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
Fix RAEDiT Stage-2 positional embedding export
Browse files
conversion_metadata.json
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{
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"source": {
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"weights_repo_or_path": "nyu-visionx/RAE-collections",
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"config_repo_or_path": "/home/ubuntu/
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"config_path": "configs/stage2/training/ImageNet256/DiTDH-S_DINOv2-B.yaml",
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"vae_model_name_or_path": "nyu-visionx/RAE-dinov2-wReg-base-ViTXL-n08"
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},
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{
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"source": {
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"weights_repo_or_path": "nyu-visionx/RAE-collections",
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"config_repo_or_path": "/home/ubuntu/rae-upstream-configs",
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"config_path": "configs/stage2/training/ImageNet256/DiTDH-S_DINOv2-B.yaml",
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"vae_model_name_or_path": "nyu-visionx/RAE-dinov2-wReg-base-ViTXL-n08"
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},
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transformer/config.json
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{
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"_class_name": "RAEDiT2DModel",
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"_diffusers_version": "0.38.0.dev0",
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"_name_or_path": "/
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"class_dropout_prob": 0.1,
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"depth": [
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12,
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"_class_name": "RAEDiT2DModel",
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"_diffusers_version": "0.38.0.dev0",
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"_name_or_path": "/tmp/tmppwg9zch4/transformer",
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"class_dropout_prob": 0.1,
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"depth": [
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12,
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transformer/diffusion_pytorch_model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 786240304
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version https://git-lfs.github.com/spec/v1
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oid sha256:e5d5b95a07bed32ef39f1958e3dcf436bccda3c92b4ed9978c3105b25f4ace4b
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size 786240304
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