Added shuffeling to prompts

config/examples/train_slider.example.yml

@@ -184,6 +184,10 @@ config:
             # if you are doing more than one target it may be good to set less important ones
             # to a lower number like 0.1 so they don't outweigh the primary target
             weight: 1.0
+            # shuffle the prompts split by the comma. We will run every combination randomly
+            # this will make the LoRA more robust. You probably want this on unless prompt order
+            # is important for some reason
+            shuffle: true
 
 
         # anchors are prompts that we will try to hold on to while training the slider

jobs/process/TrainSliderProcess.py

@@ -86,6 +86,9 @@ class TrainSliderProcess(BaseSDTrainProcess):
             # remove duplicates
             prompts_to_cache = list(dict.fromkeys(prompts_to_cache))
 
+            # trim to max steps if max steps is lower than prompt count
+            prompts_to_cache = prompts_to_cache[:self.train_config.steps]
+
             # encode them
             cache = encode_prompts_to_cache(
                 prompt_list=prompts_to_cache,

toolkit/config_modules.py

@@ -4,6 +4,7 @@ from typing import List, Optional
 import random
 
 
+
 class SaveConfig:
     def __init__(self, **kwargs):
         self.save_every: int = kwargs.get('save_every', 1000)
@@ -93,6 +94,7 @@ class SliderTargetConfig:
         self.negative: str = kwargs.get('negative', '')
         self.multiplier: float = kwargs.get('multiplier', 1.0)
         self.weight: float = kwargs.get('weight', 1.0)
+        self.shuffle: bool = kwargs.get('shuffle', False)
 
 
 class SliderConfigAnchors:
@@ -105,8 +107,6 @@ class SliderConfigAnchors:
 class SliderConfig:
     def __init__(self, **kwargs):
         targets = kwargs.get('targets', [])
-        targets = [SliderTargetConfig(**target) for target in targets]
-        self.targets: List[SliderTargetConfig] = targets
         anchors = kwargs.get('anchors', [])
         anchors = [SliderConfigAnchors(**anchor) for anchor in anchors]
         self.anchors: List[SliderConfigAnchors] = anchors
@@ -115,6 +115,18 @@ class SliderConfig:
         self.prompt_tensors: str = kwargs.get('prompt_tensors', None)
         self.batch_full_slide: bool = kwargs.get('batch_full_slide', True)
 
+        # 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
+        for target in targets:
+            if target.shuffle:
+                target_permutations = get_slider_target_permutations(target)
+                self.targets = self.targets + target_permutations
+            else:
+                self.targets.append(target)
+
 
 class GenerateImageConfig:
     def __init__(

toolkit/prompt_utils.py

@@ -7,6 +7,10 @@ from tqdm import tqdm
 
 from toolkit.stable_diffusion_model import PromptEmbeds
 from toolkit.train_tools import get_torch_dtype
+import itertools
+
+if TYPE_CHECKING:
+    from toolkit.config_modules import SliderTargetConfig
 
 
 class ACTION_TYPES_SLIDER:
@@ -226,6 +230,40 @@ def split_anchors(concatenated: EncodedAnchor, num_anchors: int = 4) -> List[Enc
     return anchors
 
 
+def get_permutations(s):
+    # 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]
+
+    # Get all permutations
+    permutations = list(itertools.permutations(phrases))
+
+    # Convert the tuples back to comma separated strings
+    return [', '.join(permutation) for permutation in permutations]
+
+
+def get_slider_target_permutations(target: 'SliderTargetConfig') -> List['SliderTargetConfig']:
+    from toolkit.config_modules import SliderTargetConfig
+    pos_permutations = get_permutations(target.positive)
+    neg_permutations = get_permutations(target.negative)
+
+    permutations = []
+    for pos, neg in itertools.product(pos_permutations, neg_permutations):
+        permutations.append(
+            SliderTargetConfig(
+                target_class=target.target_class,
+                positive=pos,
+                negative=neg,
+                multiplier=target.multiplier,
+                weight=target.weight
+            )
+        )
+
+    return permutations
+
+
 if TYPE_CHECKING:
     from toolkit.stable_diffusion_model import StableDiffusion
 
@@ -291,10 +329,6 @@ def encode_prompts_to_cache(
     return cache
 
 
-if TYPE_CHECKING:
-    from toolkit.config_modules import SliderTargetConfig
-
-
 @torch.no_grad()
 def build_prompt_pair_batch_from_cache(
         cache: PromptEmbedsCache,