feat: implement Ansible-based process architecture for RunPod
Major architecture overhaul to address RunPod Docker limitations: Core Infrastructure: - Add base_service.py: Abstract base class for all AI services - Add service_manager.py: Process lifecycle management - Add core/requirements.txt: Core dependencies Model Services (Standalone Python): - Add models/vllm/server.py: Qwen 2.5 7B text generation - Add models/flux/server.py: Flux.1 Schnell image generation - Add models/musicgen/server.py: MusicGen Medium music generation - Each service inherits from GPUService base class - OpenAI-compatible APIs - Standalone execution support Ansible Deployment: - Add playbook.yml: Comprehensive deployment automation - Add ansible.cfg: Ansible configuration - Add inventory.yml: Localhost inventory - Tags: base, python, dependencies, models, tailscale, validate, cleanup Scripts: - Add scripts/install.sh: Full installation wrapper - Add scripts/download-models.sh: Model download wrapper - Add scripts/start-all.sh: Start orchestrator - Add scripts/stop-all.sh: Stop all services Documentation: - Update ARCHITECTURE.md: Document distributed VPS+GPU architecture Benefits: - No Docker: Avoids RunPod CAP_SYS_ADMIN limitations - Fully reproducible via Ansible - Extensible: Add models in 3 steps - Direct Python execution (no container overhead) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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models/flux/server.py
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193
models/flux/server.py
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#!/usr/bin/env python3
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"""
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Flux.1 Image Generation Service
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OpenAI-compatible image generation using Flux.1 Schnell model.
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Provides /v1/images/generations endpoint.
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"""
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import base64
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import io
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import os
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from typing import Optional
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import torch
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from diffusers import FluxPipeline
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from fastapi import HTTPException
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from PIL import Image
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from pydantic import BaseModel, Field
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# Import base service class
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import sys
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../..'))
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from core.base_service import GPUService
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class ImageGenerationRequest(BaseModel):
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"""Image generation request (OpenAI-compatible)"""
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model: str = Field(default="flux-schnell", description="Model name")
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prompt: str = Field(..., description="Text description of the image to generate")
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n: int = Field(default=1, ge=1, le=4, description="Number of images to generate")
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size: str = Field(default="1024x1024", description="Image size (e.g., 512x512, 1024x1024)")
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response_format: str = Field(default="b64_json", description="Response format: url or b64_json")
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quality: str = Field(default="standard", description="Image quality: standard or hd")
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style: str = Field(default="natural", description="Image style: natural or vivid")
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class ImageGenerationResponse(BaseModel):
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"""Image generation response (OpenAI-compatible)"""
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created: int = Field(..., description="Unix timestamp")
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data: list = Field(..., description="List of generated images")
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class FluxService(GPUService):
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"""Flux.1 Schnell image generation service"""
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def __init__(self):
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# Get port from environment or use default
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port = int(os.getenv("PORT", "8002"))
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super().__init__(name="flux-schnell", port=port)
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# Service-specific attributes
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self.pipeline: Optional[FluxPipeline] = None
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self.model_name = os.getenv("MODEL_NAME", "black-forest-labs/FLUX.1-schnell")
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async def initialize(self):
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"""Initialize Flux.1 pipeline"""
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await super().initialize()
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self.logger.info(f"Loading Flux.1 pipeline: {self.model_name}")
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# Load pipeline
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self.pipeline = FluxPipeline.from_pretrained(
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self.model_name,
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torch_dtype=torch.bfloat16,
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cache_dir=os.getenv("HF_CACHE_DIR", "/workspace/huggingface_cache")
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)
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# Move to GPU
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if torch.cuda.is_available():
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self.pipeline = self.pipeline.to("cuda")
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self.logger.info("Flux.1 pipeline loaded on GPU")
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else:
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self.logger.warning("GPU not available, running on CPU (very slow)")
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# Enable memory optimizations
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if hasattr(self.pipeline, 'enable_model_cpu_offload'):
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# This moves models to GPU only when needed, saving VRAM
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self.pipeline.enable_model_cpu_offload()
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self.logger.info("Flux.1 pipeline initialized successfully")
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async def cleanup(self):
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"""Cleanup resources"""
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await super().cleanup()
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if self.pipeline:
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self.logger.info("Flux.1 pipeline cleanup")
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self.pipeline = None
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def parse_size(self, size_str: str) -> tuple[int, int]:
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"""Parse size string like '1024x1024' into (width, height)"""
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try:
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parts = size_str.lower().split('x')
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if len(parts) != 2:
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return (1024, 1024)
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width = int(parts[0])
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height = int(parts[1])
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return (width, height)
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except:
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return (1024, 1024)
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def image_to_base64(self, image: Image.Image) -> str:
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"""Convert PIL Image to base64 string"""
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buffered = io.BytesIO()
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image.save(buffered, format="PNG")
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img_bytes = buffered.getvalue()
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return base64.b64encode(img_bytes).decode('utf-8')
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def create_app(self):
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"""Create FastAPI routes"""
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@self.app.get("/")
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async def root():
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"""Root endpoint"""
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return {
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"service": "Flux.1 Schnell Image Generation",
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"model": self.model_name,
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"max_images": 4
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}
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@self.app.get("/v1/models")
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async def list_models():
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"""List available models (OpenAI-compatible)"""
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return {
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"object": "list",
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"data": [
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{
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"id": "flux-schnell",
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"object": "model",
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"created": 1234567890,
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"owned_by": "black-forest-labs",
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"permission": [],
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"root": self.model_name,
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"parent": None,
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}
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]
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}
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@self.app.post("/v1/images/generations")
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async def generate_image(request: ImageGenerationRequest) -> ImageGenerationResponse:
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"""Generate images from text prompt (OpenAI-compatible)"""
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if not self.pipeline:
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raise HTTPException(status_code=503, detail="Model not initialized")
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self.logger.info(f"Generating {request.n} image(s): {request.prompt[:100]}...")
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try:
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# Parse image size
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width, height = self.parse_size(request.size)
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self.logger.info(f"Size: {width}x{height}")
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# Generate images
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images = []
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for i in range(request.n):
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self.logger.info(f"Generating image {i+1}/{request.n}")
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# Flux.1 Schnell uses 4 inference steps for speed
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image = self.pipeline(
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prompt=request.prompt,
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width=width,
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height=height,
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num_inference_steps=4, # Schnell is optimized for 4 steps
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guidance_scale=0.0, # Schnell doesn't use guidance
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).images[0]
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# Convert to base64
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if request.response_format == "b64_json":
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image_data = {
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"b64_json": self.image_to_base64(image)
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}
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else:
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# For URL format, we'd need to save and serve the file
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# For now, we'll return base64 anyway
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image_data = {
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"b64_json": self.image_to_base64(image)
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}
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images.append(image_data)
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self.logger.info(f"Generated {request.n} image(s) successfully")
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return ImageGenerationResponse(
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created=1234567890,
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data=images
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)
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except Exception as e:
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self.logger.error(f"Error generating image: {e}", exc_info=True)
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raise HTTPException(status_code=500, detail=str(e))
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if __name__ == "__main__":
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service = FluxService()
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service.run()
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