DIS VS FFmpeg

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

FFmpeg (https://ffmpeg.org/)

GPL2

Since FFmpeg is GPL2, doesn’t that require CompressX to disclose its source code?

IANAL, apologies if I miss understand license requirements.

https://github.com/FFmpeg/FFmpeg?tab=License-1-ov-file

This turns out to be a lot of assembly macros to help write one x86 assembly. https://github.com/FFmpeg/FFmpeg/blob/master/libavutil/x86/x...

The sibling comment recommending compiler intrinsics is probably the best way to go for writing SIMD code. A mixture of `` style types and intrinsics to specify instructions is a solid 90% solution compared to assembly.

If you want that last 10%, I think macros are putting the emphasis in the wrong place. They're a somewhat easy way to build up a language abstraction which will work if held carefully, but I'm confident the dev experience using this abstraction when you write invalid code will be deeply confusing.

I would suggest to write a parser instead of the macros. That'll tell you clearly when the syntax is invalid (though possibly not with much precision) and it'll give you a place to put semantic analysis for where valid syntax encodes nonsense. Do the equivalent of the macro expansions on the parsed tree instead of on the text. Emit asm as the "back end".

You might have heard of FFMPEG or ImageMagick for image and video edition in a programmatic way. MoviePy is a Python module for video editing (Python wrapper for FFMPEG and ImageMagick). It provides functions for cutting, concatenations, title insertions, video compositing, video processing, and the creation of custom effects. It can read and write common video and audio formats and be run on any platform with Python 2.7 or 3+.

There are many cloud-based tools and websites that can convert your images, but the problem with these tools is that you usually have to upload the files for them to be processed, and some of their services are not free. In this article, I'd like to introduce a piece of software called FFmpeg, which allows you convert the images locally with one simple command.

To run the demo locally, be sure to have Python 3.11 and FFmpeg installed.