ImageNet 256 x 256
Emerging24papers using it
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2021first seen
'ImageNet 256 x 256' is a dataset that contains images resized to 256 x 256 pixels, used to evaluate the performance of generative models in terms of image fidelity and quality.
Papers using ImageNet 256 x 256 (24)
- Mean Flows for One-step Generative ModelingEfficient Generative Modeling with Residual Vector Quantization-Based TokensRBF-Solver: A Multistep Sampler for Diffusion Probabilistic Models via Radial Basis FunctionsLaminating Representation Autoencoders for Efficient DiffusionGroup Diffusion: Enhancing Image Generation by Unlocking Cross-Sample CollaborationGuiding a Diffusion Transformer with the Internal Dynamics of ItselfTerminal Velocity MatchingUnified Continuous Generative ModelsLearning to Integrate Diffusion ODEs by Averaging the DerivativesREPA-E: Unlocking VAE for End-to-End Tuning with Latent Diffusion TransformersInductive Moment MatchingOFTSR: One-Step Flow for Image Super-Resolution with Tunable Fidelity-Realism Trade-offsDiffusion Models Beat GANs on Image SynthesisScalable Diffusion Models with TransformersSiT: Exploring Flow and Diffusion-based Generative Models with Scalable
Interpolant TransformersPatch Diffusion: Faster and More Data-Efficient Training of Diffusion
ModelsRefining Generative Process with Discriminator Guidance in Score-based
Diffusion ModelsOn Distillation of Guided Diffusion ModelsFast Training of Diffusion Models with Masked TransformersAll are Worth Words: A ViT Backbone for Diffusion ModelsSAN: Inducing Metrizability of GAN with Discriminative Normalized Linear
LayerEfficient Diffusion Training via Min-SNR Weighting StrategyDiffFit: Unlocking Transferability of Large Diffusion Models via Simple
Parameter-Efficient Fine-TuningMaskBit: Embedding-free Image Generation via Bit Tokens