Note: FaceChain currently assume single-GPU, if your environment has multiple GPU, please use the following instead: # CUDA_VISIBLE_DEVICES=0 python3 app.py # Step6 FaceChain has been selected in the BenchCouncil Open100 (2022-2023) annual ranking. (November 8th, 2023 UTC) ModelScope Library provides the foundation for building the model-ecosystem of ModelScope, including the interface and implementation to integrate various models into ModelScope.

Step1: 我的notebook -> PAI-DSW -> GPU环境 # Step2: Open the Terminal，clone FaceChain from github: GIT_LFS_SKIP_SMUDGE = 1 git clone https://github.com/modelscope/facechain.git --depth 1 # Step3: Entry the Notebook cell: Face quality assessment FQA： https://modelscope.cn/models/damo/cv_manual_face-quality-assessment_fqafilm/film: This base model may contains multiple subdirectories of different styles, currently we use film/film, no need to be changed

Face attribute recognition model FairFace： https://modelscope.cn/models/damo/cv_resnet34_face-attribute-recognition_fairface HuggingFace Space is available now! You can experience FaceChain directly with 🤗 (August 25th, 2023 UTC) Add validate & ensemble for Lora training, and InpaintTab(hide in gradio for now). (August 28th, 2023 UTC)FaceChain is a deep-learning toolchain for generating your Digital-Twin. With a minimum of 1 portrait-photo, you can create a Digital-Twin of your own and start generating personal portraits in different settings (multiple styles now supported!). You may train your Digital-Twin model and generate photos via FaceChain's Python scripts, or via the familiar Gradio interface, or via sd webui. Use the conda virtual environment, and refer to Anaconda to manage your dependencies. After installation, execute the following commands: