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@ -66,7 +66,7 @@ def get_previewer(device, latent_format):
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def preview_function(x0, step, total_steps):
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global cv2_is_top
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with torch.no_grad():
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x_sample = taesd.decoder(x0).detach() * 255.0
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x_sample = taesd.decoder(torch.nn.functional.avg_pool2d(x0, kernel_size=(2, 2))).detach() * 255.0
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x_sample = einops.rearrange(x_sample, 'b c h w -> b h w c')
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x_sample = x_sample.cpu().numpy()[..., ::-1].copy().clip(0, 255).astype(np.uint8)
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for i, s in enumerate(x_sample):
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