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"""
Dataset Validation Script for Flux Identity LoRA Training
Features:
- Scan all images in dataset directory
- Detect corrupt/unreadable files using PIL
- Report image resolutions
- Find duplicate files using perceptual hashing (imagehash)
- Verify matching caption files (.txt) for each image
- Generate validation report
"""
import os
import sys
import argparse
from pathlib import Path
from collections import defaultdict
from PIL import Image
import imagehash
from rich.console import Console
from rich.table import Table
from rich.progress import Progress, SpinnerColumn, TextColumn
console = Console()
# Supported image formats
IMAGE_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.webp', '.bmp', '.tiff', '.tif'}
def scan_images(directory: Path) -> list:
"""Scan directory for image files."""
images = []
for ext in IMAGE_EXTENSIONS:
images.extend(directory.glob(f'*{ext}'))
images.extend(directory.glob(f'*{ext.upper()}'))
return sorted(set(images))
def validate_image(image_path: Path) -> dict:
"""Validate a single image file."""
result = {
'path': image_path,
'valid': False,
'width': 0,
'height': 0,
'format': None,
'mode': None,
'error': None,
'phash': None,
'has_caption': False,
'caption_path': None
}
try:
with Image.open(image_path) as img:
img.verify()
# Reopen for actual data (verify() can leave file in bad state)
with Image.open(image_path) as img:
result['valid'] = True
result['width'] = img.width
result['height'] = img.height
result['format'] = img.format
result['mode'] = img.mode
# Calculate perceptual hash for duplicate detection
result['phash'] = str(imagehash.phash(img))
except Exception as e:
result['error'] = str(e)
# Check for caption file
caption_path = image_path.with_suffix('.txt')
if caption_path.exists():
result['has_caption'] = True
result['caption_path'] = caption_path
return result
def find_duplicates(results: list) -> dict:
"""Find duplicate images using perceptual hashing."""
hash_groups = defaultdict(list)
for r in results:
if r['phash']:
hash_groups[r['phash']].append(r['path'])
# Return only groups with duplicates
return {h: paths for h, paths in hash_groups.items() if len(paths) > 1}
def generate_report(results: list, duplicates: dict, output_file: Path = None):
"""Generate validation report."""
valid_count = sum(1 for r in results if r['valid'])
invalid_count = sum(1 for r in results if not r['valid'])
with_caption = sum(1 for r in results if r['has_caption'])
without_caption = sum(1 for r in results if r['valid'] and not r['has_caption'])
console.print("\n[bold blue]═══ Dataset Validation Report ═══[/bold blue]\n")
# Summary table
summary = Table(title="Summary", show_header=False)
summary.add_column("Metric", style="cyan")
summary.add_column("Value", style="green")
summary.add_row("Total Images Scanned", str(len(results)))
summary.add_row("Valid Images", str(valid_count))
summary.add_row("Invalid/Corrupt Images", str(invalid_count))
summary.add_row("Images with Captions", str(with_caption))
summary.add_row("Images Missing Captions", str(without_caption))
summary.add_row("Duplicate Groups Found", str(len(duplicates)))
console.print(summary)
# Resolution distribution
resolutions = defaultdict(int)
for r in results:
if r['valid']:
res = f"{r['width']}x{r['height']}"
resolutions[res] += 1
if resolutions:
console.print("\n[bold]Resolution Distribution:[/bold]")
res_table = Table()
res_table.add_column("Resolution", style="cyan")
res_table.add_column("Count", style="green")
res_table.add_column("Percentage", style="yellow")
for res, count in sorted(resolutions.items(), key=lambda x: -x[1])[:10]:
pct = (count / valid_count) * 100 if valid_count > 0 else 0
res_table.add_row(res, str(count), f"{pct:.1f}%")
console.print(res_table)
# Invalid files
invalid_files = [r for r in results if not r['valid']]
if invalid_files:
console.print("\n[bold red]Invalid/Corrupt Files:[/bold red]")
for r in invalid_files:
console.print(f" • {r['path'].name}: {r['error']}")
# Missing captions
missing_captions = [r for r in results if r['valid'] and not r['has_caption']]
if missing_captions:
console.print("\n[bold yellow]Images Missing Captions:[/bold yellow]")
for r in missing_captions[:20]: # Show first 20
console.print(f" • {r['path'].name}")
if len(missing_captions) > 20:
console.print(f" ... and {len(missing_captions) - 20} more")
# Duplicates
if duplicates:
console.print("\n[bold yellow]Potential Duplicates (by perceptual hash):[/bold yellow]")
for hash_val, paths in list(duplicates.items())[:10]:
console.print(f"\n Hash: {hash_val}")
for p in paths:
console.print(f" • {p.name}")
if len(duplicates) > 10:
console.print(f"\n ... and {len(duplicates) - 10} more duplicate groups")
# Recommendations
console.print("\n[bold blue]Recommendations:[/bold blue]")
if invalid_count > 0:
console.print(f" ⚠ Remove or fix {invalid_count} corrupt image(s)")
if without_caption > 0:
console.print(f" ⚠ Add captions for {without_caption} image(s)")
if duplicates:
total_dups = sum(len(paths) - 1 for paths in duplicates.values())
console.print(f" ⚠ Review {total_dups} potential duplicate image(s)")
# Check for resolution issues
small_images = [r for r in results if r['valid'] and (r['width'] < 512 or r['height'] < 512)]
if small_images:
console.print(f" ⚠ {len(small_images)} image(s) are smaller than 512px (may affect quality)")
if valid_count > 0 and without_caption == 0 and invalid_count == 0 and not duplicates:
console.print(" ✓ Dataset looks good! Ready for training.")
# Save report to file if requested
if output_file:
with open(output_file, 'w') as f:
f.write("Dataset Validation Report\n")
f.write("=" * 50 + "\n\n")
f.write(f"Total Images: {len(results)}\n")
f.write(f"Valid: {valid_count}\n")
f.write(f"Invalid: {invalid_count}\n")
f.write(f"With Captions: {with_caption}\n")
f.write(f"Missing Captions: {without_caption}\n")
f.write(f"Duplicate Groups: {len(duplicates)}\n")
if invalid_files:
f.write("\nInvalid Files:\n")
for r in invalid_files:
f.write(f" - {r['path']}: {r['error']}\n")
if missing_captions:
f.write("\nMissing Captions:\n")
for r in missing_captions:
f.write(f" - {r['path']}\n")
console.print(f"\n[dim]Report saved to: {output_file}[/dim]")
def main():
parser = argparse.ArgumentParser(description="Validate dataset for Flux LoRA training")
parser.add_argument(
"directory",
nargs="?",
default="/workspace/flux-project/datasets/identity/images",
help="Directory containing training images"
)
parser.add_argument(
"-o", "--output",
help="Save report to file"
)
parser.add_argument(
"--no-duplicates",
action="store_true",
help="Skip duplicate detection (faster)"
)
args = parser.parse_args()
dataset_dir = Path(args.directory)
if not dataset_dir.exists():
console.print(f"[red]Error: Directory not found: {dataset_dir}[/red]")
sys.exit(1)
console.print(f"[bold]Scanning directory: {dataset_dir}[/bold]")
# Find all images
images = scan_images(dataset_dir)
if not images:
console.print("[yellow]No images found in directory.[/yellow]")
sys.exit(0)
console.print(f"Found {len(images)} image file(s)\n")
# Validate each image
results = []
with Progress(
SpinnerColumn(),
TextColumn("[progress.description]{task.description}"),
console=console
) as progress:
task = progress.add_task("Validating images...", total=len(images))
for img_path in images:
result = validate_image(img_path)
results.append(result)
progress.advance(task)
# Find duplicates
duplicates = {}
if not args.no_duplicates:
with Progress(
SpinnerColumn(),
TextColumn("[progress.description]{task.description}"),
console=console
) as progress:
task = progress.add_task("Checking for duplicates...", total=1)
duplicates = find_duplicates(results)
progress.advance(task)
# Generate report
output_file = Path(args.output) if args.output else None
generate_report(results, duplicates, output_file)
if __name__ == "__main__":
main()
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