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Conditional Balance: Improving Multi-Conditioning Trade-Offs in Image Generation

CVPR 2025 · Image generation › Editing and style transfer · approach: Arbitrary neural style transfer · 1 citations (OpenAlex)

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Analyzes per-layer, per-timestep sensitivity of SDXL (and SD3.5-Large) attention layers to style and structure via a JSD-based clustering score, then restricts style/ControlNet conditioning to sensitive layers to balance content and style at inference without training.

Problem

Over-conditioning in multi-conditioned diffusion image generation (style, text, edge/depth structure) causes content and style to overshadow each other and produces artifacts, causing imbalanced content-style trade-offs.

Approach

Score each attention layer and timestep by the ratio of inner-cluster to outer-cluster distances (Dunn-index-like, using JSD between Gaussian (mean, std) attention statistics) over collections of images varying one aspect, then apply style or structure conditioning only to the top-ranked layers, requiring no additional training.

Key results

• Balancing with 30 selected layers (lambda_S=0.43) and lambda_T=0.15 improves content-style trade-off for StyleAligned and B-LoRA in quantitative comparisons (Fig. 8).
• User study with 42 participants and 1,134 evaluations significantly preferred balanced versions over imbalanced ones (e.g., multi-choice chi2=35.1, p<0.001; B-LoRA A/B chi2=30.0, p<0.001).
• Conditioning on less than half of SDXL layers (K=30) was found in most cases to yield the best balance between style and content.

Measured effects reported in the paper

Surprising findings

• Reducing the number of style-conditioned layers in StyleAligned improved text alignment, while random layer subsets gave unstable results.
• Geometric style is lost under structure conditioning and cannot be recovered by simply reducing the ControlNet scale, but restricting ControlNet to non-sensitive layers via timestep limiting recovers it.

Limitations

• Performance depends on the capabilities of the base model; styles unfamiliar to the model produce style mismatch.
• Layer selection requires analysis on image collections per aspect before use.

Experimental setup

Datasets: self-generated image collections (10 styles x 5 images for style analysis; 10 artists x 5 images for structure analysis) 32 styles x prompts for evaluation

Benchmarks: self-constructed complex-conditioning evaluation set (5120 images per method)

Metrics: DINO cosine similarity (style) CLIP cosine similarity (content) GRAM matrix style similarity user preference (chi-squared test)

Baselines: StyleAligned B-LoRA InstantStyle Jeong et al. IP-Adapter

Builds on: StyleAligned B-LoRA ControlNet AdaIN Dunn index

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