LivePortrait for Stable Diffusion WebUI

This extension is for AUTOMATIC1111's Stable Diffusion web UI, it allows to add a LivePortrait tab to the original Stable Diffusion WebUI to benefit from LivePortrait features.

Installation

Note

Make sure your system has FFmpeg installed. For details on FFmpeg installation, see how to install FFmpeg.

1. Open "Extensions" tab.
2. Open "Install from URL" tab in the tab.
3. Enter https://github.com/dimitribarbot/sd-webui-live-portrait.git to "URL for extension's git repository".
4. Press "Install" button.
5. It may take a few minutes to install as XPose may be compiled. At the end, you will see the message "Installed into stable-diffusion-webui\extensions\sd-webui-live-portrait. Use Installed tab to restart".
6. Go to "Installed" tab, click "Check for updates", and then click "Apply and restart UI". (The next time you can also use these buttons to update this extension.)

/!\ Important notes /!\

XPose, the face detector model used for animal mode, is currently not working with MacOS or non NVIDIA graphic cards. To allow animal mode to work correctly, follow the instructions described here.

Output

By default, generated files can be found in the stable-diffusion-webui/outputs/live-portrait folder. It can be overriden in Automatic1111's SD WebUI settings (see section below).

Settings

In the Automatic1111's SD WebUI settings tab, under the Live Portrait section, you can find the following configuration settings:

• Human face detector (live_portrait_human_face_detector entry in config.json): configures the face detector model for human inference, either the default original InsightFace, MediaPipe or Face Alignment.
• Face alignment detector (live_portrait_face_alignment_detector entry in config.json): configures the face detector model for human inference when using Face Alignment.
• Face alignment detector device (live_portrait_face_alignment_detector_device entry in config.json): configures the face detector model device for human inference when using Face Alignment.
• Face alignment detector dtype (live_portrait_face_alignment_detector_dtype entry in config.json): configures the face detector model dtype for human inference when using Face Alignment.
• Enable torch.compile for faster inference (live_portrait_flag_do_torch_compile entry in config.json): the first-time inference triggers an optimization process (about one minute), making subsequent inferences 20-30% faster. Performance gains may vary with different CUDA versions.
• Live portrait generation output directory (live_portrait_output_dir entry in config.json): configures the inference output directory. Defaults to stable-diffusion-webui/outputs/live-portrait if left empty.

Models

LivePortrait

Model files go here (automatically downloaded if the folder is not present during first run): stable-diffusion-webui/models/liveportrait (human) and stable-diffusion-webui/models/liveportrait_animals (animals).

Pickle files have all been converted to safetensors by Kijai. If necessary, they can be downloaded from: https://huggingface.co/Kijai/LivePortrait_safetensors/tree/main (thank you Kijai).

Face detectors

For human mode, you can either use the original default Insightface, or Google's MediaPipe, or Face Alignment (see Settings section above or API section below).

Biggest difference is the license: Insightface is strictly for NON-COMMERCIAL use. MediaPipe is a bit worse at detection, and can't run on GPU in Windows, though it's much faster on CPU compared to Insightface. Face Alignment can use blazeface back camera model (or SFD or RetinaFace), it's far better for smaller faces than MediaPipe, that only can use the blazeface short model. The warmup on the first run when using this can take a long time, but subsequent runs are quick.

Insightface models go here (automatically downloaded if the folder is not present during first run): stable-diffusion-webui/models/insightface/models/buffalo_l. If necessary, they can be downloaded from: https://github.com/deepinsight/insightface/releases/download/v0.7/buffalo_l.zip.

For animal mode, this extension is using XPose which is also strictly for NON-COMMERCIAL use and is not compatible with MacOS. XPose model goes here (automatically downloaded if not present during first run): stable-diffusion-webui/models/liveportrait_animals.

If necessary, it can be downloaded from: https://huggingface.co/KwaiVGI/LivePortrait/resolve/main/liveportrait_animals/xpose.pth.

API

Routes have been added to the Automatic1111's SD WebUI API:

• /live-portrait/human: inference for humans.
• /live-portrait/human/retargeting/image: retargeting for human portraits. Supports an array of input parameters to generate multiple result images.
• /live-portrait/human/retargeting/image/init: get retargeting lip ratio and eye ratio of human portraits.
• /live-portrait/human/retargeting/video: retargeting for human videos.
• /live-portrait/animal: inference for animals (not available for MacOS users or for v2.1.x pytorch versions).

Parameters are the same as LivePortrait ones (see output of command python inference.py --help in LivePortrait repository) except for:

• source: it can either be a path to an existing file (as in LivePortrait) or an url or a base64 encoded string. For url without file extension or base64 encoded string, the parameter source_file_extension must also be filled with a valid extension corresponding to the given source (e.g. .jpg).
• driving: it can either be a path to an existing file (as in LivePortrait) or an url or a base64 encoded string. For url without file extension or base64 encoded string, the parameter driving_file_extension must also be filled with a valid extension corresponding to the given driving video (e.g. .mp4).
• output_mode: video or images whether to generate an output video (the original version of LivePortrait) or multiple images at given frame indices (see frame_indices)
• frame_indices: list of frame indices (1-based) where image snapshots should be generated (to be used with the 'images' output mode).
• send_output: true if you want output videos to be sent as base64 encoded strings, false otherwise.
• save_output: true if you want output videos to be saved in output_dir (as in LivePortrait), false otherwise.
• use_model_cache: true if you want live portrait and face detector models to be cached for subsequent calls using same models, false otherwise.
• human_face_detector: insightface, mediapipe or facealignment. Face detector to be used by human inference. Default to the Human face detector UI setting if defined or insightface if not set neither in settings nor in endpoint body.
• face_alignment_detector: blazeface, blazeface_back_camera, retinaface or sfd. Face detector to be used by human inference when Face Alignment is selected as human_face_detector. Default to the Face alignment detector UI setting if defined or blazeface_back_camera if not set neither in settings nor in endpoint body.
• face_alignment_detector_device: cuda, cpu or mps. Device to be used by face detector when Face Alignment is selected as human_face_detector. Default to cuda.
• face_alignment_detector_dtype: fp16, bf16 or fp32. Device type to be used by face detector when Face Alignment is selected as human_face_detector. Default to fp16.
• source_face_index: Face index (0-based) to be cropped in the source image or video if flag_do_crop is set to True.
• driving_face_index: Face index (0-based) to be cropped in the driving video if flag_crop_driving_video is set to True.

Additional parameters for the /live-portrait/human/retargeting/image endpoint are:

• retargeting_options: an array of objects with the following arguments:
  • eye_ratio: target eyes-open ratio (0 -> 0.8)
  • lip_ratio: target lip-open ratio (0 -> 0.8)
  • head_pitch_variation: relative pitch (-15 -> 15)
  • head_yaw_variation: relative yaw (-25 -> 25)
  • head_roll_variation: relative roll (-15 -> 15)
  • mov_x: x-axis movement (-0.19 -> 0.19)
  • mov_y: y-axis movement (-0.19 -> 0.19)
  • mov_z: z-axis movement (0.9 -> 1.2)
  • lip_variation_pouting: pouting (-0.09 -> 0.09)
  • lip_variation_pursing: pursing 😐 (-20 -> 15)
  • lip_variation_grin: grin 😁 (0 -> 15)
  • lip_variation_opening: lip close <-> open (-90 -> 120)
  • smile: smile 😄 (-0.3 -> 1.3)
  • wink: wink 😉 (0 -> 39)
  • eyebrow: eyebrow 🤨 (-30 -> 30)
  • eyeball_direction_x: eye gaze (horizontal) 👀 (-30 -> 30)
  • eyeball_direction_y: eye gaze (vertical) 🙄 (-63 -> 63)
• retargeting_source_scale: the ratio of face area is smaller if scale is larger
• flag_stitching_retargeting_input: To apply stitching or not
• flag_do_crop_input_retargeting_image: whether to crop the source portrait to the face-cropping space

Additional parameters for the /live-portrait/human/retargeting/image/init endpoint are:

• eye_ratio: target eyes-open ratio (0 -> 0.8)
• lip_ratio: target lip-open ratio (0 -> 0.8)
• retargeting_source_scale: the ratio of face area is smaller if scale is larger

Additional parameters for the /live-portrait/human/retargeting/video endpoint are:

• lip_ratio: target lip-open ratio (0 -> 0.8)
• retargeting_source_scale: the ratio of face area is smaller if scale is larger
• driving_smooth_observation_variance_retargeting: motion smooth strength
• video_retargeting_silence: whether to keep the lip silent
• flag_do_crop_input_retargeting_video: whether to crop the source video to the face-cropping space

Thanks

Original author's link: https://liveportrait.github.io/

This project has been inspired and uses models converted by kijai: https://github.com/kijai/ComfyUI-LivePortraitKJ