allow flipping for point of interesting autocropping. allow num repeats. Fixed some bugs with new free u

jobs/process/BaseSDTrainProcess.py

@@ -928,6 +928,8 @@ class BaseSDTrainProcess(BaseTrainProcess):
         flush()
         # self.step_num = 0
         for step in range(self.step_num, self.train_config.steps):
+            if self.train_config.free_u:
+                self.sd.pipeline.enable_freeu(s1=0.9, s2=0.2, b1=1.1, b2=1.2)
             self.progress_bar.unpause()
             with torch.no_grad():
                 # if is even step and we have a reg dataset, use that
@@ -1000,6 +1002,8 @@ class BaseSDTrainProcess(BaseTrainProcess):
                     if is_sample_step:
                         self.progress_bar.pause()
                         # print above the progress bar
+                        if self.train_config.free_u:
+                            self.sd.pipeline.disable_freeu()
                         self.sample(self.step_num)
                         self.progress_bar.unpause()
 
@@ -1047,6 +1051,8 @@ class BaseSDTrainProcess(BaseTrainProcess):
                 #     flush()
 
         self.progress_bar.close()
+        if self.train_config.free_u:
+            self.sd.pipeline.disable_freeu()
         self.sample(self.step_num + 1)
         print("")
         self.save()

toolkit/config_modules.py

@@ -120,6 +120,7 @@ class TrainConfig:
         self.merge_network_on_save = kwargs.get('merge_network_on_save', False)
         self.max_grad_norm = kwargs.get('max_grad_norm', 1.0)
         self.start_step = kwargs.get('start_step', None)
+        self.free_u = kwargs.get('free_u', False)
 
 
 class ModelConfig:
@@ -239,7 +240,7 @@ class DatasetConfig:
         self.mask_path: str = kwargs.get('mask_path', None)  # focus mask (black and white. White has higher loss than black)
         self.mask_min_value: float = kwargs.get('mask_min_value', 0.01)  # min value for . 0 - 1
         self.poi: Union[str, None] = kwargs.get('poi', None) # if one is set and in json data, will be used as auto crop scale point of interes
-
+        self.num_repeats: int = kwargs.get('num_repeats', 1)  # number of times to repeat dataset
         # cache latents will store them in memory
         self.cache_latents: bool = kwargs.get('cache_latents', False)
         # cache latents to disk will store them on disk. If both are true, it will save to disk, but keep in memory

toolkit/data_loader.py

@@ -377,6 +377,10 @@ class AiToolkitDataset(LatentCachingMixin, BucketsMixin, CaptionMixin, Dataset):
                 # keys are file paths
                 file_list = list(self.caption_dict.keys())
 
+        if self.dataset_config.num_repeats > 1:
+            # repeat the list
+            file_list = file_list * self.dataset_config.num_repeats
+
         # this might take a while
         print(f"  -  Preprocessing image dimensions")
         bad_count = 0

toolkit/dataloader_mixins.py

@@ -156,8 +156,9 @@ class BucketsMixin:
                 # Use the maximum of the scale factors to ensure both dimensions are scaled above the bucket resolution
                 max_scale_factor = max(width_scale_factor, height_scale_factor)
 
-                file_item.scale_to_width = int(width * max_scale_factor)
-                file_item.scale_to_height = int(height * max_scale_factor)
+                # round up
+                file_item.scale_to_width = int(math.ceil(width * max_scale_factor))
+                file_item.scale_to_height = int(math.ceil(height * max_scale_factor))
 
                 file_item.crop_height = bucket_resolution["height"]
                 file_item.crop_width = bucket_resolution["width"]
@@ -294,7 +295,7 @@ class ImageProcessingDTOMixin:
                 self.load_mask_image()
             return
         try:
-            img = Image.open(self.mask_path)
+            img = Image.open(self.path)
             img = exif_transpose(img)
         except Exception as e:
             print(f"Error: {e}")
@@ -583,6 +584,14 @@ class PoiFileItemDTOMixin:
             self.poi_width = int(poi['width'])
             self.poi_height = int(poi['height'])
 
+            # handle flipping
+            if kwargs.get('flip_x', False):
+                # flip the poi
+                self.poi_x = self.width - self.poi_x - self.poi_width
+            if kwargs.get('flip_y', False):
+                # flip the poi
+                self.poi_y = self.height - self.poi_y - self.poi_height
+
     def setup_poi_bucket(self: 'FileItemDTO'):
         # we are using poi, so we need to calculate the bucket based on the poi
 

toolkit/train_tools.py

@@ -5,6 +5,9 @@ import os
 import time
 from typing import TYPE_CHECKING, Union
 import sys
+
+from torch.cuda.amp import GradScaler
+
 from toolkit.paths import SD_SCRIPTS_ROOT
 
 sys.path.append(SD_SCRIPTS_ROOT)