Added a way to add a t2i adapter guided slider training for more consitant images

toolkit/config_modules.py

@@ -186,18 +186,23 @@ class SliderConfig:
         self.prompt_file: str = kwargs.get('prompt_file', None)
         self.prompt_tensors: str = kwargs.get('prompt_tensors', None)
         self.batch_full_slide: bool = kwargs.get('batch_full_slide', True)
+        self.use_adapter: bool = kwargs.get('use_adapter', None) # depth
+        self.adapter_img_dir = kwargs.get('adapter_img_dir', None)
+        self.high_ram = kwargs.get('high_ram', False)
 
         # expand targets if shuffling
         from toolkit.prompt_utils import get_slider_target_permutations
         self.targets: List[SliderTargetConfig] = []
         targets = [SliderTargetConfig(**target) for target in targets]
         # do permutations if shuffle is true
+        print(f"Building slider targets")
         for target in targets:
             if target.shuffle:
-                target_permutations = get_slider_target_permutations(target)
+                target_permutations = get_slider_target_permutations(target, max_permutations=100)
                 self.targets = self.targets + target_permutations
             else:
                 self.targets.append(target)
+        print(f"Built {len(self.targets)} slider targets (with permutations)")
 
 
 class DatasetConfig:

toolkit/prompt_utils.py

@@ -105,6 +105,18 @@ class EncodedPromptPair:
         self.both_targets = self.both_targets.to(*args, **kwargs)
         return self
 
+    def detach(self):
+        self.target_class = self.target_class.detach()
+        self.target_class_with_neutral = self.target_class_with_neutral.detach()
+        self.positive_target = self.positive_target.detach()
+        self.positive_target_with_neutral = self.positive_target_with_neutral.detach()
+        self.negative_target = self.negative_target.detach()
+        self.negative_target_with_neutral = self.negative_target_with_neutral.detach()
+        self.neutral = self.neutral.detach()
+        self.empty_prompt = self.empty_prompt.detach()
+        self.both_targets = self.both_targets.detach()
+        return self
+
 
 def concat_prompt_embeds(prompt_embeds: list[PromptEmbeds]):
     text_embeds = torch.cat([p.text_embeds for p in prompt_embeds], dim=0)
@@ -267,15 +279,17 @@ def split_anchors(concatenated: EncodedAnchor, num_anchors: int = 4) -> List[Enc
     return anchors
 
 
-def get_permutations(s):
+def get_permutations(s, max_permutations=8):
     # Split the string by comma
     phrases = [phrase.strip() for phrase in s.split(',')]
 
     # remove empty strings
     phrases = [phrase for phrase in phrases if len(phrase) > 0]
+    # shuffle the list
+    random.shuffle(phrases)
 
     # Get all permutations
-    permutations = list(itertools.permutations(phrases))
+    permutations = list([p for p in itertools.islice(itertools.permutations(phrases), max_permutations)])
 
     # Convert the tuples back to comma separated strings
     return [', '.join(permutation) for permutation in permutations]
@@ -283,8 +297,8 @@ def get_permutations(s):
 
 def get_slider_target_permutations(target: 'SliderTargetConfig', max_permutations=8) -> List['SliderTargetConfig']:
     from toolkit.config_modules import SliderTargetConfig
-    pos_permutations = get_permutations(target.positive)
-    neg_permutations = get_permutations(target.negative)
+    pos_permutations = get_permutations(target.positive, max_permutations=max_permutations)
+    neg_permutations = get_permutations(target.negative, max_permutations=max_permutations)
 
     permutations = []
     for pos, neg in itertools.product(pos_permutations, neg_permutations):