What is recorded here?
Constructed non-Markovian diffusion processes sharing a denoising training objective, enabling implicit reverse samplers and faster generation with fewer steps.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
DDIM sampling trades computation against sample quality. It builds on diffusion training and is distinct from the original DDPM and later consistency-model training.
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People and systems
Jiaming Song; Chenlin Meng; Stefano Ermon
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
Song, Meng and Ermon: denoising diffusion implicit models
Constructed non-Markovian diffusion processes sharing a denoising training objective, enabling implicit reverse samplers and faster generation with fewer steps.
Scientific scope
DDIM sampling trades computation against sample quality. It builds on diffusion training and is distinct from the original DDPM and later consistency-model training.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Jiaming Song; Chenlin Meng; Stefano Ermon
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.