Common Failure Modes & Honest Limitations

imgai1-2's own warn-box previewed two failure modes in a single paragraph: hands, and legible text. This chapter delivers the full treatment of both, adds a third (prompt bleeding), and holds every one of them to the same standard set back then — a mechanical explanation traced to what the network's training signal actually was, never a shrug toward "AI isn't perfect yet."

Anatomical Errors, In Full

imgai1-2 already named the root cause: "hands... are small, highly variable, articulated structures that appear in countless different poses across training images, so the statistical pattern is much fuzzier than something like 'a face.'" There is no rule anywhere in the network's training resembling "exactly five digits, each with a fixed number of joints" — only an averaged statistical impression of what hand-shaped regions of pixels tend to look like, blurred across an enormous range of real poses, angles, and partial occlusions.

Newer models have genuinely gotten better at this — larger, higher-resolution training sets and dedicated fine-tuning specifically targeting hands have measurably reduced how often extra or fused fingers appear. It's worth being precise about what that improvement actually is: a better-fitted statistical pattern, not a newly learned structural rule. The underlying mechanism hasn't changed; the average has simply gotten sharper.

Text Rendering, In Full

imgai1-2's own explanation: "nothing in the training signal ever represents letters as discrete symbols with a required exact sequence, only as visual textures that loosely correlate with certain image regions." A word is, to the network, a texture that tends to co-occur with signage, book covers, and similar contexts — not a sequence of discrete, individually meaningful characters that must appear in a specific, correct order.

Some newer tools have made real, visible progress here — but, honestly, mostly by adding something extra rather than by the same diffusion mechanism simply improving with scale: dedicated architectural components specifically for rendering legible text, or specialized training passes focused on typography, layered on top of the general diffusion process rather than emerging naturally from it. This is a meaningfully different kind of improvement from the hands case above — less "the same statistical pattern got sharper," more "a second, specialized mechanism was bolted on for this one problem."

Prompt Bleeding & Concept Mixing

A prompt describing two distinct subjects with distinct attributes — a red car and a blue house — doesn't guarantee those attributes stay cleanly attached to their intended nouns. The result might show a car with blue trim, or a house with a red door, or some blend of both. This is prompt bleeding, and it has its own distinct mechanical cause.

Per imgai1-2, the entire prompt is processed into a conditioning signal that steers the denoising network — but nothing in that process works like a strict grammatical parser that hard-binds each adjective to exactly the noun a human reader would assign it to. The network's own internal attention mechanism (how it decides which parts of the text embedding influence which regions of the developing image) is itself a learned, statistical association, not a rule-based binding — so during the many steps of denoising, a color or style term can end up influencing a region of the image other than the one it was "meant" for, especially when multiple similar subjects or attributes compete for the same visual region.

What "Getting Better" Actually Means Here

Hands-On Exercises