DEPLOYMENT_GUIDE.md

Hugging Face Spaces Deployment Guide

Quick Start - Deploy to Hugging Face Spaces

Option 1: Web Upload (Easiest)

1. Create a New Space

   • Go to https://huggingface.co/new-space
   • Name your space (e.g., "stable-diffusion-style-explorer")
   • Select Gradio as the SDK
   • Choose your preferred visibility (Public/Private)
   • Click "Create Space"

2. Upload Files

   • Click "Files" tab in your new Space
   • Click "Add file" → "Upload files"
   • Upload these files from hf_app/ folder:
     • app.py
     • requirements.txt
     • README.md
   • Click "Commit changes to main"

3. Wait for Build

   • The Space will automatically build (takes 5-10 minutes first time)
   • You'll see build logs in the "Logs" tab
   • Once complete, your app will be live!

Option 2: Git Push (Advanced)

# Navigate to the hf_app directory
cd C:\Users\sidhe\TSAIV4\Session15-Assignment\hf_app

# Initialize git (if not already done)
git init

# Add Hugging Face Space as remote
# Replace YOUR_USERNAME and YOUR_SPACE_NAME
git remote add origin https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME

# Add and commit files
git add .
git commit -m "Initial commit: Stable Diffusion Style Explorer"

# Push to Hugging Face
git push origin main

Local Testing (Optional)

Test the app locally before deploying:

# Navigate to hf_app directory
cd C:\Users\sidhe\TSAIV4\Session15-Assignment\hf_app

# Install dependencies
pip install -r requirements.txt

# Run the app
python app.py

The app will open at http://localhost:7860

Hardware Requirements

For Hugging Face Spaces:

• Free Tier (CPU): Works but slow (~2-3 minutes per image)
• Upgraded (GPU): Recommended for production
  • T4 GPU: ~10-15 seconds per image
  • A10G GPU: ~5-8 seconds per image

To Upgrade Space Hardware:

1. Go to your Space settings
2. Click "Hardware" tab
3. Select GPU tier (requires payment)

Troubleshooting

Build Fails

• Check requirements.txt versions are compatible
• Review build logs in Spaces "Logs" tab
• Ensure all imports in app.py are in requirements.txt

Out of Memory

• Reduce num_inference_steps default value
• Use CPU instead of GPU (slower but more memory)
• Upgrade to larger GPU tier

Slow Generation

• Upgrade to GPU hardware
• Reduce inference steps (trade quality for speed)
• Consider caching the pipeline

Customization

Add More Styles

Edit app.py and add to the STYLES dictionary:

STYLES = {
    # ... existing styles ...
    "Your Style Name": {
        "repo": "sd-concepts-library/your-concept",
        "token": "<your-token>",
        "description": "Your style description"
    }
}

Change Base Model

Replace in app.py:

pipe = StableDiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5",  # or another model
    torch_dtype=dtype,
    safety_checker=None
).to(device)

Adjust Default Parameters

Modify the default values in the Gradio components:

• steps_single = gr.Slider(..., value=30, ...) - Change default steps
• guidance_single = gr.Slider(..., value=7.5, ...) - Change guidance scale

Next Steps

1. ✅ Deploy to Hugging Face Spaces
2. ✅ Test with different prompts and styles
3. ✅ Share your Space URL with others
4. ✅ Monitor usage in Space analytics
5. ✅ Iterate based on user feedback

Support

• Hugging Face Spaces Docs: https://huggingface.co/docs/hub/spaces
• Gradio Documentation: https://gradio.app/docs
• Diffusers Documentation: https://huggingface.co/docs/diffusers