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import torch
from model import DiffusionModel, UNet
from torchvision.utils import save_image
import argparse
from PIL import Image
def generate(prompts, model_path="diffusion_model.pth", image_size=256, device="cuda"):
# Load model
model = UNet().to(device)
model.load_state_dict(torch.load(model_path, map_location=device))
model.eval()
# Setup diffusion
betas = torch.linspace(1e-4, 0.02, 1000).to(device)
diffusion = DiffusionModel(model, betas, device)
# Generate images
with torch.no_grad():
images = diffusion.sample(prompts, image_size=image_size, batch_size=len(prompts))
# Save images
os.makedirs("generated", exist_ok=True)
for i, img in enumerate(images):
img = Image.fromarray(img.permute(1, 2, 0).cpu().numpy())
img.save(f"generated/sample_{i}.png")
print(f"Generated {len(images)} images saved in 'generated' folder")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--prompts", nargs="+", required=True, help="Text prompts for generation")
parser.add_argument("--model", default="diffusion_model.pth", help="Path to trained model")
parser.add_argument("--size", type=int, default=256, help="Image size")
args = parser.parse_args()
device = "cuda" if torch.cuda.is_available() else "cpu"
generate(args.prompts, args.model, args.size, device) |