Missing keys & size mismatch when merging LORAs

[jslegers]

I keep getting errors when trying to merge either two LORAs or a checkpoint with at least one LORA.

Here's an example of the log I get from one of my attempts :

07:26:36-762384 INFO     Merge model...                                                             
07:26:36-765246 INFO     python3 "networks/merge_lora.py" --sd_model                                
                         "/content/drive/MyDrive/models/sd_xl_base_1.0.safetensors" --save_precision
                         fp16 --precision float --save_to "" --models                               
                         "/content/drive/MyDrive/models/xl_more_art-full_v1.safetensors" --ratios   
                         0.33                                                                       
2024-07-20 07:26:45.898061: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-07-20 07:26:45.898109: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-07-20 07:26:45.904290: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-07-20 07:26:47.787948: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
loading SD model: /content/drive/MyDrive/models/sd_xl_base_1.0.safetensors
UNet2DConditionModel: 64, 8, 768, False, False
Traceback (most recent call last):
  File "/content/kohya_ss/networks/merge_lora.py", line 247, in <module>
    merge(args)
  File "/content/kohya_ss/networks/merge_lora.py", line 196, in merge
    text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.sd_model)
  File "/content/kohya_ss/networks/../library/model_util.py", line 994, in load_models_from_stable_diffusion_checkpoint
    info = unet.load_state_dict(converted_unet_checkpoint)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 2041, in load_state_dict
    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for UNet2DConditionModel:
    Missing key(s) in state_dict: "down_blocks.0.attentions.0.norm.weight", "down_blocks.0.attentions.0.norm.bias", "down_blocks.0.attentions.0.proj_in.weight", "down_blocks.0.attentions.0.proj_in.bias", "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_q.weight", "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_k.weight", "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_v.weight", 
...
"mid_block.attentions.0.transformer_blocks.5.norm3.bias", "mid_block.attentions.0.transformer_blocks.5.norm3.weight", "mid_block.attentions.0.transformer_blocks.6.attn1.to_k.weight", "mid_block.attentions.0.transformer_blocks.6.attn1.to_out.0.bias", "mid_block.attentions.0.transformer_blocks.6.attn1.to_out.0.weight", "mid_block.attentions.0.transformer_blocks.6.attn1.to_q.weight", "mid_block.attentions.0.transformer_blocks.6.attn1.to_v.weight", "mid_block.attentions.0.transformer_blocks.6.attn2.to_k.weight", "mid_block.attentions.0.transformer_blocks.6.attn2.to_out.0.bias", "mid_block.attentions.0.transformer_blocks.6.attn2.to_out.0.weight", "mid_block.attentions.0.transformer_blocks.6.attn2.to_q.weight", "mid_block.attentions.0.transformer_blocks.6.attn2.to_v.weight", "mid_block.attentions.0.transformer_blocks.6.ff.net.0.proj.bias", "mid_block.attentions.0.transformer_blocks.6.ff.net.0.proj.weight", "mid_block.attentions.0.transformer_blocks.6.ff.net.2.bias", "mid_block.attentions.0.transformer_blocks.6.ff.net.2.weight", "mid_block.attentions.0.transformer_blocks.6.norm1.bias", "mid_block.attentions.0.transformer_blocks.6.norm1.weight", "mid_block.attentions.0.transformer_blocks.6.norm2.bias", "mid_block.attentions.0.transformer_blocks.6.norm2.weight", "mid_block.attentions.0.transformer_blocks.6.norm3.bias", "mid_block.attentions.0.transformer_blocks.6.norm3.weight", "mid_block.attentions.0.transformer_blocks.7.attn1.to_k.weight", "mid_block.attentions.0.transformer_blocks.7.attn1.to_out.0.bias", "mid_block.attentions.0.transformer_blocks.7.attn1.to_out.0.weight", "mid_block.attentions.0.transformer_blocks.7.attn1.to_q.weight", "mid_block.attentions.0.transformer_blocks.7.attn1.to_v.weight", "mid_block.attentions.0.transformer_blocks.7.attn2.to_k.weight", "mid_block.attentions.0.transformer_blocks.7.attn2.to_out.0.bias", "mid_block.attentions.0.transformer_blocks.7.attn2.to_out.0.weight", "mid_block.attentions.0.transformer_blocks.7.attn2.to_q.weight", "mid_block.attentions.0.transformer_blocks.7.attn2.to_v.weight", "mid_block.attentions.0.transformer_blocks.7.ff.net.0.proj.bias", "mid_block.attentions.0.transformer_blocks.7.ff.net.0.proj.weight", "mid_block.attentions.0.transformer_blocks.7.ff.net.2.bias", "mid_block.attentions.0.transformer_blocks.7.ff.net.2.weight", "mid_block.attentions.0.transformer_blocks.7.norm1.bias", "mid_block.attentions.0.transformer_blocks.7.norm1.weight", "mid_block.attentions.0.transformer_blocks.7.norm2.bias", "mid_block.attentions.0.transformer_blocks.7.norm2.weight", "mid_block.attentions.0.transformer_blocks.7.norm3.bias", "mid_block.attentions.0.transformer_blocks.7.norm3.weight", "mid_block.attentions.0.transformer_blocks.8.attn1.to_k.weight", "mid_block.attentions.0.transformer_blocks.8.attn1.to_out.0.bias", "mid_block.attentions.0.transformer_blocks.8.attn1.to_out.0.weight", "mid_block.attentions.0.transformer_blocks.8.attn1.to_q.weight", "mid_block.attentions.0.transformer_blocks.8.attn1.to_v.weight", "mid_block.attentions.0.transformer_blocks.8.attn2.to_k.weight", "mid_block.attentions.0.transformer_blocks.8.attn2.to_out.0.bias", "mid_block.attentions.0.transformer_blocks.8.attn2.to_out.0.weight", "mid_block.attentions.0.transformer_blocks.8.attn2.to_q.weight", "mid_block.attentions.0.transformer_blocks.8.attn2.to_v.weight", "mid_block.attentions.0.transformer_blocks.8.ff.net.0.proj.bias", "mid_block.attentions.0.transformer_blocks.8.ff.net.0.proj.weight", "mid_block.attentions.0.transformer_blocks.8.ff.net.2.bias", "mid_block.attentions.0.transformer_blocks.8.ff.net.2.weight", "mid_block.attentions.0.transformer_blocks.8.norm1.bias", "mid_block.attentions.0.transformer_blocks.8.norm1.weight", "mid_block.attentions.0.transformer_blocks.8.norm2.bias", "mid_block.attentions.0.transformer_blocks.8.norm2.weight", "mid_block.attentions.0.transformer_blocks.8.norm3.bias", "mid_block.attentions.0.transformer_blocks.8.norm3.weight", "mid_block.attentions.0.transformer_blocks.9.attn1.to_k.weight", "mid_block.attentions.0.transformer_blocks.9.attn1.to_out.0.bias", "mid_block.attentions.0.transformer_blocks.9.attn1.to_out.0.weight", "mid_block.attentions.0.transformer_blocks.9.attn1.to_q.weight", "mid_block.attentions.0.transformer_blocks.9.attn1.to_v.weight", "mid_block.attentions.0.transformer_blocks.9.attn2.to_k.weight", "mid_block.attentions.0.transformer_blocks.9.attn2.to_out.0.bias", "mid_block.attentions.0.transformer_blocks.9.attn2.to_out.0.weight", "mid_block.attentions.0.transformer_blocks.9.attn2.to_q.weight", "mid_block.attentions.0.transformer_blocks.9.attn2.to_v.weight", "mid_block.attentions.0.transformer_blocks.9.ff.net.0.proj.bias", "mid_block.attentions.0.transformer_blocks.9.ff.net.0.proj.weight", "mid_block.attentions.0.transformer_blocks.9.ff.net.2.bias", "mid_block.attentions.0.transformer_blocks.9.ff.net.2.weight", "mid_block.attentions.0.transformer_blocks.9.norm1.bias", "mid_block.attentions.0.transformer_blocks.9.norm1.weight", "mid_block.attentions.0.transformer_blocks.9.norm2.bias", "mid_block.attentions.0.transformer_blocks.9.norm2.weight", "mid_block.attentions.0.transformer_blocks.9.norm3.bias", "mid_block.attentions.0.transformer_blocks.9.norm3.weight". 
    size mismatch for down_blocks.1.attentions.0.proj_in.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([640, 640, 1, 1]).
    size mismatch for down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([640, 2048]) from checkpoint, the shape in current model is torch.Size([640, 768]).
    size mismatch for down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([640, 2048]) from checkpoint, the shape in current model is torch.Size([640, 768]).
    size mismatch for down_blocks.1.attentions.0.proj_out.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([640, 640, 1, 1]).
    size mismatch for down_blocks.1.attentions.1.proj_in.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([640, 640, 1, 1]).
    size mismatch for down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([640, 2048]) from checkpoint, the shape in current model is torch.Size([640, 768]).
    size mismatch for down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([640, 2048]) from checkpoint, the shape in current model is torch.Size([640, 768]).
    size mismatch for down_blocks.1.attentions.1.proj_out.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([640, 640, 1, 1]).
    size mismatch for down_blocks.2.attentions.0.proj_in.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
    size mismatch for down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
    size mismatch for down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
    size mismatch for down_blocks.2.attentions.0.proj_out.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
    size mismatch for down_blocks.2.attentions.1.proj_in.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
    size mismatch for down_blocks.2.attentions.1.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
    size mismatch for down_blocks.2.attentions.1.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
    size mismatch for down_blocks.2.attentions.1.proj_out.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
    size mismatch for up_blocks.0.resnets.2.norm1.weight: copying a param with shape torch.Size([1920]) from checkpoint, the shape in current model is torch.Size([2560]).
    size mismatch for up_blocks.0.resnets.2.norm1.bias: copying a param with shape torch.Size([1920]) from checkpoint, the shape in current model is torch.Size([2560]).
    size mismatch for up_blocks.0.resnets.2.conv1.weight: copying a param with shape torch.Size([1280, 1920, 3, 3]) from checkpoint, the shape in current model is torch.Size([1280, 2560, 3, 3]).
    size mismatch for up_blocks.0.resnets.2.conv_shortcut.weight: copying a param with shape torch.Size([1280, 1920, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 2560, 1, 1]).
    size mismatch for up_blocks.1.attentions.0.norm.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.0.norm.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.0.proj_in.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
    size mismatch for up_blocks.1.attentions.0.proj_in.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_q.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
    size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_k.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
    size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_v.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
    size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_out.0.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
    size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_out.0.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.ff.net.0.proj.weight: copying a param with shape torch.Size([5120, 640]) from checkpoint, the shape in current model is torch.Size([10240, 1280]).
    size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.ff.net.0.proj.bias: copying a param with shape torch.Size([5120]) from checkpoint, the shape in current model is torch.Size([10240]).
    size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.ff.net.2.weight: copying a param with shape torch.Size([640, 2560]) from checkpoint, the shape in current model is torch.Size([1280, 5120]).
    size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.ff.net.2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_q.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
    size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([640, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
    size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([640, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
    size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_out.0.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
    size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_out.0.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.norm1.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.norm1.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.norm2.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.norm2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.norm3.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.norm3.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.0.proj_out.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
    size mismatch for up_blocks.1.attentions.0.proj_out.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.1.norm.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.1.norm.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.1.proj_in.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
    size mismatch for up_blocks.1.attentions.1.proj_in.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_q.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
    size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_k.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
    size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_v.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
    size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_out.0.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
    size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_out.0.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.ff.net.0.proj.weight: copying a param with shape torch.Size([5120, 640]) from checkpoint, the shape in current model is torch.Size([10240, 1280]).
    size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.ff.net.0.proj.bias: copying a param with shape torch.Size([5120]) from checkpoint, the shape in current model is torch.Size([10240]).
    size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.ff.net.2.weight: copying a param with shape torch.Size([640, 2560]) from checkpoint, the shape in current model is torch.Size([1280, 5120]).
    size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.ff.net.2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_q.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
    size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([640, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
    size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([640, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
    size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_out.0.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
    size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_out.0.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.norm1.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.norm1.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.norm2.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.norm2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.norm3.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.norm3.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.1.proj_out.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
    size mismatch for up_blocks.1.attentions.1.proj_out.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.2.norm.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.2.norm.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.2.proj_in.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
    size mismatch for up_blocks.1.attentions.2.proj_in.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_q.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
    size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_k.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
    size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_v.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
    size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_out.0.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
    size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_out.0.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.ff.net.0.proj.weight: copying a param with shape torch.Size([5120, 640]) from checkpoint, the shape in current model is torch.Size([10240, 1280]).
    size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.ff.net.0.proj.bias: copying a param with shape torch.Size([5120]) from checkpoint, the shape in current model is torch.Size([10240]).
    size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.ff.net.2.weight: copying a param with shape torch.Size([640, 2560]) from checkpoint, the shape in current model is torch.Size([1280, 5120]).
    size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.ff.net.2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_q.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
    size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([640, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
    size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([640, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
    size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_out.0.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
    size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_out.0.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.norm1.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.norm1.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.norm2.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.norm2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.norm3.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.norm3.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.attentions.2.proj_out.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
    size mismatch for up_blocks.1.attentions.2.proj_out.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.resnets.0.norm1.weight: copying a param with shape torch.Size([1920]) from checkpoint, the shape in current model is torch.Size([2560]).
    size mismatch for up_blocks.1.resnets.0.norm1.bias: copying a param with shape torch.Size([1920]) from checkpoint, the shape in current model is torch.Size([2560]).
    size mismatch for up_blocks.1.resnets.0.conv1.weight: copying a param with shape torch.Size([640, 1920, 3, 3]) from checkpoint, the shape in current model is torch.Size([1280, 2560, 3, 3]).
    size mismatch for up_blocks.1.resnets.0.conv1.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.resnets.0.time_emb_proj.weight: copying a param with shape torch.Size([640, 1280]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
    size mismatch for up_blocks.1.resnets.0.time_emb_proj.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.resnets.0.norm2.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.resnets.0.norm2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.resnets.0.conv2.weight: copying a param with shape torch.Size([640, 640, 3, 3]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 3, 3]).
    size mismatch for up_blocks.1.resnets.0.conv2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.resnets.0.conv_shortcut.weight: copying a param with shape torch.Size([640, 1920, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 2560, 1, 1]).
    size mismatch for up_blocks.1.resnets.0.conv_shortcut.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.resnets.1.norm1.weight: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([2560]).
    size mismatch for up_blocks.1.resnets.1.norm1.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([2560]).
    size mismatch for up_blocks.1.resnets.1.conv1.weight: copying a param with shape torch.Size([640, 1280, 3, 3]) from checkpoint, the shape in current model is torch.Size([1280, 2560, 3, 3]).
    size mismatch for up_blocks.1.resnets.1.conv1.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.resnets.1.time_emb_proj.weight: copying a param with shape torch.Size([640, 1280]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
    size mismatch for up_blocks.1.resnets.1.time_emb_proj.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.resnets.1.norm2.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.resnets.1.norm2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.resnets.1.conv2.weight: copying a param with shape torch.Size([640, 640, 3, 3]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 3, 3]).
    size mismatch for up_blocks.1.resnets.1.conv2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.resnets.1.conv_shortcut.weight: copying a param with shape torch.Size([640, 1280, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 2560, 1, 1]).
    size mismatch for up_blocks.1.resnets.1.conv_shortcut.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.resnets.2.norm1.weight: copying a param with shape torch.Size([960]) from checkpoint, the shape in current model is torch.Size([1920]).
    size mismatch for up_blocks.1.resnets.2.norm1.bias: copying a param with shape torch.Size([960]) from checkpoint, the shape in current model is torch.Size([1920]).
    size mismatch for up_blocks.1.resnets.2.conv1.weight: copying a param with shape torch.Size([640, 960, 3, 3]) from checkpoint, the shape in current model is torch.Size([1280, 1920, 3, 3]).
    size mismatch for up_blocks.1.resnets.2.conv1.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.resnets.2.time_emb_proj.weight: copying a param with shape torch.Size([640, 1280]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
    size mismatch for up_blocks.1.resnets.2.time_emb_proj.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.resnets.2.norm2.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.resnets.2.norm2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.resnets.2.conv2.weight: copying a param with shape torch.Size([640, 640, 3, 3]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 3, 3]).
    size mismatch for up_blocks.1.resnets.2.conv2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.resnets.2.conv_shortcut.weight: copying a param with shape torch.Size([640, 960, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 1920, 1, 1]).
    size mismatch for up_blocks.1.resnets.2.conv_shortcut.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.1.upsamplers.0.conv.weight: copying a param with shape torch.Size([640, 640, 3, 3]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 3, 3]).
    size mismatch for up_blocks.1.upsamplers.0.conv.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.2.resnets.0.norm1.weight: copying a param with shape torch.Size([960]) from checkpoint, the shape in current model is torch.Size([1920]).
    size mismatch for up_blocks.2.resnets.0.norm1.bias: copying a param with shape torch.Size([960]) from checkpoint, the shape in current model is torch.Size([1920]).
    size mismatch for up_blocks.2.resnets.0.conv1.weight: copying a param with shape torch.Size([320, 960, 3, 3]) from checkpoint, the shape in current model is torch.Size([640, 1920, 3, 3]).
    size mismatch for up_blocks.2.resnets.0.conv1.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
    size mismatch for up_blocks.2.resnets.0.time_emb_proj.weight: copying a param with shape torch.Size([320, 1280]) from checkpoint, the shape in current model is torch.Size([640, 1280]).
    size mismatch for up_blocks.2.resnets.0.time_emb_proj.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
    size mismatch for up_blocks.2.resnets.0.norm2.weight: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
    size mismatch for up_blocks.2.resnets.0.norm2.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
    size mismatch for up_blocks.2.resnets.0.conv2.weight: copying a param with shape torch.Size([320, 320, 3, 3]) from checkpoint, the shape in current model is torch.Size([640, 640, 3, 3]).
    size mismatch for up_blocks.2.resnets.0.conv2.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
    size mismatch for up_blocks.2.resnets.0.conv_shortcut.weight: copying a param with shape torch.Size([320, 960, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 1920, 1, 1]).
    size mismatch for up_blocks.2.resnets.0.conv_shortcut.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
    size mismatch for up_blocks.2.resnets.1.norm1.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.2.resnets.1.norm1.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
    size mismatch for up_blocks.2.resnets.1.conv1.weight: copying a param with shape torch.Size([320, 640, 3, 3]) from checkpoint, the shape in current model is torch.Size([640, 1280, 3, 3]).
    size mismatch for up_blocks.2.resnets.1.conv1.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
    size mismatch for up_blocks.2.resnets.1.time_emb_proj.weight: copying a param with shape torch.Size([320, 1280]) from checkpoint, the shape in current model is torch.Size([640, 1280]).
    size mismatch for up_blocks.2.resnets.1.time_emb_proj.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
    size mismatch for up_blocks.2.resnets.1.norm2.weight: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
    size mismatch for up_blocks.2.resnets.1.norm2.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
    size mismatch for up_blocks.2.resnets.1.conv2.weight: copying a param with shape torch.Size([320, 320, 3, 3]) from checkpoint, the shape in current model is torch.Size([640, 640, 3, 3]).
    size mismatch for up_blocks.2.resnets.1.conv2.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
    size mismatch for up_blocks.2.resnets.1.conv_shortcut.weight: copying a param with shape torch.Size([320, 640, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 1280, 1, 1]).
    size mismatch for up_blocks.2.resnets.1.conv_shortcut.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
    size mismatch for up_blocks.2.resnets.2.norm1.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([960]).
    size mismatch for up_blocks.2.resnets.2.norm1.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([960]).
    size mismatch for up_blocks.2.resnets.2.conv1.weight: copying a param with shape torch.Size([320, 640, 3, 3]) from checkpoint, the shape in current model is torch.Size([640, 960, 3, 3]).
    size mismatch for up_blocks.2.resnets.2.conv1.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
    size mismatch for up_blocks.2.resnets.2.time_emb_proj.weight: copying a param with shape torch.Size([320, 1280]) from checkpoint, the shape in current model is torch.Size([640, 1280]).
    size mismatch for up_blocks.2.resnets.2.time_emb_proj.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
    size mismatch for up_blocks.2.resnets.2.norm2.weight: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
    size mismatch for up_blocks.2.resnets.2.norm2.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
    size mismatch for up_blocks.2.resnets.2.conv2.weight: copying a param with shape torch.Size([320, 320, 3, 3]) from checkpoint, the shape in current model is torch.Size([640, 640, 3, 3]).
    size mismatch for up_blocks.2.resnets.2.conv2.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
    size mismatch for up_blocks.2.resnets.2.conv_shortcut.weight: copying a param with shape torch.Size([320, 640, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 960, 1, 1]).
    size mismatch for up_blocks.2.resnets.2.conv_shortcut.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
    size mismatch for mid_block.attentions.0.proj_in.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
    size mismatch for mid_block.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
    size mismatch for mid_block.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
    size mismatch for mid_block.attentions.0.proj_out.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
07:27:08-379298 INFO     Done merging...

[b-fission]

It looks like you forgot to press the "SDXL" checkbox in the gui. Your log shows networks/merge_lora.py is running instead of networks/sdxl_merge_lora.py

[jslegers]

It looks like you forgot to press the "SDXL" checkbox in the gui. Your log shows networks/merge_lora.py is running instead of networks/sdxl_merge_lora.py

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