import torch
from diffusers import DiffusionPipeline, LCMScheduler, UNet2DConditionModel

import logging

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

unet = UNet2DConditionModel.from_pretrained(
    "gvecchio/StableMaterials",
    subfolder="unet_lcm",
    torch_dtype=torch.float16,
)

pipe = DiffusionPipeline.from_pretrained(
    "gvecchio/StableMaterials",
    trust_remote_code=True,
    unet=unet,
    torch_dtype=torch.float16
).to(device)

pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)


def generate_material(prompt, seed=-1, resolution=512, refinement=False):
    try:
        seed = seed if seed != -1 else torch.randint(0, 10000, (1,)).item()
        
        logger.info(f"Generating images for prompt: {prompt} with seed: {seed}")
        generator = torch.Generator(device=pipe.device).manual_seed(seed)

        image = pipe(
            prompt=[prompt],
            tileable=True,
            num_images_per_prompt=1,
            num_inference_steps=4,
            generator=generator,
        ).images[0]

        image = image.resize((resolution, resolution))

        if refinement:
            pass

        return image
    except Exception as e:
        logger.error(f"Exception occurred while generating images: {e}")
        raise