aljaz/DALLE2-pytorch
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move the inverse normalization function called before image embeddings are derived from clip to within the diffusion prior and decoder classes

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Phil Wang
2022-05-14 00:37:10 -07:00
parent 2db0c9794c
commit 124d8577c8
2 changed files with 4 additions and 4 deletions
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6
dalle2_pytorch/dalle2_pytorch.py
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@@ -278,7 +278,7 @@ class OpenAIClipAdapter(BaseClipAdapter):
def embed_image(self, image):
assert not self.cleared
image = resize_image_to(image, self.image_size)
image = self.clip_normalize(unnormalize_img(image))
image = self.clip_normalize(image)
image_embed = self.clip.encode_image(image)
return EmbeddedImage(l2norm(image_embed.float()), None)
@@ -1037,7 +1037,7 @@ class DiffusionPrior(BaseGaussianDiffusion):
assert not (self.condition_on_text_encodings and (not exists(text_encodings) and not exists(text))), 'text encodings must be present if you specified you wish to condition on it on initialization'
if exists(image):
image_embed, _ = self.clip.embed_image(image)
image_embed, _ = self.clip.embed_image(unnormalize_img(image))
# calculate text conditionings, based on what is passed in
@@ -2011,7 +2011,7 @@ class Decoder(BaseGaussianDiffusion):
if not exists(image_embed):
assert exists(self.clip), 'if you want to derive CLIP image embeddings automatically, you must supply `clip` to the decoder on init'
image_embed, _ = self.clip.embed_image(image)
image_embed, _ = self.clip.embed_image(unnormalize_img(image))
text_encodings = text_mask = None
if exists(text) and not exists(text_encodings):