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license: mit
library_name: diffusers
pipeline_tag: image-to-image
tags:
- diffusers
- gat
- gan
- class-conditional
- imagenet
inference: true
widget:
- output:
url: GAT-XL-2-256/demo.png
language:
- en
---
# BiliSakura/GAT-diffusers
Self-contained [Generative Adversarial Transformers (GAT)](https://arxiv.org/abs/2509.24935) checkpoints for Hugging Face diffusers.
Converted from the official GAT XL-2 checkpoint using `libs/GAT-diffusers/scripts/convert_gat_checkpoint.py`.
GAT performs one-step class-conditional image generation in Stable Diffusion VAE latent space (`sd-vae-ft-ema`).
## Demo
`GAT-XL-2-256` — class **207** (*golden retriever*), seed **0**, `truncation_psi=0.3`:
<p align="center">
<img src="GAT-XL-2-256/demo.png" alt="GAT-XL-2-256 demo (class 207, seed 0)" width="256"/>
</p>
## Variants
| Model | Resolution | Params | Checkpoint |
| --- | --- | --- | --- |
| GAT-XL/2 | 256×256 | 675M | `GAT-XL-2-256/` |
## Usage
```python
from pathlib import Path
import torch
from diffusers import DiffusionPipeline
model_dir = Path("./GAT-XL-2-256").resolve()
pipe = DiffusionPipeline.from_pretrained(
str(model_dir),
custom_pipeline=str(model_dir / "pipeline.py"),
trust_remote_code=True,
torch_dtype=torch.float32,
local_files_only=True,
).to("cuda")
image = pipe(
class_labels="golden retriever",
truncation_psi=0.3,
generator=torch.Generator("cuda").manual_seed(0),
).images[0]
```
## Conversion
```bash
conda activate rsgen
python libs/GAT-diffusers/scripts/convert_gat_checkpoint.py \
--ckpt models/BiliSakura/GAT-diffusers/gat-xl-2-256.pt \
--output-dir models/BiliSakura/GAT-diffusers/GAT-XL-2-256 \
--resolution 256
```
|