def __init__(self):
super().__init__()
size = 128
self.project_in = nn.Conv2d(3, size, 3, padding=1)
self.unet = DiffusionUNetBackbone2(base=size,
factors=[[1, 1, 2], [2, 2, 2],
[2, 2, 2]],
attention_levels=[],
cond_size=4 * size,
dropout=0.1,
norm=True,
middle_attention=False,
kernel=cross_low_rank_mvp,
attention_block=AttentionBlock)
self.out = nn.Conv2d(size, 3, 3, padding=1)
def __init__(self):
super().__init__()
size = 128
self.project_in = nn.Conv2d(3, size, 3, padding=1)
self.unet = DiffusionUNetBackbone2(base=size,
factors=[[1, 1, 2], [2, 2, 2],
[2, 2, 2]],
attention_levels=[1, 2],
cond_size=4 * size,
dropout=0.1,
norm=True,
middle_attention=True,
kernel=cross_attention,
attention_block=AttentionBlock)
self.out = nn.Conv2d(size, 3, 3, padding=1)
with torch.no_grad():
for param in self.out.parameters():
param.zero_()
self.factor = nn.Parameter(torch.zeros(1, requires_grad=True))
def __init__(self):
super().__init__()
size = 128
self.project_in = nn.Conv2d(3, size, 3, padding=1)
self.unet = DiffusionUNetBackbone2(
base=size, factors=[
[1, 1, 2],
[2, 2, 2],
[2, 2, 2]
], attention_levels=[1, 2],
cond_size=4 * size,
dropout=0.1, norm=True,
middle_attention=True,
kernel=cross_attention,
attention_block=AttentionBlock
)
self.out = nn.Conv2d(size, 3, 3, padding=1)
self.time = SineEmbedding(512, depth=2)
self.Z = nn.Linear(512, 1)
self.factor = nn.Linear(512, 1)