Zero-Shot Text-to-Image Generation

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11 shown of 11 papers with code (16 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Zero-Shot Text-to-Image Generation
  24 Feb 2021 12 repositories listed Syntology ran 7 of 7 samples · 0 unverified · 3 pointer-only (licence)
  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
  13 Apr 2022 8 repositories listed Syntology ran 29 of 38 samples · 9 unverified · 1 pointer-only (licence)
  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
  26 May 2021 4 repositories listed Syntology ran 4 of 6 samples · 2 unverified
  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
  27 Nov 2021 3 repositories listed Syntology ran 4 of 18 samples · 14 unverified
  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
  20 Dec 2021 2 repositories listed Syntology ran 9 of 15 samples · 6 unverified
  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.
• DreamLLM: Synergistic Multimodal Comprehension and Creation
  20 Sep 2023 1 repository listed Syntology ran 3 of 3 samples · 0 unverified · 2 pointer-only (licence)
  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.
• Shifted Diffusion for Text-to-image Generation
  24 Nov 2022 1 repository listed Syntology ran 7 of 17 samples · 10 unverified
  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…
• Blended Latent Diffusion
  6 Jun 2022 1 repository listed Syntology ran 9 of 13 samples · 4 unverified · 1 pointer-only (licence)
  Our solution leverages a recent text-to-image Latent Diffusion Model (LDM), which speeds up diffusion by operating in a lower-dimensional latent space.
• FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimization
  2 Dec 2021 1 repository listed
  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…
• Blended Diffusion for Text-driven Editing of Natural Images
  29 Nov 2021 1 repository listed Syntology ran 14 of 22 samples · 8 unverified · 1 pointer-only (licence)
  Natural language offers a highly intuitive interface for image editing.
• L-Verse: Bidirectional Generation Between Image and Text
  22 Nov 2021 1 repository listed
  Unlike other models, BiART can distinguish between image (or text) as a conditional reference and a generation target.

Syntology lines on 9 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.