Can the Same Generator Be Its Own Corrector without Text Feedback?

Text-to-image generators have achieved remarkable visual quality, yet a single generation can still violate the prompt through incorrect counting, attribute binding, or spatial relations. Correcting such errors usually takes more models: a VLM or LLM diagnoses the image and writes an edit instruction, and a separate editor carries it out. An underexplored question is whether the image generator itself can take over this role: rather than treating an imperfect output as terminal, can the same generator interpret it as an intermediate state and move it toward a more prompt-consistent result, without any text feedback? We introduce SelfFix, which learns a prompt-conditioned transition from an initial generation to a selectively corrected one inside the generator itself. SelfFix keeps the generator frozen and adds a lightweight adapter of 37.5M parameters; switching the adapter off recovers the original generator bit for bit, so generation is lossless. Given only the original prompt and the current image, the corrector learns to recognize prompt-image inconsistencies, and a Content-Aware Correction (CAC) module decides for every token whether to preserve the input content or revise it. This correction behavior requires no external LLM or VLM analysis, textual error description, or editing instruction at inference. On GenEval, a single correction raises SANA-1.0-1.6B from 0.66 to 0.77, and with a budget of 20 generator calls SelfFix reaches 0.91, surpassing fine-tuned and VLM-guided reflection baselines under the same budget as well as SANA-1.5-4.8B with Best-of-2048. Training only 2.3% of the parameters, SelfFix also outperforms full fine-tuning of the generator. On Emu Edit, it aligns the image with the target caption better than re-generation, on both Emu Edit and PIE-Bench it preserves far more of the source, and on real photographs it revises only the mismatched object, indicating that the correction transfers beyond the training templates.