RUDRA — HDR Decoders for Diffusion Models

Radiometric Dynamic-Range Conditioning for HDR-Aware Diffusion Models FXTD Studios / Radiance Research

Distilled decoders that turn diffusion latents into scene-linear HDR / OpenEXR instead of tone-mapped SDR. They replace the standard VAE decode inside the Radiance HDR VAE Decode ComfyUI node, preserving highlights, exposure, and wide-gamut color.

➡️ Code, training, and docs: https://github.com/fxtdstudios/RUDRA

Files

Each file is a trained RadianceTurboDecoder / RadianceFullDecoder for one backbone:

rudra_{turbo|full}_decoder_{backbone}_ema.safetensors

Backbone	Recommended file	Quality (PSNR_log)
Flux.1	rudra_full_decoder_flux_ema.safetensors	29.77
Wan	rudra_full_decoder_wan_ema.safetensors	32.45
LTX	rudra_full_decoder_ltx-video_ema.safetensors	25.47
SDXL	rudra_turbo_decoder_sdxl_ema.safetensors	33.86
Qwen-Image	rudra_turbo_decoder_qwen_ema.safetensors	26.67
Flux.2 Klein	rudra_turbo_decoder_flux2-klein_ema.safetensors	28.57
Z-Image	use the Flux decoder (shares the FLUX.1 VAE)	—

turbo (0.5 M params) is fast and strong on simple latents (SDXL); full (5.6 M) wins on Flux/Wan/LTX. Both are provided where trained.

Usage (ComfyUI)

1. Download into ComfyUI/models/radiance/:

   huggingface-cli download fxtdstudios/RUDRA --include "rudra_*.safetensors" \
       --local-dir "ComfyUI/models/radiance"
   

2. In the Radiance HDR VAE Decode node: set rudra_decoder = Enabled, pick decoder_size (rudra_turbo or rudra_full) per the table above, and set target_space to your output color space (Linear / ACEScg / Rec.2020 / LogC4…).

Notes

• Backbone-specific: a decoder is tied to its VAE latent space — use the matching file for the model feeding the node (Flux decoder for a Flux workflow, etc.).
• Flux.2 Klein uses a 128-channel / 16× VAE, so its decoder has an extra upsample stage (requires the updated fast_vae.py from the GitHub repo).
• Quality is reported as held-out log-space PSNR; perceptual HDR evaluation uses ColorVideoVDP (JOD). SDXL/Qwen/Klein were trained on a smaller pair set and can be improved with more data.

Citation

RUDRA: Radiometric Dynamic-Range Conditioning for HDR-Aware Diffusion Models. FXTD Studios / Radiance Research.

License: change the license: field above to match your release terms.