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ali-vilab avatar

ali-vilab/VACE

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VACE

VACE is a set of software tools and frameworks for reference-guided video generation, diffusion-based editing, and video-to-video translation. It provides utilities to produce new video content and modify existing sequences by using reference materials to guide visual style, subject matter, and composition.

The framework enables video-to-video translation and synthesis, allowing for the update of visual styles and depth. It also functions as a video editor for modifying properties and content through reference-guided transformations.

The system covers localized video editing and inpainting, which allows for the replacement or modification of specific objects and areas using masks or bounding boxes. It also includes capabilities for general video content transformation and visual structure generation.

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Features

  • Latent Diffusion Models - Utilizes latent diffusion models as the primary generative framework for denoising compressed video representations.
  • Spatio-Temporal Attention - Employs spatio-temporal attention mechanisms to ensure visual stability and consistency across sequential video frames.
  • Cross-Attention Conditioning - Implements cross-attention conditioning to map reference images and masks to visual features in the latent space.
  • Video-to-Video Synthesis - Performs video-to-video synthesis by injecting original structural information into a diffusion process to maintain consistency.
  • Reference-Guided Generation - Injects external style and subject information into the model to guide the appearance of the final video output.
  • Reference-Based Video Generators - Synthesizes new video content that follows the visual style and composition of provided reference materials.
  • Latent Inpainting Masks - Provides latent inpainting masks to replace specific video regions by blending generated content into the latent space.
  • Diffusion-Based Editors - Provides a video editor that modifies properties and content using diffusion models and reference-guided transformations.
  • Generative Video Editing - Enables generative video editing to replace or inpaint objects within sequences using masks.
  • Video Inpainting Systems - Implements video inpainting systems to replace or modify specific objects using masks and bounding boxes.
  • Video Transformation and Enhancement - Provides tools for high-end aesthetic refinement and stylistic video manipulation using reference materials.
  • Video Generation - All-in-one video creation and editing framework.
3,645 stars·250 forks·Python·apache-2.0·81 views

Star history

Star history chart for ali-vilab/vaceStar history chart for ali-vilab/vace

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does ali-vilab/vace do?

VACE is a set of software tools and frameworks for reference-guided video generation, diffusion-based editing, and video-to-video translation. It provides utilities to produce new video content and modify existing sequences by using reference materials to guide visual style, subject matter, and composition.

What are the main features of ali-vilab/vace?

The main features of ali-vilab/vace are: Latent Diffusion Models, Spatio-Temporal Attention, Cross-Attention Conditioning, Video-to-Video Synthesis, Reference-Guided Generation, Reference-Based Video Generators, Latent Inpainting Masks, Diffusion-Based Editors.

Which projects share features with ali-vilab/vace?

Projects with overlapping indexed features include: showlab/tune-a-video — Tune-A-Video is a text-to-video diffusion framework designed to convert pretrained text-to-image diffusion models into… zai-org/cogvideo — CogVideo is a video generation framework and large language model architecture designed for synthesizing… compvis/latent-diffusion — Latent Diffusion is a framework for high-resolution image synthesis that performs the denoising process within a… guoyww/animatediff — AnimateDiff is a latent diffusion video generator and text-to-video diffusion framework. It converts existing… ailab-cvc/videocrafter — Videocrafter is a latent diffusion model designed for AI video synthesis. It functions as both a text-to-video and… picsart-ai-research/text2video-zero — Text2Video-Zero is a text-to-video diffusion model and framework designed to synthesize temporally consistent video…

Projects sharing features with VACE

These projects share indexed features with VACE. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • showlab/tune-a-videoshowlab avatar

    showlab/Tune-A-Video

    4,364View on GitHub↗

    Tune-A-Video is a text-to-video diffusion framework designed to convert pretrained text-to-image diffusion models into video generators. It utilizes a spatio-temporal attention mechanism and single text-video pair training to enable the synthesis of moving sequences from text prompts. The project provides tools for one-shot video personalization, allowing a model to be tuned on a single reference video to preserve specific characters or artistic styles across new generations. It also functions as a video editor that modifies subjects, backgrounds, and styles through noise-sampling prompt guid

    Python
    View on GitHub↗4,364
  • zai-org/cogvideozai-org avatar

    zai-org/CogVideo

    12,790View on GitHub↗

    CogVideo is a video generation framework and large language model architecture designed for synthesizing high-resolution video clips from natural language descriptions and images. It functions as a text-to-video and image-to-video generator, while also providing a model for video captioning to analyze visual content into descriptive text summaries. The system supports animating static images into motion sequences and transforming series of images into video based on prompts. It includes capabilities for extending the length of generated video clips to create longer sequences of motion. The f

    Pythoncogvideoximage-to-videollm
    View on GitHub↗12,790
  • compvis/latent-diffusionCompVis avatar

    CompVis/latent-diffusion

    14,072View on GitHub↗

    Latent Diffusion is a framework for high-resolution image synthesis that performs the denoising process within a compressed latent space. It uses variational autoencoders to encode images into a lower-dimensional representation, reducing the computational cost of noise prediction compared to operating on raw pixels. The project enables text-to-image generation by integrating natural language descriptions through cross-attention conditioning. It also supports image inpainting and restoration, filling masked or missing image areas with generated content, and example-based synthesis using retrie

    Jupyter Notebook
    View on GitHub↗14,072
  • guoyww/animatediffguoyww avatar

    guoyww/AnimateDiff

    12,144View on GitHub↗

    AnimateDiff is a latent diffusion video generator and text-to-video diffusion framework. It converts existing text-to-image diffusion models into animation generators by applying specialized motion modules, allowing for the creation of video sequences without modifying the original base model. The project provides an image-to-video animation framework that uses sparse RGB images, sketches, or structural keyframe constraints to guide generation. It further distinguishes itself with a motion adapter system that injects cinematic camera movements, such as zooming, panning, and tilting, into anim

    Python
    View on GitHub↗12,144
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