Text Rendering in AI-Generated Images Quiz

Answers and explanations appear as you take the quiz.

1. Why did CLIP-based image models struggle to spell words correctly?
   • CLIP had no vocabulary at all
   • CLIP turns prompts into subword tokens that capture meaning, not letter sequences
   • CLIP processes only numbers
   • CLIP was trained only on images that contained no text
2. Which kind of encoder did Google's character-aware study find spells better?
   • A smaller CLIP encoder
   • An audio encoder
   • An image-only encoder
   • A byte-level encoder such as ByT5 that sees individual characters
3. Why is a small error tolerable in a generated face but not in generated text?
   • A single wrong stroke can change which letter it is
   • Faces are generated by a separate model
   • Text is always drawn at a higher resolution
   • Faces use a different kind of noise
4. What limitation in training data made it hard to link words to letter shapes?
   • Training images used too many fonts
   • All training images were black and white
   • Captions rarely wrote out the text visible in the image
   • Only English-language images were included
5. What did research systems such as TextDiffuser and GlyphControl add to image generation?
   • Invisible watermarks
   • Explicit layouts or pre-rendered letter images to guide the model
   • Audio narration
   • Faster sampling and nothing else
6. Which model does the guide say paired its image generator with a T5 encoder?
   • Stable Diffusion 3
   • Stable Diffusion 1.5
   • Stable Diffusion 1.4
   • The original CLIP model
7. For production work that needs exact text, what does the guide call the most reliable approach?
   • Writing a much longer prompt
   • Lowering the resolution
   • Adding a heavy negative prompt
   • Adding text outside the model, or inpainting a masked area with the corrected word
8. According to the guide, how do modern image models produce letters?
   • By copying shapes from a font file
   • By running OCR on the prompt
   • By generating them as pixels, which is why long passages and small print still go wrong
   • By looking up images of words on the web