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Diffusion models have demonstrated impressive generative capabilities, but their \textit{exposure bias} problem, described as the input mismatch between training and sampling, lacks in-depth exploration.
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Generalization in generation: A closer look at exposure bias
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Pseudo numerical methods for diffusion models on manifolds
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GLIDE: Towards photorealistic image generation and editing with text-guided diffusion models
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High-resolution image synthesis with latent diffusion models
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Diffusion models beat GANs on image synthesis
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DiffWave: A versatile diffusion model for audio synthesis
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Photorealistic text-to-image diffusion models with deep language understanding
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Progressive distillation for fast sampling of diffusion models
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