{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [], "gpuType": "T4", "machine_shape": "hm", "private_outputs": true, "accelerator": "GPU" }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "intro" }, "source": [ "# FLUX.1-dev-ControlNet-Union-Pro-2.0 (FP8 Quantized) Demo\n\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ABDALLALSWAITI/FLUX.1-dev-ControlNet-Union-Pro-2.0-fp8/blob/main/FLUX_FP8_Demo.ipynb)\n\nThis notebook demonstrates how to use the FP8 quantized version of the FLUX.1-dev-ControlNet-Union-Pro-2.0 model. This is a direct quantization of the original model to FP8 format." ] }, { "cell_type": "code", "metadata": { "id": "setup" }, "source": [ "# Install dependencies\n!pip install -q diffusers transformers accelerate controlnet_aux opencv-python\n\n# Clone the repository\n!git clone https://huggingface.co/ABDALLALSWAITI/FLUX.1-dev-ControlNet-Union-Pro-2.0-fp8\n!cp FLUX.1-dev-ControlNet-Union-Pro-2.0-fp8/pipeline_flux_controlnet.py .\n!cp FLUX.1-dev-ControlNet-Union-Pro-2.0-fp8/controlnet_flux.py ." ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "load_model" }, "source": [ "import torch\nfrom pipeline_flux_controlnet import FluxControlNetPipeline\nfrom controlnet_flux import FluxControlNetModel\n\n# Check if FP8 is supported\nfp8_supported = False\ntry:\n test = torch.tensor([1.0], dtype=torch.float8_e4m3fn)\n fp8_supported = True\n print('FP8 is supported!')\nexcept:\n print('FP8 not supported, using BF16 instead')\n\n# Load the model\ncontrolnet = FluxControlNetModel.from_pretrained(\n 'FLUX.1-dev-ControlNet-Union-Pro-2.0-fp8',\n torch_dtype=torch.bfloat16)\n\nprint('Model loaded successfully!')" ], "execution_count": null, "outputs": [] } ] }