Tag: prompt2prompt

• Rerender A Video Zero-Shot Text-Guided Video-to-Video Translation (18 Sep 2023)
  

  This is my reading note on Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation. The paper proposes a method to edit a video given style mentioned in prompt. The method performed diffusion to edit key frames and then propagate the edited key frames to other frames using optical flow. For key frame editing, several attention based constraint is applied to reserve details and consistency, including shape aware, style aware, pixel aware and fidelity aware.

• MagiCapture High-Resolution Multi-Concept Portrait Customization (11 Sep 2023)
  

  This is my reading note on MagiCapture High-Resolution Multi-Concept Portrait Customization. This paper proposes a diffusion method to apply a style to a specific face image. Both the style and face are given as images. To do this, this paper fine tune existing model with LORA given several new loss functions: one is face identity loss for the face region given a face recognition model; another one is background similarity for the style. The two loss are applied to the latent vector.

• Key-Locked Rank One Editing for Text-to-Image Personalization (07 Sep 2023)
  

  This is my reading note on Key-Locked Rank One Editing for Text-to-Image Personalization. This paper proposes a personalized image generation method base on controlling attention module of the diffusion model. Especially key captures the layout of concept and value captures the identity of the new concept. A rank one update is applied to the attention weight to this purpose.

• BLIP-Diffusion Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing (21 Aug 2023)
  

  This is my reading note for BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing. The paper proposes a method for generating an image with text prompt and target visual concept. To do that the paper trained blip model to align visual features with text prompt and then concatenate the visual embedding to the text prompt to generate the need. Code and models will be released at https://github.com/salesforce/LAVIS/tree/main/projects/blip-diffusion. Project page at https://dxli94.github.io/BLIP-Diffusion-website/.