Unconditional Image Generation
Diffusers
Safetensors
RAEDiTPipeline
rae
rae-dit
diffusion-transformer
imagenet-256
arxiv:2510.11690
Instructions to use plugyawn/rae-dit-s-ep14-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use plugyawn/rae-dit-s-ep14-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("plugyawn/rae-dit-s-ep14-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
| { | |
| "_class_name": "RAEDiTPipeline", | |
| "_diffusers_version": "0.38.0.dev0", | |
| "id2label": null, | |
| "scheduler": [ | |
| "diffusers", | |
| "FlowMatchEulerDiscreteScheduler" | |
| ], | |
| "transformer": [ | |
| "diffusers", | |
| "RAEDiT2DModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderRAE" | |
| ] | |
| } | |