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EdgeFusion: On-Device Text-to-Image Generation
Paper • 2404.11925 • Published • 21 -
Dynamic Typography: Bringing Words to Life
Paper • 2404.11614 • Published • 43 -
ControlNet++: Improving Conditional Controls with Efficient Consistency Feedback
Paper • 2404.07987 • Published • 47 -
Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models
Paper • 2404.07724 • Published • 12
Collections
Discover the best community collections!
Collections including paper arxiv:2404.02883
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Bigger is not Always Better: Scaling Properties of Latent Diffusion Models
Paper • 2404.01367 • Published • 20 -
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 • 57 -
Improved Denoising Diffusion Probabilistic Models
Paper • 2102.09672 • Published • 2
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On the Scalability of Diffusion-based Text-to-Image Generation
Paper • 2404.02883 • Published • 17 -
InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation
Paper • 2404.02733 • Published • 20 -
CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching
Paper • 2404.03653 • Published • 33 -
ControlNet++: Improving Conditional Controls with Efficient Consistency Feedback
Paper • 2404.07987 • Published • 47
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On the Scalability of Diffusion-based Text-to-Image Generation
Paper • 2404.02883 • Published • 17 -
MonoPatchNeRF: Improving Neural Radiance Fields with Patch-based Monocular Guidance
Paper • 2404.08252 • Published • 5 -
Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning
Paper • 2303.15647 • Published • 4
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Demystifying CLIP Data
Paper • 2309.16671 • Published • 20 -
Model Stock: All we need is just a few fine-tuned models
Paper • 2403.19522 • Published • 10 -
Bigger is not Always Better: Scaling Properties of Latent Diffusion Models
Paper • 2404.01367 • Published • 20 -
On the Scalability of Diffusion-based Text-to-Image Generation
Paper • 2404.02883 • Published • 17
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U-Net: Convolutional Networks for Biomedical Image Segmentation
Paper • 1505.04597 • Published • 7 -
Image Segmentation using U-Net Architecture for Powder X-ray Diffraction Images
Paper • 2310.16186 • Published • 2 -
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes
Paper • 1709.07330 • Published • 2 -
Deep LOGISMOS: Deep Learning Graph-based 3D Segmentation of Pancreatic Tumors on CT scans
Paper • 1801.08599 • Published • 2
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Faster Diffusion: Rethinking the Role of UNet Encoder in Diffusion Models
Paper • 2312.09608 • Published • 13 -
CodeFusion: A Pre-trained Diffusion Model for Code Generation
Paper • 2310.17680 • Published • 69 -
ZeroNVS: Zero-Shot 360-Degree View Synthesis from a Single Real Image
Paper • 2310.17994 • Published • 8 -
Progressive Knowledge Distillation Of Stable Diffusion XL Using Layer Level Loss
Paper • 2401.02677 • Published • 21
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TCNCA: Temporal Convolution Network with Chunked Attention for Scalable Sequence Processing
Paper • 2312.05605 • Published • 1 -
VMamba: Visual State Space Model
Paper • 2401.10166 • Published • 37 -
Rethinking Patch Dependence for Masked Autoencoders
Paper • 2401.14391 • Published • 22 -
Deconstructing Denoising Diffusion Models for Self-Supervised Learning
Paper • 2401.14404 • Published • 16
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One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning
Paper • 2306.07967 • Published • 24 -
Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation
Paper • 2306.07954 • Published • 113 -
TryOnDiffusion: A Tale of Two UNets
Paper • 2306.08276 • Published • 73 -
Seeing the World through Your Eyes
Paper • 2306.09348 • Published • 33
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The Chosen One: Consistent Characters in Text-to-Image Diffusion Models
Paper • 2311.10093 • Published • 57 -
NeuroPrompts: An Adaptive Framework to Optimize Prompts for Text-to-Image Generation
Paper • 2311.12229 • Published • 26 -
Diffusion Model Alignment Using Direct Preference Optimization
Paper • 2311.12908 • Published • 47 -
VMC: Video Motion Customization using Temporal Attention Adaption for Text-to-Video Diffusion Models
Paper • 2312.00845 • Published • 36