|
|
--- |
|
|
license: mit |
|
|
tags: |
|
|
- art |
|
|
--- |
|
|
# DeWm |
|
|
/djuːm/ DeWaterMark. |
|
|
|
|
|
Doc only. For code please head for https://github.com/huzpsb/DeWm/ |
|
|
|
|
|
Tired of images covered in watermarks? Look no further! |
|
|
This tool uses AI (StableDiffusion) to automatically remove watermarks from illustrations. |
|
|
|
|
|
**Disclaimer:** All sample images in this repository were generated by me. |
|
|
Removing watermarks from images does **not** mean you automatically gain authorization to use those images. |
|
|
More importantly, removing watermarks involves modifying the images, which is prohibited under many licenses. Please do not misuse the models provided in this repository! |
|
|
|
|
|
**Training Method:** No comment. |
|
|
|
|
|
## Usage: |
|
|
1. Use `dev.py` to process the original image and generate a mask. |
|
|
2. In Stable Diffusion (SD), select inpainting mode and upload the mask. |
|
|
That's it! |
|
|
|
|
|
### Tips for Better Results (not absolute; experiment on your own): |
|
|
1. Choose a model with a similar art style; it can significantly improve the quality of the final result. |
|
|
2. Avoid using blurred masks. Set mask blur to at most 1–2. Adjust the mathematical morphology operations provided in the function to refine the mask as needed. |
|
|
3. When in doubt, over-select rather than under-select. At worst, you can manually erase areas incorrectly selected by the model, which is still easier than manually blacking out watermarks. |
|
|
4. For semi-transparent watermarks, set masked areas to use the original image. Otherwise, use the filled option. |
|
|
5. I recommend using Euler a sampler with 20 steps. |
|
|
6. Keep the CFG scale low. If you can get by with 2, don’t use 3. The goal is to avoid breaking the image. Otherwise, the style may look overly "AI-generated." Ignore this if you prefer the AI aesthetic. |
|
|
7. Set denoising strength to 0.2, with a maximum of 0.25. Going higher will likely break the image because this process uses a fine mask. |
|
|
8. In extreme cases, first perform a "Anti-Anti-AI" operation using [DeTox](https://github.com/huzpsb/DeTox). |
|
|
|
|
|
### Additional Uses: |
|
|
The inpainting process can also remove residual edges left by the watermark. |
|
|
Therefore, if you're planning to use ControlNet, you can first remove the watermark as described and then calculate edges from the watermark-free image. |
|
|
|
|
|
### TODO: |
|
|
If feasible, create a universal style inpainting model independent of (or based on) Stable Diffusion. |
|
|
|
|
|
## Examples: |
|
|
(Supports more types of transparent, image-based, and other watermarks; the following is for demonstration purposes.) |
|
|
|
|
|
Original Image: |
|
|
 |
|
|
Generated Mask (you could manually erase false positives, but skipped here for demonstration): |
|
|
 |
|
|
Stable Diffusion Inpainting: |
|
|
 |
