mirror of
https://github.com/brycedrennan/imaginAIry
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37 lines
1.1 KiB
Python
37 lines
1.1 KiB
Python
import numpy as np
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import torch
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from PIL import Image
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from imaginairy.model_manager import get_cached_url_path
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from imaginairy.utils import get_device
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from imaginairy.utils.model_cache import memory_managed_model
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from imaginairy.vendored.basicsr.rrdbnet_arch import RRDBNet
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from imaginairy.vendored.realesrgan import RealESRGANer
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@memory_managed_model("realesrgan_upsampler", memory_usage_mb=70)
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def realesrgan_upsampler():
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model = RRDBNet(
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num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4
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)
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url = "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth"
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model_path = get_cached_url_path(url)
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device = get_device()
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upsampler = RealESRGANer(
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scale=4, model_path=model_path, model=model, tile=512, device=device
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)
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upsampler.device = torch.device(device)
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upsampler.model.to(device)
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return upsampler
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def upscale_image(img):
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img = img.convert("RGB")
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np_img = np.array(img, dtype=np.uint8)
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upsampler_output, img_mode = realesrgan_upsampler().enhance(np_img[:, :, ::-1])
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return Image.fromarray(upsampler_output[:, :, ::-1], mode=img_mode)
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