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https://github.com/openvinotoolkit/stable-diffusion-webui.git
synced 2024-12-15 23:22:48 +03:00
Fixes LDSR upscaler producing black bars
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@ -101,8 +101,8 @@ class LDSR:
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down_sample_rate = target_scale / 4
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down_sample_rate = target_scale / 4
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wd = width_og * down_sample_rate
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wd = width_og * down_sample_rate
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hd = height_og * down_sample_rate
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hd = height_og * down_sample_rate
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width_downsampled_pre = int(wd)
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width_downsampled_pre = int(np.ceil(wd))
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height_downsampled_pre = int(hd)
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height_downsampled_pre = int(np.ceil(hd))
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if down_sample_rate != 1:
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if down_sample_rate != 1:
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print(
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print(
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@ -110,7 +110,12 @@ class LDSR:
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im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
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im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
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else:
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else:
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print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
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print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
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logs = self.run(model["model"], im_og, diffusion_steps, eta)
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# pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts
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pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size
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im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
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logs = self.run(model["model"], im_padded, diffusion_steps, eta)
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sample = logs["sample"]
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sample = logs["sample"]
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sample = sample.detach().cpu()
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sample = sample.detach().cpu()
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@ -120,6 +125,9 @@ class LDSR:
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sample = np.transpose(sample, (0, 2, 3, 1))
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sample = np.transpose(sample, (0, 2, 3, 1))
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a = Image.fromarray(sample[0])
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a = Image.fromarray(sample[0])
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# remove padding
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a = a.crop((0, 0) + tuple(np.array(im_og.size) * 4))
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del model
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del model
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gc.collect()
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gc.collect()
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torch.cuda.empty_cache()
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torch.cuda.empty_cache()
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