diff --git a/plugins/interruptible.py b/plugins/interruptible.py
index e011523..22c8507 100644
--- a/plugins/interruptible.py
+++ b/plugins/interruptible.py
@@ -54,95 +54,3 @@ class InterruptiblePlugin(BasePlugin):
             # if an epoch has eg 100 steps and num_divisions is 2, then validation should occur after steps 49 and 99
             validate_every_n_steps = epoch_length_steps / num_divisions
             return [math.ceil((i+1)*validate_every_n_steps) - 1 for i in range(num_divisions)]
-
-"""
-class InterruptiblePlugin_(BasePlugin):
-    def __init__(self, log_folder, args):
-        self.log_folder = log_folder
-        self.project_name = args.project_name
-        self.max_epochs = args.max_epochs
-
-        self.every_n_epochs = 1
-
-
-    @classmethod
-    def make_resume_path(cls, resume_ckpt_folder):
-        return os.path.join(resume_ckpt_folder, 'resumable_data.pt')
-
-    def load_resume_state(self, resume_ckpt_path: str, ed_state: EveryDreamTrainingState):
-        resume_path = self.make_resume_path(resume_ckpt_path)
-        try:
-            with open(resume_path, 'rb') as f:
-                resumable_data = torch.load(f)
-                ed_state.optimizer.load_state_dict(resumable_data['ed_optimizer'])
-                ed_state.train_batch.load_state_dict(resumable_data['ed_batch'])
-        except Exception as e:
-            print(f"InterruptiblePlugin unable to load resume state from {resume_path}: {e}")
-            return
-
-
-    def on_epoch_start(self, ed_state: EveryDreamTrainingState, **kwargs):
-        epoch = kwargs['epoch']
-        epoch_length = kwargs['epoch_length']
-        if epoch == 0:
-            resume_ckpt_path = kwargs['resume_ckpt_path']
-            self.load_resume_state(resume_ckpt_path, ed_state)
-        self.steps_to_save_this_epoch = self._get_save_step_indices(epoch, epoch_length)
-
-    def _get_save_step_indices(self, epoch, epoch_length_steps: int) -> list[int]:
-        if self.every_n_epochs >= 1:
-            if ((epoch+1) % self.every_n_epochs) == 0:
-                # last step only
-                return [epoch_length_steps-1]
-            else:
-                return []
-        else:
-            # subdivide the epoch evenly, by rounding self.every_n_epochs to the nearest clean division of steps
-            num_divisions = max(1, min(epoch_length_steps, round(1/self.every_n_epochs)))
-            # validation happens after training:
-            # if an epoch has eg 100 steps and num_divisions is 2, then validation should occur after steps 49 and 99
-            validate_every_n_steps = epoch_length_steps / num_divisions
-            return [math.ceil((i+1)*validate_every_n_steps) - 1 for i in range(num_divisions)]
-
-    def on_step_end(self, epoch: int, local_step: int, global_step: int, ed_state: EveryDreamTrainingState):
-        if local_step in self.steps_to_save_this_epoch:
-            self.save_and_remove_prior(epoch, global_step, ed_state)
-
-    def _save_and_remove_prior(self, epoch: int, global_step: int, ed_state: EveryDreamTrainingState):
-            rolling_save_path = self.make_save_path(epoch, global_step, prepend="rolling-")
-            ed_optimizer: EveryDreamOptimizer = ed_state.optimizer
-            save_model(rolling_save_path,
-                       ed_state=ed_state, save_ckpt_dir=None, yaml_name=None, save_ckpt=False, save_optimizer_flag=True)
-
-                       kwargs['unet'], kwargs['text_encoder'], kwargs['tokenizer'],
-                       kwargs['noise_scheduler'], kwargs['vae'], ed_optimizer,
-                         save_ckpt_dir=None, yaml_name=None, save_optimizer_flag=True, save_ckpt=False)
-
-            train_batch: EveryDreamBatch = kwargs['train_batch']
-            resumable_data = {
-                'grad_scaler': ed_optimizer.scaler.state_dict(),
-                'epoch': epoch,
-                'global_step': global_step,
-                'train_batch': train_batch.state_dict()
-            }
-            if ed_optimizer.lr_scheduler_te is not None:
-                resumable_data['lr_scheduler_te'] = ed_optimizer.lr_scheduler_te.state_dict()
-            if ed_optimizer.lr_scheduler_unet is not None:
-                resumable_data['lr_scheduler_unet'] = ed_optimizer.lr_scheduler_unet.state_dict()
-
-            torch.save(resumable_data, os.path.join(rolling_save_path, 'resumable_data.pt'))
-
-            self.prev_epoch = epoch
-            self.prev_global_step = global_step
-        if epoch > 0:
-            prev_rolling_save_path = self.make_save_path(epoch, self.prev_global_step, prepend="rolling-")
-            shutil.rmtree(prev_rolling_save_path, ignore_errors=True)
-
-        pass
-
-    def make_save_path(self, epoch, global_step, prepend: str="") -> str:
-        basename = f"{prepend}{self.project_name}-ep{epoch:02}"
-        if global_step is not None:
-            basename += f"-gs{global_step:05}"
-        return os.path.join(self.log_folder, "ckpts", basename)
-"""
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