built a space for creating prompts for FLUX

gokaygokay/FLUX-Prompt-Generator


You can create long prompts from images or simple words. Enhance your short prompts with prompt enhancer. You can configure various settings such as artform, photo type, character details, scene details, style, and artist to create tailored prompts.

And you can combine all of them with custom prompts using llms (Mixtral, Mistral, Llama 3, and Mistral-Nemo).

The UI is a bit complex, but it includes almost everything you need. Choosing random option is the most fun!

And i've created some other spaces for using FLUX models with captioners and enhancers.

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gokaygokay/FLUX.1-dev-with-Captioner

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gokaygokay/FLUX.1-Schnell-with-Captioner

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Remember when @mistralAI said large enough and casually dropped Mistral-Large-Instruct-2407? 🤯🚀

It's now on http://lmsys.org! 🌐 It works amazing for instruction following, hard prompts, coding, and longer queries with only 123 billion parameters. 💡💻

It outperforms GPT4-Turbo and Claude 3 Opus on Coding, Hard Prompts, Math, and Longer Query categories. 📈🔢

It also outperforms Llama 3.1 405B on Instruction Following while being 3x smaller. 🐎🔍

It also does exceedingly well on the Ai2 ZebraLogic logistic reasoning benchmark despite being much smaller than the other models. 🦓🤔

Mistral is not here to take part but to take over! 🏆🌟

Model: https://mistral.ai/news/mistral-large-2407/

PyTorch implementation of the Self-Compression & Differentiable Quantization Algorithm introduced in “Self-Compressing Neural Networks” paper.

The algorithm shows dynamic neural network compression during training - with reduced size of weight, activation tensors and bits required to represent weights.

It’s basically shrinking the neural network size (weights and activations) as it’s being trained without compromising performance - this helps reduce compute and inference cost.


Code: https://github.com/Jaykef/ai-algorithms
Paper: https://arxiv.org/pdf/2301.13142