- Add prompt_length dropdown (Short/Medium/Long/Free) to control output length
- Default to "Free" (no constraint) for maximum flexibility
- Updated prompts.toml with Z-Image best practices:
- Structured format: composition → subject → clothing → lighting
- Geometric face angles (front view, 45°, profile)
- Specific lighting keywords (soft diffused daylight, rim lighting)
- Cleanup constraints at end (no text, no watermark)
- Added get_length_constraint() to config_loader.py
- Length constraints applied to both single-shot and agentic pipelines
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add _enhance_prompt_with_instructions() method that makes a second
LLM call to intelligently apply user modifications to generated prompts
- Fix DynamicCache compatibility for newer transformers versions
(seen_tokens and get_max_length attributes)
- Fix output token extraction to only decode new tokens, preventing
system prompt from appearing in output
- Improve user guidance handling with MODIFICATION directive in
system prompts for better instruction following
- Only call enhancement LLM when user_guidance is provided
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Implements externalized configuration using TOML files for:
- Provider tiers (advanced/standard/basic) and stop strings
- Tier-specific system/user prompts for local vs cloud models
- Component prompts for agentic analysis by tier
- Output filter patterns for example text removal
Local models (Ollama, LM Studio) now get simplified prompts
without JSON output or complex numbered instructions.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
