Zero-Shot Text-to-Image Generation
11 papers with code • 0 benchmarks • 0 datasets
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Libraries
Use these libraries to find Zero-Shot Text-to-Image Generation models and implementationsMost implemented papers
Zero-Shot Text-to-Image Generation
openai/DALL-E
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Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset.
Hierarchical 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.
CogView: Mastering Text-to-Image Generation via Transformers
THUDM/CogView
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NeurIPS 2021
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.
LAFITE: Towards Language-Free Training for Text-to-Image Generation
drboog/Lafite
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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.
GLIDE: 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.
L-Verse: Bidirectional Generation Between Image and Text
tgisaturday/L-Verse
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CVPR 2022
Unlike other models, BiART can distinguish between image (or text) as a conditional reference and a generation target.
Blended Diffusion for Text-driven Editing of Natural Images
omriav/blended-diffusion
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CVPR 2022
Natural language offers a highly intuitive interface for image editing.
FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimization
We approach text-to-image generation by combining the power of the retrained CLIP representation with an off-the-shelf image generator (GANs), optimizing in the latent space of GAN to find images that achieve maximum CLIP score with the given input text.
Blended Latent Diffusion
omriav/blended-latent-diffusion
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Our solution leverages a recent text-to-image Latent Diffusion Model (LDM), which speeds up diffusion by operating in a lower-dimensional latent space.
Shifted Diffusion for Text-to-image Generation
drboog/Shifted_Diffusion
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CVPR 2023
Unlike the baseline diffusion model used in DALL-E 2, our method seamlessly encodes prior knowledge of the pre-trained CLIP model in its diffusion process by designing a new initialization distribution and a new transition step of the diffusion.
