Paper Citation Record · LEDGER

Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis

As of 17 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 2 inbound Pith citation observations for arXiv:2505.10046.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.10046 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T20:44:20.190064Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-28T20:52:37.899434Z

Reference resolution

51 of 51 outbound references displayed

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External citation measurements

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Outbound references

Observation 84166abf-e733-4b08-8131-92ecb78713f0 · outbound

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

Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Flamingo: a visual language model for few-shot learning

Reference 1

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This paper cites Improving image generation with bet- ter captions.https://cdn.

Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Improving image generation with bet- ter captions.https://cdn

Reference 2

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This paper cites PaliGemma: A versatile 3B VLM for transfer.

Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis PaliGemma: A versatile 3B VLM for transfer

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This paper cites Conceptual 12M: Pushing web-scale image-text pre-training to recognize long-tail visual concepts.

Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Conceptual 12M: Pushing web-scale image-text pre-training to recognize long-tail visual concepts

Reference 4

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This paper cites Pixart-σ: Weak-to-strong training of diffu- sion transformer for 4k text-to-image generation.

Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Pixart-σ: Weak-to-strong training of diffu- sion transformer for 4k text-to-image generation

Reference 5

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Observation e5d32a4a-2b8f-4010-8297-357af72623d9 · outbound

This paper cites Pixart-α: Fast training of diffusion trans- former for photorealistic text-to-image synthesis.

Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Pixart-α: Fast training of diffusion trans- former for photorealistic text-to-image synthesis

Reference 6

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This paper cites Dreamllm: Synergistic multimodal com- prehension and creation.

Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Dreamllm: Synergistic multimodal com- prehension and creation

Reference 7

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis An image is worth 16x16 words: Transformers for image recognition at scale

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis conceptual-captions-cc12m- llavanext.https://huggingface.co/datasets/ CaptionEmporium / conceptual - captions - cc12m-llavanext, 2024

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Observation a2d01e66-b909-4997-8f7d-94bdd26dd061 · outbound

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

Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Scaling recti- fied flow transformers for high-resolution image synthesis

Reference 10

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This paper cites Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers.

Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers

Reference 11

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This paper cites GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignment.

Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignment

Reference 12

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis The Llama 3 Herd of Models

Reference 13

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

Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Gans trained by a two time-scale update rule converge to a local nash equilib- rium

Reference 14

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Observation cba3a9ee-932d-4840-977a-abba7820078c · outbound

This paper cites ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment.

Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment

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This paper cites Segment any- thing.

Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Segment any- thing

Reference 16

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Observation d5f0aabc-8081-4ed7-8bc5-be63e2764acb · outbound

This paper cites Announcing black forest labs.https: / / blackforestlabs.

Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Announcing black forest labs.https: / / blackforestlabs

Reference 17

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation

Reference 18

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models

Reference 20

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Playground v3: Improving Text-to-Image Alignment with Deep-Fusion Large Language Models

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis LLM4GEN: Leveraging Semantic Representation of LLMs for Text-to-Image Generation

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Decoupled weight decay regularization

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Exploring the Role of Large Language Models in Prompt Encoding for Diffusion Models

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Gemma: Open Models Based on Gemini Research and Technology

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Kosmos-g: Generating images in context with multimodal large language models

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Scalable diffusion models with transformers

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Learning transferable visual models from natural language supervision

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Exploring the limits of transfer learning with a unified text-to-text transformer

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Hierarchical Text-Conditional Image Generation with CLIP Latents

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Gemma 2: Improving Open Language Models at a Practical Size

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis High-resolution image syn- thesis with latent diffusion models

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis U-net: Convolutional networks for biomedical image segmentation

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This paper cites Stretching Each Dollar: Diffusion Training from Scratch on a Micro-Budget.

Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Stretching Each Dollar: Diffusion Training from Scratch on a Micro-Budget

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis LMFusion: Adapting Pretrained Language Models for Multimodal Generation

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Layer by Layer: Uncovering Hidden Representations in Language Models

Reference 39

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This paper cites Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 2024.

Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 2024

Reference 40

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Journeydb: A benchmark for generative im- age understanding

Reference 41

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This paper cites Is noise conditioning necessary for denoising genera- tive models?arXiv:2502.13129, 2025.

Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Is noise conditioning necessary for denoising genera- tive models?arXiv:2502.13129, 2025

Reference 42

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 43

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This paper cites Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation.

Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation

Reference 44

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Self-correcting llm-controlled diffusion models

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis OmniGen: Unified Image Generation

Reference 46

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers

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Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer

Reference 48

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This paper cites Mastering text-to-image diffu- sion: Recaptioning, planning, and generating with multi- modal llms.

Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Mastering text-to-image diffu- sion: Recaptioning, planning, and generating with multi- modal llms

Reference 49

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Observation 24a5858c-0b98-4d62-94c8-41a92f6c5dd5 · outbound

This paper cites Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model.

Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T21:21:22.538806Z

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Observation 7c23bef9-2e55-45cc-88e1-922db9ce5157 · outbound

This paper cites Lumina-Next: Making Lumina-T2X Stronger and Faster with Next-DiT.

Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis Lumina-Next: Making Lumina-T2X Stronger and Faster with Next-DiT

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-15T21:21:22.544386Z

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Unavailable: canonical work link unavailable.

Pith citing papers

Observation 20536b70-f7f7-486f-aba4-ca14b5b728e9 · inbound

Exploring Time Conditioning in Diffusion Generative Models from Disjoint Noisy Data Manifolds cites this paper.

Exploring Time Conditioning in Diffusion Generative Models from Disjoint Noisy Data Manifolds Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:31:15.483764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1453aca1-62fc-4390-8b98-9d0544cdf4a0 · inbound

UNISON: A Unified Sound Generation and Editing Framework via Deep LLM Fusion cites this paper.

UNISON: A Unified Sound Generation and Editing Framework via Deep LLM Fusion Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-06-28T20:52:37.900963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.