Sketcher: A Production-Grade AI Application for Turning Sketches into Anime Characters in Seconds

Sketcher is a production-grade AI web application based on Stable Diffusion XL and ControlNet, which can quickly convert hand-drawn sketches into high-quality characters in anime, cartoon, and watercolor styles. The project adopts a front-end and back-end separation architecture: the front-end is built with Next.js and deployed on Vercel, while the back-end uses Modal to provide GPU-accelerated inference services. It aims to lower the threshold for creation and shorten the conversion time from idea to finished product.

• Original Author/Maintainer: AbdulahadIltaf
• Source Platform: GitHub
• Release Date: 2026-06-16

Sketcher is an AI web application for production environments. Its core generation pipeline is based on Stable Diffusion XL combined with ControlNet to achieve precise sketch condition control. The front-end and back-end separation architecture ensures efficient operation and low-latency access worldwide.

Core Features

• Intelligent sketch-to-image conversion: Lowers the creation threshold, allowing users to generate detailed character images without professional painting skills
• Multi-style output: Supports three styles—anime, 3D toy, and watercolor—to meet diverse needs
• Interactive canvas: Provides functions like freehand drawing, tracking overlay layers, and real-time preview, compatible with mobile touch operations.

Front-end Layer

Developed with the Next.js framework, uses Canvas API to handle drawing input, deployed on Vercel to leverage edge network and serverless features

Back-end Layer

Inference services written in Python, deployed on the Modal platform (a serverless GPU computing platform designed for ML, with on-demand instance startup)

Generation Pipeline

• SDXL: The base diffusion model responsible for high-quality image generation
• ControlNet: Precisely follows the sketch's composition, pose, and outline, solving the structural control pain points of traditional text-to-image generation.

Back-end Deployment (Modal)

1. Install the Modal client: pip install modal-client
2. Deployment command: modal deploy backend.py
3. Obtain the API endpoint and configure it in the front-end

Front-end Deployment (Vercel)

1. Initialize a Next.js project
2. Integrate canvas components and API calls
3. Push the code to GitHub and deploy with one click on Vercel.

• Adopt Modal's GPU pay-as-you-go billing model (A10G/L4 instances)
• Container-level model caching to avoid cold start overhead
• Stateless API design supports horizontal scaling, automatically adjusting the number of instances based on traffic.

Sketcher plans to expand the following features in the future:

• Facial consistency control
• Text prompt refinement
• Batch generation mode
• User gallery and history records
• LoRA style training support.

Sketcher demonstrates the modern AI application development paradigm: using open-source large models (SDXL, ControlNet) + cloud-native infrastructure (Vercel, Modal) to quickly build production-grade applications. It provides developers in the AI image generation field with a reference implementation that has a clear structure and decoupled components.