---
license: apache-2.0
base_model: Lightricks/LTX-Video
tags:
  - ltx-video
  - ltx-2.3
  - lora
  - ic-lora
  - sdr-to-hdr
  - hdr
  - color-grading
  - video-to-video
  - logc3
pipeline_tag: video-to-video
library_name: diffusers
---

# LTX-2.3 SDR → HDR IC-LoRA

A LoRA adapter for **LTX-Video 2.3 (22B)** that converts SDR video into HDR (LogC3 encoded), trained as an **IC-LoRA** (in-context LoRA) using the `video_to_video` strategy from `ltxv-trainer`.

The model takes an SDR clip as a conditioning reference and generates a matching HDR (LogC3) version, suitable for grading downstream in DaVinci Resolve, Baselight, or Nuke.

Inspired by Lightricks' **LumiVid** paper ([arXiv:2604.11788](https://arxiv.org/abs/2604.11788)) — same core idea (LogC3 target, IC-LoRA-style reference conditioning, ~10K steps).

## Checkpoint

`lora_weights_step_07000.safetensors` — step 7,000 of a planned 10,000-step run.

## Usage (ComfyUI)

Use `LTXICLoRALoaderModelOnly` from [Lightricks/ComfyUI-LTXVideo](https://github.com/Lightricks/ComfyUI-LTXVideo):

1. Load LTX-2.3 base model
2. Load this LoRA via `LTXICLoRALoaderModelOnly`
3. Connect your SDR clip as the **reference video**
4. Run the IC-LoRA pipeline — Stage 1 (low-res, LoRA active) → Stage 2 (upsample, no LoRA)

**No trigger word** — the LoRA is always active when loaded.

**Recommended CFG:** up to **1.5** works well.

## Training Details

| | |
|---|---|
| Base model | LTX-Video 2.3 22B (`ltx-2.3-22b-dev`) |
| Text encoder | Gemma 3 12B |
| Strategy | IC-LoRA (`video_to_video` in [ltxv-trainer](https://github.com/Lightricks/LTX-Video-Trainer)) |
| LoRA rank / alpha | 32 / 32 |
| Target modules | `attn1/2.to_{k,q,v,out.0}`, `ff.net.0.proj`, `ff.net.2` |
| Resolution | 1280 × 736 |
| Frames per clip | 49 |
| Batch size | 1 |
| Learning rate | 2e-4, cosine schedule |
| Steps | 7,000 (target 10,000) |
| Precision | bf16 + gradient checkpointing |
| Log curve | **LogC3** (validated optimal via KL divergence in LumiVid) |

## Dataset

- **200 clips** rendered from PolyHaven HDRIs with virtual camera moves
- Paired SDR ↔ LogC3 HDR clips
- SDR side intentionally degraded (compression, blur, contrast, white-balance shift) to mimic real-world camera capture

## Intended Use

- Converting SDR (Rec.709) video to LogC3 HDR for further grading
- VFX / DI pipelines that already speak LogC3 (Nuke, Baselight, DaVinci Resolve)
- Outputs are designed to be exported to ProRes 4444 or OpenEXR sequences

## Limitations

- Trained primarily on PolyHaven HDRI environments — limited human/motion diversity in v1
- Output is **LogC3, not display-referred HDR** — you still need a grading pass to map to PQ/HLG/Rec.2100
- The model can do a surprisingly good job of *hallucinating* believable detail back into clipped highlights (skies, practicals, blown-out windows), but this is generative reconstruction — it is not faithful recovery of the original photons. Use with eyes open in any pipeline where photographic accuracy matters.
- Inference is two-stage (low-res → upsample); expect LTX-2.3's typical compute footprint

## Citation

If this is useful, please reference the LumiVid paper that inspired the approach:

```
@article{lumivid2026,
  title={LumiVid: Distilling LDR Video Diffusion Models for HDR Video Generation},
  author={Lightricks},
  journal={arXiv:2604.11788},
  year={2026}
}
```

## License

Apache 2.0. Base model license (LTX-Video) applies to inference use.