Papers › Dual Diffusion for Unified Image Generation and Understanding

Dual Diffusion for Unified Image Generation and Understanding

31 Dec 2024CVPR 2025 1arXiv:2501.00289archive 2025-07-28

Zijie Li, Henry Li, Yichun Shi, Amir Barati Farimani, Yuval Kluger, Linjie Yang, Peng Wang

Diffusion models have gained tremendous success in text-to-image generation, yet still lag behind with visual understanding tasks, an area dominated by autoregressive vision-language models. We propose a large-scale and fully end-to-end diffusion model for multi-modal understanding and generation that significantly improves on existing diffusion-based multimodal models, and is the first of its kind to support the full suite of vision-language modeling capabilities. Inspired by the multimodal diffusion transformer (MM-DiT) and recent advances in discrete diffusion language modeling, we leverage a cross-modal maximum likelihood estimation framework that simultaneously trains the conditional likelihoods of both images and text jointly under a single loss function, which is back-propagated through both branches of the diffusion transformer. The resulting model is highly flexible and capable of a wide range of tasks including image generation, captioning, and visual question answering. Our model attained competitive performance compared to recent unified image understanding and generation models, demonstrating the potential of multimodal diffusion modeling as a promising alternative to autoregressive next-token prediction models.

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zijieli-Jlee/Dual-Diffusion mentioned on GitHubpytorchMIT report

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calculate_shift zijieli-Jlee/Dual-Diffusion/sd3_modules/dual_diff_pipeline.py community (archive-listed) ran · violated contract MIT (permissive) · 4e3ad3679e5c37b2 · report
retrieve_timesteps zijieli-Jlee/Dual-Diffusion/sd3_modules/dual_diff_pipeline.py community (archive-listed) ran · our draft was wrong MIT (permissive) · 0b56680ee1708fee · report
compute_density_for_timestep_sampling zijieli-Jlee/Dual-Diffusion/sd3_modules/sd3_training_utils.py community (archive-listed) unverified MIT (permissive) · a6cb835becfb9125 · report
compute_loss_weighting_for_sd3 zijieli-Jlee/Dual-Diffusion/sd3_modules/sd3_training_utils.py community (archive-listed) unverified MIT (permissive) · 43e67a426c782b94 · report
create_logger zijieli-Jlee/Dual-Diffusion/train_dual_diffusion_sd3.py community (archive-listed) unverified MIT (permissive) · cab1caee58ae7b81 · report
gradnorm zijieli-Jlee/Dual-Diffusion/sd3_modules/sd_loss_utils.py community (archive-listed) unverified MIT (permissive) · 7a16f09fe2ddb9f5 · report
load_json zijieli-Jlee/Dual-Diffusion/custom_dataset/iterable_data.py community (archive-listed) unverified MIT (permissive) · d91cfae999630304 · report
prepare_text_inputs zijieli-Jlee/Dual-Diffusion/sd3_modules/dual_diff_pipeline.py community (archive-listed) unverified MIT (permissive) · b23cf8649eb8c230 · report
prepare_text_inputs zijieli-Jlee/Dual-Diffusion/sd3_modules/sd_loss_utils.py community (archive-listed) unverified MIT (permissive) · fcf8e44c9540403a · report

Tasks

Image GenerationLanguage ModelingLanguage ModellingQuestion AnsweringText to Image GenerationText-to-Image GenerationVisual Question Answering

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Methods

Diffusion

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