Large-scale image generation has benefited from advances in data scale, quality, rebalancing, and recaptioning, yet conventional pipelines typically optimize task-specific datasets in isolation. A central challenge is not only how to curate each task-specific corpus, but also how to organize heterogeneous supervision according to the dependencies among generative capabilities. We present a capability-driven data infrastructure that couples capability-specific supervision construction with capability-aligned curriculum scheduling. Its three specialized yet interoperable data engines build complementary relational supervision for text-image grounding, inter-image transformation, and image-knowledge association, while caption experts align T2I and editing supervision across tasks and granularities. A multi-stage curriculum jointly evolves task composition, visual-concept distribution, data quality, and image resolution along the dependency order of capability acquisition, with capability-aware evaluation closing the loop through targeted retrieval, expert construction, and gap-aware resampling. At scale, the framework curates a 440M-image T2I corpus, 120M editing pairs, and over 27M image-entity pairs. With this infrastructure, we train multimodal diffusion models at two scales from scratch, with 3B and 6B sizes respectively. We conduct quantitative evaluation on CPI-Bench, along with qualitative evaluations across diverse text-to-image and editing scenarios. Experimental results present broad visual coverage, versatile rendering, and effective transfer across generative capabilities.
Figures & tables
Figure 1: Capability-specific data construction in our framework. Shared collection and wrangling feed three specialized but interoperable engines for visual expression, editing association, and knowledge-grounded reasoning.
Figure 2: Distribution of the curated T2I corpus. The inner ring distinguishes collected and constructed data, while the outer ring reports the composition of visual domains retained for training.
Figure 3: Composition of the image-editing corpus. The left Sankey diagram shows the relative mixture of single-image and multi-image editing tasks, and the right panels illustrate representative task families.
Figure 4: Multi-granularity T2I supervision. Starting from a comprehensive annotation of an image, we construct entity descriptions, tags, short prompts, and long-form captions at multiple levels of details.
Figure 5: Pipeline for constructing T2I-aligned editing instructions. Dual-view perception produces a target-image caption and a raw editing instruction; task-specific actors align local or global changes with reusable T2I descriptions, followed by consistency verification and iterative revision.
Figure 6: Capability-gap-driven active feedback loop. Capability-aware evaluation identifies failure cases, which seed neighboring-data retrieval and expert-driven construction. Gap-aware resampling then increases the weights of persistent failures and down-weights resolved gaps in subsequent training.
Figure 7: Qualitative T2I results across illustration, graphic design, knowledge visualization, portraiture, landscapes, multi panel composition, and photographic style control.
Figure 8: Qualitative comparison on challenging single image editing cases. The examples cover hybrid editing, reasoning based transformation, object segmentation, and style transfer.
Figure 9: Visualization of degradation-aware restoration for different degradation types.
Model
Parameters
CPI-General
CPI-Practical
Overall ↑
Our Model-3B
3B
3.95
3.91
3.93
Our Model-6B
6B
3.96
3.92
3.94
Table 1: Image editing quantitative evaluation on CPI-General-Bench and CPI-Practical-Bench. Overall denotes their arithmetic mean.
Figure 10: Qualitative comparison on multi image editing. The examples evaluate viewpoint alignment, reference relation understanding, and composition capabilities. Comparison cases are grouped by with/without explicit image index reference.
Text-rich image generation is one of the most challenging settings in image generation, since models must simultaneously produce visually realistic images and render legible, semantically aligned, and layout-consistent text. Existing data pipelines usually follow a static crawl-filter-freeze paradigm. They collect candidate samples, filter them once, and freeze the accepted data for training. However, rejected samples are usually discarded, although they often contain useful failure signals such as OCR errors and semantic mismatches. As a result, later construction rounds may repeat the same failure modes. To address these limitations, we propose DataEvolver, a self-evolving multi-agent framework for text-rich image data construction. DataEvolver treats data construction as feedback-driven construction policy evolution. A Retriever collects candidate samples, a Verifier assigns quality scores and rejection causes, a Critic summarizes round-level feedback into semantic feedback, and a Generator completes under-covered regions through targeted synthesis. The updated feedback memory then guides the next construction round. Experiments on text-rich image generation benchmarks show that DataEvolver produces more useful training data than fixed-dataset baselines under matched data budgets. At the 0.75M scale on PixArt-alpha, DataEvolver improves OCR-F1 over the strongest baseline by 85.3 percent on TextScenesHQ and 35.3 percent on LongTextBench. The improvements are consistent across both evaluated benchmarks and also transfer to Show-o2, indicating that the benefit of DataEvolver is not tied to a single downstream generator. These results suggest that rejected samples can provide actionable feedback for improving text-rich image data construction.
Instruction-based image editing has emerged as a key capability for unified multimodal models (UMMs), yet constructing large-scale, diverse, and high-quality editing datasets without costly proprietary APIs remains challenging. Previous image editing datasets either rely on closed-source models for annotation, which prevents cost-effective scaling, or employ fixed synthetic editing pipelines, which suffer from limited quality and generalizability. To address these challenges, we propose ScaleEditor, a fully open-source hierarchical multi-agent framework for end-to-end construction of large-scale, high-quality image editing datasets. Our pipeline consists of three key components: source image expansion with world-knowledge infusion, adaptive multi-agent editing instruction-image synthesis, and a task-aware data quality verification mechanism. Using ScaleEditor, we curate ScaleEdit-12M, the largest open-source image editing dataset to date, spanning 23 task families across diverse real and synthetic domains. Fine-tuning UniWorld-V1 and Bagel on ScaleEdit yields consistent gains, improving performance by up to 10.4% on ImgEdit and 35.1% on GEdit for general editing benchmarks and by up to 150.0% on RISE and 26.5% on KRIS-Bench for knowledge-infused benchmarks. These results demonstrate that open-source, agentic pipelines can approach commercial-grade data quality while retaining cost-effectiveness and scalability. Both the framework and dataset will be open-sourced.
Guanzhou Chen, Erfei Cui, Changyao Tian +6
Shanghai Jiao Tong University · Shanghai AI Laboratory · CUHK MMLab +3
Modern image generation demands a single model that unifies diverse capabilities, including text-to-image (T2I), local editing, and global editing. However, these capabilities are rarely naturally aligned and often conflict. For instance, editing tends to degrade T2I performance, while global and local editing interfere with each other. Consequently, effectively composing these capabilities has become a central challenge for image generation model training. To tackle this, we introduce DanceOPD, an on-policy generative field distillation framework for flow-matching models that routes each sample to one capability field, queries one low-noise student-induced state, and trains with a simple velocity MSE objective. With each capability source defined as a velocity field over the shared flow state space, the student learns from fields queried on its own rollout states to compose expert capabilities. This formulation also absorbs operator-defined fields such as classifier-free guidance. Comprehensive experiments on T2I, editing, realism-field absorption, and CFG absorption show that our approach improves multi-capability composition, strengthening target capabilities while preserving anchor generation quality. We believe this work establishes a practical route for generative field distillation in flow-matching models.