We just published this tutorial about ARMA(p,q) models for modeling time series, and how to fit them using Python. But while it’s a tutorial, it has a few twists. First, it’s interactive: you’ll learn by solving problems and making choices. Second, it’s a story: you play a character in a plot that gives you real-life problems to solve. And third, it’s illustrated: we spent many hours hacking with Stable Diffusion, GIMP, and matplotlib.<p>This is chapter 3 in our interactive course, Everyday Data
We just published this tutorial about ARMA(p,q) models for modeling time series, and how to fit them using Python. But while it’s a tutorial, it has a few twists. First, it’s interactive: you’ll learn by solving problems and making choices. Second, it’s a story: you play a character in a plot that gives you real-life problems to solve. And third, it’s illustrated: we spent many hours hacking with Stable Diffusion, GIMP, and matplotlib.<p>This is chapter 3 in our interactive course, Everyday Data Science. [1] The first half of the chapter is free. You can get the whole course forever for $29. These chapters are a lot of effort to produce, so please let us know what you think :-)<p>- Andrew Carr [2] and Jim Fisher [3]<p>[1]: <a href="https://news.ycombinator.com/item?id=32118530" rel="nofollow">https://news.ycombinator.com/item?id=32118530</a> [2]: <a href="https://twitter.com/andrew_n_carr" rel="nofollow">https://twitter.com/andrew_n_carr</a> [3]: <a href="https://jameshfisher.com/" rel="nofollow">https://jameshfisher.com/</a>
Design and optimize ComfyUI node workflows for Stable Diffusion. Covers ControlNet, IP-Adapter, inpainting, upscaling, and multi-pass generation pipelines.
Generate stunning photorealistic portraits with SDXL. Covers lighting setups, camera simulation, skin texture, and professional photography techniques.
Detailed guide for crafting textual descriptions specifically for SDXL image generation, covering the dual-encoder system, optimal prompt lengths, and style-specific formulas for photorealism, illustration, and concept art.
Technical deep-dive into prompt engineering covering token limits, attention mechanisms, prompt weighting with parentheses and numerical values, embedding manipulation, and A/B testing different prompt structures with reproducible experiments.
Covers the full prompt engineering workflow including subject specification, style references, quality boosters, camera and lighting terminology, negative prompt strategies, and CFG scale tuning for different prompt styles.
Exhaustive guide to negative prompts covering common negative terms, negative embeddings (EasyNegative, bad_prompt), weighting strategies, model-specific negative prompts, and common mistakes like over-weighting.
Workflows from the Neura Market marketplace related to this Stable Diffusion resource