Fix some Lora's not working
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5ab7f213be
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@ -165,8 +165,10 @@ def load_lora(name, filename):
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module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
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module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
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elif type(sd_module) == torch.nn.MultiheadAttention:
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elif type(sd_module) == torch.nn.MultiheadAttention:
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module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
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module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
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elif type(sd_module) == torch.nn.Conv2d:
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elif type(sd_module) == torch.nn.Conv2d and weight.shape[2:] == (1, 1):
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module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
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module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
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elif type(sd_module) == torch.nn.Conv2d and weight.shape[2:] == (3, 3):
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module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (3, 3), bias=False)
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else:
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else:
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print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
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print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
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continue
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continue
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@ -232,6 +234,8 @@ def lora_calc_updown(lora, module, target):
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if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1):
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if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1):
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updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3)
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updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3)
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elif up.shape[2:] == (3, 3) or down.shape[2:] == (3, 3):
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updown = torch.nn.functional.conv2d(down.permute(1, 0, 2, 3), up).permute(1, 0, 2, 3)
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else:
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else:
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updown = up @ down
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updown = up @ down
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