IOPaint/README.md

<h1 align="center">Lama Cleaner</h1>
<p align="center">A free and open-source inpainting tool powered by SOTA AI model.</p>

<p align="center">
  <a href="https://github.com/Sanster/lama-cleaner">
    <img alt="total download" src="https://pepy.tech/badge/lama-cleaner" />
  </a>
  <a href="https://pypi.org/project/lama-cleaner/">
    <img alt="version" src="https://img.shields.io/pypi/v/lama-cleaner" />
  </a>
  <a href="https://colab.research.google.com/drive/1e3ZkAJxvkK3uzaTGu91N9TvI_Mahs0Wb?usp=sharing">
    <img alt="Open in Colab" src="https://colab.research.google.com/assets/colab-badge.svg" />
  </a>

  <a href="https://huggingface.co/spaces/Sanster/Lama-Cleaner-lama">
    <img alt="Hugging Face Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue" />
  </a>

  <a href="https://www.python.org/downloads/">
    <img alt="python version" src="https://img.shields.io/badge/python-3.7+-blue.svg" />
  </a>
  <a href="https://hub.docker.com/r/cwq1913/lama-cleaner">
    <img alt="version" src="https://img.shields.io/docker/pulls/cwq1913/lama-cleaner" />
  </a>
</p>

https://user-images.githubusercontent.com/3998421/196976498-ba1ad3ab-fa18-4c55-965f-5c6683141375.mp4

## Features

- Completely free and open-source
- Fully self-hosted
- Classical image inpainting algorithm powered by [cv2](https://docs.opencv.org/3.4/df/d3d/tutorial_py_inpainting.html)
- Multiple SOTA AI models
    1. [LaMa](https://github.com/saic-mdal/lama)
    1. [LDM](https://github.com/CompVis/latent-diffusion)
    1. [ZITS](https://github.com/DQiaole/ZITS_inpainting)
    1. [MAT](https://github.com/fenglinglwb/MAT)
    1. [FcF](https://github.com/SHI-Labs/FcF-Inpainting)
    1. [SD1.5](https://github.com/runwayml/stable-diffusion)
    1. [Manga](https://github.com/msxie92/MangaInpainting)
- Support CPU & GPU
- Various inpainting [strategy](#inpainting-strategy)
- Run as a desktop APP
- [Interactive Segmentation](https://github.com/Sanster/lama-cleaner/releases/tag/0.28.0) on any object

## Usage

<details>
<summary>1. Remove any unwanted things on the image</summary>

| Usage                         | Before                                        | After                                               |
|-------------------------------|-----------------------------------------------|-----------------------------------------------------|
| Remove unwanted things        | ![unwant_object2](./assets/unwant_object.jpg) | ![unwant_object2](./assets/unwant_object_clean.jpg) |
| Remove unwanted person        | ![unwant_person](./assets/unwant_person.jpg)  | ![unwant_person](./assets/unwant_person_clean.jpg)  |
| Remove text                   | ![text](./assets/unwant_text.jpg)             | ![text](./assets/unwant_text_clean.jpg)             |
| Remove watermark              | ![watermark](./assets/watermark.jpg)          | ![watermark_clean](./assets/watermark_cleanup.jpg)  |
| Remove text balloons on manga | ![manga](./assets/manga.png)                  | ![manga_clean](./assets/manga_clean.png)            |

</details>

<details>
<summary>2. Fix old photo</summary>

| Usage         | Before                              | After                                           |
| ------------- | ----------------------------------- | ----------------------------------------------- |
| Fix old photo | ![oldphoto](./assets/old_photo.jpg) | ![oldphoto_clean](./assets/old_photo_clean.jpg) |

</details>

<details>
<summary>3. Replace something on the image </summary>

| Usage                  | Before                   | After                                                          |
| ---------------------- | ------------------------ | -------------------------------------------------------------- |
| Text Driven Inpainting | ![dog](./assets/dog.jpg) | Prompt: a fox sitting on a bench<br/> ![fox](./assets/fox.jpg) |

</details>

## Quick Start

The easiest way to use Lama Cleaner is to install it using `pip`:

```bash
pip install lama-cleaner

# Models will be downloaded at first time used
lama-cleaner --model=lama --device=cpu --port=8080
# Lama Cleaner is now running at http://localhost:8080
```

For stable-diffusion model, you need
to [accepting the terms to access](https://huggingface.co/runwayml/stable-diffusion-inpainting), and
get an access token from here [huggingface access token](https://huggingface.co/docs/hub/security-tokens)

If you prefer to use docker, you can check out [docker](#docker)

If you hava no idea what is docker or pip, please check [One Click Installer](./scripts/README.md)

Available command line arguments:

| Name                 | Description                                                                                                         | Default  |
| -------------------- |---------------------------------------------------------------------------------------------------------------------| -------- |
| --model              | lama/ldm/zits/mat/fcf/sd1.5/manga See details in [Inpaint Model](#inpainting-model)                                 | lama     |
| --hf_access_token    | stable-diffusion need [huggingface access token](https://huggingface.co/docs/hub/security-tokens) to download model |          |
| --sd-run-local       | Once the model as downloaded, you can pass this arg and remove `--hf_access_token`                                  |          |
| --sd-disable-nsfw    | Disable stable-diffusion NSFW checker.                                                                              |          |
| --sd-cpu-textencoder | Always run stable-diffusion TextEncoder model on CPU.                                                               |          |
| --sd-enable-xformers | Enable xFormers optimizations. See: [facebookresearch/xformers](https://github.com/facebookresearch/xformers)       |          |
| --device             | cuda or cpu                                                                                                         | cuda     |
| --port               | Port for backend flask web server                                                                                   | 8080     |
| --gui                | Launch lama-cleaner as a desktop application                                                                        |          |
| --gui_size           | Set the window size for the application                                                                             | 1200 900 |
| --input              | Path to image you want to load by default                                                                           | None     |
| --debug              | Enable debug mode for flask web server                                                                              |          |

## Inpainting Model

| Model | Description                                                                                                                                                                                                            | Config                                                                                                                                                                                                                                                                            |
| ----- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| cv2   | :+1: No GPU is required, and for simple backgrounds, the results may even be better than AI models.                                                                                                                    |                                                                                                                                                                                                                                                                                   |
| LaMa  | :+1: Generalizes well on high resolutions(~2k)<br/>                                                                                                                                                                    |                                                                                                                                                                                                                                                                                   |
| LDM   | :+1: Possible to get better and more detail result <br/> :+1: The balance of time and quality can be achieved by adjusting `steps` <br/> :neutral_face: Slower than GAN model<br/> :neutral_face: Need more GPU memory | `Steps`: You can get better result with large steps, but it will be more time-consuming <br/> `Sampler`: ddim or [plms](https://arxiv.org/abs/2202.09778). In general plms can get [better results](https://github.com/Sanster/lama-cleaner/releases/tag/0.13.0) with fewer steps |
| ZITS  | :+1: Better holistic structures compared with previous methods <br/> :neutral_face: Wireframe module is **very** slow on CPU                                                                                           | `Wireframe`: Enable edge and line detect                                                                                                                                                                                                                                          |
| MAT   | TODO                                                                                                                                                                                                                   |                                                                                                                                                                                                                                                                                   |
| FcF   | :+1: Better structure and texture generation <br/> :neutral_face: Only support fixed size (512x512) input                                                                                                              |                                                                                                                                                                                                                                                                                   |
| SD1.5 | :+1: SOTA text-to-image diffusion model                                                                                                                                                                                |                                                                                                                                                                                                                                                                                   |

<details>
<summary> See model comparison detail</summary>

**LaMa vs LDM**

| Original Image                                                                                                                            | LaMa                                                                                                                                                   | LDM                                                                                                                                                   |
| ----------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
| ![photo-1583445095369-9c651e7e5d34](https://user-images.githubusercontent.com/3998421/156923525-d6afdec3-7b98-403f-ad20-88ebc6eb8d6d.jpg) | ![photo-1583445095369-9c651e7e5d34_cleanup_lama](https://user-images.githubusercontent.com/3998421/156923620-a40cc066-fd4a-4d85-a29f-6458711d1247.png) | ![photo-1583445095369-9c651e7e5d34_cleanup_ldm](https://user-images.githubusercontent.com/3998421/156923652-0d06c8c8-33ad-4a42-a717-9c99f3268933.png) |

**LaMa vs ZITS**

| Original Image                                                                                                         | ZITS                                                                                                                       | LaMa                                                                                                                       |
| ---------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------- |
| ![zits_original](https://user-images.githubusercontent.com/3998421/180464918-eb13ebfb-8718-461c-9e8b-7f6d8bb7a84f.png) | ![zits_compare_zits](https://user-images.githubusercontent.com/3998421/180464914-4db722c9-047f-48fe-9bb4-916ba09eb5c6.png) | ![zits_compare_lama](https://user-images.githubusercontent.com/3998421/180464903-ffb5f770-4372-4488-ba76-4b4a8c3323f5.png) |

Image is from [ZITS](https://github.com/DQiaole/ZITS_inpainting) paper. I didn't find a good example to show the
advantages of ZITS and let me know if you have a good example. There can also be possible problems with my code, if you
find them, please let me know too!

**LaMa vs FcF**

| Original Image                                                                                                    | LaMa                                                                                                                   | FcF                                                                                                                   |
| ----------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------- |
| ![texture](https://user-images.githubusercontent.com/3998421/188305027-a4260545-c24e-4df7-9739-ac5dc3cae879.jpeg) | ![texture_lama](https://user-images.githubusercontent.com/3998421/188305024-2064ed3e-5af4-4843-ac10-7f9da71e15f8.jpeg) | ![texture_fcf](https://user-images.githubusercontent.com/3998421/188305006-a08d2896-a65f-43d5-b9a5-ef62c3129f0c.jpeg) |

**LaMa vs Manga**

Manga model works better on high-quality manga image then LaMa model.

Original Image
![manga](./assets/manga.png)

| Model |  1080x740  |  1470x1010 |
|--------|------------|------------|
|Manga|![manga_1080x740](https://user-images.githubusercontent.com/3998421/202676629-54f40f20-c55b-4e6d-bcc7-0a4e81fbb27d.png)|![manga_1470x1010](https://user-images.githubusercontent.com/3998421/202675839-4f8012d5-1c10-47ea-9628-20512e86f192.png)|
|[LaMa](https://github.com/saic-mdal/lama)|![lama_1080x740](https://user-images.githubusercontent.com/3998421/202675704-53fa7a3d-ec74-4044-a19c-c673d74bdd28.png)|![lama_1470x1010](https://user-images.githubusercontent.com/3998421/202675746-1e642367-f5d0-4b48-aa8b-5d82f2e29082.png)|

</details>

## Inpainting Strategy

Lama Cleaner provides three ways to run inpainting model on images, you can change it in the settings dialog.

| Strategy     | Description                                                                                            | VRAM   | Speed             |
| ------------ | ------------------------------------------------------------------------------------------------------ | ------ | ----------------- |
| **Original** | Use the resolution of the original image                                                               | High   | :zap:             |
| **Resize**   | Resize the image to a smaller size before inpainting. The area outside the mask will not loss quality. | Midium | :zap: :zap:       |
| **Crop**     | Crop masking area from the original image to do inpainting                                             | Low    | :zap: :zap: :zap: |

## Download Model Manually

If you have problems downloading the model automatically when lama-cleaner start,
you can download it manually. By default lama-cleaner will load model from `TORCH_HOME=~/.cache/torch/hub/checkpoints/`,
you can set `TORCH_HOME` to other folder and put the models there.

- GitHub:
    - [LaMa](https://github.com/Sanster/models/releases/tag/add_big_lama)
    - [LDM](https://github.com/Sanster/models/releases/tag/add_ldm)
    - [ZITS](https://github.com/Sanster/models/releases/tag/add_zits)
    - [MAT](https://github.com/Sanster/models/releases/tag/add_mat)
    - [FcF](https://github.com/Sanster/models/releases/tag/add_fcf)
- Baidu:
    - https://pan.baidu.com/s/1vUd3BVqIpK6e8N_EA_ZJfw
    - passward: flsu

## Development

Only needed if you plan to modify the frontend and recompile yourself.

### Frontend

Frontend code are modified from [cleanup.pictures](https://github.com/initml/cleanup.pictures), You can experience their
great online services [here](https://cleanup.pictures/).

- Install dependencies:`cd lama_cleaner/app/ && yarn`
- Start development server: `yarn start`
- Build: `yarn build`

## Docker

You can use [pre-build docker image](https://github.com/Sanster/lama-cleaner#run-docker-cpu) to run Lama Cleaner. The
model will be downloaded to the cache directory when first time used.
You can mount existing cache directory to start the container,
so you don't have to download the model every time you start the container.

The cache directories for different models correspond as follows:

- lama/ldm/zits/mat/fcf: /root/.cache/torch
- sd1.5: /root/.cache/huggingface

### Run Docker (cpu)

```
docker run -p 8080:8080 \
-v /path/to/torch_cache:/root/.cache/torch \
-v /path/to/huggingface_cache:/root/.cache/huggingface \
--rm cwq1913/lama-cleaner:cpu-0.26.1 \
lama-cleaner --device=cpu --port=8080 --host=0.0.0.0
```

### Run Docker (gpu)

- cuda11.6
- pytorch1.12.1
- minimum nvidia driver 510.39.01+

```
docker run --gpus all -p 8080:8080 \
-v /path/to/torch_cache:/root/.cache/torch \
-v /path/to/huggingface_cache:/root/.cache/huggingface \
--rm cwq1913/lama-cleaner:gpu-0.26.1 \
lama-cleaner --device=cuda --port=8080 --host=0.0.0.0
```

Then open [http://localhost:8080](http://localhost:8080)

### Build Docker image

cpu only

```
docker build -f --build-arg version=0.x.0 ./docker/CPUDockerfile -t lamacleaner .
```

gpu & cpu

```
docker build -f --build-arg version=0.x.0 ./docker/GPUDockerfile -t lamacleaner .
```
update README.md 2022-07-22 17:20:12 +02:00			`<h1 align="center">Lama Cleaner</h1>`
			`<p align="center">A free and open-source inpainting tool powered by SOTA AI model.</p>`

			`<p align="center">`
			`<a href="https://github.com/Sanster/lama-cleaner">`
			`<img alt="total download" src="https://pepy.tech/badge/lama-cleaner" />`
			`</a>`
			`<a href="https://pypi.org/project/lama-cleaner/">`
			`<img alt="version" src="https://img.shields.io/pypi/v/lama-cleaner" />`
			`</a>`
			`<a href="https://colab.research.google.com/drive/1e3ZkAJxvkK3uzaTGu91N9TvI_Mahs0Wb?usp=sharing">`
			`<img alt="Open in Colab" src="https://colab.research.google.com/assets/colab-badge.svg" />`
			`</a>`
update README 2022-11-21 07:02:44 +01:00
Add huggingface space 2022-11-16 14:18:51 +01:00			`<a href="https://huggingface.co/spaces/Sanster/Lama-Cleaner-lama">`
			`<img alt="Hugging Face Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue" />`
			`</a>`
update README 2022-11-21 07:02:44 +01:00
fix cv2 params 2022-10-09 15:32:13 +02:00			`<a href="https://www.python.org/downloads/">`
			`<img alt="python version" src="https://img.shields.io/badge/python-3.7+-blue.svg" />`
			`</a>`
update readme 2022-10-08 06:55:51 +02:00			`<a href="https://hub.docker.com/r/cwq1913/lama-cleaner">`
			`<img alt="version" src="https://img.shields.io/docker/pulls/cwq1913/lama-cleaner" />`
			`</a>`
update README.md 2022-07-22 17:20:12 +02:00			`</p>`

update readme 2022-10-20 16:36:32 +02:00			`https://user-images.githubusercontent.com/3998421/196976498-ba1ad3ab-fa18-4c55-965f-5c6683141375.mp4`
update README.md 2022-07-22 17:20:12 +02:00
			`## Features`

			`- Completely free and open-source`
			`- Fully self-hosted`
update README 2022-09-26 03:16:11 +02:00			`- Classical image inpainting algorithm powered by [cv2](https://docs.opencv.org/3.4/df/d3d/tutorial_py_inpainting.html)`
update README.md 2022-07-22 17:20:12 +02:00			`- Multiple SOTA AI models`
update README 2022-11-21 07:02:44 +01:00			`1. [LaMa](https://github.com/saic-mdal/lama)`
			`1. [LDM](https://github.com/CompVis/latent-diffusion)`
			`1. [ZITS](https://github.com/DQiaole/ZITS_inpainting)`
			`1. [MAT](https://github.com/fenglinglwb/MAT)`
			`1. [FcF](https://github.com/SHI-Labs/FcF-Inpainting)`
			`1. [SD1.5](https://github.com/runwayml/stable-diffusion)`
			`1. [Manga](https://github.com/msxie92/MangaInpainting)`
update README.md 2022-07-22 17:20:12 +02:00			`- Support CPU & GPU`
update README 2022-09-04 15:23:58 +02:00			`- Various inpainting [strategy](#inpainting-strategy)`
update README.md 2022-07-22 17:20:12 +02:00			`- Run as a desktop APP`
Update README.md 2022-12-04 04:23:54 +01:00			`- [Interactive Segmentation](https://github.com/Sanster/lama-cleaner/releases/tag/0.28.0) on any object`
update README.md 2022-07-22 17:20:12 +02:00
			`## Usage`

update docker 2022-10-05 17:15:09 +02:00			`<details>`
			`<summary>1. Remove any unwanted things on the image</summary>`

update README 2022-11-21 07:02:44 +01:00			`\| Usage \| Before \| After \|`
			`\|-------------------------------\|-----------------------------------------------\|-----------------------------------------------------\|`
			`\| Remove unwanted things \| ![unwant_object2](./assets/unwant_object.jpg) \| ![unwant_object2](./assets/unwant_object_clean.jpg) \|`
			`\| Remove unwanted person \| ![unwant_person](./assets/unwant_person.jpg) \| ![unwant_person](./assets/unwant_person_clean.jpg) \|`
			`\| Remove text \| ![text](./assets/unwant_text.jpg) \| ![text](./assets/unwant_text_clean.jpg) \|`
			`\| Remove watermark \| ![watermark](./assets/watermark.jpg) \| ![watermark_clean](./assets/watermark_cleanup.jpg) \|`
			`\| Remove text balloons on manga \| ![manga](./assets/manga.png) \| ![manga_clean](./assets/manga_clean.png) \|`
update docker 2022-10-05 17:15:09 +02:00
			`</details>`

			`<details>`
			`<summary>2. Fix old photo</summary>`

			`\| Usage \| Before \| After \|`
			`\| ------------- \| ----------------------------------- \| ----------------------------------------------- \|`
			`\| Fix old photo \| ![oldphoto](./assets/old_photo.jpg) \| ![oldphoto_clean](./assets/old_photo_clean.jpg) \|`

			`</details>`

			`<details>`
			`<summary>3. Replace something on the image </summary>`

			`\| Usage \| Before \| After \|`
			`\| ---------------------- \| ------------------------ \| -------------------------------------------------------------- \|`
			`\| Text Driven Inpainting \| ![dog](./assets/dog.jpg) \| Prompt: a fox sitting on a bench<br/> ![fox](./assets/fox.jpg) \|`

			`</details>`
update readme 2021-12-12 07:54:37 +01:00
update README.md 2022-07-22 17:20:12 +02:00			`## Quick Start`
init 2021-11-15 08:22:34 +01:00
update docker 2022-10-05 17:15:09 +02:00			The easiest way to use Lama Cleaner is to install it using `pip`:

update README 2022-04-18 16:40:23 +02:00			```bash
			`pip install lama-cleaner`
add ldm model 2022-03-04 06:44:53 +01:00
update docker 2022-10-05 17:15:09 +02:00			`# Models will be downloaded at first time used`
update README.md 2022-07-22 17:20:12 +02:00			`lama-cleaner --model=lama --device=cpu --port=8080`
			`# Lama Cleaner is now running at http://localhost:8080`
update README 2022-04-18 16:40:23 +02:00			```

update README 2022-11-21 07:02:44 +01:00			`For stable-diffusion model, you need`
			`to [accepting the terms to access](https://huggingface.co/runwayml/stable-diffusion-inpainting), and`
Update README.md update readme 2022-10-21 03:05:58 +02:00			`get an access token from here [huggingface access token](https://huggingface.co/docs/hub/security-tokens)`

update docker 2022-10-05 17:15:09 +02:00			`If you prefer to use docker, you can check out [docker](#docker)`

update one click installer 2022-10-24 12:32:35 +02:00			`If you hava no idea what is docker or pip, please check [One Click Installer](./scripts/README.md)`

update docker 2022-10-05 17:15:09 +02:00			`Available command line arguments:`
add ldm model 2022-03-04 06:44:53 +01:00
update one click installer 2022-10-24 12:32:35 +02:00			`\| Name \| Description \| Default \|`
update README 2022-11-21 07:02:44 +01:00			`\| -------------------- \|---------------------------------------------------------------------------------------------------------------------\| -------- \|`
			`\| --model \| lama/ldm/zits/mat/fcf/sd1.5/manga See details in [Inpaint Model](#inpainting-model) \| lama \|`
Update README.md update readme 2022-10-21 03:05:58 +02:00			`\| --hf_access_token \| stable-diffusion need [huggingface access token](https://huggingface.co/docs/hub/security-tokens) to download model \| \|`
update one click installer 2022-10-24 12:32:35 +02:00			\| --sd-run-local \| Once the model as downloaded, you can pass this arg and remove `--hf_access_token` \| \|
			`\| --sd-disable-nsfw \| Disable stable-diffusion NSFW checker. \| \|`
			`\| --sd-cpu-textencoder \| Always run stable-diffusion TextEncoder model on CPU. \| \|`
Add argument for enabling xFormers optimizations 2022-11-29 02:34:22 +01:00			`\| --sd-enable-xformers \| Enable xFormers optimizations. See: [facebookresearch/xformers](https://github.com/facebookresearch/xformers) \| \|`
update one click installer 2022-10-24 12:32:35 +02:00			`\| --device \| cuda or cpu \| cuda \|`
			`\| --port \| Port for backend flask web server \| 8080 \|`
			`\| --gui \| Launch lama-cleaner as a desktop application \| \|`
			`\| --gui_size \| Set the window size for the application \| 1200 900 \|`
			`\| --input \| Path to image you want to load by default \| None \|`
			`\| --debug \| Enable debug mode for flask web server \| \|`
add ldm model 2022-03-04 06:44:53 +01:00
update README.md 2022-07-22 17:20:12 +02:00			`## Inpainting Model`
update README 2022-06-12 16:06:29 +02:00
update README 2022-09-26 03:16:11 +02:00			\| Model \| Description \| Config \|
			\| ----- \| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- \|
			\| cv2 \| :+1: No GPU is required, and for simple backgrounds, the results may even be better than AI models. \| \|
			\| LaMa \| :+1: Generalizes well on high resolutions(~2k)<br/> \| \|
Fix typo 2022-09-19 10:06:37 +02:00			\| LDM \| :+1: Possible to get better and more detail result <br/> :+1: The balance of time and quality can be achieved by adjusting `steps` <br/> :neutral_face: Slower than GAN model<br/> :neutral_face: Need more GPU memory \| `Steps`: You can get better result with large steps, but it will be more time-consuming <br/> `Sampler`: ddim or [plms](https://arxiv.org/abs/2202.09778). In general plms can get [better results](https://github.com/Sanster/lama-cleaner/releases/tag/0.13.0) with fewer steps \|
update README 2022-09-26 03:16:11 +02:00			\| ZITS \| :+1: Better holistic structures compared with previous methods <br/> :neutral_face: Wireframe module is very slow on CPU \| `Wireframe`: Enable edge and line detect \|
			\| MAT \| TODO \| \|
			\| FcF \| :+1: Better structure and texture generation <br/> :neutral_face: Only support fixed size (512x512) input \| \|
update readme 2022-10-20 15:03:43 +02:00			\| SD1.5 \| :+1: SOTA text-to-image diffusion model \| \|
update README 2022-06-12 16:06:29 +02:00
update readme 2022-10-08 16:15:21 +02:00			`<details>`
			`<summary> See model comparison detail</summary>`

			`LaMa vs LDM`
update README 2022-06-12 16:06:29 +02:00
update README.md 2022-07-22 17:20:12 +02:00			`\| Original Image \| LaMa \| LDM \|`
			`\| ----------------------------------------------------------------------------------------------------------------------------------------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------ \| ----------------------------------------------------------------------------------------------------------------------------------------------------- \|`
			`\| ![photo-1583445095369-9c651e7e5d34](https://user-images.githubusercontent.com/3998421/156923525-d6afdec3-7b98-403f-ad20-88ebc6eb8d6d.jpg) \| ![photo-1583445095369-9c651e7e5d34_cleanup_lama](https://user-images.githubusercontent.com/3998421/156923620-a40cc066-fd4a-4d85-a29f-6458711d1247.png) \| ![photo-1583445095369-9c651e7e5d34_cleanup_ldm](https://user-images.githubusercontent.com/3998421/156923652-0d06c8c8-33ad-4a42-a717-9c99f3268933.png) \|`
update README 2022-06-12 16:06:29 +02:00
update readme 2022-10-08 16:15:21 +02:00			`LaMa vs ZITS`
update README 2022-06-12 16:06:29 +02:00
update README.md 2022-07-22 17:20:12 +02:00			`\| Original Image \| ZITS \| LaMa \|`
			`\| ---------------------------------------------------------------------------------------------------------------------- \| -------------------------------------------------------------------------------------------------------------------------- \| -------------------------------------------------------------------------------------------------------------------------- \|`
			`\| ![zits_original](https://user-images.githubusercontent.com/3998421/180464918-eb13ebfb-8718-461c-9e8b-7f6d8bb7a84f.png) \| ![zits_compare_zits](https://user-images.githubusercontent.com/3998421/180464914-4db722c9-047f-48fe-9bb4-916ba09eb5c6.png) \| ![zits_compare_lama](https://user-images.githubusercontent.com/3998421/180464903-ffb5f770-4372-4488-ba76-4b4a8c3323f5.png) \|`
update README 2022-06-12 16:06:29 +02:00
update README 2022-11-21 07:02:44 +01:00			`Image is from [ZITS](https://github.com/DQiaole/ZITS_inpainting) paper. I didn't find a good example to show the`
			`advantages of ZITS and let me know if you have a good example. There can also be possible problems with my code, if you`
			`find them, please let me know too!`
update README 2022-06-12 16:06:29 +02:00
update readme 2022-10-08 16:15:21 +02:00			`LaMa vs FcF`
update README 2022-06-12 16:06:29 +02:00
update README 2022-11-21 07:02:44 +01:00			`\| Original Image \| LaMa \| FcF \|`
update README 2022-09-04 15:23:58 +02:00			`\| ----------------------------------------------------------------------------------------------------------------- \| ---------------------------------------------------------------------------------------------------------------------- \| --------------------------------------------------------------------------------------------------------------------- \|`
			`\| ![texture](https://user-images.githubusercontent.com/3998421/188305027-a4260545-c24e-4df7-9739-ac5dc3cae879.jpeg) \| ![texture_lama](https://user-images.githubusercontent.com/3998421/188305024-2064ed3e-5af4-4843-ac10-7f9da71e15f8.jpeg) \| ![texture_fcf](https://user-images.githubusercontent.com/3998421/188305006-a08d2896-a65f-43d5-b9a5-ef62c3129f0c.jpeg) \|`
update README 2022-06-12 16:06:29 +02:00
update README 2022-11-21 07:02:44 +01:00			`LaMa vs Manga`

			`Manga model works better on high-quality manga image then LaMa model.`

			`Original Image`
			`![manga](./assets/manga.png)`

			`\| Model \| 1080x740 \| 1470x1010 \|`
			`\|--------\|------------\|------------\|`
			`\|Manga\|![manga_1080x740](https://user-images.githubusercontent.com/3998421/202676629-54f40f20-c55b-4e6d-bcc7-0a4e81fbb27d.png)\|![manga_1470x1010](https://user-images.githubusercontent.com/3998421/202675839-4f8012d5-1c10-47ea-9628-20512e86f192.png)\|`
			`\|[LaMa](https://github.com/saic-mdal/lama)\|![lama_1080x740](https://user-images.githubusercontent.com/3998421/202675704-53fa7a3d-ec74-4044-a19c-c673d74bdd28.png)\|![lama_1470x1010](https://user-images.githubusercontent.com/3998421/202675746-1e642367-f5d0-4b48-aa8b-5d82f2e29082.png)\|`

update readme 2022-10-08 16:15:21 +02:00			`</details>`

update README 2022-09-04 15:23:58 +02:00			`## Inpainting Strategy`

			`Lama Cleaner provides three ways to run inpainting model on images, you can change it in the settings dialog.`

update readme 2022-10-08 16:15:21 +02:00			`\| Strategy \| Description \| VRAM \| Speed \|`
			`\| ------------ \| ------------------------------------------------------------------------------------------------------ \| ------ \| ----------------- \|`
			`\| Original \| Use the resolution of the original image \| High \| :zap: \|`
			`\| Resize \| Resize the image to a smaller size before inpainting. The area outside the mask will not loss quality. \| Midium \| :zap: :zap: \|`
			`\| Crop \| Crop masking area from the original image to do inpainting \| Low \| :zap: :zap: :zap: \|`
init 2021-11-15 08:22:34 +01:00
fix: README typo/errors 2022-11-02 11:24:04 +01:00			`## Download Model Manually`
update README 2022-07-25 04:16:01 +02:00
			`If you have problems downloading the model automatically when lama-cleaner start,`
			you can download it manually. By default lama-cleaner will load model from `TORCH_HOME=~/.cache/torch/hub/checkpoints/`,
			you can set `TORCH_HOME` to other folder and put the models there.

Typo fix from Github to GitHub 2022-11-16 03:07:20 +01:00			`- GitHub:`
update README 2022-11-21 07:02:44 +01:00			`- [LaMa](https://github.com/Sanster/models/releases/tag/add_big_lama)`
			`- [LDM](https://github.com/Sanster/models/releases/tag/add_ldm)`
			`- [ZITS](https://github.com/Sanster/models/releases/tag/add_zits)`
			`- [MAT](https://github.com/Sanster/models/releases/tag/add_mat)`
			`- [FcF](https://github.com/Sanster/models/releases/tag/add_fcf)`
update README 2022-07-25 04:16:01 +02:00			`- Baidu:`
update README 2022-11-21 07:02:44 +01:00			`- https://pan.baidu.com/s/1vUd3BVqIpK6e8N_EA_ZJfw`
			`- passward: flsu`
update README 2022-07-25 04:16:01 +02:00
init 2021-11-15 08:22:34 +01:00			`## Development`

update readme 2022-02-05 12:58:25 +01:00			`Only needed if you plan to modify the frontend and recompile yourself.`

Minor correction to README 2022-04-25 15:08:19 +02:00			`### Frontend`
init 2021-11-15 08:22:34 +01:00
add ldm model 2022-03-04 06:44:53 +01:00			`Frontend code are modified from [cleanup.pictures](https://github.com/initml/cleanup.pictures), You can experience their`
			`great online services [here](https://cleanup.pictures/).`
init 2021-11-15 08:22:34 +01:00
			- Install dependencies:`cd lama_cleaner/app/ && yarn`
style refine 2022-04-04 15:51:33 +02:00			- Start development server: `yarn start`
init 2021-11-15 08:22:34 +01:00			- Build: `yarn build`
Added Dockerfile 2021-11-15 20:11:46 +01:00
Docker run options 2021-11-15 23:41:59 +01:00			`## Docker`
fix cache_dir in main.py 2021-11-16 14:21:41 +01:00
update README 2022-11-21 07:02:44 +01:00			`You can use [pre-build docker image](https://github.com/Sanster/lama-cleaner#run-docker-cpu) to run Lama Cleaner. The`
			`model will be downloaded to the cache directory when first time used.`
update docker 2022-10-05 17:15:09 +02:00			`You can mount existing cache directory to start the container,`
			`so you don't have to download the model every time you start the container.`
Docker run options 2021-11-15 23:41:59 +01:00
update docker 2022-10-05 17:15:09 +02:00			`The cache directories for different models correspond as follows:`
fix cache_dir in main.py 2021-11-16 14:21:41 +01:00
update docker 2022-10-05 17:15:09 +02:00			`- lama/ldm/zits/mat/fcf: /root/.cache/torch`
update readme 2022-10-20 15:03:43 +02:00			`- sd1.5: /root/.cache/huggingface`
Docker commands 2021-11-15 23:51:27 +01:00
			`### Run Docker (cpu)`
fix cache_dir in main.py 2021-11-16 14:21:41 +01:00
Docker commands 2021-11-15 23:51:27 +01:00			```
update docker 2022-10-05 17:15:09 +02:00			`docker run -p 8080:8080 \`
			`-v /path/to/torch_cache:/root/.cache/torch \`
			`-v /path/to/huggingface_cache:/root/.cache/huggingface \`
Update README.md 2022-11-17 15:20:42 +01:00			`--rm cwq1913/lama-cleaner:cpu-0.26.1 \`
update docker 2022-10-05 17:15:09 +02:00			`lama-cleaner --device=cpu --port=8080 --host=0.0.0.0`
Added Dockerfile 2021-11-15 20:11:46 +01:00			```

Docker commands 2021-11-15 23:51:27 +01:00			`### Run Docker (gpu)`
fix cache_dir in main.py 2021-11-16 14:21:41 +01:00
update readme 2022-10-08 06:55:51 +02:00			`- cuda11.6`
			`- pytorch1.12.1`
			`- minimum nvidia driver 510.39.01+`

Added Dockerfile 2021-11-15 20:11:46 +01:00			```
update docker 2022-10-05 17:15:09 +02:00			`docker run --gpus all -p 8080:8080 \`
			`-v /path/to/torch_cache:/root/.cache/torch \`
			`-v /path/to/huggingface_cache:/root/.cache/huggingface \`
Update README.md 2022-11-17 15:20:42 +01:00			`--rm cwq1913/lama-cleaner:gpu-0.26.1 \`
update docker 2022-10-05 17:15:09 +02:00			`lama-cleaner --device=cuda --port=8080 --host=0.0.0.0`
Added Dockerfile 2021-11-15 20:11:46 +01:00			```

fix cache_dir in main.py 2021-11-16 14:21:41 +01:00			`Then open [http://localhost:8080](http://localhost:8080)`
update docker 2022-10-05 17:15:09 +02:00
			`### Build Docker image`

			`cpu only`

			```
update Dockerfile 2022-11-01 02:09:43 +01:00			`docker build -f --build-arg version=0.x.0 ./docker/CPUDockerfile -t lamacleaner .`
update docker 2022-10-05 17:15:09 +02:00			```

			`gpu & cpu`

			```
update Dockerfile 2022-11-01 02:09:43 +01:00			`docker build -f --build-arg version=0.x.0 ./docker/GPUDockerfile -t lamacleaner .`
update docker 2022-10-05 17:15:09 +02:00			```