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

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This commit is contained in:
Jaret Burkett
2023-09-28 14:08:56 -06:00
parent c5d49ba661
commit 8509da60cb
3 changed files with 189 additions and 34 deletions
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7
toolkit/config_modules.py
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@@ -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: