Unconditional Image Generation
Diffusers
Safetensors
English
afm
adversarial-flow-models
class-conditional
imagenet
Instructions to use BiliSakura/AFM-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/AFM-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/AFM-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
Ctrl+K
- AFM-B-2-1NFE-guided
- AFM-B-2-1NFE-noguide
- AFM-L-2-1NFE-guided
- AFM-L-2-1NFE-noguide
- AFM-M-2-1NFE-guided
- AFM-M-2-1NFE-noguide
- AFM-XL-2-112layer-1NFE-guided
- AFM-XL-2-1NFE-guided
- AFM-XL-2-1NFE-noguide
- AFM-XL-2-2NFE-guided
- AFM-XL-2-2NFE-noguide
- AFM-XL-2-4NFE-guided
- AFM-XL-2-56layer-1NFE-guided
- 2.33 kB
- 3.75 kB