DreamLLM: What We Can Conclude From This Comprehensive Framework?

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

7 CONCLUSIONS

How can the learning synergy between multimodal content understanding and creation emerge? In this paper, we present DREAMLLM, a comprehensive framework for developing MLLMs that not only understands but also creates multimodal content via diffusion models. Through score distillation of conditional-image synthesis distributions, we avoid the need for intermediate representation targets.

The employment of interleaved documents further enriches the multimodal distributions, fostering the learning of multimodal encoding and decoding. Our extensive empirical evaluations across diverse VL benchmarks demonstrate the effectiveness of DREAMLLM and the emerging learning synergy between multimodal content understanding and creation. Besides, this work initiates the first step towards interleaved content creation. As a general learning framework, we hope it will spur further research in the multimodal machine learning field.

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