Instructions to use diffusers/consistency-models-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use diffusers/consistency-models-test with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("diffusers/consistency-models-test", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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Download README.md from diffusers/consistency-models-test: direct link, hf CLI and curl.
- Browser
- Download file 472 Bytes
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https://huggingface.co/diffusers/consistency-models-test/resolve/main/README.md
- Command line
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hf download hf://diffusers/consistency-models-test/README.md
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curl -L -o README.md https://huggingface.co/diffusers/consistency-models-test/resolve/main/README.md
472 Bytes
metadata
license: mit
duplicated_from: dg845/consistency-models-test
These UNet2DModel checkpoints are small randomly-initialized U-Nets which accept 32x32 images for use in testing consistency models.
"test_unet_class_cond" is class-conditional (e.g. contains a class label embedding), while "test_unet" is not.
Please refer to the original model card for more information about consistency models.