yi1108/printfilm

PRINTFILM: AI short-video marketing and AI short-drama creation platform

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Summary Information

Updated 1 hour ago
Added to GitGenius on September 26th, 2026
Created on September 10th, 2026
Open Issues & Pull Requests: 5 (+0)
GitHub issues: Enabled
Number of forks: 571
Total Stargazers: 5,128 (+2)
Total Subscribers: 293 (+0)

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Issue Activity (beta)

Open issues: 5
New in 7 days: 2
Closed in 7 days: 0
Avg open age: 1 days
Stale 30+ days: 0
Stale 90+ days: 0

Recent activity

Opened in 7 days: 2
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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Detailed Description

Printfilm is an AI-powered platform for creating short-form videos and animated dramas from scripts to finished content.

The platform addresses the challenge of producing video content at scale by automating the pipeline from concept to final output. It uses a template-driven workflow that converts outlines or scripts into storyboards, generates images, produces video, and composites the final piece. For short videos, voiceover is generated during output synthesis without requiring separate audio recording. The system unifies two product lines—AI-animated dramas and AI short videos—on a shared generation backend, allowing both to leverage the same image and video synthesis capabilities.

Teams should choose this tool if they need to produce multiple short videos or episodic animated content efficiently. It suits projects focused on customer acquisition, educational content, or serial storytelling where character and scene reusability matters. The platform includes over twenty built-in style templates and offers both full synthesis (image plus video plus compositing) and faster image-text modes. A tools center provides single-step capabilities like text-to-image, image-to-image, and video-to-video without requiring the complete pipeline. The system includes an admin backend for managing users, orders, templates, and task routing, plus open APIs for image and video generation.

Development activity shows consistent feature expansion with both product lines receiving attention. The codebase demonstrates architectural maturity through its in-process task platform design that eliminates the need for separate worker processes. Model flexibility is built in, allowing configuration of different AI providers through the admin interface without code changes. The project maintains a complete Docker deployment path with public images requiring no authentication, and includes optional billing infrastructure for usage-based pricing.