Exploring the latent space of diffusion models directly through singular value decomposition | Paper Citation Record

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This paper cites SegDiff: Image Segmentation with Diffusion Probabilistic Models.

Exploring the latent space of diffusion models directly through singular value decomposition SegDiff: Image Segmentation with Diffusion Probabilistic Models

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This paper cites Multidiffusion: Fusing diffusion paths for controlled image generation.

Exploring the latent space of diffusion models directly through singular value decomposition Multidiffusion: Fusing diffusion paths for controlled image generation

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This paper cites Label-Efficient Semantic Segmentation with Diffusion Models.

Exploring the latent space of diffusion models directly through singular value decomposition Label-Efficient Semantic Segmentation with Diffusion Models

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Exploring the latent space of diffusion models directly through singular value decomposition High- frequency space diffusion model for accelerated mri

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This paper cites Textdiffuser: Diffusion models as text painters.

Exploring the latent space of diffusion models directly through singular value decomposition Textdiffuser: Diffusion models as text painters

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This paper cites Infogan: Interpretable rep- resentation learning by information maximizing generative adversarial nets.

Exploring the latent space of diffusion models directly through singular value decomposition Infogan: Interpretable rep- resentation learning by information maximizing generative adversarial nets

Reference 6

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This paper cites ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models.

Exploring the latent space of diffusion models directly through singular value decomposition ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models

Reference 7

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This paper cites Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANs.

Exploring the latent space of diffusion models directly through singular value decomposition Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANs

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This paper cites Custom-Edit: Text-Guided Image Editing with Customized Diffusion Models.

Exploring the latent space of diffusion models directly through singular value decomposition Custom-Edit: Text-Guided Image Editing with Customized Diffusion Models

Reference 9

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Exploring the latent space of diffusion models directly through singular value decomposition Diffusion models in vision: A survey

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Exploring the latent space of diffusion models directly through singular value decomposition Diffusion models beat gans on image synthesis

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This paper cites Prompt tuning inversion for text-driven image editing using diffusion models.

Exploring the latent space of diffusion models directly through singular value decomposition Prompt tuning inversion for text-driven image editing using diffusion models

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This paper cites Direct Inversion: Optimization-Free Text-Driven Real Image Editing with Diffusion Models.

Exploring the latent space of diffusion models directly through singular value decomposition Direct Inversion: Optimization-Free Text-Driven Real Image Editing with Diffusion Models

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This paper cites Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models.

Exploring the latent space of diffusion models directly through singular value decomposition Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models

Reference 14

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This paper cites Diffusion Brush: A Latent Diffusion Model-based Editing Tool for AI-generated Images.

Exploring the latent space of diffusion models directly through singular value decomposition Diffusion Brush: A Latent Diffusion Model-based Editing Tool for AI-generated Images

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Exploring the latent space of diffusion models directly through singular value decomposition Domain targeted synthetic plant style transfer using stable diffusion lora and controlnet

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This paper cites Classifier-Free Diffusion Guidance.

Exploring the latent space of diffusion models directly through singular value decomposition Classifier-Free Diffusion Guidance

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This paper cites Denoising dif- fusion probabilistic models.

Exploring the latent space of diffusion models directly through singular value decomposition Denoising dif- fusion probabilistic models

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This paper cites Cascaded diffu- sion models for high fidelity image generation.

Exploring the latent space of diffusion models directly through singular value decomposition Cascaded diffu- sion models for high fidelity image generation

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This paper cites Kv inversion: Kv embeddings learning for text-conditioned real image action editing.

Exploring the latent space of diffusion models directly through singular value decomposition Kv inversion: Kv embeddings learning for text-conditioned real image action editing

Reference 20

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This paper cites Diff- styler: Controllable dual diffusion for text-driven image styl- ization.

Exploring the latent space of diffusion models directly through singular value decomposition Diff- styler: Controllable dual diffusion for text-driven image styl- ization

Reference 21

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This paper cites Inverse problems in atmospheric science and their applica- tion.

Exploring the latent space of diffusion models directly through singular value decomposition Inverse problems in atmospheric science and their applica- tion

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Exploring the latent space of diffusion models directly through singular value decomposition Diffusion Model-Based Image Editing: A Survey

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Exploring the latent space of diffusion models directly through singular value decomposition An edit friendly ddpm noise space: Inversion and manipulations

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Exploring the latent space of diffusion models directly through singular value decomposition Imagic: Text-based real image editing with diffusion models

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Exploring the latent space of diffusion models directly through singular value decomposition Diffu- sionclip: Text-guided diffusion models for robust image ma- nipulation

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Exploring the latent space of diffusion models directly through singular value decomposition User-friendly Image Editing with Minimal Text Input: Leveraging Captioning and Injection Techniques

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This paper cites Adam: A Method for Stochastic Optimization.

Exploring the latent space of diffusion models directly through singular value decomposition Adam: A Method for Stochastic Optimization

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This paper cites Diffusion Models already have a Semantic Latent Space.

Exploring the latent space of diffusion models directly through singular value decomposition Diffusion Models already have a Semantic Latent Space

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This paper cites Diffusion models already have a semantic latent space.

Exploring the latent space of diffusion models directly through singular value decomposition Diffusion models already have a semantic latent space

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Exploring the latent space of diffusion models directly through singular value decomposition Srdiff: Single image super-resolution with diffusion probabilistic models

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Exploring the latent space of diffusion models directly through singular value decomposition Self-discovering interpretable diffusion latent di- rections for responsible text-to-image generation

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Exploring the latent space of diffusion models directly through singular value decomposition SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations

Reference 33

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Exploring the latent space of diffusion models directly through singular value decomposition DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models

Reference 34

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Exploring the latent space of diffusion models directly through singular value decomposition Clustergan: Latent space clustering in generative adversarial networks

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Exploring the latent space of diffusion models directly through singular value decomposition Contrastive denoising score for text-guided latent diffusion image editing

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Exploring the latent space of diffusion models directly through singular value decomposition Improved denoising diffusion probabilistic models

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Exploring the latent space of diffusion models directly through singular value decomposition Shape-guided diffusion with inside-outside atten- tion

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Exploring the latent space of diffusion models directly through singular value decomposition Understanding the latent space of diffusion models through the lens of riemannian geometry

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Exploring the latent space of diffusion models directly through singular value decomposition Understanding the latent space of dif- fusion models through the lens of riemannian geometry

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Exploring the latent space of diffusion models directly through singular value decomposition Enhancing dreambooth with lora for generating unlimited characters with stable diffusion

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Exploring the latent space of diffusion models directly through singular value decomposition Localizing object-level shape variations with text-to-image diffusion models

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Exploring the latent space of diffusion models directly through singular value decomposition Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

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

Exploring the latent space of diffusion models directly through singular value decomposition Learning transferable visual models from natural language supervi- sion

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Exploring the latent space of diffusion models directly through singular value decomposition High-resolution image synthesis with latent diffusion models

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Exploring the latent space of diffusion models directly through singular value decomposition Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

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Exploring the latent space of diffusion models directly through singular value decomposition Photorealistic text-to-image diffusion models with deep language understanding

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Exploring the latent space of diffusion models directly through singular value decomposition In- terpreting the latent space of gans for semantic face editing

Reference 48

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This paper cites Dragdiffusion: Harnessing diffusion models for interactive point-based image editing.

Exploring the latent space of diffusion models directly through singular value decomposition Dragdiffusion: Harnessing diffusion models for interactive point-based image editing

Reference 49

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Exploring the latent space of diffusion models directly through singular value decomposition Denoising Diffusion Implicit Models

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Exploring the latent space of diffusion models directly through singular value decomposition Score-Based Generative Modeling through Stochastic Differential Equations

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Exploring the latent space of diffusion models directly through singular value decomposition Unitune: Text-driven image editing by fine tuning a diffusion model on a single image

Reference 52

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This paper cites Unsupervised discov- ery of interpretable directions in the gan latent space.

Exploring the latent space of diffusion models directly through singular value decomposition Unsupervised discov- ery of interpretable directions in the gan latent space

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This paper cites Stylediffusion: Controllable disentangled style transfer via diffusion models.

Exploring the latent space of diffusion models directly through singular value decomposition Stylediffusion: Controllable disentangled style transfer via diffusion models

Reference 54

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This paper cites De- blurring via stochastic refinement.

Exploring the latent space of diffusion models directly through singular value decomposition De- blurring via stochastic refinement

Reference 55

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This paper cites Uncovering the disentanglement capability in text- to-image diffusion models.

Exploring the latent space of diffusion models directly through singular value decomposition Uncovering the disentanglement capability in text- to-image diffusion models

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This paper cites The ocean circulation inverse problem.

Exploring the latent space of diffusion models directly through singular value decomposition The ocean circulation inverse problem

Reference 57

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Exploring the latent space of diffusion models directly through singular value decomposition Open-vocabulary panop- tic segmentation with text-to-image diffusion models

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T13:00:05.765482Z

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Exploring the latent space of diffusion models directly through singular value decomposition Raphael: Text-to-image generation via large mixture of diffusion paths

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T13:00:05.757809Z

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Exploring the latent space of diffusion models directly through singular value decomposition Paint by example: Exemplar-based image editing with diffusion mod- els

Reference 60

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Exploring the latent space of diffusion models directly through singular value decomposition Diffusion models: A comprehensive survey of methods and applications

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T13:00:05.746313Z

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This paper cites Magicremover: Tuning-free Text-guided Image inpainting with Diffusion Models.

Exploring the latent space of diffusion models directly through singular value decomposition Magicremover: Tuning-free Text-guided Image inpainting with Diffusion Models

Reference 62

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Exploring the latent space of diffusion models directly through singular value decomposition Exploring Diffusion Time-steps for Unsupervised Representation Learning

Reference 63

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This paper cites Text-to-image Diffusion Models in Generative AI: A Survey.

Exploring the latent space of diffusion models directly through singular value decomposition Text-to-image Diffusion Models in Generative AI: A Survey

Reference 64

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Exploring the latent space of diffusion models directly through singular value decomposition Adding conditional control to text-to-image diffusion models

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-09T13:00:05.548317Z

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Observation 9878c506-d736-4d81-9279-d38f30869aab · outbound

This paper cites Sine: Single image editing with text- to-image diffusion models.

Exploring the latent space of diffusion models directly through singular value decomposition Sine: Single image editing with text- to-image diffusion models

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T13:00:05.734636Z

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This paper cites Uni-controlnet: All-in-one control to text-to-image diffusion models.

Exploring the latent space of diffusion models directly through singular value decomposition Uni-controlnet: All-in-one control to text-to-image diffusion models

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T13:00:05.727127Z

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Observation 74eb94e9-3121-496c-b810-2f7008fd8b55 · outbound

This paper cites Conditional text image generation with diffu- sion models.

Exploring the latent space of diffusion models directly through singular value decomposition Conditional text image generation with diffu- sion models

Reference 68

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