RUDRA

Radiometric Dynamic-Range Conditioning for HDR-Aware Diffusion Models FXTD Studios · github.com/fxtdstudios/RUDRA

Two independent families live in this repository.

family	what it does	files
sdr2hdr/	takes an ordinary 8-bit frame and reconstructs scene-linear HDR	6
decoders (root)	decode diffusion latents straight to scene-linear HDR, skipping the tone-mapped VAE	14

They share a name and a radiometric convention and nothing else. Pick the one that matches your problem.

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sdr2hdr/ — single-image inverse tone mapping

A compact U-Net that predicts a residual on an analytic inverse-ACES tone map, not the image itself. Six channels in (SDR RGB plus the analytic baseline), five out (a log-domain residual and two masks). The curve does the mapping; the network supplies only what a curve cannot know.

Start with sdr2hdr_shadow_v1.safetensors.

file	params	what it is
sdr2hdr_shadow_v1	1,217,318	the shipped model. v5 backbone plus a trained shadow gate, seed 20260901
sdr2hdr_shadow_s2	1,217,318	same recipe, seed 2
sdr2hdr_shadow_s3	1,217,318	same recipe, seed 3
sdr2hdr_image_v5	1,196,197	the backbone alone, no gate
sdr2hdr_image_v6	4,770,117	4× capacity ablation, 64 base channels
sdr2hdr_temporal_v1	37,011	temporal refiner. Not an SDR2HDRNet and will not load as one

Input is 8-bit normalised sRGB RGB. Output is scene-linear RGB normalised to 10,000 nits, with diffuse white at 203 (ITU-R BT.2408).

Loading

Each file carries its own architecture config in its safetensors metadata, so nothing else is needed to build the right network:

import json, torch
from safetensors import safe_open
from safetensors.torch import load_file
from rudra.sdr2hdr import SDR2HDRNet          # pip install -e . from the GitHub repo

path = "sdr2hdr/sdr2hdr_shadow_v1.safetensors"
with safe_open(path, framework="pt") as f:
    config = json.loads(f.metadata()["config"])
model = SDR2HDRNet.from_config(config)
model.load_state_dict(load_file(path), strict=True)
model.eval()

SHA256SUMS and index.json sit beside the weights.

What it scores

429 held-out frames at native 1280×720, PU21-PSNR and ColorVideoVDP JOD, against the analytic inverse-ACES baseline the model sits on top of. hard is the deployment condition (unknown tone curve, 4:2:0 chroma, banding, JPEG); clean is well-graded input the baseline already handles.

model	clean dB	clean JOD	hard dB	hard JOD
shadow_v1	+0.07	+0.113	+1.24	+0.389
shadow_s2	+0.71	+0.068	+0.96	+0.308
shadow_s3	+0.45	+0.089	+1.18	+0.358
mean ± sd	+0.41 ± 0.33	+0.090 ± 0.023	+1.12 ± 0.15	+0.352 ± 0.041
image_v5	−3.00	−0.046	+1.43	+0.443
image_v6	−2.75	+0.004	+0.96	+0.343

The three gate seeds are the only configurations that come out positive on all four measures.

Limitations, stated plainly

• No published method has been scored on our split. Every number above is against our own analytic baseline. Read them as internal progress, not as a comparison, until that gap is closed.
• The backbone alone regresses on clean input. image_v5 loses 3.0 dB of PU21-PSNR on well-graded frames, and the failure concentrates in low-dynamic-range scenes: the 60 worst average −10.76 dB at a median ground-truth peak of 238 nits, the 60 best +3.41 dB at 19,590 nits. The gate is what fixes it.
• The metrics disagree, and it matters. That same 3.0 dB loss is −0.046 JOD in CVVDP, far below a just-noticeable difference. Do not quote the PU21 row without the JOD beside it.
• The achievable gain is bounded by the input, not the model. A linear readout of the features explains 3% of the variance in the per-frame scale an oracle would use, and 79% of that variance sits within a condition rather than between. Capacity (4×), corpus (6×) and objective were each varied and none moved it.
• Three seeds is a spread, not a distribution.
• The temporal refiner is unevaluated. Its held-out set is 4 validation and 5 test clips of one scene each, too small to report.

Training data is documented in the GitHub README: Poly Haven HDRIs (CC0), HdM-HDR-2014 and HdM-HFR-2017 (academic licence), Netflix Chimera (CC BY 4.0), plus proprietary FXTD footage that is not redistributed.

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Decoders — diffusion latents to scene-linear HDR

Diffusion models are trained on tone-mapped images, so their VAEs decode to a world where nothing is brighter than white. These replace that decoder and return scene-linear radiance instead.

Two sizes per backbone: full reconstructs more faithfully, turbo is roughly a tenth the size and fast enough to sit in a live graph.

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

Then enable rudra_decoder in the Radiance HDR VAE Decode node and pick a decoder_size.

file	size
rudra_full_decoder_flux_ema.safetensors	21.5 MB
rudra_full_decoder_ltx-video_ema.safetensors	22.0 MB
rudra_full_decoder_sdxl_ema.safetensors	21.4 MB
rudra_full_decoder_wan_ema.safetensors	21.5 MB
rudra_full_decoder_zimage_ema.safetensors	21.5 MB
rudra_turbo_decoder_flux_ema.safetensors	2.2 MB
rudra_turbo_decoder_flux2_ema.safetensors	2.0 MB
rudra_turbo_decoder_flux2-klein_ema.safetensors	3.0 MB
rudra_turbo_decoder_ltx_ema.safetensors	2.4 MB
rudra_turbo_decoder_ltx-video_ema.safetensors	3.5 MB
rudra_turbo_decoder_qwen_ema.safetensors	2.2 MB
rudra_turbo_decoder_sdxl_ema.safetensors	2.1 MB
rudra_turbo_decoder_wan_ema.safetensors	2.2 MB
rudra_turbo_decoder_zimage_ema.safetensors	2.2 MB

Measured reconstruction quality, for the size we recommend per backbone. The table covers the recommended decoder per backbone, not every file above:

backbone	VAE latent	recommended	PSNR_log
Flux.1	16ch / 8×	full	29.77
Wan	16ch / 8×	full	32.45
LTX	128ch / 8×	full	25.47
SDXL	4ch / 8×	turbo	33.86
Qwen-Image	16ch / 8×	turbo	26.67
Flux.2 Klein	128ch / 16×	turbo	28.57

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Licence and citation

Apache 2.0. If the SDR→HDR work is useful in yours:

@misc{rudra2026,
  title  = {What an 8-Bit Frame Can and Cannot Say About the Scene Behind It:
            bounds for inverse tone mapping, and a gate that reaches one of them},
  author = {{FXTD Studios}},
  year   = {2026},
  note   = {https://github.com/fxtdstudios/RUDRA}
}