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import spaces
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def load_pipeline():
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from diffusers import StableDiffusionXLPipeline
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import torch
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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pipe = StableDiffusionXLPipeline.from_pretrained(
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"John6666/rae-diffusion-xl-v2-sdxl-spo-pcm",
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custom_pipeline="lpw_stable_diffusion_xl",
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torch_dtype=torch.float16,
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)
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pipe.to(device)
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return pipe
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def save_image(image, metadata, output_dir):
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import os
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import uuid
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import json
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from PIL import PngImagePlugin
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filename = str(uuid.uuid4()) + ".png"
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os.makedirs(output_dir, exist_ok=True)
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filepath = os.path.join(output_dir, filename)
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metadata_str = json.dumps(metadata)
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info = PngImagePlugin.PngInfo()
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info.add_text("metadata", metadata_str)
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image.save(filepath, "PNG", pnginfo=info)
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return filepath
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pipe = load_pipeline()
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@spaces.GPU
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def generate_image(prompt, neg_prompt):
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metadata = {
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"prompt": prompt + ", anime, masterpiece, best quality, very aesthetic, absurdres",
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"negative_prompt": neg_prompt + ", bad hands, bad feet, 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",
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"resolution": f"{1024} x {1024}",
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"guidance_scale": 7.0,
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"num_inference_steps": 28,
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"sampler": "Euler",
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}
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try:
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images = pipe(
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prompt=prompt + ", anime, masterpiece, best quality, very aesthetic, absurdres",
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negative_prompt=neg_prompt + ", bad hands, bad feet, 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",
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width=1024,
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height=1024,
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guidance_scale=7.0,
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num_inference_steps=28,
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output_type="pil",
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clip_skip=1,
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).images
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if images:
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image_paths = [
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save_image(image, metadata, "./outputs")
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for image in images
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]
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return image_paths
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except Exception as e:
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print(e)
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return []
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