README.md

---

license: mit
language: en
tags:
  - text-to-image
  - diffusion
  - cpu-optimized
  - bytedream
  - clip
pipeline_tag: text-to-image
---


# Byte Dream - Text-to-Image Model

## Overview
Byte Dream is a production-ready text-to-image diffusion model optimized for CPU inference. 
It uses CLIP ViT-B/32 for text encoding and a custom UNet architecture for image generation.

## Features
- ✅ **CPU Optimized**: Runs efficiently on CPU (no GPU required)
- ✅ **High Quality**: Generates 512x512 images
- ✅ **Fast Inference**: Optimized for speed
- ✅ **Easy to Use**: Simple Python API and web interface
- ✅ **Open Source**: MIT License

## Installation

```bash

pip install torch pillow transformers

git lfs install

git clone https://huggingface.co/Enzo8930302/ByteDream

cd ByteDream

```

## Usage

### Quick Start
```python

from bytedream import ByteDreamGenerator



# Load model

generator = ByteDreamGenerator(hf_repo_id="Enzo8930302/ByteDream")



# Generate image

image = generator.generate(

    prompt="A beautiful sunset over mountains, digital art",

    num_inference_steps=50,

    guidance_scale=7.5,

)

image.save("output.png")

```

### Using Cloud API
```python

from bytedream import ByteDreamHFClient



client = ByteDreamHFClient(

    repo_id="Enzo8930302/ByteDream",

    use_api=True,

)



image = client.generate(

    prompt="Futuristic city at night, cyberpunk",

)

image.save("output.png")

```

## Training

Train on your own dataset:

```bash

# Create dataset

python create_test_dataset.py



# Train model

python train.py --config config.yaml --train_data dataset

```

## Web Interface

Launch Gradio web interface:

```bash

python app.py

```

Or deploy to Hugging Face Spaces:

```bash

python deploy_to_spaces.py --repo_id YourUsername/ByteDream-Space

```

## Model Architecture

- **Text Encoder**: CLIP ViT-B/32 (512 dimensions)
- **UNet**: Custom architecture with cross-attention
- **VAE**: Autoencoder for latent space
- **Scheduler**: DDIM sampling

### Parameters
- Cross-attention dimension: 512
- Block channels: [128, 256, 512, 512]
- Attention heads: 4
- Layers per block: 1

## Examples

### Prompts that work well:
- "A serene lake at sunset with mountains"
- "Futuristic city with flying cars, cyberpunk"
- "Majestic dragon flying over castle, fantasy"
- "Peaceful garden with cherry blossoms"

### Tips:
- Use detailed, descriptive prompts
- Add style keywords (digital art, oil painting, etc.)
- Use negative prompts to avoid unwanted elements
- Higher guidance scale = more faithful to prompt

## Files Structure

```

ByteDream/

├── bytedream/          # Core package

│   ├── __init__.py

│   ├── generator.py    # Main generator

│   ├── model.py        # Model architecture

│   ├── pipeline.py     # Pipeline

│   ├── scheduler.py    # Scheduler

│   ├── hf_api.py       # HF API client

│   └── utils.py

├── train.py            # Training script

├── infer.py            # Inference

├── app.py              # Web UI

├── config.yaml         # Config

└── requirements.txt    # Dependencies

```

## Requirements

- Python 3.8+
- PyTorch
- Pillow
- Transformers
- Gradio (for web UI)

See `requirements.txt` for full list.

## License

MIT License

## Citation

```bibtex

@software{bytedream2024,

  title={Byte Dream: CPU-Optimized Text-to-Image Generation},

  year={2024}

}

```

## Links

- [GitHub](https://github.com/yourusername/bytedream)
- [Documentation](https://huggingface.co/Enzo8930302/ByteDream/blob/main/README.md)
- [Spaces Demo](https://huggingface.co/spaces/Enzo8930302/ByteDream-Space)

## Support

For issues or questions, please open an issue on GitHub.

---

**Created by Enzo and the Byte Dream Team** 🎨