Table of Links Abstract and 1 Introduction 2 Background & Problem Statement 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 3 DreamLLM 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.2 Model Training 4 Experiments and 4.1 Multimodal Comprehension 4.2 Text-Conditional Image Synthesis 4.3 Multimodal Joint Creation & Comprehension 5 Discussions 5.1 Synergy between creation & Comprehension? 5. 2 What is learned by DreamLLM? 6 Related Works 7 Conclusions and References A Additional Experiments B Additional Qualitative Examples C Implementation Details D Additional Related Works E Limitations, Failure Cases & Future Works 5.2 WHAT IS LEARNED BY DREAMLLM? Dream Query Attention In DREAMLLM, the conditional embedding is derived from MLLMs with some learned dream queries. Fig. 6 demonstrates a visualization of the learned cross-attention mechanism between these queries and the diffusion latent. Similar to (Hertz et al., 2023), we visualize the attention map averaged across all timestamps. It is seen that: i) The query attention is structured, disentangled, and semantically-oriented. This is evidenced by the fact that distinct queries adeptly capture different subject and background semantics. ii) Despite varying prompts, attention patterns exhibit remarkable similarity as shown in Fig. 6 (a) and (b). This contrasts with the token attentions from the original SD, which are typically text-token dependent. We postulate that this arises from the model’s causal nature, leading to a consistent semantic structure order. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors:
(1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII;
(2) Chunrui Han, MEGVII Technology;
(3) Yuang Peng, Tsinghua University and Internship at MEGVII;
(4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII;
(5) Zheng Ge, MEGVII Technology;
(6) Jinrong Yang, HUST and Internship at MEGVII;
(7) Liang Zhao, MEGVII Technology;
(8) Jianjian Sun, MEGVII Technology;
(9) Hongyu Zhou, MEGVII Technology;
(10) Haoran Wei, MEGVII Technology;
(11) Xiangwen Kong, MEGVII Technology;
(12) Xiangyu Zhang, MEGVII Technology and a Project leader;
(13) Kaisheng Ma, Tsinghua University and a Corresponding author;
(14) Li Yi, Tsinghua University, a Corresponding authors and Project leader. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background & Problem Statement 2 Background & Problem Statement 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 3 DreamLLM 3 DreamLLM 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.2 Model Training 3.2 Model Training 4 Experiments and 4.1 Multimodal Comprehension 4 Experiments and 4.1 Multimodal Comprehension 4.2 Text-Conditional Image Synthesis 4.2 Text-Conditional Image Synthesis 4.3 Multimodal Joint Creation & Comprehension 4.3 Multimodal Joint Creation & Comprehension 5 Discussions 5.1 Synergy between creation & Comprehension? 5.1 Synergy between creation & Comprehension? 5. 2 What is learned by DreamLLM? 5. 2 What is learned by DreamLLM? 6 Related Works 6 Related Works 7 Conclusions and References 7 Conclusions and References A Additional Experiments A Additional Experiments B Additional Qualitative Examples B Additional Qualitative Examples C Implementation Details C Implementation Details D Additional Related Works D Additional Related Works E Limitations, Failure Cases & Future Works E Limitations, Failure Cases & Future Works 5.2 WHAT IS LEARNED BY DREAMLLM? Dream Query Attention In DREAMLLM, the conditional embedding is derived from MLLMs with some learned dream queries. Fig. 6 demonstrates a visualization of the learned cross-attention mechanism between these queries and the diffusion latent. Similar to (Hertz et al., 2023), we visualize the attention map averaged across all timestamps. It is seen that: i) The query attention is structured, disentangled, and semantically-oriented. Dream Query Attention This is evidenced by the fact that distinct queries adeptly capture different subject and background semantics. ii) Despite varying prompts, attention patterns exhibit remarkable similarity as shown in Fig. 6 (a) and (b). This contrasts with the token attentions from the original SD, which are typically text-token dependent. We postulate that this arises from the model’s causal nature, leading to a consistent semantic structure order. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv Authors: (1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII; (2) Chunrui Han, MEGVII Technology; (3) Yuang Peng, Tsinghua University and Internship at MEGVII; (4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII; (5) Zheng Ge, MEGVII Technology; (6) Jinrong Yang, HUST and Internship at MEGVII; (7) Liang Zhao, MEGVII Technology; (8) Jianjian Sun, MEGVII Technology; (9) Hongyu Zhou, MEGVII Technology; (10) Haoran Wei, MEGVII Technology; (11) Xiangwen Kong, MEGVII Technology; (12) Xiangyu Zhang, MEGVII Technology and a Project leader; (13) Kaisheng Ma, Tsinghua University and a Corresponding author; (14) Li Yi, Tsinghua University, a Corresponding authors and Project leader. Authors: Authors: (1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII; (2) Chunrui Han, MEGVII Technology; (3) Yuang Peng, Tsinghua University and Internship at MEGVII; (4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII; (5) Zheng Ge, MEGVII Technology; (6) Jinrong Yang, HUST and Internship at MEGVII; (7) Liang Zhao, MEGVII Technology; (8) Jianjian Sun, MEGVII Technology; (9) Hongyu Zhou, MEGVII Technology; (10) Haoran Wei, MEGVII Technology; (11) Xiangwen Kong, MEGVII Technology; (12) Xiangyu Zhang, MEGVII Technology and a Project leader; (13) Kaisheng Ma, Tsinghua University and a Corresponding author; (14) Li Yi, Tsinghua University, a Corresponding authors and Project leader.

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