replicate/qwen-image-lora-trainer

replicate/qwen-image-lora-trainer

Language: Python

Stars: 18

Forks: 4

Open issues: 3

Created: 2025-08-05T21:38:46Z

Pushed: 2025-11-15T02:16:08Z

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README:

Qwen Image LoRA


Fine-tunable Qwen Image model with exceptional composition abilities. Train custom LoRAs for any style or subject.

Training

Train your own LoRA on Replicate or locally:

cog train -i dataset=@your-images.zip -i default_caption="A photo of a person named <>"

Training runs on Nvidia H100 GPU hardware and outputs a ZIP file with your LoRA weights.

Inference

Generate images using your trained LoRA:

cog predict -i prompt="A beautiful sunset" -i replicate_weights=@your-trained-lora.zip

Local Development

git clone --recursive https://github.com/your-repo/qwen-image-lora-trainer.git
cd qwen-image-lora-trainer

Then use cog train and cog predict as shown above.

Dataset Format

Your training ZIP should contain images (.jpg, .png, .webp) and optionally matching .txt caption files:

dataset.zip
├── photo1.jpg
├── photo1.txt # "A photo of a person named <>"
├── photo2.jpg
└── photo3.jpg # Will use default_caption

Important: Qwen Prompting

Critical: Qwen is extremely sensitive to prompting and differs from other image models. Do NOT use abstract tokens like "TOK", "sks", or meaningless identifiers.

Instead, use descriptive, familiar words that closely match your actual images:

• ✅ "person", "man", "woman", "dog", "cat", "building", "car"
• ❌ "TOK", "sks", "subj", random tokens

Every token carries meaning - the model learns by overriding specific descriptive concepts rather than learning new tokens. Be precise and descriptive about what's actually in your images.

Notes

• Training typically takes 15-30 minutes depending on dataset size
• Runs on Nvidia H100 GPU hardware on Replicate