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Not All Noises Are Created Equally:Diffusion Noise Selection and Optimization

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arxiv 2407.14041 v2 pith:F6CZUWXO submitted 2024-07-19 cs.CV

Zipeng Qi , Lichen Bai , Haoyi Xiong , Zeke Xie This is my paper
classification cs.CV
keywords noisediffusionmodelsnoisesmethodoptimizationsampledselection
verification ladder T0 review T1 audit T2 compute T3 formal
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Diffusion models that can generate high-quality data from randomly sampled Gaussian noises have become the mainstream generative method in both academia and industry. Are randomly sampled Gaussian noises equally good for diffusion models? While a large body of works tried to understand and improve diffusion models, previous works overlooked the possibility to select or optimize the sampled noise the possibility of selecting or optimizing sampled noises for improving diffusion models. In this paper, we mainly made three contributions. First, we report that not all noises are created equally for diffusion models. We are the first to hypothesize and empirically observe that the generation quality of diffusion models significantly depend on the noise inversion stability. This naturally provides us a noise selection method according to the inversion stability. Second, we further propose a novel noise optimization method that actively enhances the inversion stability of arbitrary given noises. Our method is the first one that works on noise space to generally improve generated results without fine-tuning diffusion models. Third, our extensive experiments demonstrate that the proposed noise selection and noise optimization methods both significantly improve representative diffusion models, such as SDXL and SDXL-turbo, in terms of human preference and other objective evaluation metrics. For example, the human preference winning rates of noise selection and noise optimization over the baselines can be up to 57% and 72.5%, respectively, on DrawBench.

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Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. UniNDM: A Unified Noise-driven Detection and Mitigation Framework Against Sexual Content in Text-to-Image Generation

    cs.CV 2026-07 conditional novelty 7.0 of 10

    UniNDM detects sexual intent from early-stage diffusion noise and mitigates it via LLM-generated negative prompts and initial-noise optimization, across U-Net and DiT models.

  2. HyperNet-Adaptation for Diffusion-Based Test Case Generation

    cs.LG 2026-01 accept novelty 7.0 of 10

    HyNeA adapts a diffusion model's hypernetwork per test case to generate realistic, failure-inducing inputs for deep learning systems without curated failure data.

  3. LatSearch: Latent Reward-Guided Search for Faster Inference-Time Scaling in Video Diffusion

    cs.CV 2026-03 accept novelty 6.0 of 10

    LatSearch improves video diffusion quality and efficiency by scoring intermediate latents with a trained reward model and performing reward-guided resampling plus final pruning.

  4. ReGuidance: A Simple Diffusion Wrapper for Boosting Sample Quality on Hard Inverse Problems

    cs.LG 2025-06 conditional novelty 6.0 of 10

    A two-step wrapper (invert candidate to latent, then run DPS from that latent) improves hard inpainting results, with mixed or negative superresolution results and toy-model theory.

  5. Test-Time Scaling of Diffusion Models via Noise Trajectory Search

    cs.LG 2025-05 conditional novelty 6.0 of 10

    An epsilon-greedy search over per-step noise trajectories improves proxy rewards in diffusion image generation without retraining.

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

    cs.LG 2025-05 conditional novelty 6.0 of 10

    A one-step RL method learns a prompt-conditioned initial noise distribution for a frozen diffusion model, improving scores on the training reward models, with the largest gains at low inference steps.

  7. Anchoring and Steering Diffusion: Enhancing the Faithfulness of Text-to-Image Generation at Inference Time

    cs.CV 2026-07 conditional novelty 5.0 of 10

    AnchorSteer improves text-to-image faithfulness by anchoring initial noise with CLIP/DAS-derived semantics (LP-SDS) and correcting errors during denoising with a VLM-driven Think-Erase-Retouch loop.

  8. Inference-Time Scaling of Diffusion Models via Progressive Seed Pruning

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Progressive Seed Pruning—start many noise seeds, score early partially-denoised images, prune aggressively—improves prompt-aligned image generation at fixed denoising compute over best-of-N, resampling, and tree-searc...

  9. SimDiffRec: Semantic Similarity-Guided Diffusion for Contrastive Sequential Recommendation

    cs.IR 2025-07 conditional novelty 5.0 of 10

    SimDiffRec augments user sequences by replacing items at high-confidence diffusion positions with the model's top prediction and using averaged similar-item embeddings as noise, reporting consistent but unverified gai...

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