Z²-Sampling implicitly realizes zero-cost zigzag trajectories for curvature-aware semantic alignment in diffusion models by reducing multi-step paths via operator dualities and temporal caching while synthesizing a directional derivative penalty.
Synthetic shifts to initial seed vector exposes the brittle nature of latent-based diffusion models
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RF-Sampling enhances flow matching models by implicitly performing gradient ascent on text-image alignment scores via linear textual combinations and flow inversion.
ABSS ranks diffusion seeds by early cross-attention strength to prompt core tokens and retains only the top-k for full generation, yielding consistent gains in alignment and quality on Stable Diffusion variants.
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$Z^2$-Sampling: Zero-Cost Zigzag Trajectories for Semantic Alignment in Diffusion Models
Z²-Sampling implicitly realizes zero-cost zigzag trajectories for curvature-aware semantic alignment in diffusion models by reducing multi-step paths via operator dualities and temporal caching while synthesizing a directional derivative penalty.
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Reflective Flow Sampling Enhancement
RF-Sampling enhances flow matching models by implicitly performing gradient ascent on text-image alignment scores via linear textual combinations and flow inversion.
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Boosting Text-to-Image Diffusion Models via Core Token Attention-Based Seed Selection
ABSS ranks diffusion seeds by early cross-attention strength to prompt core tokens and retains only the top-k for full generation, yielding consistent gains in alignment and quality on Stable Diffusion variants.