Multiclass Unconditional Image Generation
Hi folks,I’m currently performing unconditional image generation.Input: My input is multiclass image data with 10 class of images (for example, cifar10).Output: Expected output is to generate images for each class.Problem: I’m able to generate images, but not able to extract/connect the class label into which the generated image belong to.For example, I want to generate specifically Frog images from trained model based on cifar10 modelI’m not able to find the solution even in Github repo as well.I would highly appreciate if someone could guide me on how to extract the class label.Thanks and regardsHere is the code snippet:!pip install diffusersfrom diffusers import DDPMPipeline, DDIMPipeline, PNDMPipelinemodel_id = “google/ddpm-cifar10-32”// load model and schedulerddpm = DDPMPipeline.from_pretrained(model_id)  # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference// run pipeline in inference (sample random noise and denoise)image = ddpm().images[0]// save imageimage.save(“ddpm_generated_image.png”)