error in loading model

[mahilaMoghadami]

I get this error for inferencing and running your demo notebook:
Loading pipeline components...: 100%
 7/7 [00:37<00:00,  2.70s/it]
/usr/local/lib/python3.10/dist-packages/photomaker/pipeline.py:218: FutureWarning: You are using torch.load with weights_only=False (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for weights_only will be flipped to True. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via torch.serialization.add_safe_globals. We recommend you start setting weights_only=True for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
state_dict = torch.load(model_file, map_location="cpu")
Loading PhotoMaker v2 components [1] id_encoder from [/root/.cache/huggingface/hub/models--TencentARC--PhotoMaker/snapshots/f68f8e6309bf213d28d68230abff0ccc92de9f30]...
4096

RuntimeError Traceback (most recent call last)
in <cell line: 12>()
10 ).to(device)
11
---> 12 pipe.load_photomaker_adapter(
13 os.path.dirname(photomaker_ckpt),
14 subfolder="",

2 frames
/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in load_state_dict(self, state_dict, strict, assign)
2213
2214 if len(error_msgs) > 0:
-> 2215 raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
2216 self.class.name, "\n\t".join(error_msgs)))
2217 return _IncompatibleKeys(missing_keys, unexpected_keys)

RuntimeError: Error(s) in loading state_dict for PhotoMakerIDEncoder_CLIPInsightfaceExtendtoken:
Missing key(s) in state_dict: "qformer_perceiver.token_proj.0.weight", "qformer_perceiver.token_proj.0.bias", "qformer_perceiver.token_proj.2.weight", "qformer_perceiver.token_proj.2.bias", "qformer_perceiver.token_norm.weight", "qformer_perceiver.token_norm.bias", "qformer_perceiver.perceiver_resampler.proj_in.weight", "qformer_perceiver.perceiver_resampler.proj_in.bias", "qformer_perceiver.perceiver_resampler.proj_out.weight", "qformer_perceiver.perceiver_resampler.proj_out.bias", "qformer_perceiver.perceiver_resampler.norm_out.weight", "qformer_perceiver.perceiver_resampler.norm_out.bias", "qformer_perceiver.perceiver_resampler.layers.0.0.norm1.weight", "qformer_perceiver.perceiver_resampler.layers.0.0.norm1.bias", "qformer_perceiver.perceiver_resampler.layers.0.0.norm2.weight", "qformer_perceiver.perceiver_resampler.layers.0.0.norm2.bias", "qformer_perceiver.perceiver_resampler.layers.0.0.to_q.weight", "qformer_perceiver.perceiver_resampler.layers.0.0.to_kv.weight", "qformer_perceiver.perceiver_resampler.layers.0.0.to_out.weight", "qformer_perceiver.perceiver_resampler.layers.0.1.0.weight", "qformer_perceiver.perceiver_resampler.layers.0.1.0.bias", "qformer_perceiver.perceiver_resampler.layers.0.1.1.weight", "qformer_perceiver.perceiver_resampler.layers.0.1.3.weight", "qformer_perceiver.perceiver_resampler.layers.1.0.norm1.weight", "qformer_perceiver.perceiver_resampler.layers.1.0.norm1.bias", "qformer_perceiver.perceiver_resampler.layers.1.0.norm2.weight", "qformer_p...

[bozagina]

I got the same problem