Lumina-mGPT 2.0：自回归模型华丽复兴，媲美顶尖扩散模型

Core Viewpoint - Lumina-mGPT 2.0 is an innovative stand-alone autoregressive image model that integrates various tasks such as text-to-image generation, subject-driven generation, and controllable generation, showcasing significant advancements in image generation technology [5][9][21]. Group 1: Core Technology and Breakthroughs - Lumina-mGPT 2.0 employs a fully independent training architecture, utilizing a pure decoder Transformer model, which allows for two parameter versions (2 billion and 7 billion) and avoids biases from pre-trained models [4][5]. - The model incorporates a high-quality image tokenizer, SBER-MoVQGAN, which was selected based on its optimal reconstruction quality on the MS-COCO dataset [7]. - A unified multi-task processing framework is introduced, enabling seamless support for various tasks including text-to-image generation and image editing [9]. Group 2: Efficient Inference Strategies - The model introduces two optimizations to enhance generation speed while maintaining quality, including model quantization to 4-bit integers and a sampling method that reduces GPU memory consumption by 60% [11][13]. - The optimizations allow for parallel decoding, significantly accelerating the generation process [13]. Group 3: Experimental Results - In text-to-image generation benchmarks, Lumina-mGPT 2.0 achieved a GenEval score of 0.80, ranking it among the top generative models, particularly excelling in tests involving "two objects" and "color attributes" [14][15]. - The model demonstrated superior performance in the Graph200K multi-task benchmark, confirming the feasibility of a pure autoregressive model for multi-modal generation tasks [17]. Group 4: Future Directions - Despite optimizations, Lumina-mGPT 2.0 still faces challenges with sampling time, which affects user experience, indicating a need for further enhancements [21]. - The focus will expand from multi-modal generation to include multi-modal understanding, aiming to improve overall functionality and performance [21].