The Art of AI Image Editing: Why Instructions Still Aren't Quite Right

Instruction-guided image editing is supposed to be the next big thing. But while the tech world promises easy transformations, reality paints a messier picture. The heart of the issue? High-quality image editing relies heavily on precise instructions, and current models aren't cutting it.

The Struggle of Current Models

Let's face it: even strong vision-language models (VLMs) fumble when tasked with describing the visual changes between images. Three major failings keep cropping up: orientation inconsistency, viewpoint ambiguity, and missing fine details. A human evaluation of 400 image pairs found error rates soaring above 47% with open-source VLM baselines. That's not just a minor glitch, it's a showstopper for scalable training.

Enter EditCaption

Where there's a problem, there's innovation. Enter EditCaption, a two-stage post-training pipeline that's making waves. It starts with building a 100K supervised fine-tuning dataset using GLM-based auto-captioning, EditScore filtering, and human tweaking. Then, it gets even smarter with 10K human-annotated pairs, each detailing the main error type and its severity.

State-of-the-Art Performance

The results? Pretty impressive. Using a 235B model, the system with SFT+HAE-DPO shows off state-of-the-art performance. Across three benchmarks, Eval-400, HQ-Edit, and ByteMorph-Bench, this model scores 4.720, 4.672, and 4.651, respectively. It's outshining Gemini-3-Pro on all fronts. Critical error rates have plummeted from 47.75% to 17.50%, and correct rates have shot up to 70.25%, leaving Gemini-3-Pro at 66.00%.

Why It Matters

Why should we care? Because no one wants to rely on instructions that lead to missteps. If nobody would play it without the model, the model won't save it. The game comes first. The economy comes second. If AI can't handle the 'play' part, it's game over.

So, what does the future hold? This development might be a major shift, but it's only the beginning. With AI models, the grind never ends. We need more innovation and less error. AI's got the potential, but the journey's just started.