Image Generation On Imagenet 64X64

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이 벤치마크에서 각 모델의 성능 결과

		Paper Title	Repository
DenseFlow-74-10	3.35 (different downsampling)	Densely connected normalizing flows
2-rectified flow++ (NFE=1)	-	Improving the Training of Rectified Flows
Performer (6 layers)	3.719	Rethinking Attention with Performers
GDD-I	-	Diffusion Models Are Innate One-Step Generators
Sparse Transformer 59M (strided)	3.44	Generating Long Sequences with Sparse Transformers
CTM (NFE 1)	-	Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion
CD (Diffusion + Distillation, NFE=2)	-	Consistency Models
CT (Direct Generation, NFE=1)	-	Consistency Models
MaCow (Unf)	3.75	MaCow: Masked Convolutional Generative Flow
TCM	-	Truncated Consistency Models	-
Very Deep VAE	3.52	Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images
CDM	-	Cascaded Diffusion Models for High Fidelity Image Generation	-
Combiner-Axial	3.42	Combiner: Full Attention Transformer with Sparse Computation Cost
GLIDE + CLS-FREE	-	Composing Ensembles of Pre-trained Models via Iterative Consensus	-
SiD	-	Score identity Distillation: Exponentially Fast Distillation of Pretrained Diffusion Models for One-Step Generation
RIN	-	Scalable Adaptive Computation for Iterative Generation
Logsparse (6 layers)	4.351	Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting
Efficient-VDVAE	3.30 (different downsampling)	Efficient-VDVAE: Less is more
MRCNF	3.44	Multi-Resolution Continuous Normalizing Flows
Gated PixelCNN (van den Oord et al., [2016c])	3.57	Conditional Image Generation with PixelCNN Decoders

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Image Generation On Imagenet 64X64

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