Key points
- DDPM learns to reverse a fixed noising process, generating images by iterative denoising.
- It is the direct mathematical foundation of latent diffusion, AnimateDiff, Video LDM and MDM.
- It is taught as 'the diffusion model' in ML-for-creatives courses.
Summary
Ho, Jain and Abbeel (2020) demonstrated that a neural network trained to reverse a fixed Markov noising chain can generate high-quality images by iteratively denoising Gaussian noise. The denoising score-matching objective gives DDPM a stable training regime compared to GANs, and the resulting sample quality matched or exceeded the state of the art. DDPM is the pixel-space origin of the entire diffusion stack: Stable Diffusion, AnimateDiff, Video LDM and the Motion Diffusion Model all build on its mathematical foundation. It is the paper courses name when introducing diffusion models to creative-AI students.
Related items
- High-Resolution Image Synthesis with Latent Diffusion Models
- Align Your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
Source
Source: NeurIPS 2020 ↗ (Research)
Cite this item
Ho, J., Jain, A. & Abbeel, P. (2020). ‘Denoising Diffusion Probabilistic Models (DDPM)’, NeurIPS 2020. Available at: https://arxiv.org/abs/2006.11239
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