Rethinking Memorization Mitigation in Diffusion Models: Reinforcing Text Conditioning

arXiv:2610.01723 · cs.CV · Submitted 2026-10-01 · Read on arXiv

cs.CV

Submitted: 2026-10-01

Updated: 2026-10-01

Terminology

• Alignment — 24× in this paper · explained in 1 episode
• Diffusion Model — 21× in this paper · explained in 8 episodes
• Diffusion Models — 20× in this paper · explained in 6 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Verification — 3× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Calibration — 2× in this paper · explained in 7 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Frozen model — 1× in this paper · explained in 3 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes

Sources

• Exploring Local Memorization in Diffusion Models via Bright Ending Attention
• The 2021 Image Similarity Dataset and Challenge
• Hierarchical Text-Conditional Image Generation with CLIP Latents
• A Reproducible Extraction of Training Images from Diffusion Models

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