LoRA Tag

A machine learning engineer prepares a dataset for training a character LoRA and faces 500 images that need detailed captions

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A machine learning engineer prepares a dataset for training a character LoRA and faces 500 images that need detailed captions. She could write them manually, spending 5-10 minutes per image describing poses, clothing, facial expressions, and backgrounds. Or she batches all 500 into LoRA Tag, selects "Character" as the LoRA type, picks her quality level, and gets organized caption files paired with each image in minutes instead of days.

The system generates descriptions using OpenAI Vision, identifying specific elements like character features, artistic styles, environmental details, and poses. A concept artist training a style LoRA uploads 100 images at once and gets captions that focus on art techniques, color palettes, and compositional elements because he selected "Style" as his LoRA type. Each prompt optimizes for what matters most to that training approach. Object and Concept LoRA types work similarly, tailoring descriptions to their training needs.

Quality levels range from quick drafts to exhaustive descriptions. The free plan handles standard quality, good enough for basic training datasets. Professional tier provides detailed quality with more sophisticated descriptions. Business and Enterprise tiers access all quality levels including the most thorough analysis. A research team testing different caption depths can compare how standard versus exhaustive descriptions affect model performance. They edit captions before downloading if something needs tweaking, or regenerate individual captions that missed important details. Global tags let them add consistent metadata across entire batches.

The batch processing matters most. Upload 100 images simultaneously with a 10MB file size limit per image on paid plans. The system pairs each caption as a .txt file with its corresponding image, maintaining the organizational structure training pipelines expect. No manual renaming or file matching. Priority processing on Professional and higher tiers means faster turnaround when deadlines press. Business plan adds bulk processing capabilities and custom tags for specialized workflows. Enterprise includes white glove support and custom integrations for teams running large-scale training operations.

Where it struggles: monthly image limits create hard stops. Free accounts get 10 images monthly. Starter handles 600, Professional does 1,200, Business covers 3,000, and Enterprise reaches 10,000. A solo creator building multiple LoRAs per month might burn through 1,200 images quickly. Top-ups exist but add costs. Starter top-ups run $5 for 600 images, Professional charges $8 for 1,200, Business needs $20 for 3,000 more. Annual plans save 20% but require upfront commitment.

The quality restrictions bite too with free users can't access detailed or exhaustive modes. Professional subscribers can't reach the most thorough analysis levels. Only Business and Enterprise provide everything. A hobbyist testing whether exhaustive captions improve results must upgrade immediately or stay limited.

Free tier starts with 10 images monthly at standard quality. Testing the service works fine. Starter costs $6.99 monthly for 600 images. Professional runs $10.99 for 1,200 images and priority processing. Business hits $24.99 for 3,000 images with custom tags. Enterprise reaches $69.99 for 10,000 images and dedicated support.

Skip this if you caption fewer than 10 images monthly or prefer manual control over every description detail. Teams not training LoRAs won't benefit from the specialized prompt optimization. Anyone needing unlimited processing should look elsewhere. But for LoRA creators facing hundreds of images and tight timelines, it eliminates the most tedious part of dataset preparation.