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(requested) support AMD 8GB GPUs via Windows DirectML

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this update is requested by users
This commit is contained in:
lllyasviel
2023-12-30 06:30:59 -08:00
committed by GitHub
parent c0e11c3451
commit 8e62a72a63
31 changed files with 402 additions and 148 deletions
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ldm_patched/ldm/modules/diffusionmodules/model.py
+1 -1
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@@ -41,7 +41,7 @@ def nonlinearity(x):
def Normalize(in_channels, num_groups=32):
return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
return ops.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
class Upsample(nn.Module):
ldm_patched/ldm/modules/diffusionmodules/upscaling.py
+2 -2
View File
@@ -43,8 +43,8 @@ class AbstractLowScaleModel(nn.Module):
def q_sample(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
return (extract_into_tensor(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start +
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise)
def forward(self, x):
return x, None
ldm_patched/ldm/modules/diffusionmodules/util.py
+3 -3
View File
@@ -51,9 +51,9 @@ class AlphaBlender(nn.Module):
if self.merge_strategy == "fixed":
# make shape compatible
# alpha = repeat(self.mix_factor, '1 -> b () t () ()', t=t, b=bs)
alpha = self.mix_factor
alpha = self.mix_factor.to(image_only_indicator.device)
elif self.merge_strategy == "learned":
alpha = torch.sigmoid(self.mix_factor)
alpha = torch.sigmoid(self.mix_factor.to(image_only_indicator.device))
# make shape compatible
# alpha = repeat(alpha, '1 -> s () ()', s = t * bs)
elif self.merge_strategy == "learned_with_images":
@@ -61,7 +61,7 @@ class AlphaBlender(nn.Module):
alpha = torch.where(
image_only_indicator.bool(),
torch.ones(1, 1, device=image_only_indicator.device),
rearrange(torch.sigmoid(self.mix_factor), "... -> ... 1"),
rearrange(torch.sigmoid(self.mix_factor.to(image_only_indicator.device)), "... -> ... 1"),
)
alpha = rearrange(alpha, self.rearrange_pattern)
# make shape compatible