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fxtdstudios/
RUDRA

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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
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
LTX (2.3)	`rudra_full_decoder_ltx-video_ema.safetensors`	25.47
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)

Download into ComfyUI/models/radiance/:
bash
1huggingface-cli download fxtdstudios/RUDRA --include "rudra_*.safetensors" \ 2 --local-dir "ComfyUI/models/radiance"
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 t