Image generation is the umbrella field; **text-to-image** (prompt → image), **image-to-image** (reference + prompt → edited image), **inpainting / outpainting** (fill a masked region), and **controllable generation** (ControlNet, IP-Adapter — obey a pose / depth-map / style reference) are the sub-techniques. Under the hood, almost all modern systems are diffusion models operating in latent space, conditioned on CLIP or T5 text embeddings. DiT (diffusion transformer) backbones, pioneered by Stable Diffusion 3 and FLUX, are displacing U-Net as the dominant architecture.
The 2026 vendor landscape: **Midjourney v7** leads artistic aesthetics, **FLUX.1 [pro]** leads prompt coherence in open weights, **Ideogram** dominates in-image text rendering, **DALL-E 3** remains the default for prompt-following inside ChatGPT, **Stable Diffusion 3.5** is the workhorse for self-hosted fine-tuning. **Recraft** and **Adobe Firefly** compete on commercial-safe training data.
For enterprise APAC use, the decision framework is consent-and-rights (are we safe to train on this data, do we own the output), **brand consistency** (IP-Adapter or LoRA fine-tuning to lock visual style), and **workflow integration** (Figma plugins, asset management, approval gates). The technology is past the novelty phase; the moat now lives in integration, rights management, and taste, not the model itself.
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