| ---
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| license: mit
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| language: en
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| tags:
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| - text-to-image
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| - diffusion
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| - cpu-optimized
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| - bytedream
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| - clip
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| pipeline_tag: text-to-image
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| ---
|
|
|
| # Byte Dream - Text-to-Image Model
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|
|
| ## Overview
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| Byte Dream is a production-ready text-to-image diffusion model optimized for CPU inference.
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| It uses CLIP ViT-B/32 for text encoding and a custom UNet architecture for image generation.
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|
|
| ## Features
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| - ✅ **CPU Optimized**: Runs efficiently on CPU (no GPU required)
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| - ✅ **High Quality**: Generates 512x512 images
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| - ✅ **Fast Inference**: Optimized for speed
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| - ✅ **Easy to Use**: Simple Python API and web interface
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| - ✅ **Open Source**: MIT License
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|
|
| ## Installation
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|
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| ```bash
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| pip install torch pillow transformers
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| git lfs install
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| git clone https://huggingface.co/Enzo8930302/ByteDream
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| cd ByteDream
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| ```
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|
|
| ## Usage
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|
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| ### Quick Start
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| ```python
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| from bytedream import ByteDreamGenerator
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|
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| # Load model
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| generator = ByteDreamGenerator(hf_repo_id="Enzo8930302/ByteDream")
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|
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| # Generate image
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| image = generator.generate(
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| prompt="A beautiful sunset over mountains, digital art",
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| num_inference_steps=50,
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| guidance_scale=7.5,
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| )
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| image.save("output.png")
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| ```
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|
|
| ### Using Cloud API
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| ```python
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| from bytedream import ByteDreamHFClient
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|
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| client = ByteDreamHFClient(
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| repo_id="Enzo8930302/ByteDream",
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| use_api=True,
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| )
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|
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| image = client.generate(
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| prompt="Futuristic city at night, cyberpunk",
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| )
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| image.save("output.png")
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| ```
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|
|
| ## Training
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|
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| Train on your own dataset:
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|
|
| ```bash
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| # Create dataset
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| python create_test_dataset.py
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|
|
| # Train model
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| python train.py --config config.yaml --train_data dataset
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| ```
|
|
|
| ## Web Interface
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|
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| Launch Gradio web interface:
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|
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| ```bash
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| python app.py
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| ```
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|
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| Or deploy to Hugging Face Spaces:
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|
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| ```bash
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| python deploy_to_spaces.py --repo_id YourUsername/ByteDream-Space
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| ```
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|
|
| ## Model Architecture
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|
|
| - **Text Encoder**: CLIP ViT-B/32 (512 dimensions)
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| - **UNet**: Custom architecture with cross-attention
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| - **VAE**: Autoencoder for latent space
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| - **Scheduler**: DDIM sampling
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|
|
| ### Parameters
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| - Cross-attention dimension: 512
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| - Block channels: [128, 256, 512, 512]
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| - Attention heads: 4
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| - Layers per block: 1
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|
|
| ## Examples
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|
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| ### Prompts that work well:
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| - "A serene lake at sunset with mountains"
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| - "Futuristic city with flying cars, cyberpunk"
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| - "Majestic dragon flying over castle, fantasy"
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| - "Peaceful garden with cherry blossoms"
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|
|
| ### Tips:
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| - Use detailed, descriptive prompts
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| - Add style keywords (digital art, oil painting, etc.)
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| - Use negative prompts to avoid unwanted elements
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| - Higher guidance scale = more faithful to prompt
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|
|
| ## Files Structure
|
|
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| ```
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| ByteDream/
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| ├── bytedream/ # Core package
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| │ ├── __init__.py
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| │ ├── generator.py # Main generator
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| │ ├── model.py # Model architecture
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| │ ├── pipeline.py # Pipeline
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| │ ├── scheduler.py # Scheduler
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| │ ├── hf_api.py # HF API client
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| │ └── utils.py
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| ├── train.py # Training script
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| ├── infer.py # Inference
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| ├── app.py # Web UI
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| ├── config.yaml # Config
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| └── requirements.txt # Dependencies
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| ```
|
|
|
| ## Requirements
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|
|
| - Python 3.8+
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| - PyTorch
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| - Pillow
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| - Transformers
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| - Gradio (for web UI)
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|
|
| See `requirements.txt` for full list.
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|
|
| ## License
|
|
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| MIT License
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|
|
| ## Citation
|
|
|
| ```bibtex
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| @software{bytedream2024,
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| title={Byte Dream: CPU-Optimized Text-to-Image Generation},
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| year={2024}
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| }
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| ```
|
|
|
| ## Links
|
|
|
| - [GitHub](https://github.com/yourusername/bytedream)
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| - [Documentation](https://huggingface.co/Enzo8930302/ByteDream/blob/main/README.md)
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| - [Spaces Demo](https://huggingface.co/spaces/Enzo8930302/ByteDream-Space)
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|
|
| ## Support
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|
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| For issues or questions, please open an issue on GitHub.
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|
|
| ---
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|
|
| **Created by Enzo and the Byte Dream Team** 🎨
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|
|
