# Squeeze-MLLM: A New Breakthrough in Subject-Driven Image Generation Powered by Multimodal Large Language Models

> This article introduces the Squeeze-MLLM framework, which deeply integrates multimodal large language models (MLLMs) with diffusion models, combining a Dual-Layer Aggregation (DLA) module and a multi-stage denoising strategy. It achieves high-quality text-guided image generation while maintaining subject identity consistency, significantly outperforming existing methods.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-25T17:59:35.000Z
- 最近活动: 2026-05-26T04:18:05.190Z
- 热度: 144.7
- 关键词: 多模态大语言模型, 主题驱动图像生成, 扩散模型, 身份保持, 跨模态理解, 双层聚合, 多阶段去噪, 图像合成, VAE条件, 计算机视觉
- 页面链接: https://www.zingnex.cn/en/forum/thread/squeeze-mllm
- Canonical: https://www.zingnex.cn/forum/thread/squeeze-mllm
- Markdown 来源: floors_fallback

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## [Introduction] Squeeze-MLLM: A New Breakthrough in Subject-Driven Image Generation Powered by Multimodal Large Language Models

**Core Insights**: The Squeeze-MLLM framework deeply integrates multimodal large language models (MLLMs) with diffusion models, combining the Dual-Layer Aggregation (DLA) module and a multi-stage denoising strategy. It achieves high-quality text-guided image generation while maintaining subject identity consistency, significantly outperforming existing methods.
**Basic Information**:
- Original author team: Researchers including zsh2000
- Source platform: arXiv
- Publication date: May 25, 2026
- Original paper link: http://arxiv.org/abs/2605.26111v1
- Project homepage: https://zsh2000.github.io/squeeze-mllm-subject-gen/

## Research Background and Core Challenges

Subject-driven image generation aims to produce new images that both preserve the subject identity and align with text instructions based on reference images and text prompts. It is applied in scenarios like personalized creation and e-commerce display. Existing methods face two major challenges:
1. Separate encoding limits cross-modal reasoning ability, making it difficult to understand complex semantic relationships between text and images;
2. Lack of effective identity preservation mechanisms, which easily leads to "copy-paste" artifacts.
Although some studies have combined MLLMs with diffusion models, they ignore identity preservation, resulting in poor identity consistency in generated results.

## Core Design of the Squeeze-MLLM Framework

Core ideas of the Squeeze-MLLM framework:
- **Joint Encoding**: Let MLLMs process both text and reference images simultaneously, understanding their relationships in a unified semantic space to avoid mechanical concatenation;
- **VAE Identity Condition**: Introduce a variational autoencoder to extract fine-grained features, ensuring precise preservation of subject details such as texture and color.
The combination of these two achieves an organic unification of semantic understanding and identity preservation.

## Dual-Layer Aggregation (DLA) Module: Multi-Level Feature Fusion

The Dual-Layer Aggregation (DLA) module aggregates features from different layers of MLLMs:
- Shallow features: Retain details like edges and textures;
- Deep features: Contain semantics such as object categories and scene understanding.
By adaptively adjusting weights, it provides rich conditional signals for diffusion models, flexibly adapting to different generation needs (e.g., precise identity preservation or complex semantic understanding).

## Multi-Stage Denoising Strategy: Progressive Balance Between Semantics and Identity

The multi-stage denoising strategy balances semantics and identity in stages:
- **Early stage**: Rely on MLLM semantic conditions to establish correct composition and conceptual expression;
- **Late stage**: Increase the weight of VAE identity conditions to restore subject details.
This simulates the human painting process (outlines first, then details), effectively alleviating "copy-paste" artifacts.

## Experimental Results: Significantly Outperforming Existing Methods

Experimental results show that Squeeze-MLLM significantly outperforms existing methods:
- **Human preference**: Generated images have higher subjective scores, balancing semantic understanding and identity preservation;
- **Quantitative metrics**: Excellent performance in dimensions like identity preservation, text-image alignment, and image quality;
- **Artifact mitigation**: Achieves more flexible and natural subject transfer, reducing traces of rigid splicing.

## Technical Insights and Future Outlook

**Technical Insights**:
1. MLLMs have great potential in visual generation tasks and can handle both text and image inputs uniformly;
2. The multi-level feature fusion strategy of DLA can be applied to multi-scale visual tasks;
3. The dynamic conditional adjustment of multi-stage denoising provides new ideas for conditional control of diffusion models.
**Future Outlook**: Explore more aggregation mechanisms, extend dynamic conditional adjustment to other diffusion applications, and look forward to more intelligent image generation systems.