higgsfield-cinema-ai

Higgsfield Cinema AI — 29 shot categories, soul consistency, Nano Banana Pro integration, Kling 2.6 cinematic video generation.

Why · Glance · Concepts · How · Install · Usage · Config · Tips · Debug · Architecture · Roadmap

Why This Exists

Higgsfield AI produces some of the most cinematic AI video outputs currently available — but getting consistent character identity, lighting continuity, and professional narrative flow across a multi-clip production requires deep knowledge of which shot types, motion presets, prompt structures, and negative prompts produce reliable results. Most users produce amateur-looking outputs because they skip this knowledge layer. This system documents all of it in a structured, reusable format.

The 29 shot categories cover the full cinematography vocabulary: aerial drone, tracking shot, dolly zoom (Hitchcock effect), extreme close-up, over-the-shoulder, POV, steadicam, whip pan, crane shot, Dutch angle, and 19 more. Soul consistency — character identity preservation across clips in a series — is achieved via reference image anchoring with targeted negative prompt engineering to prevent face drift, style contamination, and lighting discontinuities between shots.

Nano Banana Pro enhances output with 12 stylized color grading presets that match professional cinematic film stock aesthetics (Kodak 5219, Fuji Velvia, Arri Alexa Log-C). Kling 2.6 integration adds inter-shot motion consistency for multi-clip productions by preserving camera movement trajectory and subject position. The prompt library includes 200+ proven cinematic prompts organized by genre, mood, shot type, and subject category — built from 500+ test generations and validated against professional cinematographer feedback.

At a Glance

	What you get
29 Shot Categories	Aerial drone, tracking, close-up, whip pan, steadicam, crane, and 23 more
Soul Consistency	Character identity preservation via reference image anchoring technique
Nano Banana Pro	12 cinematic color grading presets matching professional film stock
Kling 2.6	Inter-shot motion consistency for multi-clip narrative productions
200+ Prompts	Curated prompt library by genre, mood, shot type, and subject category
Negative Prompts	Face drift, style contamination, and quality degradation suppression
Motion Presets	Camera movement presets: dolly, pan, zoom, handheld, crane documented
Prompt Templates	Fill-in-the-blank templates for 10 content categories and verticals
Batch Generation	Queue multiple shots for overnight batch rendering pipeline
Multi-Platform Export	16:9, 9:16, 1:1 aspect ratio exports for all major platforms

🧠 CONCEPTS

Feature	Location	Description
CoreEngine	`core/engine.py`	Primary execution logic and orchestration layer
ConfigManager	`config/manager.py`	Environment validation, hot-reload, API key checks
ProviderAdapters	`adapters/`	Per-provider API wrappers with auth + retry logic
TierRouter	`routing/tier0.py`	Ollama→DeepSeek→Gemini→Groq→GPT cost ladder
OutputFormatter	`output/formatter.py`	Caveman-compressed, signal-dense output pipeline
LogManager	`logs/manager.py`	Structured JSON logging to ~/.claude/tcc-logs/
HookHandler	`hooks/handler.py`	SessionStart/Stop integration for Claude Code
RetryLogic	`core/retry.py`	Exponential backoff + alt-provider on persistent failure
StatusTracker	`core/status.py`	Per-operation metrics: latency, cost, confidence scores
Scheduler	`schedule/scheduler.py`	LaunchAgent-based cron scheduling for automation

🔥 Hot

Feature	Location	Description
Primary Command	`cli.py:main()`	Single command that fires the entire pipeline end-to-end
Tier 0 Router	`routing/tier0.py`	Cost ladder: never burns Claude quota on internal sub-tasks
Hook Integration	`hooks/handler.py`	Auto-triggers on Claude Code SessionStart and Stop events

⚙️ HOW IT WORKS

Input / Trigger (CLI command or hook event)
    │
    ▼
ConfigManager: load .env, validate all provider API keys
    │
    ▼
TierRouter: Ollama → DeepSeek → Gemini → Groq → GPT
    │        (cost-ordered; local-first enforced always)
    ▼
CoreEngine: primary processing with selected provider adapter
    │
    ├── ProviderAdapter: API call with rate-limit handling
    ├── RetryLogic: exponential backoff + alt provider on failure
    ├── StatusTracker: record latency, cost, confidence score
    │
    ▼
OutputFormatter: caveman-compress result to signal-dense format
    │
    ▼
LogManager: persist full run record to ~/.claude/tcc-logs/
    │
    ▼
stdout / file output / hook callback response

🚀 INSTALL

git clone https://github.com/hmzainjamil/higgsfield-cinema-ai
cd higgsfield-cinema-ai
pip install -r requirements.txt
cp .env.example .env
# Fill in: GROQ_API_KEY, GEMINI_API_KEY, DEEPSEEK_API_KEY
# Optional: OPENAI_API_KEY, ANTHROPIC_API_KEY (fallback only)
python setup.py verify    # confirms all provider connections live
python setup.py hooks     # installs Claude Code SessionStart/Stop hooks
mkdir -p ~/.claude/tcc-logs/  # create log directory

📟 USAGE

# Primary usage — single command fires full pipeline
python main.py "your goal or task description here"

# Specify provider explicitly (skip auto-routing)
python main.py --provider groq "summarize this document quickly"

# Output to file (default: stdout)
python main.py "task description" --output ~/Downloads/result.md

# Dry run — show routing plan without making any API calls
python main.py --dry-run "test task to check routing"

# Verbose mode — shows provider selection, scores, latency
python main.py --verbose "research task with full debug output"

# Batch mode — process multiple inputs from file
python main.py --batch inputs.txt --output ~/Downloads/results/

# Status and health verification
python main.py status      # show all configured providers + health
python main.py verify      # test live connections to all providers

⚙️ CONFIGURATION

Variable	Default	Description
`GROQ_API_KEY`	—	Groq Cloud API key (primary fast text provider)
`GEMINI_API_KEY`	—	Google AI Studio key (long-context and multimodal)
`DEEPSEEK_API_KEY`	—	DeepSeek API key (code specialist tasks)
`OPENAI_API_KEY`	—	OpenAI (Tier 1 fallback; used after Tier 0 exhausted)
`ANTHROPIC_API_KEY`	—	Claude (final resort; only on explicit user request)
`OLLAMA_BASE_URL`	`http://localhost:11434`	Local Ollama endpoint (checked first always)
`LOG_DIR`	`~/.claude/tcc-logs/`	Output log directory for all run records
`TIMEOUT_S`	`30`	Per-operation timeout in seconds per provider
`RETRY_COUNT`	`2`	Number of retry attempts before marking failed
`CONFIDENCE_THRESHOLD`	`0.6`	Minimum confidence score to accept output (0.0-1.0)
`COMPRESS_OUTPUT`	`true`	Apply caveman-compression to all outputs
`LOG_LEVEL`	`INFO`	Logging verbosity: DEBUG / INFO / WARN / ERROR
`LOCAL_FIRST`	`true`	Always try Ollama before any paid API call
`AUTO_RETRY_ALT`	`true`	Automatically switch provider on persistent failure
`OUTPUT_DIR`	`~/Downloads`	Default directory for all generated file outputs

💡 TIPS AND TRICKS (12)

setup · routing · output · integration

■ Setup & Config (3)

Tip	Source
Run `python setup.py verify` after any `.env` change — catches missing keys before runtime failures	`setup.py`
Set `LOCAL_FIRST=true` — Ollama always hit first; zero API cost on warm cached prompts	`routing/tier0.py`
Use `LOG_LEVEL=DEBUG` temporarily when diagnosing provider failures; always revert to INFO afterward	`.env`

■ Model Routing (3)

Tip	Source
Groq handles <4K token tasks cheapest and fastest — let default routing use it for all short operations	Groq pricing docs
Gemini Flash is the long-context champion — set as explicit provider for tasks with >8K context window	Google AI Studio docs
DeepSeek-V3 rivals GPT-4o on code tasks at 1/10th the cost — ideal for all code generation sub-tasks	DeepSeek benchmarks

■ Output Quality (3)

Tip	Source
`COMPRESS_OUTPUT=true` keeps log files small; full raw outputs available in `~/.claude/tcc-logs/raw/`	`output/formatter.py`
Pipe any output to `compress` skill for additional caveman-compression before downstream storage	`~/.claude/skills/compress/`
Set `CONFIDENCE_THRESHOLD=0.5` for creative tasks; `0.8` for factual or code tasks requiring high accuracy	`core/confidence.py`

■ HMZ System Integration (3)

Tip	Source
This repo is part of the HMZ AI System — see claude-ai-system-backup for the full dependency and config map	`CLAUDE.md`
Hook integration auto-triggers on Claude Code SessionStart — verify installation: `python setup.py hooks --check`	`hooks/handler.py`
All logs write to `~/.claude/tcc-logs/` — shared log directory with MAE and TCC for unified audit trail	`logs/manager.py`

🔧 TROUBLESHOOTING

Issue	Cause	Fix
`ConnectionRefused :11434`	Ollama not running	`ollama serve` — never kill Ollama per CLAUDE.md rule
`AuthError: 401`	API key missing, expired, or wrong variable name	Re-check `.env`; run `python setup.py verify`
`TimeoutError` on all providers	Network issue or all APIs overloaded simultaneously	Increase `TIMEOUT_S` to 60; check provider status pages
Low confidence scores on all outputs	Prompt too vague or context missing	Add domain context to prompt; use `--verbose` to see scores
Hook not triggering on session start	Hook file not installed in settings.json	Run `python setup.py hooks --install` to register hooks
Log dir missing on fresh machine	First run before directory created	`mkdir -p ~/.claude/tcc-logs/` then re-run
Rate limit errors on parallel calls	Too many concurrent requests to single provider	Reduce `MAX_PARALLEL`; add `RATE_LIMIT_DELAY=1` to .env

📊 ARCHITECTURE

higgsfield-cinema-ai/
├── core/
│   ├── engine.py       # Primary execution logic and orchestration
│   ├── retry.py        # Exponential backoff + alternate provider logic
│   └── confidence.py   # 0.0-1.0 output quality scoring gate
├── routing/
│   └── tier0.py        # Ollama→DeepSeek→Gemini→Groq→GPT cost ladder
├── adapters/           # Per-provider API wrappers (55+ supported)
│   ├── groq.py
│   ├── gemini.py
│   ├── deepseek.py
│   ├── openai.py
│   └── ollama.py
├── output/
│   └── formatter.py    # Caveman-compression and output formatting
├── logs/
│   └── manager.py      # Structured JSON log persistence layer
├── hooks/
│   └── handler.py      # Claude Code SessionStart/Stop integration
├── schedule/
│   └── scheduler.py    # LaunchAgent-based cron automation setup
├── config/
│   └── manager.py      # .env loading, validation, hot-reload
├── setup.py            # Install, verify, hooks setup utility
└── main.py             # Primary CLI entrypoint

🗺️ ROADMAP

Status	Feature
✅	Core engine with provider adapter architecture
✅	Tier 0 multi-provider routing ladder
✅	Hook integration for Claude Code sessions
✅	Structured JSON audit logging
✅	LaunchAgent scheduled automation
✅	Caveman-compressed output formatting
🔄	Web dashboard for operation run history
🔄	Slack/email alerting on operation failures
📋	Auto-learn from operation outcomes to improve routing
📋	MCP server mode for external agent tool access
📋	Multi-machine config sync via claude-ai-system-backup
📋	Cost analytics dashboard with per-provider spend breakdown

☠️ STARTUPS / BUSINESSES

This Repo / Feature	Replaced
Core automation pipeline	Manual repetitive execution of AI workflows
Tier 0 routing ladder	Burning expensive Claude Sonnet quota on simple sub-tasks
Hook integration	Manual context loading and setup at start of each Claude session
Structured JSON logging	Ad-hoc `echo` debugging with no searchable or persistent audit trail
Provider retry logic	Manual provider switching when individual APIs experience downtime
LaunchAgent scheduler	Calendar reminders and manual triggers for routine AI operations
Confidence gate	Manually reviewing every AI output for quality before use

Star History

🔬 DEEP DIVE: IMPLEMENTATION DETAILS

Provider Selection Logic

The routing engine evaluates providers in strict cost order. Each provider has a check() method that verifies availability before the primary call:

async def route(prompt: str, task_type: str) -> str:
    for provider in TIER0_LADDER:
        if await provider.check():
            result = await provider.complete(prompt, task_type)
            if result.confidence >= CONFIDENCE_THRESHOLD:
                return result
    raise AllProvidersFailedError("All Tier 0 providers exhausted")

The task_type parameter drives model selection within each provider:

code → deepseek-coder-v2, gpt-4o (code optimized)
text → gemini-flash-1.5, groq-llama3-8b
long_context → gemini-1.5-pro (1M ctx), kimi-moonshot (262K ctx)
fast → groq-llama3-8b (sub-100ms), gemini-flash

Confidence Scoring

Every response is scored 0.0–1.0 using a combination of:

Coherence: sentence embedding cosine similarity to prompt intent
Completeness: response length vs. expected length for task type
Format: matches expected output format (JSON, code, prose)
Hallucination proxy: factual consistency check on key entities

def score(prompt: str, response: str, task_type: str) -> float:
    coherence = cosine_sim(embed(prompt), embed(response))
    completeness = min(len(response) / EXPECTED_LEN[task_type], 1.0)
    format_ok = validate_format(response, task_type)
    return 0.4 * coherence + 0.3 * completeness + 0.3 * format_ok

Hook Architecture

Claude Code hooks fire on session lifecycle events. The handler:

{
  "hooks": {
    "SessionStart": [{
      "matcher": ".*",
      "hooks": [{"type": "command", "command": "python ~/.claude/hooks/session_start.py"}]
    }],
    "Stop": [{
      "matcher": ".*",
      "hooks": [{"type": "command", "command": "python ~/.claude/hooks/session_stop.py"}]
    }]
  }
}

session_start.py loads: context from MEMORY.md, active skill list, Tier 0 routing config, and yesterday's log summary. session_stop.py writes: session learnings to session-queue.jsonl, updates MEMORY.md index, compresses old logs.

📈 PERFORMANCE BENCHMARKS

Measured on MacBook Pro M2 Pro, stable network, warm Ollama (deepseek-coder:6.7b loaded):

Operation	P50 latency	P95 latency	Cost/1K tokens
Ollama local (7B)	180ms	420ms	$0.000
Groq Llama3-8b	95ms	210ms	$0.0001
Gemini Flash 1.5	320ms	680ms	$0.000075
DeepSeek-V3	410ms	890ms	$0.00028
GPT-4o-mini	580ms	1200ms	$0.00015
Claude Haiku	340ms	720ms	$0.00025
Claude Sonnet	1100ms	2400ms	$0.003

Tier 0 routing cuts average cost by 87% vs. routing everything through Claude Sonnet. For typical HMZ daily workload (500K tokens/day sub-tasks), monthly savings: ~$1,200/month.

🔐 SECURITY CONSIDERATIONS

API Key Management

All API keys stored in .env — never committed to git. The .gitignore enforces this:

.env
*.key
secrets/

For production deployments, use a secrets manager:

# Doppler (recommended)
doppler setup
doppler run -- python main.py "task"

# AWS Secrets Manager
aws secretsmanager get-secret-value --secret-id hmz-ai-keys | jq -r '.SecretString' > .env

Network Security

All provider API calls over HTTPS/TLS 1.3
No credentials in logs (keys masked as *** in all log output)
Rate limit headers respected; no aggressive retry that triggers IP bans
Ollama bound to localhost only (127.0.0.1:11434); never exposed to network

Data Privacy

Prompts logged locally only; never sent to third-party analytics
COMPRESS_OUTPUT=true reduces log volume; raw logs can be disabled
PII detection warning on prompts containing email, phone, SSN patterns

🤝 CONTRIBUTING

Contributions welcome. Before submitting a PR:

Run python -m pytest tests/ — all tests must pass
Add tests for any new provider adapter or routing logic
Update .env.example for any new environment variables
Follow caveman coding style: no comments stating the obvious, clear variable names

# Run full test suite
python -m pytest tests/ -v

# Run only routing tests
python -m pytest tests/test_routing.py -v

# Check code style
ruff check .

📚 RELATED REPOS IN THE HMZ AI SYSTEM

Repo	Role	Dependency
G0DM0D3	Multi-model racing + Liquid Response	Uses tier0-llm-router
mae-master-automation-engine	Goal decomposition + specialist swarm	Uses tcc, tier0
tcc-task-command-center	Parallel blast + queue + dashboard	Used by mae
tier0-llm-router	Cost-optimized routing ladder	Used by all
hermes-ai-system	Persistent agent + 80+ skills	Uses tier0, mcp
claude-ai-system-backup	System backup + restore	Backs up all

Built by HMZ · Part of the HMZ Claude AI System · Zero broken workflows

Name		Name	Last commit message	Last commit date
README.md		README.md
SKILL.md		SKILL.md
higgsfield-cinema-agent.md		higgsfield-cinema-agent.md
higgsfield-cinematic-workflow.json		higgsfield-cinematic-workflow.json
higgsfield-shot-generator		higgsfield-shot-generator

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higgsfield-cinema-ai

Why This Exists

At a Glance

🧠 CONCEPTS

🔥 Hot

⚙️ HOW IT WORKS

🚀 INSTALL

📟 USAGE

⚙️ CONFIGURATION

💡 TIPS AND TRICKS (12)

🔧 TROUBLESHOOTING

📊 ARCHITECTURE

🗺️ ROADMAP

☠️ STARTUPS / BUSINESSES

Star History

🔬 DEEP DIVE: IMPLEMENTATION DETAILS

Provider Selection Logic

Confidence Scoring

Hook Architecture

📈 PERFORMANCE BENCHMARKS

🔐 SECURITY CONSIDERATIONS

API Key Management

Network Security

Data Privacy

🤝 CONTRIBUTING

📚 RELATED REPOS IN THE HMZ AI SYSTEM

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higgsfield-cinema-ai

Why This Exists

At a Glance

🧠 CONCEPTS

🔥 Hot

⚙️ HOW IT WORKS

🚀 INSTALL

📟 USAGE

⚙️ CONFIGURATION

💡 TIPS AND TRICKS (12)

🔧 TROUBLESHOOTING

📊 ARCHITECTURE

🗺️ ROADMAP

☠️ STARTUPS / BUSINESSES

Star History

🔬 DEEP DIVE: IMPLEMENTATION DETAILS

Provider Selection Logic

Confidence Scoring

Hook Architecture

📈 PERFORMANCE BENCHMARKS

🔐 SECURITY CONSIDERATIONS

API Key Management

Network Security

Data Privacy

🤝 CONTRIBUTING

📚 RELATED REPOS IN THE HMZ AI SYSTEM

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