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Resize the support noise and inpainting mask to match the image size #125

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Resize the support noise and inpainting mask to match the image size #125

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18 changes: 18 additions & 0 deletions deepfloyd_if/modules/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -177,6 +177,24 @@ def model_fn(x_t, ts, **kwargs):
noise = torch.randn(
(batch_size * bs_scale, 3, image_h, image_w), device=self.device, dtype=self.model.dtype)
else:
if support_noise.shape != (1, 3, image_h, image_w):
# Resize support noise and mask to image size.
support_noise = torch.nn.functional.interpolate(
support_noise,
size=(image_h, image_w),
mode="bilinear",
align_corners=False,
)
if inpainting_mask is not None:
inpainting_mask = torch.nn.functional.interpolate(
inpainting_mask.float(),
size=(image_h, image_w),
mode="bilinear",
align_corners=False,
)
inpainting_mask = inpainting_mask > 0.5 # Back to bool.
inpainting_mask = inpainting_mask.float() # Back to 0/1.

assert support_noise_less_qsample_steps < len(diffusion.timestep_map) - 1
assert support_noise.shape == (1, 3, image_h, image_w)
q_sample_steps = torch.tensor([int(len(diffusion.timestep_map) - 1 - support_noise_less_qsample_steps)])
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