aljaz/DALLE2-pytorch
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allow for control over use of nearest interp method of downsampling low res conditioning, in addition to being able to turn it off

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Phil Wang
2022-07-08 11:44:43 -07:00
parent ec7cab01d9
commit 8c823affff
2 changed files with 19 additions and 5 deletions
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20
dalle2_pytorch/dalle2_pytorch.py
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@@ -125,14 +125,23 @@ def log(t, eps = 1e-12):
def l2norm(t): def l2norm(t):
return F.normalize(t, dim = -1) return F.normalize(t, dim = -1)
def resize_image_to(image, target_image_size, clamp_range = None): def resize_image_to(
image,
target_image_size,
clamp_range = None,
nearest = False,
**kwargs
):
orig_image_size = image.shape[-1] orig_image_size = image.shape[-1]
if orig_image_size == target_image_size: if orig_image_size == target_image_size:
return image return image
if not nearest:
scale_factors = target_image_size / orig_image_size scale_factors = target_image_size / orig_image_size
out = resize(image, scale_factors = scale_factors) out = resize(image, scale_factors = scale_factors, **kwargs)
else:
out = F.interpolate(image, target_image_size, mode = 'nearest', align_corners = False)
if exists(clamp_range): if exists(clamp_range):
out = out.clamp(*clamp_range) out = out.clamp(*clamp_range)
@@ -1781,12 +1790,15 @@ class LowresConditioner(nn.Module):
def __init__( def __init__(
self, self,
downsample_first = True, downsample_first = True,
downsample_mode_nearest = False,
blur_sigma = 0.6, blur_sigma = 0.6,
blur_kernel_size = 3, blur_kernel_size = 3,
input_image_range = None input_image_range = None
): ):
super().__init__() super().__init__()
self.downsample_first = downsample_first self.downsample_first = downsample_first
self.downsample_mode_nearest = downsample_mode_nearest
self.input_image_range = input_image_range self.input_image_range = input_image_range
self.blur_sigma = blur_sigma self.blur_sigma = blur_sigma
@@ -1802,7 +1814,7 @@ class LowresConditioner(nn.Module):
blur_kernel_size = None blur_kernel_size = None
): ):
if self.training and self.downsample_first and exists(downsample_image_size): if self.training and self.downsample_first and exists(downsample_image_size):
cond_fmap = resize_image_to(cond_fmap, downsample_image_size, clamp_range = self.input_image_range) cond_fmap = resize_image_to(cond_fmap, downsample_image_size, clamp_range = self.input_image_range, nearest = self.downsample_mode_nearest)
if self.training: if self.training:
# when training, blur the low resolution conditional image # when training, blur the low resolution conditional image
@@ -1845,6 +1857,7 @@ class Decoder(nn.Module):
image_sizes = None, # for cascading ddpm, image size at each stage 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) 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_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_sigma = 0.6, # cascading ddpm - blur sigma blur_sigma = 0.6, # cascading ddpm - blur sigma
blur_kernel_size = 3, # cascading ddpm - blur kernel size blur_kernel_size = 3, # cascading ddpm - blur kernel size
clip_denoised = True, clip_denoised = True,
@@ -1987,6 +2000,7 @@ class Decoder(nn.Module):
self.to_lowres_cond = LowresConditioner( self.to_lowres_cond = LowresConditioner(
downsample_first = lowres_downsample_first, downsample_first = lowres_downsample_first,
downsample_mode_nearest = lowres_downsample_mode_nearest,
blur_sigma = blur_sigma, blur_sigma = blur_sigma,
blur_kernel_size = blur_kernel_size, blur_kernel_size = blur_kernel_size,
input_image_range = self.input_image_range input_image_range = self.input_image_range

2
dalle2_pytorch/version.py
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@@ -1 +1 @@
__version__ = '0.16.17' __version__ = '0.16.18'