pytorch_clip_guided_loss VS BMSG-GAN

This approach isn't perfect at all, but it is really simple and works after writing just a few lines of code. You can find our implementation of the ClipRCNN here: https://github.com/bes-dev/pytorch_clip_guided_loss/tree/master/examples/object_detection

There are different ways to generate images by their text descriptions. But one of the most powerful approaches to generate synthetic art is CLIP guided image generation. We provide a new python library that incapsulates the whole logic of the CLIP guided loss into one PyTorch primitive with a simple API. We provide CLIP guided loss using different CLIP models (such as original CLIP models by OpenAI and ruCLIP model by SberAI), multiple prompts (texts or images) as targets for optimization, and automatic detection and translation of the input texts. Also, we provide our tiny implementation of the VQGAN-CLIP based on our library and VQVAE by SberAI (in my opinion, this is the best version of the VQGAN that is publicly available) to make text to image. Our library is all you need to integrate text-powered losses into your image synthesis pipelines by adding a few lines of code. You can find our library here (pypi package is available): https://github.com/bes-dev/pytorch_clip_guided_loss