Image-to-Image
Diffusers
ONNX
Safetensors
StableDiffusionXLInpaintPipeline
stable-diffusion-xl
inpainting
virtual try-on v2
Instructions to use SpringAI/TryonSpringHD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SpringAI/TryonSpringHD 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("SpringAI/TryonSpringHD", 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
Download scheduler/scheduler_config.json from SpringAI/TryonSpringHD: direct link, hf CLI and curl.
- Browser
- Download file 523 Bytes
-
https://huggingface.co/SpringAI/TryonSpringHD/resolve/main/scheduler/scheduler_config.json
- Command line
-
hf download hf://SpringAI/TryonSpringHD/scheduler/scheduler_config.json
-
curl -L -o scheduler_config.json https://huggingface.co/SpringAI/TryonSpringHD/resolve/main/scheduler/scheduler_config.json
523 Bytes
| { | |
| "_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 | |
| } | |