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MPS Upscalers Fix
Get ESRGAN, SCUNet, and SwinIR working correctly on MPS by ensuring memory is contiguous for tensor views before sending to MPS device.
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@ -81,3 +81,7 @@ def autocast(disable=False):
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return contextlib.nullcontext()
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return torch.autocast("cuda")
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# MPS workaround for https://github.com/pytorch/pytorch/issues/79383
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def mps_contiguous(input_tensor, device): return input_tensor.contiguous() if device.type == 'mps' else input_tensor
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def mps_contiguous_to(input_tensor, device): return mps_contiguous(input_tensor, device).to(device)
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@ -190,7 +190,7 @@ def upscale_without_tiling(model, img):
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img = img[:, :, ::-1]
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img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
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img = torch.from_numpy(img).float()
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img = img.unsqueeze(0).to(devices.device_esrgan)
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img = devices.mps_contiguous_to(img.unsqueeze(0), devices.device_esrgan)
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with torch.no_grad():
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output = model(img)
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output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
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@ -54,9 +54,8 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
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img = img[:, :, ::-1]
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img = np.moveaxis(img, 2, 0) / 255
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img = torch.from_numpy(img).float()
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img = img.unsqueeze(0).to(device)
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img = devices.mps_contiguous_to(img.unsqueeze(0), device)
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img = img.to(device)
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with torch.no_grad():
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output = model(img)
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output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
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@ -111,7 +111,7 @@ def upscale(
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img = img[:, :, ::-1]
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img = np.moveaxis(img, 2, 0) / 255
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img = torch.from_numpy(img).float()
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img = img.unsqueeze(0).to(devices.device_swinir)
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img = devices.mps_contiguous_to(img.unsqueeze(0), devices.device_swinir)
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with torch.no_grad(), precision_scope("cuda"):
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_, _, h_old, w_old = img.size()
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h_pad = (h_old // window_size + 1) * window_size - h_old
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