So, a couple of points. I'm sure there are several computer vision experts on this sub but here are some of my opnions. Anyone please feel free to correct me.

1. If you mean you have 5000 images for segmentation then I think this would be sufficient data to train, for example, a UNET. If you are not getting the accuracy you want, perhaps look at how other people have been segmenting images in your domain for ideas.
2. If you mean, you have image at 5k resolution, how many images do you have? You would likely run into the problem of small data for training generative models as well. I assume you are already using domain-specific image augmentation techniques.
3. When training a generative model (such as a diffusion model) you are inherently learning the distribution of data. If you are able to generate high integrity images using this method, is there a way you could directly use this model to perform the segmentation task? (I am not familiar with the literature of diffusion models but I know other generative models, such as GANs have been used to perform image segmentation).
4. I'm not sure how you could also generate labels with a generative model (perhaps there are smart ways of modifying the architecture to facilitate this) in addition to the images. Perhaps other people can chime in here.

These points account for performing this segmentation task to a high accuracy for this specific task and not for developing a novel segmentation algorithm. If the latter is what you are looking for, then these points will not be very useful for you. Good luck!