Vixy
e4294b57e6
- SDXL image generation using RealVisXL_V4.0 - IP-Adapter FaceID integration for consistent face generation - Simplified API (removed client_id requirement) - New params: face_image, face_strength - 'vixy' shortcut for face-locked generation - Queue-based async job processing - FastAPI with proper error handling Co-authored-by: Alex <alex@k4zka.online>
340 lines
12 KiB
Python
Executable File
340 lines
12 KiB
Python
Executable File
"""
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SDXL Image Generator
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Handles image generation using Stable Diffusion XL with Jetson optimizations.
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Supports IP-Adapter FaceID for consistent face generation.
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"""
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import torch
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import logging
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import cv2
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import numpy as np
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from typing import Optional, Dict, Any, List, Union
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from pathlib import Path
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from diffusers import StableDiffusionXLPipeline, DDIMScheduler
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from PIL import Image
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import asyncio
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import config
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logger = logging.getLogger(__name__)
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class SDXLGenerator:
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"""SDXL image generator with optimizations for Jetson AGX Orin."""
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def __init__(self):
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self.pipeline = None
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self.device = None
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self.model_loaded = False
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self._load_lock = asyncio.Lock()
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# IP-Adapter FaceID components
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self.ip_model = None
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self.face_app = None
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self.face_embeds_cache = {} # Cache for precomputed face embeddings
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self.ip_adapter_loaded = False
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async def initialize(self):
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"""Load SDXL model with Jetson optimizations."""
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async with self._load_lock:
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if self.model_loaded:
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logger.info("Model already loaded, skipping initialization")
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return
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logger.info("Initializing SDXL model...")
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logger.info(f"Model: {config.SDXL_MODEL_ID}")
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logger.info(f"FP16: {config.USE_FP16}")
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logger.info(f"Attention slicing: {config.ENABLE_ATTENTION_SLICING}")
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# Determine device
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if torch.cuda.is_available():
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self.device = "cuda"
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logger.info(f"Using GPU: {torch.cuda.get_device_name(0)}")
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logger.info(f"VRAM available: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.2f} GB")
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else:
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self.device = "cpu"
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logger.warning("CUDA not available, using CPU (will be very slow)")
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# Load pipeline
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try:
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dtype = torch.float16 if config.USE_FP16 else torch.float32
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# Use DDIM scheduler for IP-Adapter compatibility
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noise_scheduler = DDIMScheduler(
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num_train_timesteps=1000,
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beta_start=0.00085,
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beta_end=0.012,
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beta_schedule="scaled_linear",
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clip_sample=False,
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set_alpha_to_one=False,
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steps_offset=1,
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)
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self.pipeline = StableDiffusionXLPipeline.from_pretrained(
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config.SDXL_MODEL_ID,
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torch_dtype=dtype,
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scheduler=noise_scheduler,
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use_safetensors=True,
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cache_dir=str(config.MODELS_DIR),
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add_watermarker=False,
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)
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# Move to device
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self.pipeline = self.pipeline.to(self.device)
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# Apply optimizations
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if config.ENABLE_ATTENTION_SLICING:
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self.pipeline.enable_attention_slicing()
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logger.info("Attention slicing enabled")
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if config.ENABLE_VAE_SLICING:
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self.pipeline.enable_vae_slicing()
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logger.info("VAE slicing enabled")
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if config.ENABLE_CPU_OFFLOAD and self.device == "cuda":
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self.pipeline.enable_model_cpu_offload()
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logger.info("CPU offload enabled")
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self.model_loaded = True
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logger.info("SDXL model loaded successfully!")
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except Exception as e:
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logger.error(f"Failed to load SDXL model: {e}")
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raise
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async def initialize_ip_adapter(self):
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"""Load IP-Adapter FaceID components (lazy loading)."""
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if self.ip_adapter_loaded:
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return
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logger.info("Initializing IP-Adapter FaceID...")
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try:
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# Import IP-Adapter components
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from ip_adapter.ip_adapter_faceid import IPAdapterFaceIDXL
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from insightface.app import FaceAnalysis
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# Initialize InsightFace for face detection/embedding
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self.face_app = FaceAnalysis(
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name="buffalo_l",
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providers=['CUDAExecutionProvider', 'CPUExecutionProvider']
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)
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self.face_app.prepare(ctx_id=0, det_size=(640, 640))
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logger.info("InsightFace initialized")
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# Load IP-Adapter FaceID model
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ip_ckpt = str(config.IP_ADAPTER_PATH)
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self.ip_model = IPAdapterFaceIDXL(
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self.pipeline,
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ip_ckpt,
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self.device
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)
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self.ip_adapter_loaded = True
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logger.info("IP-Adapter FaceID loaded successfully!")
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except ImportError as e:
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logger.warning(f"IP-Adapter dependencies not available: {e}")
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logger.warning("Face-locked generation will not be available")
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except Exception as e:
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logger.error(f"Failed to load IP-Adapter FaceID: {e}")
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raise
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def extract_face_embedding(self, image: Union[str, Path, Image.Image, np.ndarray]) -> torch.Tensor:
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"""
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Extract face embedding from an image.
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Args:
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image: Path to image, PIL Image, or numpy array
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Returns:
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Face embedding tensor
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"""
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if self.face_app is None:
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raise RuntimeError("InsightFace not initialized. Call initialize_ip_adapter() first.")
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# Convert to numpy array if needed
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if isinstance(image, (str, Path)):
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img_cv = cv2.imread(str(image))
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elif isinstance(image, Image.Image):
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img_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
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else:
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img_cv = image
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# Detect faces and extract embedding
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faces = self.face_app.get(img_cv)
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if len(faces) == 0:
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raise ValueError("No face detected in image")
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# Use first detected face
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face_embed = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0)
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logger.info(f"Face embedding extracted: shape {face_embed.shape}")
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return face_embed
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def precompute_face_embeddings(self, face_images: List[Union[str, Path]]) -> torch.Tensor:
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"""
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Precompute and average face embeddings from multiple reference images.
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Args:
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face_images: List of paths to face reference images
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Returns:
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Averaged face embedding tensor
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"""
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embeddings = []
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for img_path in face_images:
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try:
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embed = self.extract_face_embedding(img_path)
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embeddings.append(embed)
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logger.info(f"Extracted embedding from {img_path}")
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except Exception as e:
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logger.warning(f"Failed to extract face from {img_path}: {e}")
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if len(embeddings) == 0:
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raise ValueError("No faces could be extracted from any reference images")
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# Average the embeddings for better consistency
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avg_embedding = torch.mean(torch.stack(embeddings), dim=0)
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logger.info(f"Averaged {len(embeddings)} face embeddings")
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return avg_embedding
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async def generate_image(
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self,
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prompt: str,
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negative_prompt: Optional[str] = None,
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width: int = config.DEFAULT_WIDTH,
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height: int = config.DEFAULT_HEIGHT,
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num_inference_steps: int = config.DEFAULT_STEPS,
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guidance_scale: float = config.DEFAULT_GUIDANCE_SCALE,
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seed: Optional[int] = None,
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progress_callback = None,
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face_image: Optional[Union[str, Path, List[str]]] = None,
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face_strength: float = 0.6,
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) -> Image.Image:
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"""
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Generate an image from a text prompt.
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Args:
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prompt: Text prompt
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negative_prompt: Negative prompt
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width: Image width
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height: Image height
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num_inference_steps: Number of diffusion steps
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guidance_scale: Guidance scale
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seed: Random seed for reproducibility
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progress_callback: Optional async callback(step, total) for progress updates
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face_image: Optional path(s) to face reference image(s) for face locking
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face_strength: Strength of face conditioning (0.0-1.0, default 0.6)
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Returns:
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PIL Image
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Raises:
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RuntimeError: If model not loaded
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"""
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if not self.model_loaded:
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raise RuntimeError("Model not loaded. Call initialize() first.")
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logger.info(f"Generating image: '{prompt[:50]}...'")
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logger.info(f"Parameters: {width}x{height}, steps={num_inference_steps}, guidance={guidance_scale}")
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# Check if face-locked generation requested
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use_face_id = face_image is not None
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if use_face_id:
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# Initialize IP-Adapter if needed
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await self.initialize_ip_adapter()
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if not self.ip_adapter_loaded:
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logger.warning("IP-Adapter not available, generating without face lock")
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use_face_id = False
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else:
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logger.info(f"Face-locked generation enabled, strength={face_strength}")
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# Set random seed if provided
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generator = None
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if seed is not None:
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generator = torch.Generator(device=self.device).manual_seed(seed)
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logger.info(f"Using seed: {seed}")
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try:
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loop = asyncio.get_event_loop()
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if use_face_id:
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# Extract face embedding(s)
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if isinstance(face_image, list):
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face_embed = self.precompute_face_embeddings(face_image)
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else:
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face_embed = self.extract_face_embedding(face_image)
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# Generate with IP-Adapter FaceID
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image = await loop.run_in_executor(
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None,
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lambda: self.ip_model.generate(
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prompt=prompt,
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negative_prompt=negative_prompt,
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faceid_embeds=face_embed,
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width=width,
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height=height,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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num_samples=1,
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seed=seed,
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s_scale=face_strength,
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)[0]
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)
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else:
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# Progress callback wrapper (only for standard pipeline)
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def callback_wrapper(step: int, timestep: int, latents: torch.FloatTensor):
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if progress_callback:
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progress = int((step / num_inference_steps) * 100)
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try:
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asyncio.create_task(progress_callback(progress))
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except:
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pass
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# Standard generation without face lock
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image = await loop.run_in_executor(
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None,
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lambda: self.pipeline(
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prompt=prompt,
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negative_prompt=negative_prompt,
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width=width,
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height=height,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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generator=generator,
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callback=callback_wrapper,
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callback_steps=1
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).images[0]
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)
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logger.info("Image generated successfully")
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return image
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except Exception as e:
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logger.error(f"Error generating image: {e}")
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raise
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def get_model_info(self) -> Dict[str, Any]:
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"""Get information about the loaded model."""
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return {
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"model_id": config.SDXL_MODEL_ID,
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"device": self.device,
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"fp16": config.USE_FP16,
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"attention_slicing": config.ENABLE_ATTENTION_SLICING,
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"vae_slicing": config.ENABLE_VAE_SLICING,
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"cpu_offload": config.ENABLE_CPU_OFFLOAD,
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"loaded": self.model_loaded,
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"ip_adapter_loaded": self.ip_adapter_loaded,
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}
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# Global generator instance
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generator = SDXLGenerator()
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