jonstreeter
0256779967
- Add extract_trigger_words() method supporting Civitai, Kohya, AI Toolkit, SimpleTuner, OneTrainer - Add auto_trigger toggle to inject trigger words at LoRA tag positions - Add trigger_words output socket with all extracted triggers - Enhanced lora_info output to show trigger words per LoRA - Fix duplicate LoRA tags causing rendering issues (v1.4.1) - Update README with new examples and documentation - Update CHANGELOG with v1.4.1 and v1.5.0 entries
93 lines
2.9 KiB
Python
93 lines
2.9 KiB
Python
"""
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Test script to verify LoRA tag parsing logic
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"""
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def parse_lora_tag(tag_content, default_weight):
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"""
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Simplified version of parse_lora_tag for testing
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"""
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parts = [p.strip() for p in tag_content.split(':')]
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if len(parts) < 1 or not parts[0]:
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return None
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result = {
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'name': parts[0],
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'high_noise': default_weight,
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'low_noise': default_weight,
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'clip': default_weight
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}
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try:
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if len(parts) == 2:
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# Single weight: affects model only, clip uses default
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weight = float(parts[1])
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result['high_noise'] = weight
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result['low_noise'] = weight
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result['clip'] = default_weight # Use default, not the specified weight
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elif len(parts) == 3:
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# Dual noise: different high/low for model, clip uses default
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result['high_noise'] = float(parts[1])
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result['low_noise'] = float(parts[2])
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result['clip'] = default_weight # Use default
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elif len(parts) >= 4:
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# Full control: high, low, clip all separate
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result['high_noise'] = float(parts[1])
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result['low_noise'] = float(parts[2])
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result['clip'] = float(parts[3])
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except (ValueError, IndexError) as e:
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print(f"Error parsing tag '{tag_content}': {e}")
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return None
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return result
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def test_parsing():
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"""Test the parse_lora_tag function with different formats"""
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default_weight = 1.0
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test_cases = [
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# (tag_content, expected_result, description)
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("name:0.8", {"name": "name", "high_noise": 0.8, "low_noise": 0.8, "clip": 1.0},
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"Single weight - should affect model only, clip uses default"),
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("name:1.2:0.6", {"name": "name", "high_noise": 1.2, "low_noise": 0.6, "clip": 1.0},
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"Dual weight - should affect high/low model, clip uses default"),
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("name:1.2:0.6:0.9", {"name": "name", "high_noise": 1.2, "low_noise": 0.6, "clip": 0.9},
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"Triple weight - full control over model and clip"),
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("name", {"name": "name", "high_noise": 1.0, "low_noise": 1.0, "clip": 1.0},
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"Name only - all weights use default"),
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]
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print("Testing LoRA tag parsing:\n")
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all_passed = True
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for tag_content, expected, description in test_cases:
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result = parse_lora_tag(tag_content, default_weight)
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if result == expected:
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print(f"PASS: {description}")
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print(f" Tag: <lora:{tag_content}>")
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print(f" Result: {result}\n")
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else:
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print(f"FAIL: {description}")
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print(f" Tag: <lora:{tag_content}>")
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print(f" Expected: {expected}")
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print(f" Got: {result}\n")
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all_passed = False
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if all_passed:
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print("\nAll tests passed!")
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else:
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print("\nSome tests failed!")
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return all_passed
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if __name__ == "__main__":
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test_parsing()
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