Instructions to use thingthatis/SDXL-Refiner-olive-optimized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use thingthatis/SDXL-Refiner-olive-optimized 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("thingthatis/SDXL-Refiner-olive-optimized", 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
Download vae_decoder/model.onnx from thingthatis/SDXL-Refiner-olive-optimized: direct link, hf CLI and curl.
- Browser
- Download file 198 MB
-
https://huggingface.co/thingthatis/SDXL-Refiner-olive-optimized/resolve/main/vae_decoder/model.onnx
- Command line
-
hf download hf://thingthatis/SDXL-Refiner-olive-optimized/vae_decoder/model.onnx
-
curl -L -o model.onnx https://huggingface.co/thingthatis/SDXL-Refiner-olive-optimized/resolve/main/vae_decoder/model.onnx
198 MB
- Xet hash:
- b708334c2caacc1da5d9b29234b8b5e10b782ccd1750e54f579236629cfca564
- Size of remote file:
- 198 MB
- SHA256:
- 5beeb46a31b7dceb1422add50f092bdcd7a41b9b4599f7a4a01e71892b1614e9
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