https://youtu.be/R6h02YY6gUs

Qwen Image is literally unchallenged at understanding complex prompts and writing amazing text on generated images. This model feels almost as if it’s illegal to be open source and free. It is my new tool for generating thumbnail images. Even with low-effort prompting, the results are excellent.
This tutorial literally shows how these images were generated with Gemini 2.5 Pro made prompts :
Qwen Image Dominates Text-to-Image: 700+ Tests Reveal Why It’s Better Than FLUX — Presets Published
https://youtu.be/R6h02YY6gUs

Gemini 2.5 Pro is freely available on Google Studio AI
All images generated in easy to use SwarmUI and they are unmodified raw generations
SwarmUI and ComfyUI install tutorial :
Master Local AI Art & Video Generation with SwarmUI (ComfyUI Backend): The Ultimate 2025 Tutorial
https://www.youtube.com/watch?v=fTzlQ0tjxj0

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6 Must-read books about AI and Machine Learning:

Sharing some free, useful resources for you. In this collection, we’ve gathered the most recent books to give you up-to-date information on key fundamental topics. Hope this helps you master AI and machine learning:

1. Machine Learning Systems by Vijay Janapa Reddi → https://www.mlsysbook.ai/
Provides a framework for building effective ML solutions, covering data engineering, optimization, hardware-aware training, inference acceleration, architecture choice, and other key principles

2. Generative Diffusion Modeling: A Practical Handbook by Zihan Ding, Chi Jin → https://arxiv.org/abs/2412.17162
Offers a unified view of diffusion models: probabilistic, score-based, consistency, rectified flow, pre/post-training. It aligns notations with code to close the “paper-to-code” gap.

3. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges → https://arxiv.org/abs/2104.13478
Explores unified geometric principles to analyze neural networks' architectures: CNNs, RNNs, GNNs, Transformers, and guide the design of the future ones

4. Mathematical Foundations of Geometric Deep Learning by Haitz Saez de Ocariz Borde and Michael Bronstein → https://arxiv.org/abs/2508.02723
Dives into the the key math concepts behind geometric Deep Learning: geometric and analytical structures, vector calculus, differential geometry, etc.

5. Interpretable Machine Learning by Christoph Molnar → https://github.com/christophM/interpretable-ml-book
Practical guide to simple, transparent models (e.g., decision trees) and model-agnostic methods like LIME, Shapley values, permutation importance, and accumulated local effects.

6. Understanding Deep Learning by Simon J.D. Prince → https://udlbook.github.io/udlbook/
Explores core deep learning concenpts: models, training, evaluation, RL, architectures for images, text, and graphs, addressing open theoretical questions

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