Introduction
Consistency Models investigates one- and few-step generation through either distillation or standalone training, supplying a methodological background for later interactive image-generation workflows.
View full image ↗Figure 1: mapping points on one probability-flow trajectory to a shared data endpoint. Source: v2; the image version is distinct from the initial submission date.
Song, Yang; Dhariwal, Prafulla; Chen, Mark; Sutskever, Ilya · original paper / arXiv · Rights in the paper and depicted works remain with their holders. Research quotation does not establish an open licence; republication rights await independent review.
Source · Consistency Models ↗RESEARCH ACCOUNT
Consistency Models investigates one- and few-step generation through either distillation or standalone training, supplying a methodological background for later interactive image-generation workflows.
ANTI-AI ARCHIVE · Revised 2026-10-03
What the paper investigates
The proposed models map noise directly to data while allowing additional sampling steps to trade computation for quality. The paper distinguishes distillation from an existing diffusion model and standalone training, and demonstrates editing tasks without dedicated task-specific training.
Section sources: Consistency Models
Its place in generative-art history
Editorial interpretation: shorter waiting times can change the rhythm of viewing, revising and generating. Comparison with LCM helps distinguish a general method from later adaptations to different resolutions and tool environments.
Section sources: Consistency Models
Reading limits and versions
A single sampling step does not guarantee real-time operation on every device or equal quality across all tasks.
Section sources: Consistency Models
Sources for this account
Read the arXiv abstract, authors and version history, plus the selected figure/page on PDF page 1; proofs and full experiments were not independently audited.
Read the arXiv abstract, authors and version history, plus the selected figure/page on PDF page 1; proofs and full experiments were not independently audited. The account and translation are AI-assisted, pending independent human review. Section references identify evidence without claiming independent verification of every historical statement.
Continue with a comparative question
Consistency Models: compare methods, control or evaluation conditions with MeanFlow.
Consistency Models: compare methods, control or evaluation conditions with Latent Consistency Models.
Consistency Models: compare methods, control or evaluation conditions with SDXL Turbo.
Dates & version record
Date displayed for this node: 2023-03-02 · Historical date recorded for the source: 2023-03-02
The timeline uses the initial arXiv submission, distinct from conference publication, model release and the archive addition on 3 October 2026. The account and image refer to v2; its date is recorded in the source history.
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Original sources & further reading
These links lead to the cited paper, article, institution or conference page. External texts retain their source languages.
Read the arXiv abstract, authors and version history, plus the selected figure/page on PDF page 1; proofs and full experiments were not independently audited.
Provenance, translation & verification
Archive node #221 · Initial research-paper submission; methods, evaluation and production conditions
Research materials & supplement references · 1
Recent research papers: additions and reconciled sources since 2022 · Read the arXiv abstract, authors and version history, plus the selected figure/page on PDF page 1; proofs and full experiments were not independently audited. · 015
Date as recorded: 2023-03-02
Consistency Models ↗
Consistency Models
Archive account based on the listed research materials, not a full translation of the linked work. AI-assisted translation; independent human review pending.
Official material was read within the stated scope; see the source note for reading limits and outstanding checks.
Cite this node
ANTI-AI ARCHIVE. Consistency Models. Art-history node #221. https://salondesrefuses.cn/en/art-history/338
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