Image-to-Image
Diffusers
ONNX
Safetensors
StableDiffusionXLInpaintPipeline
stable-diffusion-xl
inpainting
virtual try-on
Instructions to use efdev/IDM-VTON with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use efdev/IDM-VTON with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("efdev/IDM-VTON", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
| { | |
| "_class_name": "DDPMScheduler", | |
| "_diffusers_version": "0.21.0.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": "epsilon", | |
| "sample_max_value": 1.0, | |
| "set_alpha_to_one": false, | |
| "skip_prk_steps": true, | |
| "steps_offset": 1, | |
| "timestep_spacing": "leading", | |
| "trained_betas": null, | |
| "use_karras_sigmas": false, | |
| "rescale_betas_zero_snr": true | |
| } | |