ChatGPT Image Editing Harbors Spatial Artifacts: Iterative Edits Reveal Grid Patterns
DickHorner · reddit · 2026-08-14
A developer conducted an in-depth investigation into a recurring low-level artifact in ChatGPT (and similar LLM) image generation and editing. They found that after multiple rounds of generative editing, low-detail areas (like backgrounds and skin) are prone to faint cloudy or mottled textures.
Through a series of experiments, the author revealed several core phenomena:
- Spatial Binding: Shifting the image by 20 pixels before repairing alters the artifact's distribution, proving it is tied to a fixed spatial phase relationship with the model's output canvas.
- Implicit Subject Protection: Comparing intermediate outputs suggested the model generates an internal coarse silhouette mask, keeping faces and bodies more stable than background walls.
- Black Image Test: Generating completely black images yielded sparse non-zero pixels rather than true zeroes. Multiple independently generated black images showed a massive overlap in non-zero pixel masks (correlation of 0.848), confirming the existence of a fixed underlying spatial grid pattern during generation.
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