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Parameter meaning: ly261666/cv_portrait_model: The stable diffusion base model of the ModelScope model hub, which will be used for training, no need to be changed. Support super resolution🔥🔥🔥, provide multiple resolution choice (512 512, 768768, 1024 1024, 20482048). (November 13th, 2023 UTC) When inferring, please edit the code in run_inference.py: # 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 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 Face recognition model RTS： https://modelscope.cn/models/damo/cv_ir_face-recognition-ood_rts More Information

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. Colab notebook is available now! You can experience FaceChain directly with our Colab Notebook. (August 15th, 2023 UTC) Input: User-uploaded images in the training phase, preset input prompt words for generating personal portraits 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 You can find the generated personal digital image photos in the output_dir. Algorithm Introduction Architectural Overview Add validate & ensemble for Lora training, and InpaintTab(hide in gradio for now). (August 28th, 2023 UTC)

Use the conda virtual environment, and refer to Anaconda to manage your dependencies. After installation, execute the following commands: 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. FaceChain has been selected in the BenchCouncil Open100 (2022-2023) annual ranking. (November 8th, 2023 UTC) Support a series of new style models in a plug-and-play fashion. Refer to: Features (August 16th, 2023 UTC)Add robust face lora training module, enhance the performance of one pic training & style-lora blending. (August 27th, 2023 UTC) Human parsing model M2FP： https://modelscope.cn/models/damo/cv_resnet101_image-multiple-human-parsing Face attribute recognition model FairFace： https://modelscope.cn/models/damo/cv_resnet34_face-attribute-recognition_fairface 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 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.

ly261666/cv_portrait_model: The stable diffusion base model of the ModelScope model hub, which will be used for training, no need to be changed.

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 detection model DamoFD： https://modelscope.cn/models/damo/cv_ddsar_face-detection_iclr23-damofd film/film: This base model may contains multiple subdirectories of different styles, currently we use film/film, no need to be changed