Instructions to use engineerA314/Wan2.1-Fun-V1.1-1.3B-InP-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use engineerA314/Wan2.1-Fun-V1.1-1.3B-InP-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("engineerA314/Wan2.1-Fun-V1.1-1.3B-InP-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: 746 Bytes
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"_class_name": "UniPCMultistepScheduler",
"_diffusers_version": "0.37.1",
"beta_end": 0.02,
"beta_schedule": "linear",
"beta_start": 0.0001,
"disable_corrector": [],
"dynamic_thresholding_ratio": 0.995,
"final_sigmas_type": "zero",
"flow_shift": 3.0,
"lower_order_final": true,
"num_train_timesteps": 1000,
"predict_x0": true,
"prediction_type": "flow_prediction",
"rescale_betas_zero_snr": false,
"sample_max_value": 1.0,
"solver_order": 2,
"solver_p": null,
"solver_type": "bh2",
"steps_offset": 0,
"thresholding": false,
"timestep_spacing": "linspace",
"trained_betas": null,
"use_beta_sigmas": false,
"use_exponential_sigmas": false,
"use_flow_sigmas": true,
"use_karras_sigmas": false
}
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