LEARNING PATH
IMAGE
Control composition and constraints to create consistent images.
Fundamentals of image generation: Translating Diffusion, Flow Matching, and Transformer to production decisions
The generation from noise back to an image is understood as the relationship between latent representation, conditioning, Flow Matching, and DiT, and is used to determine speed and control.
Image model research notes: don't confuse offering models, open weights, and licenses
Research notes based on official sources checked on 2026-10-04. Compare API availability, open weights, and license conditions for OpenAI GPT Image, Google Imagen, and Stable Diffusion.
Image production flow: Reference, character consistency, Control, LoRA, Inpainting
Instead of relying on one-shot generation, we separate the responsibilities of reference, control, local correction, and LoRA when necessary, and transform it into a reproducible image production process.
Practical image workshop: Creating a single advertising visual using a verifiable process
An exercise in creating still images containing products and people by dividing them into specifications, references, composition, creation, local corrections, typesetting, and release checks.
Image generation frontier notes: Reading FLUX.3, GPT-Image 2.5, Nano Banana 2 in process
From the official information confirmed on 2026-10-04, we summarize that the focus of image generation has shifted from one-shot image quality comparison to layout, editing, history, and high-speed iterative process design.