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
File size: 890 Bytes
ba20fec | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | {
"_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
]
}
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