Rethinking Multi-Condition DiTs: Eliminating Redundant Attention via Position-Alignment and Keyword-Scoping
Paper • 2602.06850 • Published
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End of preview. Expand in Data Studio
PKA-SubjectSpatial-10K
Dataset for Rethinking Multi-Condition DiTs: Eliminating Redundant Attention via Position-Alignment and Keyword-Scoping, submitted to IEEE TVCG.
PKA-SubjectSpatial-10K is the curated subject-and-spatial-control subset used to train the three released PKA Compact = 2 LoRAs. It contains target images, descriptions, subject references, and multiple spatial conditions for multi-condition image generation.
Local snapshot
- Public release name:
PKA-SubjectSpatial-10K - On-disk size: approximately 13 GB
- Format: Parquet, 50 shards per split
- Train: 8,200 rows
- Test: 1,575 rows
- Total: 9,775 rows
- Collection field: all rows are currently labeled
collection_1 - Valid descriptions: 8,136/8,200 train and 1,564/1,575 test
The public name is PKA-SubjectSpatial-10K, which communicates the subject/spatial-control scope and approximate size. The snapshot contains 9,775 examples.
Schema
image: target RGB imagecollection: source collection labelquality_assessment: composite structure, image quality, and object consistency scoresdescription: item, two descriptions, category, and validity flag- Control images:
sketch,canny,depth,normal,segmentation,albedo, andirradiance
The local Parquet schema stores the control images as binary values. The training recipes released here use subject references together with canny or depth, or canny together with depth.
Training setup
The released FLUX.1-dev LoRAs are trained for 20,000 optimizer steps with LoRA rank/alpha 4, batch size 1, gradient accumulation 4, and the Prodigy optimizer. Conditions are encoded at 256 x 256 for 512 x 512 generation.
Notes
- The release uses Apache-2.0.
- 75 rows have
description_valid=false; the flag is retained so downstream users can filter them explicitly. - All current rows use the collection label
collection_1. - The dataset repository is https://huggingface.co/datasets/zc1696190340/PKA-SubjectSpatial-10K.
Citation
@article{zhou2026rethinking,
title={Rethinking Multi-Condition DiTs: Eliminating Redundant Attention via Position-Alignment and Keyword-Scoping},
author={Zhou, Chao and Wei, Tianyi and Chen, Yiling and Zhou, Wenbo and Zhang, Weiming and Yu, Nenghai},
journal={arXiv preprint arXiv:2602.06850},
year={2026}
}
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