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Golden Noise for Diffusion Models: A Learning Framework

Zikai Zhou, Shitong Shao, Lichen Bai, Shufei Zhang, Zhiqiang Xu, Bo Han, Zeke Xie

arXiv:2411.09502v5cs.LGcs.CV

TL;DR

Text-to-image diffusion results depend on both the prompt and noise, while existing golden-noise approaches can be difficult to generalize or deploy efficiently. This paper learns prompt-conditioned golden noise with NPNet and reports improved image-quality metrics across models and settings, with limited added inference and memory costs.

  • Problem

    Noise strongly affects synthesized-image quality and prompt faithfulness, but prior golden-noise methods can be task-specific, slow, and require pipeline changes or subject tokens.

    Source: §1 · p. 2

  • Method

    The framework learns a small prompt-derived perturbation and trains NPNet on noise pairs collected into NPD to transform random noise into golden noise.

    Source: §1 · p. 2, §3.1 · p. 4

  • Results

    NPNet improves all six reported human-preference metrics across three diffusion models, seven samplers, and four datasets, with 3% extra inference time and approximately 3% of standard-pipeline memory.

    Source: §1 · p. 2

  • Takeaways & Limitations

    NPNet is reported as a plug-and-play module, and fine-tuning with 600 Hunyuan-DiT-produced noise pairs achieves the highest results over the tested baselines for Hunyuan-DiT.

    Source: §1 · p. 2, §4.1 · p. 7

    Performance is limited by noise-prompt data quality, which depends on re-denoise sampling and data selection and may incur bias from AI-feedback-based selection.

    Source: F. Discussion · p. 20

Abstract

from arXiv · show

Text-to-image diffusion model is a popular paradigm that synthesizes personalized images by providing a text prompt and a random Gaussian noise. While people observe that some noises are ``golden noises'' that can achieve better text-image alignment and higher human preference than others, we still lack a machine learning framework to obtain those golden noises. To learn golden noises for diffusion sampling, we mainly make three contributions in this paper. First, we identify a new concept termed the \textit{noise prompt}, which aims at turning a random Gaussian noise into a golden noise by adding a small desirable perturbation derived from the text prompt. Following the concept, we first formulate the \textit{noise prompt learning} framework that systematically learns ``prompted'' golden noise associated with a text prompt for diffusion models. Second, we design a noise prompt data collection pipeline and collect a large-scale \textit{noise prompt dataset}~(NPD) that contains 100k pairs of random noises and golden noises with the associated text prompts. With the prepared NPD as the training dataset, we trained a small \textit{noise prompt network}~(NPNet) that can directly learn to transform a random noise into a golden noise. The learned golden noise perturbation can be considered as a kind of prompt for noise, as it is rich in semantic information and tailored to the given text prompt. Third, our extensive experiments demonstrate the impressive effectiveness and generalization of NPNet on improving the quality of synthesized images across various diffusion models, including SDXL, DreamShaper-xl-v2-turbo, and Hunyuan-DiT. Moreover, NPNet is a small and efficient controller that acts as a plug-and-play module with very limited additional inference and computational costs, as it just provides a golden noise instead of a random noise without accessing the original pipeline.

1. Introduction

The section frames noise as a major influence on text-to-image quality and fidelity, then introduces prompt-conditioned noise learning and NPNet as a way to obtain golden noise. Experiments report improvements across multiple diffusion models, samplers, datasets, and preference metrics.

1. Introduction

  • Noise variations can substantially change synthesized images, affecting both aesthetics and semantic faithfulness to the prompt.The section notes that even minor changes to noise can dramatically influence the output.Source: §1 · p. 2
  • Prior golden-noise approaches can improve semantic faithfulness and image quality, but may be task-specific, slow, require pipeline modifications, or depend on subject tokens.These constraints motivate the questions of efficient prediction and generalization across prompts, noises, and diffusion models.Source: §1 · p. 2
  • Noise prompts turn random noise into golden noise by adding a small, text-prompt-derived perturbation, and the framework learns prompted golden noise for diffusion models.The perturbation is described as semantically rich and tailored to the text prompt.Source: §1 · p. 2
  • NPD supplies noise pairs for training NPNet, which learns to transform random Gaussian noise into golden noise using a re-denoise collection pipeline and AI-driven feedback.The pipeline is designed to collect valuable noise pairs for training.Source: §1 · p. 2
  • Across three diffusion models, seven samplers, and four datasets, NPNet improves all six reported human-preference metrics, with 3% extra inference time and approximately 3% of standard-pipeline memory.The evaluated models include SDXL, DreamShaper-xl-v2-turbo, and Hunyuan-DiT.Source: §1 · p. 2

2. Preliminaries

The preliminaries introduce diffusion noise processes, DDIM sampling and inversion, and classifier-free guidance. They also situate the paper’s workflow around converting noise before it enters a text-to-image model.

2. Preliminaries

  • At inference, NPNet processes source noise, target noise, and the encoded prompt to produce golden noise before the diffusion model receives it.The workflow describes NPNet as a direct noise-conversion step before model sampling.Source: §2 · p. 3
  • The forward process forms x_t from the original image x_0 and Gaussian noise ϵ_t using predefined schedules α_t and σ_t.The timestep t ranges from 0 to T.Source: §2 · p. 3
  • DDIM is presented as deterministic, non-Markov reverse sampling that can speed synthesis while maintaining sample quality; DDIM-Inversion is the corresponding way to add noise.The section notes that related work is placed in Appendix B.Source: §2 · pp. 2–3
  • Classifier-free guidance steers synthesis toward conditions such as text prompts, and its predicted noise is used in the DDIM update to obtain x_t−1.The passage identifies c as the text prompt and ω as the guidance scale.Source: §2 · p. 3

3. Noise Prompt Learning

Noise prompt learning trains a network to convert random noise into prompt-conditioned golden noise. Its data pipeline constructs and filters noise pairs, while NPNet predicts target-noise components and combines them to control injected semantic information.

3. Noise Prompt Learning

  • Noise prompt learning converts random Gaussian noise into golden noise by adding a small, text-derived perturbation rich in semantic information.The framework learns prompted golden noises associated with text prompts for diffusion models.Source: §3 · p. 3
  • The training workflow learns an optimal network ϕ* from paired source noises, target noises, prompts, and a loss function.The paper presents data, training, and inference stages for the framework.Source: §3 · p. 4, §3.2 · p. 5

3.1. Noise Prompt Dataset Collection

  • Re-denoise sampling applies DDIM sampling followed by DDIM inversion with CFG scales ωl > ωw, producing target noise with greater semantic information and fidelity.The proposed mechanism attributes semantic injection to the CFG scale inconsistency; the original noise is the source noise and the inverted noise is the target noise.Source: §3.1 · p. 4
  • A human-preference model filters re-denoised pairs because added noise can produce images below the originals’ quality.Pairs are retained when the target image’s preference score exceeds the source score by threshold m.Source: §3.1 · p. 4

3.2. Noise Prompt Network

  • NPNet predicts target-noise singular values, using observed similarity between source and target noise singular vectors, which may point in opposite directions.The predicted singular values are used with an SVD inverse transformation to reconstruct target noise.Source: §3.2 · p. 4
  • NPNet also predicts the residual between source and target noise, injecting text semantics through the diffusion model’s frozen text encoder.The text embedding is used to improve residual-prediction accuracy.Source: §3.2 · pp. 4–5
  • Trainable α and β adjust the influence of injected semantic information in NPNet’s predictions, with α reported as very small but still influential.The paper says the parameters together tune semantic relevance and image diversity.Source: §3.2 · p. 5
  • At inference, NPNet takes initial noise and the frozen text encoder’s prompt embedding and transforms the noise into golden noise for the diffusion model.NPNet is inserted into sampling as the noise provider.Source: §3.2 · p. 5

4. Empirical Analysis

NPNet is evaluated across text-to-image models, datasets, samplers, and inference settings, with reported gains in image quality and broad transfer. The analysis also examines its component contributions, efficiency, and integration with ControlNet.

4. Empirical Analysis

  • The experiments use three training datasets of 100k, 80k, and 600 noise pairs for SDXL, DreamShaper-xl-v2-turbo, and Hunyuan-DiT, respectively.Testing includes Pick-a-Pic, HPD v2, DrawBench, GenEval, and T2I-CompBench.Source: §4 · p. 5

4.1. Main Results

  • On SDXL and DreamShaper-xl-v2-turbo, NPNet almost achieves the best results across all 6 metrics and 3 datasets evaluated, according to the reported experiments.Figure 3 compares SDXL winning rates across Pick-a-Pic, DrawBench, and HPD v2, reporting consistent transformation across nearly all evaluated datasets and metrics.Source: §4.1 · pp. 5, 7, Figure 3 · p. 6
  • Fine-tuning the SDXL-trained NPNet on only 600 Hunyuan-DiT-produced noise pairs yields the highest results over Hunyuan-DiT baselines.The paper presents this as evidence of cross-model generalizability.Source: Table 3 · p. 6, §4.1 · p. 7

4.2. Analysis of Generalization and Robustness

  • Training on Pick-a-Pic NPD generalizes to other datasets, DreamShaper without fine-tuning, few-step models, and out-of-distribution seed ranges without harming image diversity.The paper also reports maintained performance across seven stochastic and deterministic samplers, and robustness across common inference-step settings.Source: §4.2 · pp. 7–8
  • NPNet can be combined with DPO and AYS at 10 inference steps, and joint use with prior noise-optimization methods improves their performance and winning rates.The authors describe the method as orthogonal to DPO and AYS.Source: §4.2 · p. 7, Table 5 · p. 7
  • Both singular-value prediction and residual prediction contribute to the final results, with singular-value prediction playing the larger role.The authors also report empirical validation of their data-selection strategies.Source: Table 4 · p. 7, §4.2 · p. 8

4.3. Efficiency Analysis and Downstream Tasks

  • At matched inference steps, NPNet adds only a 0.4-second delay and uses 500 MB of memory.Figure 5 also places NPNet near methods characterized as efficient and high-performing, with fewer steps and less time.Source: §4.3 · p. 8, Figure 5 · p. 8
  • Integrating NPNet with ControlNet yields images described as more detailed, higher-quality, and more semantically faithful.The paper presents NPNet as a plug-and-play module for this controlled-synthesis task.Source: §4.3 · p. 8

5. Conclusion

The conclusion presents noise prompting as a way to add a small, text-derived perturbation to random noise and train a model to produce prompted golden noise. It reports NPNet's performance improvements and plug-and-play efficiency, while proposing video, 3D content, and AIGC deployment as future applications.

5. Conclusion

  • Noise prompting adds a small perturbation derived from the text prompt to random noise to produce golden noise associated with that prompt.The paper frames the perturbation as a semantic prompt for noise.Source: §5 · p. 8
  • The framework uses an HPSv2-filtered noise-pair collection pipeline and backbones for noise-prompt models to train NPNet.The conclusion describes NPNet as plug-and-play, straightforward, and nearly time-efficient.Source: §5 · p. 8
  • The authors report significant performance improvements and propose video, 3D content, and seamless AIGC-product deployment as future applications.These are presented as prospective uses of NPNet.Source: §5 · p. 8

A.1. Benchmarks

The appendix describes the evaluation benchmarks and metrics, the diffusion models tested, NPNet's architecture, and experimental settings. Together, these details specify the paper's evaluation scope and implementation choices.

A.1. Benchmarks

  • Testing uses prompts from Pick-a-Pic, HPD v2, DrawBench, GenEval, and T2I-CompBench, with stated subsets of 100, 100, 200, and 553 prompts for the first four datasets.Pick-a-Pic contains user preference examples, DrawBench tests semantic attributes, HPD v2 collects preference choices, and GenEval focuses on object compositionality.Source: §A.1 · p. 11

A.2. Evaluation Metrics

  • The evaluation metrics include preference predictors PickScore, HPSv2, ImageReward, and MPS, alongside AES for aesthetics and CLIPScore for image-text correspondence.MPS scores image preferences across dimensions including aesthetics, semantic alignment, detail quality, and overall assessment.Source: §A.2 · pp. 11–13
  • MPS compares two images, with their preference scores summing to 1; the appendix identifies it as a multidimensional preference score.The main evaluation uses six metrics.Source: §A.2 · pp. 11–13

A.3. T2I Diffusion Models

  • The tested diffusion models are SDXL, DreamShaper-xl-v2-turbo, and Hunyuan-DiT.DreamShaper is fine-tuned from SDXL, while Hunyuan-DiT is a text-to-image diffusion transformer.Source: §A.3 · p. 13

A.4. Model Architecture

  • NPNet has singular-value and residual-prediction branches; the former processes SVD components with parallel MLPs and self-attention, while the latter uses a Swin-Tiny transformer with sampling layers.The singular-value branch reconstructs refined components, and the residual branch can add the input through an optional residual connection.Source: §A.4 · p. 13

A.5. Hyper-parameter Settings

  • Training uses 30 epochs and batch size 64; CFG values are 5.5, 3.5, and 5.0 for SDXL, DreamShaper, and Hunyuan-DiT, respectively.The inverse CFG is 1.0 for all three models; quantitative experiments generally use 50 inference steps.Source: §A.5 · p. 13

B. Related Work

Prior noise-optimization methods can be costly or difficult to transfer, while this work reframes the task as learning a plug-and-play noise prompt. The supplied experiments examine its generalization, data selection, image quality, and generated-image distributions.

B. Related Work

  • Earlier noise-optimization approaches can require substantial time or pipeline changes, whereas NPNet is presented as a plug-and-play alternative with minimal overhead.The discussion also notes manual subject-token selection and adaptation difficulties for prior methods.Source: B. Related Work · p. 14, C. Discussion with the Previous Works · p. 15

C. Discussion with the Previous Works

D.1. Motivation

  • The authors motivate a singular-value prediction branch by observing similar singular vectors in source and target noises, sometimes pointing in opposite directions.They also report that re-denoising sampling enhances semantic information in the initial noise.Source: §D.1 · p. 15

D.2. Exploration of Data Selection Strategies

  • Using HPSv2 to filter noise pairs is reported to preserve data diversity and improve NPNet performance; raising the filtering threshold improves pair quality but reduces data and synthesis diversity.The threshold result is reported for experiments with m varied from the baseline setting.Source: §D.2 · p. 16

D.3. Evaluate the Quality of Synthesized Images

  • On SDXL, NPNet is reported to improve image quality over standard synthesis in a 5,000-image, 512 × 512 class-conditional ImageNet evaluation focused on fish prompts.The supplied Figure 12 caption identifies FID as the comparison metric but gives no values.Source: Figure 12 · p. 15, §D.3 · p. 16

D.4. Evaluate the probability density of synthesized images with NPNet

  • In a 50-image SDXL comparison against one ImageNet class, NPNet shifts generated images toward higher-density regions of the reference distribution.The text cautions that text-to-image and class-conditioned generation may have distribution shifts.Source: Figure 13 · p. 16, §D.4 · p. 16

D.5. Generalization and Robustness

The experiments report NPNet generalization across models, inference settings, and altered random seeds, alongside robustness checks for training hyperparameters.

Generalization to Models, Datasets and Inference Steps

  • NPNet trained on SDXL is reported to perform nearly best across inference steps when applied directly to DreamShaper-xl-v2-turbo without fine-tuning.Winning-rate comparisons also cover DreamShaper-xl-v2-turbo and Hunyuan-DiT across Pick-a-Pic, DrawBench, and HPD v2.Source: Generalization to Models, Datasets and… · p. 16, Figure 14 · p. 17

Generalization to Random Seeds

  • The authors report strong performance across altered, out-of-distribution random-seed ranges, despite noting a possible seed-specific overfitting concern.They artificially modified test-set seeds for the Pick-a-Pic evaluation.Source: Generalization to Models, Datasets and… · pp. 17–18

Robustness to Inference Steps and Hyper-parameters

  • Across varied inference steps, the reported performance curves remain at the top for DreamShaper-xl-v2-turbo and Hunyuan-DiT, with winning-rate tests also spanning three models and two datasets.The supplied passages report the comparisons but provide no numerical values.Source: Robustness to Inference Steps and… · p. 18

Robustness to Hyper-parameters

  • The reported optimal settings are CFG 5.5, batch size 64, and 30 training epochs; the paper uses these settings throughout its experiments.The authors also attribute robustness and generalization in part to having a large training dataset.Source: Robustness to Inference Steps and… · p. 18

D.6. Efficiency Analysis and Ablation Studies

The section reports modest additional inference and memory costs, discusses the role of text embeddings, and evaluates NPNet on the larger GenEval benchmark.

D.6. Efficiency Analysis and Ablation Studies

  • NPNet adds 0.4 seconds of inference delay and requires 500 MB of extra memory, alongside a reported improvement in image quality.The time comparison is under the same inference-step conditions.Source: §D.6 · p. 18
  • The text-embedding term helps adjust semantic influence, which the authors associate with prompt-image relevance and synthesized-image diversity.Although α is small in one ablation, the authors report the term’s importance in another.Source: §D.6 · p. 18

D.7. Experiments on Large-scale Dataset

  • On GenEval, all reported metrics improve with NPNet across different text-to-image models, which the authors interpret as improved compositional image properties.The supplied passages identify Tables 13 and 14 but do not provide metric values.Source: §D.7 · p. 18

E. Theoretical Understanding of Re-denoise Sampling

The theoretical discussion describes re-denoise sampling as an additional forward and reverse step and relates it to semantic information in the initial noise. It attributes that effect to classifier-free-guidance inconsistency across timesteps.

E. Theoretical Understanding of Re-denoise Sampling

  • The theorem frames re-denoise sampling using initial Gaussian noise and DDIM-Inversion and DDIM operators, with k denoting the DDIM sampling step.The supplied theorem-related prose defines the CFG scales at T and T − k and the text prompt c.Source: E. Theoretical Understanding of… · pp. 18, 20
  • One re-denoise step consists of one additional forward-sampling step and one reverse-sampling step applied to the initial Gaussian noise.The proof describes DDIM-Inversion as the sampling algorithm with x_t and x_(t−1) interchanged.Source: E. Theoretical Understanding of… · pp. 18, 20
  • When the guidance scales at timesteps T and T − k differ, re-denoise sampling is described as injecting semantic information into the initial Gaussian noise under future-timestep CFG.The prose identifies the guidance scales as ω_l at T and ω_w at T − k, and c as the text prompt.Source: E. Theoretical Understanding of… · pp. 18, 20

F. Discussion

The discussion identifies constraints in noise-prompt data quality and NPNet architecture, then outlines improvements to data collection, model design, and generalization as future directions.

Limitations

  • Noise-prompt data quality limits performance and is constrained by re-denoise sampling and data selection, with potential bias from AI-driven feedback.The authors note that the data collection process is not yet comprehensively understood.Source: F. Discussion · p. 20
  • NPNet's architecture remains rudimentary, and more elegant or efficient designs may exist without a principled design framework for this task.Ablations support the components used, but architecture optimization remains an open challenge.Source: F. Discussion · p. 20

Future Directions

  • Future work includes more performant and trustworthy data collection, streamlined model structures, and better generalization to out-of-distribution prompts or beyond T2I.One proposed direction is using a pretrained diffusion model to synthesize golden noise more precisely.Source: F. Discussion · p. 20

G. More Visualization Results

The additional visualizations and experiments examine NPNet's size, semantic input, generalization, compatibility with other methods, and use in downstream settings. They report improvements across models and human-preference metrics, alongside implementation and dataset details.

More Visualization Results

  • The appendix includes qualitative comparisons across SDXL and multiple methods, and identifies the datasets as slated for release.The comparison figures include LCM, PCM, SDXL-Lightning, Re-sampling, PAG, CFG++, FreeU, APG, SAG, and SEG.Source: G. More Visualization Results · p. 20, Figure 18 · p. 25, Figure 23 · p. 29
  • NPNet requires about 500 MB, and Table 16's caption identifies text embedding as crucial for injecting semantic information into noise-prompt learning.Source: Figure 15 · p. 21, Table 16 · p. 21
  • The appendix explores scaling with training-sample counts on SDXL and Pick-a-Pic, and tests seed generalization using test seeds outside the training range.The training seeds range from 0 to 1024; the test range is adjusted to examine out-of-distribution seeds.Source: Table 17 · p. 22, Table 19 · p. 22
  • NPNet generalizes to few-step models LCM, PCM, and SDXL-Lightning, boosting their performance when the SDXL-trained network is used.The evaluation is on GenEval.Source: Table 18 · p. 22
  • Winning-rate comparisons span SDXL, DreamShaper-xl-v2-turbo, and Hunyuan-DiT across inference steps and datasets, with captions reporting NPNet effectiveness.Figure 16 visualizes performance by inference steps on Pick-a-Pic and DrawBench; Figure 17 compares winning rates on DrawBench and HPD v2.Source: Figure 16 · p. 23, Figure 17 · p. 24
  • Combining NPNet with other noise-optimization methods improves their winning rates across three datasets on SDXL.The reported gains are described as occurring to some extent in the figure caption and as significant in the table caption.Source: Figure 19 · p. 26, Table 20 · p. 27
  • NPNet can be applied to ControlNet conditions including OpenPose, Canny, and depth without modifying the T2I pipeline.The accompanying example presents NPNet as a plug-and-play module that is easy to implement.Source: Figure 20 · p. 28, Figure 21 · p. 28
  • NPNet visualizations across three diffusion models show improved overall quality, aesthetic style, semantic faithfulness, and all six human-preference metrics.Images for each prompt use the same random seed.Source: Figure 22 · p. 29

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