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Running
on
L4
Running
on
L4
update.
Browse files- CodeFormer/basicsr/utils/realesrgan_utils.py +1 -0
- app.py +4 -4
CodeFormer/basicsr/utils/realesrgan_utils.py
CHANGED
@@ -211,6 +211,7 @@ class RealESRGANer():
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del output_img_t
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torch.cuda.empty_cache()
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except RuntimeError as error:
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print(f"Failed inference for RealESRGAN: {error}")
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# ------------------- process the alpha channel if necessary ------------------- #
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del output_img_t
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torch.cuda.empty_cache()
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except RuntimeError as error:
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+
output_img = cv2.resize(output_img, (w_input * self.scale, h_input * self.scale), interpolation=cv2.INTER_LINEAR)
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print(f"Failed inference for RealESRGAN: {error}")
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# ------------------- process the alpha channel if necessary ------------------- #
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app.py
CHANGED
@@ -110,10 +110,10 @@ def inference(image, background_enhance, face_upsample, upscale, codeformer_fide
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draw_box = False
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detection_model = "retinaface_resnet50"
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print('Inp:', image, background_enhance, face_upsample, upscale, codeformer_fidelity)
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-
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if background_enhance is None
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if face_upsample is None
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if upscale is None
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img = cv2.imread(str(image), cv2.IMREAD_COLOR)
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print('\timage size:', img.shape)
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draw_box = False
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detection_model = "retinaface_resnet50"
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print('Inp:', image, background_enhance, face_upsample, upscale, codeformer_fidelity)
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+
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background_enhance = background_enhance if background_enhance is not None else True
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face_upsample = face_upsample if face_upsample is not None else True
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upscale = upscale if (upscale is not None and upscale > 0) else 2
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img = cv2.imread(str(image), cv2.IMREAD_COLOR)
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print('\timage size:', img.shape)
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