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.