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Remove unexpected artifacts in output images
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@ -15,14 +15,14 @@ def prepare_control(
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) -> torch.Tensor:
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# load and encode the conditioning image
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img_cond = cond_image.convert("RGB")
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img_cond = img_cond.resize((width, height), Image.Resampling.LANCZOS)
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img_cond = img_cond.resize((width, height), Image.Resampling.BICUBIC)
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img_cond = np.array(img_cond)
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img_cond = torch.from_numpy(img_cond).float()
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img_cond = torch.from_numpy(img_cond).float() / 127.5 - 1.0
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img_cond = rearrange(img_cond, "h w c -> 1 c h w")
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ae_dtype = next(iter(ae.parameters())).dtype
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ae_device = next(iter(ae.parameters())).device
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img_cond = img_cond.to(device=ae_device, dtype=ae_dtype)
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generator = torch.Generator(device=ae_device).manual_seed(seed)
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img_cond = ae.encode(img_cond, sample=True, generator=generator)
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img_cond = ae.encode(img_cond, sample=False, generator=generator)
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img_cond = rearrange(img_cond, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
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return img_cond
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