diff options
| -rw-r--r-- | extensions-builtin/Lora/lora.py | 6 |
1 files changed, 5 insertions, 1 deletions
diff --git a/extensions-builtin/Lora/lora.py b/extensions-builtin/Lora/lora.py index 6f246921..bcf36d77 100644 --- a/extensions-builtin/Lora/lora.py +++ b/extensions-builtin/Lora/lora.py @@ -165,8 +165,10 @@ def load_lora(name, filename): module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
elif type(sd_module) == torch.nn.MultiheadAttention:
module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
- elif type(sd_module) == torch.nn.Conv2d:
+ elif type(sd_module) == torch.nn.Conv2d and weight.shape[2:] == (1, 1):
module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
+ elif type(sd_module) == torch.nn.Conv2d and weight.shape[2:] == (3, 3):
+ module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (3, 3), bias=False)
else:
print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
continue
@@ -232,6 +234,8 @@ def lora_calc_updown(lora, module, target): if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1):
updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3)
+ elif up.shape[2:] == (3, 3) or down.shape[2:] == (3, 3):
+ updown = torch.nn.functional.conv2d(down.permute(1, 0, 2, 3), up).permute(1, 0, 2, 3)
else:
updown = up @ down
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