Instructions to use IDsala/stable-video-diffusion-img2vid-xt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use IDsala/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("IDsala/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
| { | |
| "_class_name": "StableVideoDiffusionPipeline", | |
| "_diffusers_version": "0.24.0.dev0", | |
| "_name_or_path": "diffusers/svd-xt", | |
| "feature_extractor": [ | |
| "transformers", | |
| "CLIPImageProcessor" | |
| ], | |
| "image_encoder": [ | |
| "transformers", | |
| "CLIPVisionModelWithProjection" | |
| ], | |
| "scheduler": [ | |
| "diffusers", | |
| "EulerDiscreteScheduler" | |
| ], | |
| "unet": [ | |
| "diffusers", | |
| "UNetSpatioTemporalConditionModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKLTemporalDecoder" | |
| ] | |
| } | |