Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation | Paper Citation Record

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This paper cites https://www.midjourney.com , 2023.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation https://www.midjourney.com , 2023

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This paper cites Flamingo: a visual language model for few-shot learning.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Flamingo: a visual language model for few-shot learning

Reference 2

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This paper cites Universal guidance for diffusion models.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Universal guidance for diffusion models

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This paper cites A computational approach to edge detection.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation A computational approach to edge detection

Reference 4

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This paper cites Realtime multi-person 2d pose estimation using part affinity fields.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Realtime multi-person 2d pose estimation using part affinity fields

Reference 5

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This paper cites Training-free layout control with cross-attention guidance.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Training-free layout control with cross-attention guidance

Reference 6

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This paper cites https://huggingface.co/CompVis/ stable-diffusion-v-1-4-original , 2023.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation https://huggingface.co/CompVis/ stable-diffusion-v-1-4-original , 2023

Reference 7

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This paper cites Diffusion models beat gans on image synthesis.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Diffusion models beat gans on image synthesis

Reference 8

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This paper cites Activation functions in deep learning: A com- prehensive survey and benchmark.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Activation functions in deep learning: A com- prehensive survey and benchmark

Reference 9

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This paper cites Sigmoid- weighted linear units for neural network function approxima- tion in reinforcement learning.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Sigmoid- weighted linear units for neural network function approxima- tion in reinforcement learning

Reference 10

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This paper cites Frido: Fea- ture pyramid diffusion for complex scene image synthesis.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Frido: Fea- ture pyramid diffusion for complex scene image synthesis

Reference 11

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This paper cites Attrlost- gan: Attribute controlled image synthesis from reconfig- urable layout and style.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Attrlost- gan: Attribute controlled image synthesis from reconfig- urable layout and style

Reference 12

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This paper cites https : / / https : / / huggingface.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation https : / / https : / / huggingface

Reference 13

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This paper cites Generative adversarial nets.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Generative adversarial nets

Reference 14

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This paper cites Towards light-weight and real-time line segment detection.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Towards light-weight and real-time line segment detection

Reference 15

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This paper cites HyperNetworks.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation HyperNetworks

Reference 16

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

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Gans trained by a two time-scale update rule converge to a local nash equilib- rium

Reference 17

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This paper cites Denoising dif- fusion probabilistic models.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Denoising dif- fusion probabilistic models

Reference 18

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This paper cites Parameter-efficient transfer learning for nlp.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Parameter-efficient transfer learning for nlp

Reference 19

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

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation LoRA: Low-Rank Adaptation of Large Language Models

Reference 20

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This paper cites High-Resolution Complex Scene Synthesis with Transformers.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation High-Resolution Complex Scene Synthesis with Transformers

Reference 21

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This paper cites Cubic convolution interpolation for digital im- age processing.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Cubic convolution interpolation for digital im- age processing

Reference 22

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This paper cites Adam: A Method for Stochastic Optimization.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Adam: A Method for Stochastic Optimization

Reference 23

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Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Gradient accumulation in pytorch

Reference 24

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This paper cites Visual genome: Connecting language and vision using crowdsourced dense image annotations.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Visual genome: Connecting language and vision using crowdsourced dense image annotations

Reference 25

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This paper cites Grounded language-image pre-training.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Grounded language-image pre-training

Reference 26

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This paper cites Bachgan: High-resolution im- age synthesis from salient object layout.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Bachgan: High-resolution im- age synthesis from salient object layout

Reference 27

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This paper cites Gligen: Open-set grounded text-to-image generation.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Gligen: Open-set grounded text-to-image generation

Reference 28

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This paper cites Image synthesis from layout with locality- aware mask adaption.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Image synthesis from layout with locality- aware mask adaption

Reference 29

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This paper cites Microsoft coco: Common objects in context.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Microsoft coco: Common objects in context

Reference 30

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This paper cites Pseudo Numerical Methods for Diffusion Models on Manifolds.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Pseudo Numerical Methods for Diffusion Models on Manifolds

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This paper cites Design guidelines for prompt engineering text-to-image generative models.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Design guidelines for prompt engineering text-to-image generative models

Reference 32

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Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Mixed Precision Training

Reference 33

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This paper cites Representing scenes as neu- ral radiance fields for view synthesis., 2021, 65.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Representing scenes as neu- ral radiance fields for view synthesis., 2021, 65

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This paper cites T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models

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

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

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Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Improved denoising diffusion probabilistic models

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Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Dall-e-3

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Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Im2text: Describing images using 1 million captioned pho- tographs

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This paper cites A survey on performance metrics for object-detection algo- rithms.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation A survey on performance metrics for object-detection algo- rithms

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This paper cites Best prompts for text-to-image models and how to find them.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Best prompts for text-to-image models and how to find them

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This paper cites Flickr30k entities: Collecting region-to-phrase corre- spondences for richer image-to-sentence models.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Flickr30k entities: Collecting region-to-phrase corre- spondences for richer image-to-sentence models

Reference 42

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Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Multilayer percep- tron and neural networks

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This paper cites Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation

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This paper cites UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild

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Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Learning transferable visual models from natural language supervi- sion

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Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Zero-shot text-to-image generation

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Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer

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This paper cites You only look once: Unified, real-time object de- tection.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation You only look once: Unified, real-time object de- tection

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

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation High-resolution image synthesis with latent diffusion models

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This paper cites U- net: Convolutional networks for biomedical image segmen- tation.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation U- net: Convolutional networks for biomedical image segmen- tation

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This paper cites Limitations of face image generation.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Limitations of face image generation

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This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven 18 generation.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Dreambooth: Fine tuning text-to-image diffusion models for subject-driven 18 generation

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

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Photorealistic text-to-image diffusion models with deep language understanding

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Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 55

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Observation 3bec2208-10f0-4080-91e2-124f05f55e88 · outbound

This paper cites pytorch-fid: FID Score for PyTorch.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation pytorch-fid: FID Score for PyTorch

Reference 56

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This paper cites Objects365: A large-scale, high-quality dataset for object detection.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Objects365: A large-scale, high-quality dataset for object detection

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:14:44.617263Z

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Observation de19db63-626c-4862-82b7-d408e4c8c8f9 · outbound

This paper cites Conceptual captions: A cleaned, hypernymed, im- age alt-text dataset for automatic image captioning.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Conceptual captions: A cleaned, hypernymed, im- age alt-text dataset for automatic image captioning

Reference 58

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Observation d1d1f4db-63d8-44a8-a4bc-f6626418c8a5 · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Deep unsupervised learning using nonequilibrium thermodynamics

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:14:44.597076Z

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Observation 0c17c7f5-752e-413f-b1c4-ccc871b6c652 · outbound

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

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Generative modeling by esti- mating gradients of the data distribution

Reference 60

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Observation b34d761d-7a03-47a0-893f-c0173d29bfc5 · outbound

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

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Score-Based Generative Modeling through Stochastic Differential Equations

Reference 61

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Observation 0106fb5e-2414-4cd5-b98b-57e9b8c8e826 · outbound

This paper cites Image synthesis from reconfig- urable layout and style.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Image synthesis from reconfig- urable layout and style

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:14:44.580328Z

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Observation df9ae7bd-3a94-40b9-b4ac-b73c25ac4893 · outbound

This paper cites Learning layout and style recon- figurable gans for controllable image synthesis.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Learning layout and style recon- figurable gans for controllable image synthesis

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:14:44.569859Z

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Observation 111589ab-a591-4257-a6c3-3ed7ca876990 · outbound

This paper cites Object-centric image genera- tion from layouts.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Object-centric image genera- tion from layouts

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:14:44.559207Z

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Observation bc3cb536-5a41-4fa0-bea8-127208a7715f · outbound

This paper cites DIODE: A Dense Indoor and Outdoor DEpth Dataset.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation DIODE: A Dense Indoor and Outdoor DEpth Dataset

Reference 65

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Observation ce22718a-6b35-41a2-bcaf-1cf0a49c7f17 · outbound

This paper cites Attention is all you need.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Attention is all you need

Reference 66

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Observation 667db6ff-5591-4409-870e-729c1d4c2a53 · outbound

This paper cites Continual learning with hypernetworks.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Continual learning with hypernetworks

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-10T20:14:44.280111Z

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Observation b792b2ab-73dd-430f-886e-8df50f4e3416 · outbound

This paper cites Pretraining is All You Need for Image-to-Image Translation.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Pretraining is All You Need for Image-to-Image Translation

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-10T20:14:44.283725Z

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Observation f9f7e92f-1658-4a49-968b-7d2f728f3274 · outbound

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Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Instancediffusion: Instance- level control for image generation

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-10T20:14:44.287085Z

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Observation c855e139-93d9-4c00-bce4-508d27e16bd5 · outbound

This paper cites Boxdiff: Text-to-image synthesis with training-free box-constrained diffusion.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Boxdiff: Text-to-image synthesis with training-free box-constrained diffusion

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-10T20:14:44.291064Z

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Observation e50025bc-ffd4-4915-80ef-8baaed24fa68 · outbound

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Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Holistically-nested edge de- tection

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:14:44.532346Z

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Observation 9cb65e0e-4c7f-4c31-b64b-062866f42352 · outbound

This paper cites Modeling image composition for complex scene generation.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Modeling image composition for complex scene generation

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:14:44.522300Z

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Observation e2fe1491-c148-457b-81a1-eb63363e2779 · outbound

This paper cites Reco: Region-controlled text-to-image genera- tion.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Reco: Region-controlled text-to-image genera- tion

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-10T20:14:44.301686Z

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Observation 2b8052b4-417d-4b9b-934a-95a88cfd1e32 · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Adding conditional control to text-to-image diffusion models

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:14:44.505355Z

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Observation 123f70c7-4508-4798-b52a-79a2dff5f11a · outbound

This paper cites Image generation from layout.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Image generation from layout

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:14:44.492829Z

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Observation 09dee146-a184-4d4a-88e7-5a6e48ddafe6 · outbound

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

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Uni-controlnet: All-in-one control to text-to-image diffusion models

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:14:44.481695Z

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Observation 544f663d-2bfa-4ff9-8157-a6857e9a4b06 · outbound

This paper cites Scene parsing through ade20k dataset.

Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Scene parsing through ade20k dataset

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-10T20:14:44.317232Z

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Observation 8409a590-d40e-4765-bdcf-021e50fbb2f4 · outbound

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Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation Unresolved cited work

Reference 2023

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