---
title: LTX-2.5-22b-IC-LoRA-Day-To-Night
canonical_url: "https://www.modelscope.cn/models/Lightricks/LTX-2.5-22b-IC-LoRA-Day-To-Night"
md_url: "https://www.modelscope.cn/models/Lightricks/LTX-2.5-22b-IC-LoRA-Day-To-Night.md"
repository: Lightricks/LTX-2.5-22b-IC-LoRA-Day-To-Night
last_updated: 2026-10-22
license: other
base_model:
  - Lightricks/LTX-2.5
base_model_relation: adapter
parameters: 163.6M
tensor_type:
  - BF16
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
downloads: 126
stars: 0
tags:
  - ltx-video
  - ic-lora
  - ltx-2.5
  - video-to-video
  - day-to-night
  - relighting
  - diffusion-single-file
  - ltx
---

# LTX-2.5-22b-IC-LoRA-Day-To-Night

> LTX-2.5-22b-IC-LoRA-Day-To-Night - Lightricks 在 ModelScope 开源的模型。LTX-2.5 22B IC-LoRA Day-to-Night Relighting

Lightricks/LTX-2.5-22b-IC-LoRA-Day-To-Night 是 ModelScope 魔搭社区上的 163.6M 参数机器学习模型，采用 other 许可，基于 Lightricks/LTX-2.5 构建。

- **Repository**: Lightricks/LTX-2.5-22b-IC-LoRA-Day-To-Night
- **License**: other
- **Parameters**: 163.6M
- **Base model**: Lightricks/LTX-2.5
- **Tags**: ltx-video, ic-lora, ltx-2.5, video-to-video, day-to-night, relighting, diffusion-single-file, ltx
- **Downloads**: 126
- **Stars**: 0
- **Last updated**: 2026-10-22

Source: https://www.modelscope.cn/models/Lightricks/LTX-2.5-22b-IC-LoRA-Day-To-Night

---

# LTX-2.5 22B IC-LoRA Day-to-Night Relighting

This is a **Day-to-Night Relighting** IC-LoRA trained on top of **LTX-2.5-22B**, which re-renders a daytime video as the same shot at night while preserving composition, framing, camera movement, and subject motion frame-for-frame.

It is based on the [LTX-2.5](https://huggingface.co/Lightricks/LTX-2.5) foundation model.

## Example Outputs

<Gallery />

## Model Files

`ltx-2.5-22b-ic-lora-day-to-night-0.9.safetensors`

The recommended default and only shipped checkpoint (final, step 3000).

## Model Details

- **Base Model:** LTX-2.5-22B Video
- **Training Type:** IC-LoRA (video-to-video)
- **Control Type:** Daytime reference video — the model conditions on a day clip and relights it to night.
- **Reference Downscale Factor:** 1 (the reference video is processed at the same resolution as the output).
- **Pipeline details:** No special pre/post-processing. The daytime reference is resized + center-cropped to the target resolution at inference; outputs are generated directly in pixel space via the VAE.

## Intended Use & Out-of-Scope

**Intended use:** Converting short real-world day videos into a photorealistic nighttime version of the *same* shot, keeping motion and layout intact (e.g. outdoor scenes, landscapes, streets, people in motion). Best at the trained resolutions and ~4s (97 frames) clip length.

**Out of scope:** Inventing new scenes or camera moves, stylized/non-photoreal looks, and clips much longer than the training length (longer references can drift toward the end). Indoor and heavily artificial-light scenes work but are outside the primary training distribution.

## Control Signal Requirements

- **Control signal type:** Daytime video (the shot to be relit).
- **Expected input:** A single reference video.
- **Preprocessing:** Re-encode to a clean H.264 MP4 at the output frame rate (24 fps) before inference; resample the frame rate if the source differs (the reference loader does not resample temporally). Spatial resizing/cropping to the target resolution is handled automatically.
- **Alignment:** The generated night video matches the reference frame-for-frame. Output frame count should satisfy `frames % 8 == 1` and dimensions must be divisible by 32; the reference is sampled to the requested number of frames.
- **Mask support:** Not supported.

## How It Works

The reference (day) video is encoded by the VAE and supplied as in-context conditioning alongside the text prompt. The model generates a new video that keeps the reference's geometry and motion but replaces daytime lighting with night lighting. The prompt steers the *style* of night (e.g. moonlight, color temperature, brightness), while the reference dictates structure and movement.

## Usage

### 🔌 ComfyUI

1. Copy the LoRA weights into `models/loras`.
2. Load the **LTX-2.5-22B** base model and add `ltx-2.5-22b-ic-lora-day-to-night-0.9.safetensors` as the LoRA.
3. Start at strength `1.0` and adjust to taste.
4. Use an [IC-LoRA (video-to-video) workflow](https://github.com/Lightricks/ComfyUI-LTXVideo/blob/master/example_workflows/2.5/LTX-2.5_V2V_ICLoRA_Single_Stage_Distilled.json) from the [LTX-2 ComfyUI repository](https://github.com/Lightricks/ComfyUI-LTXVideo/), which already wires the reference-video conditioning nodes. Connect your daytime clip as the reference input. Since the reference downscale factor is 1, a standard reference loader is fine.

## Recommended Settings

- **LoRA strength / weight:** 1.0
- **Inference steps:** 30
- **Guidance scale:** 3.0–4.0 (lower ≈ brighter, more preserved detail; higher ≈ darker, stronger night)
- **Resolution & frames:** Trained at **768×448** (landscape) and **448×768** (portrait), **97 frames @ 24 fps** (~4s). These give the best results; longer clips are possible but may drift.
- **Spatial guidance:** STG mode `stg_v`, scale `1.0`, block `[29]`.
- **Prompting:** Describe the desired night look — e.g. *"A realistic nighttime scene … photorealistic moonlight, deep natural shadows. Only the lighting changes from day to night; identical composition, framing, camera movement and motion."* Recommended negative prompt: `daytime, bright sunlight, blue sky, overexposed, worst quality, inconsistent motion, blurry, jittery, distorted`. The reference drives structure, so the prompt mainly controls lighting/brightness/color temperature.

## References

- **Code:** [GitHub Repository](https://github.com/Lightricks/LTX-2)
- **ComfyUI:** [ComfyUI-LTXVideo](https://github.com/Lightricks/ComfyUI-LTXVideo/)

## Tips & Troubleshooting

- **Output too dark / crushed shadows:** Lower the guidance scale (e.g. 4.0 → 3.0) and add `pitch black, underexposed, crushed shadows, too dark` to the negative prompt.
- **Color temperature:** Steer it in the prompt — "warm tungsten interior light" vs. "cool white LED light" produce noticeably different night palettes.
- **Motion timing looks off:** Make sure the reference is resampled to 24 fps before inference; the reference loader reads frames at native rate without temporal resampling.
- **Drift on long clips:** For maximum fidelity, run the first ~4s (97 frames); longer references can lose consistency toward the end.

## Dataset

The model was trained using a proprietary dataset of 192 motion-aligned day/night video pairs, where each pair is the identical shot rendered in daylight and at night.

## Training

- **Technique:** IC-LoRA (rank 32, alpha 32, dropout 0.0) on the DiT transformer (attention q/k/v/out and feed-forward projections).
- **Hyperparameters:** bf16 mixed precision, AdamW, learning rate 2e-4, linear scheduler, batch size 1, gradient checkpointing, flow-matching with shifted-logit-normal timestep sampling, first-frame conditioning probability 0.2.
- **Steps:** 3000 (final checkpoint recommended).
- **Resolution buckets:** 768×448×97 and 448×768×97.
- **Infrastructure:** LTX-2 Community Trainer.

## License

See the **[LTX-2.x Community License](https://github.com/Lightricks/LTX-2/blob/main/LICENSE-2_x)** for full terms.

## Acknowledgments

- Base model by **[Lightricks](https://ltx.io/)**
- Training infrastructure: **[LTX-2 Community Trainer](https://github.com/Lightricks/LTX-2/tree/main/packages/ltx-trainer)**