ReAlign distills LLM-generated reasoning texts into a lightweight AIGI forgery detector via contrastive image-text alignment to improve generalization on complex forgeries.
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Seedream 3.0 Technical Report
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abstract
We present Seedream 3.0, a high-performance Chinese-English bilingual image generation foundation model. We develop several technical improvements to address existing challenges in Seedream 2.0, including alignment with complicated prompts, fine-grained typography generation, suboptimal visual aesthetics and fidelity, and limited image resolutions. Specifically, the advancements of Seedream 3.0 stem from improvements across the entire pipeline, from data construction to model deployment. At the data stratum, we double the dataset using a defect-aware training paradigm and a dual-axis collaborative data-sampling framework. Furthermore, we adopt several effective techniques such as mixed-resolution training, cross-modality RoPE, representation alignment loss, and resolution-aware timestep sampling in the pre-training phase. During the post-training stage, we utilize diversified aesthetic captions in SFT, and a VLM-based reward model with scaling, thereby achieving outputs that well align with human preferences. Furthermore, Seedream 3.0 pioneers a novel acceleration paradigm. By employing consistent noise expectation and importance-aware timestep sampling, we achieve a 4 to 8 times speedup while maintaining image quality. Seedream 3.0 demonstrates significant improvements over Seedream 2.0: it enhances overall capabilities, in particular for text-rendering in complicated Chinese characters which is important to professional typography generation. In addition, it provides native high-resolution output (up to 2K), allowing it to generate images with high visual quality.
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representative citing papers
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citing papers explorer
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ReAlign: Generalizable Image Forgery Detection via Reasoning-Aligned Representation
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HierEdit: Region-Aware Hierarchical Diffusion for Efficient High-Resolution Editing
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Unlocking Complex Visual Generation via Closed-Loop Verified Reasoning
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Qwen-Image-VAE-2.0 Technical Report
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L2P: Unlocking Latent Potential for Pixel Generation
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Leveraging Verifier-Based Reinforcement Learning in Image Editing
Edit-R1 builds a CoT-based reasoning reward model (RRM) via SFT and GCPO, then applies it with GRPO to improve image editing models such as FLUX.1-kontext.
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SpatialFusion: Endowing Unified Image Generation with Intrinsic 3D Geometric Awareness
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LLaDA2.0-Uni: Unifying Multimodal Understanding and Generation with Diffusion Large Language Model
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Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation
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Generative Refinement Networks for Visual Synthesis
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Self-Adversarial One Step Generation via Condition Shifting
APEX derives self-adversarial gradients from condition-shifted velocity fields in flow models to achieve high-fidelity one-step generation, outperforming much larger models and multi-step teachers.
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Nucleus-Image: Sparse MoE for Image Generation
A 17B-parameter sparse MoE diffusion transformer activates 2B parameters per pass and reaches competitive quality on image generation benchmarks without post-training.
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IdGlow: Dynamic Identity Modulation for Multi-Subject Generation
IdGlow is a progressive two-stage diffusion framework that uses task-adaptive timestep scheduling, temporal gating, VLM prompt synthesis, and group-level DPO to balance identity preservation and scene coherence in multi-subject image generation.
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PixelGen: Improving Pixel Diffusion with Perceptual Supervision
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VDE Bench: Evaluating The Capability of Image Editing Models to Modify Visual Documents
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Seedance 1.5 pro: A Native Audio-Visual Joint Generation Foundation Model
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DeCo: Frequency-Decoupled Pixel Diffusion for End-to-End Image Generation
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Emu3.5: Native Multimodal Models are World Learners
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MONET: A Massive, Open, Non-redundant and Enriched Text-to-image dataset
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Decomposing Subject-Driven Image Generation via Intermediate Structural Prediction
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A Systematic Post-Train Framework for Video Generation
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Pref-GRPO: Pairwise Preference Reward-based GRPO for Stable Text-to-Image Reinforcement Learning
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Qwen-Image Technical Report
Qwen-Image is a foundation model that reaches state-of-the-art results in image generation and editing by combining a large-scale text-focused data pipeline with curriculum learning and dual semantic-reconstructive encoding for editing consistency.
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Qwen-Image-2.0 Technical Report
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Seedance 1.0: Exploring the Boundaries of Video Generation Models
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Wan-Image: Pushing the Boundaries of Generative Visual Intelligence
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Seedance 2.0: Advancing Video Generation for World Complexity
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Seedream 4.0: Toward Next-generation Multimodal Image Generation
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- Beyond Text Prompts: Visual-to-Visual Generation as A Unified Paradigm