Instructions to use yisol/IDM-VTON with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use yisol/IDM-VTON with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import AutoPipelineForInpainting from diffusers.utils import load_image # switch to "mps" for apple devices pipe = AutoPipelineForInpainting.from_pretrained("yisol/IDM-VTON", dtype=torch.float16, device_map="cuda") img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" image = load_image(img_url).resize((1024, 1024)) mask_image = load_image(mask_url).resize((1024, 1024)) prompt = "a tiger sitting on a park bench" generator = torch.Generator(device="cuda").manual_seed(0) image = pipe( prompt=prompt, image=image, mask_image=mask_image, guidance_scale=8.0, num_inference_steps=20, # steps between 15 and 30 work well for us strength=0.99, # make sure to use `strength` below 1.0 generator=generator, ).images[0] - Notebooks
- Google Colab
- Kaggle
Using a VTON model for real time production purpose
I am searching a way, in which I can use the VTON model inside an app for real-time production purpose. I came to know that to do this, I have to pre-process my images with model image, cloth image, segmentation masks, pose estimation key points etc. However, the client wants if I can use any pre-trained model on my images and test the result on 10 images.
How can I do that without any UIs like ComfyUI or Stable Diffusion WebUI. I am not finding any concrete source of creating this model in my local system. I have tried to use Pose estimation + key points detection + Grounded SAM + Thin Plate Spline algorithm to achive the result. But, I am not successful in this yet.
Any advice/code/reference you can provide or suggest will help me a lot