feat: add architecture overview diagram

docs/architecture-diagram.md

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+# DiffSynth-Engine Architecture Diagram
+
+This mermaid diagram shows the overall architecture and flow of the DiffSynth-Engine, which is a high-performance inference engine for diffusion models.
+
+```mermaid
+graph TB
+    %% Input Layer
+    A[User Input: Prompt, Image, Parameters] --> B[Configuration]
+    B --> B1[Pipeline Config<br/>FluxPipelineConfig/SDXLPipelineConfig/etc.]
+
+    %% Model Fetching & Loading
+    B1 --> C[Model Fetching System]
+    C --> C1[fetch_model<br/>HuggingFace/CivitAI/ModelScope]
+    C1 --> C2[State Dict Loading<br/>SafeTensors/GGUF]
+    C2 --> C3[Model Conversion<br/>Diffusers → DiffSynth Format]
+
+    %% Pipeline Factory
+    C3 --> D{Pipeline Type}
+    D -->|Text-to-Image| E[FluxImagePipeline]
+    D -->|SDXL| F[SDXLImagePipeline]
+    D -->|SD| G[SDImagePipeline]
+    D -->|Video| H[WanVideoPipeline]
+    D -->|Qwen Image| I[QwenImagePipeline]
+
+    %% Main Pipeline Flow (using Flux as example)
+    E --> J[Model Initialization]
+
+    %% Text Processing
+    J --> K[Text Processing]
+    K --> K1[CLIPTokenizer + T5TokenizerFast]
+    K1 --> K2[FluxTextEncoder1 + FluxTextEncoder2]
+    K2 --> K3[Text Embeddings]
+
+    %% Image Processing (if img2img)
+    J --> L[Image Processing]
+    L --> L1[Image Preprocessing]
+    L1 --> L2[FluxVAEEncoder]
+    L2 --> L3[Latent Space]
+
+    %% Noise & Sampling Setup
+    J --> M[Noise & Sampling Setup]
+    M --> M1[Noise Generation + Dynamic Shifting]
+    M --> M2[RecifitedFlowScheduler → Timesteps]
+    M --> M3[FlowMatchEulerSampler → Strategy]
+
+    %% Core Denoising Loop
+    K3 --> N[Core Denoising Loop]
+    L3 --> N
+    M1 --> N
+    M2 --> N
+    M3 --> N
+
+    N --> N1[FluxDiT Transformer<br/>+ ControlNet/IP-Adapter]
+    N1 --> N2[Noise Prediction]
+    N2 --> N3[Sampler Step]
+    N3 --> N4{More Steps?}
+    N4 -->|Yes| N1
+    N4 -->|No| O[Final Latents]
+
+    %% Image Decoding
+    O --> P[FluxVAEDecoder]
+    P --> Q[Generated Image]
+
+    %% Performance Optimizations
+    R[Performance Features]
+    R --> R1[Memory Management<br/>CPU/GPU Offloading<br/>Sequential Offloading]
+    R --> R2[Parallel Processing<br/>Tensor/Sequence Parallel<br/>CFG Parallel]
+    R --> R3[Quantization<br/>FP8/GGUF Support<br/>Model Compilation]
+
+    R1 --> J
+    R2 --> J
+    R3 --> J
+
+    %% Model Customization
+    S[Model Customization]
+    S --> S1[LoRA Support<br/>Fused/Unfused Loading]
+    S --> S2[Conditioning<br/>IP-Adapter/Redux]
+    S --> S3[Control<br/>ControlNet/Inpainting]
+
+    S1 --> N1
+    S2 --> N1
+    S3 --> N1
+
+    %% Tools & Extensions
+    T[Tools & Extensions]
+    T --> T1[FluxInpaintingTool]
+    T --> T2[FluxOutpaintingTool]
+    T --> T3[FluxReferenceTools]
+    T --> T4[FluxReplaceTool]
+
+    T --> E
+
+    %% Algorithm Foundation
+    U[Algorithm Foundation]
+    U --> U1[Noise Schedulers<br/>Beta/DDIM/Exponential/Karras]
+    U --> U2[Samplers<br/>Euler/DPM++/DDPM/FlowMatch]
+
+    U1 --> M
+    U2 --> M
+
+    style A fill:#e1f5fe
+    style Q fill:#c8e6c9
+    style N1 fill:#fff3e0
+    style E fill:#f3e5f5
+    style C fill:#fce4ec
+    style R fill:#e8f5e8
+    style S fill:#fff8e1
+```
+
+## Architecture Overview
+
+The DiffSynth-Engine follows a modular architecture with these key components:
+
+### 1. **Pipeline Layer** 
+- **FluxImagePipeline**: Primary image generation pipeline using Flux models
+- **SDXLImagePipeline**: Stable Diffusion XL pipeline
+- **SDImagePipeline**: Standard Stable Diffusion pipeline  
+- **WanVideoPipeline**: Video generation pipeline
+- **QwenImagePipeline**: Qwen image generation pipeline
+
+### 2. **Text Processing**
+- **Tokenizers**: CLIPTokenizer and T5TokenizerFast for text preprocessing
+- **Text Encoders**: CLIP and T5 models for text embedding generation
+- **Prompt Encoding**: Converts text prompts to numerical embeddings
+
+### 3. **Image Processing** 
+- **VAE Encoder**: Encodes images to latent space representation
+- **VAE Decoder**: Decodes latents back to pixel space
+- **Preprocessing**: Image normalization and format conversion
+
+### 4. **Noise Scheduling & Sampling**
+- **Schedulers**: Define noise schedules (Beta, DDIM, Exponential, etc.)
+- **Samplers**: Implement sampling strategies (Euler, DPM++, DDPM, etc.)
+- **Timestep Management**: Controls the denoising process progression
+
+### 5. **Core Denoising**
+- **DiT (Diffusion Transformer)**: Main neural network for noise prediction
+- **Attention Mechanisms**: Self-attention and cross-attention layers
+- **ControlNet Integration**: Optional conditioning for guided generation
+
+### 6. **Advanced Features**
+- **LoRA Support**: Low-rank adaptation for model customization
+- **IP-Adapter & Redux**: Image-based conditioning
+- **Parallel Processing**: Multi-GPU and distributed inference
+- **Memory Management**: CPU/GPU offloading and optimization
+- **Quantization**: FP8 and other precision optimizations
+
+### 7. **Model Management**
+- **State Dict Handling**: Loading and converting model weights
+- **Device Management**: GPU/CPU memory allocation
+- **Model Lifecycle**: Loading, offloading, and cleanup
+
+The engine supports multiple diffusion model formats (Flux, SD, SDXL, Wan, Qwen) while providing a unified interface and extensive optimization features for high-performance inference.