Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation | Paper Citation Record

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This paper cites write newline.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation write newline

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This paper cites The k-means algorithm: A comprehensive survey and performance evaluation.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation The k-means algorithm: A comprehensive survey and performance evaluation

Reference 2

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Unresolved cited work

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This paper cites Automatic story generation: A survey of approaches.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Automatic story generation: A survey of approaches

Reference 4

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This paper cites Using my artistic style? you must obtain my authorization.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Using my artistic style? you must obtain my authorization

Reference 5

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This paper cites Customttt: Motion and appearance customized video generation via test-time training.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Customttt: Motion and appearance customized video generation via test-time training

Reference 6

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This paper cites Relactrl: Relevance-guided efficient control for diffusion transformers.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Relactrl: Relevance-guided efficient control for diffusion transformers

Reference 7

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This paper cites GameGen-X: Interactive Open-world Game Video Generation.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation GameGen-X: Interactive Open-world Game Video Generation

Reference 8

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This paper cites PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis

Reference 9

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This paper cites Panda-70m: Captioning 70m videos with multiple cross-modality teachers.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Panda-70m: Captioning 70m videos with multiple cross-modality teachers

Reference 10

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This paper cites Ctr-driven advertising image generation with multimodal large language models.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Ctr-driven advertising image generation with multimodal large language models

Reference 11

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

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Scaling rectified flow transformers for high-resolution image synthesis

Reference 12

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This paper cites FancyVideo: Towards Dynamic and Consistent Video Generation via Cross-frame Textual Guidance.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation FancyVideo: Towards Dynamic and Consistent Video Generation via Cross-frame Textual Guidance

Reference 13

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This paper cites DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data

Reference 14

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Observation cfa2aa51-d149-4c6a-ac4e-8189d353621f · outbound

This paper cites An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 15

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Check locate rectify: A training-free layout calibration system for text-to-image generation

Reference 16

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This paper cites Variational autoencoder: An unsupervised model for encoding and decoding fmri activity in visual cortex.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Variational autoencoder: An unsupervised model for encoding and decoding fmri activity in visual cortex

Reference 17

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This paper cites AnyStory: Towards Unified Single and Multiple Subject Personalization in Text-to-Image Generation.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation AnyStory: Towards Unified Single and Multiple Subject Personalization in Text-to-Image Generation

Reference 18

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This paper cites FreeEdit: Mask-free Reference-based Image Editing with Multi-modal Instruction.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation FreeEdit: Mask-free Reference-based Image Editing with Multi-modal Instruction

Reference 19

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This paper cites PlanGen: Towards Unified Layout Planning and Image Generation in Auto-Regressive Vision Language Models.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation PlanGen: Towards Unified Layout Planning and Image Generation in Auto-Regressive Vision Language Models

Reference 20

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This paper cites Context-aware layout to image generation with enhanced object appearance.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Context-aware layout to image generation with enhanced object appearance

Reference 21

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Style aligned image generation via shared attention

Reference 22

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

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Reference 23

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 24

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Interactdiffusion: Interaction control in text-to-image diffusion models

Reference 25

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This paper cites Learning disentangled identifiers for action-customized text-to-image generation.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Learning disentangled identifiers for action-customized text-to-image generation

Reference 26

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Reversion: Diffusion-based relation inversion from images

Reference 27

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation GPT-4o System Card

Reference 28

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Res-tuning: A flexible and efficient tuning paradigm via unbinding tuner from backbone

Reference 29

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This paper cites Miradata: A large-scale video dataset with long durations and structured captions.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Miradata: A large-scale video dataset with long durations and structured captions

Reference 30

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Story generation with crowdsourced plot graphs

Reference 31

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Blip-diffusion: Pre-trained subject representation for controllable text-to-image generation and editing

Reference 32

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Planning and Rendering: Towards Product Poster Generation with Diffusion Models

Reference 33

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Photomaker: Customizing realistic human photos via stacked id embedding

Reference 34

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation RAGAR: Retrieval Augmented Personalized Image Generation Guided by Recommendation

Reference 35

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Intelligent grimm-open-ended visual storytelling via latent diffusion models

Reference 36

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Grounding dino: Marrying dino with grounded pre-training for open-set object detection

Reference 37

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Bridge diffusion model: Bridge chinese text-to-image diffusion model with english communities

Reference 38

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation One-Prompt-One-Story: Free-Lunch Consistent Text-to-Image Generation Using a Single Prompt

Reference 39

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Out-of-Distribution Detection: A Task-Oriented Survey of Recent Advances

Reference 40

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Uni-Layout: Integrating Human Feedback in Unified Layout Generation and Evaluation

Reference 41

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Unified Multi-Modal Latent Diffusion for Joint Subject and Text Conditional Image Generation

Reference 42

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Hico: Hierarchical controllable diffusion model for layout-to-image generation

Reference 43

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Observation 8ed8abbb-803c-4f57-94fc-fd899bf49ee9 · outbound

This paper cites Storynizor: Consistent Story Generation via Inter-Frame Synchronized and Shuffled ID Injection.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Storynizor: Consistent Story Generation via Inter-Frame Synchronized and Shuffled ID Injection

Reference 44

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Story-adapter: A training-free iterative framework for long story visualization

Reference 45

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Lego: Learning to Disentangle and Invert Personalized Concepts Beyond Object Appearance in Text-to-Image Diffusion Models

Reference 46

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation

Reference 47

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Scalable diffusion models with transformers

Reference 48

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 49

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Learning transferable visual models from natural language supervision

Reference 50

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 51

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 52

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Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation High-resolution image synthesis with latent diffusion models

Reference 53

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

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation U-net: Convolutional networks for biomedical image segmentation

Reference 54

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

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 55

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Observation bbf0a86e-b3c7-4069-8cda-0f070345e064 · outbound

This paper cites Carvekit: Automated high-quality background removal framework.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Carvekit: Automated high-quality background removal framework

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:33:26.671466Z

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 14878054-7e70-4519-9123-535504af5875 · outbound

This paper cites EventVAD: Training-Free Event-Aware Video Anomaly Detection.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation EventVAD: Training-Free Event-Aware Video Anomaly Detection

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T17:33:24.505916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f15e1c6-d563-498c-8d6f-0ddd02e0f476 · outbound

This paper cites TR-DQ: Time-Rotation Diffusion Quantization.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation TR-DQ: Time-Rotation Diffusion Quantization

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T17:33:24.509659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7aca3f69-d998-4cb2-994f-ebd472c998ce · outbound

This paper cites In-Context Meta LoRA Generation.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation In-Context Meta LoRA Generation

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T17:33:24.513403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1012e227-1a41-40c3-be63-102d8887161e · outbound

This paper cites Storybooth: Training-free multi-subject consistency for improved visual storytelling.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Storybooth: Training-free multi-subject consistency for improved visual storytelling

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:33:26.661107Z

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 ee9570da-aeae-4c8f-ae6f-26d6e4c34579 · outbound

This paper cites Styledrop: Text-to-image synthesis of any style.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Styledrop: Text-to-image synthesis of any style

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:33:26.649823Z

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 7465c189-c960-43b5-b1a1-f1e2c786dbf6 · outbound

This paper cites Instantx flux.1-dev ip-adapter page, 2024.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Instantx flux.1-dev ip-adapter page, 2024

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:33:26.511142Z

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 05cc723f-532d-4a31-b1d9-3bd5f3c5aafc · outbound

This paper cites Training-free consistent text-to-image generation.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Training-free consistent text-to-image generation

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:33:26.434035Z

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 c802d7af-d252-423c-829b-a9528f1ff267 · outbound

This paper cites Converting video formats with ffmpeg.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Converting video formats with ffmpeg

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:33:26.416528Z

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 493d489e-35f0-41e7-af30-f0e5743f9da2 · outbound

This paper cites Face0: Instantaneously conditioning a text-to-image model on a face.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Face0: Instantaneously conditioning a text-to-image model on a face

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-15T17:33:24.533405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d28c5608-8fc7-49e3-9565-f479c24dc70b · outbound

This paper cites Attention is all you need.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Attention is all you need

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-15T17:33:24.536634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39d6403e-4df9-4a85-a89d-7d0e4d3161be · outbound

This paper cites Is this loss informative? faster text-to-image customization by tracking objective dynamics.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Is this loss informative? faster text-to-image customization by tracking objective dynamics

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:33:26.394968Z

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 8074d3a6-3acf-412b-a3dc-4ea4311d74f5 · outbound

This paper cites P+: Extended Textual Conditioning in Text-to-Image Generation.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation P+: Extended Textual Conditioning in Text-to-Image Generation

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-15T17:33:24.692476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6bdc817-5fc0-4052-86fe-7d21afef8c8d · outbound

This paper cites Qihoo-t2x: An efficiency-focused diffusion transformer via proxy tokens for text-to-any-task.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Qihoo-t2x: An efficiency-focused diffusion transformer via proxy tokens for text-to-any-task

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:33:26.386538Z

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 5ab2929e-b66d-4dc2-8167-7f61abd8df2c · outbound

This paper cites WISA: World Simulator Assistant for Physics-Aware Text-to-Video Generation.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation WISA: World Simulator Assistant for Physics-Aware Text-to-Video Generation

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-15T17:33:24.883696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef5dbec5-4de4-44cd-8e70-9ef2870f1d0e · outbound

This paper cites Videofactory: Swap attention in spatiotemporal diffusions for text-to-video generation.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Videofactory: Swap attention in spatiotemporal diffusions for text-to-video generation

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:33:26.377558Z

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 2eac308c-dd49-4730-bcb0-12c84322c412 · outbound

This paper cites Spnet: Learning stereo matching with slanted plane aggregation.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Spnet: Learning stereo matching with slanted plane aggregation

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:33:26.368976Z

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 1d4832bb-3a12-4737-9dde-d6afb7c9d02f · outbound

This paper cites InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-15T17:33:24.895483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f12da01-1b8b-4999-907b-4e9d43b6216f · outbound

This paper cites Adstereo: Efficient stereo matching with adaptive downsampling and disparity alignment.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Adstereo: Efficient stereo matching with adaptive downsampling and disparity alignment

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:33:26.358610Z

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 4d108ca4-d67b-4da1-835a-0623ebe08d67 · outbound

This paper cites Learning Robust Stereo Matching in the Wild with Selective Mixture-of-Experts.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Learning Robust Stereo Matching in the Wild with Selective Mixture-of-Experts

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-15T17:33:24.902704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33e4b485-7113-4f2a-a49e-af5766b3c201 · outbound

This paper cites Dualnet: Robust self-supervised stereo matching with pseudo-label supervision.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Dualnet: Robust self-supervised stereo matching with pseudo-label supervision

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:33:26.297882Z

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 bf120898-3370-458a-8fbe-53b31e199dcf · outbound

This paper cites StyleAdapter: A Unified Stylized Image Generation Model.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation StyleAdapter: A Unified Stylized Image Generation Model

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-15T17:33:24.910052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d02d64f1-c3d1-4d7d-9d7b-93cedf51295c · outbound

This paper cites Dropoutgs: Dropping out gaussians for better sparse-view rendering.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Dropoutgs: Dropping out gaussians for better sparse-view rendering

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:33:26.197045Z

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 187c33fb-64fc-4b67-84e9-c9c9c3c11eff · outbound

This paper cites Freestyle layout-to-image synthesis.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Freestyle layout-to-image synthesis

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:33:26.135150Z

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 f5696252-57a0-4544-b1fe-4e0df41d83db · outbound

This paper cites FaceStudio: Put Your Face Everywhere in Seconds.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation FaceStudio: Put Your Face Everywhere in Seconds

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-15T17:33:24.920689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 026d94fe-7633-4f3e-b69e-6c4944e7ad2c · outbound

This paper cites SEED-Story: Multimodal Long Story Generation with Large Language Model.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation SEED-Story: Multimodal Long Story Generation with Large Language Model

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-15T17:33:24.931590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 570aa5e5-cc7a-492a-865d-4fe5ed740e49 · outbound

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

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Reco: Region-controlled text-to-image generation

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:33:26.126220Z

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 9a153d8a-319f-473b-bc32-b9716c4347e6 · outbound

This paper cites IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-15T17:33:25.042293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 076aab37-bdeb-4466-8d02-74ac8923bb14 · outbound

This paper cites Controlnet-xs: Rethinking the control of text-to-image diffusion models as feedback-control systems.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Controlnet-xs: Rethinking the control of text-to-image diffusion models as feedback-control systems

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:33:26.116863Z

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 e3585d81-7fe0-4675-907b-3b983bb69ad0 · outbound

This paper cites CreatiLayout: Siamese Multimodal Diffusion Transformer for Creative Layout-to-Image Generation.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation CreatiLayout: Siamese Multimodal Diffusion Transformer for Creative Layout-to-Image Generation

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-15T17:33:25.049145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cbcded5b-0c13-4992-88b1-ddc2120d7399 · outbound

This paper cites A survey on personalized content synthesis with diffusion models.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation A survey on personalized content synthesis with diffusion models

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-15T17:33:25.052551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a779cdd0-d223-42ff-b991-83b083c247a5 · outbound

This paper cites Generative active learning for image synthesis personalization.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Generative active learning for image synthesis personalization

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:33:26.107500Z

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 5b39d0a7-5129-4cdc-98b6-e70f24729b1d · outbound

This paper cites Towards highly realistic artistic style transfer via stable diffusion with step-aware and layer-aware prompt.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Towards highly realistic artistic style transfer via stable diffusion with step-aware and layer-aware prompt

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:33:26.097492Z

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 8b287e06-a5d5-4fc3-9684-9011bc1dad8a · outbound

This paper cites Artbank: Artistic style transfer with pre-trained diffusion model and implicit style prompt bank.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Artbank: Artistic style transfer with pre-trained diffusion model and implicit style prompt bank

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:33:26.085143Z

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 067a07b0-cb44-4ff3-b48d-a592e4d65299 · outbound

This paper cites Lgast: Towards high-quality arbitrary style transfer with local--global style learning.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Lgast: Towards high-quality arbitrary style transfer with local--global style learning

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:33:26.072781Z

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 efa75ff7-9d5c-439f-bebd-cc72a0b060f0 · outbound

This paper cites U-StyDiT: Ultra-high Quality Artistic Style Transfer Using Diffusion Transformers.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation U-StyDiT: Ultra-high Quality Artistic Style Transfer Using Diffusion Transformers

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-15T17:33:25.071626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e12b688-2419-4e73-9d55-a9afb5a66f3b · outbound

This paper cites Spast: Arbitrary style transfer with style priors via pre-trained large-scale model.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Spast: Arbitrary style transfer with style priors via pre-trained large-scale model

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:33:26.061449Z

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 4d046ab3-eae6-4e84-bb24-0c7f0ba38190 · outbound

This paper cites Vectorsketcher: Learning to create a vector-based free-hand sketch.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Vectorsketcher: Learning to create a vector-based free-hand sketch

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:33:26.050314Z

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 68d5348a-b873-4d39-a6ca-8987b713da9e · outbound

This paper cites Image generation from layout.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Image generation from layout

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:33:25.980060Z

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 29f365ef-545d-478f-928e-e5ce52f440e7 · outbound

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

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Uni-controlnet: All-in-one control to text-to-image diffusion models

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-15T17:33:25.085390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5059c8ea-c45a-43d9-9e8d-e91dc928317d · outbound

This paper cites Layoutdiffusion: Controllable diffusion model for layout-to-image generation.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Layoutdiffusion: Controllable diffusion model for layout-to-image generation

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:33:25.862408Z

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 b6342a0b-8efa-4d95-add4-142dc6b16b32 · outbound

This paper cites Enhancing Detail Preservation for Customized Text-to-Image Generation: A Regularization-Free Approach.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Enhancing Detail Preservation for Customized Text-to-Image Generation: A Regularization-Free Approach

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-15T17:33:25.201953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ea8d041-acb5-43e4-ad50-1523a30eb732 · outbound

This paper cites Storydiffusion: Consistent self-attention for long-range image and video generation.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation Storydiffusion: Consistent self-attention for long-range image and video generation

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:33:25.851440Z

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 a1bd2745-0f78-427c-8f7b-06784ac589c7 · outbound

This paper cites StoryMaker: Towards Holistic Consistent Characters in Text-to-image Generation.

Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation StoryMaker: Towards Holistic Consistent Characters in Text-to-image Generation

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-15T17:33:25.283022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Reference resolution

Outbound references

Pith citing papers