Paper Citation Record · LEDGER

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds

As of 14 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 4 inbound Pith citation observations for arXiv:2411.18810.

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

pith.paper-citation-record.v1
2411.18810 v5

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T10:57:49.371253Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:49:37.766161Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T15:26:33.563528Z

Reference resolution

49 of 49 outbound references displayed

verified exact0
verified fuzzy22
unresolved24
parse uncertain1
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e42eba22-8907-4fc5-9e98-75ff82254f8e · outbound

This paper cites https://github.com/discus0434/aesthetic-predictor-v2-5/, May 2024.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds https://github.com/discus0434/aesthetic-predictor-v2-5/, May 2024

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:49.994096Z

Source-reported events for the cited work

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Observation 484c4387-0028-4e76-a8c2-c02050e8c462 · outbound

This paper cites GPT-4 Technical Report.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds GPT-4 Technical Report

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T10:57:49.174963Z

Source-reported events for the cited work

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Observation da68f209-3c9e-418f-b2b4-0e308f6fc464 · outbound

This paper cites Multidiffusion: Fusing diffusion paths for controlled image generation.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Multidiffusion: Fusing diffusion paths for controlled image generation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:49.981436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ef58a434-b118-4b32-b348-f1cc3436c40f · outbound

This paper cites Attend-and-excite: Attention-based semantic guidance for text-to-image diffusion models, 2023.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Attend-and-excite: Attention-based semantic guidance for text-to-image diffusion models, 2023

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T10:57:49.184116Z

Source-reported events for the cited work

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Observation d4ba5275-c7a3-4ac3-ada4-68b8dc969943 · outbound

This paper cites Pixart- : Fast training of diffusion transformer for photorealistic text-to-image synthesis, 2023 a.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Pixart- : Fast training of diffusion transformer for photorealistic text-to-image synthesis, 2023 a

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:49.959875Z

Source-reported events for the cited work

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Observation 27923059-b696-407f-a307-6f9ec2be8da5 · outbound

This paper cites Training-Free Layout Control with Cross-Attention Guidance.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Training-Free Layout Control with Cross-Attention Guidance

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T10:57:49.192489Z

Source-reported events for the cited work

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Observation 5fa48778-7326-4586-97ca-fdd76c993a70 · outbound

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

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Training-free layout control with cross-attention guidance

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:49.947265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b62da3f1-b623-4e92-827e-7a92131c36f5 · outbound

This paper cites Reason out Your Layout: Evoking the Layout Master from Large Language Models for Text-to-Image Synthesis.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Reason out Your Layout: Evoking the Layout Master from Large Language Models for Text-to-Image Synthesis

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T10:57:49.201325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c0acd0b-90e9-4229-af31-015d6f4f81a3 · outbound

This paper cites Laion-aesthetics.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Laion-aesthetics

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:49.934663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4ac97fd7-f71f-4fe7-b2d6-5db295b62aad · outbound

This paper cites Zero-shot spatial layout conditioning for text-to-image diffusion models.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Zero-shot spatial layout conditioning for text-to-image diffusion models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:49.922041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2d5fde3d-7418-48b6-93e9-b9e28d960249 · outbound

This paper cites Be Yourself: Bounded Attention for Multi-Subject Text-to-Image Generation.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Be Yourself: Bounded Attention for Multi-Subject Text-to-Image Generation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T10:57:49.213633Z

Source-reported events for the cited work

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Observation 7b64d488-5396-442f-8e10-57ef6ad67d32 · outbound

This paper cites Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T10:57:49.217878Z

Source-reported events for the cited work

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Observation 14de4da3-90c1-4695-a7af-6474c9451e4f · outbound

This paper cites E xploiting the S ignal- L eak B ias in D iffusion M odels.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds E xploiting the S ignal- L eak B ias in D iffusion M odels

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:49.909024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 06125bbd-e93e-4482-9a19-1c1378d6db58 · outbound

This paper cites Layoutgpt: Compositional visual planning and generation with large language models.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Layoutgpt: Compositional visual planning and generation with large language models

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:49.893568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ede91823-9119-4985-982c-e46081538ddb · outbound

This paper cites Ranni: Taming text-to-image diffusion for accurate instruction following.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Ranni: Taming text-to-image diffusion for accurate instruction following

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:49.880749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9c65ef00-106b-4d16-84c0-ab0c8424bba0 · outbound

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

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Initno: Boosting text-to-image diffusion models via initial noise optimization

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:49.867209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ecb1abda-3533-49c7-8993-4559d0d51268 · outbound

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

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Initno: Boosting text-to-image diffusion models via initial noise optimization

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:49.854067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 22d39ff1-689f-4163-9897-3f9bd7b65b34 · outbound

This paper cites Diffusion with offset noise, Jan 2023.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Diffusion with offset noise, Jan 2023

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:49.841324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b7e92756-c100-41ac-9710-cc5b51013b8f · outbound

This paper cites CogVLM2: Visual Language Models for Image and Video Understanding.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds CogVLM2: Visual Language Models for Image and Video Understanding

Reference 20

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Source-reported events for the cited work

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Observation 6ff037ce-4bd2-4b6a-b980-e0192ad3a5ef · outbound

This paper cites T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation

Reference 21

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Source-reported events for the cited work

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Observation b33e24ba-97cb-4302-b2d9-1a65b4c58ed0 · outbound

This paper cites Ssmg: Spatial-semantic map guided diffusion model for free-form layout-to-image generation.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Ssmg: Spatial-semantic map guided diffusion model for free-form layout-to-image generation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:49.819805Z

Source-reported events for the cited work

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Observation a4235b81-7931-4838-8eb8-13506e1b8575 · outbound

This paper cites Improved precision and recall metric for assessing generative models.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Improved precision and recall metric for assessing generative models

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:49.806086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c175443e-d859-4197-89ed-be7bd10ee762 · outbound

This paper cites LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models

Reference 24

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Source-reported events for the cited work

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Observation e3fc5f69-dba8-4336-9717-46a95f8f249d · outbound

This paper cites Cpgan: Content-parsing generative adversarial networks for text-to-image synthesis.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Cpgan: Content-parsing generative adversarial networks for text-to-image synthesis

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:49.792583Z

Source-reported events for the cited work

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Observation 9af30f51-d278-4a41-a939-bf491924b226 · outbound

This paper cites Common diffusion noise schedules and sample steps are flawed.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Common diffusion noise schedules and sample steps are flawed

Reference 26

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Observation 532cad2b-ed64-436e-896f-d5658eb6158d · outbound

This paper cites Directed diffusion: Direct control of object placement through attention guidance.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Directed diffusion: Direct control of object placement through attention guidance

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:49.771370Z

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Observation 51d810c3-1c53-4bf0-a9fc-52c9f0675536 · outbound

This paper cites The Lottery Ticket Hypothesis in Denoising: Towards Semantic-Driven Initialization.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds The Lottery Ticket Hypothesis in Denoising: Towards Semantic-Driven Initialization

Reference 28

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Observation 92ee7cee-5d36-4c26-ad9a-66b4d306123d · outbound

This paper cites Hello gpt-4o.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Hello gpt-4o

Reference 29

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Observation 4f76da10-17d2-4f46-9d72-077dfc547aef · outbound

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

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 30

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Observation 2dc1ae30-880a-4398-98f2-85b70d1f7c82 · outbound

This paper cites Layoutllm-t2i: Eliciting layout guidance from llm for text-to-image generation.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Layoutllm-t2i: Eliciting layout guidance from llm for text-to-image generation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:49.744426Z

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Observation 79482881-7888-4d34-a3d4-16a8f271d3f2 · outbound

This paper cites Learning transferable visual models from natural language supervision.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Learning transferable visual models from natural language supervision

Reference 32

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Observation 698603ca-e668-48c1-9831-461cfc7265b9 · outbound

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

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 33

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Observation 5ad0fce8-4eb9-4d78-a612-b463ea793719 · outbound

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

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 34

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This paper cites Generative adversarial text to image synthesis.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Generative adversarial text to image synthesis

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:49.713871Z

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Observation 29394909-5924-4bc5-ac7c-dfe854dd9f19 · outbound

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

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds High-resolution image synthesis with latent diffusion models

Reference 36

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Observation f91869c9-1098-4f26-ae6f-7f38987f22da · outbound

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

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Photorealistic text-to-image diffusion models with deep language understanding

Reference 37

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Observation 6e005745-aa51-4366-9fbd-4ef667a6135b · outbound

This paper cites Generating images of rare concepts using pre-trained diffusion models.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Generating images of rare concepts using pre-trained diffusion models

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:49.684043Z

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Observation fc412c15-c32c-488f-aa31-5e5316589f3d · outbound

This paper cites A Picture is Worth a Thousand Words: Principled Recaptioning Improves Image Generation.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds A Picture is Worth a Thousand Words: Principled Recaptioning Improves Image Generation

Reference 39

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Observation 5d37650f-ee26-4c0b-8005-3f5c6f3e9d1c · outbound

This paper cites The Curse of Recursion: Training on Generated Data Makes Models Forget.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds The Curse of Recursion: Training on Generated Data Makes Models Forget

Reference 40

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Observation c8b273b5-31fa-44d0-940b-a573e9ccc435 · outbound

This paper cites Df-gan: A simple and effective baseline for text-to-image synthesis.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Df-gan: A simple and effective baseline for text-to-image synthesis

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:49.670576Z

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Observation 1bf25062-465d-4c84-a4d1-cdc94fed4f19 · outbound

This paper cites Visualizing data using t-sne.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Visualizing data using t-sne

Reference 42

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Observation 462e3d3f-6721-42ec-b6a8-cdf10ae81daf · outbound

This paper cites Cogvlm: Visual expert for pretrained language models, 2023.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Cogvlm: Visual expert for pretrained language models, 2023

Reference 43

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Observation 16da978b-fdc5-481f-8bbf-a3ff412c23a2 · outbound

This paper cites Le, and Dimitris Samaras.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Le, and Dimitris Samaras

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:49.640742Z

Source-reported events for the cited work

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Observation 33512a56-0e13-4d0d-8809-f29bfc87927e · outbound

This paper cites Assessing Sample Quality via the Latent Space of Generative Models.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Assessing Sample Quality via the Latent Space of Generative Models

Reference 45

Resolution: metadata mismatch; local_arxiv, observed 2026-08-12T10:57:49.412092Z

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Observation abe53e43-a2d4-4c62-9a98-6b8a5e62a1a4 · outbound

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

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Layoutdiffusion: Controllable diffusion model for layout-to-image generation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:57:49.627722Z

Source-reported events for the cited work

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Observation 75d0ba1e-a747-46da-a28f-1e2a9fb695c6 · outbound

This paper cites write newline.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds write newline

Reference 47

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Observation 7ce378db-a42a-4ee6-aec1-3218d24f3a22 · outbound

This paper cites @esa (Ref.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds @esa (Ref

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-12T10:57:49.362742Z

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Observation 3c596bd4-a83c-4260-b2e4-9217c6470239 · outbound

This paper cites an unresolved cited work.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Unresolved cited work

Reference 49

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Observation b606f84f-2ab9-4bc3-8f20-c59157684c55 · outbound

This paper cites Prompt-to-Prompt Image Editing with Cross Attention Control.

All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Prompt-to-Prompt Image Editing with Cross Attention Control

Reference 50

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Unavailable: canonical work link unavailable.

Pith citing papers

Observation c107d766-d4c4-424b-bf99-9f319d5b5217 · inbound

ISAC: Training-Free Instance-to-Semantic Attention Control for Multi-Instance Generation cites this paper.

ISAC: Training-Free Instance-to-Semantic Attention Control for Multi-Instance Generation All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T13:49:37.766161Z

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Observation 190e206c-c50b-4b2c-9c18-d083856e65c5 · inbound

PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework cites this paper.

PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T04:23:42.523167Z

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Observation 509327cf-cb4c-462c-911e-b1b024a66641 · inbound

GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Design cites this paper.

GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Design All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-05T18:05:40.320166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 806bce9a-28e0-4a7e-975c-95cb3625c6f7 · inbound

CARINOX: Inference-time Scaling with Category-Aware Reward-based Initial Noise Optimization and Exploration cites this paper.

CARINOX: Inference-time Scaling with Category-Aware Reward-based Initial Noise Optimization and Exploration All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-18T15:26:33.566376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.