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IC-Light

(★ 8,502)

More relighting!

파일 탐색기

최종 버전 다운로드 (.zip)

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.gitignore
briarmbg.py
db_examples.py
gradio_demo.py
gradio_demo_bg.py
LICENSE
README.md
requirements.txt

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/lllyasviel/IC-Light

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd IC-Light

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. 공식 설치 스크립트

쉬움 추천

사전 준비물

Python 3 pip 명령어를 쓰려면 Python이 필요합니다.

pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

✅ 설치 후 새 터미널을 열고, 프로그램의 버전 확인 명령(예: --version)으로 정상 설치됐는지 확인하세요.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

3. Python

쉬움

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

pip install -r requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

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# IC-Light

IC-Light is a project to manipulate the illumination of images.

The name "IC-Light" stands for **"Imposing Consistent Light"** (we will briefly describe this at the end of this page).

Currently, we release two types of models: text-conditioned relighting model and background-conditioned model. Both types take foreground images as inputs.

**Note that "iclightai dot com" is a scam website. They have no relationship with us. Do not give scam websites money! This GitHub repo is the only official IC-Light.**

# News

[Alternative model](https://github.com/lllyasviel/IC-Light/discussions/109) for stronger illumination modifications.

Some news about flux is [here](https://github.com/lllyasviel/IC-Light/discussions/98). (A fix [update](https://github.com/lllyasviel/IC-Light/discussions/98#discussioncomment-11370266) is added at Nov 25, more demos will be uploaded soon.)

# Get Started

Below script will run the text-conditioned relighting model:

git clone https://github.com/lllyasviel/IC-Light.git
    cd IC-Light
    conda create -n iclight python=3.10
    conda activate iclight
    pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121
    pip install -r requirements.txt
    python gradio_demo.py

Or, to use background-conditioned demo:

python gradio_demo_bg.py

Model downloading is automatic.

Note that the "gradio_demo.py" has an official [huggingFace Space here](https://huggingface.co/spaces/lllyasviel/IC-Light).

# Screenshot

### Text-Conditioned Model

(Note that the "Lighting Preference" are just initial latents - eg., if the Lighting Preference is "Left" then initial latent is left white right black.)

---

**Prompt: beautiful woman, detailed face, warm atmosphere, at home, bedroom**

Lighting Preference: Left

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/87265483-aa26-4d2e-897d-b58892f5fdd7)

---

**Prompt: beautiful woman, detailed face, sunshine from window**

Lighting Preference: Left

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/148c4a6d-82e7-4e3a-bf44-5c9a24538afc)

---

**beautiful woman, detailed face, neon, Wong Kar-wai, warm**

Lighting Preference: Left

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/f53c9de2-534a-42f4-8272-6d16a021fc01)

---

**Prompt: beautiful woman, detailed face, sunshine, outdoor, warm atmosphere**

Lighting Preference: Right

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/25d6ea24-a736-4a0b-b42d-700fe8b2101e)

---

**Prompt: beautiful woman, detailed face, sunshine, outdoor, warm atmosphere**

Lighting Preference: Left

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/dd30387b-0490-46ee-b688-2191fb752e68)

---

**Prompt: beautiful woman, detailed face, sunshine from window**

Lighting Preference: Right

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/6c9511ca-f97f-401a-85f3-92b4442000e3)

---

**Prompt: beautiful woman, detailed face, shadow from window**

Lighting Preference: Left

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/e73701d5-890e-4b15-91ee-97f16ea3c450)

---

**Prompt: beautiful woman, detailed face, sunset over sea**

Lighting Preference: Right

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/ff26ac3d-1b12-4447-b51f-73f7a5122a05)

---

**Prompt: handsome boy, detailed face, neon light, city**

Lighting Preference: Left

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/d7795e02-46f7-444f-93e7-4d6460840437)

---

**Prompt: beautiful woman, detailed face, light and shadow**

Lighting Preference: Left

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/706f70a8-d1a0-4e0b-b3ac-804e8e231c0f)

(beautiful woman, detailed face, soft studio lighting)

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/fe0a72df-69d4-4e11-b661-fb8b84d0274d)

---

**Prompt: Buddha, detailed face, sci-fi RGB glowing, cyberpunk**

Lighting Preference: Left

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/68d60c68-ce23-4902-939e-11629ccaf39a)

---

**Prompt: Buddha, detailed face, natural lighting**

Lighting Preference: Left

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/1841d23d-0a0d-420b-a5ab-302da9c47c17)

---

**Prompt: toy, detailed face, shadow from window**

Lighting Preference: Bottom

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/dcb97439-ea6b-483e-8e68-cf5d320368c7)

---

**Prompt: toy, detailed face, sunset over sea**

Lighting Preference: Right

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/4f78b897-621d-4527-afa7-78d62c576100)

---

**Prompt: dog, magic lit, sci-fi RGB glowing, studio lighting**

Lighting Preference: Bottom

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/1db9cac9-8d3f-4f40-82e2-e3b0cafd8613)

---

**Prompt: mysteriou human, warm atmosphere, warm atmosphere, at home, bedroom**

Lighting Preference: Right

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/5d5aa7e5-8cbd-4e1f-9f27-2ecc3c30563a)

---

### Background-Conditioned Model

The background conditioned model does not require careful prompting. One can just use simple prompts like "handsome man, cinematic lighting".

---

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/0b2a889f-682b-4393-b1ec-2cabaa182010)

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/477ca348-bd47-46ff-81e6-0ffc3d05feb2)

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/5bc9d8d9-02cd-442e-a75c-193f115f2ad8)

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/a35e4c57-e199-40e2-893b-cb1c549612a9)

---

A more structured visualization:

![r1](https://github.com/lllyasviel/IC-Light/assets/19834515/c1daafb5-ac8b-461c-bff2-899e4c671ba3)

# Imposing Consistent Light

In HDR space, illumination has a property that all light transports are independent.

As a result, the blending of appearances of different light sources is equivalent to the appearance with mixed light sources:

![cons](https://github.com/lllyasviel/IC-Light/assets/19834515/27c67787-998e-469f-862f-047344e100cd)

Using the above [light stage](https://www.pauldebevec.com/Research/LS/) as an example, the two images from the "appearance mixture" and "light source mixture" are consistent (mathematically equivalent in HDR space, ideally).

We imposed such consistency (using MLPs in latent space) when training the relighting models.

As a result, the model is able to produce highly consistent relight - **so** consistent that different relightings can even be merged as normal maps! Despite the fact that the models are latent diffusion.

![r2](https://github.com/lllyasviel/IC-Light/assets/19834515/25068f6a-f945-4929-a3d6-e8a152472223)

From left to right are inputs, model outputs relighting, devided shadow image, and merged normal maps. Note that the model is not trained with any normal map data. This normal estimation comes from the consistency of relighting.

You can reproduce this experiment using this button (it is 4x slower because it relight image 4 times)

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/d9c37bf7-2136-446c-a9a5-5a341e4906de)

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/fcf5dd55-0309-4e8e-9721-d55931ea77f0)

Below are bigger images (feel free to try yourself to get more results!)

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/12335218-186b-4c61-b43a-79aea9df8b21)

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/2daab276-fdfa-4b0c-abcb-e591f575598a)

For reference, [geowizard](https://fuxiao0719.github.io/projects/geowizard/) (geowizard is a really great work!):

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/4ba1a96d-e218-42ab-83ae-a7918d56ee5f)

And, [switchlight](https://arxiv.org/pdf/2402.18848) (switchlight is another great work!):

![image](https://github.com/lllyasviel/IC-Light/assets/19834515/fbdd961f-0b26-45d2-802e-ffd734affab8)

# Model Notes

* **iclight_sd15_fc.safetensors** - The default relighting model, conditioned on text and foreground. You can use initial latent to influence the relighting.

* **iclight_sd15_fcon.safetensors** - Same as "iclight_sd15_fc.safetensors" but trained with offset noise. Note that the default "iclight_sd15_fc.safetensors" outperform this model slightly in a user study. And this is the reason why the default model is the model without offset noise.

* **iclight_sd15_fbc.safetensors** - Relighting model conditioned with text, foreground, and background.

Also, note that the original [BRIA RMBG 1.4](https://huggingface.co/briaai/RMBG-1.4) is for non-commercial use. If you use IC-Light in commercial projects, replace it with other background replacer like [BiRefNet](https://github.com/ZhengPeng7/BiRefNet).

# Cite

@inproceedings{
        zhang2025scaling,
        title={Scaling In-the-Wild Training for Diffusion-based Illumination Harmonization and Editing by Imposing Consistent Light Transport},
        author={Lvmin Zhang and Anyi Rao and Maneesh Agrawala},
        booktitle={The Thirteenth International Conference on Learning Representations},
        year={2025},
        url={https://openreview.net/forum?id=u1cQYxRI1H}
    }

# Related Work

Also read ...

[Total Relighting: Learning to Relight Portraits for Background Replacement](https://augmentedperception.github.io/total_relighting/)

[Relightful Harmonization: Lighting-aware Portrait Background Replacement](https://arxiv.org/abs/2312.06886)

[SwitchLight: Co-design of Physics-driven Architecture and Pre-training Framework for Human Portrait Relighting](https://arxiv.org/pdf/2402.18848)