Exploiting the Signal-Leak Bias in Diffusion Models

• URL: http://arxiv.org/abs/2309.15842v2
• Date: Tue, 24 Oct 2023 11:09:55 GMT
• Title: Exploiting the Signal-Leak Bias in Diffusion Models
• Abstract summary: There is a bias in the inference pipeline of most diffusion models. This bias arises from a signal leak whose distribution deviates from the noise distribution. We exploit this signal-leak bias in existing diffusion models to allow more control over the generated images.
• Score: 3.093293209977702
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There is a bias in the inference pipeline of most diffusion models. This bias arises from a signal leak whose distribution deviates from the noise distribution, creating a discrepancy between training and inference processes. We demonstrate that this signal-leak bias is particularly significant when models are tuned to a specific style, causing sub-optimal style matching. Recent research tries to avoid the signal leakage during training. We instead show how we can exploit this signal-leak bias in existing diffusion models to allow more control over the generated images. This enables us to generate images with more varied brightness, and images that better match a desired style or color. By modeling the distribution of the signal leak in the spatial frequency and pixel domains, and including a signal leak in the initial latent, we generate images that better match expected results without any additional training.

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