dc-ae-f32c32-in-1.0
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dc-ae-f32c32-in-1.0-diffusers
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View details ↗dc-ae-f32c32-sana-1.0
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mit-han-labWe present Deep Compression Autoencoder (DC-AE), a new family of autoencoder models for accelerating high-resolution diffusion models. Existing autoencoder models have demonstrated impressive results at a moderate spatial compression ratio (e.g., 8x), but fail to maintain satisfactory reconstruction accuracy for high spatial compression ratios (e.g., 64x). We address this challenge by introducing two key techniques: (1) Residual Autoencoding, where we design our models to learn residuals based on the space-to-channel transformed features to alleviate the optimization difficulty of high spatial-compression autoencoders; (2) Decoupled High-Resolution Adaptation, an efficient decoupled three-phases training strategy for mitigating the generalization penalty of high spatial-compression autoencoders. With these designs, we improve the autoencoder's spatial compression ratio up to 128 while maintaining the reconstruction quality. Applying our DC-AE to latent diffusion models, we achieve significant speedup without accuracy drop. For example, on ImageNet 512x512, our DC-AE provides 19.1x inference speedup and 17.9x training speedup on H100 GPU for UViT-H while achieving a better FID, compared with the widely used SD-VAE-f8 autoencoder. If DC-AE is useful or relevant to your research, please kindly recognize our contributions by citing our papers:
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View details ↗dc-ae-f32c32-sana-1.1
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View details ↗dc-ae-f64c128-in-1.0-diffusers
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View details ↗nunchaku-flux.1-dev
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View details ↗nunchaku-flux.1-kontext-dev
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View details ↗StreamingVLM
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View details ↗svdq-int4-flux.1-dev
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View details ↗svdq-int4-flux.1-schnell
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