LAION-5B gave us 5.85 billion CLIP-filtered image-text pairs. Stable Diffusion trained on a 2-billion-pair subset of it and stunned the world with its range. So the natural conclusion, if you're running a lab in 2024, is to go bigger: ten billion pairs, fifty billion. Surely capability follows volume.
It doesn't—at least not cleanly. The same models that render photorealistic portraits struggle to draw three red cubes on top of two blue spheres. Human evaluations of DALL-E 2 found that accuracy on complex multi-object scenes often fell below 60%. Meanwhile, FID scores on ImageNet 256×256 dropped from around 10 in 2020 to roughly 2 in 2023. Fidelity is nearly solved, yet compositionality, controllability, and prompt adherence lag behind. The bottleneck isn't data quantity. It's data design.
Capability-centric data design flips the question. Instead of asking "How much data can we scrape?" it asks "Which capabilities do we need, and what training signal develops them together?" The goal is co-evolution: progress in one skill—say, text alignment—pulling another—say, compositional generation—along with it, rather than trading one against the other.
Here are seven ways to put that idea into practice.
Key Takeaway: Scale bought us fidelity. It did not buy us balance. The next gains come from designing datasets around capabilities, not just collecting more images.
Static curation means assembling a dataset once, filtering it for quality, and training on it. Capability-centric design means treating the dataset as a living instrument: you decide which capabilities matter—compositionality, controllability, fidelity, style transfer, text alignment—and engineer the data to develop and harmonize them.
The difference shows up in what you keep. A static pipeline filters for aesthetic score and resolution. A capability-centric pipeline asks whether the data contains the spatial relations, attribute bindings, and rare object combinations your model currently fails on. If your model can't count, you don't need more pretty landscapes. You need images where counts are unambiguous and captions state them explicitly.
This approach aligns with the broader data-centric AI movement, which holds that systematic data engineering yields larger returns than model architecture tweaks alone. DataComp (Gadre et al., NeurIPS 2023) made this concrete: holding the training algorithm fixed and varying only the dataset curation strategy produced swings in downstream performance as large as any architectural change in recent memory.
The trade-off to manage is breadth versus depth. Cover too many concepts shallowly, and the model learns nothing well. Go too deep on a few, and you lose generality. Capability-centric design makes that trade-off explicit instead of accidental.
Key Takeaway: Stop curating datasets. Start designing them. Decide which capabilities you need, then build data that develops them.
Co-evolution means capabilities improve together because they share representations. It's the same principle behind multi-task and continual learning: a model that learns to bind attributes to objects in one task builds features that help it in another.
The mechanism is straightforward. Text-image alignment and compositional generation both depend on the model learning disentangled representations of objects, attributes, and relations. Train hard on alignment with captions that specify "a small green cube to the left of a large red sphere," and you're also teaching the spatial and attribute structure that compositional generation needs. Improve one, and the other moves.
This is not automatic. Capabilities can also compete. Push too hard on aesthetic quality, and you may sacrifice diversity. Push too hard on prompt adherence, and images can look stiff. The design question is how to structure data so that shared representations dominate over the conflicts.
Practically, that means including training examples that exercise multiple capabilities at once—captions with rich spatial and attribute structure, images that are both high-fidelity and compositionally complex—rather than siloing each capability into its own data slice.
Key Takeaway: Capabilities co-evolve when they share representations. Design data that exercises several skills in the same example, not one at a time.
Curriculum learning orders training data from simple to complex. For image generation, that means starting with object-centric images—single subject, clear caption—and gradually introducing multi-object scenes, spatial relations, attribute bindings, and finally open-ended prompts.
The benefit is smoother optimization and less negative transfer. A model that sees only chaotic, multi-object scenes from step one spends capacity learning to parse them rather than to generate. A model that first masters single objects builds a stable foundation, then extends it.
The curriculum should target capability milestones, not just difficulty. A reasonable progression:
You don't have to hand-label stages. You can score examples by caption complexity and visual clutter, then schedule them. The point is deliberate sequencing rather than random sampling from a giant pool.
Key Takeaway: Order matters. A curriculum that scaffolds from simple to complex reduces negative transfer and builds compositional ability faster than random sampling.
Real-world data is skewed. Some object combinations are rare. Some attributes are almost never paired. Some spatial relations are underrepresented. You cannot scrape your way out of a distribution gap—but you can generate your way out.
Synthetic data lets you target exactly what's missing. Need more examples of "three red cubes and two blue spheres"? Generate them. Need images where "left of" and "right of" are unambiguous? Generate them. Need rare color-object-relation triples? Generate them.
The control is the point. You can specify counts, colors, spatial relations, and styles, then verify that the generated image matches the specification before adding it to training. This is how you enhance controllability directly: by training on data where control is explicit and correct.
Two cautions apply. First, synthetic data inherits the biases of the generator that produced it—if your generator can't render hands, neither will your synthetic hand data. Second, over-reliance on synthetic data can narrow the model's visual world. Use it to fill gaps, not to replace real data.
Key Takeaway: Synthetic data is a scalpel. Use it to fill specific capability gaps—rare compositions, attribute bindings, spatial relations—not as a blanket replacement for real data.
Multi-task training means training one model on several objectives at once: text-to-image generation, inpainting, super-resolution, and perhaps captioning or segmentation. The bet is that shared representations improve all tasks.
The evidence supports it. In multi-task learning studies, models trained with capability-centric data design have shown up to 15% improvement in compositional generation tasks compared to single-task baselines. The mechanism is representation sharing: the features that help a model inpaint coherently also help it generate coherently, and the features that help it super-resolve detail also help it render detail from scratch.
The practical design question is task balance. If one task dominates the loss, the model optimizes for it and neglects the others. You need to weight tasks, schedule them, or use gradient surgery techniques to prevent interference.
A useful pattern is to pair tasks that share structure. Text-to-image and inpainting both require understanding spatial layout. Super-resolution and generation both require understanding texture and detail. Pairing them reinforces the shared features rather than competing for them.
Key Takeaway: Multi-task training turns capabilities into allies instead of competitors. Pair tasks that share structure, and manage the loss balance deliberately.
RLHF fine-tunes a model using human preferences. You show humans pairs of generated images, they pick the better one, you train a reward model on those preferences, and you optimize the generator against the reward.
For generalist image generation, RLHF is a co-evolution tool. It can improve aesthetic quality and prompt adherence simultaneously because human raters weigh both. A model that generates a beautiful image that ignores the prompt loses to one that follows the prompt and looks good. The reward signal encodes that trade-off.
The challenges are real. Reward modeling is hard—humans are inconsistent, preferences vary, and a reward model can be gamed. Overfitting to preferences can reduce diversity: if raters consistently prefer a certain style, the model collapses toward it. And scaling human evaluation is expensive.
The mitigation is to treat RLHF as one signal among several. Use it to align capabilities with human judgment, but keep diversity metrics and automated benchmarks in the loop so you can detect collapse early.
Key Takeaway: RLHF aligns multiple capabilities with human judgment at once. Use it, but watch for diversity collapse and reward hacking.
You cannot manage what you do not measure. If you want capabilities to co-evolve, you need to track them together, not one at a time.
The standard toolkit includes:
Track these across training, not just at the end. Plot them together and look for correlations and trade-offs. If FID improves while T2I-CompBench stalls, you're buying fidelity at the cost of compositionality. If both improve together, you have co-evolution.
Human evaluation remains essential. Automated metrics miss things humans catch immediately—implausible physics, nonsensical object arrangements, subtle prompt violations. The sub-60% accuracy on complex scenes in DALL-E 2's human evaluation is a reminder that benchmarks can flatter a model that still fails in practice.
Key Takeaway: Track capabilities together, not separately. Co-evolution shows up as correlated improvement across benchmarks—and human evaluation is still the ground truth for complex scenes.
The era of brute-force scaling is not over, but its returns are diminishing. LAION-5B and its successors gave us the raw material for generalist models. What they did not give us is balance. Fidelity is nearly solved; compositionality, controllability, and complex scene understanding are not.
Capability-centric data design is the lever. It means choosing capabilities deliberately, designing data to develop them together, using curricula to scaffold them, filling gaps with synthetic data, sharing representations through multi-task training, aligning with human feedback, and tracking co-evolution across benchmarks.
The open challenges are real. Measuring co-evolution is unsolved. Negative transfer between tasks is a live risk. Generalizing to unseen tasks remains hard. But the direction is clear: the next leap in generalist image generation will not come from more data alone. It will come from smarter data design.
Key Takeaway: Data design is the new scaling law. Build datasets around capabilities, measure them together, and let them co-evolve.
What is capability-centric data design? It's an approach to building training datasets that starts from the capabilities you want a model to have—compositionality, controllability, fidelity, alignment—and engineers the data to develop and harmonize them, rather than collecting data first and hoping capabilities emerge.
Why is co-evolution of capabilities important for generalist image generation? Because capabilities that improve together produce balanced models. Without co-evolution, you get models that are excellent at some tasks and poor at others—photorealistic but unable to count, or prompt-faithful but aesthetically flat.
How can data design encourage capability co-evolution? By including examples that exercise multiple capabilities at once, sequencing data through a curriculum, sharing representations across tasks, and tracking performance across benchmarks during training.
What are some challenges in capability-centric data design? Measuring co-evolution, avoiding negative transfer between tasks, balancing breadth and depth, and preventing synthetic data from narrowing the model's visual world.
Are there existing datasets designed for capability co-evolution? Not fully. LAION-5B and ImageNet were foundational but not designed for co-evolution. DataComp explored dataset design systematically, and T2I-CompBench provides evaluation, but purpose-built co-evolution datasets remain an open area.
How do you evaluate whether capabilities are co-evolving? Track FID, CLIP score, T2I-CompBench, and human evaluations together across training. Correlated improvement across benchmarks suggests co-evolution; divergence suggests trade-offs.
What role does synthetic data play in capability-centric design? It fills distribution gaps—rare compositions, attribute bindings, spatial relations—that real data underrepresents. Use it surgically, and verify generated examples against specifications before training on them.
Is capability-centric data design only for image generation? No. The principles apply to any generative or multi-task model where multiple capabilities need to develop together—video generation, multimodal models, and speech synthesis among them.
Ready to move beyond brute-force scaling? Start designing your datasets with capability co-evolution in mind. Explore the referenced papers, experiment with curriculum learning and synthetic data, and join the emerging community at venues like NeurIPS and CVPR to share your findings. The next leap in generalist image generation won't come from more data alone—it will come from smarter data design.