DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling | Paper Citation Record

Observation 21c58512-10a9-420c-8d74-1edd75c91538 · outbound

This paper cites GPT-4 Technical Report.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling GPT-4 Technical Report

Reference 1

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This paper cites Universal guidance for diffusion models.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Universal guidance for diffusion models

Reference 2

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This paper cites Improving image generation with better captions.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Improving image generation with better captions

Reference 3

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This paper cites Training Diffusion Models with Reinforcement Learning.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Training Diffusion Models with Reinforcement Learning

Reference 4

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This paper cites Solving 3d inverse problems using pre-trained 2d diffusion models.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Solving 3d inverse problems using pre-trained 2d diffusion models

Reference 5

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Observation 51ee30d8-acdf-4952-b3ff-bd26933b7143 · outbound

This paper cites Directly Fine-Tuning Diffusion Models on Differentiable Rewards.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Directly Fine-Tuning Diffusion Models on Differentiable Rewards

Reference 6

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Observation 3f09d32a-f7c1-4948-a10c-f3c8334ed289 · outbound

This paper cites Manipulating Embeddings of Stable Diffusion Prompts.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Manipulating Embeddings of Stable Diffusion Prompts

Reference 7

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Observation b2750a26-257d-40a9-b9fa-3d41c61ad386 · outbound

This paper cites Diffusion models beat gans on image synthesis.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Diffusion models beat gans on image synthesis

Reference 8

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Observation 86e602d3-9e5e-45ab-b91a-f04ec441352f · outbound

This paper cites RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment

Reference 9

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This paper cites Scaling recti- fied flow transformers for high-resolution image synthesis.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Scaling recti- fied flow transformers for high-resolution image synthesis

Reference 10

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Observation fe2d5d03-9342-4d2a-84d1-fbded927de97 · outbound

This paper cites ReNO: Enhancing One-step Text-to-Image Models through Reward-based Noise Optimization.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling ReNO: Enhancing One-step Text-to-Image Models through Reward-based Noise Optimization

Reference 11

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Observation 53221506-11db-45d9-b969-87b8c1e70f35 · outbound

This paper cites Re- inforcement learning for fine-tuning text-to-image diffusion models.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Re- inforcement learning for fine-tuning text-to-image diffusion models

Reference 12

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Observation dc70493b-20eb-4328-9b0e-a4fa9baa1aa6 · outbound

This paper cites Aligning diffusion models with noise- conditioned perception.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Aligning diffusion models with noise- conditioned perception

Reference 13

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Observation 27f61534-32a3-41ac-9db9-7b50689f3c27 · outbound

This paper cites Geneval: An object-focused framework for evaluating text- to-image alignment.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Geneval: An object-focused framework for evaluating text- to-image alignment

Reference 14

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Observation bceac320-9a14-4cae-aef3-b4acaae86723 · outbound

This paper cites Diffusion-RPO: Aligning Diffusion Models through Relative Preference Optimization.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Diffusion-RPO: Aligning Diffusion Models through Relative Preference Optimization

Reference 15

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This paper cites Initno: Boosting text-to-image diffu- sion models via initial noise optimization.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Initno: Boosting text-to-image diffu- sion models via initial noise optimization

Reference 16

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Observation f3051112-1de9-4446-adc5-c0262c4bc2ff · outbound

This paper cites Prompt-to-Prompt Image Editing with Cross Attention Control.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Prompt-to-Prompt Image Editing with Cross Attention Control

Reference 17

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Observation 58485b3e-7bcb-4159-aadd-35c1af56e9c7 · outbound

This paper cites Classifier-Free Diffusion Guidance.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Classifier-Free Diffusion Guidance

Reference 18

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Observation 831f26f7-f341-4b4e-8d65-39db8e669411 · outbound

This paper cites Denoising dif- fusion probabilistic models.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Denoising dif- fusion probabilistic models

Reference 19

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This paper cites Pick-a-pic: An open dataset of user preferences for text-to-image generation.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Pick-a-pic: An open dataset of user preferences for text-to-image generation

Reference 20

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This paper cites Flux.1-schnell, 2024.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Flux.1-schnell, 2024

Reference 21

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Observation 0757cc70-4749-4a7c-99af-8f3ae76dc145 · outbound

This paper cites Aligning Text-to-Image Models using Human Feedback.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Aligning Text-to-Image Models using Human Feedback

Reference 22

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Observation e1e8df4a-4342-4e8f-bf1c-591f4e6a67fd · outbound

This paper cites Aligning Diffusion Models by Optimizing Human Utility.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Aligning Diffusion Models by Optimizing Human Utility

Reference 23

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Observation 42cd921c-f1a5-4cb0-8719-2f6b19bc37c3 · outbound

This paper cites Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization

Reference 24

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This paper cites Alignment of dif- fusion models: Fundamentals, challenges, and future.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Alignment of dif- fusion models: Fundamentals, challenges, and future

Reference 25

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Observation 6862f4f4-0378-4648-a3ab-8d03d13861d3 · outbound

This paper cites More control for free! im- age synthesis with semantic diffusion guidance.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling More control for free! im- age synthesis with semantic diffusion guidance

Reference 26

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Observation d945acab-6e62-46f4-8aa6-6ecd663813c7 · outbound

This paper cites Dreamguider: Improved Training free Diffusion-based Conditional Generation.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Dreamguider: Improved Training free Diffusion-based Conditional Generation

Reference 27

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Observation 87dd58d1-62db-4fa1-8f61-aaa5653260e9 · outbound

This paper cites GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Reference 28

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Observation 7afa36a0-2a4c-4917-aa8f-802f0853fd4c · outbound

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

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 29

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This paper cites Aligning Text-to-Image Diffusion Models with Reward Backpropagation.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Aligning Text-to-Image Diffusion Models with Reward Backpropagation

Reference 30

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This paper cites Direct preference optimization: Your language model is secretly a reward model.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Direct preference optimization: Your language model is secretly a reward model

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Observation 0682d329-e124-40cd-8ca6-4a0a4a3b5c31 · outbound

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

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling High-resolution image synthesis with latent diffusion models

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Observation b896313c-069f-427b-bcba-2b00fbe40eac · outbound

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

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Photorealistic text-to-image diffusion models with deep language understanding

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This paper cites Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation

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This paper cites Laion-aesthetics.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Laion-aesthetics

Reference 35

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Observation 2ef6c14d-9ec0-47c1-81be-b2996b0aac44 · outbound

This paper cites Understanding and improv- ing training-free loss-based diffusion guidance, 2024.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Understanding and improv- ing training-free loss-based diffusion guidance, 2024

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Observation 6d6fa47e-8cd6-4e78-ba74-8c12206db9f0 · outbound

This paper cites Pseudoinverse-guided diffusion models for inverse problems.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Pseudoinverse-guided diffusion models for inverse problems

Reference 37

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This paper cites Generative modeling by esti- mating gradients of the data distribution.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Generative modeling by esti- mating gradients of the data distribution

Reference 38

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

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Score-Based Generative Modeling through Stochastic Differential Equations

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This paper cites Eggen: Image genera- tion with multi-entity prior learning through entity guidance.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Eggen: Image genera- tion with multi-entity prior learning through entity guidance

Reference 40

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This paper cites Inference-Time Alignment of Diffusion Models with Direct Noise Optimization.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Inference-Time Alignment of Diffusion Models with Direct Noise Optimization

Reference 41

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DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling LLaMA: Open and Efficient Foundation Language Models

Reference 42

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This paper cites Plug-and-play diffusion features for text-driven image-to-image translation.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Plug-and-play diffusion features for text-driven image-to-image translation

Reference 43

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This paper cites End-to-end diffusion latent optimization improves classifier guidance.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling End-to-end diffusion latent optimization improves classifier guidance

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Observation 50f8d2ef-7b06-471f-ad1a-f548e6db3cbc · outbound

This paper cites Diffusion model align- ment using direct preference optimization.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Diffusion model align- ment using direct preference optimization

Reference 45

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This paper cites Magic: Multi-modality guided image completion.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Magic: Multi-modality guided image completion

Reference 46

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This paper cites Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 47

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This paper cites Deep Reward Supervisions for Tuning Text-to-Image Diffusion Models.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Deep Reward Supervisions for Tuning Text-to-Image Diffusion Models

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This paper cites Imagere- ward: Learning and evaluating human preferences for text- to-image generation.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Imagere- ward: Learning and evaluating human preferences for text- to-image generation

Reference 49

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This paper cites Using human feedback to fine-tune diffusion models without any reward model.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Using human feedback to fine-tune diffusion models without any reward model

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This paper cites A Dense Reward View on Aligning Text-to-Image Diffusion with Preference.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling A Dense Reward View on Aligning Text-to-Image Diffusion with Preference

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This paper cites TFG: Unified Training-Free Guidance for Diffusion Models.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling TFG: Unified Training-Free Guidance for Diffusion Models

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This paper cites Scaling Autoregressive Models for Content-Rich Text-to-Image Generation.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

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Observation b1d03871-ea8c-4bf6-afff-a5deb6d7c209 · outbound

This paper cites Freedom: Training-free energy-guided condi- tional diffusion model.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Freedom: Training-free energy-guided condi- tional diffusion model

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Observation 06eedd97-047f-4638-83db-d9956045d96f · outbound

This paper cites Object- conditioned energy-based attention map alignment in text-to- image diffusion models.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Object- conditioned energy-based attention map alignment in text-to- image diffusion models

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:07:11.072478Z

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Observation 58fed634-d100-4d1a-a92d-4b09ba1d767f · outbound

This paper cites Differentiable augmentation for data-efficient gan training.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Differentiable augmentation for data-efficient gan training

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:07:11.055824Z

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Observation 0233c7dc-c76f-4770-bc5b-78be9e2c420b · outbound

This paper cites an unresolved cited work.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-12T05:07:11.039276Z

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Observation b1254319-6cb7-4fec-99fd-a65bc63ab174 · outbound

This paper cites Details of Prompts used in Experiments The text prompts used to generate the images in Fig.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Details of Prompts used in Experiments The text prompts used to generate the images in Fig

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:07:11.021961Z

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Observation d959df94-3bd1-4ec0-8694-70d7c10785a5 · outbound

This paper cites an unresolved cited work.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-12T05:07:11.004782Z

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Observation 510730c2-3a60-4817-ac36-c1edfe5e060c · outbound

This paper cites prompt":.

DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling prompt":

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:07:10.988638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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