Instructions to use chenguolin/sv3d-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use chenguolin/sv3d-diffusers 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("chenguolin/sv3d-diffusers", 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
Download scheduler/scheduler_config.json from chenguolin/sv3d-diffusers: direct link, hf CLI and curl.
- Browser
- Download file 594 Bytes
-
https://huggingface.co/chenguolin/sv3d-diffusers/resolve/main/scheduler/scheduler_config.json
- Command line
-
hf download hf://chenguolin/sv3d-diffusers/scheduler/scheduler_config.json
-
curl -L -o scheduler_config.json https://huggingface.co/chenguolin/sv3d-diffusers/resolve/main/scheduler/scheduler_config.json
594 Bytes
| { | |
| "_class_name": "EulerDiscreteScheduler", | |
| "_diffusers_version": "0.30.3", | |
| "beta_end": 0.012, | |
| "beta_schedule": "scaled_linear", | |
| "beta_start": 0.00085, | |
| "clip_sample": false, | |
| "final_sigmas_type": "zero", | |
| "interpolation_type": "linear", | |
| "num_train_timesteps": 1000, | |
| "prediction_type": "v_prediction", | |
| "rescale_betas_zero_snr": false, | |
| "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 | |
| } | |