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license: cc-by-nc-4.0
library_name: diffusers
pipeline_tag: text-to-image
tags:
- diffusers
- dit
- image-generation
- class-conditional
- imagenet
widget:
- output:
url: DiT-XL-2-512/demo.png
language:
- en
---
# BiliSakura/DiT-diffusers
Diffusers-ready checkpoints for **Diffusion Transformers (DiT)**, re-packaged for local/offline use with a project-owned custom `DiTPipeline`.
> **Re-distribution notice:** weights and configs in this repo are re-distributed from [`facebook/DiT-XL-2-512`](https://huggingface.co/facebook/DiT-XL-2-512). Original work: [Scalable Diffusion Models with Transformers (ICCV 2023)](https://openaccess.thecvf.com/content/ICCV2023/html/Peebles_Scalable_Diffusion_Models_with_Transformers_ICCV_2023_paper.html). License: [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).
This repo is derived from the development bundle in [Visual-Generative-Foundation-Model-Collection](https://github.com/Bili-Sakura/Visual-Generative-Foundation-Model-Collection). Inference only needs:
- This model repo (`BiliSakura/DiT-diffusers`)
- PyPI `diffusers`, `torch`, `safetensors`
## Important note
This repo intentionally does **not** use Diffusers built-in `diffusers.DiTPipeline`.
Instead, each model subfolder contains `pipeline.py` with a custom class named `DiTPipeline`.
## Available checkpoints
| Subfolder | Resolution | Source |
| --- | --- | --- |
| [`DiT-XL-2-256/`](DiT-XL-2-256/) | 256×256 | [`facebook/DiT-XL-2-256`](https://huggingface.co/facebook/DiT-XL-2-256) |
| [`DiT-XL-2-512/`](DiT-XL-2-512/) | 512×512 | [`facebook/DiT-XL-2-512`](https://huggingface.co/facebook/DiT-XL-2-512) |
Each subfolder is a self-contained Diffusers model repo with:
- `model_index.json` (includes ImageNet `id2label`)
- `pipeline.py` (custom `DiTPipeline`)
- `transformer/diffusion_pytorch_model.safetensors`
- `vae/diffusion_pytorch_model.safetensors`
- `scheduler/scheduler_config.json`
## Demo
```python
from pathlib import Path
import torch
from diffusers import DiffusionPipeline
model_dir = Path("path/to/DiT-XL-2-512")
pipe = DiffusionPipeline.from_pretrained(
str(model_dir),
local_files_only=True,
custom_pipeline=str(model_dir / "pipeline.py"),
trust_remote_code=True,
torch_dtype=torch.bfloat16,
).to("cuda")
generator = torch.Generator(device="cuda").manual_seed(0)
out = pipe(
class_labels=[207],
num_inference_steps=250,
guidance_scale=4.0,
generator=generator,
).images[0]
out
```
## Repo layout
```text
BiliSakura/DiT-diffusers/
├── README.md
├── DiT-XL-2-256/
└── DiT-XL-2-512/
├── README.md
├── model_index.json
├── pipeline.py
├── demo.png
├── transformer/
│ ├── config.json
│ └── diffusion_pytorch_model.safetensors
├── vae/
│ ├── config.json
│ └── diffusion_pytorch_model.safetensors
└── scheduler/
└── scheduler_config.json
```
|