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Apply methods described in "Git Re-basin"-paper [1] to arbitrary models --- [1] Ainsworth et al. (https://arxiv.org/abs/2209.04836)

License: MIT License

Python 100.00%
deep-learning git-re-basin git-rebasin pytorch rebasin

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rebasin's Issues

Extremely slow tracing on SDXL (is initialize_parallel_paths a forkbomb?)

Hi,
I'm having a weird situation, where when paired with the diffusers SDXL model, the program makes it ~130 layers deep of initialize_parallel_paths with CPU memory usage climbing to 80G after a few minutes (both models passed into the program were on GPU),
weirdly taking close to no CPU or GPU utilization while running.

Hi! I have questions regarding the visualization feature.

Thanks for the amazing project, I have an inquiry regarding the visualization feature mentioned in the tutorial.
What does this mean? Can you elaborate a little further please?

Thanks!

print(pcd.pinit.model_graph) returns an output like this:

ParallelPaths(
LinearPath( LinearPath(
DefaultModule( DefaultModule(
module.type: Conv2d module.type: Conv2d
input.shape: [(128, 64, 56, 56)] input.shape: [(128, 64, 56, 56)]
output.shape: [(128, 64, 56, 56)] output.shape: [(128, 256, 56, 56)]
weight.in_dim.permutation: None weight.in_dim.permutation: None
weight.out_dim.permutation.shape: 64 weight.out_dim.permutation.shape: 256
) )

OneDimModule(                                OneDimModule(                              
  module.type: BatchNorm2d                     module.type: BatchNorm2d                 
  input.shape: [(128, 64, 56, 56)]             input.shape: [(128, 256, 56, 56)]        
  output.shape: [(128, 64, 56, 56)]            output.shape: [(128, 256, 56, 56)]       
  weight.in_dim.permutation.shape: 64          weight.in_dim.permutation.shape: 256     
  weight.out_dim.permutation.shape: 64         weight.out_dim.permutation.shape: 256    
)                                            )                                          
                                                                                        
DefaultModule(                             )                                            
  module.type: Conv2d                                         |                         
  input.shape: [(128, 64, 56, 56)]                            |                         
  output.shape: [(128, 64, 56, 56)]                           |                         
  weight.in_dim.permutation.shape: 64                         |                         
  weight.out_dim.permutation.shape: 64                        |                         
)                                                             |                         
                                                              |                         
OneDimModule(                                                 |                         
  module.type: BatchNorm2d                                    |                         
  input.shape: [(128, 64, 56, 56)]                            |                         
  output.shape: [(128, 64, 56, 56)]                           |                         
  weight.in_dim.permutation.shape: 64                         |                         
  weight.out_dim.permutation.shape: 64                        |                         
)                                                             |                         
                                                              |                         
DefaultModule(                                                |                         
  module.type: Conv2d                                         |                         
  input.shape: [(128, 64, 56, 56)]                            |                         
  output.shape: [(128, 256, 56, 56)]                          |                         
  weight.in_dim.permutation.shape: 64                         |                         
  weight.out_dim.permutation.shape: 256                       |                         
)                                                             |                         
                                                              |                         
OneDimModule(                                                 |                         
  module.type: BatchNorm2d                                    |                         
  input.shape: [(128, 256, 56, 56)]                           |                         
  output.shape: [(128, 256, 56, 56)]                          |                         
  weight.in_dim.permutation.shape: 256                        |                         
  weight.out_dim.permutation.shape: 256                       |                         
)                                                             |                         
                                                              |

) |
)

(please refer to attached txt file for full result):

vis.txt

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