---
license: mit
---

# SDv1.5 Artifacts-500 LoRA Usage Guide

## Introduction
  `SDv1.5 Artifacts-500 LoRA` is a fine-tuned model based on Stable Diffusion v1.5, specifically optimized for generating patterns from the `artifacts-500` dataset(artifacts-500.zip). Using LoRA (Low-Rank Adaptation) technology, the model has been adapted to produce higher-quality patterns relevant to the dataset.

## Usage Instructions

### 1. Download Stable Diffusion v1.5 Weights
Before you begin, ensure you have downloaded the pre-trained weights for Stable Diffusion v1.5. You can download the weights from the [official Stable Diffusion repository](https://hf-mirror.com/stablediffusiontutorials/stable-diffusion-v1.5).

### 2. Prepare LoRA Weights
We have trained LoRA weights for the Artifacts-500 dataset. You can download the trained LoRA weights from the following links:
- LoRA weights after 100 epochs: [artifacts_100epoch_lora.safetensors](link)

### 3. Test the Model
After downloading the weights, you can use the `generate.py` script to test the model's performance. Follow these steps:

#### Install Dependencies
Ensure you have the following Python libraries installed:
```bash
pip install diffusers transformers torch
```

### 4. Run the Test Script
To test the model with the LoRA weights trained for 1 epoch:
```bash
python generate.py
```
The param `lcm_speedup` decide use lcm speed up or not.

#### View the Results
The generated images will be saved to the specified paths:

Results after 100 epochs: `100epoch_test_results.png`

Here are the example results:
- ![100 epoch test results](test_lora_grid_30_steps.png)