Qwen Image 2512 LoRA Trainer

Qwen Image 2512 LoRA Trainer

Playground

Qwen-Image-2512 LoRA Trainer lets you train custom LoRA models 10x faster with style, character, and object training. From concept to model in minutes, not hours—upload a ZIP file containing images to start. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.

Features

Qwen Image 2512 LoRA Trainer

Qwen Image 2512 LoRA Trainer is a high-performance custom model training service for the Qwen Image 2512 text-to-image generation model. It allows you to train lightweight LoRA (Low-Rank Adaptation) adapters for personalized styles, characters, and concepts — with exceptional bilingual text rendering preserved throughout.

Training Architecture

The trainer is designed around Qwen Image’s 20B MMDiT architecture and produces specialized LoRA adapters optimized for the model’s unique capabilities:

• Base LoRA adapter Trains on the core Qwen Image representation to capture your target style, character, or object, while keeping the base model frozen and stable.

• Text-rendering preservation The training process is optimized to maintain Qwen Image’s superior Chinese and English text rendering capabilities even after fine-tuning.

• Bilingual prompt compatibility Trained LoRAs work seamlessly with both Chinese and English prompts, preserving the model’s multilingual strengths.

This architecture ensures that your LoRA:

• Remains compact and easy to share
• Is plug-and-play with supported UIs and pipelines (e.g. ComfyUI, AI Toolkit)
• Preserves the text rendering and bilingual capabilities of Qwen Image 2512

Training Process

1. Data Upload Prepare and upload a ZIP file containing your training images. Include 10-20 high-quality images for best results.

2. Configure Trigger Word Set a unique trigger word (e.g., “p3r5on”) that will activate your trained style or character in prompts.

3. Adjust Training Parameters

   • steps — Total training iterations (default: 1000)
   • learning_rate — Training speed (default: 0.0004)
   • lora_rank — Adapter capacity (default: 16)

4. LoRA Training The system runs a tailored LoRA optimization loop:

   • Freezes the base model weights
   • Trains only the low-rank adapter layers
   • Applies Qwen-optimized settings for best results

5. Model Export After training completes, you receive:

   • A LoRA adapter file (.safetensors) compatible with Qwen Image 2512
   • Ready to use with Qwen Image 2512 LoRA

Parameters

Pricing

Billing Rules

• Base price: $1 per 1,000 steps
• Total cost = $1 × (steps / 1000)
• Billed proportionally to the total number of steps in your job

Best Use Cases

• Character Consistency — Train on character images to maintain identity across generations.
• Brand Styles — Create custom visual styles for consistent marketing materials.
• Art Styles — Capture specific artistic aesthetics for creative projects.
• Product Visualization — Train on product photos for consistent e-commerce imagery.

Pro Tips

• Use 10-20 high-quality, diverse images of your subject for best results.
• Choose a unique trigger word that won’t conflict with common words.
• Start with default settings, then adjust if needed.
• Higher lora_rank captures more detail but increases file size.
• Lower learning_rate is more stable but requires more steps.

Notes

• Higher parameter values (steps, lora_rank) will increase training time.
• Training time scales with the number of images and total steps configured.
• For faster iterations, start with lower settings and increase gradually.

Try More Trainers

• Z-Image LoRA Trainer — High-performance LoRA trainer for Z-Image models with Turbo-compatible optimization.

• Wan 2.2 Image LoRA Trainer — LoRA trainer for the Wan 2.2 image model, ideal for custom styles that integrate into the Wan video/image ecosystem.

• Flux Dev LoRA Trainer — LoRA trainer tailored for the Flux Dev model, focusing on high-fidelity creative visuals.

# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/qwen-image-2512-lora-trainer" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "trigger_word": "p3r5on",
    "steps": 1000,
    "learning_rate": 0.0004,
    "lora_rank": 16
}'

# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"