42 lines
1.7 KiB
Python
42 lines
1.7 KiB
Python
# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
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# except for the third-party components listed below.
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# Hunyuan 3D does not impose any additional limitations beyond what is outlined
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# in the repsective licenses of these third-party components.
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# Users must comply with all terms and conditions of original licenses of these third-party
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# components and must ensure that the usage of the third party components adheres to
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# all relevant laws and regulations.
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# For avoidance of doubts, Hunyuan 3D means the large language models and
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# their software and algorithms, including trained model weights, parameters (including
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# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
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# fine-tuning enabling code and other elements of the foregoing made publicly available
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# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
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import numpy as np
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from PIL import Image
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class imageSuperNet:
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def __init__(self, config) -> None:
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from realesrgan import RealESRGANer
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from basicsr.archs.rrdbnet_arch import RRDBNet
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
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upsampler = RealESRGANer(
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scale=4,
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model_path=config.realesrgan_ckpt_path,
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dni_weight=None,
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model=model,
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tile=0,
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tile_pad=10,
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pre_pad=0,
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half=True,
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gpu_id=None,
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)
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self.upsampler = upsampler
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def __call__(self, image):
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output, _ = self.upsampler.enhance(np.array(image))
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output = Image.fromarray(output)
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return output
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