---
license: other
license_name: ltx-2-community-license
license_link: https://huggingface.co/Lightricks/LTX-2.3
base_model: Lightricks/LTX-2.3
tags:
  - lora
  - ic-lora
  - ltx-video
  - video-to-video
  - video-restoration
  - lens-flare-removal
  - vfx
language:
  - en
pipeline_tag: video-to-video
---

# LensRemover — LTX-2.3-22B IC-LoRA for Lens-Flare & Optical-Artifact Removal

**LensRemover** is a video-to-video IC-LoRA for **LTX-2.3-22B** that removes **lens flares, veiling glare,lens dirt and another optical artifacts** from footage while keeping the underlying scene intact. You feed it the *flared* clip as the conditioning reference, and it regenerates a *clean* version of the same shot.

It is a **dual-encoding** LoRA — a single model that works on both **sRGB / Rec.709** and **ARRI LogC3** inputs, selected at inference with a trigger word.

I used Slava Sexton - OCIO Management in the workflow to handle the colors. It’s the best I𝗢/𝗖𝗼𝗹𝗼𝗿 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 for Comfy out there. 

𝗖𝗼𝗺𝗳𝘆𝗨𝗜-𝗢𝗖𝗜𝗢: https://github.com/SlavaSexton/ComfyUI-OCIO

## 🎬 Showcase

All clips below are on a **holdout scene the model never saw in training**, shown as `INPUT (flare) | LensRemover (clean)` side-by-side.

<video controls autoplay loop muted src="https://huggingface.co/JanKanta/ltx-2.3-22b-ic-lora-lens_remover/resolve/main/Showcase.mp4" width="100%"></video>

**sRGB / Rec.709**

<video controls autoplay loop muted src="https://huggingface.co/JanKanta/ltx-2.3-22b-ic-lora-lens_remover/resolve/main/srgb.mp4" width="100%"></video>

**ARRI LogC3**

<video controls autoplay loop muted src="https://huggingface.co/JanKanta/ltx-2.3-22b-ic-lora-lens_remover/resolve/main/logc3.mp4" width="100%"></video>

The flare is removed in both color encodings, and the composition, subject and motion are preserved — no identity drift.

## 🚀 How to Use It

This is a **V2V IC-LoRA**: the conditioning is a *reference video* (the flared clip), not an image.

1. Load base **LTX-2.3-22B** + this LoRA `lens-eraiser-ltx23-ic-lora.safetensors` (strength **1.0**).
2. Set the **reference video** = your flared clip.
3. Prompt with the **trigger word that matches your input's color encoding**:
   - sRGB / Rec.709 input → start the prompt with **`rgb, `**
   - ARRI LogC3 input → start the prompt with **`logc3, `**
4. Add a short scene description after the trigger, e.g. `rgb, a woman walking through an autumn forest`.
5. Generate → you get the cleaned version of the shot.

> **Tip:** For LogC3 / EXR pipelines, encode your footage to LogC3 first (matching the training curve), run with the `logc3` trigger, then decode back. Because sRGB ↔ LogC3 is a reversible curve, one LoRA covers both worlds.

## 🧩 Workflow

A ready-to-run **ComfyUI workflow** is included: `LensRemover_comfyui_workflow.json`.

- Base model: `ltx-2.3-22b-dev` + this IC-LoRA (rank 32, α 32)
- Reference conditioning at full resolution (`downscale_factor = 1`)
- Recommended working resolution: **960×544, 97 frames, 24 fps** (the training bucket)
- Text encoder: Gemma-3-12B

## 📐 Model Details

| | |
|---|---|
| Base model | LTX-2.3-22B (`dev`) |
| Type | IC-LoRA, video-to-video |
| Rank / Alpha | 32 / 32 |
| Target modules | `attn1/attn2` (q, k, v, out) + `ff.net` |
| Conditioning | reference video (flared → clean), reference strength 1.0 |
| Resolution | 960×544 × 97 frames @ 24 fps |
| Trigger words | `rgb` (sRGB/Rec.709), `logc3` (ARRI LogC3) |
| File | `lens-eraiser-ltx23-ic-lora.safetensors` (~327 MB) |

## 📊 Dataset & Training

- **100 paired clips** = **50 unique scenes, each in two color encodings** (sRGB/Rec.709 and ARRI LogC3). Each pair maps a **flared** clip → its **clean** target.
- **Dual-encoding recipe:** the LogC3 pairs reuse the caption of their sRGB twin, with a split **trigger word baked into the caption** (`rgb, …` / `logc3, …`) so one model learns to route by encoding.
- 98 pairs trained, 1 scene held out **in both encodings** for validation.
- **3000 steps** on a single 80 GB GPU, bf16, gradient checkpointing.
- Captions generated with a multimodal model and lightly cleaned.

The model converges early — flare removal is already effective within a few hundred steps — and stays stable through 3000 with no over-fitting or drift.

## ⚠️ Limitations

- **Latent Space:** Encoded dates are not the same as the output in terms of details — they need to be polished in post-production, but a wide range of dates are there, and they are pretty close to the input.
- **Flare variety:** trained on a specific set of added optical effects; unfamiliar real-lens flares may generalize less well.
- **Resolution:** trained at 960×544 — for larger footage, downscale to a working resolution (or tile) and match the aspect.I tested it on FullHD/2K and it is quite stable in 1 step, but 4K is quite unstable.
- **Generative model:** this regenerates the shot guided by the reference; on high-value footage expect the possibility of subtle content changes. Full-resolution reference conditioning keeps fidelity high.

## 🙏 Attribution

Built on **LTX-2.3-22B** by Lightricks, trained with the official LTX-2 IC-LoRA training pipeline. Released under the **LTX-2 Community License** — please follow the base model's terms.

*Feedback welcome — if you try it on your footage, let me know how it holds up.*