ByteDance’s Seed research team introduced Seedream 5.5 in a blog post on 9 November. The new model generates and edits images for professional image production. The Decoder, which covers AI news, reported on the announcement the same day.

Repeated editing and transparent backgrounds

When someone edits an image in rounds, each building on the previous result, small errors can accumulate: details disappear and the image degrades. ByteDance said Seedream 5.5 was trained specifically to preserve unedited regions with pixel accuracy and without colour shifts. This supports continued work on the same image across 100 rounds of editing, the company said.

The company illustrated this with a demo in which it edited an image of the same person 100 times in succession. Even then, the model accurately edited small objects such as rings and glasses frames, the company said.

Second, the model stores each pixel’s transparency, known as the alpha channel. It produces RGBA images directly, with fully or partially transparent backgrounds. Users can place a generated object or character into a layout or composite without separate background removal or edge clean-up. Objects and characters can be generated from a text description or a reference image.

The company said the model also renders light, materials and skin more naturally, and draws small, dense text and multi-stroke Chinese characters more accurately than before.

The company’s own assessments

ByteDance tested the model in six use cases: e-commerce, marketing, film and television, games, office productivity and fashion design. Experienced image-generation evaluators scored the quality and usability of the results on the MagicArena platform.

Compared with the previous version, Seedream 5.0 Pro, both the evaluators’ quality scores (MOS) and the share of usable images rose in all six use cases. In film and television and in office productivity, the share of usable images more than doubled.

Experts assessed text structure and realism in image pairs using the GSB (Good/Same/Bad) method, in which the evaluator says which image is better or whether both are equally good. The new model beat the previous version in all four comparisons, across both text-to-image and image-to-image tasks. For realism in image-to-image tasks, for example, the GSB score was +55.62 per cent.

ByteDance said the model outperforms GPT Image 2.5 Sunburst and Nano Banana 2.1 in text structure in both task types. In realism, the company reports a clear lead in text-to-image tasks. The Decoder presents the comparison with competitors as ByteDance’s claim.

In a test of Chinese and English text, the severe text corruption rate was 0.4 per cent and the minor text corruption rate 1.7 per cent. The company said both rates are clearly lower than the previous version’s and among the best in a comparison of frontier models.

The Decoder reported that the model is gradually rolling out in ByteDance’s Jimeng and Doubao services, Doubao Work and the Volcengine Ark platform. ByteDance said a Pro version is coming and that it plans to expand the model’s use in agent workflows.