The Beijing Academy of Artificial Intelligence has released OmniGen2, an open-source multimodal generative model built around a unified transformer framework that handles text-to-image generation, image editing, and subject-driven creation. Compared with its predecessor, OmniGen2 separates the processing of text and visuals, adds a feedback-based training loop, and uses a custom benchmark—OmniContext—to measure how well outputs adhere to prompts.
Some earlier designs shared parameters across text and image modes. OmniGen2 introduces two distinct pathways: an autoregressive transformer for text output and a diffusion-based transformer for image synthesis. A new position encoding scheme called Omni-RoPE manages sequence tokens, spatial coordinates, and modality markers, delivering high-fidelity results in both generation and editing.
To preserve the Qwen2.5-VL-3B large language model’s text skills, OmniGen2 feeds VAE-derived features solely into the diffusion branch. This design protects language performance while providing rich visual signals for the image pathway.
OmniGen2’s standout element is its reflection mechanism. During training, the model reviews its own outputs, spots inconsistencies, and applies corrections. This self-review approach mimics test-time adjustments, improving instruction-following accuracy and visual coherence, even when tasks involve subtle edits like color shifts, object count changes, or repositioning. A multi-stage feedback dataset taught the model how to refine outputs and know when to stop, narrowing the gap between open-source and commercial systems.
For in-context evaluation, the team built OmniContext, a benchmark featuring three task types—SINGLE, MULTIPLE, and SCENE—spanning Character, Object, and Scene scenarios. OmniGen2 led open-source scores with an overall rating of 7.18, outpacing BAGEL and UniWorld-V1. Ratings rely on three metrics—Prompt Following (PF), Subject Consistency (SC), and an overall score computed as the geometric mean—each validated by a GPT-4.1-based agent that checks prompt alignment, cross-image consistency, and realism.
Training consumed 140 million text-to-image pairs plus 10 million proprietary images, augmented by curated sets for in-context tasks and editing. A video-based pipeline extracted semantically aligned frame pairs while Qwen2.5-VL models generated instruction labels covering fine-grained manipulations, motion shifts, and compositional edits.
Most language model weights remained frozen to retain general text understanding, while the diffusion module was built from the ground up and tuned for joint visual-textual attention. A special token “<|img|>” triggers image creation within text streams, streamlining multimodal outputs.
OmniGen2 posted strong benchmarks: in text-to-image it scored 0.86 on GenEval and 83.57 on DPG-Bench; for image editing it achieved Subject Consistency of 7.16; and in OmniContext it recorded 7.81 on SINGLE, 7.23 on MULTIPLE, and 6.71 on SCENE tasks. Its reflection feature also delivered high correction accuracy and reliable termination behavior for flawed generations.
By open-sourcing its code, trained models, and data, the project aims to spur further work on controllable, consistent image-text synthesis. Future plans include adding reinforcement learning to refine the reflection loop and broadening support for more languages and noisy inputs.
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