mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
be8b99eed5
- Replace AnyModelLoader with ModelLoaderRegistry - Fix type check errors in multiple files - Remove apparently unneeded `get_model_config_enum()` method from model manager - Remove last vestiges of old model manager - Updated tests and documentation resolve conflict with seamless.py
136 lines
5.1 KiB
Python
136 lines
5.1 KiB
Python
"""Utilities for parsing model files, used mostly by probe.py"""
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import json
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from pathlib import Path
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from typing import Dict, Optional, Union
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import safetensors
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import torch
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from picklescan.scanner import scan_file_path
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def _fast_safetensors_reader(path: str) -> Dict[str, torch.Tensor]:
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checkpoint = {}
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device = torch.device("meta")
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with open(path, "rb") as f:
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definition_len = int.from_bytes(f.read(8), "little")
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definition_json = f.read(definition_len)
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definition = json.loads(definition_json)
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if "__metadata__" in definition and definition["__metadata__"].get("format", "pt") not in {
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"pt",
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"torch",
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"pytorch",
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}:
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raise Exception("Supported only pytorch safetensors files")
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definition.pop("__metadata__", None)
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for key, info in definition.items():
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dtype = {
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"I8": torch.int8,
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"I16": torch.int16,
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"I32": torch.int32,
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"I64": torch.int64,
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"F16": torch.float16,
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"F32": torch.float32,
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"F64": torch.float64,
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}[info["dtype"]]
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checkpoint[key] = torch.empty(info["shape"], dtype=dtype, device=device)
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return checkpoint
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def read_checkpoint_meta(path: Union[str, Path], scan: bool = False) -> Dict[str, torch.Tensor]:
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if str(path).endswith(".safetensors"):
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try:
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path_str = path.as_posix() if isinstance(path, Path) else path
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checkpoint = _fast_safetensors_reader(path_str)
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except Exception:
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# TODO: create issue for support "meta"?
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checkpoint = safetensors.torch.load_file(path, device="cpu")
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else:
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if scan:
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scan_result = scan_file_path(path)
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if scan_result.infected_files != 0:
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raise Exception(f'The model file "{path}" is potentially infected by malware. Aborting import.')
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checkpoint = torch.load(path, map_location=torch.device("meta"))
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return checkpoint
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def lora_token_vector_length(checkpoint: Dict[str, torch.Tensor]) -> Optional[int]:
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"""
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Given a checkpoint in memory, return the lora token vector length
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:param checkpoint: The checkpoint
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"""
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def _get_shape_1(key: str, tensor: torch.Tensor, checkpoint: Dict[str, torch.Tensor]) -> Optional[int]:
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lora_token_vector_length = None
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if "." not in key:
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return lora_token_vector_length # wrong key format
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model_key, lora_key = key.split(".", 1)
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# check lora/locon
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if lora_key == "lora_down.weight":
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lora_token_vector_length = tensor.shape[1]
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# check loha (don't worry about hada_t1/hada_t2 as it used only in 4d shapes)
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elif lora_key in ["hada_w1_b", "hada_w2_b"]:
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lora_token_vector_length = tensor.shape[1]
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# check lokr (don't worry about lokr_t2 as it used only in 4d shapes)
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elif "lokr_" in lora_key:
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if model_key + ".lokr_w1" in checkpoint:
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_lokr_w1 = checkpoint[model_key + ".lokr_w1"]
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elif model_key + "lokr_w1_b" in checkpoint:
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_lokr_w1 = checkpoint[model_key + ".lokr_w1_b"]
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else:
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return lora_token_vector_length # unknown format
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if model_key + ".lokr_w2" in checkpoint:
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_lokr_w2 = checkpoint[model_key + ".lokr_w2"]
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elif model_key + "lokr_w2_b" in checkpoint:
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_lokr_w2 = checkpoint[model_key + ".lokr_w2_b"]
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else:
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return lora_token_vector_length # unknown format
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lora_token_vector_length = _lokr_w1.shape[1] * _lokr_w2.shape[1]
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elif lora_key == "diff":
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lora_token_vector_length = tensor.shape[1]
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# ia3 can be detected only by shape[0] in text encoder
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elif lora_key == "weight" and "lora_unet_" not in model_key:
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lora_token_vector_length = tensor.shape[0]
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return lora_token_vector_length
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lora_token_vector_length = None
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lora_te1_length = None
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lora_te2_length = None
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for key, tensor in checkpoint.items():
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if key.startswith("lora_unet_") and ("_attn2_to_k." in key or "_attn2_to_v." in key):
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lora_token_vector_length = _get_shape_1(key, tensor, checkpoint)
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elif key.startswith("lora_unet_") and (
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"time_emb_proj.lora_down" in key
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): # recognizes format at https://civitai.com/models/224641
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lora_token_vector_length = _get_shape_1(key, tensor, checkpoint)
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elif key.startswith("lora_te") and "_self_attn_" in key:
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tmp_length = _get_shape_1(key, tensor, checkpoint)
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if key.startswith("lora_te_"):
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lora_token_vector_length = tmp_length
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elif key.startswith("lora_te1_"):
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lora_te1_length = tmp_length
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elif key.startswith("lora_te2_"):
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lora_te2_length = tmp_length
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if lora_te1_length is not None and lora_te2_length is not None:
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lora_token_vector_length = lora_te1_length + lora_te2_length
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if lora_token_vector_length is not None:
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break
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return lora_token_vector_length
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