Paper Citation Record · LEDGER

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation

As of 16 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 6 inbound Pith citation observations for arXiv:2411.18301.

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

pith.paper-citation-record.v1
2411.18301 v1

Coverage vector

measured 48 of 48 reference resolution

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measured 54 of 54 standing notices

One-hop event checks from named stored sources.

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measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T10:46:55.020093Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T22:02:50.398472Z

Reference resolution

48 of 48 outbound references displayed

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External citation measurements

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Outbound references

Observation 9b7ac967-ad2d-469f-b8f1-0833d46bd194 · outbound

This paper cites A-star: Test-time attention segregation and retention for text-to-image synthesis.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation A-star: Test-time attention segregation and retention for text-to-image synthesis

Reference 1

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Observation b6b36b74-55fa-402f-a2f7-786da14c8a48 · outbound

This paper cites Separate-and-enhance: Composi- tional finetuning for text-to-image diffusion models.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Separate-and-enhance: Composi- tional finetuning for text-to-image diffusion models

Reference 2

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Observation fa70afa9-7a6c-4d04-81f7-b2c69bd1fbbb · outbound

This paper cites Make It Count: Text-to-Image Generation with an Accurate Number of Objects.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Make It Count: Text-to-Image Generation with an Accurate Number of Objects

Reference 3

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Observation 40e1ba90-e595-4ba4-9e11-527794ca7a5d · outbound

This paper cites Attend-and-excite: Attention-based se- mantic guidance for text-to-image diffusion models.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Attend-and-excite: Attention-based se- mantic guidance for text-to-image diffusion models

Reference 4

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Observation d6dea554-32b1-4bff-afb4-d1a4bf206666 · outbound

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

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Training-free layout control with cross-attention guidance

Reference 5

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Observation 83f0ef26-6420-4f2f-8cac-dd06f889bf94 · outbound

This paper cites Reproducible scal- ing laws for contrastive language-image learning.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Reproducible scal- ing laws for contrastive language-image learning

Reference 6

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Observation 7cf3d7aa-6c68-418a-8afe-ab3ca7c4052b · outbound

This paper cites Be yourself: Bounded attention for multi-subject text-to-image generation.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Be yourself: Bounded attention for multi-subject text-to-image generation

Reference 7

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Observation 1b5e9962-00ee-4125-b24f-dd661b0b1715 · outbound

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

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Scaling recti- fied flow transformers for high-resolution image synthesis

Reference 8

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Observation 1daa7a5c-3cf4-46b9-9a32-f40123d61b80 · outbound

This paper cites Training- free structured diffusion guidance for compositional text-to- image synthesis.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Training- free structured diffusion guidance for compositional text-to- image synthesis

Reference 9

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Observation bdccbab0-6763-4c00-b01c-b515802fe92d · outbound

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

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Initno: Boosting text-to-image diffu- sion models via initial noise optimization

Reference 10

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Observation 8d9f2fa3-cece-4ba7-aef5-a1ea67106a9a · outbound

This paper cites Optimizing prompts for text-to-image generation.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Optimizing prompts for text-to-image generation

Reference 11

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

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Gans trained by a two time-scale update rule converge to a local nash equilib- rium

Reference 12

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Observation 49d280ce-a727-4b10-8bd7-f62a4de6b2e1 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Classifier-Free Diffusion Guidance

Reference 13

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This paper cites Denoising dif- fusion probabilistic models.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Denoising dif- fusion probabilistic models

Reference 14

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Observation 1a7572d9-e64a-4f5d-afbc-e07231b7c4a4 · outbound

This paper cites Scal- ing up gans for text-to-image synthesis.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Scal- ing up gans for text-to-image synthesis

Reference 15

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Observation 579048d3-32b6-456e-9780-b08236d76551 · outbound

This paper cites Counting Guidance for High Fidelity Text-to-Image Synthesis.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Counting Guidance for High Fidelity Text-to-Image Synthesis

Reference 16

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Observation a996a307-abfb-422b-ba7c-1e7d880a0cf2 · outbound

This paper cites Compositional visual generation with composable diffusion models.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Compositional visual generation with composable diffusion models

Reference 17

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Observation e2196b6c-4a59-4244-8517-5ccbd57801d6 · outbound

This paper cites Grounding dino: Mar- rying dino with grounded pre-training for open-set object detection.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Grounding dino: Mar- rying dino with grounded pre-training for open-set object detection

Reference 18

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Observation edeb82ba-38fb-4ce5-a7a5-c462eabb3979 · outbound

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

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Design guidelines for prompt engineering text-to-image generative models

Reference 19

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Observation 7118c30c-170f-499b-9d9c-6a3c05c1c738 · outbound

This paper cites Flow straight and fast: Learning to generate and transfer data with rectified flow.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Flow straight and fast: Learning to generate and transfer data with rectified flow

Reference 20

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Observation 990cc766-901a-481e-8f4e-b0dac7a31e8f · outbound

This paper cites Conform: Contrast is all you need for high- fidelity text-to-image diffusion models.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Conform: Contrast is all you need for high- fidelity text-to-image diffusion models

Reference 21

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Observation 1e700a80-f3a4-4760-b714-24a165f5a6cd · outbound

This paper cites Glide: Towards photorealis- tic image generation and editing with text-guided diffusion models.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Glide: Towards photorealis- tic image generation and editing with text-guided diffusion models

Reference 22

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This paper cites Gpt-4o mini: Advancing cost-efficient intelligence.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Gpt-4o mini: Advancing cost-efficient intelligence

Reference 23

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Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Unresolved cited work

Reference 24

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Observation 71b2bb50-a706-4842-8904-281f3d02f709 · outbound

This paper cites Scalable diffusion models with transformers.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Scalable diffusion models with transformers

Reference 25

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

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 26

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Observation b4ca5c12-b16d-42e1-93e7-ad7b07fa1124 · outbound

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

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Learning transferable visual models from natural language supervi- sion

Reference 27

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This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 28

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Observation 4d9d0ce6-8b5b-413d-a542-f780ee614bf0 · outbound

This paper cites Hierarchical text-conditional image gener- ation with clip latents.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Hierarchical text-conditional image gener- ation with clip latents

Reference 29

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This paper cites Linguistic bind- ing in diffusion models: Enhancing attribute correspondence through attention map alignment.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Linguistic bind- ing in diffusion models: Enhancing attribute correspondence through attention map alignment

Reference 30

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

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation High-resolution image synthesis with latent diffusion models

Reference 31

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

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation U- net: Convolutional networks for biomedical image segmen- tation

Reference 32

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

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Photorealistic text-to-image diffusion models with deep language understanding

Reference 33

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This paper cites Denois- ing diffusion implicit models.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Denois- ing diffusion implicit models

Reference 34

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This paper cites Predicated diffusion: Predicate logic-based attention guidance for text-to-image diffusion models.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Predicated diffusion: Predicate logic-based attention guidance for text-to-image diffusion models

Reference 35

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This paper cites Diffu- siondb: A large-scale prompt gallery dataset for text-to- image generative models.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Diffu- siondb: A large-scale prompt gallery dataset for text-to- image generative models

Reference 36

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Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Hairclipv2: Unifying hair editing via proxy feature blending

Reference 37

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Observation 386e4eb0-ce3a-4e38-9ec9-68c1cb288040 · outbound

This paper cites Investigating Prompt Engineering in Diffusion Models.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Investigating Prompt Engineering in Diffusion Models

Reference 38

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Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Attngan: Fine- grained text to image generation with attentional generative adversarial networks

Reference 39

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Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Scaling autoregressive models for content-rich text-to-image generation

Reference 40

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Observation 4a5d5aa7-326c-437b-b9d7-3813dbe6e9f3 · outbound

This paper cites Cross-modal contrastive learning for text-to- image generation.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Cross-modal contrastive learning for text-to- image generation

Reference 41

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This paper cites Enhancing semantic fidelity in text- to-image synthesis: Attention regulation in diffusion mod- els.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Enhancing semantic fidelity in text- to-image synthesis: Attention regulation in diffusion mod- els

Reference 42

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

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Object- conditioned energy-based attention map alignment in text-to- image diffusion models

Reference 43

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Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Unresolved cited work

Reference 45

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This paper cites a chicken and a duck and a goose.

Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation a chicken and a duck and a goose

Reference 46

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Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Unresolved cited work

Reference 47

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Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation Bad seed, rejected sampling!

Reference 48

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Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation 7, 8, 12

Reference 2024

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Pith citing papers

Observation 64cd234c-1b99-421e-83c6-3df2b3ec6c14 · inbound

Self-Cross Diffusion Guidance for Text-to-Image Synthesis of Similar Subjects cites this paper.

Self-Cross Diffusion Guidance for Text-to-Image Synthesis of Similar Subjects Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation

Reference 49

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JEDI: The Force of Jensen-Shannon Divergence in Disentangling Diffusion Models cites this paper.

JEDI: The Force of Jensen-Shannon Divergence in Disentangling Diffusion Models Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation

Reference 20

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Observation 4de35d14-f731-4746-ba70-481e00658dbf · inbound

Scale Your Instructions: Enhance the Instruction-Following Fidelity of Unified Image Generation Model by Self-Adaptive Attention Scaling cites this paper.

Scale Your Instructions: Enhance the Instruction-Following Fidelity of Unified Image Generation Model by Self-Adaptive Attention Scaling Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T15:19:24.182520Z

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Observation 9bfe246e-1ad4-4f52-8f8e-d93c200fa060 · inbound

Detail++: Training-Free Detail Enhancer for T2I Diffusion Models cites this paper.

Detail++: Training-Free Detail Enhancer for T2I Diffusion Models Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:53.470401Z

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Observation 50c1ff24-d2f5-4c3c-a041-996742e60fc0 · inbound

LaRender: Training-Free Occlusion Control in Image Generation via Latent Rendering cites this paper.

LaRender: Training-Free Occlusion Control in Image Generation via Latent Rendering Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-05T22:02:50.403741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d5ea9f5f-5257-4c32-a219-cd3e9235ba5b · inbound

RubricRL: Simple Generalizable Rewards for Text-to-Image Generation cites this paper.

RubricRL: Simple Generalizable Rewards for Text-to-Image Generation Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-03T20:15:30.592173Z

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Unavailable: canonical work link unavailable.