stable-diffusion-webui-rembg VS DIS

Extension REMBG for Automatic1111

This tool will mask the output image of your generations: https://github.com/AUTOMATIC1111/stable-diffusion-webui-rembg

Remove background with your favourite image editing software (I used Photoshop) or rembg, for example

With the following prompt, we get a good image of Pikachu in the DD style which can then have the background made transparent with https://github.com/AUTOMATIC1111/stable-diffusion-webui-rembg

Also, this auto removes backgrounds

So, the only difference from the listed method is to add an extra step to preprocessing. Before captioning images, I used this extension to batch remove backgrounds. Then, I took those PNGs and used Photoshop to batch save them as JPEGs, resulting in cutout images of the subject with white backgrounds. I then proceeded as listed in that comment.

Hello, I had some succes using this repo : https://github.com/xuebinqin/DIS

DIS

Rembg only adds to my frustration. It's a half-assed solution providing sub-par results and acting as a band-aid for the fundamental absence of any transparency handling in diffusion models. DIS is better, but it's a pain to set up and still often needs retouching. Trying to remove backgrounds post-generation will always be an uphill battle because the information just isn't there.

also try to look into https://github.com/xuebinqin/DIS cuz it may help to either achieve the result straight away, or to make a dataset for layering lora if each picture takes to much touch up

Also checkout https://github.com/xuebinqin/U-2-Net. They have a new version in this repo: https://github.com/xuebinqin/DIS

If you want some tricks to make this smoother, try masking the wolf upfront in each frame and then only replacing the wolf itself with the pixels from sd on the second frame and onward. Keep the first frame's sd output for the background stable or change it less quickly. I've found good results segmenting with this, but lots of good options on foreground segmentation are available.

For instance, click "open in colab" at the top of this Image Segmentation notebook