Trying to load garment_extractor.safetensors into StableDiffusionXLControlNetPipeline failed

[wxhzzsf]

Hi there, I wrote a script and tried to run the ipadapter and openpose control net demo with SDXL. However, when loading stable_ckpt/garment_extractor.safetensors into sd_pipe.unet, there are following errors. It seems the garment_extractor.safetensors architecture doesn't fit the SDXL architecture. Is there an easy way to adapt the stable_ckpt/garment_extractor.safetensors so that it runs on SDXL? My code is the attachments.
Here are the errors:
Traceback (most recent call last):
File "/root/autodl-tmp/MagicClothing/stable_version/stable_gradio_ipadapter_openpose_xl.py", line 69, in
ip_model = StableIPAdapterFaceID(pipe, garment_extractor_path, garment_ip_layer_path, image_encoder_path, ip_ckpt, device, args.enable_cloth_guidance)
File "/root/autodl-tmp/MagicClothing/garment_adapter/garment_ipadapter_faceid_stable.py", line 75, in init
super().init(sd_pipe, ref_path, image_encoder_path, ip_ckpt, device, enable_cloth_guidance, num_tokens, torch_dtype, set_seg_model)
File "/root/autodl-tmp/MagicClothing/garment_adapter/garment_ipadapter_faceid.py", line 351, in init
ref_unet.load_state_dict(state_dict, strict=False)
File "/root/autodl-tmp/envs/oms-diffusion/lib/python3.10/site-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:
size mismatch for down_blocks.1.attentions.0.proj_in.weight: copying a param with shape torch.Size([640, 640, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([640, 768]) from checkpoint, the shape in current model is torch.Size([640, 2048]).
size mismatch for down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([640, 768]) from checkpoint, the shape in current model is torch.Size([640, 2048]).
size mismatch for down_blocks.1.attentions.0.proj_out.weight: copying a param with shape torch.Size([640, 640, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for down_blocks.1.attentions.1.proj_in.weight: copying a param with shape torch.Size([640, 640, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([640, 768]) from checkpoint, the shape in current model is torch.Size([640, 2048]).
size mismatch for down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([640, 768]) from checkpoint, the shape in current model is torch.Size([640, 2048]).
size mismatch for down_blocks.1.attentions.1.proj_out.weight: copying a param with shape torch.Size([640, 640, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for down_blocks.2.attentions.0.proj_in.weight: copying a param with shape torch.Size([1280, 1280, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
size mismatch for down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 2048]).
size mismatch for down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 2048]).
size mismatch for down_blocks.2.attentions.0.proj_out.weight: copying a param with shape torch.Size([1280, 1280, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
size mismatch for down_blocks.2.attentions.1.proj_in.weight: copying a param with shape torch.Size([1280, 1280, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
size mismatch for down_blocks.2.attentions.1.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 2048]).
size mismatch for down_blocks.2.attentions.1.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 2048]).
size mismatch for down_blocks.2.attentions.1.proj_out.weight: copying a param with shape torch.Size([1280, 1280, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
size mismatch for up_blocks.0.resnets.2.norm1.weight: copying a param with shape torch.Size([2560]) from checkpoint, the shape in current model is torch.Size([1920]).
size mismatch for up_blocks.0.resnets.2.norm1.bias: copying a param with shape torch.Size([2560]) from checkpoint, the shape in current model is torch.Size([1920]).
size mismatch for up_blocks.0.resnets.2.conv1.weight: copying a param with shape torch.Size([1280, 2560, 3, 3]) from checkpoint, the shape in current model is torch.Size([1280, 1920, 3, 3]).
size mismatch for up_blocks.0.resnets.2.conv_shortcut.weight: copying a param with shape torch.Size([1280, 2560, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 1920, 1, 1]).
size mismatch for up_blocks.1.attentions.0.norm.weight: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.0.norm.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.0.proj_in.weight: copying a param with shape torch.Size([1280, 1280, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for up_blocks.1.attentions.0.proj_in.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.norm1.weight: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.norm1.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_q.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_k.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_v.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_out.0.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_out.0.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.norm2.weight: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.norm2.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_q.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([640, 2048]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([640, 2048]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_out.0.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_out.0.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.norm3.weight: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.norm3.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.ff.net.0.proj.weight: copying a param with shape torch.Size([10240, 1280]) from checkpoint, the shape in current model is torch.Size([5120, 640]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.ff.net.0.proj.bias: copying a param with shape torch.Size([10240]) from checkpoint, the shape in current model is torch.Size([5120]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.ff.net.2.weight: copying a param with shape torch.Size([1280, 5120]) from checkpoint, the shape in current model is torch.Size([640, 2560]).
size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.ff.net.2.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.0.proj_out.weight: copying a param with shape torch.Size([1280, 1280, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for up_blocks.1.attentions.0.proj_out.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.1.norm.weight: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.1.norm.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.1.proj_in.weight: copying a param with shape torch.Size([1280, 1280, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for up_blocks.1.attentions.1.proj_in.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.norm1.weight: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.norm1.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_q.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_k.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_v.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_out.0.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_out.0.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.norm2.weight: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.norm2.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_q.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([640, 2048]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([640, 2048]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_out.0.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_out.0.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.norm3.weight: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.norm3.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.ff.net.0.proj.weight: copying a param with shape torch.Size([10240, 1280]) from checkpoint, the shape in current model is torch.Size([5120, 640]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.ff.net.0.proj.bias: copying a param with shape torch.Size([10240]) from checkpoint, the shape in current model is torch.Size([5120]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.ff.net.2.weight: copying a param with shape torch.Size([1280, 5120]) from checkpoint, the shape in current model is torch.Size([640, 2560]).
size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.ff.net.2.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.1.proj_out.weight: copying a param with shape torch.Size([1280, 1280, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for up_blocks.1.attentions.1.proj_out.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.2.norm.weight: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.2.norm.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.2.proj_in.weight: copying a param with shape torch.Size([1280, 1280, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for up_blocks.1.attentions.2.proj_in.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.norm1.weight: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.norm1.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_q.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_k.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_v.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_out.0.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_out.0.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.norm2.weight: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.norm2.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_q.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([640, 2048]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([640, 2048]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_out.0.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_out.0.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.norm3.weight: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.norm3.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.ff.net.0.proj.weight: copying a param with shape torch.Size([10240, 1280]) from checkpoint, the shape in current model is torch.Size([5120, 640]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.ff.net.0.proj.bias: copying a param with shape torch.Size([10240]) from checkpoint, the shape in current model is torch.Size([5120]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.ff.net.2.weight: copying a param with shape torch.Size([1280, 5120]) from checkpoint, the shape in current model is torch.Size([640, 2560]).
size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.ff.net.2.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.attentions.2.proj_out.weight: copying a param with shape torch.Size([1280, 1280, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 640]).
size mismatch for up_blocks.1.attentions.2.proj_out.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.resnets.0.norm1.weight: copying a param with shape torch.Size([2560]) from checkpoint, the shape in current model is torch.Size([1920]).
size mismatch for up_blocks.1.resnets.0.norm1.bias: copying a param with shape torch.Size([2560]) from checkpoint, the shape in current model is torch.Size([1920]).
size mismatch for up_blocks.1.resnets.0.conv1.weight: copying a param with shape torch.Size([1280, 2560, 3, 3]) from checkpoint, the shape in current model is torch.Size([640, 1920, 3, 3]).
size mismatch for up_blocks.1.resnets.0.conv1.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.resnets.0.time_emb_proj.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([640, 1280]).
size mismatch for up_blocks.1.resnets.0.time_emb_proj.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.resnets.0.norm2.weight: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.resnets.0.norm2.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.resnets.0.conv2.weight: copying a param with shape torch.Size([1280, 1280, 3, 3]) from checkpoint, the shape in current model is torch.Size([640, 640, 3, 3]).
size mismatch for up_blocks.1.resnets.0.conv2.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.resnets.0.conv_shortcut.weight: copying a param with shape torch.Size([1280, 2560, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 1920, 1, 1]).
size mismatch for up_blocks.1.resnets.0.conv_shortcut.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.resnets.1.norm1.weight: copying a param with shape torch.Size([2560]) from checkpoint, the shape in current model is torch.Size([1280]).
size mismatch for up_blocks.1.resnets.1.norm1.bias: copying a param with shape torch.Size([2560]) from checkpoint, the shape in current model is torch.Size([1280]).
size mismatch for up_blocks.1.resnets.1.conv1.weight: copying a param with shape torch.Size([1280, 2560, 3, 3]) from checkpoint, the shape in current model is torch.Size([640, 1280, 3, 3]).
size mismatch for up_blocks.1.resnets.1.conv1.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.resnets.1.time_emb_proj.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([640, 1280]).
size mismatch for up_blocks.1.resnets.1.time_emb_proj.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.resnets.1.norm2.weight: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.resnets.1.norm2.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.resnets.1.conv2.weight: copying a param with shape torch.Size([1280, 1280, 3, 3]) from checkpoint, the shape in current model is torch.Size([640, 640, 3, 3]).
size mismatch for up_blocks.1.resnets.1.conv2.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.resnets.1.conv_shortcut.weight: copying a param with shape torch.Size([1280, 2560, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 1280, 1, 1]).
size mismatch for up_blocks.1.resnets.1.conv_shortcut.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.resnets.2.norm1.weight: copying a param with shape torch.Size([1920]) from checkpoint, the shape in current model is torch.Size([960]).
size mismatch for up_blocks.1.resnets.2.norm1.bias: copying a param with shape torch.Size([1920]) from checkpoint, the shape in current model is torch.Size([960]).
size mismatch for up_blocks.1.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([640, 960, 3, 3]).
size mismatch for up_blocks.1.resnets.2.conv1.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.resnets.2.time_emb_proj.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([640, 1280]).
size mismatch for up_blocks.1.resnets.2.time_emb_proj.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.resnets.2.norm2.weight: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.resnets.2.norm2.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.resnets.2.conv2.weight: copying a param with shape torch.Size([1280, 1280, 3, 3]) from checkpoint, the shape in current model is torch.Size([640, 640, 3, 3]).
size mismatch for up_blocks.1.resnets.2.conv2.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.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([640, 960, 1, 1]).
size mismatch for up_blocks.1.resnets.2.conv_shortcut.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.1.upsamplers.0.conv.weight: copying a param with shape torch.Size([1280, 1280, 3, 3]) from checkpoint, the shape in current model is torch.Size([640, 640, 3, 3]).
size mismatch for up_blocks.1.upsamplers.0.conv.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.2.resnets.0.norm1.weight: copying a param with shape torch.Size([1920]) from checkpoint, the shape in current model is torch.Size([960]).
size mismatch for up_blocks.2.resnets.0.norm1.bias: copying a param with shape torch.Size([1920]) from checkpoint, the shape in current model is torch.Size([960]).
size mismatch for up_blocks.2.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([320, 960, 3, 3]).
size mismatch for up_blocks.2.resnets.0.conv1.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for up_blocks.2.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([320, 1280]).
size mismatch for up_blocks.2.resnets.0.time_emb_proj.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for up_blocks.2.resnets.0.norm2.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for up_blocks.2.resnets.0.norm2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for up_blocks.2.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([320, 320, 3, 3]).
size mismatch for up_blocks.2.resnets.0.conv2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for up_blocks.2.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([320, 960, 1, 1]).
size mismatch for up_blocks.2.resnets.0.conv_shortcut.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for up_blocks.2.resnets.1.norm1.weight: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.2.resnets.1.norm1.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.2.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([320, 640, 3, 3]).
size mismatch for up_blocks.2.resnets.1.conv1.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for up_blocks.2.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([320, 1280]).
size mismatch for up_blocks.2.resnets.1.time_emb_proj.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for up_blocks.2.resnets.1.norm2.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for up_blocks.2.resnets.1.norm2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for up_blocks.2.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([320, 320, 3, 3]).
size mismatch for up_blocks.2.resnets.1.conv2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for up_blocks.2.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([320, 640, 1, 1]).
size mismatch for up_blocks.2.resnets.1.conv_shortcut.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for up_blocks.2.resnets.2.norm1.weight: copying a param with shape torch.Size([960]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.2.resnets.2.norm1.bias: copying a param with shape torch.Size([960]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for up_blocks.2.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([320, 640, 3, 3]).
size mismatch for up_blocks.2.resnets.2.conv1.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for up_blocks.2.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([320, 1280]).
size mismatch for up_blocks.2.resnets.2.time_emb_proj.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for up_blocks.2.resnets.2.norm2.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for up_blocks.2.resnets.2.norm2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for up_blocks.2.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([320, 320, 3, 3]).
size mismatch for up_blocks.2.resnets.2.conv2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for up_blocks.2.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([320, 640, 1, 1]).
size mismatch for up_blocks.2.resnets.2.conv_shortcut.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([320]).
size mismatch for mid_block.attentions.0.proj_in.weight: copying a param with shape torch.Size([1280, 1280, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
size mismatch for mid_block.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 2048]).
size mismatch for mid_block.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 2048]).
size mismatch for mid_block.attentions.0.proj_out.weight: copying a param with shape torch.Size([1280, 1280, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).