trainingTools.h1

Upload 10-50 high-quality images (for LoRA) or 100-1000+ images (for full fine-tuning). Our automated pipeline handles resizing, cropping, and captioning. Smart deduplication removes near-identical images, and quality scoring flags blurry or low-resolution samples before training begins. Supports JPEG, PNG, and WebP formats.

Choose your base model (FLUX.1, SDXL, or SD 1.5), training method (LoRA, DreamBooth, or Textual Inversion), and hyperparameters. Presets for common use cases (character, style, object) auto-configure learning rate, batch size, and training steps. Advanced users can fine-tune every parameter manually.

Training runs on dedicated GPU instances (A100 / H100). LoRA training typically completes in 15-45 minutes; full DreamBooth runs take 1-3 hours. Real-time progress monitoring shows loss curves and sample generations at each checkpoint. Automatic early stopping prevents overfitting. Build on your trained models with LoRA Generation.

Test your trained model instantly in the playground with various prompts and settings. Compare outputs side-by-side with the base model to verify quality. One-click deployment makes your model available via API and the WaveSpeed playground. Version control lets you roll back to any previous checkpoint.