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Stable Video Diffusion 1.1 License Agreement
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principles.
Stable Video Diffusion (SVD) 1.1 Image-to-Video is a diffusion model that takes in a still image as a conditioning frame, and generates a video from it.
(SVD 1.1) Image-to-Video is a latent diffusion model trained to generate short video clips from an image conditioning.
This model was trained to generate 25 frames at resolution 1024x576 given a context frame of the same size, finetuned from SVD Image-to-Video [25 frames].
Fine tuning was performed with fixed conditioning at 6FPS and Motion Bucket Id 127 to improve the consistency of outputs without the need to adjust hyper parameters. These conditions are still adjustable and have not been removed. Performance outside of the fixed conditioning settings may vary compared to SVD 1.0.
- Developed by: Stability AI
- Funded by: Stability AI
- Model type: Generative image-to-video model
- Finetuned from model: SVD Image-to-Video [25 frames]
For research purposes, we recommend our generative-models Github repository (https://github.com/Stability-AI/generative-models), which implements the most popular diffusion frameworks (both training and inference).
The model is intended for research purposes only. Possible research areas and tasks include
- Research on generative models.
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
Excluded uses are described below.
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. The model should not be used in any way that violates Stability AI's Acceptable Use Policy.
- The generated videos are rather short (<= 4sec), and the model does not achieve perfect photorealism.
- The model may generate videos without motion, or very slow camera pans.
- The model cannot be controlled through text.
- The model cannot render legible text.
- Faces and people in general may not be generated properly.
- The autoencoding part of the model is lossy.
The model is intended for research purposes only.
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