Unconditional Image Generation
Diffusers
Safetensors
English
edm2
image-generation
class-conditional
imagenet
Instructions to use BiliSakura/EDM2-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/EDM2-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BiliSakura/EDM2-diffusers", 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

- Xet hash:
- 8ef043a4acf3756ce6d81e7ce7620b8a0aa469f708d3add2797ce7ec585de33c
- Size of remote file:
- 375 kB
- SHA256:
- 9da85f6cb881c112c3240fa5f62b3331af9700a656b882e01ebf9df4ea05660f
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