Scalable GANs with Transformers
Paper • 2509.24935 • Published • 5
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("BiliSakura/GAT-diffusers", dtype=torch.bfloat16, device_map="cuda")
prompt = "Turn this cat into a dog"
input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")
image = pipe(image=input_image, prompt=prompt).images[0]BiliSakura/GAT-diffusers
Self-contained Generative Adversarial Transformers (GAT) 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:
Variants
| Model | Resolution | Params | Checkpoint |
|---|---|---|---|
| GAT-XL/2 | 256×256 | 675M | GAT-XL-2-256/ |
Usage
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
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
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