Commit abfa22c1 authored by brkirch's avatar brkirch
Browse files

Revert "MPS Upscalers Fix" 

This reverts commit 768b95394a8500da639b947508f78296524f1836.
parent 98947d17

modules/devices.py

+0 −9
Original line number Diff line number Diff line
@@ -94,12 +94,3 @@ def autocast(disable=False): 
        return contextlib.nullcontext()



    return torch.autocast("cuda")
 




# MPS workaround for https://github.com/pytorch/pytorch/issues/79383

def mps_contiguous(input_tensor, device):

    return input_tensor.contiguous() if device.type == 'mps' else input_tensor





def mps_contiguous_to(input_tensor, device):

    return mps_contiguous(input_tensor, device).to(device)

modules/esrgan_model.py

+1 −1
Original line number Diff line number Diff line
@@ -199,7 +199,7 @@ def upscale_without_tiling(model, img): 
    img = img[:, :, ::-1]

    img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255

    img = torch.from_numpy(img).float()

    img = devices.mps_contiguous_to(img.unsqueeze(0), devices.device_esrgan)

    img = img.unsqueeze(0).to(devices.device_esrgan)

    with torch.no_grad():

        output = model(img)

    output = output.squeeze().float().cpu().clamp_(0, 1).numpy()

modules/scunet_model.py

+2 −1
Original line number Diff line number Diff line
@@ -54,8 +54,9 @@ class UpscalerScuNET(modules.upscaler.Upscaler): 
        img = img[:, :, ::-1]

        img = np.moveaxis(img, 2, 0) / 255

        img = torch.from_numpy(img).float()

        img = devices.mps_contiguous_to(img.unsqueeze(0), device)

        img = img.unsqueeze(0).to(device)



        img = img.to(device)

        with torch.no_grad():

            output = model(img)

        output = output.squeeze().float().cpu().clamp_(0, 1).numpy()

modules/swinir_model.py

+1 −1
Original line number Diff line number Diff line
@@ -111,7 +111,7 @@ def upscale( 
    img = img[:, :, ::-1]

    img = np.moveaxis(img, 2, 0) / 255

    img = torch.from_numpy(img).float()

    img = devices.mps_contiguous_to(img.unsqueeze(0), devices.device_swinir)

    img = img.unsqueeze(0).to(devices.device_swinir)

    with torch.no_grad(), precision_scope("cuda"):

        _, _, h_old, w_old = img.size()

        h_pad = (h_old // window_size + 1) * window_size - h_old

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