人工智慧圖像生成

Generated images can support illustration and exploration, but they are not evidence that a depicted event occurred or that an object has a physically valid structure.

重點摘要

• State visual constraints clearly.
• Inspect the final display context.
• Separate illustration from documentary evidence.

Different model families generate images in different ways. Diffusion models learn a denoising process; other systems use autoregressive or alternative approaches. A product may combine generation with editing, upscaling, and postprocessing, so the complete workflow matters. Describe the visual purpose and constraints. Subject, composition, lighting, palette, and required empty space can guide an illustration. Exact text, repeated geometry, small objects, and consistent identities across outputs need direct inspection rather than assumptions about prompt compliance. Evaluate at the final display size and in context. A thumbnail can hide distorted edges or unreadable text that becomes obvious in a banner or print layout. Upscaling increases pixel dimensions but does not necessarily recover accurate detail. Keep provenance and usage requirements clear. Review recognizable people, third-party material, and the tool’s terms before publication. Label illustrations so they cannot reasonably be mistaken for documentary evidence when that distinction matters. Preserve the actual final asset and relevant generation settings for reproducibility.

1. Imagine generating an educational diagram with three labeled components for a mobile article.
2. Inspect the labels, relationships, and small-screen legibility rather than judging only the overall style.
3. If exact labels or geometry are unreliable, rebuild those elements as editable text or vector shapes and verify the final composition.

This constructed workflow evaluates communication quality instead of equating resolution with correctness.