DC-DiT learns dynamic chunking to allocate fewer tokens to smooth or noisy regions and more to detailed or late-stage areas, cutting inference FLOPs up to 36.8% while improving FID up to 37.8% on class-conditional ImageNet generation.
Elastictok: Adaptive tokenization for image and video
6 Pith papers cite this work. Polarity classification is still indexing.
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Prior-Aligned AutoEncoders shape latent manifolds with spatial coherence, local continuity, and global semantics to improve latent diffusion, achieving SOTA gFID 1.03 on ImageNet 256x256 with up to 13x faster convergence.
Elastic Looped Transformers share weights across recurrent blocks and apply intra-loop self-distillation to deliver 4x parameter reduction while matching competitive FID and FVD scores on ImageNet and UCF-101.
APT adaptively varies patch sizes within a single image to reduce ViT token count, delivering 40-50% throughput gains on large models with no downstream performance loss.
FAST applies discrete cosine transform to robot action sequences for efficient tokenization, enabling autoregressive VLAs to succeed on high-frequency dexterous tasks and scale to 10k hours of data while matching diffusion VLA performance with up to 5x faster training.
Swift Sampling is a training-free frame selection method that uses Taylor expansions on video latent trajectories to pick temporally surprising frames, outperforming uniform sampling on long-video QA tasks.
citing papers explorer
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DC-DiT: Adaptive Compute and Elastic Inference for Visual Generation via Dynamic Chunking
DC-DiT learns dynamic chunking to allocate fewer tokens to smooth or noisy regions and more to detailed or late-stage areas, cutting inference FLOPs up to 36.8% while improving FID up to 37.8% on class-conditional ImageNet generation.
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What Matters for Diffusion-Friendly Latent Manifold? Prior-Aligned Autoencoders for Latent Diffusion
Prior-Aligned AutoEncoders shape latent manifolds with spatial coherence, local continuity, and global semantics to improve latent diffusion, achieving SOTA gFID 1.03 on ImageNet 256x256 with up to 13x faster convergence.
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ELT: Elastic Looped Transformers for Visual Generation
Elastic Looped Transformers share weights across recurrent blocks and apply intra-loop self-distillation to deliver 4x parameter reduction while matching competitive FID and FVD scores on ImageNet and UCF-101.
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Accelerating Vision Transformers with Adaptive Patch Sizes
APT adaptively varies patch sizes within a single image to reduce ViT token count, delivering 40-50% throughput gains on large models with no downstream performance loss.
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FAST: Efficient Action Tokenization for Vision-Language-Action Models
FAST applies discrete cosine transform to robot action sequences for efficient tokenization, enabling autoregressive VLAs to succeed on high-frequency dexterous tasks and scale to 10k hours of data while matching diffusion VLA performance with up to 5x faster training.
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Swift Sampling: Selecting Temporal Surprises via Taylor Series
Swift Sampling is a training-free frame selection method that uses Taylor expansions on video latent trajectories to pick temporally surprising frames, outperforming uniform sampling on long-video QA tasks.