Multimodal Latent Space Concept Bleed Diagnostic Script

Role: Principal Generative AI Tooling Engineer specializing in latent diffusion architecture and compositional prompt debugging.

Context

• Target Model Architecture: {{base_diffusion_model}}
• Problematic Compositional Prompt: {{target_prompt_string}}
• Corrupted or Bleeding Attributes: {{bleeding_attributes}}
• Prompt Parser Syntax: {{token_weighting_syntax}}
• Control Negative Prompt: {{negative_prompt_baseline}}
• Target Runtime Framework: {{output_script_runtime}}

Task

Produce a production-grade, executable diagnostic script in {{output_script_runtime}} that isolates cross-attention attribute leakage, quantifies token-to-patch spatial overlap, and outputs a calibrated prompt remediation patch for {{base_diffusion_model}}.

Method

1. Parse the target prompt {{target_prompt_string}} into token IDs and register forward hooks on the cross-attention layers of {{base_diffusion_model}}.
2. Isolate attention maps specifically for the tokens defined in {{bleeding_attributes}} across all denoising timesteps.
3. Compute the spatial IoU (Intersection over Union) and cosine similarity between the activation masks of colliding token groups.
4. Execute an automated prompt mutation loop varying {{token_weighting_syntax}} to evaluate weight dampening curves.
5. Implement a masked negative prompt injection test utilizing {{negative_prompt_baseline}} to detect latent suppression efficacy.
6. Generate a tabular metric summary reporting peak cross-attention leakage scores and token displacement coordinates.
7. Output an optimized candidate prompt string that mathematically minimizes token overlap below the collision threshold.

Constraints

• The script MUST be fully executable code with explicit imports, error handlers, and zero pseudo-code.
• The script MUST NOT require external proprietary visualization suites beyond standard scientific computing libraries.
• You MUST structure attention hook callbacks to avoid GPU VRAM out-of-memory errors during multi-timestep capture.
• Include inline documentation explaining how each metric maps back to physical attribute transfer in the image.

Output format

1. Environment Setup & Dependency Requirements (shell block)
2. Executable Diagnostic Harness Script (fully typed Python code block)
3. Diagnostic Metric Interpretation Guide (markdown table with metric name, threshold value, and failure condition)
4. Remediation Patch Recommendations (markdown list with modified prompt syntax examples)

Self-review

• Confirm that all 6 variables are referenced in the script logic or setup parameters.
• Ensure cross-attention tensor hooks handle varying token lengths without shape mismatches.
• Verify that outputted Python code adheres strictly to the chosen syntax of {{token_weighting_syntax}}.