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import model_loader
import pipeline
from PIL import Image
from pathlib import Path
from transformers import CLIPTokenizer
import torch
from config import Config, default_config, DeviceConfig
# Device configuration
ALLOW_CUDA = False
ALLOW_MPS = False
device = "cpu"
if torch.cuda.is_available() and ALLOW_CUDA:
device = "cuda"
elif (torch.backends.mps.is_built() or torch.backends.mps.is_available()) and ALLOW_MPS:
device = "mps"
print(f"Using device: {device}")
# Initialize configuration
config = Config(
device=DeviceConfig(device=device),
seed=42,
tokenizer=CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
)
# Update diffusion parameters
config.diffusion.strength = 0.75
config.diffusion.cfg_scale = 8.0
config.diffusion.n_inference_steps = 50
# Load models with SE blocks enabled
model_file = "data/v1-5-pruned-emaonly.ckpt"
config.models = model_loader.load_models(model_file, device, use_se=True)
# Generate image
prompt = "A ultra sharp photorealtici painting of a futuristic cityscape at night with neon lights and flying cars"
uncond_prompt = ""
output_image = pipeline.generate(
prompt=prompt,
uncond_prompt=uncond_prompt,
config=config
)
# Save output
output_image = Image.fromarray(output_image)
output_image.save("output.png")
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