REVIEW 33 cited by
OmniGen: Unified Image Generation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The emergence of Large Language Models (LLMs) has unified language generation tasks and revolutionized human-machine interaction. However, in the realm of image generation, a unified model capable of handling various tasks within a single framework remains largely unexplored. In this work, we introduce OmniGen, a new diffusion model for unified image generation. OmniGen is characterized by the following features: 1) Unification: OmniGen not only demonstrates text-to-image generation capabilities but also inherently supports various downstream tasks, such as image editing, subject-driven generation, and visual-conditional generation. 2) Simplicity: The architecture of OmniGen is highly simplified, eliminating the need for additional plugins. Moreover, compared to existing diffusion models, it is more user-friendly and can complete complex tasks end-to-end through instructions without the need for extra intermediate steps, greatly simplifying the image generation workflow. 3) Knowledge Transfer: Benefit from learning in a unified format, OmniGen effectively transfers knowledge across different tasks, manages unseen tasks and domains, and exhibits novel capabilities. We also explore the model's reasoning capabilities and potential applications of the chain-of-thought mechanism. This work represents the first attempt at a general-purpose image generation model, and we will release our resources at https://github.com/VectorSpaceLab/OmniGen to foster future advancements.
Forward citations
Cited by 33 Pith papers
-
DSH-Bench: A Difficulty- and Scenario-Aware Benchmark with Hierarchical Subject Taxonomy for Subject-Driven Text-to-Image Generation
DSH-Bench supplies a hierarchical 58-category subject set, difficulty/scenario labels, and a human-aligned SICS metric that exposes systematic failures of 19 subject-driven T2I models.
-
MultiRef: Controllable Image Generation with Multiple Visual References
MultiRef-bench shows that current image generators that accept multiple visual references still fail to combine them reliably, with the best tested model OmniGen reaching only 66.6% synthetic and 79.0% real-world alig...
-
Simplifying Bayesian Optimization Via In-Context Direct Optimum Sampling
FIBO directly samples the optimum from a pretrained in-context generative model, bypassing surrogate fitting and acquisition function maximization, and matches GP-based Bayesian optimization in quality while being far faster.
-
LoRAShop: Training-Free Multi-Concept Image Generation and Editing with Rectified Flow Transformers
LoRAShop localizes each LoRA's effect to attention-derived spatial masks inside a Flux transformer, enabling training-free multi-concept image generation and editing.
-
R2I-Bench: Benchmarking Reasoning-Driven Text-to-Image Generation
A 3,068-prompt benchmark with per-instance Q&A scoring shows that current text-to-image models, including reasoning-enhanced ones, handle reasoning-driven prompts poorly, with mathematical reasoning near zero.
-
USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning
USO trains one DiT model for subject-driven, style-driven, and joint generation by disentangling content and style from triplet data and adding a style-reward objective, claiming SOTA on USO-Bench.
-
Trade-offs in Image Generation: How Do Different Dimensions Interact?
A new benchmark and VLM-as-judge metric map trade-offs among ten image-generation dimensions across 14 models, with a visualization called DTM.
-
X-Omni: Reinforcement Learning Makes Discrete Autoregressive Image Generative Models Great Again
GRPO reinforcement learning applied to a discrete autoregressive image generator with a diffusion decoder improves instruction following, image quality, and long-text rendering in a unified multimodal model.
-
Scale Your Instructions: Enhance the Instruction-Following Fidelity of Unified Image Generation Model by Self-Adaptive Attention Scaling
SaaS, a self-adaptive attention-scaling method, improves instruction-following fidelity of unified image generation models without training by boosting the cross-attention activation of each sub-instruction in regions...
-
MADI: Masking-Augmented Diffusion with Inference-Time Scaling for Visual Editing
A masking-augmented diffusion objective plus pause-token inference scaling modestly improves instruction adherence and source preservation for OmniGen-based image editing.
-
Beyond Simple Edits: X-Planner for Complex Instruction-Based Image Editing
X-Planner, an MLLM-based planner, decomposes complex image-editing instructions into localized sub-edits with masks and boxes, improving editing quality on standard and new complex benchmarks.
-
FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization
A method for multi-subject image personalization that fuses independently trained LoRA modules at inference time on visual autoregressive models.
-
UniCode$^2$: Cascaded Large-scale Codebooks for Unified Multimodal Understanding and Generation
UniCode² builds a 500K-entry codebook from clustered SigLIP embeddings and uses a cascaded frozen-plus-trainable codebook to unify multimodal understanding and generation with stable training and high token utilization.
-
ComplexBench-Edit: Benchmarking Complex Instruction-Driven Image Editing via Compositional Dependencies
Introduces a benchmark for chain-dependent image editing instructions plus a region-aware consistency metric, and shows a chain-of-thought prompt improves a Gemini-based editor.
-
From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models
Frozen CogVideoX1.5, adapted with LoRA on 3 to 30 input-output videos, performs segmentation, pose estimation, and abstract reasoning (ARC-AGI 16.75%) with modest but real generalization.
-
UNIC: Unified In-Context Video Editing
One diffusion transformer handles ID insert, swap, delete, stylization, propagation, and re-camera control in a single model using in-context token concatenation with task-aware positional encoding and bias.
-
Image Editing As Programs with Diffusion Models
IEAP decomposes complex editing instructions into atomic operations executed sequentially on a diffusion transformer, and reports state-of-the-art results on MagicBrush and AnyEdit.
-
HaploOmni: Unified Single Transformer for Multimodal Video Understanding and Generation
A training recipe that initializes different depth segments of one transformer from pretrained ViT, LLM, and DiT models, then jointly tunes them to do multimodal understanding and generation.
-
AlignGen: Boosting Personalized Image Generation with Cross-Modality Prior Alignment
AlginGen improves zero-shot personalized image generation by training a learnable token and a selective attention mask that align textual and visual priors, achieving the best balance of concept preservation and promp...
-
OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation
A benchmark and five-million-clip dataset for evaluating and training subject-to-video generation models, with three new metrics for subject consistency, naturalness, and text alignment.
-
Jodi: Unification of Visual Generation and Understanding via Joint Modeling
A single diffusion transformer with role-switch training performs joint generation, controllable generation, and multi-label perception across image and seven label domains.
-
KRIS-Bench: Benchmarking Next-Level Intelligent Image Editing Models
A new benchmark, KRIS-Bench, evaluates image editing models on knowledge-grounded reasoning across factual, conceptual, and procedural tasks, and finds large performance gaps in current models.
-
OmniStyle: Filtering High Quality Style Transfer Data at Scale
A new million-triplet dataset and a diffusion transformer model that performs text-guided and image-guided style transfer, with a filtering pipeline used to curate high-quality training examples.
-
JarvisHub: An Open Harness for Canvas-Native Multimodal Creative Agents
JarvisHub open-sources a three-layer canvas-state, protocol-bridge, and agent-runtime harness so multimodal creative agents can inspect and update a shared editable project graph over long workflows.
-
Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing
A compact 4B image generation/editing system with a fast one-step VAE, native-resolution packing, RL alignment, and 4-step distillation reports competitive benchmarks against 6B–80B open models.
-
LiDARDraft: Generating LiDAR Point Cloud from Versatile Inputs
LiDARDraft represents text, image, and point-cloud inputs as 3D layouts and uses them to condition LiDAR point-cloud diffusion, reporting improved FRD/MMD/JSD/FPD on KITTI-360.
-
DreamPoster: A Unified Framework for Image-Conditioned Generative Poster Design
DreamPoster fine-tunes Seedream3.0 with a deconstruction-recaptioning dataset pipeline and a three-stage curriculum to turn image-plus-text inputs into finished posters, reporting substantially higher usability than G...
-
XVerse: Consistent Multi-Subject Control of Identity and Semantic Attributes via DiT Modulation
XVerse learns token-specific offsets that modify the text-stream modulation of a diffusion transformer, enabling multi-subject identity and attribute control in image generation.
-
Draw ALL Your Imagine: A Holistic Benchmark and Agent Framework for Complex Instruction-based Image Generation
A new benchmark and agent framework for complex text-to-image generation, with an unvalidated AI-judge evaluation and claims that the agent outperforms GPT-4o on the authors' own benchmark.
-
Dimension-Reduction Attack! Video Generative Models are Experts on Controllable Image Synthesis
A video diffusion model, HunyuanVideo-I2V, is adapted with mixup transitions, frame-skip position embeddings, and attention masking to outperform image-only models on several controllable image generation benchmarks.
-
ComfyMind: Toward General-Purpose Generation via Tree-Based Planning and Reactive Feedback
A ComfyUI-based multi-agent system with semantic workflow modules and tree-based local-feedback planning reports near-perfect pass rates on ComfyBench and competitive scores on GenEval and Reason-Edit.
-
SeedEdit 3.0: Fast and High-Quality Generative Image Editing
SeedEdit 3.0 reports a 56.1% usability rate on internal real-image editing tests, beating SeedEdit 1.6, GPT-4o, and Gemini 2.0, with 8x faster inference after distillation and quantization.
-
StyleAR: Customizing Multimodal Autoregressive Model for Style-Aligned Text-to-Image Generation
StyleAR enables autoregressive image generation models to do style-aligned text-to-image generation using only binary text-image data, via self-reconstruction training and style-enhanced tokens.
Discussion (0). Sign in to comment.