aljaz/DALLE2-pytorch
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just always use nearest neighbor interpolation when resizing for low resolution conditioning, for https://github.com/lucidrains/DALLE2-pytorch/pull/181

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This commit is contained in:
Phil Wang
2022-07-13 20:59:43 -07:00
parent f141144a6d
commit 0b40cbaa54
2 changed files with 6 additions and 13 deletions
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17
dalle2_pytorch/dalle2_pytorch.py
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@@ -146,7 +146,7 @@ def resize_image_to(
scale_factors = target_image_size / orig_image_size
out = resize(image, scale_factors = scale_factors, **kwargs)
else:
out = F.interpolate(image, target_image_size, mode = 'nearest', align_corners = False)
out = F.interpolate(image, target_image_size, mode = 'nearest')
if exists(clamp_range):
out = out.clamp(*clamp_range)
@@ -1957,7 +1957,6 @@ class LowresConditioner(nn.Module):
def __init__(
self,
downsample_first = True,
downsample_mode_nearest = False,
blur_prob = 0.5,
blur_sigma = 0.6,
blur_kernel_size = 3,
@@ -1965,8 +1964,6 @@ class LowresConditioner(nn.Module):
):
super().__init__()
self.downsample_first = downsample_first
self.downsample_mode_nearest = downsample_mode_nearest
self.input_image_range = input_image_range
self.blur_prob = blur_prob
@@ -1983,7 +1980,7 @@ class LowresConditioner(nn.Module):
blur_kernel_size = None
):
if self.downsample_first and exists(downsample_image_size):
cond_fmap = resize_image_to(cond_fmap, downsample_image_size, clamp_range = self.input_image_range, nearest = self.downsample_mode_nearest)
cond_fmap = resize_image_to(cond_fmap, downsample_image_size, clamp_range = self.input_image_range, nearest = True)
# blur is only applied 50% of the time
# section 3.1 in https://arxiv.org/abs/2106.15282
@@ -2010,7 +2007,7 @@ class LowresConditioner(nn.Module):
cond_fmap = gaussian_blur2d(cond_fmap, cast_tuple(blur_kernel_size, 2), cast_tuple(blur_sigma, 2))
cond_fmap = resize_image_to(cond_fmap, target_image_size, clamp_range = self.input_image_range)
cond_fmap = resize_image_to(cond_fmap, target_image_size, clamp_range = self.input_image_range, nearest = True)
return cond_fmap
class Decoder(nn.Module):
@@ -2033,7 +2030,6 @@ class Decoder(nn.Module):
image_sizes = None, # for cascading ddpm, image size at each stage
random_crop_sizes = None, # whether to random crop the image at that stage in the cascade (super resoluting convolutions at the end may be able to generalize on smaller crops)
lowres_downsample_first = True, # cascading ddpm - resizes to lower resolution, then to next conditional resolution + blur
lowres_downsample_mode_nearest = False, # cascading ddpm - whether to use nearest mode downsampling for lower resolution
blur_prob = 0.5, # cascading ddpm - when training, the gaussian blur is only applied 50% of the time
blur_sigma = 0.6, # cascading ddpm - blur sigma
blur_kernel_size = 3, # cascading ddpm - blur kernel size
@@ -2183,11 +2179,8 @@ class Decoder(nn.Module):
lowres_conditions = tuple(map(lambda t: t.lowres_cond, self.unets))
assert lowres_conditions == (False, *((True,) * (len(self.unets) - 1))), 'the first unet must be unconditioned (by low resolution image), and the rest of the unets must have `lowres_cond` set to True'
self.lowres_downsample_mode_nearest = lowres_downsample_mode_nearest
self.to_lowres_cond = LowresConditioner(
downsample_first = lowres_downsample_first,
downsample_mode_nearest = lowres_downsample_mode_nearest,
blur_prob = blur_prob,
blur_sigma = blur_sigma,
blur_kernel_size = blur_kernel_size,
@@ -2510,7 +2503,7 @@ class Decoder(nn.Module):
shape = (batch_size, channel, image_size, image_size)
if unet.lowres_cond:
lowres_cond_img = resize_image_to(img, target_image_size = image_size, clamp_range = self.input_image_range, nearest = self.lowres_downsample_mode_nearest)
lowres_cond_img = resize_image_to(img, target_image_size = image_size, clamp_range = self.input_image_range, nearest = True)
is_latent_diffusion = isinstance(vae, VQGanVAE)
image_size = vae.get_encoded_fmap_size(image_size)
@@ -2580,7 +2573,7 @@ class Decoder(nn.Module):
assert not (not self.condition_on_text_encodings and exists(text_encodings)), 'decoder specified not to be conditioned on text, yet it is presented'
lowres_cond_img = self.to_lowres_cond(image, target_image_size = target_image_size, downsample_image_size = self.image_sizes[unet_index - 1]) if unet_number > 1 else None
image = resize_image_to(image, target_image_size)
image = resize_image_to(image, target_image_size, nearest = True)
if exists(random_crop_size):
aug = K.RandomCrop((random_crop_size, random_crop_size), p = 1.)

2
dalle2_pytorch/version.py
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@@ -1 +1 @@
__version__ = '0.23.9'
__version__ = '0.23.10'