Ambient Diffusion Omni: Training Good Models with Bad Data
We show how to use low-quality, synthetic, and out-of-distribution images to
improve the quality of a diffusion model. Typically, diffusion models are
trained on curated datasets that emerge from highly filtered data pools from
the Web and other sources. We show that there is immense value in the
lower-quality images that are often discarded. We present Ambient Diffusion
Omni, a simple, principled framework to train diffusion models that can extract
signal from all available images during training. Our framework exploits two
properties of natural images -- spectral power law decay and locality. We first
validate our framework by successfully training diffusion models with images
synthetically corrupted by Gaussian blur, JPEG compression, and motion blur. We
then use our framework to achieve state-of-the-art ImageNet FID, and we show
significant improvements in both image quality and diversity for text-to-image
generative modeling. The core insight is that noise dampens the initial skew
between the desired high-quality distribution and the mixed distribution we
actually observe. We provide rigorous theoretical justification for our
approach by analyzing the trade-off between learning from biased data versus
limited unbiased data across diffusion times.
68523b4a0164cd131671055d
Giannis Daras
68523b4a0164cd131671055e
Adrian Rodriguez-Munoz
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2025-06-18T10:29:49.294Z
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/avatars/8a9ac73d93785f48e63184d612b9fff1.svg
Adrian Rodriguez Munoz
user
adrianrm
68523b4a0164cd131671055f
Adam Klivans
68523b4a0164cd1316710560
Antonio Torralba
68523b4a0164cd1316710561
Constantinos Daskalakis
Ambient Diffusion Omni: Training Good Models with Bad Data
We show how to use low-quality, synthetic, and out-of-distribution images to
improve the quality of a diffusion model. Typically, diffusion models are
trained on curated datasets that emerge from highly filtered data pools from
the Web and other sources. We show that there is immense value in the
lower-quality images that are often discarded. We present Ambient Diffusion
Omni, a simple, principled framework to train diffusion models that can extract
signal from all available images during training. Our framework exploits two
properties of natural images -- spectral power law decay and locality. We first
validate our framework by successfully training diffusion models with images
synthetically corrupted by Gaussian blur, JPEG compression, and motion blur. We
then use our framework to achieve state-of-the-art ImageNet FID, and we show
significant improvements in both image quality and diversity for text-to-image
generative modeling. The core insight is that noise dampens the initial skew
between the desired high-quality distribution and the mixed distribution we
actually observe. We provide rigorous theoretical justification for our
approach by analyzing the trade-off between learning from biased data versus
limited unbiased data across diffusion times.
Ambient Diffusion Omni framework leverages low-quality images to enhance diffusion models by utilizing properties of natural images and shows improvements in ImageNet FID and text-to-image quality.
diffusion models synthetic images out-of-distribution images Ambient Diffusion Omni spectral power law decay locality Gaussian blur JPEG compression motion blur ImageNet FID text-to-image generative modeling noise dampening biased data limited unbiased data