ImageNet 256 x 256
Emerging29papers using it
2021first seen
Papers using ImageNet 256 x 256 (29)
- Mean Flows for One-step Generative ModelingEfficient Generative Modeling with Residual Vector Quantization-Based TokensDiffusion Image Generation with Explicit Modeling of Data Manifold GeometryWhat Matters for Diffusion-Friendly Latent Manifold? Prior-Aligned Autoencoders for Latent DiffusionRBF-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 MatchingLatent Denoising Makes Good TokenizersUnified Continuous Generative ModelsLearning to Integrate Diffusion ODEs by Averaging the DerivativesREPA-E: Unlocking VAE for End-to-End Tuning with Latent Diffusion TransformersInductive Moment MatchingDiffusion Models Beat GANs on Image SynthesisSiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant TransformersScalable Diffusion Models with 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-TuningRelay Diffusion: Unifying diffusion process across resolutions for image synthesisThink While You Generate: Discrete Diffusion with Planned DenoisingMaskBit: Embedding-free Image Generation via Bit TokensOFTSR: One-Step Flow for Image Super-Resolution with Tunable Fidelity-Realism Trade-offs