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Image & Video AI

Scene Card Studio

Scene Card Studio is a Python application for turning personal photos into editable visual narratives. It analyzes observable image properties, organizes direction in Scene Cards, compiles versioned prompts for image generation and supports deterministic layouts.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Scene Card Studio — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a photo or related collection needs an explicit narrative treatment before image generation, or when a structured visual layout is part of the intended result.

Who it is for

  • People creating directed visual stories from a single personal photo or a related photo sequence.
  • Users who want to edit narrative choices before preparing prompts for image generation.

Documented uses

  • Turn a photograph or photo sequence into draft Scene Cards and an editable narrative direction.
  • Create versioned prompts that specify how an image or story should be read and visually expressed.
  • Prepare deterministic layouts for photo narratives.
  • Choose between one-photo output, per-photo direction for a sequence and multi-photo synthesis.
Before you choose

Requirements & limits

The repository is public and licensed under Apache-2.0. The documented setup requires Python 3.10 or later; Pillow is used for automatic analysis and PNG rendering.

  • The local analyzer measures dimensions, orientation, palette, brightness and saturation; it does not identify semantic content such as people, objects or gestures.
  • Semantic fields and transformation rules need completion from visual evidence before the prompt-generation stage.
  • The analysis Workprint is clearly labelled and is not a generated final image.
  • Cloud upload requires consent specific to the provider, purpose and files.
  • The workflow separates Narrative Systems, which govern how a story is read, from Expression Profiles, which govern visual expression.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Scene Card Studio and prepare a photo direction bundle

Install the Python application and use its documented direct command to prepare a local direction bundle from a photo.

Before you start

  • Python 3.10 or later
  • Pillow for automatic analysis and PNG rendering
  • A local photograph and a brief describing the intended direction

Steps

  1. Clone the Scene Card Studio repository and change into its directory.

  2. Install the project with the command below.

    Documented command
    python -m pip install -e .

  3. Run scene-card-studio direct photos/portrait.jpg --brief "quiet family portrait with restrained silver-gelatin depth" using a photo path and your own brief.

Expected outcome

The direct workflow prepares draft Scene Cards, a Prompt Manifest, an analysis Workprint and a run summary. The Workprint is not a generated final image.

Guide limitations

  • The local analyzer measures image properties but does not infer semantic details such as people, objects, gestures or spatial meaning.
  • Complete semantic fields from visual evidence before compiling for image generation; upload requires provider-, purpose- and file-specific consent.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
swping999/scene-card-studio · Official homepage title · retained in its source language
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