Class-conditional image generation
Hey everyone,for a university project I want to play around with class-conditional DDIM image generation based on a custom created dataset of microscopy images. Basically I want to generate synthetic microscopy images of a specific biological application based on a set of features that should be visible.I have a dataset of roughly 3400 images that I tagged manually with 4 different features.I.e. for each image I have a vector of length 4, with 1 indicating that the specific feature is visible in the image. I want this to be necessary to control what kind of images are generated, like in stable diffusion but without a whole text prompt.I need ideas or implementation hints on how to implement that using huggingface and accelerate.I dont how how to add the feature vector in the pipeline for training and inference.Thanks for any kind of help!
2023-10-12T09:28:03Z
Have you found a way to add the feature vector in the pipeline for training and inference?