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Character Canvases for AutoAI


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SOP

Creating Effective Character Canvases for AutoAI

Objective

This SOP outlines the steps to create accurate and effective character canvases for AutoAI to ensure character consistency and quality outputs.

Key Steps

1. Understand Character Canvas Structure

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Identify the key columns required for the character canvas:
character_name (case sensitive: lowercase 'c' and 'n')
prompt
image_url (optional, can be left empty)
outfit_details (case sensitive) for costume specifics.
2. Define Character Attributes
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Include specific details in the prompt:
Age: Use specific numbers (e.g., '13 years old', 'mid-20s'). Avoid vague terms like 'adult'.
Facial features: Include at least 5-7 distinct features (e.g., eye color, hair type, face shape).
Body structure: Provide at least two attributes (e.g., height, body build).
3. Create Separate Rows for Character Variations
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For characters with different spellings or surnames, create separate rows:
Example: For 'ZOE' and 'ZOEY', create two rows with the same prompt and image URL.
4. Enhance Prompt Details
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Avoid vague descriptions. Instead, provide detailed attributes:
Example: Instead of 'long, dark hair', specify 'long, dark brown, shoulder-length hair'.
Include facial attributes like 'oval-shaped face', 'sharp nose', etc.
5. Manage Costume Details Separately
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Use the outfit_details column to specify costumes:
Example: 'Wearing a dark grey jacket over a white round neck t-shirt'.
Ensure that costume details do not conflict with character prompts.
6. Review and Validate Character Canvases
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After completing the character canvas, review all details for accuracy and completeness.
Ensure all character features are unique and specific to maintain consistency.

Note:

Avoid vague terms and descriptions to ensure clarity in character prompts.
Ensure all column names are case sensitive and formatted correctly to avoid errors in AutoAI outputs.

For Efficiency:

Use templates for common character types to save time.
Regularly review and update character canvases based on feedback from AutoAI outputs.
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