temporal-controlnet-depth-svd-v1
Maintainer: CiaraRowles
40
🛠️
| 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
The temporal-controlnet-depth-svd-v1 model is a tool that uses a controlnet style encoder with an SVD base to enhance the temporal consistency of video diffusion projects. This model was developed by CiaraRowles and is designed to provide precise temporal control for your video diffusion needs. It can be used to generate videos that closely follow a given input image, with a focus on maintaining temporal coherence.
Similar models include the Stable Video Diffusion Image-to-Video and Stable Video Diffusion 1.1 Image-to-Video models from Stability AI, which also generate video from image inputs. The TemporalNet2 model, also developed by CiaraRowles, is an evolution of the original TemporalNet concept that uses both the last frame and an optical flow map to improve generation consistency.
Model Inputs and Outputs
Inputs
- Input Images: The model takes a series of input images that will be used as the basis for the generated video.
- Controlnet Model: The model requires the inference repo from the provided GitHub link to be downloaded and installed.
Outputs
- Generated Video: The model outputs a video that is generated based on the input images, with a focus on maintaining temporal coherence.
Capabilities
The temporal-controlnet-depth-svd-v1 model is designed to enhance the temporal consistency of video diffusion projects. It can generate videos that closely follow a given input image, with a smooth and coherent flow of motion. The model is particularly adept at handling central object motion and simpler motions that the SVD base can handle well.
What Can I Use It For?
The temporal-controlnet-depth-svd-v1 model can be used for a variety of research and creative projects involving video generation. Possible use cases include:
- Research on generative models and understanding their limitations and biases
- Generation of artistic and design-focused videos
- Educational or creative tools that utilize video generation capabilities
For commercial use of the model, please refer to the Stability AI membership page.
Things to Try
When working with the temporal-controlnet-depth-svd-v1 model, it's important to focus on central object motion and simpler movements that the SVD base can handle well. Avoid overly complex motions or obscure objects, as the model may have difficulty processing those. Additionally, the model tends to extract motion features primarily from the central object and occasionally the background, so it's best to keep the input images focused and straightforward.
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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