This model is no longer supported.
Customize your input with more control.
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Hint: Drag and drop files from your computer, images from web pages, paste from clipboard (Ctrl/Cmd+V), or provide a URL.
Customize your input with more control.
Your request will cost $0.00095 per step (minimum of 500 steps is charged). For $0.95 you can fine-tune a LoRA for 1000 steps.
Fine-tune your training parameters and start right now.
Fine-tunes the Qwen-Image-2512 model using LoRA to teach it new subjects, objects, or styles from your images and captions.
image_data_url (required)URL to a zip archive containing image + caption pairs. Each image (e.g., photo.jpg) should have a matching caption file (e.g., photo.txt).
Supported formats: .jpg, .jpeg, .png, .webp, .heic, .heif, .avif, .bmp, .psd, .tiff
default_caption (default = null)Fallback caption for images without a .txt file. If not set and a caption is missing, training fails.
steps (default = 2000)The ideal number of training steps depends on the difficulty of your task.
Simple tasks (e.g., learning a basic object or loose style) often work well with 1000–2000 steps.
Complex tasks (e.g., accurate character likeness, detailed faces, or precise artistic styles) usually require more steps — commonly 2000–4000+.
Tip: Start with the default (2000) and experiment. Train a few versions with different step counts, then compare the results. More steps can improve learning but may cause overfitting if your dataset is small or not diverse enough.
learning_rate (default = 5e-4)Use 1e-4 for slower/conservative learning, 1e-3 for faster/aggressive learning.
Image-caption pairing: Each image (e.g., photo.jpg) is paired with its caption file (photo.txt or photo.TXT).
Aspect ratio bucketing: Images are assigned to the nearest bucket matching their aspect ratio, preserving natural proportions.
| Bucket (H×W) | AR | Orientation |
|---|---|---|
| 1344×576 | 3:7 | Portrait |
| 1280×720 | 9:16 | Portrait |
| 1248×832 | 2:3 | Portrait |
| 1152×864 | 3:4 | Portrait |
| 1152×896 | 7:9 | Portrait |
| 1024×1024 | 1:1 | Square |
| 896×1152 | 9:7 | Landscape |
| 864×1152 | 4:3 | Landscape |
| 832×1248 | 3:2 | Landscape |
| 720×1280 | 16:9 | Landscape |
| 576×1344 | 7:3 | Landscape |
Caption dropout: 5% of the time, captions are dropped during training to help the model generalize.
Write clear, descriptive captions in a consistent style:
a golden retriever sitting on a red couch, soft afternoon lightingdogTip: Use an image caption model like Moondream-3
Trigger phrases: For subject training, include a unique trigger word or phrase in every caption (e.g., a photo of ohwx person). Use the same trigger when generating new images later.
| Problem | Signs | Solution |
|---|---|---|
| Overfitting | Outputs match training images exactly, poor generalization | Reduce steps, add more varied images |
| Underfitting | Outputs barely resemble your training data | Increase steps, improve captions or dataset quality |
| Caption errors | Training fails | Add missing .txt files or set default_caption |