TemporalNet2
Maintainer: CiaraRowles
120
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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
TemporalNet2 is an evolution of the original TemporalNet model, designed to enhance the temporal consistency of generated outputs. The key difference is that TemporalNet2 uses both the last frame and an optical flow map between frames to guide the generation, improving the consistency of the output. This takes some modifications to the original ControlNet code, as outlined in the maintainer's description.
Model inputs and outputs
TemporalNet2 is a ControlNet model that takes in a sequence of input frames and generates a video output with improved temporal consistency. It can be used in conjunction with Stable Diffusion to create temporally coherent video content.
Inputs
- Input Images: A sequence of input frames to be processed
- Init Image: A pre-stylized initial image to prevent drastic style changes
Outputs
- Output Video: A generated video with improved temporal consistency compared to the input frames
Capabilities
TemporalNet2 significantly reduces flickering and inconsistencies in generated video outputs, particularly at higher denoise levels. By leveraging both the last frame and an optical flow map, it can better maintain the visual coherence of the generated sequence.
What can I use it for?
TemporalNet2 can be a valuable tool for content creators and animators looking to generate temporally consistent video content using Stable Diffusion. It can be used to create smooth, visually coherent animations, video loops, and other dynamic media. The maintainer also suggests using it in conjunction with the HED model for additional benefits.
Things to try
Experimenting with the control net settings, such as the guidance scale and conditioning scale, can help find the right balance between maintaining the QR code shape and preserving the desired style. Additionally, generating the output at a higher resolution of 768x768 can improve the overall quality and detail of the generated video.
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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