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fix self conditioning shape in diffusion prior

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This commit is contained in:
Phil Wang
2022-08-12 12:29:25 -07:00
parent 9440411954
commit 301a97197f
2 changed files with 4 additions and 4 deletions
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6
dalle2_pytorch/dalle2_pytorch.py
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@@ -1004,9 +1004,9 @@ class DiffusionPriorNetwork(nn.Module):
# setup self conditioning
self_cond = None
if self.self_cond:
self_cond = default(self_cond, lambda: torch.zeros(batch, 1, self.dim, device = device, dtype = dtype))
self_cond = default(self_cond, lambda: torch.zeros(batch, self.dim, device = device, dtype = dtype))
self_cond = rearrange(self_cond, 'b d -> b 1 d')
# in section 2.2, last paragraph
# "... consisting of encoded text, CLIP text embedding, diffusion timestep embedding, noised CLIP image embedding, final embedding for prediction"
@@ -1287,7 +1287,7 @@ class DiffusionPrior(nn.Module):
image_embed_noisy = self.noise_scheduler.q_sample(x_start = image_embed, t = times, noise = noise)
self_cond = None
if self.net.self_cond and random.random() < 0.5:
if self.net.self_cond and random.random() < 1.5:
with torch.no_grad():
self_cond = self.net(image_embed_noisy, times, **text_cond).detach()

2
dalle2_pytorch/version.py
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@@ -1 +1 @@
__version__ = '1.6.1'
__version__ = '1.6.2'