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
File size: 594 Bytes
d168518 f869c4e d168518 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"_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
}
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