aljaz/DALLE2-pytorch
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default to l2 loss

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Phil Wang
2022-05-11 19:24:41 -07:00
parent 6f76652d11
commit 6021945fc8
2 changed files with 3 additions and 3 deletions
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4
dalle2_pytorch/dalle2_pytorch.py
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@@ -831,7 +831,7 @@ class DiffusionPrior(BaseGaussianDiffusion):
image_channels = 3, image_channels = 3,
timesteps = 1000, timesteps = 1000,
cond_drop_prob = 0., cond_drop_prob = 0.,
loss_type = "l1", loss_type = "l2",
predict_x_start = True, predict_x_start = True,
beta_schedule = "cosine", beta_schedule = "cosine",
condition_on_text_encodings = True, # the paper suggests this is needed, but you can turn it off for your CLIP preprocessed text embed -> image embed training condition_on_text_encodings = True, # the paper suggests this is needed, but you can turn it off for your CLIP preprocessed text embed -> image embed training
@@ -1614,7 +1614,7 @@ class Decoder(BaseGaussianDiffusion):
timesteps = 1000, timesteps = 1000,
image_cond_drop_prob = 0.1, image_cond_drop_prob = 0.1,
text_cond_drop_prob = 0.5, text_cond_drop_prob = 0.5,
loss_type = 'l1', loss_type = 'l2',
beta_schedule = 'cosine', beta_schedule = 'cosine',
predict_x_start = False, predict_x_start = False,
predict_x_start_for_latent_diffusion = False, predict_x_start_for_latent_diffusion = False,

2
setup.py
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@@ -10,7 +10,7 @@ setup(
'dream = dalle2_pytorch.cli:dream' 'dream = dalle2_pytorch.cli:dream'
], ],
}, },
version = '0.2.10', version = '0.2.11',
license='MIT', license='MIT',
description = 'DALL-E 2', description = 'DALL-E 2',
author = 'Phil Wang', author = 'Phil Wang',