Instructions to use JGuo666/TurboClear with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JGuo666/TurboClear 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("JGuo666/TurboClear", 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
TurboClear weights
This repository contains the first TurboClear checkpoint release used by the public inference code.
Files
sdxl/state_dict.pth: one-step DMD student generator state.fusion/fusion_module.pth: learnable spatial fusion head trained after the generator.
The SDXL/ObjectClear base model is not duplicated here. Check its license and download it separately before inference. OBER data and evaluation images are not redistributed.
Local inference
Download the files, then pass the local sdxl directory as WEIGHT_PATH and
fusion/fusion_module.pth as FUSION_MODULE_PATH to
inference/inference_turboclear.sh.
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