genimage.py

import spaces


def load_pipeline():
    from diffusers import StableDiffusionXLPipeline
    import torch
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    pipe = StableDiffusionXLPipeline.from_pretrained(
        "John6666/rae-diffusion-xl-v2-sdxl-spo-pcm",
        torch_dtype=torch.float16,
    )
    pipe.to(device)
    return pipe


def save_image(image, metadata, output_dir):
    import os
    import uuid
    import json
    from PIL import PngImagePlugin
    filename = str(uuid.uuid4()) + ".png"
    os.makedirs(output_dir, exist_ok=True)
    filepath = os.path.join(output_dir, filename)
    metadata_str = json.dumps(metadata)
    info = PngImagePlugin.PngInfo()
    info.add_text("metadata", metadata_str)
    image.save(filepath, "PNG", pnginfo=info)
    return filepath


pipe = load_pipeline()


@spaces.GPU
def generate_image(prompt, neg_prompt):
    metadata = {
        "prompt": prompt,
        "negative_prompt": neg_prompt,
        "resolution": f"{1024} x {1024}",
        "guidance_scale": 7.5,
        "num_inference_steps": 16,
        "sampler": "Euler",
    }
    try: 
        images = pipe(
            prompt=prompt + ", masterpiece, best quality, very aesthetic, absurdres",
            negative_prompt=neg_prompt + ", lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract], photo, deformed, disfigured, low contrast, photo, deformed, disfigured, low contrast",
            width=1024,
            height=1024,
            guidance_scale=7.5,
            num_inference_steps=16,
            output_type="pil",
            clip_skip=1,
        ).images
        if images:
            image_paths = [
                save_image(image, metadata, "./outputs")
                for image in images
            ]
        return image_paths
    except Exception as e:
        return []