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53 lines
2.1 KiB
Python
53 lines
2.1 KiB
Python
from typing import Any
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import torch
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from invokeai.backend.flux.ip_adapter.xlabs_ip_adapter_flux import XlabsIpAdapterParams
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def is_state_dict_xlabs_ip_adapter(sd: dict[str | int, Any]) -> bool:
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"""Is the state dict for an XLabs FLUX IP-Adapter model?
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This is intended to be a reasonably high-precision detector, but it is not guaranteed to have perfect precision.
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"""
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# If all of the expected keys are present, then this is very likely an XLabs IP-Adapter model.
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expected_keys = {
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"double_blocks.0.processor.ip_adapter_double_stream_k_proj.bias",
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"double_blocks.0.processor.ip_adapter_double_stream_k_proj.weight",
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"double_blocks.0.processor.ip_adapter_double_stream_v_proj.bias",
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"double_blocks.0.processor.ip_adapter_double_stream_v_proj.weight",
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"ip_adapter_proj_model.norm.bias",
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"ip_adapter_proj_model.norm.weight",
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"ip_adapter_proj_model.proj.bias",
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"ip_adapter_proj_model.proj.weight",
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}
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if expected_keys.issubset(sd.keys()):
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return True
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return False
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def infer_xlabs_ip_adapter_params_from_state_dict(state_dict: dict[str | int, torch.Tensor]) -> XlabsIpAdapterParams:
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num_double_blocks = 0
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context_dim = 0
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hidden_dim = 0
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# Count the number of double blocks.
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double_block_index = 0
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while f"double_blocks.{double_block_index}.processor.ip_adapter_double_stream_k_proj.weight" in state_dict:
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double_block_index += 1
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num_double_blocks = double_block_index
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hidden_dim = state_dict["double_blocks.0.processor.ip_adapter_double_stream_k_proj.weight"].shape[0]
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context_dim = state_dict["double_blocks.0.processor.ip_adapter_double_stream_k_proj.weight"].shape[1]
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clip_embeddings_dim = state_dict["ip_adapter_proj_model.proj.weight"].shape[1]
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clip_extra_context_tokens = state_dict["ip_adapter_proj_model.proj.weight"].shape[0] // context_dim
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return XlabsIpAdapterParams(
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num_double_blocks=num_double_blocks,
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context_dim=context_dim,
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hidden_dim=hidden_dim,
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clip_embeddings_dim=clip_embeddings_dim,
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clip_extra_context_tokens=clip_extra_context_tokens,
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)
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