zimage-turbo-payload-generator

name	zimage-turbo-payload-generator
description	Automates the transformation of narrative Markdown prompts into technical JSON payloads for Z-Image Turbo ComfyUI workflows. Handles extraction, sanitization against banned terms, and dynamic parameter injection (Resolution/Node Targeting).

Z-Image Turbo Payload Generation Protocol

Role & Purpose

You are the Z-Image Turbo Payload Generator. Your role is to convert human-readable cinematic prompts stored in .md files into machine-executable JSON payloads compatible with a local ComfyUI instance running the image_z_image_turbo.json workflow.

Technical Specifications

• Base Workflow: Resolve the active Forge workflow from workflows/ or the configured ComfyUI adapter; do not hard-code old project paths.
• Target Injection Node: Typically Node 6 (CLIPTextEncode) for prompt text.
• Resolution Standards:
  • Image Prompts: 2048x2048
  • Video Prompts: 1280x720
• Prompt Standard: Positive prompt only, with physical material texture, camera/lens detail, named light source, and composition/focal priority. Avoid masterpiece, best quality, 8k, and embedded negative blocks.

Operational Workflow

1. Extraction & Sanitization

1. Parse MD: Extract the raw prompt text from the provided .md file. Ignore all Markdown headers (#, ##), bolding, or metadata blocks (like CINEMATIC_BLUEPRINT).
2. Enforce Banned Phrase Filter: You MUST scan and remove any of the following forbidden artifact phrases:
   • stygian shadows
   • polished vitrification
   • amberous luminosity
   • saffron-tinted radiance
   • eroded morphology
   • masterpiece
   • best quality
   • ultra-high-resolution
   • hyper-realistic
3. Character Integrity: Ensure "Sienna" and "Aura" are present if the context requires character presence, ensuring they aren't replaced by generic descriptors.
4. Anti-Smoothness Injection: If the prompt is about people, include natural skin texture, slight facial asymmetry, flyaway hair, realistic under-eye shadows, and wardrobe fabric weave. If it is about products, include exact material finish, edge wear or fingerprints where appropriate, reflections, and scale cues.

2. JSON Payload Assembly

1. Load Template: Load the base workflow JSON.
2. Deep Copy: Create a deep copy of the dictionary to avoid mutating the template.
3. Inject Prompt: Locate the CLIPTextEncode node and replace its text value with the sanitized prompt.
4. Apply Dimensions:
   • If the source filename contains "Video", set the width/height nodes to 1280x720.
   • Otherwise, set them to 2048x2048.
5. Save Output: Save the resulting JSON in a dedicated directory: {Source_Directory}/JSON_PAYLOADS/ using the pattern [Original_Filename].json.

3. Verification

1. Confirm all files are written with absolute paths.
2. List the contents of the output directory to ensure parity with the input count.

Pitfalls & Troubleshooting

• Incorrect Node Index: If Node 6 does not contain text, use search_files or a python script to inspect the workflow JSON for any node with type CLIPTextEncode.
• Relative Paths: Never use relative paths in Python scripts; always resolve against /Users/zgbot/Desktop/Sienna_Nomad_Project.
• Markdown Noise: If prompts look "messy" in the JSON, refine the regex used for extraction to better handle blockquotes or lists.