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File "I:\SD\sd-webui-aki-v4\modules\img2img.py", line 170, in img2img
processed = modules.scripts.scripts_img2img.run(p, *args)
File "I:\SD\sd-webui-aki-v4\modules\scripts.py", line 407, in run
processed = script.run(p, *script_args)
File "I:\SD\sd-webui-aki-v4\extensions\depthmap2mask\scripts\depth2image_depthmask.py", line 97, in run
d_m = sdmg.calculate_depth_maps(p.init_images[0],img_x,img_y,model_type,invert_depth)
File "I:\SD\sd-webui-aki-v4\extensions/depthmap2mask/scripts/depthmap_for_depth2img.py", line 306, in calculate_depth_maps
prediction = model.forward(sample)
File "I:\SD\sd-webui-aki-v4\repositories\midas\midas\dpt_depth.py", line 166, in forward
return super().forward(x).squeeze(dim=1)
File "I:\SD\sd-webui-aki-v4\repositories\midas\midas\dpt_depth.py", line 114, in forward
layers = self.forward_transformer(self.pretrained, x)
File "I:\SD\sd-webui-aki-v4\repositories\midas\midas\backbones\swin_common.py", line 10, in forward_swin
return forward_default(pretrained, x)
File "I:\SD\sd-webui-aki-v4\repositories\midas\midas\backbones\utils.py", line 64, in forward_default
exec(f"pretrained.model.{function_name}(x)")
File "", line 1, in
File "I:\SD\sd-webui-aki-v4\py310\lib\site-packages\timm\models\swin_transformer_v2.py", line 598, in forward_features
x = self.patch_embed(x)
File "I:\SD\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "I:\SD\sd-webui-aki-v4\py310\lib\site-packages\timm\models\layers\patch_embed.py", line 35, in forward
x = self.proj(x)
File "I:\SD\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "I:\SD\sd-webui-aki-v4\extensions-builtin\Lora\lora.py", line 319, in lora_Conv2d_forward
return torch.nn.Conv2d_forward_before_lora(self, input)
File "I:\SD\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\conv.py", line 463, in forward
return self._conv_forward(input, self.weight, self.bias)
File "I:\SD\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\conv.py", line 459, in _conv_forward
return F.conv2d(input, weight, bias, self.stride,
RuntimeError: Input type (float) and bias type (struct c10::Half) should be the same
The text was updated successfully, but these errors were encountered:
File "I:\SD\sd-webui-aki-v4\modules\img2img.py", line 170, in img2img
processed = modules.scripts.scripts_img2img.run(p, *args)
File "I:\SD\sd-webui-aki-v4\modules\scripts.py", line 407, in run
processed = script.run(p, *script_args)
File "I:\SD\sd-webui-aki-v4\extensions\depthmap2mask\scripts\depth2image_depthmask.py", line 97, in run
d_m = sdmg.calculate_depth_maps(p.init_images[0],img_x,img_y,model_type,invert_depth)
File "I:\SD\sd-webui-aki-v4\extensions/depthmap2mask/scripts/depthmap_for_depth2img.py", line 306, in calculate_depth_maps
prediction = model.forward(sample)
File "I:\SD\sd-webui-aki-v4\repositories\midas\midas\dpt_depth.py", line 166, in forward
return super().forward(x).squeeze(dim=1)
File "I:\SD\sd-webui-aki-v4\repositories\midas\midas\dpt_depth.py", line 114, in forward
layers = self.forward_transformer(self.pretrained, x)
File "I:\SD\sd-webui-aki-v4\repositories\midas\midas\backbones\swin_common.py", line 10, in forward_swin
return forward_default(pretrained, x)
File "I:\SD\sd-webui-aki-v4\repositories\midas\midas\backbones\utils.py", line 64, in forward_default
exec(f"pretrained.model.{function_name}(x)")
File "", line 1, in
File "I:\SD\sd-webui-aki-v4\py310\lib\site-packages\timm\models\swin_transformer_v2.py", line 598, in forward_features
x = self.patch_embed(x)
File "I:\SD\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "I:\SD\sd-webui-aki-v4\py310\lib\site-packages\timm\models\layers\patch_embed.py", line 35, in forward
x = self.proj(x)
File "I:\SD\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "I:\SD\sd-webui-aki-v4\extensions-builtin\Lora\lora.py", line 319, in lora_Conv2d_forward
return torch.nn.Conv2d_forward_before_lora(self, input)
File "I:\SD\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\conv.py", line 463, in forward
return self._conv_forward(input, self.weight, self.bias)
File "I:\SD\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\conv.py", line 459, in _conv_forward
return F.conv2d(input, weight, bias, self.stride,
RuntimeError: Input type (float) and bias type (struct c10::Half) should be the same
The text was updated successfully, but these errors were encountered: