GenerativeModels
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MONAI Generative Models makes it easy to train, evaluate, and deploy generative models and related applications
Score-MRI ([paper](https://arxiv.org/abs/2110.05243), [code](https://github.com/HJ-harry/score-MRI)) is one of the simplest MRI reconstruction algorithms that can be implemented with a general pre-trained checkpoint. Once a diffusion model checkpoint is ready in the model...
Hello, thank you for sharing this wonderful package. Hello, I would like to ask, how many GPU cards did you use and how long did it take to train the...
Now that concat-based conditioning is supported in the infereres we should update tutorials to use the new infererers. AFAIK it is these two tutorials: https://github.com/Project-MONAI/GenerativeModels/blob/main/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution.ipynb https://github.com/Project-MONAI/GenerativeModels/blob/main/tutorials/generative/image_to_image_translation/tutorial_segmentation_with_ddpm.ipynb
For use-cases where there are very few training images (e.g. perhaps rare diseases), it could be nice to augment the data using one-shot generative models. Could this be something of...
One of the big disadvantages of the FID is that to obtain meaningful results, the total number of samples should be equal/bigger than the latent space of the model used...
Implement #293
Create a tutorial that uses monai.fl tools to train a generative models. It can use any FL tools for it (reccomend start with NVFlare), but it should use MONAIAlgo or...
Similar to [RoentGen: Vision-Language Foundation Model for Chest X-ray Generation](https://arxiv.org/pdf/2211.12737.pdf), it would be good to be able to easily download the Stable diffusion model and then finetune it into a...
Implements #154
In order to make simpler the use of non-imaging data as conditioning, some new transformations could be added. For example, for text we have been using custom transformations to load...