Intrinsic Distribution Matching (IDM) extends distribution matching distillation (DMD) for one‑step and few‑step text‑to‑image generation by adding an explicit moment constraint that matches feature means and per‑coordinate standard deviations using the generator’s exponential‑moving‑average copy. This approach avoids a separate fake‑score network and an external feature encoder, while retaining the full DMD correction. Experiments on Z‑Image‑Turbo 6B and Qwen‑Image 20B with full‑parameter and LoRA tuning show IDM achieving the highest scores on Chinese and English LongText‑Bench, and ablations confirm the benefit of the moment loss.
arXiv:2609.35763v3 Announce Type: replace Abstract: Distributional training provides collective supervision for one-step visual generation by matching real and generated features in frozen representa...
arXiv:2605. 30116v2 Announce Type: replace-cross Abstract: Distribution Matching Distillation (DMD) is a widely used paradigm for accelerating inference in few-step video diffusion models.
arXiv:2512.17730v2 Announce Type: replace Abstract: Detectors of AI-generated images tend to inherit the biases of the data they are trained on: models fitted to GAN imagery learn to treat GAN-specif...
arXiv:2605.19256v2 Announce Type: replace Abstract: Distribution Matching Distillation (DMD) provides an effective distribution-level correction for few-step generation, while relying on an auxiliary...
arXiv:2607. 26860v1 Announce Type: new Abstract: We propose amortized moment matching, utilizing neural networks to learn data moments as distributional training signals.
arXiv:2609.10224v1 Announce Type: new Abstract: Vision-language models such as CLIP embed images and text in a shared space, where modality-specific distributions often remain separated. Existing acc...
The paper introduces Function Projection for Flow Matching (FP‑FM), an algorithm that conditions generative models directly on samples from a target distribution. FP‑FM learns basis functions spanning velocity fields for training distributions and adapts to new distributions via a simple least‑squares projection, enabling efficient sample generation without additional training at inference time. Variants of the method trade expressivity for computational cost by enriching coefficient calculations, and the approach shows markedly better precision and recall than baselines, especially on unseen distributions.
arXiv:2608.29395v1 Announce Type: new Abstract: Vision-language models such as CLIP and SigLIP provide strong zero-shot recognition, but their predictions can degrade when deployed on target data tha...
arXiv:2608. 06240v1 Announce Type: cross Abstract: Unpaired image-to-image translation must decide, per image, what to change and what to preserve without paired supervision.
DMAD (Distribution Matching as Adversarial Distillation) reinterprets distribution matching as a classification task, enabling a few‑step student model to learn log‑density ratios directly via two discriminator heads on a shared backbone. This eliminates the need for an auxiliary diffusion model, reducing memory and computation overhead. Experiments show DMAD achieves state‑of‑the‑art Fréchet Inception Distance scores on ImageNet‑64x64, COCO‑10K, and VBench, and outperforms competing few‑step methods in joint audio‑video generation on MiniMax‑H3‑33B.
The paper introduces VDOT++, a unified distillation framework for few-step video generation across text-to-video, image-to-video, and condition-based tasks. It extends the previous VDOT method by incorporating an asymmetric unbalanced optimal transport distillation that allows student tokens to carry less mass, and an α1 ground cost that uses a weighted median to preserve modes. The approach combines distribution matching with adversarial refinement and cross-scale distillation, achieving competitive performance on multiple video benchmarks with only four sampling steps.
arXiv:2609.30840v1 Announce Type: cross Abstract: One-step generators enable high-quality visual generation with a single network evaluation, but their post-training is difficult: general implicit ge...