``Working with z of shape (1, 256, 16, 16) = 65536 dimensions.
loaded pretrained LPIPS loss from taming/modules/autoencoder/lpips/vgg.pth
VQLPIPSWithDiscriminator running with hinge loss.
Restored from models/vqgan_imagenet_f16_16384.ckpt
Using device: cuda:0
Optimising using: Adam
Using text prompts: ['underwater city']
Using seed: 1698681138380486500
0/? [00:00<?, ?it/s]
Oops: runtime error: solve: MAGMA library not found in compilation. Please rebuild with MAGMA.
Try reducing --num-cuts to save memory
/kaggle/working/clipit/clipit.py in do_run(args)
997 print("Oops: runtime error: ", e)
998 print("Try reducing --num-cuts to save memory")
--> 999 raise e
1000 except KeyboardInterrupt:
1001 pass
/kaggle/working/clipit/clipit.py in do_run(args)
989 while True:
990 try:
--> 991 train(args, cur_iteration)
992 if cur_iteration == args.iterations:
993 break
/kaggle/working/clipit/clipit.py in train(args, cur_it)
902
903 for i in range(args.batches):
--> 904 lossAll = ascend_txt(args)
905
906 if i == 0 and cur_it % args.save_every == 0:
/kaggle/working/clipit/clipit.py in ascend_txt(args)
723 for cutoutSize in cutoutsTable:
724 make_cutouts = cutoutsTable[cutoutSize]
--> 725 cur_cutouts[cutoutSize] = make_cutouts(out)
726
727 if args.spot_prompts:
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, *kwargs)
1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1050 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1051 return forward_call(input, **kwargs)
1052 # Do not call functions when jit is used
1053 full_backward_hooks, non_full_backward_hooks = [], []
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, *kwargs)
1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1050 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1051 return forward_call(input, **kwargs)
1052 # Do not call functions when jit is used
1053 full_backward_hooks, non_full_backward_hooks = [], []
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/container.py in forward(self, input)
137 def forward(self, input):
138 for module in self:
--> 139 input = module(input)
140 return input
141
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, *kwargs)
1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1050 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1051 return forward_call(input, **kwargs)
1052 # Do not call functions when jit is used
1053 full_backward_hooks, non_full_backward_hooks = [], []
/opt/conda/lib/python3.7/site-packages/kornia/augmentation/augmentation.py in forward(self, input, params, return_transform)
1141 input_pad = self.compute_padding(input_temp.shape)
1142 _input = self.precrop_padding(input_temp, input_pad) # type: ignore
-> 1143 out = super().forward(_input, params, return_transform)
1144
1145 # Update the actual input size for inverse
/opt/conda/lib/python3.7/site-packages/kornia/geometry/transform/imgwarp.py in get_perspective_transform(src, dst)
281
282 # solve the system Ax = b
--> 283 X, LU = _torch_solve_cast(b, A)
284
285 # create variable to return
``Workingwith z of shape (1, 256, 16, 16) = 65536 dimensions. loaded pretrained LPIPS loss from taming/modules/autoencoder/lpips/vgg.pth VQLPIPSWithDiscriminator running with hinge loss. Restored from models/vqgan_imagenet_f16_16384.ckpt Using device: cuda:0 Optimising using: Adam Using text prompts: ['underwater city'] Using seed: 1698681138380486500 0/? [00:00<?, ?it/s] Oops: runtime error: solve: MAGMA library not found in compilation. Please rebuild with MAGMA. Try reducing --num-cuts to save memoryRuntimeError Traceback (most recent call last) /tmp/ipykernel_58/2225298613.py in
21 settings = clipit.apply_settings()
22 clipit.do_init(settings)
---> 23 clipit.do_run(settings)
/kaggle/working/clipit/clipit.py in do_run(args) 997 print("Oops: runtime error: ", e) 998 print("Try reducing --num-cuts to save memory") --> 999 raise e 1000 except KeyboardInterrupt: 1001 pass
/kaggle/working/clipit/clipit.py in do_run(args) 989 while True: 990 try: --> 991 train(args, cur_iteration) 992 if cur_iteration == args.iterations: 993 break
/kaggle/working/clipit/clipit.py in train(args, cur_it) 902 903 for i in range(args.batches): --> 904 lossAll = ascend_txt(args) 905 906 if i == 0 and cur_it % args.save_every == 0:
/kaggle/working/clipit/clipit.py in ascend_txt(args) 723 for cutoutSize in cutoutsTable: 724 make_cutouts = cutoutsTable[cutoutSize] --> 725 cur_cutouts[cutoutSize] = make_cutouts(out) 726 727 if args.spot_prompts:
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, *kwargs) 1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks 1050 or _global_forward_hooks or _global_forward_pre_hooks): -> 1051 return forward_call(input, **kwargs) 1052 # Do not call functions when jit is used 1053 full_backward_hooks, non_full_backward_hooks = [], []
/kaggle/working/clipit/clipit.py in forward(self, input, spot) 352 # TF.to_pil_image(batch[j_wide].cpu()).save(f"cachedim{curiteration:02d}{jwide:02d}{spot}.png") 353 else: --> 354 batch1, transforms1 = self.augs_zoom(torch.cat(cutouts[:self.cutn_zoom], dim=0)) 355 batch2, transforms2 = self.augs_wide(torch.cat(cutouts[self.cutn_zoom:], dim=0)) 356 # print(batch1.shape, batch2.shape)
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, *kwargs) 1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks 1050 or _global_forward_hooks or _global_forward_pre_hooks): -> 1051 return forward_call(input, **kwargs) 1052 # Do not call functions when jit is used 1053 full_backward_hooks, non_full_backward_hooks = [], []
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/container.py in forward(self, input) 137 def forward(self, input): 138 for module in self: --> 139 input = module(input) 140 return input 141
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, *kwargs) 1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks 1050 or _global_forward_hooks or _global_forward_pre_hooks): -> 1051 return forward_call(input, **kwargs) 1052 # Do not call functions when jit is used 1053 full_backward_hooks, non_full_backward_hooks = [], []
/opt/conda/lib/python3.7/site-packages/kornia/augmentation/augmentation.py in forward(self, input, params, return_transform) 1141 input_pad = self.compute_padding(input_temp.shape) 1142 _input = self.precrop_padding(input_temp, input_pad) # type: ignore -> 1143 out = super().forward(_input, params, return_transform) 1144 1145 # Update the actual input size for inverse
/opt/conda/lib/python3.7/site-packages/kornia/augmentation/base.py in forward(self, input, params, return_transform) 243 244 self._params = params --> 245 output = self.apply_func(in_tensor, in_transform, self._params, return_transform) 246 return _transform_output_shape(output, ori_shape) if self.keepdim else output 247
/opt/conda/lib/python3.7/site-packages/kornia/augmentation/base.py in apply_func(self, in_tensor, in_transform, params, return_transform) 202 # if all data needs to be augmented 203 elif torch.sum(to_apply) == len(to_apply): --> 204 trans_matrix = self.compute_transformation(in_tensor, params) 205 output = self.apply_transform(in_tensor, params, trans_matrix) 206 else:
/opt/conda/lib/python3.7/site-packages/kornia/augmentation/augmentation.py in compute_transformation(self, input, params) 1063 1064 def compute_transformation(self, input: torch.Tensor, params: Dict[str, torch.Tensor]) -> torch.Tensor: -> 1065 transform: torch.Tensor = get_perspective_transform(params['src'].to(input), params['dst'].to(input)) 1066 return transform 1067
/opt/conda/lib/python3.7/site-packages/kornia/geometry/transform/imgwarp.py in get_perspective_transform(src, dst) 281 282 # solve the system Ax = b --> 283 X, LU = _torch_solve_cast(b, A) 284 285 # create variable to return
/opt/conda/lib/python3.7/site-packages/kornia/utils/helpers.py in _torch_solve_cast(input, A) 94 dtype = torch.float32 95 ---> 96 out1, out2 = torch.solve(input.to(dtype), A.to(dtype)) 97 98 return (out1.to(input.dtype), out2.to(input.dtype))
RuntimeError: solve: MAGMA library not found in compilation. Please rebuild with MAGMA.```
here is the kaggle code please fix it 😭 https://www.kaggle.com/shreeshaaithal/notebookf22d408364