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_encoder/model.onnx from thingthatis/SDXL-Refiner-olive-optimized: direct link, hf CLI and curl.
- Browser
- Download file 137 MB
-
https://huggingface.co/thingthatis/SDXL-Refiner-olive-optimized/resolve/main/vae_encoder/model.onnx
- Command line
-
hf download hf://thingthatis/SDXL-Refiner-olive-optimized/vae_encoder/model.onnx
-
curl -L -o model.onnx https://huggingface.co/thingthatis/SDXL-Refiner-olive-optimized/resolve/main/vae_encoder/model.onnx
137 MB
- Xet hash:
- 30926e5a95eb1b4ec5d963e2b0f7652601e78df416b4c889a68b94b4b4512433
- Size of remote file:
- 137 MB
- SHA256:
- 3f9b98c7d05fbb259a8c43186e8cfa7f7771a30b22d18d1533c5c1e22b9642b7
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