Stable Video Diffusion (SVD) Image-to-Video is a diffusion model that takes in a still image as a conditioning frame, and generates a video from it.
Model Details
Model Description
(SVD) 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 576x1024 given a context frame of the same size, finetuned from SVD Image-to-Video [14 frames].
We also finetune the widely used f8-decoder for temporal consistency.
For convenience, we additionally provide the model with the
standard frame-wise decoder here.
Developed by: Stability AI
Funded by: Stability AI
Model type: Generative image-to-video model
Finetuned from model: SVD Image-to-Video [14 frames]
Model Sources
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 chart above evaluates user preference for SVD-Image-to-Video over GEN-2 and PikaLabs.
SVD-Image-to-Video is preferred by human voters in terms of video quality. For details on the user study, we refer to the research paper
Uses
Direct Use
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.
Out-of-Scope Use
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.
Limitations and Bias
Limitations
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.
