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A port of DiffusionBee to Tauri and Svelte for future expansion.

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DiffusionBee Tauri Port

This document outlines the plan to port the DiffusionBee application from Electron to Tauri, leveraging Svelte for the frontend.

Development Progress

Current Status: Stories #1-4 Implementation (Partially Complete)

We have successfully implemented the foundation for Stories #1-4 with the following components:

✅ Completed Features

  • Tauri + Svelte Setup: Modern tech stack with Tauri 2.0 and Svelte 5.0
  • Rust Backend Structure: Python integration framework with proper error handling
  • Svelte State Management: Comprehensive store system for generation state
  • UI Components: Clean, modern interface for text-to-image generation
  • Error Handling: Basic error handling and user feedback
  • Progress Tracking: UI for generation progress (frontend complete)
  • Advanced Controls: Sliders for inference steps and guidance scale
  • Settings Management: Configurable output directory and parameter persistence
  • Modern UI Theme: Dark glassmorphism theme with animations

⚠️ Blocking Issues

  • Python Backend Integration: Blocked by tf-nightly + keras-nightly compatibility issues
  • Model Loading: Python backend cannot load due to TensorFlow/Keras version conflicts
  • Image Generation: Cannot generate actual images until backend issues are resolved

🔄 Workarounds Implemented

  • Bootstrapper Script: Created run_backend.py to handle Keras version compatibility
  • Placeholder Images: Frontend displays placeholder images for testing UI
  • Settings Persistence: Working configuration management between sessions

⏳ Next Steps (Blocked by Backend Issues)

  • Resolve Python backend dependency conflicts
  • Implement real Python backend communication
  • Add file system operations for image storage
  • Implement progress tracking from Python backend
  • Add image history functionality

Running the Application

# Install dependencies
bun install

# Start development server
bun run dev

# Run Tauri in development mode
bun run tauri dev

# Build for production
bun run build
bun run tauri build

Backend Issues and Workarounds

Current Problem

The Python backend is blocked by compatibility issues between tf-nightly and keras-nightly packages. The error occurs when TensorFlow tries to access keras.__version__ which is missing in the new standalone Keras package.

Workarounds Implemented

  1. Bootstrapper Script: Created run_backend.py in the backend directory that sets keras.__version__ = "3.0.0" before importing the backend
  2. Frontend Placeholders: The UI works with placeholder images for testing
  3. Settings Persistence: Configuration management works independently of the backend

To Test Backend (Experimental)

cd backends/stable_diffusion
poetry run python run_backend.py --help

Note: The backend may still fail due to other compatibility issues even with the bootstrapper script.

Product Requirements Document (PRD)

1. Introduction & Vision

The goal of this project is to create a more performant, secure, and lightweight version of DiffusionBee by porting it from Electron to Tauri. The new application will retain the core features of the original while providing a better user experience through a native webview and a smaller bundle size.

2. Core Features (MVP)

The Minimum Viable Product (MVP) will include the following features:

  • Text-to-Image Generation: Generate images from a text prompt.
  • Image-to-Image Generation: Generate images from an initial image and a text prompt.
  • Inpainting: Edit parts of an image by masking and providing a text prompt.
  • History: View a gallery of previously generated images.
  • Model Management: Download and switch between different Stable Diffusion models.
  • Settings: Configure application settings.

3. Technology Stack

  • Frontend: Svelte 5 + SvelteKit
  • Backend: Rust (Tauri)
  • AI Backend: Python (utilizing the existing backends directory)

4. Target Audience

The target audience includes existing DiffusionBee users and new users who are looking for a native and efficient Stable Diffusion UI for their desktop.

5. Non-Functional Requirements

  • Performance: The application must be fast, responsive, and have a low memory footprint.
  • Cross-Platform: The application must be buildable for macOS, Windows, and Linux.
  • Security: The application should be secure, benefiting from Tauri's security features.

Coding Style Guide

General

  • Development Management: Only work on one story at a time taken from the USER-STORIES.md file, update the story with comments when finished.
  • Version Control: Use Git with a new branch for every story named "story-#".
  • Dependencies: Always use the latest stable versions of dependencies.
  • Logging: Always log all messages to ./diffusion.logs file in an appending manner.
  • Security Review: Always review code for security flaws before finishing a task.
  • Simplicity First: Always review code to make sure it is simple and straightforward before finishing a task.
  • Commits: Follow the Conventional Commits specification.
  • Validation: Validate at least minimally with bun run tauri dev, ensure you are in the porting directory, ported-diffusion-app, first.
  • Documentation: All new features should be documented in this README or relevant files.

Frontend (Svelte / JavaScript)

  • Formatting: Use Prettier for automatic code formatting. A .prettierrc file should be included in the project.
  • Linting: Use ESLint to identify and fix code quality issues.
  • Component Design: Components should be small, reusable, and focused on a single responsibility.
  • State Management: For complex state, use Svelte stores.
  • API Interaction: All communication with the Tauri backend should be done through the invoke function, with clear command names.

Backend (Rust)

  • Formatting: Use rustfmt to maintain a consistent code style.
  • Linting: Use clippy to catch common mistakes and improve code quality.
  • Error Handling: Use Result and ? for robust error handling. Avoid unwrap() in production code.
  • Modularity: Organize code into modules to keep it maintainable.
  • Tauri Commands: All Tauri commands should be well-documented and handle errors gracefully.

I'll review the codebase, README.md, and USER-STORIES.md to understand the project structure and goals. Let me start by examining the current state of the project.

Read file: ported-diffusion-app/package.json Read file: ported-diffusion-app/src-tauri/Cargo.toml Read file: ported-diffusion-app/src-tauri/src/main.rs Read file: ported-diffusion-app/src-tauri/src/lib.rs Read file: ported-diffusion-app/src/routes/+page.svelte Ran tool Read file: backends/stable_diffusion/diffusionbee_backend.py Read file: electron_app/package.json Now let me examine the current state of the Tauri app more closely:

Read file: ported-diffusion-app/src-tauri/tauri.conf.json

Codebase Review Summary

Based on my analysis of the codebase, README.md, and USER-STORIES.md, here's my comprehensive review:

Project Overview

This is a DiffusionBee Tauri Port project that aims to convert the existing Electron-based DiffusionBee application to a Tauri-based desktop app using Svelte for the frontend. The project is currently in its very early stages.

Current State Analysis

What's Already Done:

  1. Basic Tauri + Svelte Setup: The project has a minimal Tauri 2.0 + Svelte 5.0 + SvelteKit setup
  2. Project Structure: Proper separation between frontend (src/) and backend (src-tauri/)
  3. Build Configuration: Basic Tauri configuration with proper build commands
  4. Existing Backend: The original Python backend (backends/) is available and functional

What's Missing (Major Gaps):

  1. No Core Features Implemented: The current app only has a basic "Hello World" example
  2. No Python Backend Integration: The existing Python backend isn't connected to Tauri
  3. No UI Components: No Svelte components for the actual DiffusionBee features
  4. No State Management: No Svelte stores or state management system
  5. No Error Handling: Basic error handling structure missing

Technical Architecture Assessment

Strengths:

  • Modern Tech Stack: Tauri 2.0 + Svelte 5.0 is cutting-edge
  • Performance: Tauri will provide better performance than Electron
  • Security: Tauri's security model is superior to Electron
  • Existing Backend: The Python backend is mature and feature-complete

Challenges:

  • Python Integration: Need to bridge Tauri (Rust) with the existing Python backend
  • Complex UI: The original Vue.js UI is quite sophisticated and needs to be rebuilt in Svelte
  • File Management: Need to handle model downloads, image storage, etc.

Recommendations for Development

Phase 1: Foundation (Current Priority)

  1. Python Backend Integration: Create Tauri commands to communicate with the Python backend
  2. Basic UI Framework: Set up Svelte stores and basic layout components
  3. File System Operations: Implement model management and image storage

Phase 2: Core Features

  1. Text-to-Image: Implement the basic text-to-image generation
  2. Image Gallery: Create the history/gallery view
  3. Settings: Basic configuration management

Phase 3: Advanced Features

  1. Image-to-Image: Build on the foundation
  2. Inpainting: More complex UI interactions
  3. Model Management: Download and switching functionality

Specific Technical Recommendations

1. Python Backend Integration Strategy

// Example Tauri command structure needed:
#[tauri::command]
async fn generate_image(prompt: String, options: ImageOptions) -> Result<ImageResult, String> {
    // Call Python backend via subprocess or IPC
}

2. State Management Architecture

// Svelte stores needed:
- imageGenerationStore
- modelManagementStore  
- settingsStore
- historyStore

3. UI Component Structure

src/
├── lib/
│   ├── components/
│   │   ├── ImageGenerator.svelte
│   │   ├── ImageGallery.svelte
│   │   ├── ModelManager.svelte
│   │   └── Settings.svelte
│   ├── stores/
│   └── utils/

Risk Assessment

High Risk Areas:

  1. Python Integration Complexity: Bridging Rust and Python can be challenging
  2. Performance: Need to ensure the Python backend doesn't block the UI
  3. File Management: Handling large model files and images efficiently

Medium Risk Areas:

  1. UI Recreation: Rebuilding the complex Vue.js UI in Svelte
  2. Cross-Platform Compatibility: Ensuring the Python backend works on all platforms

Next Steps Recommendation

Based on the USER-STORIES.md, I recommend starting with:

  1. Story #1: "As a user, I want to be able to enter a text prompt and generate an image"

    • This will force you to solve the Python integration problem
    • Create the basic UI framework
    • Establish the communication pattern between Tauri and Python

  2. Infrastructure First: Before diving into features, establish:

    • Python backend communication
    • Basic Svelte store structure
    • Error handling patterns
    • File system operations

Would you like me to help you start implementing any of these specific areas? I can help with:

  • Setting up the Python backend integration
  • Creating the basic Svelte component structure
  • Implementing the first user story
  • Setting up proper error handling and state management

What would you like to focus on first?

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