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--- |
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pipeline_tag: image-to-video |
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license: other |
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license_name: stable-video-diffusion-nc-community |
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license_link: LICENSE |
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--- |
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# Stable Video Diffusion Image-to-Video Model Card |
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<!-- Provide a quick summary of what the model is/does. --> |
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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. |
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## Model Details |
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### Model Description |
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(SVD) Image-to-Video is a latent diffusion model trained to generate short video clips from an image conditioning. |
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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]](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid). |
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We also finetune the widely used [f8-decoder](https://huggingface.co/docs/diffusers/api/models/autoencoderkl#loading-from-the-original-format) for temporal consistency. |
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For convenience, we additionally provide the model with the |
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standard frame-wise decoder [here](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/blob/main/svd_xt_image_decoder.safetensors). |
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- **Developed by:** Stability AI |
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- **Funded by:** Stability AI |
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- **Model type:** Generative image-to-video model |
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- **Finetuned from model:** SVD Image-to-Video [14 frames] |
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### Model Sources |
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For research purposes, we recommend our `generative-models` Github repository (https://github.com/Stability-AI/generative-models), |
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which implements the most popular diffusion frameworks (both training and inference). |
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- **Repository:** https://github.com/Stability-AI/generative-models |
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- **Paper:** https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets |
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## Evaluation |
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The chart above evaluates user preference for SVD-Image-to-Video over [GEN-2](https://research.runwayml.com/gen2) and [PikaLabs](https://www.pika.art/). |
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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](https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets) |
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## Uses |
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### Direct Use |
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The model is intended for research purposes only. Possible research areas and tasks include |
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- Research on generative models. |
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- Safe deployment of models which have the potential to generate harmful content. |
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- Probing and understanding the limitations and biases of generative models. |
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- Generation of artworks and use in design and other artistic processes. |
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- Applications in educational or creative tools. |
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Excluded uses are described below. |
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### Out-of-Scope Use |
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The model was not trained to be factual or true representations of people or events, |
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and therefore using the model to generate such content is out-of-scope for the abilities of this model. |
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The model should not be used in any way that violates Stability AI's [Acceptable Use Policy](https://stability.ai/use-policy). |
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## Limitations and Bias |
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### Limitations |
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- The generated videos are rather short (<= 4sec), and the model does not achieve perfect photorealism. |
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- The model may generate videos without motion, or very slow camera pans. |
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- The model cannot be controlled through text. |
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- The model cannot render legible text. |
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- Faces and people in general may not be generated properly. |
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- The autoencoding part of the model is lossy. |
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### Recommendations |
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The model is intended for research purposes only. |
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## How to Get Started with the Model |
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Check out https://github.com/Stability-AI/generative-models |
