LUA-FLUX

LUA-FLUX: Latent Upscale Adapter for FLUX

<p align="center"> <a href="https://github.com/vaskers5/LUA"><img src="https://img.shields.io/badge/GitHub-Code-blue" alt="GitHub"></a> <a href="https://arxiv.org/abs/2511.10629"><img src="https://img.shields.io/badge/arXiv-2511.10629-b31b1b" alt="arXiv"></a> <a href="https://huggingface.co/papers/2511.10629"><img src="https://img.shields.io/badge/HuggingFace-Paper-orange" alt="Paper"></a> <a href="https://www.youtube.com/watch?v=7jV9NYu32dk"><img src="https://img.shields.io/badge/YouTube-Demo-red" alt="Demo"></a> </p>

Official weights for LUA (Latent Upscale Adapter) from the paper "One Small Step in Latent, One Giant Leap for Pixels".

LUA is a lightweight (~250M params) SwinIR-based module that upscales diffusion model latents before VAE decoding. It enables high-resolution image generation (2K/4K) from a 1024x1024 base generation, significantly faster than generating at full resolution directly.

Key Features

• Drop-in adapter: works with any FLUX model, no fine-tuning needed
• Dual-head: x2 (1024->2048) and x4 (1024->4096) upscaling from a single model
• Fast: operates in latent space (~250ms for x2, ~900ms for x4 on A100)
• Quality: comparable perceptual quality to direct high-resolution generation

Quick Start

from lua import load_model, upscale_latent

# Auto-downloads this checkpoint
lua_model = load_model(device="cuda")

# Upscale a FLUX VAE latent (B, 16, H, W)
upscaled = upscale_latent(lua_model, latent, head="x2")  # or "x4"

See the GitHub repo for full usage examples including CLI and Gradio demo.

Model Details

Citation

@article{razin2024lua,
  title={One Small Step in Latent, One Giant Leap for Pixels: Fast Latent Upscale Adapter for Your Diffusion Models},
  author={Razin, Aleksandr and Kazantsev, Danil and Makarov, Ilya},
  journal={arXiv preprint arXiv:2511.10629},
  year={2024}
}

License

Apache 2.0