Cheers: Decoupling Patch Details from Semantic Representations Enables Unified Multimodal Comprehension and Generation

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• Cheers decouples patch-level details from semantic representations to stabilize semantics for multimodal understanding and improve fidelity for image generation.method 70%
• Cheers utilizes a unified vision tokenizer to encode and compress image latent states into semantic tokens for efficient LLM conditioning.method 70%
• Cheers employs an LLM-based Transformer that unifies autoregressive decoding for text generation and diffusion decoding for image generation.method 70%
• Cheers achieves 4x token compression, enabling more efficient high-resolution image encoding and generation.result 70%
• Cheers matches or surpasses advanced UMMs in both visual understanding and generation on popular benchmarks.result 70%
• Cheers outperforms Tar-1.5B on GenEval and MMBench benchmarks while requiring only 20% of the training cost.result 70%
• Challenges in scaling up model architectures or adapting to new data domains may affect output quality.limitation 70%

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