Hmm… from what I can tell from the public CSVs, maybe something like this:
I think there are several plausible ML uses here, and the one that looks especially natural to me is controlled evaluation / diagnostics, not only using the outputs as generic synthetic training data.
The interesting part may not be that the images are synthetic by itself, but that your generator can potentially turn design parameters into known, controllable interventions with exact metadata.
From the public CSVs, the three examples already seem to point toward somewhat different task families:
| Collection | What the metadata looks useful for |
|---|---|
| Wavy Gradient | |
| Reading visual parameters such as colors, blur, twirl, and noise; controlled one-parameter changes | |
| Pinwheel Shapes | |
| Recovering a procedural transformation recipe; reasoning over multi-step transformations | |
| Flowy Image Blend | |
| Reading animation/compositing properties from video, or generating video from a structured composition specification |
That seems reasonably close to work that is already appearing in graphic-design evaluation. For example, GraphicDesignBench (GDB) has executable benchmarks for layout, typography, SVG, templates, and animation. Its property-extraction tasks include things such as font size, letter spacing, motion type, duration, and timing parameters.
So I would probably keep the current collections as they are, and—if you wanted a research/evaluation-oriented version as well—derive a small benchmark layer from them rather than redesigning the original data.
For example, a very small Wavy subset like this seems potentially high-leverage:
same palette
same twirl
same noise
blur = 40
vs.
blur = 70
Then a model can be asked:
What changed?
Which parameter changed?
What were the before/after values?
That gives a rather different kind of dataset from broad parameter-space sampling, even though it can come from exactly the same generator.
The older TRANCE / Transformation Driven Visual Reasoning benchmark is a useful conceptual precedent: it evaluates inference of a single-step transformation or a sequence of transformations from initial and final visual states. More recent counterfactual VLM evaluations use a similar general idea of changing one factor while trying to leave the others fixed.
So if I had to pick only one small experiment, a tiny one-factor-at-a-time paired subset would probably tell people a lot about what is special about this generator setup without requiring you to change the larger collections.
The overall pattern I see is therefore:
your existing generator
+
your existing asset-level metadata
|
+-- broad visual dataset
|
+-- small controlled evaluation dataset
|
+-- optional provenance/reproducibility layer
Those can be separate outputs rather than competing design goals.
If I were trying one thing first, I would probably take Wavy Gradient, generate a small number of matched one-parameter pairs, and publish that together with the existing scalar metadata as a tiny evaluation sample.
That seems like a fairly low-cost way to demonstrate something that is harder to get from ordinary scraped/curated image datasets: not merely knowing what metadata happens to correlate with an image, but knowing exactly what was deliberately changed while the rest of the generation setup stayed fixed.