Text-to-Image API invents mutant baboons for a dog basketball prompt
Description
Screenshot of a developer portal demoing a "Text To Image API." The page is split: on the left a generated picture shows four grotesquely blended animals - baboons and dogs merged together with distorted faces - standing on patchy grass while a plush-like gray creature sits in the foreground. On the right, the interface displays the heading "Text To Image API," author information, the caption "Creates an image from scratch from a text description," and a blue-bordered textarea containing the prompt text: "Dogs playing basketball." A gray "Submit" button sits below the prompt, along with icons linking to API documentation and SDKs. The absurd mismatch between the requested scene and the output pokes fun at generative-AI failure cases, highlighting the limitations of diffusion-based image models and the developer challenge of exposing such models through an API
Comments
9Comment deleted
Text-to-image in prod: we serialize DogsPlayingBasketballDTO, the model deserializes BaboonMutantCluster - exactly what happens when your whole contract is a free-form string and hope
This is what happens when your image generation model was trained on 'dogs sitting for treats' dataset but never actually saw a single NBA game - now every prompt becomes an excuse for the model to show off its extensive knowledge of 'good boys waiting patiently' instead of whatever you actually asked for
When your text-to-image API has perfect uptime, flawless documentation, and enterprise-grade security - but the model was trained exclusively on literal interpretations of idioms. Sure, the dogs are technically 'playing' and there's probably a basketball somewhere in the training set, just not in the same embedding space. At least it's deterministic: garbage prompt in, confused canines out
We added a seed for reproducibility; now our e2e suite deterministically verifies that “dogs playing basketball” returns primate - dog chimeras - turns out versioning a probability distribution is still a breaking change
Prompt parsed perfectly until the latent space clustered 'basketball' with 'dogpile' - embedding drift hits again
Prompt: "dogs playing basketball." Output: latent space kennel orbiting a sphere; CLIP score 0.89, so product says it’s ready; apparently we replaced acceptance criteria with cosine similarity
Is it bad if i can understand why this is generated like this even tho I haven’t ever trained AI and I am not planning to except for text recognition? Comment deleted
Looks like dog porn! Comment deleted
but it was the first one, the next were +- cats Comment deleted