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-# Stable Diffusion Style Transfer with Color Distance Loss
-
-This project implements a Stable Diffusion-based image generation system with custom style transfers and color enhancement through a distance loss function.
-
-## 🎨 Features
-
-### Style Transfer
-- Implements 5 different artistic styles:
-  - Ronaldo Style (Sports scenes)
-  - Canna Lily (Nature and flowers)
-  - Three Stooges (Comedy scenes)
-  - Pop Art (Vibrant artistic style)
-  - Bird Style (Wildlife imagery)
-
-### Color Distance Loss
-The project implements a unique color enhancement through RGB channel separation:
-- Calculates distances between RGB channels
-- Enhances color vibrancy and contrast
-- Creates more distinct color separation
-- Reduces color mixing and muddiness
-
-### Interactive Interface
-- User-friendly Gradio web interface
-- Side-by-side comparison of original and enhanced images
-- Real-time style selection
-- Example prompts for each style
-
-## 🚀 Setup and Installation
-
-1. Install dependencies:
-bash
-pip install -r requirements.txt
-
-2. Required files:
-- Style embeddings (.bin files):
-  - ronaldo.bin
-  - canna-lily-flowers102.bin
-  - threestooges.bin
-  - pop_art.bin
-  - bird_style.bin
-
-3. Run the application:
-bash
-python app.py
-
-## 🎮 Usage
-
-1. Enter a text prompt describing your desired image
-2. Select a style using the radio buttons
-3. Click "Generate Images" to create:
-   - Left: Original styled image
-   - Right: Image with color distance loss applied
-
-## 🔧 Technical Details
-
-### Distance Loss Function
-def Distance_loss(images):
-# Extract RGB channels
-red = images[:,0:1]
-green = images[:,1:2]
-blue = images[:,2:3]
-# Calculate channel distances
-rg_distance = ((red - green) 2).mean()
-rb_distance = ((red - blue) 2).mean()
-gb_distance = ((green - blue) 2).mean()
-return (rg_distance + rb_distance + gb_distance) 100
-
-This loss function:
-- Separates RGB channels
-- Calculates squared distances between channels
-- Enhances color distinctiveness
-- Applies during the generation process
-
-## 📝 Example Prompts
-
-- Sports: "a soccer player celebrating a goal"
-- Nature: "beautiful flowers in a garden"
-- Comedy: "three comedians performing a skit"
-- Art: "a colorful portrait in pop art style"
-- Wildlife: "birds flying in a natural landscape"
-
-## 🛠️ Requirements
-
-- Python 3.8+
-- PyTorch
-- Diffusers
-- Transformers
-- Gradio
-- CUDA-capable GPU recommended
-
-## 📊 Results
-
-The color distance loss typically produces:
-- More vibrant colors
-- Better color separation
-- Enhanced contrast
-- More distinctive style characteristics
-
-## 🤝 Contributing
-
-Feel free to:
-- Submit issues
-- Fork the repository
-- Submit pull requests
-
-## 📜 License
-
-This project is open-source and available under the MIT License.