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Xiaohongshu Open-Sources FireRed-Image-Edit v1.1 AI Model, Tackles Consistency and Complex Edits
On March 9, 2026, Xiaohongshu's Super Intelligence team officially launched the FireRed-Image-Edit v1.1 model. Arriving less than a month after v1.0, this release signals a major acceleration in the platform's iteration speed for multimodal large models.
The new version delivers deep optimization for complex tasks like ID-consistent editing, multi-element fusion, portrait enhancement, and font style reference, while preserving its predecessor's strengths. It exhibits significantly improved semantic comprehension and visual generation. From a technical standpoint, v1.1 now supports full-process optimization for training and deployment, cutting inference time to 4.5 seconds and capping memory usage at 30GB—a major boost for practical, industrial-scale implementation.
As the core engine powering Xiaohongshu's general intelligence ambitions, the Super Intelligence team has fully open-sourced the project's code, technical papers, model weights, and its complete training and inference framework. This initiative not only bolsters Xiaohongshu's internal workflows for content creation, distribution, search, and advertising but also addresses an industry gap by providing a sophisticated, open-source toolkit for fine-grained image editing.
With the global large model race now focused on deep application, the rapid advancement and open-sourcing of the FireRed series highlights how leading platforms are working to democratize multimodal technology. By shifting focus from pure model development to high-performance engineering, Xiaohongshu is forging a distinct, content-centric AI advantage, poised to accelerate the adoption of multimodal intelligence in content-driven e-commerce and social platforms.
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On March 9, 2026, Xiaohongshu's Super Intelligence team officially launched the FireRed-Image-Edit v1.1 model. Arriving less than a month after v1.0, this release signals a major acceleration in the platform's iteration speed for multimodal large models.
The new version delivers deep optimization for complex tasks like ID-consistent editing, multi-element fusion, portrait enhancement, and font style reference, while preserving its predecessor's strengths. It exhibits significantly improved semantic comprehension and visual generation. From a technical standpoint, v1.1 now supports full-process optimization for training and deployment, cutting inference time to 4.5 seconds and capping memory usage at 30GB—a major boost for practical, industrial-scale implementation.
As the core engine powering Xiaohongshu's general intelligence ambitions, the Super Intelligence team has fully open-sourced the project's code, technical papers, model weights, and its complete training and inference framework. This initiative not only bolsters Xiaohongshu's internal workflows for content creation, distribution, search, and advertising but also addresses an industry gap by providing a sophisticated, open-source toolkit for fine-grained image editing.
With the global large model race now focused on deep application, the rapid advancement and open-sourcing of the FireRed series highlights how leading platforms are working to democratize multimodal technology. By shifting focus from pure model development to high-performance engineering, Xiaohongshu is forging a distinct, content-centric AI advantage, poised to accelerate the adoption of multimodal intelligence in content-driven e-commerce and social platforms.
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