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ARXIV:2603.12240 · TOKEN COMPRESSION · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE
ARXIV:2603.12240TOKEN COMPRESSIONSUBMITTED 19 MAR · 18:48 UTCFRESHNESS STALEarXiv
BiGain is a training-free framework that enhances both generation quality and classification accuracy in accelerated diffusion models through innovative token compression techniques.
Opportunity summary
Pain BiGain is a training-free framework that enhances both generation quality and classification accuracy in accelerated diffusion models through innovative token compression techniques.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
BiGain is a training-free framework that enhances both generation quality and classification accuracy in accelerated diffusion models through innovative token compression techniques. We revisit token compression with a joint objective and present BiGain, a…
Acceleration methods for diffusion models (e.g., token merging or downsampling) typically optimize synthesis quality under reduced compute, yet often ignore discriminative capacity. We revisit token compression with a joint objective and present BiGain, a…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Across DiT- and U-Net-based backbones and ImageNet-1K, ImageNet-100, Oxford-IIIT Pets, and COCO-2017, our operators consistently improve the speed-accuracy trade-off for diffusion-based classification, while maintaining…
Token Compression moved forward this cycle; last verified April 2026. Public score 7.0/10.
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BiGain is a training-free framework that enhances both generation quality and classification accuracy in accelerated diffusion models through innovative token compression techniques.
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10.48550/arXiv.2603.12240BiGain is a training-free framework that enhances both generation quality and classification accuracy in accelerated diffusion models through innovative token compression techniques.
Abstract
Acceleration methods for diffusion models (e.g., token merging or downsampling) typically optimize synthesis quality under reduced compute, yet often ignore discriminative capacity. We revisit token compression with a joint objective and present BiGain, a training-free, plug-and-play framework that preserves generation quality while improving classification in accelerated diffusion models. Our key insight is frequency separation: mapping feature-space signals into a frequency-aware representation disentangles fine detail from global semantics, enabling compression that respects both generative fidelity and discriminative utility. BiGain reflects this principle with two frequency-aware operators: (1) Laplacian-gated token merging, which encourages merges among spectrally smooth tokens while discouraging merges of high-contrast tokens, thereby retaining edges and textures; and (2) Interpolate-Extrapolate KV Downsampling, which downsamples keys/values via a controllable interextrapolation between nearest and average pooling while keeping queries intact, thereby conserving attention precision. Across DiT- and U-Net-based backbones and ImageNet-1K, ImageNet-100, Oxford-IIIT Pets, and COCO-2017, our operators consistently improve the speed-accuracy trade-off for diffusion-based classification, while maintaining or enhancing generation quality under comparable acceleration. For instance, on ImageNet-1K, with 70% token merging on Stable Diffusion 2.0, BiGain increases classification accuracy by 7.15% while improving FID by 0.34 (1.85%). Our analyses indicate that balanced spectral retention, preserving high-frequency detail and low/mid-frequency semantics, is a reliable design rule for token compression in diffusion models. To our knowledge, BiGain is the first framework to jointly study and advance both generation and classification under accelerated diffusion, supporting lower-cost deployment.
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BiGain is a training-free framework that enhances both generation quality and classification accuracy in accelerated diffusion models through innovative token compression techniques. We revisit token compression with a joint objective and present BiGain, a training-free, plug-an...
METHOD
Acceleration methods for diffusion models (e.g., token merging or downsampling) typically optimize synthesis quality under reduced compute, yet often ignore discriminative capacity. We revisit token compression with a joint objective and present BiGain, a training-free, plug-and...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Across DiT- and U-Net-based backbones and ImageNet-1K, ImageNet-100, Oxford-IIIT Pets, and COCO-2017, our operators consistently improve the speed-accuracy trade-off for diffusion-based classification, wh...
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Token Compression moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
BiGain is a training-free framework that enhances both generation quality and classification accuracy in accelerated diffusion models through innovative token compression techniques. We revisit token compression with a joint objective and present BiGain, a training-free, plug-and-play framework that preserves generation quality while improving classification in accelerated diffusion models.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Acceleration methods for diffusion models (e.g., token merging or downsampling) typically optimize synthesis quality under reduced compute, yet often ignore discriminative capacity. We revisit token compression with a joint objective and present BiGain, a training-free, plug-and-play framework that preserves generation quality while improving classification in accelerated diffusion models.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Across DiT- and U-Net-based backbones and ImageNet-1K, ImageNet-100, Oxford-IIIT Pets, and COCO-2017, our operators consistently improve the speed-accuracy trade-off for diffusion-based classification, while maintaining or enhancing generation quality under comparable acceleration.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Token Compression moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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BiGain is a training-free framework that enhances both generation quality and classification accuracy in accelerated diffusion models through innovative token compression techniques.
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