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## Usage
| 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) |
| Fix old photo | ![oldphoto](./assets/old_photo.jpg) | ![oldphoto_clean](./assets/old_photo_clean.jpg) |
| Text Driven Inpainting | ![dog](./assets/dog.jpg) | Prompt: a fox sitting on a bench<br/> ![fox](./assets/fox.jpg) |
<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) |
</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
# Model will be downloaded automatically
# 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
```
Available arguments:
If you prefer to use docker, you can check out [docker](#docker)
| Name | Description | Default |
|-------------------|-------------------------------------------------------------------------------------------------------------------------------| -------- |
| --model | lama/ldm/zits/mat/fcf/sd1.4 See details in [Inpaint Model](#inpainting-model) | lama |
| --hf_access_token | stable-diffusion(sd) model 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. | |
Available command line arguments:
| Name | Description | Default |
| -------------------- | ----------------------------------------------------------------------------------------------------------------------------- | -------- |
| --model | lama/ldm/zits/mat/fcf/sd1.4 See details in [Inpaint Model](#inpainting-model) | lama |
| --hf_access_token | stable-diffusion(sd) model 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. | |
| --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 | |
| --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
@@ -141,27 +166,53 @@ great online services [here](https://cleanup.pictures/).
## Docker
Run within a Docker container. Set the `CACHE_DIR` to models location path. Optionally add a `-d` option to
the `docker run` command below to run as a daemon.
You can use [pre-build docker image]() 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.
### Build Docker image
The cache directories for different models correspond as follows:
```
docker build -f Dockerfile -t lamacleaner .
```
- lama/ldm/zits/mat/fcf: /root/.cache/torch
- sd1.4: /root/.cache/huggingface
### Run Docker (cpu)
```
docker run -p 8080:8080 -e CACHE_DIR=/app/models -v $(pwd)/models:/app/models -v $(pwd):/app --rm lamacleaner \
python3 main.py --device=cpu --port=8080 --host=0.0.0.0
docker run -p 8080:8080 \
-v /path/to/torch_cache:/root/.cache/torch \
-v /path/to/huggingface_cache:/root/.cache/huggingface \
--rm lamacleaner \
lama-cleaner --device=cpu --port=8080 --host=0.0.0.0
```
### Run Docker (gpu)
```
docker run --gpus all -p 8080:8080 -e CACHE_DIR=/app/models -v $(pwd)/models:/app/models -v $(pwd):/app --rm lamacleaner \
python3 main.py --device=cuda --port=8080 --host=0.0.0.0
docker run --gpus all -p 8080:8080 \
-v /path/to/torch_cache:/root/.cache/torch \
-v /path/to/huggingface_cache:/root/.cache/huggingface \
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 ./docker/CPUDockerfile -t lamacleaner .
```
gpu & cpu
- cuda11.6
- pytorch1.12.1
```
docker build -f ./docker/GPUDockerfile -t lamacleaner .
```
## One Click Installer
TODO