Story to Prompts

One-shot conversion from story/scene to text-to-image prompts. No interactive confirmation — output the final result directly.

Output Language

Detect language from user input:

• - Chinese input → primary prompt in Chinese, secondary in English
• English input → primary prompt in English, secondary in Chinese
• Explicit language override (e.g. "output in English", "用中文输出") → follow user instruction
• All structural text (titles, character sheets, scene descriptions) matches the primary language

Entry Point

Determine mode based on user input:

• - Multi-scene mode: Input contains multiple events/plot points, or user explicitly requests N images
• Single-scene mode: Input describes only one scene/画面, or user asks for a prompt for "one scene"

Split Strategy (Multi-scene Mode)

Priority for determining image count and split:

1. 1. User specifies count (e.g. "4 images", "拆成6张") → use directly
2. User does not specify → split by spatiotemporal boundaries:

- Identify distinct time-space units (location change, time jump) - Each independent time-space = one image - Within the same time-space, if multiple key actions exist, split into 2-3 images with different shot types

1. 3. Default range: 3-6 images unless the story is extremely simple or very long

Workflow (Multi-scene Mode)

Complete all steps in one pass. Output final result only.

Step 1: Extract Story Baseline

Determine internally (do not output separately):

• - Story core (one sentence)
• Character fixed features (age, hair, clothing, signature accessories)
• Unified visual style
• Color palette
• Lighting style

Step 2: Structure Split

Determine N images, assign for each:

• - Shot type (refer to references/shot-types.md narrative rhythm template, adjacent images must differ)
• Camera angle
• Narrative function (establishing / progression / climax / resolution)

Step 3: Generate Prompt per Image

Requirements for each prompt:

• - Repeat character fixed features in every prompt (consistency)
• Vary viewpoint, composition, posture across images (diversity)
• Only include characters/objects mentioned in the current scene (appearance rule)
• Include negative prompt (anti-failure)
• Follow the writing spec below

Step 4: Score and Optimize

Self-evaluate each prompt on 10 dimensions and optimize:

Structure Completeness (40 pts)

1. 1. Core intent clarity (10): Is the goal unambiguous?
2. Subject and hierarchy (10): Is the main subject clear with size ratio?
3. Composition and ratio constraints (10): Aspect ratio, viewpoint, composition technique?
4. Style anchor clarity (10): Specific style/medium specified?

Generation Quality Control (40 pts)

1. 5. Motif unity (10): Do visual details serve a unified theme?
2. Material and lighting description (10): Specific material and light logic?
3. Constraints and negative prompts (10): Anti-failure constraints present?
4. Text-image integration (10): Text layout handled or explicitly absent?

Productization and Reusability (20 pts)

1. 9. Parameterization (10): Easy to adjust and reuse?
2. Failure anticipation (10): Common AI errors preemptively blocked?

Logic check per prompt: character consistency, scene continuity, physics plausibility, style coherence. Fix contradictions if found.

Target: each prompt ≥ 80 points (High Quality). If below, self-optimize and output the improved version.

Workflow (Single-scene Mode)

Simpler, one pass:

1. 1. Extract character features and visual style from the scene
2. Determine optimal shot type and composition
3. Generate prompt (same requirements as Step 3-4 above)
4. Output

Output Format

Primary language marked ★, secondary marked ☆:

CODEBLOCK0

Prompt Writing Spec

Structure (by priority):
CODEBLOCK1

Bilingual output rules:

• - Primary language prompt: complete and detailed, ready to copy-paste
• Secondary language prompt: equally complete, adapted to target language prompt conventions (not a literal translation)

Consistency rules:

• - Character fixed features (age, hair, clothing) must be explicitly repeated in every prompt
• Style, color palette, lighting baseline must carry through all images
• Key props appearance must remain consistent

Diversity rules:

• - Adjacent images use different shot types
• Encourage different composition techniques
• Character posture, expression, position may vary
• Lighting intensity may be adjusted, style remains constant

Reference Files

Read on demand:

• - references/shot-types.md — Shot types, camera angles, narrative rhythm templates
• INLINECODE2 — 12 composition patterns with prompt fragments
• INLINECODE3 — 30+ style parameters (keywords, quality tags, avoid list, lighting)