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 text_encoder_2/model.onnx.data from thingthatis/SDXL-Refiner-olive-optimized: direct link, hf CLI and curl.
- Browser
- Download file 2.78 GB
-
https://huggingface.co/thingthatis/SDXL-Refiner-olive-optimized/resolve/main/text_encoder_2/model.onnx.data
- Command line
-
hf download hf://thingthatis/SDXL-Refiner-olive-optimized/text_encoder_2/model.onnx.data
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curl -L -o model.onnx.data https://huggingface.co/thingthatis/SDXL-Refiner-olive-optimized/resolve/main/text_encoder_2/model.onnx.data
2.78 GB
- Xet hash:
- 8372d427a34c4769da7c029247d5d79eaf162562debe3484f76f44b9d20424f5
- Size of remote file:
- 2.78 GB
- SHA256:
- 3da7ac65349fbd092e836e3eeca2c22811317bc804fd70af157b4550f2d4bcb5
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