| --- |
| license: apache-2.0 |
| library_name: comfyui |
| pipeline_tag: image-to-image |
| tags: |
| - hdr |
| - diffusion |
| - comfyui |
| - vae-decoder |
| - openexr |
| - radiance |
| - rudra |
| --- |
| |
| # 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) |
| |
| 1. Download into `ComfyUI/models/radiance/`: |
| ```bash |
| 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 t |
