Instructions to use dg845/cm_edm_test_class_cond with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dg845/cm_edm_test_class_cond with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dg845/cm_edm_test_class_cond", 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 | |
| A small random test class-conditional [EDM model](https://arxiv.org/pdf/2206.00364.pdf) checkpoint based on the [consistency models EDM implementation](https://github.com/openai/consistency_models). |