AnimateDiff-Lightning
Maintainer: ByteDance
563
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
AnimateDiff-Lightning is a lightning-fast text-to-video generation model developed by ByteDance. It can generate videos more than ten times faster than the original AnimateDiff model. The model is distilled from the AnimateDiff SD1.5 v2 model, and the repository contains checkpoints for 1-step, 2-step, 4-step, and 8-step distilled models. The 2-step, 4-step, and 8-step models produce great generation quality, while the 1-step model is only provided for research purposes.
Model inputs and outputs
AnimateDiff-Lightning takes text prompts as input and generates corresponding videos as output. The model can be used with a variety of base models, including realistic and anime/cartoon styles, to produce high-quality animated videos.
Inputs
- Text prompts for the desired video content
Outputs
- Animated videos corresponding to the input text prompts
Capabilities
AnimateDiff-Lightning is capable of generating high-quality animated videos from text prompts. The model can produce realistic or stylized videos, depending on the base model used. It also supports additional features, such as using Motion LoRAs to enhance the motion in the generated videos.
What can I use it for?
AnimateDiff-Lightning can be used for a variety of applications, such as creating animated explainer videos, generating custom animated content for social media, or even producing animated short films. The model's ability to generate videos quickly and with high quality makes it a valuable tool for content creators and businesses looking to produce engaging visual content.
Things to try
When using AnimateDiff-Lightning, it's recommended to experiment with different base models and settings to find the best results for your specific use case. The model performs well with stylized base models, and using 3 inference steps on the 2-step model can produce great results. You can also explore the use of Motion LoRAs to enhance the motion in the generated videos.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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