Dataset Card for StyleCruxGen

A visual dataset exploring the interplay between style, object, and environment using diffusion-generated imagery.

Dataset Details

Dataset Description

StyleCruxGen is a synthetic multi-style image dataset generated using the Stable Diffusion XL (SDXL) model. It contains 4245 high-resolution images (1024px) featuring 8 object-environment pairs rendered across photorealistic and 10 artistic styles. Each image is guided by a structured prompt with style placement variations (prepend, mid, append) to support prompt sensitivity studies. The dataset is accompanied by image resolution variants (512, 384, 256) and a metadata CSV describing all generation parameters. The 10 styles used in the dataset: pixel art, watercolor, sketch, voxel art, charcoal drawing, embroidery, neon glow, mosaic art, graffiti and glass painting. Two types of control images were also created:

Style_only (these images don't have any object environment pair), placement_of_style column in csv has style_only values for these type of images
Content_only (these images don't have any style associated with it in the SDXL prompts), placement_of_style column in csv has content_only values for these type of images

Curated by: Bodhisatta Maiti
Funded by: None
Shared by: Bodhisatta Maiti
License: CC BY-NC-SA 4.0

Dataset Sources

Uses

Direct Use

StyleCruxGen can be used for:

Style classification and clustering
Evaluating prompt faithfulness of diffusion models
Analyzing style-conditioned image generation capabilities
Prompt sensitivity analysis based on style cue placement
Style disentanglement benchmarking
Cross-style image retrieval
Domain robustness testing and visual generalization
Evaluation of style transfer models
Vision-language model sensitivity to stylistic shifts
Failure case analysis in diffusion-based generation

Out-of-Scope Use

Commercial use or redistribution of images without proper attribution
Training downstream models for medical, biometric, or identity-sensitive tasks
Any use violating the non-commercial terms of the license

Dataset Structure

Each image sample includes:

filename (filename column in csv): image file name
object (object_name column in csv): one of 8 base objects
environment (env_name column in csv): environment context in which the object is placed
style (style column in csv): one of photorealistic and 10 styles (e.g., watercolor, voxel art, charcoal drawing)
prompt_structure (placement_of_style column in csv): one of prepend, mid, append, content-only, or style-only
guidance_scale (guidance_scale column in csv): generation parameter
style_description (prompt column in csv): descriptive phrase used in the prompt

Images are stored in folders by resolution. All metadata is provided in a unified CSV file.

Dataset Creation

Curation Rationale

The dataset was created to fill a gap in publicly available multi-style benchmarks that consider both content and prompt structure variation. It supports model evaluation, style fidelity analysis, and retrieval tasks under stylistic and contextual shifts.

Source Data

Data Collection and Processing

Images were generated using Stable Diffusion XL (base 1.0) via structured text prompts. Prompts vary the placement of style cues and maintain consistent object and environment pairings. Each prompt was used to generate 5 image variants. The code used to generate the dataset is open-source.

Who are the source data producers?

The content was synthesized using the SDXL model. Prompts and object–environment–style mappings were manually curated by the dataset author.

Annotations

Annotation process

No human annotations were added post-generation. Style labels and metadata are embedded at generation time.

Who are the annotators?

All annotations were programmatically generated by the dataset author.

Personal and Sensitive Information

No real-world identities, personal data, or biometric information are present. All content is fully synthetic.

Bias, Risks, and Limitations

Some styles may fail to render clearly for certain objects.
Style fidelity may vary across prompts and should not be assumed to be perfect.
Diffusion models may occasionally produce artifacts such as signatures or unprompted background elements.

Recommendations

Researchers using the dataset for style fidelity evaluation should apply additional style recognition filters.
Caution is advised when attributing semantic meaning to stylistic differences without further validation.

Citation

Maiti, B. (2025). StyleCruxGen: A Visual Dataset Exploring Style, Object, and Environment Variation [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15795551

Glossary

Prompt placement: Location of the style cue in the sentence (prepend, mid, append)
Style fidelity: The visual consistency between the intended style and the generated image

More Information

Please contact the dataset author for collaborations or dataset feedback.

Dataset Card Authors

Bodhisatta Maiti

Dataset Card Contact

[email protected]

image imagewidth (px) 256 1.02k