Instructions to use stabilityai/stable-video-diffusion-img2vid-xt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use stabilityai/stable-video-diffusion-img2vid-xt with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
Update README.md
#18
by Shalfunnn - opened
README.md
CHANGED
|
@@ -8,13 +8,13 @@
|
|
| 8 |
|
| 9 |
<!-- Provide a quick summary of what the model is/does. -->
|
| 10 |

|
| 11 |
-
Stable Video Diffusion (SVD) Image-to-Video is a diffusion model that takes in a still image as a conditioning frame
|
| 12 |
|
| 13 |
## Model Details
|
| 14 |
|
| 15 |
### Model Description
|
| 16 |
|
| 17 |
-
(SVD) Image-to-Video is a latent diffusion model trained to generate short video clips from
|
| 18 |
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).
|
| 19 |
We also finetune the widely used [f8-decoder](https://huggingface.co/docs/diffusers/api/models/autoencoderkl#loading-from-the-original-format) for temporal consistency.
|
| 20 |
For convenience, we additionally provide the model with the
|
|
|
|
| 8 |
|
| 9 |
<!-- Provide a quick summary of what the model is/does. -->
|
| 10 |

|
| 11 |
+
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.
|
| 12 |
|
| 13 |
## Model Details
|
| 14 |
|
| 15 |
### Model Description
|
| 16 |
|
| 17 |
+
(SVD) Image-to-Video is a latent diffusion model trained to generate short video clips from image conditioning.
|
| 18 |
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).
|
| 19 |
We also finetune the widely used [f8-decoder](https://huggingface.co/docs/diffusers/api/models/autoencoderkl#loading-from-the-original-format) for temporal consistency.
|
| 20 |
For convenience, we additionally provide the model with the
|