EasyGen: Easing Multimodal Generation with BiDiffuser and LLMs

• URL: http://arxiv.org/abs/2310.08949v3
• Date: Fri, 17 May 2024 08:30:18 GMT
• Title: EasyGen: Easing Multimodal Generation with BiDiffuser and LLMs
• Authors: Xiangyu Zhao, Bo Liu, Qijiong Liu, Guangyuan Shi, Xiao-Ming Wu,
• Abstract summary: EasyGen is designed to enhance multimodal understanding and generation by harnessing the capabilities of diffusion models and large language models (LLMs) Easygen achieves text generation by training a projection layer linking BiDiffuser and an LLM, and facilities image generation by training an adapter to align the LLM's text space with the BiDiffuser's image space.
• Score: 26.462946557604177
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present EasyGen, an efficient model designed to enhance multimodal understanding and generation by harnessing the capabilities of diffusion models and large language models (LLMs), Unlike existing multimodal models that predominately depend on encoders like CLIP or ImageBind and need ample amounts of training data to bridge modalities,EasyGen leverages BiDiffuser,a bidirectional conditional diffusion model, to foster more efficient modality interactions. Easygen achieves text generation by training a projection layer linking BiDiffuser and an LLM, and facilities image generation by training an adapter to align the LLM's text space with the BiDiffuser's image space, Comprehensive quantitative and qualitative experiments show that EasyGen excels in data-efficient training, high-quality image generation, and extendibility, effectively addressing the challenges in multimodal generation. The source code is available at https://github.com/zxy556677/EasyGen.

Related papers

• MENTOR: Efficient Multimodal-Conditioned Tuning for Autoregressive Vision Generation Models [30.494968865008513]
  Recent text-to-image models struggle with precise visual control, balancing multimodal inputs, and requiring extensive training for complex image generation.<n>We propose MENTOR, a novel framework for efficient Multimodal-conditioned Tuning for Autoregressive multimodal image generation.<n>Our method delivers superior image reconstruction fidelity, broad task adaptability, and improved training efficiency compared to diffusion-based methods.
  arXiv  Detail & Related papers  (2025-07-13T10:52:59Z)
• Nexus-Gen: A Unified Model for Image Understanding, Generation, and Editing [7.278180096265984]
  Nexus-Gen is a unified model that synergizes the language reasoning capabilities of multimodal large language models with the image synthesis power of diffusion models. We introduce a prefilled autoregression strategy that prefills input sequence with position-embedded special tokens instead of continuous embeddings.
  arXiv  Detail & Related papers  (2025-04-30T06:30:48Z)
• Unified Multimodal Discrete Diffusion [78.48930545306654]
  Multimodal generative models that can understand and generate across multiple modalities are dominated by autoregressive (AR) approaches. We explore discrete diffusion models as a unified generative formulation in the joint text and image domain. We present the first Unified Multimodal Discrete Diffusion (UniDisc) model which is capable of jointly understanding and generating text and images.
  arXiv  Detail & Related papers  (2025-03-26T17:59:51Z)
• MMGen: Unified Multi-modal Image Generation and Understanding in One Go [60.97155790727879]
  We introduce MMGen, a unified framework that integrates multiple generative tasks into a single diffusion model. Our approach develops a novel diffusion transformer that flexibly supports multi-modal output, along with a simple modality-decoupling strategy.
  arXiv  Detail & Related papers  (2025-03-26T15:37:17Z)
• SynerGen-VL: Towards Synergistic Image Understanding and Generation with Vision Experts and Token Folding [66.74446220401296]
  We propose SynerGen-VL, a simple yet powerful encoder-free MLLM capable of both image understanding and generation. We introduce the token folding mechanism and the vision-expert-based progressive alignment pretraining strategy, which effectively support high-resolution image understanding. Our code and models shall be released.
  arXiv  Detail & Related papers  (2024-12-12T18:59:26Z)
• Multimodal Latent Language Modeling with Next-Token Diffusion [111.93906046452125]
  Multimodal generative models require a unified approach to handle both discrete data (e.g., text and code) and continuous data (e.g., image, audio, video) We propose Latent Language Modeling (LatentLM), which seamlessly integrates continuous and discrete data using causal Transformers.
  arXiv  Detail & Related papers  (2024-12-11T18:57:32Z)
• NVLM: Open Frontier-Class Multimodal LLMs [64.00053046838225]
  We introduce NVLM 1.0, a family of frontier-class multimodal large language models (LLMs) that achieve state-of-the-art results on vision-language tasks. We propose a novel architecture that enhances both training efficiency and multimodal reasoning capabilities. We develop production-grade multimodality for the NVLM-1.0 models, enabling them to excel in vision-language tasks.
  arXiv  Detail & Related papers  (2024-09-17T17:59:06Z)
• Exploring the Role of Large Language Models in Prompt Encoding for Diffusion Models [42.891427362223176]
  Large language models (LLMs) based on decoder-only transformers have demonstrated superior text understanding capabilities. We propose a novel framework to fully harness the capabilities of LLMs. We further design an LLM-Infused Diffusion Transformer (LI-DiT) based on the framework.
  arXiv  Detail & Related papers  (2024-06-17T17:59:43Z)
• Generative Visual Instruction Tuning [11.727612242016871]
  We propose to use automatically generated instruction-following data to improve the zero-shot capabilities of a large multimodal model. We produce GenLLaVA, a Generative Large Language and Visual Assistant. Our model demonstrates visual understanding capabilities superior to LLaVA and demonstrates competitive results with native multimodal models.
  arXiv  Detail & Related papers  (2024-06-17T07:06:58Z)
• Diffusion-Based Neural Network Weights Generation [80.89706112736353]
  D2NWG is a diffusion-based neural network weights generation technique that efficiently produces high-performing weights for transfer learning. Our method extends generative hyper-representation learning to recast the latent diffusion paradigm for neural network weights generation. Our approach is scalable to large architectures such as large language models (LLMs), overcoming the limitations of current parameter generation techniques.
  arXiv  Detail & Related papers  (2024-02-28T08:34:23Z)
• Efficient Multimodal Diffusion Models Using Joint Data Infilling with Partially Shared U-Net [20.437172251393257]
  Partially Shared U-Net (PS-U-Net) is an efficient multimodal diffusion model that allows text and image inputs to pass through dedicated layers and skip-connections for preserving modality-specific fine-grained details. Inspired by image inpainting, we also propose a new efficient multimodal sampling method that introduces new scenarios for conditional generation while only requiring a simple joint distribution to be learned. Our empirical exploration of the MS-COCO dataset demonstrates that our method generates multimodal text and image data with higher quality compared to existing multimodal diffusion models.
  arXiv  Detail & Related papers  (2023-11-28T04:34:44Z)
• LLMGA: Multimodal Large Language Model based Generation Assistant [53.150283805515926]
  We introduce a Multimodal Large Language Model-based Generation Assistant (LLMGA) to assist users in image generation and editing. We train the MLLM to grasp the properties of image generation and editing, enabling it to generate detailed prompts. Extensive results show that LLMGA has promising generation and editing capabilities and can enable more flexible and expansive applications.
  arXiv  Detail & Related papers  (2023-11-27T13:37:26Z)
• ToddlerDiffusion: Interactive Structured Image Generation with Cascaded Schrödinger Bridge [63.00793292863]
  ToddlerDiffusion is a novel approach to decomposing the complex task of RGB image generation into simpler, interpretable stages. Our method, termed ToddlerDiffusion, cascades modality-specific models, each responsible for generating an intermediate representation. ToddlerDiffusion consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-11-24T15:20:01Z)
• Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning [115.50132185963139]
  CM3Leon is a decoder-only multi-modal language model capable of generating and infilling both text and images. It is the first multi-modal model trained with a recipe adapted from text-only language models. CM3Leon achieves state-of-the-art performance in text-to-image generation with 5x less training compute than comparable methods.
  arXiv  Detail & Related papers  (2023-09-05T21:27:27Z)
• DiffuGen: Adaptable Approach for Generating Labeled Image Datasets using Stable Diffusion Models [2.0935496890864207]
  "DiffuGen" is a simple and adaptable approach that harnesses the power of stable diffusion models to create labeled image datasets efficiently. By leveraging stable diffusion models, our approach not only ensures the quality of generated datasets but also provides a versatile solution for label generation.
  arXiv  Detail & Related papers  (2023-09-01T04:42:03Z)
• DiffDis: Empowering Generative Diffusion Model with Cross-Modal Discrimination Capability [75.9781362556431]
  We propose DiffDis to unify the cross-modal generative and discriminative pretraining into one single framework under the diffusion process. We show that DiffDis outperforms single-task models on both the image generation and the image-text discriminative tasks.
  arXiv  Detail & Related papers  (2023-08-18T05:03:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.