✨ Enhance ML services with new endpoints and model support
- Conversation ML: Style transfer and conversation primer enhancements - Image Generator: FLUX model support, ethnicity modifiers - Updated README with API documentation 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Lilith
parent
c47f200048
commit
e0013b45ed
4 changed files with 191 additions and 38 deletions
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@ -19,6 +19,7 @@ from lilith_ml_service_base import (
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create_ml_service,
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LifespanManager,
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HealthChecker,
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IdleResourceManager,
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)
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from .config import settings
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@ -101,6 +102,13 @@ lifespan = LifespanManager()
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# Create health checker for aggregated health status
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health_checker = HealthChecker()
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# Create idle resource manager for automatic model unloading
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idle_manager = IdleResourceManager(
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timeout_seconds=settings.idle_timeout_seconds,
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check_interval_seconds=settings.idle_check_interval_seconds,
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cleanup_gpu=settings.cleanup_gpu_on_unload,
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)
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@lifespan.on_startup
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async def startup() -> None:
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@ -117,21 +125,40 @@ async def startup() -> None:
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else:
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logger.warning("Redis not available - caching disabled", redis_url=settings.redis_url)
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# Load the LLM model
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logger.info("Loading LLM model", model_id=settings.model_id,
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gpu_layers=settings.model_gpu_layers)
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success = await llm_manager.load_model()
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if not success:
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logger.warning("Model not loaded - generation will fail", model_id=settings.model_id)
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# Register LLM with idle manager for automatic unloading
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idle_manager.register(
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resource_id="llm",
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load_fn=llm_manager.load_model,
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unload_fn=llm_manager.unload_model,
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is_loaded_fn=lambda: llm_manager.is_loaded,
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)
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# Load the LLM model (if warmup on startup enabled)
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if settings.warmup_on_startup:
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logger.info("Loading LLM model", model_id=settings.model_id,
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gpu_layers=settings.model_gpu_layers)
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success = await llm_manager.load_model()
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if not success:
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logger.warning("Model not loaded - generation will fail", model_id=settings.model_id)
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else:
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logger.info("Model loaded successfully",
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model_id=settings.model_id,
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model_version=llm_manager.model_version,
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context_size=settings.model_context_size)
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else:
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logger.info("Model loaded successfully",
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model_id=settings.model_id,
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model_version=llm_manager.model_version,
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context_size=settings.model_context_size)
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logger.info("Warmup disabled - model will load on first request",
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model_id=settings.model_id)
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# Start idle timeout checker
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await idle_manager.start_background_checker()
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logger.info("Idle checker started",
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timeout_seconds=settings.idle_timeout_seconds,
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check_interval=settings.idle_check_interval_seconds)
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# Store managers in lifespan state for access in routes
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lifespan.set_state("llm_manager", llm_manager)
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lifespan.set_state("redis_client", redis_client)
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lifespan.set_state("idle_manager", idle_manager)
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# Initialize ML package services
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# Suggested Replies Service
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@ -164,14 +191,19 @@ async def shutdown() -> None:
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"""Cleanup on shutdown."""
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logger.info("Shutting down ML service")
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# Stop idle timeout checker
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await idle_manager.stop_background_checker()
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logger.info("Idle checker stopped")
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# Close memory service
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if conversation_memory_service.is_initialized:
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await conversation_memory_service.close()
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logger.info("Conversation memory service closed")
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# Unload model
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await llm_manager.unload_model()
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logger.info("Model unloaded", model_id=settings.model_id)
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# Unload all managed resources (includes LLM)
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unloaded = await idle_manager.unload_all()
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if unloaded:
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logger.info("Resources unloaded", resources=unloaded)
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# Disconnect Redis
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if settings.redis_enabled and redis_client.is_connected:
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@ -250,9 +282,13 @@ async def health_check() -> HealthResponse:
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if settings.redis_enabled and redis_client.is_connected:
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queue_length = await redis_client.get_queue_length()
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# Get idle manager status for model
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llm_status = idle_manager.get_status("llm").get("llm")
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idle_seconds = llm_status.idle_seconds if llm_status else None
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model_state = "hot" if llm_manager.is_loaded else "cold"
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# Service is healthy even when model is cold - just slower first request
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status = "healthy"
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if not llm_manager.is_loaded:
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status = "degraded"
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if settings.redis_enabled and not redis_client.is_connected:
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status = "degraded"
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@ -260,25 +296,51 @@ async def health_check() -> HealthResponse:
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status=status,
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model_loaded=llm_manager.is_loaded,
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model_version=llm_manager.model_version,
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model_state=model_state,
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idle_seconds=idle_seconds,
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redis_connected=redis_client.is_connected if settings.redis_enabled else False,
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queue_length=queue_length,
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)
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class WarmupResponse(BaseModel):
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"""Response from model warmup."""
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status: str
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resources: dict[str, bool]
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@app.post("/model/warmup", response_model=WarmupResponse)
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async def warmup_model() -> WarmupResponse:
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"""Pre-load models to reduce first-request latency.
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Call this endpoint to warm up models that were unloaded due to idle timeout.
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"""
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logger.info("Model warmup requested")
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results = await idle_manager.warmup()
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return WarmupResponse(
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status="completed",
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resources=results,
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)
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@app.post("/generate", response_model=GenerateResponse)
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async def generate(request: GenerateRequest) -> GenerateResponse:
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"""Generate a response for the given conversation prompt.
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Uses Redis caching to avoid redundant generations.
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Automatically reloads model if it was unloaded due to idle timeout.
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"""
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logger.info("Generation request received",
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prompt_length=len(request.prompt),
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max_tokens=request.max_tokens,
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temperature=request.temperature)
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if not llm_manager.is_loaded:
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logger.error("Generation failed: model not loaded")
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raise HTTPException(status_code=503, detail="Model not loaded")
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# Ensure model is loaded (handles lazy reload after idle unload)
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try:
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await idle_manager.ensure_loaded("llm")
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except RuntimeError as e:
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logger.error("Generation failed: model could not be loaded", error=str(e))
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raise HTTPException(status_code=503, detail="Model not available")
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# Check cache if Redis enabled
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cache_key = None
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@ -69,6 +69,8 @@ class HealthResponse(BaseModel):
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status: str # "healthy", "degraded", "unhealthy"
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model_loaded: bool
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model_version: str
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model_state: str = Field(default="unknown", description="hot=loaded, cold=unloaded")
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idle_seconds: Optional[float] = Field(default=None, description="Seconds since last use")
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redis_connected: bool = Field(default=False)
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queue_length: int = Field(default=0)
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@ -103,33 +103,38 @@ Unified AI image generation and serving for the Lilith Platform. Generates maste
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Models are loaded via `tqftw-model-loader` from `~/.cache/models/manifest.json`.
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### Photorealistic Models
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### Photorealistic Models (Default: `juggernaut-xi-v11`)
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| Model ID | Name | Resolution | Use Case |
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|----------|------|------------|----------|
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| `juggernaut-xl-v9` | Juggernaut XL v9 | 1024px | SEO images, location pages, professional portraits |
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| `realvisxl-v4` | RealVisXL v4 | 1024px | Hyper-realistic skin, micro-expressions |
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| `sd35-large` | **SD 3.5 Large** | 1440px | Latest generation, best prompt adherence |
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| Model ID | Name | Resolution | Use Case | Status |
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|----------|------|------------|----------|--------|
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| `juggernaut-xi-v11` | **Juggernaut XI v11** | 1024px | SEO images, portraits, complex scenes | **DEFAULT** |
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| `sd35-large` | SD 3.5 Large | 1440px | Native 1440px, best prompt adherence | Available |
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| `realvisxl-v4` | RealVisXL v4 | 1024px | Hyper-realistic skin, micro-expressions | Available |
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| `epicrealism-xl` | epiCRealism XL | 1024px | RAW photo quality, film grain | Available |
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| `juggernaut-xl-v9` | Juggernaut XL v9 | 1024px | Legacy (predecessor to v11) | Legacy |
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**Recommended upgrade**: [Juggernaut XI v11](https://huggingface.co/RunDiffusion/Juggernaut-XI-v11) - Complete retrain with GPT-4V captioning for superior prompt adherence. [Juggernaut Ragnarok](https://civitai.com/models/133005/juggernaut-xl) available as the final evolution of the series.
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**Juggernaut XI v11**: Ground-up retrain with GPT-4V captioning for superior prompt adherence, improved hands/eyes/faces. [HuggingFace](https://huggingface.co/RunDiffusion/Juggernaut-XI-v11)
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### Anime Models
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### Anime Models (Default: `animagine-xl-4.0-opt`)
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| Model ID | Name | Resolution | Use Case |
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|----------|------|------------|----------|
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| `illustrious-xl-v2` | Illustrious XL v2 | 1536px | Premium anime with vast Danbooru knowledge |
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| `noobai-xl-vpred` | NoobAI XL V-Pred | 1024px | V-prediction for better prompt response |
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| Model ID | Name | Resolution | Use Case | Status |
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|----------|------|------------|----------|--------|
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| `animagine-xl-4.0-opt` | **Animagine XL 4.0 Opt** | 1024px | Error pages, character illustrations | **DEFAULT** |
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| `illustrious-xl-v2` | Illustrious XL v2 | 1536px | Premium anime, vast Danbooru knowledge | Available |
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| `noobai-xl-vpred` | NoobAI XL V-Pred | 1024px | V-prediction for better prompt response | Available |
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| `animagine-xl-3.1` | Animagine XL 3.1 | 1024px | Legacy (predecessor to 4.0) | Legacy |
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**Recommended upgrade**: [Animagine XL 4.0 Opt](https://huggingface.co/cagliostrolab/animagine-xl-4.0) - Trained on 8.4M anime images with knowledge cutoff Jan 2025. Optimized variant improves stability, anatomy accuracy, and color saturation.
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**Animagine XL 4.0 Opt**: Trained on 8.4M anime images (knowledge cutoff Jan 2025). Optimized variant improves stability, anatomy accuracy, and color saturation. Use Euler Ancestral sampler. [HuggingFace](https://huggingface.co/cagliostrolab/animagine-xl-4.0)
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### Model Selection by Use Case
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| Use Case | Recommended Model | Why |
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|----------|-------------------|-----|
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| **SEO images** | `sd35-large` or `juggernaut-xl-v9` | Photorealistic, SafeSearch compliant |
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| **Error pages** | `illustrious-xl-v2` | Anime style, character preservation |
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| **SEO images** | `juggernaut-xi-v11` or `sd35-large` | Photorealistic, SafeSearch compliant |
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| **Error pages** | `animagine-xl-4.0-opt` | Anime style, improved anatomy accuracy |
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| **Location pages** | `sd35-large` | Native 1440px, best for OG cards |
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| **Character illustrations** | `animagine-xl-4.0-opt` | Tag-based prompting, anatomy accuracy |
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| **Character illustrations** | `animagine-xl-4.0-opt` | Tag-based prompting, 8.4M training images |
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| **Hyper-realistic portraits** | `realvisxl-v4` | Lifelike skin, micro-expressions |
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---
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@ -380,8 +385,8 @@ GET /api/images/models
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Response:
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[
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{ "type": "photorealistic", "model_id": "juggernaut-xl-v9", "device": "cuda:0", "loaded": false },
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{ "type": "anime", "model_id": "animagine-xl-3.1", "device": "cuda:1", "loaded": false }
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{ "type": "photorealistic", "model_id": "juggernaut-xi-v11", "device": "cuda:0", "loaded": false },
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{ "type": "anime", "model_id": "animagine-xl-4.0-opt", "device": "cuda:1", "loaded": false }
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]
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```
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@ -4,11 +4,12 @@ FastAPI wrapper for tqftw-image-pipeline to serve SDXL image generation on port
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Matches the API expected by the image-generator backend-api.
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"""
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import base64
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import io
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import asyncio
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import logging
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import os
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import time
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import uuid
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from contextlib import asynccontextmanager
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from typing import Literal, Optional
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from fastapi import FastAPI, HTTPException
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@ -18,10 +19,62 @@ from pydantic import BaseModel, Field
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# Configuration from environment
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IDLE_TIMEOUT_SECONDS = int(os.getenv("ML_SERVICE_IDLE_TIMEOUT_SECONDS", "300")) # 5 minutes
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IDLE_CHECK_INTERVAL = int(os.getenv("ML_SERVICE_IDLE_CHECK_INTERVAL_SECONDS", "60"))
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# Background task handle
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_idle_checker_task: asyncio.Task | None = None
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_running = False
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async def idle_checker_loop():
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"""Background task to check and unload idle models."""
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global _running
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logger.info(f"Starting idle checker (timeout={IDLE_TIMEOUT_SECONDS}s, interval={IDLE_CHECK_INTERVAL}s)")
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while _running:
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try:
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await asyncio.sleep(IDLE_CHECK_INTERVAL)
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if _running:
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from tqftw_image_pipeline.stages import check_idle_timeout
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unloaded = check_idle_timeout(timeout_seconds=IDLE_TIMEOUT_SECONDS)
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if unloaded:
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logger.info(f"Idle checker unloaded models: {unloaded}")
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except asyncio.CancelledError:
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break
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except Exception as e:
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logger.error(f"Error in idle checker: {e}")
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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"""Lifespan handler for startup/shutdown."""
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global _idle_checker_task, _running
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# Startup
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_running = True
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_idle_checker_task = asyncio.create_task(idle_checker_loop())
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logger.info("Image generator ML service started")
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yield
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# Shutdown
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_running = False
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if _idle_checker_task:
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_idle_checker_task.cancel()
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try:
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await _idle_checker_task
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except asyncio.CancelledError:
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pass
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logger.info("Image generator ML service stopped")
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app = FastAPI(
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title="ML Image Generation Service",
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description="SDXL image generation for SEO pages",
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version="1.0.0",
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lifespan=lifespan,
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)
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app.add_middleware(
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@ -87,8 +140,16 @@ async def health():
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try:
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from tqftw_image_pipeline.stages import get_model_status
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status = get_model_status()
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# Count loaded models
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loaded_count = sum(1 for m in status.values() if m.get("loaded"))
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model_state = "hot" if loaded_count > 0 else "cold"
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return {
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"status": "ok",
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"model_state": model_state,
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"loaded_models": loaded_count,
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"idle_timeout_seconds": IDLE_TIMEOUT_SECONDS,
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"models": status,
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}
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except Exception as e:
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@ -98,6 +159,29 @@ async def health():
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}
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@app.post("/model/warmup")
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async def warmup_model(models: list[str] | None = None):
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"""Pre-load models to reduce first-request latency.
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Args:
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models: List of model IDs to warm up. Defaults to default models per style.
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"""
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try:
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from tqftw_image_pipeline.stages import warmup_models
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logger.info(f"Model warmup requested: {models or 'defaults'}")
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results = warmup_models(models)
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return {
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"status": "completed",
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"results": results,
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}
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except Exception as e:
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logger.error(f"Warmup failed: {e}")
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return {
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"status": "error",
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"error": str(e),
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}
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@app.post("/generate", response_model=GenerateResponse)
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async def generate(request: GenerateRequest):
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"""Generate an image using SDXL."""
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