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Collections including paper arxiv:2404.01367
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Be Yourself: Bounded Attention for Multi-Subject Text-to-Image Generation
Paper • 2403.16990 • Published • 24 -
ViTAR: Vision Transformer with Any Resolution
Paper • 2403.18361 • Published • 50 -
Getting it Right: Improving Spatial Consistency in Text-to-Image Models
Paper • 2404.01197 • Published • 29 -
Bigger is not Always Better: Scaling Properties of Latent Diffusion Models
Paper • 2404.01367 • Published • 19
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Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
Paper • 2404.02905 • Published • 63 -
InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation
Paper • 2404.02733 • Published • 20 -
Cross-Attention Makes Inference Cumbersome in Text-to-Image Diffusion Models
Paper • 2404.02747 • Published • 11 -
Bigger is not Always Better: Scaling Properties of Latent Diffusion Models
Paper • 2404.01367 • Published • 19
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Bigger is not Always Better: Scaling Properties of Latent Diffusion Models
Paper • 2404.01367 • Published • 19 -
On the Scalability of Diffusion-based Text-to-Image Generation
Paper • 2404.02883 • Published • 17 -
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
Paper • 2403.03206 • Published • 52 -
Improved Denoising Diffusion Probabilistic Models
Paper • 2102.09672 • Published • 2
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Lumiere: A Space-Time Diffusion Model for Video Generation
Paper • 2401.12945 • Published • 85 -
Long-form factuality in large language models
Paper • 2403.18802 • Published • 23 -
ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion
Paper • 2403.18818 • Published • 23 -
TC4D: Trajectory-Conditioned Text-to-4D Generation
Paper • 2403.17920 • Published • 15
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Demystifying CLIP Data
Paper • 2309.16671 • Published • 19 -
Model Stock: All we need is just a few fine-tuned models
Paper • 2403.19522 • Published • 9 -
Bigger is not Always Better: Scaling Properties of Latent Diffusion Models
Paper • 2404.01367 • Published • 19 -
On the Scalability of Diffusion-based Text-to-Image Generation
Paper • 2404.02883 • Published • 17