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
128 lines
3.7 KiB
Python
128 lines
3.7 KiB
Python
"""
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Simple test script for the LoRA Tag Power Loader parser
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Run this to verify tag parsing logic works correctly
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"""
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import re
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def parse_lora_tag(tag_content, default_weight=1.0):
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"""
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Parse LoRA tag with hybrid format.
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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: use everywhere
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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'] = weight
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elif len(parts) == 3:
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# Dual noise: different high/low, clip follows high
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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'] = result['high_noise']
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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_tag_parsing():
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"""Test various tag formats"""
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test_cases = [
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# (input, description)
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("style_lora:0.8", "Single weight"),
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("detail:1.2:0.3", "Dual noise"),
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("character:1.0:0.8:0.6", "Full control"),
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("background:0.7", "Name only with weight"),
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("lighting:1.5:0.5", "High/low different"),
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("texture", "Name only (should use defaults)"),
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("complex_name:0.9:0.9:0.7", "Same high/low, different clip"),
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]
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print("=" * 70)
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print("LoRA Tag Parser Test")
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print("=" * 70)
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for tag, description in test_cases:
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result = parse_lora_tag(tag)
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print(f"\nTest: {description}")
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print(f"Input: <lora:{tag}>")
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if result:
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print(f"Result:")
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print(f" Name: {result['name']}")
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print(f" High Noise: {result['high_noise']:.2f}")
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print(f" Low Noise: {result['low_noise']:.2f}")
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print(f" Clip: {result['clip']:.2f}")
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else:
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print("Result: FAILED TO PARSE")
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print("\n" + "=" * 70)
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def test_text_extraction():
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"""Test extracting tags from full text"""
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test_texts = [
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"A beautiful portrait <lora:style:0.8> with lighting",
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"Epic <lora:cinematic:1.2:0.4> landscape with <lora:detail:0.9:0.5>",
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"Simple text without any loras",
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"<lora:test1:0.5> <lora:test2:1.0:0.3> <lora:test3:0.8:0.6:0.5>",
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]
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tag_pattern = r"<lora:([^>]+)>"
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print("\n" + "=" * 70)
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print("Text Extraction Test")
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print("=" * 70)
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for text in test_texts:
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print(f"\nInput text: {text}")
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matches = re.finditer(tag_pattern, text)
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lora_tags = [match.group(1) for match in matches]
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if lora_tags:
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print(f"Found {len(lora_tags)} tag(s):")
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for i, tag in enumerate(lora_tags, 1):
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parsed = parse_lora_tag(tag)
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if parsed:
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print(f" {i}. {parsed['name']} - H:{parsed['high_noise']:.2f} L:{parsed['low_noise']:.2f} C:{parsed['clip']:.2f}")
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else:
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print("No tags found")
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# Show cleaned text
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cleaned = re.sub(tag_pattern, "", text)
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cleaned = re.sub(r'\s+', ' ', cleaned).strip()
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print(f"Cleaned text: {cleaned}")
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print("\n" + "=" * 70)
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if __name__ == "__main__":
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test_tag_parsing()
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test_text_extraction()
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print("\n✓ All tests completed!")
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