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Qwen-Image LoRA Trainer: Training

Train custom Qwen-Image LoRA adapters with Qwen-Image LoRA Trainer. Fast zip uploads, auto-tuned hyperparameters, and pay-per-step pricing on Muapi. Train custom Qwen-Image LoRA models 10x faster. Fine-tune styles, characters, or object concepts from a ZIP dataset with auto-tuned learning rates and steps.

Interactive model controls

Train custom Qwen-Image LoRA models 10x faster. Fine-tune styles, characters, or object concepts from a ZIP dataset with auto-tuned learning rates and steps.

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Overview

About this model

Qwen-Image LoRA Trainer provides accelerated fine-tuning for Qwen-Image models directly from custom dataset zip archives. Train character concepts, visual styles, and proprietary objects 10x faster with flexible step configurations from 1,000 to 10,000, custom learning rates, and adjustable rank dimensions. Ideal for developers and artists building consistent AI graphics.

1Qwen Model Customization: Fine-tune Qwen-Image checkpoints for targeted visual domain adaptation.
2Character & Subject Consistency: Teach the model specific visual subjects and digital avatars.
3Artistic Style Training: Train custom LoRA adapters to capture distinct illustration and rendering styles.
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Pricing & Value

Cost analysis

muapiapp$2.00 per 1,000 steps

Pay per training step with auto-tuned defaults and zero setup overhead.

Fal.aiNot available

Qwen-Image LoRA Trainer is not hosted on Fal.ai.

ReplicateNot available

Qwen-Image LoRA Trainer is not hosted on Replicate.

** Competitor pricing is estimated based on similar model architectures and usage tiers.

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Technical Details

Configuration schema

Dataset Zip URLstring

URL to zip archive with images. Try to use at least 4 images in general the more the better. In addition to images the archive can contain text files with captions.

Default Valueundefined
Trigger Wordstring

Optional trigger word. If a caption file exists, it is prepended when not already present.

Default Valueexample
Stepsint

Number of steps to train the LoRA on.

Default Value1000
Learning Ratefloat

Learning rate for Qwen LoRA training.

Default Value0.0004
LoRA Rankint

LoRA rank dimension.

Default Value16
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Implementation Guide

Developer documentation

How to Use Qwen-Image LoRA Trainer API

  1. Prepare your dataset: Create a .zip archive containing high-quality training images along with optional .txt caption files.

  2. Set training parameters: Customize trigger_word, steps (default 1000), learning_rate (default 0.0004), and lora_rank (default 16).

  3. Submit prediction via API:

curl -X POST "https://api.muapi.ai/api/v1/qwen-image-lora-trainer" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "data": "https://example.com/my-qwen-dataset.zip",
    "trigger_word": "qwen_character",
    "steps": 1000,
    "learning_rate": 0.0004,
    "lora_rank": 16
  }'
  1. Fetch trained weights: Retrieve the completed model weights URL from the returned prediction result.
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Common Questions

Frequently asked

What is Qwen-Image LoRA Trainer?

Qwen-Image LoRA Trainer is an asynchronous API that fine-tunes custom Qwen-Image LoRA adapters from a zipped image dataset with customizable steps and learning rates.

What is the recommended dataset format?

Upload a `.zip` file containing PNG or JPEG images. You can optionally include corresponding `.txt` caption files named identically to each image file.

What is the default learning rate for Qwen LoRA training?

The default learning rate for Qwen-Image LoRA Trainer is 0.0004, which is optimized for fast convergence across typical image datasets.

How many steps can I configure?

You can configure training steps anywhere between 1,000 and 10,000 steps, with 1,000 steps serving as the default recommendation.

What output is produced upon completion?

When status reaches completed, the outputs array provides direct download URLs for your trained Qwen LoRA model weights.