Paper Citation Record · LEDGER

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models

As of 21 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 1 inbound Pith citation observation for arXiv:2506.12036.

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

pith.paper-citation-record.v1
2506.12036 v3

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:52:18.041141Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-15T09:53:45.151347Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T09:55:24.199362Z

Reference resolution

30 of 30 outbound references displayed

verified exact1
verified fuzzy5
unresolved24
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6be833f7-fa53-4827-b304-210260c308f8 · outbound

This paper cites GPT-4 Technical Report.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:16.247180Z

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Unavailable: canonical work link unavailable.

Observation f0b0ed64-7221-47fe-8ada-a5c8b61c2b59 · outbound

This paper cites Directly Fine-Tuning Diffusion Models on Differentiable Rewards.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models Directly Fine-Tuning Diffusion Models on Differentiable Rewards

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:16.342672Z

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Observation f61c8208-c235-4fc2-b6b0-e698636d7db0 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:16.408169Z

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Observation 42ab3402-1e74-4d74-93fb-69a04f67b9b1 · outbound

This paper cites Planning with Diffusion for Flexible Behavior Synthesis.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models Planning with Diffusion for Flexible Behavior Synthesis

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:16.580182Z

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Observation 5a9988dc-901d-4d02-8f38-994064bd5b58 · outbound

This paper cites Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:16.646723Z

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Observation b42e0463-126a-4570-88f3-810e147d2c4d · outbound

This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:16.762552Z

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Observation d80da419-46cb-4d28-8b61-ed73e07e6410 · outbound

This paper cites Decoupled Weight Decay Regularization.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models Decoupled Weight Decay Regularization

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:16.822023Z

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Observation c2cd457f-19e3-4d7f-916d-b0e49641be25 · outbound

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

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:16.960290Z

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Observation 3428dd03-4d6f-41cf-9f0d-5e91a9dc9219 · outbound

This paper cites Not All Noises Are Created Equally:Diffusion Noise Selection and Optimization.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models Not All Noises Are Created Equally:Diffusion Noise Selection and Optimization

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:17.012913Z

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Observation 8725ed4d-f029-4e95-af94-00af00b408a1 · outbound

This paper cites Proximal Policy Optimization Algorithms.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models Proximal Policy Optimization Algorithms

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:17.063700Z

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Observation f17ea69b-6b92-4f9e-90ce-d094e9a6feae · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:17.138799Z

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Observation 6e10a50e-a9c3-4f19-aed6-bfa28fc3e79b · outbound

This paper cites Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:17.372585Z

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Observation f9140106-4d70-4047-acb7-47740d8813d4 · outbound

This paper cites GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:17.425877Z

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Observation 49f672d8-3b0d-4384-9cab-82399c4fea80 · outbound

This paper cites Scaling Autoregressive Models for Content-Rich Text-to-Image Generation.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:17.476303Z

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Unavailable: canonical work link unavailable.

Observation 3d23a271-48a7-4c83-9994-66e13a33debd · outbound

This paper cites Characteristic Guidance: Non-linear Correction for Diffusion Model at Large Guidance Scale.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models Characteristic Guidance: Non-linear Correction for Diffusion Model at Large Guidance Scale

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:17.523017Z

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Observation b3d7255e-7c86-4bf0-b906-806c2c490566 · outbound

This paper cites Golden Noise for Diffusion Models: A Learning Framework.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models Golden Noise for Diffusion Models: A Learning Framework

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:17.577217Z

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Observation 75b81ba0-a36e-4b7a-9c78-897e86c90dea · outbound

This paper cites Training is conducted with a batch size of 16, gradient accumulation over 4 steps, and a fixed learning rate of 1×10 −4.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models Training is conducted with a batch size of 16, gradient accumulation over 4 steps, and a fixed learning rate of 1×10 −4

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:19.366594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3019d052-33f5-4176-9e8d-23c3d6b7e0f4 · outbound

This paper cites an unresolved cited work.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:52:19.238104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation dda3696c-569b-4cfc-a045-aa804211a1f5 · outbound

This paper cites As a result, even state-of-the-art generative models can produce outputs that lack sharpness or fail to fully respect their conditioning inputs.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models As a result, even state-of-the-art generative models can produce outputs that lack sharpness or fail to fully respect their conditioning inputs

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:19.111966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fcd5ba43-66f9-4931-bf57-2ba12f65bca0 · outbound

This paper cites auto-guidance.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models auto-guidance

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:18.934609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 82e7cef4-8f98-4792-bcf8-6e4adbebf3e0 · outbound

This paper cites Initial noise for diffusion models.Recent studies by Qi et al.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models Initial noise for diffusion models.Recent studies by Qi et al

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:18.821553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 075f142e-f761-42e1-8222-34e6051c3551 · outbound

This paper cites an unresolved cited work.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:52:18.714196Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 74bf7633-94b8-4aea-a825-ee4a756aadc7 · outbound

This paper cites By contrast, our method needs only standard prompt datasets, which are far more readily available, and does not require specialized noise and prompt annotations.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models By contrast, our method needs only standard prompt datasets, which are far more readily available, and does not require specialized noise and prompt annotations

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:18.606918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2ba64f1a-5bcb-4f9f-856a-c8854baa7869 · outbound

This paper cites Diffusion Models without Classifier-free Guidance.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models Diffusion Models without Classifier-free Guidance

Reference 1998

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Observation 250f57d2-8d74-4594-b18f-7b49ace70a6d · outbound

This paper cites Denoising Diffusion Implicit Models.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models Denoising Diffusion Implicit Models

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:17.210455Z

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Observation c27cf674-0905-4e07-a33c-9adfd959d491 · outbound

This paper cites LCM-LoRA: A Universal Stable-Diffusion Acceleration Module.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models LCM-LoRA: A Universal Stable-Diffusion Acceleration Module

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:16.886800Z

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Observation c622a0d0-ae29-4000-a33d-96d8931c33fe · outbound

This paper cites Classifier-Free Diffusion Guidance.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models Classifier-Free Diffusion Guidance

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:16.526558Z

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Observation 326a4c5e-e17e-4c16-9046-6313ce3ec403 · outbound

This paper cites Training Diffusion Models with Reinforcement Learning.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models Training Diffusion Models with Reinforcement Learning

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:16.292529Z

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Observation 01dcb167-e932-45cc-9985-c688cc3748a5 · outbound

This paper cites ReNeg: Learning Negative Embedding with Reward Guidance.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models ReNeg: Learning Negative Embedding with Reward Guidance

Reference 2024

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:52:18.369441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 64588200-3ee5-4d5c-8518-c6c3625ef98b · outbound

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

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models Prompt-to-Prompt Image Editing with Cross Attention Control

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:16.449504Z

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Pith citing papers

Observation c5734329-ffbb-4dd5-8058-303b4805e6ec · inbound

You've Got a Golden Ticket: Improving Generative Robot Policies With A Single Noise Vector cites this paper.

You've Got a Golden Ticket: Improving Generative Robot Policies With A Single Noise Vector A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-15T09:55:24.201491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.