Conversational Image Editing with Multimodal Models Quiz

Answers and explanations appear as you take the quiz.

1. What is the key difference between classic diffusion inpainting and native multimodal chat editing?
   • Inpainting only works on black-and-white images
   • Inpainting regenerates only a masked region, while multimodal models usually generate a whole new image from the conversation
   • Multimodal models require you to paint a mask first
   • Inpainting cannot use a text prompt
2. Why can details you did not ask to change shift during a multimodal edit?
   • The model deliberately adds random changes
   • The chat interface compresses text
   • The original file is deleted after upload
   • The model typically generates a whole new image, so preservation is learned rather than guaranteed
3. What does access to the conversation history let a multimodal model do?
   • Follow requests that refer to earlier turns, such as returning to a previous color
   • Guarantee pixel-identical output
   • Edit the layers of your original file
   • Increase the image resolution automatically
4. Which 2022 research model showed that images could be edited from a written instruction without a mask?
   • SynthID
   • C2PA
   • InstructPix2Pix
   • Nano Banana
5. What is a good step when drift has built up over many chat turns?
   • Keep adding more instructions in the same chat indefinitely
   • Start a fresh chat from the best version or refer back to the original upload
   • Lower the image to a thumbnail
   • Switch to black and white
6. A retoucher needs every untouched pixel to stay exactly the same. Which approach fits best?
   • Asking a chat model to 'change nothing else'
   • Running the edit many times and picking one at random
   • Generating a new image from scratch
   • Mask-based inpainting or compositing the edited region back over the original
7. Which provenance signal does Google add to images from its models?
   • An invisible SynthID watermark
   • A visible logo in every corner
   • A QR code
   • No signal of any kind
8. Which statement about conversational editing is a misconception?
   • The output may not match your original resolution
   • Preservation of unchanged areas is learned, not guaranteed
   • The model edits your original file and its layers directly
   • Stating what must stay the same can help reduce drift