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ae68218 17770cb ae68218 17770cb ae68218 17770cb ae68218 17770cb ae68218 17770cb ae68218 17770cb ae68218 17770cb ae68218 17770cb ae68218 17770cb ae68218 17770cb ae68218 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 | """
Image generation utilities using Stable Diffusion.
This module provides the ImageGenerator class for generating images
using the Stable Diffusion model with configurable parameters.
"""
import logging
import os
from datetime import datetime
from typing import List, Optional, Tuple, Union
import torch
from diffusers import AutoPipelineForText2Image
from PIL import Image
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class ImageGenerator:
"""
A class for generating images using Stable Diffusion.
This class handles the initialization of the Stable Diffusion pipeline
and provides methods for generating images with configurable parameters.
"""
def __init__(self, model_id: str = "stabilityai/sdxl-turbo"):
"""
Initialize the ImageGenerator with a Stable Diffusion model.
Args:
model_id: The Hugging Face model ID for Stable Diffusion.
Defaults to "stabilityai/sdxl-turbo".
Raises:
RuntimeError: If the model fails to load or initialize.
"""
self.model_id = model_id
self.pipeline = None
self.device = self._get_device()
try:
logger.info(f"Loading Stable Diffusion model: {model_id}")
logger.info(f"Using device: {self.device}")
# Load the pipeline with appropriate device settings
self.pipeline = AutoPipelineForText2Image.from_pretrained(
model_id,
torch_dtype=torch.float16 if self.device == "cuda" else torch.float32,
variant="fp16"
)
# Move to device
self.pipeline = self.pipeline.to(self.device)
# Enable memory efficient attention if available
if hasattr(self.pipeline, 'enable_attention_slicing'):
self.pipeline.enable_attention_slicing()
logger.info("Model loaded successfully")
except Exception as e:
logger.error(f"Failed to load model: {str(e)}")
raise RuntimeError(f"Failed to initialize Stable Diffusion model: {str(e)}")
def _get_device(self) -> str:
"""
Determine the best available device for inference.
Returns:
str: The device to use ('cuda' or 'cpu').
"""
if torch.cuda.is_available():
return "cuda"
else:
logger.warning("CUDA not available, using CPU. Generation will be slower.")
return "cpu"
def generate_images(
self,
prompt: str,
num_images: int = 1,
num_inference_steps: int = 1,
guidance_scale: float = 0.0,
width: int = 512,
height: int = 512,
seed: Optional[int] = None
) -> List[Image.Image]:
"""
Generate images based on a text prompt.
Args:
prompt: The text prompt describing the desired image.
num_images: Number of images to generate (1-4).
num_inference_steps: Number of denoising steps (20-100).
guidance_scale: How closely to follow the prompt (1-20).
width: Width of the generated image (max 512).
height: Height of the generated image (max 512).
seed: Random seed for reproducible generation.
Returns:
List[Image.Image]: List of generated PIL Images.
Raises:
ValueError: If parameters are out of valid ranges.
RuntimeError: If image generation fails.
"""
# Validate parameters
if not prompt or not prompt.strip():
raise ValueError("Prompt cannot be empty")
if num_images < 1 or num_images > 4:
raise ValueError("Number of images must be between 1 and 4")
if num_inference_steps < 1 or num_inference_steps > 4:
raise ValueError("Inference steps must be between 1 and 4")
# if guidance_scale != 0.0:
# raise ValueError("Guidance scale must be 0.0 for best results")
if width > 1024 or height > 1024:
raise ValueError("Image dimensions cannot exceed 1024x1024")
if width < 512 or height < 512:
raise ValueError("Image dimensions must be at least 512x512")
try:
logger.info(f"Generating {num_images} image(s) with prompt: '{prompt[:50]}...'")
# Set seed for reproducibility
if seed is not None:
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
# Generate images
with torch.no_grad():
result = self.pipeline(
prompt=prompt,
num_images_per_prompt=num_images,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
width=width,
height=height
)
images = result.images
logger.info(f"Successfully generated {len(images)} image(s)")
return images
except Exception as e:
logger.error(f"Image generation failed: {str(e)}")
raise RuntimeError(f"Failed to generate images: {str(e)}")
def save_image_with_metadata(
image: Image.Image,
prompt: str,
output_dir: str = "outputs",
metadata: Optional[dict] = None
) -> str:
"""
Save an image with metadata to the output directory.
Args:
image: The PIL Image to save.
prompt: The prompt used to generate the image.
output_dir: Directory to save the image in.
metadata: Additional metadata to include in filename.
Returns:
str: The path to the saved image file.
Raises:
OSError: If the image cannot be saved.
"""
# Create output directory if it doesn't exist
os.makedirs(output_dir, exist_ok=True)
# Generate filename with timestamp
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
safe_prompt = "".join(c for c in prompt[:30] if c.isalnum() or c in (' ', '-', '_')).rstrip()
safe_prompt = safe_prompt.replace(' ', '_')
filename = f"generated_{timestamp}_{safe_prompt}.png"
filepath = os.path.join(output_dir, filename)
try:
# Save the image
image.save(filepath, "PNG")
logger.info(f"Image saved to: {filepath}")
# Save metadata if provided
if metadata:
metadata_file = filepath.replace('.png', '_metadata.txt')
with open(metadata_file, 'w') as f:
f.write(f"Prompt: {prompt}\n")
f.write(f"Generated: {datetime.now().isoformat()}\n")
for key, value in metadata.items():
f.write(f"{key}: {value}\n")
return filepath
except Exception as e:
logger.error(f"Failed to save image: {str(e)}")
raise OSError(f"Failed to save image: {str(e)}")
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