Upload README.md with huggingface_hub

README.md

@@ -1,3 +1,73 @@
 ---
 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 |
+| 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/`:
+   ```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 terms.