Cross-attention control in text-conditioned models enables localized and global image edits by editing only the input text prompt.
Styleclip: Text-driven manipulation of stylegan imagery
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
citation-role summary
background 4
citation-polarity summary
roles
background 4polarities
background 4representative citing papers
Textual Inversion learns a single embedding vector from a few images to represent personal concepts inside the text embedding space of a frozen text-to-image model, enabling their composition in natural language prompts.
A technique for controllable diversity in text-to-image generation by inducing structured semantic variations at the prompt level via VLM and agentic workflow.
BadBone backdoors backbone models with bi-level optimization to make prompt learning on downstream tasks vulnerable while preserving model utility.
Voxify3D generates voxel art from 3D meshes via orthographic pixel supervision, patch-based CLIP alignment, and palette-constrained Gumbel-Softmax quantization, achieving 37.12 CLIP-IQA and 77.90% user preference.
A hierarchical prior-decoder model using CLIP latents generates more diverse text-conditional images than direct methods while preserving photorealism and caption fidelity.
A 3.5-billion-parameter diffusion model with classifier-free guidance generates images preferred over DALL-E by human raters and can be fine-tuned for text-guided inpainting.
Diffusion models with architecture improvements and classifier guidance achieve superior FID scores to GANs on unconditional and conditional ImageNet image synthesis.
citing papers explorer
-
Prompt-to-Prompt Image Editing with Cross Attention Control
Cross-attention control in text-conditioned models enables localized and global image edits by editing only the input text prompt.
-
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
Textual Inversion learns a single embedding vector from a few images to represent personal concepts inside the text embedding space of a frozen text-to-image model, enabling their composition in natural language prompts.
-
Semantic Browsing: Controllable Diversity for Image Generation
A technique for controllable diversity in text-to-image generation by inducing structured semantic variations at the prompt level via VLM and agentic workflow.
-
BadBone: Backdoor Attacks Against Backbone Models in Visual Prompt Learning
BadBone backdoors backbone models with bi-level optimization to make prompt learning on downstream tasks vulnerable while preserving model utility.
-
Voxify3D: Pixel Art Meets Volumetric Rendering
Voxify3D generates voxel art from 3D meshes via orthographic pixel supervision, patch-based CLIP alignment, and palette-constrained Gumbel-Softmax quantization, achieving 37.12 CLIP-IQA and 77.90% user preference.
-
Hierarchical Text-Conditional Image Generation with CLIP Latents
A hierarchical prior-decoder model using CLIP latents generates more diverse text-conditional images than direct methods while preserving photorealism and caption fidelity.
-
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
A 3.5-billion-parameter diffusion model with classifier-free guidance generates images preferred over DALL-E by human raters and can be fine-tuned for text-guided inpainting.
-
Diffusion Models Beat GANs on Image Synthesis
Diffusion models with architecture improvements and classifier guidance achieve superior FID scores to GANs on unconditional and conditional ImageNet image synthesis.