Sharing Early Diffusion Steps Across Similar Prompts for Efficient Text-to-Image Generation

The authors propose reusing early denoising steps in diffusion models across semantically similar prompts to cut redundant computation. By clustering prompts and sharing initial steps, then specializing later steps per prompt, they reduce cost and can improve image quality. Leveraging UnClip’s prior further optimizes step allocation, making the method scalable and easy to adopt.

Key Points

• Early diffusion steps encode coarse, shared structure that can be reused across semantically similar prompts.
• A training-free pipeline clusters prompts and shares initial denoising computation before branching to prompt-specific refinement.
• The method is especially effective for models conditioned on image embeddings and leverages UnClip’s prior for better step allocation.
• Experiments show reduced compute cost alongside improved image quality compared to independent per-prompt sampling.
• The approach integrates easily with existing pipelines and scales to large prompt sets, reducing environmental and financial overhead.

Sentiment

Cautiously positive interest tempered by concern about missing comparisons to prior art; overall mildly favorable but skeptical about novelty/supporting evidence.

In Agreement

• The method is interesting and aligns with the general insight that early neural processing captures shared concepts or global structure.
• Sharing early diffusion steps across related prompts is intuitively consistent with how models progressively refine images from coarse-to-fine.

Opposed

• The paper appears to overlook or fails to compare against closely related prior work such as ParaAttention and TeaCache.
• Insufficient benchmarking or citation against similar methods undermines claims of novelty or practical advantage.