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Image Generation On Imagenet 64X64

Metrics

Bits per dim

Results

Performance results of various models on this benchmark

Paper TitleRepository
DenseFlow-74-103.35 (different downsampling)Densely connected normalizing flows
2-rectified flow++ (NFE=1)-Improving the Training of Rectified Flows
Performer (6 layers)3.719Rethinking Attention with Performers
GDD-I-Diffusion Models Are Innate One-Step Generators
Sparse Transformer 59M (strided)3.44Generating 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)3.75MaCow: Masked Convolutional Generative Flow
TCM-Truncated Consistency Models-
Very Deep VAE3.52Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images
CDM-Cascaded Diffusion Models for High Fidelity Image Generation-
Combiner-Axial3.42Combiner: 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)4.351Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting
Efficient-VDVAE3.30 (different downsampling)Efficient-VDVAE: Less is more
MRCNF3.44Multi-Resolution Continuous Normalizing Flows
Gated PixelCNN (van den Oord et al., [2016c])3.57Conditional Image Generation with PixelCNN Decoders
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Image Generation On Imagenet 64X64 | SOTA | HyperAI