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: 1,200 Bytes
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"in_channels": 16,
"out_channels": 3,
"up_block_types": [
"DecoderUpBlock2D",
"DecoderUpBlock2D",
"DecoderUpBlock2D",
"DecoderUpBlock2D"
],
"block_out_channels": [
64,
128,
256,
256
],
"layers_per_block": 2,
"norm_num_groups": 32,
"act_fn": "hardswish",
"mid_block_add_attention": false,
"conv_in_module": "Conv2d",
"conv_in_dw_bias": true,
"conv_in_pw_bias": false,
"conv_out_module": "Conv2d",
"conv_out_dw_bias": true,
"conv_out_pw_bias": false,
"use_mid_block": false,
"mid_block_type": "DecoderUNetMidBlock2D",
"mid_block_use_additional_resnet": true,
"resnet_middle_expansion": null,
"resnet_module": "DecoderResnetBlock2D",
"resnet_conv_module": "DecoderSepConv2d",
"attn_module": "Attention",
"attn_processor_type": "AttnProcessor2_0",
"kv_heads": 1,
"qk_norm": "layer_norm",
"layers_per_blocks": [
3,
3,
2,
1
],
"backward_output_channels": true,
"upsample_module": "Upsample2D",
"upsample_conv_module": "Conv2d",
"resnet_dw_bias": true,
"resnet_pw_bias": true
} |