Papers Zero-Shot Text-to-Image Generation
“Zero-Shot Text-to-Image Generation” 태그가 달린 논문 16편 · 필터 해제
One-step Diffusion Models with $f$-Divergence Distribution Matching
Sampling from diffusion models involves a slow iterative process that hinders their practical deployment, especially for interactive applications. To accelerate generation speed, recent approaches distill a multi-step di…
Image GenerationText to Image GenerationText-to-Image GenerationZero-Shot Text-to-Image GenerationGALOT: Generative Active Learning via Optimizable Zero-shot Text-to-image Generation
Active Learning (AL) represents a crucial methodology within machine learning, emphasizing the identification and utilization of the most informative samples for efficient model training. However, a significant challenge…
Active LearningImage GenerationPseudo LabelText to Image Generation+2DreamLLM: Synergistic Multimodal Comprehension and Creation
This paper presents DreamLLM, a learning framework that first achieves versatile Multimodal Large Language Models (MLLMs) empowered with frequently overlooked synergy between multimodal comprehension and creation. DreamL…
multimodal generationVisual Question AnsweringZero-Shot LearningZero-Shot Text-to-Image GenerationCoBIT: A Contrastive Bi-directional Image-Text Generation Model
The field of vision and language has witnessed a proliferation of pre-trained foundation models. Most existing methods are independently pre-trained with contrastive objective like CLIP, image-to-text generative objectiv…
DecoderImage GenerationImage to textRetrieval+5Shifted Diffusion for Text-to-image Generation
We present Corgi, a novel method for text-to-image generation. Corgi is based on our proposed shifted diffusion model, which achieves better image embedding generation from input text. Unlike the baseline diffusion model…
Image GenerationText to Image GenerationText-to-Image GenerationZero-Shot Text-to-Image GenerationWhat Does DALL-E 2 Know About Radiology?
Generative models such as DALL-E 2 could represent a promising future tool for image generation, augmentation, and manipulation for artificial intelligence research in radiology provided that these models have sufficient…
Image GenerationText to Image GenerationText-to-Image GenerationZero-Shot Text-to-Image GenerationBlended Latent Diffusion
The tremendous progress in neural image generation, coupled with the emergence of seemingly omnipotent vision-language models has finally enabled text-based interfaces for creating and editing images. Handling generic im…
Image GenerationImage Inpaintingspatial-aware image editingtext-guided-image-editing+2Hierarchical Text-Conditional Image Generation with CLIP Latents
Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations for image generation, we propose a two-stage model: a prior …
Conditional Image GenerationDecoderDiversityImage Generation+2Towards Language-Free Training for Text-to-Image Generation
One of the major challenges in training text-to-image generation models is the need of a large number of high-quality text-image pairs. While image samples are often easily accessible, the associated text description…
Image GenerationText to Image GenerationText-to-Image GenerationZero-Shot Text-to-Image GenerationGLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity. We explore diffusion models for the problem of te…
DiversityImage GenerationImage InpaintingReranking+2FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimization
Generating images from natural language instructions is an intriguing yet highly challenging task. We approach text-to-image generation by combining the power of the retrained CLIP representation with an off-the-shelf im…
counterfactualImage GenerationNavigateText to Image Generation+2Blended Diffusion for Text-driven Editing of Natural Images
Natural language offers a highly intuitive interface for image editing. In this paper, we introduce the first solution for performing local (region-based) edits in generic natural images, based on a natural language desc…
text-guided-image-editingText-to-Image GenerationZero-Shot Text-to-Image GenerationLAFITE: Towards Language-Free Training for Text-to-Image Generation
One of the major challenges in training text-to-image generation models is the need of a large number of high-quality image-text pairs. While image samples are often easily accessible, the associated text descriptions ty…
Image GenerationText to Image GenerationText-to-Image GenerationZero-Shot Text-to-Image GenerationL-Verse: Bidirectional Generation Between Image and Text
Far beyond learning long-range interactions of natural language, transformers are becoming the de-facto standard for many vision tasks with their power and scalability. Especially with cross-modal tasks between image and…
Image CaptioningImage GenerationImage ReconstructionImage to text+7CogView: Mastering Text-to-Image Generation via Transformers
Text-to-Image generation in the general domain has long been an open problem, which requires both a powerful generative model and cross-modal understanding. We propose CogView, a 4-billion-parameter Transformer with VQ-V…
Image GenerationSuper-ResolutionText to Image GenerationText-to-Image Generation+1Zero-Shot Text-to-Image Generation
Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such…
Image GenerationText to Image GenerationText-to-Image GenerationZero-Shot Text-to-Image Generation