Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples | Paper Citation Record

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This paper cites TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation

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This paper cites Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

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This paper cites Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image Generation.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image Generation

Reference 3

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This paper cites Sampling is as easy as learning the score: Theory for diffusion models with minimal data assumptions.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Sampling is as easy as learning the score: Theory for diffusion models with minimal data assumptions

Reference 4

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This paper cites Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack

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This paper cites 8-bit optimizers via block-wise quantiza- tion.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples 8-bit optimizers via block-wise quantiza- tion

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This paper cites Consistency Models Made Easy.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Consistency Models Made Easy

Reference 7

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This paper cites Griffiths and Desmond J.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Griffiths and Desmond J

Reference 8

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This paper cites Boot: Data-free distillation of denoising diffusion models with bootstrapping.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Boot: Data-free distillation of denoising diffusion models with bootstrapping

Reference 9

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This paper cites Multistep Consistency Models.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Multistep Consistency Models

Reference 10

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This paper cites CLIPScore: A Reference-free Evaluation Metric for Image Captioning.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Reference 11

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This paper cites GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

Reference 12

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This paper cites Classifier-Free Diffusion Guidance.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Classifier-Free Diffusion Guidance

Reference 13

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This paper cites Denoising diffusion probabilistic models.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Denoising diffusion probabilistic models

Reference 14

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Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Video diffusion models

Reference 15

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This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples LoRA: Low-Rank Adaptation of Large Language Models

Reference 16

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This paper cites Make-an-audio: Text-to-audio generation with prompt-enhanced diffusion models.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Make-an-audio: Text-to-audio generation with prompt-enhanced diffusion models

Reference 17

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This paper cites Consistency trajectory models: Learning probability flow ODE trajectory of diffusion.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Consistency trajectory models: Learning probability flow ODE trajectory of diffusion

Reference 18

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This paper cites PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher

Reference 19

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This paper cites Diffwave: A versatile diffusion model for audio synthesis.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Diffwave: A versatile diffusion model for audio synthesis

Reference 20

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This paper cites Bidirectional Consistency Models.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Bidirectional Consistency Models

Reference 21

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Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Deep generative models through the lens of the manifold hypothesis: A survey and new connections

Reference 22

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Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Mathematical analysis of singularities in the diffusion model under the submanifold assumption

Reference 23

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This paper cites Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed

Reference 24

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This paper cites Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference

Reference 25

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This paper cites LCM-LoRA: A Universal Stable-Diffusion Acceleration Module.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples LCM-LoRA: A Universal Stable-Diffusion Acceleration Module

Reference 26

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This paper cites On distillation of guided diffusion models.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples On distillation of guided diffusion models

Reference 27

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Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Unresolved cited work

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This paper cites Score-based generative models detect manifolds.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Score-based generative models detect manifolds

Reference 29

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This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 30

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Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Barron, and Ben Mildenhall

Reference 31

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Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Self-attention Does Not Need $O(n^2)$ Memory

Reference 32

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Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Learning transferable visual models from natural language supervision

Reference 33

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Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 34

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Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Hyper-SD: Trajectory Segmented Consistency Model for Efficient Image Synthesis

Reference 35

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Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples High-resolution image synthesis with latent diffusion models

Reference 36

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Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples U-net: Convolutional networks for biomedical image segmentation

Reference 37

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This paper cites MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation

Reference 39

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Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Photorealistic text-to-image diffusion models with deep language understanding

Reference 40

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Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Progressive distillation for fast sampling of diffusion models

Reference 41

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Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Adversarial Diffusion Distillation

Reference 42

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Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples LAION-5B: An open large-scale dataset for training next generation image-text models

Reference 43

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Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Deep unsupervised learning using nonequilibrium thermodynamics

Reference 44

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Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Denoising diffusion implicit models

Reference 45

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Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Improved techniques for training consistency models

Reference 46

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Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Improved techniques for training score-based generative models

Reference 47

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Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Score-based generative modeling through stochastic differential equations

Reference 48

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Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Consistency models

Reference 49

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This paper cites Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models

Reference 50

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This paper cites Phased Consistency Models.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Phased Consistency Models

Reference 51

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This paper cites VideoComposer: Compositional Video Synthesis with Motion Controllability.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples VideoComposer: Compositional Video Synthesis with Motion Controllability

Reference 52

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Observation 46992c8f-e674-40df-973a-9d870066693f · outbound

This paper cites Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation

Reference 53

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Observation ceebf24a-32f3-4ee5-b788-9ab0d51d62f9 · outbound

This paper cites TLCM: Training-efficient Latent Consistency Model for Image Generation with 2-8 Steps.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples TLCM: Training-efficient Latent Consistency Model for Image Generation with 2-8 Steps

Reference 54

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Observation f47f0a35-20cc-4d25-b17e-56d8a79e6781 · outbound

This paper cites Freeman, and Taesung Park.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Freeman, and Taesung Park

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:18:04.248924Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 605644b0-d075-4453-b1d4-3d0acd6b1aa9 · outbound

This paper cites Fast sampling of diffusion models via operator learning.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Fast sampling of diffusion models via operator learning

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:18:04.236640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e9f5e197-5885-47c5-ae8d-77677a05c9f9 · outbound

This paper cites Trajectory Consistency Distillation: Improved Latent Consistency Distillation by Semi-Linear Consistency Function with Trajectory Mapping.

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples Trajectory Consistency Distillation: Improved Latent Consistency Distillation by Semi-Linear Consistency Function with Trajectory Mapping

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T21:18:03.954280Z

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Unavailable: canonical work link unavailable.

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