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

B ADDITIONAL QUALITATIVE EXAMPLES

Text-condition Image Synthesis In Fig. 10 and Fig. 11, we show the image examples of DREAMLLM using the same prompts from previous works for a cross reference and comparison, including DALL-E (Ramesh et al., 2021), DALL-E 2 (i.e., unCLIP) (Ramesh et al., 2022), GLIDE (Nichol et al., 2022), Imagen (Saharia et al., 2022), and Parti (Yu et al., 2022b). Similar to Parti, we have extended some prompts with new sub-prompts for constructing more examples from different prompts.

Multimodal Dialogue In Tables 9 and 10, we present a comparative analysis of visual question answering results between our model, DREAMLLM, and other state-of-the-art models: GPT-4 (OpenAI, 2023), LLaVA (Liu et al., 2023a), BLIP-2 (Li et al., 2022), and OpenFlamingo (Awadalla et al., 2023b).

The key findings are as follows: i) DREAMLLM surpasses GPT-4 in providing more detailed and precise responses to given questions. ii) While LLaVA (Liu et al., 2023a) also offers detailed responses, it frequently introduces imaginary elements not present in the image. In contrast, DREAMLLM delivers more accurate answers, effectively avoiding this visual hallucination issue. This observation aligns with our earlier findings in Table 7, which underscore the robustness of DREAMLLM against visual hallucination.

Furthermore, we showcase additional qualitative results of the multimodal dialogue in Fig. 7, Fig. 8, and Fig. 9. These figures illustrate DREAMLLM’s proficiency in comprehending and generating long-context multimodal information in various input and output formats.