Instructions to use AndrewChoyCS/Mobile-VTON with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AndrewChoyCS/Mobile-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("AndrewChoyCS/Mobile-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": "T2IMobilePipelineV1_3_NotLoadingT5_Decoder", | |
| "_diffusers_version": "0.32.2", | |
| "denoiser": [ | |
| "Mobile_VTON.models.unets.unet_2d_condition_tryon", | |
| "UNet2DConditionModel" | |
| ], | |
| "denoiser_garment": [ | |
| "Mobile_VTON.models.unets.unet_2d_condition_garment", | |
| "UNet2DConditionModel" | |
| ], | |
| "feature_extractor": [ | |
| null, | |
| null | |
| ], | |
| "image_encoder": [ | |
| "transformers", | |
| "Dinov2Model" | |
| ], | |
| "scheduler": [ | |
| "diffusers", | |
| "FlowMatchEulerDiscreteScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "CLIPTextModelWithProjection" | |
| ], | |
| "text_encoder_2": [ | |
| "transformers", | |
| "CLIPTextModelWithProjection" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "CLIPTokenizer" | |
| ], | |
| "tokenizer_2": [ | |
| "transformers", | |
| "CLIPTokenizer" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKL" | |
| ], | |
| "vae_decoder": [ | |
| null, | |
| null | |
| ] | |
| } | |