Story to Prompts

Convert story synopses or single-scene descriptions into high-quality text-to-image prompts. Two modes: (1) multi-scene - a story outline is split into multiple coherent scenes, each with its own prompt; (2) single-scene - a single scene description gets a prompt directly. Outputs scored prompts with bilingual versions. Use when users say story to images, generate prompts for this scene, story split, storyboard prompts, text-to-image prompt, 文生图, 分镜, 故事拆分.

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. 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
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. 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) 5. Motif unity (10): Do visual details serve a unified theme? 6. Material and lighting description (10): Specific material and light logic? 7. Constraints and negative prompts (10): Anti-failure constraints present? 8. Text-image integration (10): Text layout handled or explicitly absent?

Productization and Reusability (20 pts) 9. Parameterization (10): Easy to adjust and reuse? 10. 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. 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 ☆:

text

### Image N | [Shot Type] | [Narrative Function]

**Scene Description:** [Detailed description in primary language]

**Text-to-Image Prompt ★ ([Primary Language]):**
[Complete detailed prompt, ready to copy-paste]

**Text-to-Image Prompt ☆ ([Secondary Language]):**
[Complete prompt adapted to target language conventions]

**Negative Prompt:** [negative keywords]

Score: [X]/100 | Level: [Product-grade / High Quality / Usable]
Strengths: [One sentence]
Improvements: [If applicable, one sentence]

Prompt Writing Spec

Structure (by priority):

text

[Style] + [Shot type + Composition + Camera angle] + [Subject + fixed features] + [Action/Expression] + [Environment/Background] + [Lighting/Atmosphere] + [Material/Texture] + [Quality tags] + [Negative prompt]

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
• references/composition-patterns.md — 12 composition patterns with prompt fragments
• references/style-params.md — 30+ style parameters (keywords, quality tags, avoid list, lighting)