ComfyUI Node: Pose Generate Video 🎥AniPortrait

Pose Generate Video 🎥AniPortrait:

The AniPortrait_Pose_Gen_Video node is designed to generate a video by animating a reference image based on a series of pose images. This node leverages advanced facial landmark detection and pose estimation techniques to create a seamless and realistic animation. By inputting a reference image and a sequence of pose images, the node produces a video that mimics the movements and expressions depicted in the pose images. This is particularly useful for AI artists looking to create dynamic and expressive animations from static images, enhancing the storytelling and visual appeal of their projects.

Pose Generate Video 🎥AniPortrait Input Parameters:

ref_image

The reference image that serves as the base for the animation. This image is used to extract facial landmarks and generate the initial pose. The quality and resolution of this image can significantly impact the final video output. Ensure the image is clear and well-lit for optimal results.

pose_images

A sequence of images depicting different poses or expressions. These images guide the animation process, dictating how the reference image should move and change over time. The more diverse and detailed the pose images, the more dynamic and realistic the resulting video will be.

frame_count

The total number of frames to be generated in the video. This parameter determines the length and smoothness of the animation. A higher frame count results in a longer and smoother video but requires more processing time and resources. Typical values range from 30 to 300 frames.

height

The height of the output video in pixels. This parameter, along with the width, defines the resolution of the video. Higher values result in better quality but require more computational power. Common values are 720, 1080, etc.

width

The width of the output video in pixels. This parameter, along with the height, defines the resolution of the video. Higher values result in better quality but require more computational power. Common values are 1280, 1920, etc.

seed

A numerical value used to initialize the random number generator for reproducibility. By setting a specific seed, you can ensure that the same input parameters will always produce the same output video. This is useful for consistent results across different runs.

cfg

Configuration settings for the video generation process. This parameter includes various options that control the behavior and quality of the animation. Adjusting these settings can help fine-tune the output to meet specific requirements.

steps

The number of steps or iterations used in the video generation process. More steps generally lead to higher quality and more detailed animations but also increase the processing time. Typical values range from 50 to 500 steps.

vae_path

The file path to the Variational Autoencoder (VAE) model used in the video generation process. The VAE model helps in encoding and decoding the images, contributing to the overall quality and realism of the animation.

model

The specific model used for generating the video. This parameter defines the architecture and capabilities of the video generation process. Different models may offer varying levels of detail, speed, and quality.

weight_dtype

The data type of the model weights. This parameter affects the precision and performance of the video generation process. Common data types include float32 and float16, with float16 offering faster performance but potentially lower precision.

accelerate

A boolean parameter that, when enabled, speeds up the video generation process by utilizing hardware acceleration techniques. This can significantly reduce processing time, especially for high-resolution videos or large frame counts.

fi_step

The frame interpolation step, which determines the number of intermediate frames generated between each pair of pose images. Higher values result in smoother transitions but require more processing power.

motion_module_path

The file path to the motion module used in the video generation process. This module is responsible for handling the movement and transitions between poses, contributing to the overall fluidity of the animation.

image_encoder_path

The file path to the image encoder model used in the video generation process. The image encoder helps in extracting features from the reference and pose images, which are then used to guide the animation.

denoising_unet_path

The file path to the denoising U-Net model used in the video generation process. This model helps in reducing noise and enhancing the quality of the generated video frames.

reference_unet_path

The file path to the reference U-Net model used in the video generation process. This model assists in maintaining the consistency and quality of the reference image throughout the animation.

pose_guider_path

The file path to the pose guider model used in the video generation process. This model helps in accurately mapping the pose images to the reference image, ensuring realistic and coherent animations.

Pose Generate Video 🎥AniPortrait Output Parameters:

video

The generated video that animates the reference image based on the input pose images. This output is a sequence of frames that depict the reference image moving and changing according to the poses provided. The video can be used for various creative projects, including animations, presentations, and visual storytelling.

Pose Generate Video 🎥AniPortrait Usage Tips:

• Ensure the reference image is clear and well-lit to achieve the best results.
• Use a diverse set of pose images to create more dynamic and expressive animations.
• Adjust the frame count and steps to balance between video quality and processing time.
• Enable the accelerate option if you have compatible hardware to speed up the video generation process.
• Experiment with different configuration settings (cfg) to fine-tune the animation to your specific needs.

Pose Generate Video 🎥AniPortrait Common Errors and Solutions:

"Can not detect a face in the reference image."

• Explanation: The node was unable to detect facial landmarks in the provided reference image.
• Solution: Ensure the reference image is clear, well-lit, and contains a visible face. Try using a different image if the problem persists.

"source video has {len(images)} frames, with {fps} fps"

• Explanation: This message indicates the number of frames and frames per second (fps) in the source video.
• Solution: Verify that the frame count and fps are as expected. Adjust the input parameters if necessary to achieve the desired video length and smoothness.

"pose video has {frame_count} frames"

• Explanation: This message indicates the number of frames in the pose video.
• Solution: Ensure the frame count matches your requirements. If the frame count is too low or too high, adjust the input parameters accordingly.

"AssertionError: Can not detect a face in the reference image."

• Explanation: The node failed to detect a face in the reference image, causing an assertion error.
• Solution: Double-check the reference image for clarity and visibility of the face. Use a different image if necessary.