Tag: dream-booth

• Word-As-Image for Semantic Typography (13 Oct 2023)
  

  This is my reading note for Word-As-Image for Semantic Typography. This paper utilized the differential rendering for vector graph to train a diffusion model to generate vector graph for a given text. Check my note for related paper in # VectorFusion Text-to-SVG by Abstracting Pixel-Based Diffusion Models

• VectorFusion Text-to-SVG by Abstracting Pixel-Based Diffusion Models (12 Oct 2023)
  

  This is my reading note for VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models. This paper utilized the differential rendering for vector graph to train a diffusion model to generate vector graph for a given text.

• PhotoVerse Tuning-Free Image Customization with Text-to-Image Diffusion Models (13 Sep 2023)
  

  This is my reading note for PhotoVerse: Tuning-Free Image Customization with Text-to-Image Diffusion. This paper proposes a fine tune free personalized image edit method bases on diffusion. To this end it proposes dual branch to encode text and image feature. Lora is used to update the existing model. it also proposed to use a random fusion injection to condition the attention with image and text embedding. However the paper fails to describe why this random fusion injection is needed.

• DreamBooth Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation (14 Jul 2023)
  

  This is my reading note for DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation. This paper proposes a personalized method for text to image based on diffusion. To achieve this, it firsts learn to align the visual content to be personalized to a rarely used text embedding, then this text embedding will be insert to the text to control the image generation.

• MeshDiffusion Score-based Generative 3D Mesh Modeling (02 Jul 2023)
  

  This is my reading note for MeshDiffusion: Score-based Generative 3D Mesh Modeling. This paper represents the 3D mesh as a reformed tetrahedral which is defined on a regular 3D grid with 4 channel features: 3D positional deformation of the vertex and signed distance function values to define the surface.