put a file loss_scale.txt containing a float in a training folder to apply loss scale (eg -1 for negative examples)

data/dataset.py

@@ -54,6 +54,7 @@ class ImageConfig:
     cond_dropout: float = None
     flip_p: float = None
     shuffle_tags: bool = False
+    loss_scale: float = None
 
     def merge(self, other):
         if other is None:
@@ -68,7 +69,8 @@ class ImageConfig:
             cond_dropout=overlay(other.cond_dropout, self.cond_dropout),
             flip_p=overlay(other.flip_p, self.flip_p),
             shuffle_tags=overlay(other.shuffle_tags, self.shuffle_tags),
-            batch_id=overlay(other.batch_id, self.batch_id)
+            batch_id=overlay(other.batch_id, self.batch_id),
+            loss_scale=overlay(other.loss_scale, self.loss_scale)
         )
 
     @classmethod
@@ -83,7 +85,8 @@ class ImageConfig:
             cond_dropout=data.get("cond_dropout"),
             flip_p=data.get("flip_p"),
             shuffle_tags=data.get("shuffle_tags"),
-            batch_id=data.get("batch_id")
+            batch_id=data.get("batch_id"),
+            loss_scale=data.get("loss_scale")
             )
 
         # Alternatively parse from dedicated `caption` attribute
@@ -170,6 +173,8 @@ class Dataset:
             cfgs.append(ImageConfig.from_file(fileset['local.yml']))
         if 'batch_id.txt' in fileset:
             cfgs.append(ImageConfig(batch_id=read_text(fileset['batch_id.txt'])))
+        if 'loss_scale.txt' in fileset:
+            cfgs.append(ImageConfig(loss_scale=read_float(fileset['loss_scale.txt'])))
         
         result = ImageConfig.fold(cfgs)
         if 'shuffle_tags.txt' in fileset:
@@ -264,7 +269,8 @@ class Dataset:
                     multiplier=config.multiply or 1.0,
                     cond_dropout=config.cond_dropout,
                     shuffle_tags=config.shuffle_tags,
-                    batch_id=config.batch_id
+                    batch_id=config.batch_id,
+                    loss_scale=config.loss_scale
                 )
                 items.append(item)
             except Exception as e:

data/every_dream.py

@@ -118,6 +118,7 @@ class EveryDreamBatch(Dataset):
         example["tokens"] = torch.tensor(example["tokens"])
 
         example["runt_size"] = train_item["runt_size"]
+        example["loss_scale"] = train_item["loss_scale"]
 
         return example
 
@@ -134,6 +135,7 @@ class EveryDreamBatch(Dataset):
             example["cond_dropout"] = image_train_tmp.cond_dropout
         example["runt_size"] = image_train_tmp.runt_size
         example["shuffle_tags"] = image_train_tmp.shuffle_tags
+        example["loss_scale"] = image_train_tmp.loss_scale
 
         return example
 
@@ -214,11 +216,14 @@ def collate_fn(batch):
     images = torch.stack(images)
     images = images.to(memory_format=torch.contiguous_format).float()
 
+    loss_scale = torch.tensor([example.get("loss_scale", 1) for example in batch])
+
     ret = {
         "tokens": torch.stack(tuple(tokens)),
         "image": images,
         "captions": captions,
         "runt_size": runt_size,
+        "loss_scale": loss_scale
     }
     del batch
     return ret

data/image_train_item.py

@@ -141,7 +141,8 @@ class ImageTrainItem:
                  multiplier: float=1.0,
                  cond_dropout=None,
                  shuffle_tags=False,
-                 batch_id: str=None
+                 batch_id: str=None,
+                 loss_scale: float=None
                  ):
         self.caption = caption
         self.aspects = aspects
@@ -153,6 +154,7 @@ class ImageTrainItem:
         self.cond_dropout = cond_dropout
         self.shuffle_tags = shuffle_tags
         self.batch_id = batch_id or DEFAULT_BATCH_ID
+        self.loss_scale = loss_scale or 1
         self.target_wh = None
 
         self.image_size = None

train.py

@@ -1138,8 +1138,12 @@ def main(args):
                 del target, model_pred
 
                 if batch["runt_size"] > 0:
-                    loss_scale = (batch["runt_size"] / args.batch_size)**1.5 # further discount runts by **1.5
-                    loss = loss * loss_scale
+                    runt_loss_scale = (batch["runt_size"] / args.batch_size)**1.5 # further discount runts by **1.5
+                    loss = loss * runt_loss_scale
+
+                if "loss_scale" in batch.keys():
+                    # Apply the mask to the loss
+                    loss = loss * batch["loss_scale"]
 
                 ed_optimizer.step(loss, step, global_step)