.41.2\", # Core model architecture\n",
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" \"optimum-intel[openvino]>=1.17.0\", # Intel optimization toolkit\n",
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" \"openvino>=2024.1.0\", # Inference engine (CPU optimized)\n",
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" \"diffusers>=0.27.0\", # Diffusion pipeline support\n",
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" ]\n",
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")\n",
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"\n",
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"# 3. INSTALL PROJECT: Install Ocular-Core-Lite directly from your GitHub source.\n",
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"print(\"📦 Installing Ocular-Core-Lite library...\")\n",
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"subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"git+https://github.com/humbeaniket2006-max/Ocular_Core_Lite.git\"])\n",
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"\n",
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"print(\"✅ Dependencies locked. Restarting runtime to apply changes...\")\n",
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"\n",
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"# 4. RESTART RUNTIME: Crucial step!\n",
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"# Python needs to reload the newly installed libraries. We force a crash/restart to ensure this happens.\n",
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"os.kill(os.getpid(), 9)"
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"# Initialize OpenVINO Core\n",
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"core = ov.Core()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "UslPhY2mRFCD",
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"outputId": "98b561d7-1460-47a7-8950-d31e9aa22130"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"/content/Ocular_Core_Lite\n",
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"👁️ Generating texture for: 'extreme macro photo, human eye, Blue iris, detailed texture, 8k, photorealistic'...\n",
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"/usr/local/lib/python3.12/dist-packages/openvino/runtime/__init__.py:10: DeprecationWarning: The `openvino.runtime` module is deprecated and will be removed in the 2026.0 release. Please replace `openvino.runtime` with `openvino`.\n",
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" warnings.warn(\n",
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"OpenVINO Tokenizer version is not compatible with OpenVINO version. Installed OpenVINO version: 2025.4.1,OpenVINO Tokenizers requires . OpenVINO Tokenizers models will not be added during export.\n",
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"2025-12-31 14:24:09.606657: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
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"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
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"E0000 00:00:1767191049.698336 36401 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
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"E0000 00:00:1767191049.734473 36401 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
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"W0000 00:00:1767191049.835850 36401 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
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"W0000 00:00:1767191049.835909 36401 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
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"W0000 00:00:1767191049.835917 36401 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
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"W0000 00:00:1767191049.835926 36401 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
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"/usr/local/lib/python3.12/dist-packages/transformers/utils/import_utils.py:519: FutureWarning: `is_torch_tpu_available` is deprecated and will be removed in 4.41.0. Please use the `is_torch_xla_available` instead.\n",
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" warnings.warn(\n",
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"/usr/local/lib/python3.12/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
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" warnings.warn(\n",
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"Loading Neural Engine...\n",
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"Fetching 24 files: 100% 24/24 [00:00<00:00, 112724.86it/s]\n",
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"Compiling the vae_decoder to CPU ...\n",
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"Compiling the unet to CPU ...\n",
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"Compiling the vae_encoder to CPU ...\n",
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"Compiling the text_encoder to CPU ...\n",
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"Generating...\n",
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"100% 21/21 [12:33<00:00, 35.87s/it]\n",
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"Saved to ./output.png\n",
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"./output.png\n"
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]
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}
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],
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"metadata": {},
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"outputs": [],
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"source": [
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"# @title 2. Generate Biological Texture\n",
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"import os\n",
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"\n",
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"# 1. DIRECTORY CHECK: After the runtime restarts (from Cell 1), Colab resets to the root folder.\n",
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"# We must navigate back into the project folder so the generator knows where to save files.\n",
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"if os.path.exists(\"/content/Ocular_Core_Lite\"):\n",
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" %cd /content/Ocular_Core_Lite\n",
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"else:\n",
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" print(\"⚠️ Project folder not found. Did you run Cell 1?\")\n",
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"\n",
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"# 2. INPUT: Define the text prompt for the Latent Consistency Model (LCM).\n",
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"# We use '8k' and 'photorealistic' keywords to guide the generation quality.\n",
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"prompt = \"extreme macro photo, human eye, Blue iris, detailed texture, 8k, photorealistic\" # @param {type:\"string\"}\n",
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"\n",
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"print(f\"👁️ Generating texture for: '{prompt}'...\")\n",
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"# Create a dropdown widget to select the device (CPU, GPU, AUTO)\n",
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"device = widgets.Dropdown(\n",
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" options=core.available_devices + [\"AUTO\"],\n",
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" value='AUTO',\n",
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" description='Device:',\n",
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" disabled=False,\n",
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")\n",
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"\n",
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"# 3. EXECUTE: Run the command-line tool (CLI) installed by your package.\n",
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"# This triggers the 'ocular-generate' entry point defined in your pyproject.toml.\n",
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"!ocular-generate \"{prompt}\""
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"device"
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]
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},
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{
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "fXALqNSmby8W",
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"outputId": "8829ab81-272e-42d4-9cec-948186d41c49"
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"id": "UslPhY2mRFCD",
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"outputId": "98b561d7-1460-47a7-8950-d31e9aa22130"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"❌ No images found yet.\n",
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"Searched in: /content/Ocular_Core_Lite/experiments/*.png\n",
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"Try running Cell 2 again!\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"# @title 3. View Result\n",
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"import glob\n",
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"import os\n",
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"from IPython.display import Image, display\n",
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"model_id = \"SimianLuo/LCM_Dreamshaper_v7\"\n",
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"\n",
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"# 1. SEARCH: Look for PNG images specifically inside the experiments folder.\n",
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"# We use a wildcard (*) to find any file ending in .png.\n",
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"search_path = \"/content/Ocular_Core_Lite/experiments/*.png\"\n",
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"list_of_files = glob.glob(search_path)\n",
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"print(f\"Loading {model_id} and converting to OpenVINO IR... (This may take a few minutes)\")\n",
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"\n",
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"if list_of_files:\n",
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" # 2. FILTER: If multiple images exist, pick the most recently created one.\n",
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" # This ensures we always show the result of the *latest* run.\n",
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" latest_file = max(list_of_files, key=os.path.getctime)\n",
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" print(f\"✅ Found generated image: {latest_file}\")\n",
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"# Load the model and export it to OpenVINO format automatically\n",
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"pipe = OVLatentConsistencyModelPipeline.from_pretrained(\n",
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" model_id,\n",
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" export=True,\n",
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" device=device.value \n",
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")\n",
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"\n",
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" # 3. DISPLAY: Render the image directly in the notebook output.\n",
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" display(Image(filename=latest_file))\n",
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"else:\n",
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" # Error handling if generation failed or folder is missing\n",
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" print(\"❌ No images found yet.\")\n",
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" print(f\"Searched in: {search_path}\")\n",
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" print(\"Try running Cell 2 again!\")"
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"# Compile the model explicitly for the selected device\n",
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"pipe.compile()\n",
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"print(f\"✅ Model successfully compiled for {device.value}\")"
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]
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},
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{

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