Midpoint Generative Models define a midpoint divergence from flow matching symmetry and derive its variational form as a tractable objective for training competitive one-step generators.
Cmt: Mid-training for efficient learning of consistency, mean flow, and flow map models
8 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Generalizes MeanFlow to learn finite-horizon minimum-energy control coefficients for linear swarm systems via a differential identity and stop-gradient regression objective.
Decouples Sphere Encoder into fixed pretrained encoder and spherical latent denoiser, yielding higher quality and faster inference than the joint original on Animal-Faces, Oxford-Flowers and ImageNet-1K.
Drifting with Gaussian kernels exactly matches score-matching on smoothed distributions via Tweedie's formula, while Laplace kernels approximate this closely in high dimensions.
Drift Flow Matching connects direct transport maps from Drift Models with flow-based iterative refinement to enable adaptive computation in generative modeling.
Improved MeanFlow (iMF) reaches 1.72 FID on ImageNet 256x256 with one function evaluation by reformulating the training objective as a regression on instantaneous velocity and treating guidance as flexible conditioning variables.
Stabilizes MeanFlow for large-scale diffusion distillation via discrete warm-up and trajectory alignment, reporting better results on FLUX.1-dev and HunyuanImage 3.0.
citing papers explorer
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Midpoint Generative Models
Midpoint Generative Models define a midpoint divergence from flow matching symmetry and derive its variational form as a tractable objective for training competitive one-step generators.
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Learning Sampled-data Control for Swarms via MeanFlow
Generalizes MeanFlow to learn finite-horizon minimum-energy control coefficients for linear swarm systems via a differential identity and stop-gradient regression objective.
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Efficient Image Synthesis with Sphere Latent Encoder
Decouples Sphere Encoder into fixed pretrained encoder and spherical latent denoiser, yielding higher quality and faster inference than the joint original on Animal-Faces, Oxford-Flowers and ImageNet-1K.
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A Unified View of Score-Based and Drifting Models
Drifting with Gaussian kernels exactly matches score-matching on smoothed distributions via Tweedie's formula, while Laplace kernels approximate this closely in high dimensions.
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Drift Flow Matching
Drift Flow Matching connects direct transport maps from Drift Models with flow-based iterative refinement to enable adaptive computation in generative modeling.
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Improved Mean Flows: On the Challenges of Fastforward Generative Models
Improved MeanFlow (iMF) reaches 1.72 FID on ImageNet 256x256 with one function evaluation by reformulating the training objective as a regression on instantaneous velocity and treating guidance as flexible conditioning variables.
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Stabilizing, Scaling & Enhancing MeanFlow for Large-scale Diffusion Distillation
Stabilizes MeanFlow for large-scale diffusion distillation via discrete warm-up and trajectory alignment, reporting better results on FLUX.1-dev and HunyuanImage 3.0.
- The Principles of Diffusion Models