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ModelScope Library provides the foundation for building the model-ecosystem of ModelScope, including the interface and implementation to integrate various models into ModelScope.

Use depth control, default False, only effective when using pose control use_depth_control = False # Use pose control, default False use_pose_model = False # The path of the image for pose control, only effective when using pose control pose_image = 'poses/man/pose1.png' # Fill in the folder of the images after preprocessing above, it should be the same as during training processed_dir = './processed' # The number of images to generate in inference num_generate = 5 # The stable diffusion base model used in training, no need to be changed base_model = 'ly261666/cv_portrait_model' # The version number of this base model, no need to be changed revision = 'v2.0' # This base model may contains multiple subdirectories of different styles, currently we use film/film, no need to be changed base_model_sub_dir = 'film/film' # The folder where the model weights stored after training, it must be the same as during training train_output_dir = './output' # Specify a folder to save the generated images, this parameter can be modified as needed output_dir = './generated' # Use Chinese style model, default False use_style = False Wait for 5-20 minutes to complete the training. Users can also adjust other training hyperparameters. The hyperparameters supported by training can be viewed in the file of train_lora.sh, or the complete hyperparameter list in facechain/train_text_to_image_lora.py. 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. You can also experience FaceChain directly with our ModelScope Studio. 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.Input: User-uploaded images in the training phase, preset input prompt words for generating personal portraits The ModelScope notebook has a free tier that allows you to run the FaceChain application, refer to ModelScope Notebook In addition to ModelScope notebook and ECS, I would suggest that we add that user may also start DSW instance with the option of ModelScope (GPU) image, to create a ready-to-use environment. 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: imgs: This parameter needs to be replaced with the actual value. It means a local file directory that contains the original photos used for training and generation

Face quality assessment FQA： https://modelscope.cn/models/damo/cv_manual_face-quality-assessment_fqa Colab notebook is available now! You can experience FaceChain directly with our Colab Notebook. (August 15th, 2023 UTC) film/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 Support a series of new style models in a plug-and-play fashion. Refer to: Features (August 16th, 2023 UTC)Human parsing model M2FP： https://modelscope.cn/models/damo/cv_resnet101_image-multiple-human-parsing Use the conda virtual environment, and refer to Anaconda to manage your dependencies. After installation, execute the following commands:

FaceChain has been selected in the BenchCouncil Open100 (2022-2023) annual ranking. (November 8th, 2023 UTC) processed: The folder of the processed images after preprocessing, this parameter needs to be passed the same value in inference, no need to be changed Note: FaceChain currently assume single-GPU, if your environment has multiple GPU, please use the following instead: # CUDA_VISIBLE_DEVICES=0 python3 app.py # Step6GIT_LFS_SKIP_SMUDGE = 1 git clone https://github.com/modelscope/facechain.git --depth 1 cd facechain Support super resolution🔥🔥🔥, provide multiple resolution choice (512 512, 768768, 1024 1024, 20482048). (November 13th, 2023 UTC)