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skyworkai/skyreels-v2

6,871PythonAudience · researcherComplexity · 5/5Setup · hard

TLDR

An open-source AI video generation system that creates video from text or images and can extend clips to any length by generating them in continuous segments.

Mindmap

mindmap
  root((SkyReels V2))
    Generation modes
      Text to video
      Image to video
      Video extension
      Start and end frame
    Model sizes
      1.3B parameters
      14B parameters
      540p and 720p
    Tools included
      SkyCaptioner-V1
      Prompt enhancer
    Setup
      Hugging Face download
      Multi-GPU inference
      Python scripts
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Things people build with this

USE CASE 1

Generate a short video clip from a written scene description using the text-to-video mode.

USE CASE 2

Animate a still product image into moving footage using image-to-video generation.

USE CASE 3

Extend an existing video clip by generating more footage and appending it to the end.

USE CASE 4

Specify an opening and closing frame and let the model fill in the motion between them.

Tech stack

PythonHugging FaceModelScope

Getting it running

Difficulty · hard Time to first run · 1day+

Requires significant GPU memory, the 14B model needs multi-GPU setup and models must be downloaded from Hugging Face before first run.

No license information was mentioned in the explanation.

In plain English

SkyReels V2 is an open-source AI video generation system from Skywork AI. Given a text description or a starting image, it generates video footage. The standout claim is that it can produce videos of any length by generating them in continuous segments rather than as a fixed-length clip. The developers describe the underlying approach as an AutoRegressive Diffusion-Forcing architecture, which is the mechanism that allows the model to keep extending the video beyond typical limits. The system comes in several sizes. The smallest is 1.3 billion parameters, and the largest publicly released version is 14 billion parameters. Models are available at 540p and 720p resolutions. There are three main generation modes: text-to-video (generate from a written description), image-to-video (animate a still image), and diffusion forcing (the mode used for long or infinite-length generation). A separate video extension mode lets you take an existing clip and add more footage to the end of it. A start-and-end frame control mode lets you specify both the opening and closing images and have the model fill in the motion between them. Running the larger models requires substantial computing resources. The README describes multi-GPU inference options and provides guidance on memory requirements. Models are downloaded from Hugging Face or ModelScope before running the included Python scripts. The repository also includes a video captioning model called SkyCaptioner-V1 and a prompt enhancement tool to help users write better generation prompts. The project is part of a broader series. SkyReels-V1 was released earlier and focused on human-centric video generation. SkyReels-V3 is now available as a separate repository. Related projects in the same organization include portrait animation tools and a controllable generation framework for assembling specific visual elements into a scene. The full README is longer than what was shown.

Copy-paste prompts

Prompt 1
I want to use SkyReels V2 to generate a 10-second video from a text prompt. Show me the Python command or script to run inference with the 1.3B model at 540p.
Prompt 2
What are the GPU memory requirements for each SkyReels V2 model size, and how do I run multi-GPU inference to handle the 14B model?
Prompt 3
I have a still image I want to animate with SkyReels V2. Walk me through the image-to-video pipeline step by step.
Prompt 4
How do I use the SkyCaptioner-V1 model to generate a text description of an existing video so I can use it as a generation prompt?
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