Dimensions overall score 7.0

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• Applying repulsion in the Contextual Space during the transformer's forward pass produces significantly richer diversity in generated images without sacrificing visual fidelity or semantic adherence.result 90%
• The proposed method is uniquely efficient, imposing only a small computational overhead compared to baseline methods like SGI.method 85%
• Intervening directly in the VAE Latent Space for diversity (as done by PG and SPARKE) often leads to visual artifacts like incoherent overlays and ghostly artifacts.limitation 90%
• The method applies repulsion by intervening in the multimodal attention channels, injecting the intervention between blocks where text conditioning is enriched with emergent image structure.technical 88%
• The method remains effective in modern 'Turbo' and distilled models where traditional trajectory-based interventions typically fail.result 80%
• The intervention is restricted to a chosen interval of the first few timesteps of the denoising trajectory, as these stages are most crucial for semantic meaning and global composition.technical 85%
• Baseline method CADS frequently leads to semantic drift, where diversity is achieved through weak prompt alignment.limitation 80%

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