Paper Citation Record · LEDGER

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions

As of 15 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2506.16679.

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pith.paper-citation-record.v1
2506.16679 v1

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measured 45 of 45 reference resolution

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45 of 45 outbound references displayed

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External citation measurements

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Outbound references

Observation b29b55f1-1eae-4431-8287-c32b58950ab5 · outbound

This paper cites eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers

Reference 1

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Observation b7ccddc5-04e7-49b8-b370-194bce0d1623 · outbound

This paper cites Improving image generation with better captions, 2023.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Improving image generation with better captions, 2023

Reference 2

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Observation e9c07c11-e186-4cf8-810b-83d1fd08c5fa · outbound

This paper cites Easily Ac- cessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Easily Ac- cessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale

Reference 3

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Observation 558f82cb-ca6b-4513-bf14-22cd904bf406 · outbound

This paper cites Se- mantics derived automatically from language corpora con- tain human-like biases.Science, 356(6334):183–186, 2017.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Se- mantics derived automatically from language corpora con- tain human-like biases.Science, 356(6334):183–186, 2017

Reference 4

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This paper cites How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

Reference 5

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Observation 6624f839-a4f3-4b3a-8d99-8774cb2628d5 · outbound

This paper cites Lmdeploy: A toolkit for com- pressing, deploying, and serving llm.https://github.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Lmdeploy: A toolkit for com- pressing, deploying, and serving llm.https://github

Reference 6

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Observation 6a27f1dc-6ac3-4360-9fe3-16dc6ee37eab · outbound

This paper cites DeepFloyd-IF-I-XL-v1.0: DeepFloyd’s Image Generation Model, 2023.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions DeepFloyd-IF-I-XL-v1.0: DeepFloyd’s Image Generation Model, 2023

Reference 7

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Observation d0220b11-71d3-4c05-8397-1d2acf8f482f · outbound

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

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Scaling rectified flow trans- formers for high-resolution image synthesis

Reference 8

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Observation 347fc60b-73ce-4c28-b156-dd2933804507 · outbound

This paper cites Auditing and instructing text-to-image gener- ation models on fairness.AI and Ethics, 2024.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Auditing and instructing text-to-image gener- ation models on fairness.AI and Ethics, 2024

Reference 9

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Observation a1c12695-5946-44e0-8b8d-c2ddea93614f · outbound

This paper cites Multilingual text-to-image generation magnifies gender stereotypes and prompt engineering may not help you, 2024.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Multilingual text-to-image generation magnifies gender stereotypes and prompt engineering may not help you, 2024

Reference 10

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Observation 22dec8b3-0204-4ef4-89e8-31bd5a06cafd · outbound

This paper cites Datacomp: In search of the next generation of multimodal datasets.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Datacomp: In search of the next generation of multimodal datasets

Reference 11

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Observation 61b54fcd-4a5f-434d-944c-ae818c146561 · outbound

This paper cites CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images

Reference 12

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This paper cites Hal- lusionbench: An advanced diagnostic suite for entangled language hallucination and visual illusion in large vision- language models.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Hal- lusionbench: An advanced diagnostic suite for entangled language hallucination and visual illusion in large vision- language models

Reference 13

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This paper cites Benchmarking of deep architectures for segmentation of medical images.Trans.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Benchmarking of deep architectures for segmentation of medical images.Trans

Reference 14

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Observation fc7b575b-4bc4-4a28-8285-15bc81136e46 · outbound

This paper cites CLIPScore: a reference-free evaluation met- ric for image captioning.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions CLIPScore: a reference-free evaluation met- ric for image captioning

Reference 15

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Observation c571d370-70d8-47c8-b310-bf761d777dbf · outbound

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

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Gans trained by a two time-scale update rule converge to a local nash equilib- rium

Reference 16

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This paper cites Classifier-Free Diffusion Guidance.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Classifier-Free Diffusion Guidance

Reference 17

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Observation 81b22947-d3cf-4904-9aca-c6978eecb2f8 · outbound

This paper cites Fairface: Face at- tribute dataset for balanced race, gender, and age for bias measurement and mitigation.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Fairface: Face at- tribute dataset for balanced race, gender, and age for bias measurement and mitigation

Reference 18

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This paper cites Transformers are minimax optimal nonparametric in-context learners.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Transformers are minimax optimal nonparametric in-context learners

Reference 19

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This paper cites Pick-a-pic: An open dataset of user preferences for text-to-image generation.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Pick-a-pic: An open dataset of user preferences for text-to-image generation

Reference 20

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Observation 08b35d18-f633-4871-9784-f9114dbe2b77 · outbound

This paper cites Genai-bench: Evaluat- ing and improving compositional text-to-visual generation,.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Genai-bench: Evaluat- ing and improving compositional text-to-visual generation,

Reference 21

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Observation da717749-599d-4916-9ecb-f93dbace5d54 · outbound

This paper cites LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models

Reference 22

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This paper cites Manmatha, Ashwin Swaminathan, Zhuowen Tu, Stefano Ermon, and Stefano Soatto.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Manmatha, Ashwin Swaminathan, Zhuowen Tu, Stefano Ermon, and Stefano Soatto

Reference 23

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How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Unresolved cited work

Reference 24

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This paper cites MVPTR: multi- level semantic alignment for vision-language pre-training via multi-stage learning.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions MVPTR: multi- level semantic alignment for vision-language pre-training via multi-stage learning

Reference 25

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This paper cites Playground v3: Im- proving text-to-image alignment with deep-fusion large lan- guage models, 2024.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Playground v3: Im- proving text-to-image alignment with deep-fusion large lan- guage models, 2024

Reference 26

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This paper cites Decoupled weight decay regularization.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Decoupled weight decay regularization

Reference 27

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Observation 7b80c2bb-db38-4371-8bc0-d7b1e7857db7 · outbound

This paper cites DataDecide: How to Predict Best Pretraining Data with Small Experiments.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions DataDecide: How to Predict Best Pretraining Data with Small Experiments

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How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions On aliased resizing and surprising subtleties in GAN evaluation

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This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Hierarchical Text-Conditional Image Generation with CLIP Latents

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How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions High-resolution image synthesis with latent diffusion models

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:42:48.820391Z

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This paper cites Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:42:48.515166Z

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This paper cites Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation

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This paper cites Safe latent diffusion: Mitigating inappro- priate degeneration in diffusion models.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Safe latent diffusion: Mitigating inappro- priate degeneration in diffusion models

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:42:48.294749Z

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How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions LAION- 400M: open dataset of clip-filtered 400 million image-text pairs

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This paper cites Laion-5b: An open large-scale dataset for training next gen- eration image-text models.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Laion-5b: An open large-scale dataset for training next gen- eration image-text models

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:42:47.803600Z

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This paper cites The bias amplification paradox in text-to-image generation.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions The bias amplification paradox in text-to-image generation

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:42:47.536040Z

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This paper cites Scaling autoregressive models for content-rich text-to-image generation.Transactions on Machine Learn- ing Research, 2022.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Scaling autoregressive models for content-rich text-to-image generation.Transactions on Machine Learn- ing Research, 2022

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This paper cites Sigmoid loss for language image pre-training.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Sigmoid loss for language image pre-training

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:42:47.121589Z

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This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions The unreasonable effectiveness of deep features as a perceptual metric

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How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions SGLang: Efficient Execution of Structured Language Model Programs

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How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Unresolved cited work

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How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Unresolved cited work

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How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions Unresolved cited work

Reference 44

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How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions psychologist

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