Wiro - Train Sd Lora icon

Wiro - Train Sd Lora

LoRA training is a low-rank adaptation technique used to fine-tune Stable Diffusion (SD) models

Wiro - Train Sd Lora

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Overview

This node performs LoRA (Low-Rank Adaptation) training to fine-tune Stable Diffusion models using the Wiro API. It is useful for users who want to customize and train their own diffusion models with specific datasets and training parameters. For example, it can be used to train a model on a custom image dataset with specific prompts and training configurations to generate personalized image outputs.

Use Case Examples

  1. Training a Stable Diffusion model with a custom dataset and prompts to generate unique art styles.
  2. Fine-tuning a pre-trained diffusion model with specific class and instance prompts for improved image generation accuracy.

Properties

Name Meaning
Trained Model Name The name of the model to be trained, required to identify the training job.
Train Model Description Optional description of the training model providing additional context.
Reference Model The base model to use as a reference for training.
Reference Model Private Indicates if the reference model is private.
Classification Dataset The dataset used for classification during training, required.
Instance Prompt Prompt describing the instance for training, required.
Class Prompt Prompt describing the class for training, required.
Validation Prompt Optional prompt used for validation during training.
Validation Negative Prompt Optional negative prompt used for validation.
Num Validation Images Number of images used for validation, required.
Num Class Images Number of class images used in training, required.
With Prior Preservation Boolean flag to enable prior preservation during training.
Prior Loss Weight Weight of the prior loss in training, required.
Seed Seed value for reproducibility, required.
Resolution Resolution of training images, required.
Center Crop Boolean flag to enable center cropping of images.
Train Text Encoder Boolean flag to enable training of the text encoder.
Train Batch Size Batch size used during training, required.
Sample Batch Size Batch size used for sampling, required.
Training Steps Number of training steps, required.
Gradient Accumulation Steps Number of gradient accumulation steps, required.
Gradient Checkpointing Boolean flag to enable gradient checkpointing.
Learning Rate Learning rate for training, required.
Scale Lr Boolean flag to enable learning rate scaling.
Lr Scheduler Learning rate scheduler type, required.
Lr Warm Up Steps Number of warm-up steps for learning rate, required.
Lr Num Cycles Number of cycles for learning rate scheduler, required.
Lr Power Power parameter for learning rate scheduler, required.
Use 8bit Adam Boolean flag to enable 8-bit Adam optimizer.
Adam Beta1 Beta1 parameter for Adam optimizer, required.
Adam Beta2 Beta2 parameter for Adam optimizer, required.
Adam Weight Decay Weight decay parameter for Adam optimizer, required.
Adam Epsilon Epsilon parameter for Adam optimizer, required.
Max Grad Norm Maximum gradient norm for clipping, required.
Checkpointing Steps Steps interval for checkpointing, required.
Checkpoints Total Limit Maximum number of checkpoints to keep, required.
Mixed Precision Mixed precision mode for training, required.
Prior Generation Precision Precision mode for prior generation, required.
Lora Rank Rank parameter for LoRA, required.
Allowtf32 Boolean flag to enable TF32 precision.
Selected Folder For Train Data Folder path for training data.
ZIP URL For Training Data URL to a ZIP file containing training data, which will be extracted automatically.

Output

JSON

  • taskid - Unique identifier for the training task.
  • url - URL to access the trained model or results.
  • status - Status of the training task, e.g., 'completed' or 'failed'.

Dependencies

  • Wiro API with API key and secret for authentication

Troubleshooting

  • If the API response does not contain a valid task ID or socket access token, the node throws an error indicating an invalid response from the Wiro API. Ensure the API credentials are correct and the API service is reachable.
  • Training may fail with status codes '-1', '-2', '-3', or '-4', which are mapped to 'failed' status in the output. Check the training parameters and dataset validity.
  • Network or authentication issues may cause HTTP request failures. Verify network connectivity and API key/secret validity.

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