SynergyFrame: Orchestrating Control Signals for Photorealistic Stylization

Synergizing Geometry, Lighting, and Style for High-Fidelity Texture Generation

🔧 Installation • 🚀 Quick Start • 🎯 Overview • 🔬 Method • 📖 Citation

🎯 Overview

The core mission of our work is to introduce SynergyFrame, a unified framework capable of generating high-fidelity, physically realistic textures. This framework is designed to synergize multiple control signals—specifically geometry, lighting, and style—to achieve seamless and detailed local stylization, thereby empowering and enhancing mainstream generative workflows.

💥 The Core Challenge: A Common Dilemma in Guided Generation

Existing workflows, while powerful, face significant challenges when guided by multiple, often conflicting, control signals:

• Inpainting Workflows: The strong structural and boundary constraints inherent to inpainting often suppress the effective expression of style signals. This leads to results where the stylization is either muted, coarse, or fails to integrate naturally.
• Image-to-Image Workflows: The creative freedom of these models is a double-edged sword. In unconstrained regions, they are prone to producing checkerboard artifacts or style leakage, which contaminates the original, unmasked content.

✨ The Solution: SynergyFrame

To address these challenges, we propose SynergyFrame, a universal solution built upon two foundational pillars that transform signal conflict into synergy.

Click to see the SynergyFrame Architecture Diagram

graph TD
    subgraph "Inputs"
        A[Geometry]
        B[Lighting]
        C[Style]
        D[Mask]
    end

    subgraph "SynergyFrame Core"
        P1["Pillar 1: Physics-Prior Injection<br/>(MultiScaleDepth, DirectionalShading)"]
        P2["Pillar 2: Adaptive Semantic Fusion<br/>(Semantic Sparse Attention - SSA)"]
        A & B --> P1
        C & D --> P2
        P1 --> SR
        P2 --> SR
        SR{Synergized Representation}
    end

    subgraph "Empowered Workflows"
        W1[Inpainting]
        W2[Image-to-Image]
        SR --> W1 & W2
    end

    subgraph "Outputs"
        O1["✔️ High-Fidelity Results<br/>✔️ No Style Suppression"]
        O2["✔️ Artifact-Free<br/>✔️ No Style Leakage"]
        W1 --> O1
        W2 --> O2
    end

Loading

The SynergyFrame architecture, turning conflicting signals into a synergistic force for photorealistic stylization.

🔬 Method: The Two Pillars of SynergyFrame

Pillar 1: Physics-Prior Injection (The Foundation of Realism)

To ground our stylization in physical reality, we inject crucial environmental priors at the outset.

• Multi-Scale Geometric Awareness: The MultiScaleDepthEnhancement module provides the model with a comprehensive understanding of the scene's geometric structure across various scales.
• Lighting-Aware Initialization: The DirectionalShadingModule establishes a coherent lighting environment, ensuring that the generated style interacts harmoniously with shadows and highlights.

Pillar 2: Adaptive Semantic Fusion (The Key to Universal Empowerment)

This pillar enables our framework to intelligently adapt to different workflows and resolve their core weaknesses.

• Core Mechanism: Semantic Sparse Attention (SSA): We introduce a plug-and-play Semantic Sparse Attention mechanism. SSA compels the model to perform precise semantic matching at the attention level, ensuring the style is applied only where it makes semantic sense.
• Adaptive Capabilities:
  • For Inpainting: SSA's strong semantic matching breaks through inherent style suppression, enabling fine-grained and accurate style injection even under strong structural constraints.
  • For Image-to-Image: When combined with a mask, SSA precisely constrains the style's application area, effectively preventing artifacts and content leakage.

🚀 Quick Start

Installation

# 1. Clone the repository
git clone https://github.com/jackeyloveseven/SynergyFrame.git
cd SynergyFrame

# 2. Install dependencies (example)
# Create a requirements.txt file with your dependencies
# pip install -r requirements.txt
# Example dependencies:
pip install diffusers transformers torch torchvision
pip install accelerate opencv-python pillow numpy matplotlib
pip install rembg html4vision

# 3. Download models
mkdir -p checkpoints sdxl_models models
# Place required models in their respective directories:
# - checkpoints/depth_anything_v2_vitb.pth
# - models/ip-adapter_sdxl_vit-h.bin
# - models/image_encoder/

Note: Please refer to the original model repositories for official download links.

Generation Pipelines

SynergyFrame enhances two primary workflows.

1. Empowered Inpainting (Text-Guided)

This workflow leverages strong geometric and boundary constraints, now enhanced with powerful style injection via SSA.

python SynergyFrame.py --backbone='Inpaint'

2. Empowered Image-to-Image (Style-Reference-Guided)

This workflow offers greater creative freedom, with SSA preventing style leakage and artifacts.

python SynergyFrame.py --backbone='Img2Img'

📊 Results: From Conflict to Synergy

Our narrative focuses on synergy, not opposition. SynergyFrame is not about proving one workflow's superiority but about acting as a higher-level, unified control framework. It elevates both Inpainting and Image-to-Image to new heights in local stylization tasks.

Comparative Analysis

Input	Style Reference	Baseline (e.g., Inpainting)	Ours (SynergyFrame)

*Note: The images above are for illustrative purposes. Please replace `assets/demo/*.png` with your actual result images.*

Ablation Studies

Method	Boundary Preservation	Style Fidelity	Artifact Suppression
Baseline (IP-Adapter Only)	❌	⭐⭐⭐	❌
+ Physics Priors	✅	⭐⭐⭐⭐	❌
SynergyFrame (Priors + SSA)	✅	⭐⭐⭐⭐⭐	✅

📖 Citation

If you find SynergyFrame useful for your research, please consider citing our work:

@misc{synergyframe2025,
  title={SynergyFrame: A Unified Framework for Photorealistic Local Stylization},
  author={The SynergyFrame Team},
  year={2025},
  publisher={GitHub},
  journal={GitHub repository},
  howpublished={\url{https://github.com/jackeyloveseven/SynergyFrame}}
}

🤝 Acknowledgements

Our work builds upon the incredible contributions of the open-source community. We extend our sincere gratitude to the developers and researchers behind:

• Stable Diffusion XL
• IP-Adapter
• DepthAnything V2
• ControlNet
• 🤗 Diffusers

---

🌟 Star this repository if SynergyFrame inspires your work! 🌟