diff --git a/src/pytti/ImageGuide.py b/src/pytti/ImageGuide.py
index 36122ed..7e5746e 100644
--- a/src/pytti/ImageGuide.py
+++ b/src/pytti/ImageGuide.py
@@ -289,13 +289,16 @@ def train(
         # losses_accumulator, losses_raw_accumulator = Counter(), Counter()
         losses, losses_raw = [], []  # just... don't care
         total_loss = 0
-        if self.embedder is not None:
-            for mb_i in range(gradient_accumulation_steps):
-                # logger.debug(mb_i)
+
+        for mb_i in range(gradient_accumulation_steps):
+            # logger.debug(mb_i)
+            # logger.debug(self.image_rep.shape)
+            t = 1
+            interp_losses = [0]
+            prompt_losses = {}
+            if self.embedder is not None:
                 image_embeds, offsets, sizes = self.embedder(self.image_rep, input=z)
 
-                t = 1
-                interp_losses = [0]
                 if i < interp_steps:
                     t = i / interp_steps
                     interp_losses = [
@@ -317,33 +320,33 @@ def train(
                     for prompt in prompts
                 }
 
-                losses, losses_raw = zip(
-                    *map(unpack_dict, [prompt_losses, aug_losses, image_losses])
-                    # *map(unpack_dict, [prompt_losses])
-                )
-                # logger.debug(losses)
-                losses = list(losses)
-                # logger.debug(losses)
-                # losses = Counter(losses)
-                # logger.debug(losses)
-                losses_raw = list(losses_raw)
-                # losses_raw = Counter(losses_raw)
-                # losses_accumulator += losses
-                # losses_raw_accumulator += losses_raw
-
-                for v in prompt_losses.values():
-                    v[0].mul_(t)
-
-                total_loss_mb = sum(map(lambda x: sum(x.values()), losses)) + sum(
-                    interp_losses
-                )
+            losses, losses_raw = zip(
+                *map(unpack_dict, [prompt_losses, aug_losses, image_losses])
+                # *map(unpack_dict, [prompt_losses])
+            )
+            # logger.debug(losses)
+            losses = list(losses)
+            # logger.debug(losses)
+            # losses = Counter(losses)
+            # logger.debug(losses)
+            losses_raw = list(losses_raw)
+            # losses_raw = Counter(losses_raw)
+            # losses_accumulator += losses
+            # losses_raw_accumulator += losses_raw
+
+            for v in prompt_losses.values():
+                v[0].mul_(t)
+
+            total_loss_mb = sum(map(lambda x: sum(x.values()), losses)) + sum(
+                interp_losses
+            )
 
-                total_loss_mb /= gradient_accumulation_steps
+            total_loss_mb /= gradient_accumulation_steps
 
-                # total_loss_mb.backward()
-                total_loss_mb.backward(retain_graph=True)
-                # total_loss += total_loss_mb # this is causing it to break
-                # total_loss = total_loss_mb
+            # total_loss_mb.backward()
+            total_loss_mb.backward(retain_graph=True)
+            # total_loss += total_loss_mb # this is causing it to break
+            # total_loss = total_loss_mb
 
         # losses = [{k:v} for k,v in losses_accumulator.items()]
         # losses_raw = [{k:v} for k,v in losses_raw_accumulator.items()]