pytorch_clip_guided_loss VS ai-art-generator

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

I have a github project that will help you set up large batches of prompts too.

Hey all, just a note that I've updated my AI-art generator to work with Stable Diffusion (both txt2img and imgtoimg)! If you have a decent GPU (8GB VRAM+, though more is better), you should be able to use Stable Diffusion on your local computer.

I'm a software engineer, but a complete machine-learning noob (not exactly a linux guru, either). I'm trying to use the GPU for VQGAN+CLIP image generation. Running on an RTX 3060, I get almost 4 iterations per second, so a 512x512 image takes about 2 minutes to create with default settings. Running on the Tesla M40, I get about 0.4 iterations per second (~22 minutes per 512x512 image at the same settings). A full order of magnitude slower! I'd read that older Tesla GPUs are some of the top value picks when it comes to ML applications, but obviously with this level of performance that isn't the case at all. I figure I must be going wrong somewhere.