The authors argue that diffusion models don't need to start from independent Gaussian noise for each image in a batch — instead, you can "couple" the initial noises (each remains marginally Gaussian, so the model sees the same distribution per sample, but samples are no longer independent). This reframes noise control from picking individual seeds to designing dependence structure across a gallery. Repulsive Gaussian coupling improves gallery diversity on SD1.5, SDXL, and SD3 without adding sampling cost, matching or outperforming recent noise-optimization baselines.
Main takeaways:
- Standard diffusion generation uses independent Gaussian noise per sample, but this is just one choice — you can design coupled noise with chosen dependence structure.
- Each noise remains marginally standard Gaussian, so pretrained models see the same single-sample input distribution.
- Repulsive coupling (samples push away from each other) improves gallery diversity while preserving prompt alignment and image quality.
- Matches or beats recent noise-optimization baselines on diversity metrics at the same sampling cost as independent generation.
- Subspace couplings enable fixed-object background generation with diverse, natural backgrounds.