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
| { | |
| "_class_name": "EulerDiscreteScheduler", | |
| "_diffusers_version": "0.24.0.dev0", | |
| "beta_end": 0.012, | |
| "beta_schedule": "scaled_linear", | |
| "beta_start": 0.00085, | |
| "clip_sample": false, | |
| "interpolation_type": "linear", | |
| "num_train_timesteps": 1000, | |
| "prediction_type": "v_prediction", | |
| "set_alpha_to_one": false, | |
| "sigma_max": 700.0, | |
| "sigma_min": 0.002, | |
| "skip_prk_steps": true, | |
| "steps_offset": 1, | |
| "timestep_spacing": "leading", | |
| "timestep_type": "continuous", | |
| "trained_betas": null, | |
| "use_karras_sigmas": true | |
| } | |