@@ -8,7 +8,7 @@ The pipeline is initiated via the experiment_creator.ipynb notebook, which is se
Upon execution, the notebook generates individual JSON files, encapsulating all the hyperparameter information. When running the model on the HPC, we can choose between the operations 'train', 'sample', and 'evaluate'. These operations automatically extract the necessary hyperparameters from the JSON files and perform their respective tasks. This process is managed by the main.py file. The remaining files contain all the necessary functions optimized for HPC to perform the aforementioned tasks.
Every uniquely trained diffusion model has its own experiment folder, given by its WANDB run name. It holds four different directories: settings, trained_ddpm, samples, and evaluations. The settings folder holds the JSON files specifying the diffusion model's configurations as well as the arguments for the training, sampling, and evaluation functions. The trained_ddpm folder contains .pth files storing the weights and biases of this experiment's diffusion model, which have been saved at different epoch milestones while training. Upon resuming training, the pipeline takes the specified model in trained_ddpm and continues training from there. When sampling images from these trained diffusion models, the samples are stored in different directories for the milestones under the names epoch_{i}. This is done so we know what epoch i version of the diffusion model was used to generate these samples.
Every uniquely trained diffusion model has its own experiment folder, given by its WANDB run name. It holds four different directories: settings, trained_ddpm, samples, and evaluations. The settings folder holds the JSON files specifying the diffusion model's configurations as well as the arguments for the training, sampling, and evaluation functions. The trained_ddpm folder contains .pth files storing the weights and biases of this experiment's diffusion model, which have been saved at different epoch milestones while training. Upon resuming training, the pipeline takes the specified model in trained_ddpm and continues training from there. When sampling images from these trained diffusion models, the samples are stored in different directories for the milestones under the names epoch_{i}. This is done so we know what epoch i version of the diffusion model was used to generate these samples. This is done analogously for the evaluation.
