Exploring Baidu PaddlePaddle's Multimodal Large Model Innovations and the PaddleMIX Development Kit

Introduction – The talk shares Baidu PaddlePaddle's exploration and practice in deploying multimodal large models, highlighting the open‑source PaddleMIX suite designed to lower development barriers.

1. Capabilities and Application Scenarios – Multimodal models combine understanding (image captioning, object recognition) and generation (text‑to‑image, video synthesis). They excel in fine‑grained visual tasks, commercial intelligence (GBI), and industrial defect detection, while noting challenges such as hallucination and high‑resolution processing.

2. Architecture Evolution and Characteristics – Since 2022, multimodal models have progressed toward unified handling of image, audio, and video. Two architectural styles are discussed: (a) using a large language model (LLM) as a scheduler to invoke specialized vision, audio, or video modules, and (b) integrating the LLM as a sub‑module for end‑to‑end training. Encoder designs (CLIP, ViT) and high‑resolution strategies (image slicing, multi‑branch encoders) are examined, as well as Connector modules (MLP, cross‑attention Q‑Former, Resampling) that align multimodal and textual features.

3. Multimodal Generation Architecture – Generation relies on diffusion models guided by LLM‑encoded text, with a shift from convolutional to Transformer‑based diffusion backbones (DIT, MMDIT). Larger models improve fidelity and detail.

4. PaddleMIX Development Kit – PaddleMIX 2.1 offers a comprehensive model library, end‑to‑end pipelines (data handling, pre‑training, fine‑tuning, inference), and tools such as DataCopilot for schema conversion, sampling, and data augmentation. It introduces PP‑Instagger for multimodal tagging, information‑density filtering, and MixToken for efficient fine‑tuning. Distributed training, low‑precision (FP16/BF16) support, and optimized inference (operator fusion, layout tuning, batch parallelism) deliver up to 26% speedup.

5. Q&A Highlights – Answers cover video understanding progress, DataCopilot’s role in labeling and data preparation, design rationale behind information‑density filtering, and the relationship between large language models and multimodal models, emphasizing shared training paradigms and architectural concepts.

Conclusion – The session underscores the rapid evolution of multimodal large models, the practical advantages of PaddleMIX for developers, and ongoing challenges toward a truly unified multimodal AI system.