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randomly adjust scale of unconditional noise on ip adapters if training with cfg

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Jaret Burkett
2024-02-06 03:44:54 -07:00
parent e18e0cb5f8
commit a8481c1670
1 changed files with 6 additions and 0 deletions
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6
toolkit/ip_adapter.py
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@@ -500,6 +500,9 @@ class IPAdapter(torch.nn.Module):
if drop:
if self.clip_noise_zero:
tensors_0_1 = torch.rand_like(tensors_0_1).detach()
noise_scale = torch.rand([tensors_0_1.shape[0], 1, 1, 1], device=self.device,
dtype=get_torch_dtype(self.sd_ref().dtype))
tensors_0_1 = tensors_0_1 * noise_scale
else:
tensors_0_1 = torch.zeros_like(tensors_0_1).detach()
# tensors_0_1 = tensors_0_1 * 0
@@ -514,6 +517,9 @@ class IPAdapter(torch.nn.Module):
# scale the noise down
if self.clip_noise_zero:
tensors_0_1 = torch.rand_like(tensors_0_1).detach()
noise_scale = torch.rand([tensors_0_1.shape[0], 1, 1, 1], device=self.device,
dtype=get_torch_dtype(self.sd_ref().dtype))
tensors_0_1 = tensors_0_1 * noise_scale
else:
tensors_0_1 = torch.zeros_like(tensors_0_1).detach()
# tensors_0_1 = tensors_0_1 * 0