Rectified flow learns straight-path neural ODEs for distribution transport, yielding efficient generative models and domain transfers that work well even with a single simulation step.
gDDIM: Generalized denoising diffusion implicit models
6 Pith papers cite this work. Polarity classification is still indexing.
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Diffusion models show grokking on modular addition by composing periodic operand representations in simple data regimes or by separating arithmetic computation from visual denoising across timesteps in varied regimes.
An ensemble of stage-specialized text-to-image diffusion models improves prompt alignment over single shared-parameter models while preserving visual quality and inference speed.
DPM-Solver++ enables high-quality guided sampling of diffusion models in 15-20 steps via data-prediction ODE solving and multistep stabilization.
An adapted scaling law predicts GPU energy consumption for diffusion model inference with R² > 0.9 within architectures and strong cross-architecture generalization.
I2VGen-XL applies cascaded diffusion models with a base stage for semantic preservation via hierarchical encoders and a refinement stage for detail and resolution, trained on 35 million text-video and 6 billion text-image pairs.
citing papers explorer
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Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
Rectified flow learns straight-path neural ODEs for distribution transport, yielding efficient generative models and domain transfers that work well even with a single simulation step.
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Grokking of Diffusion Models: Case Study on Modular Addition
Diffusion models show grokking on modular addition by composing periodic operand representations in simple data regimes or by separating arithmetic computation from visual denoising across timesteps in varied regimes.
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eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers
An ensemble of stage-specialized text-to-image diffusion models improves prompt alignment over single shared-parameter models while preserving visual quality and inference speed.
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DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models
DPM-Solver++ enables high-quality guided sampling of diffusion models in 15-20 steps via data-prediction ODE solving and multistep stabilization.
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Energy Scaling Laws for Diffusion Models: Quantifying Compute in Image Generation
An adapted scaling law predicts GPU energy consumption for diffusion model inference with R² > 0.9 within architectures and strong cross-architecture generalization.
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I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models
I2VGen-XL applies cascaded diffusion models with a base stage for semantic preservation via hierarchical encoders and a refinement stage for detail and resolution, trained on 35 million text-video and 6 billion text-image pairs.