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library_name: diffusers
pipeline_tag: unconditional-image-generation
tags:
- diffusers
- lightningdit
- image-generation
- class-conditional
- imagenet
- flow-matching
license: mit
inference: true
widget:
- output:
url: LightningDit-XL-1-256/demo.png
language:
- en
---
# LightningDiT-diffusers
Diffusers-ready checkpoints for **LightningDiT** (VA-VAE–aligned latent diffusion with flow matching), converted from [`hustvl/lightningdit-xl-imagenet256-800ep`](https://huggingface.co/hustvl/lightningdit-xl-imagenet256-800ep) for local/offline use.
This root folder is a model collection that contains:
- `LightningDit-XL-1-256`
Each subfolder is a self-contained Diffusers model repo with:
- `pipeline.py` (`LightningDiTPipeline`)
- `transformer/transformer_lightningdit.py` and weights
- `scheduler/scheduler_config.json` (`FlowMatchHeunDiscreteScheduler`, `shift=0.3`)
- `vae/` ([`REPA-E/vavae-hf`](https://huggingface.co/REPA-E/vavae-hf))
Each variant embeds English `id2label` in `model_index.json`, so class labels can be passed as ImageNet ids or English synonym strings.
## Demo
Class-conditional sample (ImageNet class **207**, golden retriever), `LightningDiT-XL/1` at 256×256, 250 steps, CFG 6.7, `cfg_interval_start=0.125`, `timestep_shift=0.3`, seed 0.
## Model Paths
| Model | Resolution | Local path |
| --- | ---: | --- |
| LightningDiT-XL/1 | 256×256 | `./LightningDit-XL-1-256` |
## Inference
```python
import torch
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained(
"./LightningDit-XL-1-256",
trust_remote_code=True,
torch_dtype=torch.bfloat16,
).to("cuda")
class_id = pipe.get_label_ids("golden retriever")[0]
image = pipe(
class_labels=class_id,
num_inference_steps=250,
guidance_scale=6.7,
cfg_interval_start=0.125,
timestep_shift=0.3,
generator=torch.Generator(device="cuda").manual_seed(0),
).images[0]
```
## Citation
```bibtex
@inproceedings{yao2025reconstruction,
title={Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models},
author={Yao, Jingfeng and Yang, Bin and Wang, Xinggang},
booktitle={CVPR},
year={2025}
}
```
|