https://github.com/filnow/replace-anything
replace any object you want on the image with whatever you want
https://github.com/filnow/replace-anything
flask inpainting opencv python3 segment-anything stable-diffusion
Last synced: 5 months ago
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replace any object you want on the image with whatever you want
- Host: GitHub
- URL: https://github.com/filnow/replace-anything
- Owner: filnow
- Created: 2023-04-06T09:45:38.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-02-06T16:11:00.000Z (over 1 year ago)
- Last Synced: 2025-05-08T22:58:55.459Z (5 months ago)
- Topics: flask, inpainting, opencv, python3, segment-anything, stable-diffusion
- Language: Python
- Homepage:
- Size: 22.2 MB
- Stars: 13
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# About
The website allows segmentation of objects by clicking on the desired thing in the photo and replacing it with any other object through the appropriate prompt using inpainting.
Segmentation is done using [SAM](https://github.com/facebookresearch/segment-anything), inpating is done using [Diffusers](https://github.com/huggingface/diffusers).
# Installation
Firstly you need to download a SAM model and requirements.
That can be done with setup.sh script.
```bash
git clone https://github.com/filnow/replace-anything.git
cd replace-anything
chmod +x setup.sh
./setup.sh {MODEL_NAME}
```
where {MODEL_NAME} should be replaced with one of the following options:
* vit_h: download the Huge model (2,4GB).
* vit_l: download the Large model (1,2GB).
* vit_b: download the Big model (358MB).
For example, to download the Large model, you would run:
```bash
./setup.sh vit_l
```
To start a web app you need to run this command with proper model argument.
```bash
python3 app.py --model={MODEL_NAME}
```
To use inpainting pass a flag before starting a web app.
```bash
python3 app.py --model={MODEL_NAME} --inpainting
```
After first prompt the Stable Diffusion will download automatically (about 10GB) and then you good to go.
Despite our limitations in hardware it takes abour 2s for a segmentation and 6s for inpainting for vit_l on GPU with 6GB of memory and CUDA environment.
# Results
* TODO: add results, add arrow.svg as sumbit button