arXiv AI
Aug 12
SynBoost: A Synergistic Framework for Fast Sampling of Diffusion Models
arXiv:2506. 13058v2 Announce Type: replace-cross Abstract: Diffusion probabilistic models (DPMs) have demonstrated remarkable success in visual generation.
ASSERT: Adaptive Stochastic Sampling for Robust Diffusion Models on Analog Compute-in-Memory Hardware
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arXiv Machine Learning
Oct 9
Beyond Sample Copying: Structural Memorization in Diffusion Models
The paper investigates why diffusion models, despite their practical success, can fully memorize training data when optimal. It shows that as training proceeds, the denoising objective causes overfitting at intermediate noise levels, creating a gap between validation and training performance. In a 2D toy model, this gap arises from the interplay between model error and the data distribution’s support density, leading to a smooth, generalizing flow field rather than exact recall of training points. The study also reveals that predictions from intermediate inference states become increasingly similar to training data in feature space, even as overall similarity to validation data remains unchanged.
Hugging Face Trending Papers
5d ago
Valid Early Detection of Memorization in Diffusion Models
Diffusion models can sometimes reproduce examples from their training data, a phenomenon known as memorization. Existing memorization detection methods for unconditional diffusion models typically ope...
arXiv Computer Vision
Sep 1
Memory-Efficient Training-Free Acceleration of Diffusion Transformers with BaryCache
arXiv:2608.28670v1 Announce Type: new Abstract: Diffusion Transformers achieve high-fidelity image and video generation, but their iterative sampling remains expensive, for each denoising step requir...
arXiv Statistics ML
4d ago
Valid Early Detection of Memorization in Diffusion Models
arXiv:2610.34617v1 Announce Type: cross Abstract: Diffusion models can sometimes reproduce examples from their training data, a phenomenon known as memorization. Existing memorization detection metho...
arXiv Machine Learning
Jun 10
Blind denoising diffusion models and the blessings of dimensionality
arXiv:2602. 09639v2 Announce Type: replace Abstract: Denoising diffusion models (DDMs) are state-of-the-art methods for learning densities from data across numerous domains, yet many aspects of the training and sampling pipeline remain poorly understood.
arXiv Machine Learning
Aug 4
Pseudorandom Streams within Diffusion Models Act as Learnable Inputs That Affect Generation Quality
arXiv:2608. 02575v1 Announce Type: new Abstract: Diffusion models rely on stochastic inputs, yet on finite-precision hardware, the "randomness" they consume is realized as deterministic numerical orbits generated by pseudorandom rules.
arXiv AI
Jun 16
Region-Adaptive Sampling for Diffusion Transformers
arXiv:2502. 10389v2 Announce Type: replace-cross Abstract: Diffusion models (DMs) have become the leading choice for generative tasks across diverse domains.
arXiv AI
Oct 1
From Modes to Memories: Characterizing the Scale-Space Dynamics of Diffusion Models
arXiv:2609.39648v1 Announce Type: cross Abstract: Diffusion models are typically viewed as stochastic processes that transform noise into data. We take a complementary perspective: a diffusion model...
arXiv Computer Vision
Aug 25
Pixel-Space Diffusion via Observation Operators
Pixel‑Space Diffusion via Observation Operators introduces a new framework for pixel‑space diffusion models that addresses a scale‑time mismatch in existing methods. By replacing fixed full‑image supervision with a time‑indexed observation trajectory that progresses from coarse structures to the full image, the model aligns supervision with the natural recovery order of image details. The approach employs Gaussian‑Lanczos operators and a GL‑CoDA decoder to refine features progressively, resulting in faster convergence and higher generation quality, achieving an FID of 1.52 on ImageNet‑256.
arXiv Computer Vision
Aug 21
When Guidance Goes Off-Scale: Recalibrating Diffusion Transformers under Analog Compute-in-Memory Nonidealities
arXiv:2608. 19644v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) incur high memory traffic and energy costs because sampling repeatedly evaluates large denoisers dominated by linear operations.
arXiv Computer Vision
Sep 21
Quantization-Aware Kalman Estimation for Diffusion Sampling
The paper introduces QuAKE, a Quantization-Aware Kalman Estimator designed to correct errors in diffusion model sampling when using quantized denoisers. By treating sampling as an online estimation problem, QuAKE leverages the history of quantized outputs to recover full-precision estimates, updating a posterior in closed form at each step. The method is lightweight, plug‑and‑play, and works with any high‑order multistep ODE sampler, outperforming existing correction techniques on W4A4‑quantized text‑to‑image diffusion models.