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Found 11 papers, 7 papers with code
Stable Flow: Vital Layers for Training-Free Image Editing
The main challenge is that, unlike the UNet-based models, DiT lacks a coarse-to-fine synthesis structure, making it unclear in which layers to perform the injection.
Click2Mask: Local Editing with Dynamic Mask Generation
Recent advancements in generative models have revolutionized image generation and editing, making these tasks accessible to non-experts.
PALP: Prompt Aligned Personalization of Text-to-Image Models
We term our approach prompt-aligned personalization.
The Chosen One: Consistent Characters in Text-to-Image Diffusion Models
Recent advances in text-to-image generation models have unlocked vast potential for visual creativity.
Blended-NeRF: Zero-Shot Object Generation and Blending in Existing Neural Radiance Fields
We present Blended-NeRF, a robust and flexible framework for editing a specific region of interest in an existing NeRF scene, based on text prompts, along with a 3D ROI box.
Break-A-Scene: Extracting Multiple Concepts from a Single Image
Text-to-image model personalization aims to introduce a user-provided concept to the model, allowing its synthesis in diverse contexts.
SpaText: Spatio-Textual Representation for Controllable Image Generation
Due to lack of large-scale datasets that have a detailed textual description for each region in the image, we choose to leverage the current large-scale text-to-image datasets and base our approach on a novel CLIP-based spatio-textual representation, and show its effectiveness on two state-of-the-art diffusion models: pixel-based and latent-based.
Blended Latent Diffusion
Our solution leverages a recent text-to-image Latent Diffusion Model (LDM), which speeds up diffusion by operating in a lower-dimensional latent space.
Blended Diffusion for Text-driven Editing of Natural Images
Natural language offers a highly intuitive interface for image editing.
text-guided-image-editing
Zero-Shot Text-to-Image Generation
GAN Cocktail: mixing GANs without dataset access
In the second stage, we merge the rooted models by averaging their weights and fine-tuning them for each specific domain, using only data generated by the original trained models.
