README.md

---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: creativeml-openrail-m
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
inference: true
datasets:
- Shuhaib73/stablediffusion_celeba1k
pipeline_tag: text-to-image
---

<strong style="color: rosybrown; font-size: 18px">Text-to-Image Generation with Fine-Tuned SDXL [QLoRA]</strong>


<strong style="text-decoration: underline">Example Prompts: </strong>
<p style="color: orangered">1. A young, attractive female with arched eyebrows and a pointy nose. She has wavy brown hair, wears heavy makeup with lipstick, and exudes a confident, stylish look. The scene features soft, flattering lighting that enhances her youthful features and glamorous appearance.</p>
<p style="color: orangered">2. A male with an oval face, big nose, high cheekbones, and a receding hairline. He has black hair, bushy eyebrows, and his mouth is slightly open in a smile. The subject is clean-shaven, with no beard.</p>
<p style="color: orangered">3. Male with a big nose, black hair, bushy eyebrows, high cheekbones, and a receding hairline. He has an oval face, a mouth slightly open in a smile, and is clean-shaven with no beard.</p>


<strong>Goal of this project:</strong> This project focuses on building an advanced text-to-image generation system using the Stable Diffusion XL (SDXL) model, a state-of-the-art deep learning architecture. The goal is to transform natural language text descriptions into visually coherent and high-quality images, unlocking creative possibilities in areas like art generation, design prototyping, and multimedia applications.

To enhance performance and tailor the model to specific use cases, SDXL is fine-tuned using <strong>QLoRA (Quantized Low-Rank Adaptation)</strong>. This approach leverages efficient parameter fine-tuning and memory optimization techniques, enabling high-quality adaptations with reduced computational overhead. Fine-tuning with QLoRA ensures that the model is optimized for domain-specific text-to-image tasks, delivering even more precise and creative outputs.

Simplified Architecture: ![pipeline](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/pipeline.png)


<p style="font-family:Lucida Sans ;font-size:15px;">Dataset Description: CelebFaces Attributes Dataset (CelebA).</p>

<p style="font-family: Lucida Sans ;font-size:15px;">The CelebA dataset is a widely-used, large-scale dataset in the field of computer vision, particularly for tasks related to faces. It consists of over 200,000 celebrity face images annotated with a rich set of attributes. The dataset offers diverse visual content with variations in pose, facial expressions, and backgrounds, making it suitable for a range of face-related applications.</p>


<strong>Here are few examples of generated images Using Stable Diffusion SDXL:</strong>

<strong>Before Fine-Tuning SDXL</strong>

![img_1](./generated_img1.png)

<strong>After Fine-Tuning SDXL on Custom Dataset</strong>

![img_1](./after_training_img1.png)

![img_1](./after_training_img2.png)


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#### How to use

```python
import torch
from diffusers import DiffusionPipeline

model_path = "Shuhaib73/stablediffusion_fld"

trained_pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)
trained_pipe.to("cuda")
trained_pipe.load_lora_weights(model_path)

```