---
license: mit
library_name: diffusers
pipeline_tag: unconditional-image-generation
tags:
- diffusers
- jlt
- image-generation
- class-conditional
- flux2
- clean-latent
widget:
- text: golden retriever
  output:
    url: demo.png
language:
- en
---

# JLT-diffusers

Native [diffusers](https://github.com/huggingface/diffusers) checkpoints for **JLT** (Clean-Latent Prediction in Latent Diffusion Transformers). Paper: [arXiv:2605.27102](https://arxiv.org/abs/2605.27102). Code: [akatsuki-neo/JLT](https://github.com/akatsuki-neo/JLT). Each variant folder is self-contained:

- `pipeline.py` — `JLTPipeline`
- `scheduler/scheduling_jlt.py` — `JLTScheduler` (JLT time `t: 0 → 1`, Heun / Euler)
- `transformer/transformer_jlt.py` — `JLTTransformer2DModel`
- `vae/` — bundled `AutoencoderKLFlux2`

Load with PyPI `diffusers` and `trust_remote_code=True`. No extra JLT package is required.

## Demo

![JLT-B-1 demo](demo.png)

Class: *golden retriever* (ImageNet 207) — JLT-B/1 at 256×256, 50 Heun steps, `guidance_scale=2.9`, `noise_scale=1.0`, `torch_dtype=bfloat16`, seed 42. These checkpoints are trained and sampled at 256×256 only.

## Available checkpoints

| Checkpoint | Path | Resolution | Latent | Recommended CFG |
| --- | --- | --- | --- | ---: |
| JLT-B/1 | [`JLT-B-1/`](JLT-B-1/) | 256×256 | FLUX.2, patch /1 | 2.9 |
| JLT-L/1 | [`JLT-L-1/`](JLT-L-1/) | 256×256 | FLUX.2, patch /1 | 2.9 |
| JLT-H/1 | [`JLT-H-1/`](JLT-H-1/) | 256×256 | FLUX.2, patch /1 | 2.9 |

## ImageNet class labels

Each variant keeps an English `id2label` map in `model_index.json`.

- `pipe.id2label` — id → English label
- `pipe.labels` — synonym → id
- `pipe.get_label_ids("golden retriever")`
- `pipe(class_labels="golden retriever", ...)` — string labels resolve automatically

## Inference

```python
import torch
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained(
    "BiliSakura/JLT-diffusers",
    subfolder="JLT-B-1",  # or "JLT-L-1" / "JLT-H-1"
    custom_pipeline="pipeline.py",
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
).to("cuda")

print(pipe.id2label[207])
image = pipe(
    class_labels="golden retriever",
    num_inference_steps=50,
    guidance_scale=2.9,
    generator=torch.Generator(device="cuda").manual_seed(42),
).images[0]
image.save("demo.png")
```

## Citation

```bibtex
@article{fu2026jlt,
  title={{JLT}: {C}lean-{L}atent {P}rediction in {L}atent {D}iffusion {T}ransformers},
  author={Fu, Funing and Wang, Tenghui and Zhou, Guanyu and Cen, Junyong and Zhu, Qichao},
  journal = {arXiv preprint arXiv:2605.27102},
  year={2026}
}
```