E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models | Paper Citation Record

Observation 3e320209-cd19-4c67-82ec-c0195248ed74 · outbound

This paper cites Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models

Reference 1

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Observation 17151c00-bba1-4137-9998-4145ac8d83ba · outbound

This paper cites Improving image generation with better captions.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Improving image generation with better captions

Reference 2

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Observation 647c720e-fb3b-46e6-aecf-ac0a02fb2d62 · outbound

This paper cites Poisoning web-scale training datasets is practical.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Poisoning web-scale training datasets is practical

Reference 3

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Observation 7f360b9f-d000-4469-87d5-16a680b9f6a2 · outbound

This paper cites PIXART-{\delta}: Fast and Controllable Image Generation with Latent Consistency Models.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models PIXART-{\delta}: Fast and Controllable Image Generation with Latent Consistency Models

Reference 4

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Observation c833fcc2-3928-4232-842f-2c407bd38165 · outbound

This paper cites Scaling Rectified Flow Transformers for High-Resolution Image Synthesis.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Reference 5

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Observation 8b1d44b6-7a9f-4e6c-961a-e0cd70088fe9 · outbound

This paper cites Generative adversarial networks.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Generative adversarial networks

Reference 6

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Observation 57b8b53e-d9d1-4ce4-9287-da4cef232bca · outbound

This paper cites Photorealistic video generation with diffusion models.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Photorealistic video generation with diffusion models

Reference 7

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Observation 33d298b9-8664-40b6-b18f-54857b6df47f · outbound

This paper cites CameraCtrl: Enabling Camera Control for Text-to-Video Generation.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models CameraCtrl: Enabling Camera Control for Text-to-Video Generation

Reference 8

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Observation e59362f2-be96-4621-b0d3-e93780b2549f · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilib- rium.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Gans trained by a two time-scale update rule converge to a local nash equilib- rium

Reference 9

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Observation fd17616f-8c59-471d-9299-aed51f7f2f5e · outbound

This paper cites Denoising dif- fusion probabilistic models.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Denoising dif- fusion probabilistic models

Reference 10

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Observation e9a4e266-202e-4d0a-a7c4-04e483a231c3 · outbound

This paper cites Video dif- fusion models.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Video dif- fusion models

Reference 11

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Observation f8585022-31a4-4c0a-8f4a-d9e98e49214b · outbound

This paper cites Diffusion Models for Video Prediction and Infilling.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Diffusion Models for Video Prediction and Infilling

Reference 12

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Observation 0cea043f-9f93-4eef-b325-6a257864e1ca · outbound

This paper cites Animate anyone: Consistent and controllable image- to-video synthesis for character animation.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Animate anyone: Consistent and controllable image- to-video synthesis for character animation

Reference 13

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Observation db55e84e-30ac-4e4c-916a-5af926f16813 · outbound

This paper cites Elucidating the design space of diffusion-based generative models.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Elucidating the design space of diffusion-based generative models

Reference 14

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Observation 293ac067-b8b3-4485-87ad-2390e6494da5 · outbound

This paper cites Analyzing and improving the training dynamics of diffusion models.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Analyzing and improving the training dynamics of diffusion models

Reference 15

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Observation f44eaa84-7936-4e42-9463-cdaba16183b3 · outbound

This paper cites Auto-Encoding Variational Bayes.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Auto-Encoding Variational Bayes

Reference 16

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Observation 0fe7860b-2401-4604-b0bc-bc3743dadb32 · outbound

This paper cites Photo- realistic single image super-resolution using a generative ad- versarial network.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Photo- realistic single image super-resolution using a generative ad- versarial network

Reference 17

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Observation 358806fd-1582-4214-a6a9-4613f827a1d0 · outbound

This paper cites SDXL-Lightning: Progressive Adversarial Diffusion Distillation.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models SDXL-Lightning: Progressive Adversarial Diffusion Distillation

Reference 18

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Observation 3338da25-3fbf-4bc9-ac9d-d6b7efc4ddf8 · outbound

This paper cites Microsoft coco: Common objects in context.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Microsoft coco: Common objects in context

Reference 19

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Observation 13790e9d-19b2-4bc1-8fd9-19d3e01de58a · outbound

This paper cites Visual instruction tuning.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Visual instruction tuning

Reference 20

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Observation 7875a7d8-eaf8-4002-b587-a6f9d7f475ff · outbound

This paper cites Generic perceptual loss for modeling structured output de- pendencies.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Generic perceptual loss for modeling structured output de- pendencies

Reference 21

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Observation 685395b7-2002-4250-b97a-51060e566f54 · outbound

This paper cites Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps

Reference 22

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Observation 45e3f0dd-d058-44a5-803e-eda14eda26ef · outbound

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

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference

Reference 23

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Observation f273198b-8503-4716-930e-cd8477c6170c · outbound

This paper cites Latte: Latent Diffusion Transformer for Video Generation.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Latte: Latent Diffusion Transformer for Video Generation

Reference 24

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Observation e554880a-352f-48ba-a58c-c96b69da84f6 · outbound

This paper cites On distillation of guided diffusion models.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models On distillation of guided diffusion models

Reference 25

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Observation 30fd2f80-9694-4c08-91d3-4ca2adbe2298 · outbound

This paper cites Improved denoising diffusion probabilistic models.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Improved denoising diffusion probabilistic models

Reference 26

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Observation 60d0eba9-6d0e-432f-b56e-886887472f7a · outbound

This paper cites Scalable diffusion models with transformers.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Scalable diffusion models with transformers

Reference 27

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Observation 14edc397-b36a-4ca1-8642-ca0a6e7f9e24 · outbound

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

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 28

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Observation 04f5b03f-2772-4171-adcc-7b4dccff6cde · outbound

This paper cites DreamFusion: Text-to-3D using 2D Diffusion.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models DreamFusion: Text-to-3D using 2D Diffusion

Reference 29

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Observation 1a6daec9-1db4-4a02-ae6b-b592aef013b9 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Learning transferable visual models from natural language supervi- sion

Reference 30

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Observation 2d276f8b-8a06-496b-9be2-51c10e19d350 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 31

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Observation bbfe9551-bf65-4626-b013-32b92e4dd100 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models High-resolution image synthesis with latent diffusion models

Reference 32

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Observation b87d9365-777e-40d0-b76a-5c5629c602e0 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Photorealistic text-to-image diffusion models with deep language understanding

Reference 33

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This paper cites Progressive Distillation for Fast Sampling of Diffusion Models.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Progressive Distillation for Fast Sampling of Diffusion Models

Reference 34

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This paper cites Make-A-Video: Text-to-Video Generation without Text-Video Data.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Make-A-Video: Text-to-Video Generation without Text-Video Data

Reference 35

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This paper cites Denois- ing diffusion implicit models.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Denois- ing diffusion implicit models

Reference 36

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Observation bac5d352-e554-4bfa-bed4-7203198c84fd · outbound

This paper cites Improved Techniques for Training Consistency Models.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Improved Techniques for Training Consistency Models

Reference 37

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This paper cites Consistency Models.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Consistency Models

Reference 38

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Observation a390f2a8-93b4-4315-95d6-914db169acfe · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Score-Based Generative Modeling through Stochastic Differential Equations

Reference 39

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Observation 42ca0740-81c6-4f92-b86c-f5e9f22d289b · outbound

This paper cites Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent

Reference 40

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This paper cites An em- pirical study and analysis of text-to-image generation us- ing large language model-powered textual representation.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models An em- pirical study and analysis of text-to-image generation us- ing large language model-powered textual representation

Reference 41

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Observation db79d915-a7fc-4b9a-bf31-f57370e47f92 · outbound

This paper cites Esrgan: En- hanced super-resolution generative adversarial networks.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Esrgan: En- hanced super-resolution generative adversarial networks

Reference 42

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This paper cites Tackling the Generative Learning Trilemma with Denoising Diffusion GANs.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 43

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Observation c2bb7e79-56bc-4aac-be8c-72a1e81b1dd0 · outbound

This paper cites CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Reference 44

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This paper cites CamI2V: Camera-Controlled Image-to-Video Diffusion Model.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models CamI2V: Camera-Controlled Image-to-Video Diffusion Model

Reference 45

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This paper cites Fast ode-based sampling for diffusion models in around 5 steps.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Fast ode-based sampling for diffusion models in around 5 steps

Reference 46

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Observation b165c3fa-4d00-44de-b22a-5447ac741f6f · outbound

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E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Unresolved cited work

Reference 48

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Observation b5818e26-af82-4240-9dfd-e6c0d60686cc · outbound

This paper cites • A pair of images generated by different models.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models • A pair of images generated by different models

Reference 49

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E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Unresolved cited work

Reference 50

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E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Unresolved cited work

Reference 51

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E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Unresolved cited work

Reference 52

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E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Unresolved cited work

Reference 53

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Observation 10c189ca-e6f9-4bb7-85bc-62db6e39e2a8 · outbound

This paper cites Part II: Evaluation Criteria and Guidelines.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Part II: Evaluation Criteria and Guidelines

Reference 54

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E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Unresolved cited work

Reference 55

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This paper cites Faithfulness: Assess if the generated image appears plausible and realis- tically conforms to the laws of the real world.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Faithfulness: Assess if the generated image appears plausible and realis- tically conforms to the laws of the real world

Reference 56

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Observation b694ad41-bd70-43c4-8184-930078bc985e · outbound

This paper cites Afterwards, proceed to assess all remaining image pairs sequentially.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Afterwards, proceed to assess all remaining image pairs sequentially

Reference 57

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1e52cb09-ceef-49f5-852c-1f60147a4d17 · outbound

This paper cites an unresolved cited work.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-10T23:08:55.487163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5a1cfd4e-7d25-4b1b-b2c9-81932053ade8 · outbound

This paper cites an unresolved cited work.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-10T23:08:55.469721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1376469d-8714-4e52-a83a-6c0148c47790 · outbound

This paper cites We sincerely appreciate your contribution to this research project.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models We sincerely appreciate your contribution to this research project

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:08:55.449406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f9643d7a-db85-4171-bea7-53d89e82112e · outbound

This paper cites Limitations and Future Works A.1.

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models Limitations and Future Works A.1

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:08:55.692110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers