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**/__pycache__ **/__pycache__
examples/ examples/
.idea/ .idea/
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README.md
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https://user-images.githubusercontent.com/3998421/153323093-b664bb68-2928-480b-b59b-7c1ee24a4507.mp4 https://user-images.githubusercontent.com/3998421/153323093-b664bb68-2928-480b-b59b-7c1ee24a4507.mp4
- [x] Support multiple model architectures - [x] Support multiple model architectures
1. [LaMa](https://github.com/saic-mdal/lama) 1. [LaMa](https://github.com/saic-mdal/lama)
1. [LDM](https://github.com/CompVis/latent-diffusion) 1. [LDM](https://github.com/CompVis/latent-diffusion)
- [x] High resolution support - [x] High resolution support
- [x] Multi stroke support. Press and hold the `cmd/ctrl` key to enable multi stroke mode. - [x] Multi stroke support. Press and hold the `cmd/ctrl` key to enable multi stroke mode.
- [x] Zoom & Pan - [x] Zoom & Pan
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## Quick Start ## Quick Start
Install requirements: `pip3 install -r requirements.txt` 1. Install requirements: `pip3 install -r requirements.txt`
2. Start server: `python3 main.py`, open http://localhost:8080
### Start server with LaMa model Available commands for `main.py`
```bash | Name | Description | Default |
python3 main.py --device=cuda --port=8080 --model=lama | ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------- |
``` | --model | lama or ldm. See details in **Model Comparison** | lama |
| --device | cuda or cpu | cuda |
| --ldm-steps | The larger the value, the better the result, but it will be more time-consuming | 50 |
| --crop-trigger-size | If image size large then crop-trigger-size, crop each area from original image to do inference. Mainly for performance and memory reasons on **very** large image. | 2042,2042 |
| --crop-margin | Margin around bounding box of painted stroke when crop mode triggered. | 256 |
| --port | Port for web server | 8080 |
| --gui | Launch lama-cleaner as a desktop application | |
| --gui_size | Set the window size for the application | 1200 900 |
- `--crop-trigger-size`: If image size large then crop-trigger-size, crop each area from original image to do inference. ## Model Comparison
Mainly for performance and memory reasons on **very** large image.Default is 2042,2042
- `--crop-margin`: Margin around bounding box of painted stroke when crop mode triggered. Default is 256.
### Start server with LDM model Diffusion model(ldm) is **MUCH MORE** slower than GANs(lama)(1080x720 image takes 8s on 3090), but it's possible to get better
result, see below example:
```bash | Original Image | LaMa | LDM |
python3 main.py --device=cuda --port=8080 --model=ldm --ldm-steps=50 | ----------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
``` | ![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) |
`--ldm-steps`: The larger the value, the better the result, but it will be more time-consuming
Diffusion model is **MUCH MORE** slower than GANs(1080x720 image takes 8s on 3090), but it's possible to get better
results than LaMa.
### GUI
You can run lama-cleaner as a desktop application using the following command line arguments.
`--gui`: Launch lama-cleaner as a desktop application
`--gui_size`: Set the window size for the application. Usage: --gui_size 1200 900
|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)|
Blogs about diffusion models: Blogs about diffusion models: