README.md

---
license: openrail++
library_name: diffusers
pipeline_tag: text-to-image
tags:
  - sdxl
  - text-to-image
  - image-generation
---

<!-- README Version: v1.4 -->

# Stable Diffusion XL FP16 Model Repository

Local repository containing Stable Diffusion XL (SDXL) checkpoint models in FP16 precision for high-quality text-to-image generation.

## Model Description

This repository contains two SDXL checkpoint models optimized for different use cases:

- **SDXL Base**: Full-featured SDXL 1.0 base model for high-quality image generation with standard inference steps
- **SDXL Turbo**: Fast inference variant optimized for fewer steps (1-4 steps) while maintaining quality

Both models use FP16 (16-bit floating point) precision, providing a balance between quality and VRAM efficiency.

## Repository Contents

```
E:\huggingface\sdxl-fp16\
├── checkpoints/
│   └── sdxl/
│       ├── sdxl-base.safetensors     (6.94 GB)
│       └── sdxl-turbo.safetensors    (13.88 GB)
├── diffusion_models/
│   └── sdxl/                         (empty - reserved)
└── loras/
    └── sdxl/                         (empty - reserved)
```

**Total Repository Size**: ~20.82 GB

### Model Files

| File | Size | Description |
|------|------|-------------|
| `sdxl-base.safetensors` | 6.94 GB | SDXL 1.0 base checkpoint (FP16) |
| `sdxl-turbo.safetensors` | 13.88 GB | SDXL Turbo checkpoint (FP16) |

## Hardware Requirements

### SDXL Base
- **VRAM**: 8GB minimum, 12GB+ recommended
- **Disk Space**: 7GB for model file
- **System RAM**: 16GB+ recommended
- **GPU**: NVIDIA GPU with CUDA support

### SDXL Turbo
- **VRAM**: 12GB minimum, 16GB+ recommended
- **Disk Space**: 14GB for model file
- **System RAM**: 16GB+ recommended
- **GPU**: NVIDIA GPU with CUDA support

## Usage Examples

### SDXL Base (Standard Quality)

```python
from diffusers import DiffusionPipeline
import torch

# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
    "E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
    torch_dtype=torch.float16
)

pipe.to("cuda")

# Generate image with standard settings
image = pipe(
    prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
    negative_prompt="blurry, low quality, distorted",
    num_inference_steps=50,
    guidance_scale=7.5,
    width=1024,
    height=1024
).images[0]

image.save("output.png")
```

### SDXL Turbo (Fast Generation)

```python
from diffusers import DiffusionPipeline
import torch

# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
    "E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
    torch_dtype=torch.float16
)

pipe.to("cuda")

# Generate with minimal steps (1-4 steps)
image = pipe(
    prompt="a futuristic cityscape at night, neon lights, cyberpunk",
    num_inference_steps=4,  # Turbo optimized for 1-4 steps
    guidance_scale=0.0,     # Turbo works best with guidance_scale=0
    width=1024,
    height=1024
).images[0]

image.save("turbo_output.png")
```

### Memory Optimization

```python
import torch
from diffusers import DiffusionPipeline

# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
    "E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
    torch_dtype=torch.float16
)

# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")

# Generate with optimized memory usage
image = pipe(
    prompt="your prompt here",
    num_inference_steps=30
).images[0]
```

## Model Specifications

### SDXL Base
- **Architecture**: Latent Diffusion Model with UNet
- **Parameters**: ~2.6B (UNet backbone)
- **Precision**: FP16 (16-bit floating point)
- **Format**: SafeTensors (secure, efficient)
- **Resolution**: 1024x1024 native, supports 512-2048px
- **Text Encoders**: Dual CLIP (OpenCLIP ViT-bigG, OpenAI CLIP ViT-L)
- **Inference Steps**: 30-50 recommended

### SDXL Turbo
- **Architecture**: Adversarial Diffusion Distillation (ADD)
- **Parameters**: Similar to base with distillation optimizations
- **Precision**: FP16 (16-bit floating point)
- **Format**: SafeTensors
- **Resolution**: 1024x1024 native
- **Inference Steps**: 1-4 steps (optimized)
- **Guidance Scale**: 0.0 recommended (classifier-free guidance disabled)

## Performance Tips

### Speed Optimization
- **SDXL Turbo**: Use 1-4 steps with `guidance_scale=0.0` for fastest generation
- **Attention Slicing**: Enable with `pipe.enable_attention_slicing()` for memory efficiency
- **VAE Slicing**: Enable with `pipe.enable_vae_slicing()` to reduce VRAM usage
- **Lower Resolutions**: Use 768x768 or 512x512 for faster generation
- **Batch Processing**: Process multiple prompts together when VRAM allows

### Quality Optimization
- **SDXL Base**: Use 40-50 steps for highest quality
- **Guidance Scale**: 7.0-9.0 for base model (higher = more prompt adherence)
- **Negative Prompts**: Use detailed negative prompts to avoid unwanted elements
- **Resolution**: 1024x1024 is the native resolution for best results
- **Aspect Ratios**: Multiples of 64 recommended (1024x768, 768x1024, etc.)

### VRAM Management
- **8GB VRAM**: Use attention slicing, VAE slicing, lower batch sizes
- **12GB VRAM**: Standard settings with optimizations
- **16GB+ VRAM**: Can handle higher resolutions and batch sizes

## Changelog

### v1.4 (2025-10-28)
- Final verification of repository structure and model integrity
- Confirmed all file sizes and paths are accurate
- Validated YAML frontmatter format and HuggingFace compliance
- Documentation verified complete and production-ready

### v1.3 (2025-10-28)
- Verified repository structure and model file integrity
- Confirmed YAML frontmatter compliance with HuggingFace standards
- Validated all file paths and sizes
- Updated documentation timestamp

### v1.2 (2025-10-14)
- Fixed YAML frontmatter: removed base_model fields (these are base models, not derived)
- Streamlined tags to essential categories only
- Improved metadata compliance with Hugging Face standards

### v1.1 (2025-10-14)
- Updated YAML frontmatter format (metadata now precedes version header)
- Optimized tag ordering for better discoverability
- Verified all model files and sizes

### v1.0 (2025-10-13)
- Initial repository documentation
- Added SDXL Base checkpoint (6.94 GB)
- Added SDXL Turbo checkpoint (13.88 GB)
- Organized directory structure for checkpoints, diffusion models, and LoRAs

## License

**License**: CreativeML Open RAIL++-M License

Stable Diffusion XL models are released under the [CreativeML Open RAIL++-M license](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md), which permits commercial use with the following key terms:

- ✅ Commercial use permitted
- ✅ Modification and redistribution allowed
- ⚠️ Use restrictions apply (see full license)
- ⚠️ Must include license and attribution

**Key Restrictions**: Cannot be used for illegal activities, generating harmful content, or violating privacy rights. See full license for complete terms.

## Citation

If you use these models in your research or applications, please cite:

```bibtex
@misc{podell2023sdxl,
    title={SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis},
    author={Dustin Podell and Zion English and Kyle Lacey and Andreas Blattmann and Tim Dockhorn and Jonas Müller and Joe Penna and Robin Rombach},
    year={2023},
    eprint={2307.01952},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

@inproceedings{sauer2023adversarial,
    title={Adversarial Diffusion Distillation},
    author={Sauer, Axel and Lorenz, Dominik and Blattmann, Andreas and Rombach, Robin},
    booktitle={arXiv preprint arXiv:2311.17042},
    year={2023}
}
```

## Official Resources

- [SDXL Base Model](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
- [SDXL Turbo Model](https://huggingface.co/stabilityai/sdxl-turbo)
- [SDXL Documentation](https://huggingface.co/docs/diffusers/using-diffusers/sdxl)
- [Diffusers Library](https://github.com/huggingface/diffusers)
- [SDXL Paper](https://arxiv.org/abs/2307.01952)
- [SDXL Turbo Paper](https://arxiv.org/abs/2311.17042)

## Contact & Support

- **Issues**: Report issues with models or documentation on [Hugging Face Discussions](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/discussions)
- **Community**: Join [Hugging Face Discord](https://discord.gg/hugging-face) for community support
- **Repository**: This is a local storage repository - for upstream issues, see official model pages

---

**Repository maintained locally** | Last updated: 2025-10-28 | Version: v1.4