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
| 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](https://github.com/openai/consistency_models/blob/main/model-card.md) for more information about consistency models. |