mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
658 lines
26 KiB
Python
658 lines
26 KiB
Python
import json
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import re
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Callable, Dict, Literal, Optional, Union
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import safetensors.torch
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import torch
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from diffusers import ConfigMixin, ModelMixin
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from picklescan.scanner import scan_file_path
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from invokeai.backend.model_management.models.ip_adapter import IPAdapterModelFormat
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from .models import (
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BaseModelType,
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InvalidModelException,
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ModelType,
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ModelVariantType,
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SchedulerPredictionType,
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SilenceWarnings,
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)
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from .models.base import read_checkpoint_meta
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from .util import lora_token_vector_length
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@dataclass
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class ModelProbeInfo(object):
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model_type: ModelType
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base_type: BaseModelType
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variant_type: ModelVariantType
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prediction_type: SchedulerPredictionType
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upcast_attention: bool
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format: Literal["diffusers", "checkpoint", "lycoris", "olive", "onnx"]
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image_size: int
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name: Optional[str] = None
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description: Optional[str] = None
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class ProbeBase(object):
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"""forward declaration"""
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pass
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class ModelProbe(object):
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PROBES = {
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"diffusers": {},
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"checkpoint": {},
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"onnx": {},
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}
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CLASS2TYPE = {
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"StableDiffusionPipeline": ModelType.Main,
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"StableDiffusionInpaintPipeline": ModelType.Main,
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"StableDiffusionXLPipeline": ModelType.Main,
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"StableDiffusionXLImg2ImgPipeline": ModelType.Main,
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"StableDiffusionXLInpaintPipeline": ModelType.Main,
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"AutoencoderKL": ModelType.Vae,
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"AutoencoderTiny": ModelType.Vae,
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"ControlNetModel": ModelType.ControlNet,
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"CLIPVisionModelWithProjection": ModelType.CLIPVision,
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"T2IAdapter": ModelType.T2IAdapter,
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}
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@classmethod
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def register_probe(
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cls, format: Literal["diffusers", "checkpoint", "onnx"], model_type: ModelType, probe_class: ProbeBase
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):
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cls.PROBES[format][model_type] = probe_class
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@classmethod
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def heuristic_probe(
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cls,
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model: Union[Dict, ModelMixin, Path],
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prediction_type_helper: Callable[[Path], SchedulerPredictionType] = None,
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) -> ModelProbeInfo:
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if isinstance(model, Path):
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return cls.probe(model_path=model, prediction_type_helper=prediction_type_helper)
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elif isinstance(model, (dict, ModelMixin, ConfigMixin)):
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return cls.probe(model_path=None, model=model, prediction_type_helper=prediction_type_helper)
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else:
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raise InvalidModelException("model parameter {model} is neither a Path, nor a model")
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@classmethod
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def probe(
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cls,
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model_path: Path,
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model: Optional[Union[Dict, ModelMixin]] = None,
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prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
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) -> ModelProbeInfo:
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"""
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Probe the model at model_path and return sufficient information about it
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to place it somewhere in the models directory hierarchy. If the model is
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already loaded into memory, you may provide it as model in order to avoid
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opening it a second time. The prediction_type_helper callable is a function that receives
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the path to the model and returns the SchedulerPredictionType.
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"""
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if model_path:
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format_type = "diffusers" if model_path.is_dir() else "checkpoint"
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else:
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format_type = "diffusers" if isinstance(model, (ConfigMixin, ModelMixin)) else "checkpoint"
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model_info = None
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try:
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model_type = (
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cls.get_model_type_from_folder(model_path, model)
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if format_type == "diffusers"
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else cls.get_model_type_from_checkpoint(model_path, model)
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)
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format_type = "onnx" if model_type == ModelType.ONNX else format_type
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probe_class = cls.PROBES[format_type].get(model_type)
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if not probe_class:
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return None
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probe = probe_class(model_path, model, prediction_type_helper)
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base_type = probe.get_base_type()
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variant_type = probe.get_variant_type()
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prediction_type = probe.get_scheduler_prediction_type()
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name = cls.get_model_name(model_path)
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description = f"{base_type.value} {model_type.value} model {name}"
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format = probe.get_format()
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model_info = ModelProbeInfo(
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model_type=model_type,
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base_type=base_type,
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variant_type=variant_type,
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prediction_type=prediction_type,
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name = name,
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description = description,
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upcast_attention=(
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base_type == BaseModelType.StableDiffusion2
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and prediction_type == SchedulerPredictionType.VPrediction
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),
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format=format,
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image_size=(
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1024
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if (base_type in {BaseModelType.StableDiffusionXL, BaseModelType.StableDiffusionXLRefiner})
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else (
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768
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if (
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base_type == BaseModelType.StableDiffusion2
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and prediction_type == SchedulerPredictionType.VPrediction
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)
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else 512
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)
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),
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)
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except Exception:
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raise
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return model_info
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@classmethod
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def get_model_name(cls, model_path: Path) -> str:
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if model_path.suffix in {'.safetensors', '.bin', '.pt', '.ckpt'}:
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return model_path.stem
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else:
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return model_path.name
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@classmethod
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def get_model_type_from_checkpoint(cls, model_path: Path, checkpoint: dict) -> ModelType:
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if model_path.suffix not in (".bin", ".pt", ".ckpt", ".safetensors", ".pth"):
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return None
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if model_path.name == "learned_embeds.bin":
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return ModelType.TextualInversion
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ckpt = checkpoint if checkpoint else read_checkpoint_meta(model_path, scan=True)
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ckpt = ckpt.get("state_dict", ckpt)
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for key in ckpt.keys():
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if any(key.startswith(v) for v in {"cond_stage_model.", "first_stage_model.", "model.diffusion_model."}):
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return ModelType.Main
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elif any(key.startswith(v) for v in {"encoder.conv_in", "decoder.conv_in"}):
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return ModelType.Vae
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elif any(key.startswith(v) for v in {"lora_te_", "lora_unet_"}):
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return ModelType.Lora
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elif any(key.endswith(v) for v in {"to_k_lora.up.weight", "to_q_lora.down.weight"}):
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return ModelType.Lora
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elif any(key.startswith(v) for v in {"control_model", "input_blocks"}):
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return ModelType.ControlNet
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elif key in {"emb_params", "string_to_param"}:
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return ModelType.TextualInversion
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else:
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# diffusers-ti
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if len(ckpt) < 10 and all(isinstance(v, torch.Tensor) for v in ckpt.values()):
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return ModelType.TextualInversion
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raise InvalidModelException(f"Unable to determine model type for {model_path}")
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@classmethod
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def get_model_type_from_folder(cls, folder_path: Path, model: ModelMixin) -> ModelType:
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"""
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Get the model type of a hugging-face style folder.
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"""
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class_name = None
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error_hint = None
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if model:
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class_name = model.__class__.__name__
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else:
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for suffix in ["bin", "safetensors"]:
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if (folder_path / f"learned_embeds.{suffix}").exists():
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return ModelType.TextualInversion
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if (folder_path / f"pytorch_lora_weights.{suffix}").exists():
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return ModelType.Lora
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if (folder_path / "unet/model.onnx").exists():
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return ModelType.ONNX
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if (folder_path / "image_encoder.txt").exists():
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return ModelType.IPAdapter
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i = folder_path / "model_index.json"
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c = folder_path / "config.json"
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config_path = i if i.exists() else c if c.exists() else None
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if config_path:
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with open(config_path, "r") as file:
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conf = json.load(file)
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if "_class_name" in conf:
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class_name = conf["_class_name"]
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elif "architectures" in conf:
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class_name = conf["architectures"][0]
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else:
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class_name = None
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else:
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error_hint = f"No model_index.json or config.json found in {folder_path}."
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if class_name and (type := cls.CLASS2TYPE.get(class_name)):
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return type
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else:
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error_hint = f"class {class_name} is not one of the supported classes [{', '.join(cls.CLASS2TYPE.keys())}]"
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# give up
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raise InvalidModelException(
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f"Unable to determine model type for {folder_path}" + (f"; {error_hint}" if error_hint else "")
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)
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@classmethod
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def _scan_and_load_checkpoint(cls, model_path: Path) -> dict:
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with SilenceWarnings():
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if model_path.suffix.endswith((".ckpt", ".pt", ".bin")):
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cls._scan_model(model_path, model_path)
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return torch.load(model_path)
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else:
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return safetensors.torch.load_file(model_path)
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@classmethod
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def _scan_model(cls, model_name, checkpoint):
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"""
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Apply picklescanner to the indicated checkpoint and issue a warning
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and option to exit if an infected file is identified.
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"""
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# scan model
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scan_result = scan_file_path(checkpoint)
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if scan_result.infected_files != 0:
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raise Exception("The model {model_name} is potentially infected by malware. Aborting import.")
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# ##################################################3
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# Checkpoint probing
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# ##################################################3
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class ProbeBase(object):
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def get_base_type(self) -> BaseModelType:
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pass
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def get_variant_type(self) -> ModelVariantType:
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pass
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def get_scheduler_prediction_type(self) -> SchedulerPredictionType:
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pass
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def get_format(self) -> str:
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pass
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class CheckpointProbeBase(ProbeBase):
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def __init__(
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self, checkpoint_path: Path, checkpoint: dict, helper: Callable[[Path], SchedulerPredictionType] = None
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) -> BaseModelType:
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self.checkpoint = checkpoint or ModelProbe._scan_and_load_checkpoint(checkpoint_path)
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self.checkpoint_path = checkpoint_path
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self.helper = helper
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def get_base_type(self) -> BaseModelType:
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pass
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def get_format(self) -> str:
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return "checkpoint"
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def get_variant_type(self) -> ModelVariantType:
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model_type = ModelProbe.get_model_type_from_checkpoint(self.checkpoint_path, self.checkpoint)
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if model_type != ModelType.Main:
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return ModelVariantType.Normal
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state_dict = self.checkpoint.get("state_dict") or self.checkpoint
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in_channels = state_dict["model.diffusion_model.input_blocks.0.0.weight"].shape[1]
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if in_channels == 9:
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return ModelVariantType.Inpaint
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elif in_channels == 5:
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return ModelVariantType.Depth
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elif in_channels == 4:
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return ModelVariantType.Normal
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else:
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raise InvalidModelException(
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f"Cannot determine variant type (in_channels={in_channels}) at {self.checkpoint_path}"
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)
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class PipelineCheckpointProbe(CheckpointProbeBase):
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def get_base_type(self) -> BaseModelType:
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checkpoint = self.checkpoint
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state_dict = self.checkpoint.get("state_dict") or checkpoint
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key_name = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
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if key_name in state_dict and state_dict[key_name].shape[-1] == 768:
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return BaseModelType.StableDiffusion1
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if key_name in state_dict and state_dict[key_name].shape[-1] == 1024:
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return BaseModelType.StableDiffusion2
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key_name = "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_k.weight"
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if key_name in state_dict and state_dict[key_name].shape[-1] == 2048:
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return BaseModelType.StableDiffusionXL
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elif key_name in state_dict and state_dict[key_name].shape[-1] == 1280:
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return BaseModelType.StableDiffusionXLRefiner
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else:
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raise InvalidModelException("Cannot determine base type")
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def get_scheduler_prediction_type(self) -> Optional[SchedulerPredictionType]:
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"""Return model prediction type."""
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# if there is a .yaml associated with this checkpoint, then we do not need
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# to probe for the prediction type as it will be ignored.
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if self.checkpoint_path and self.checkpoint_path.with_suffix(".yaml").exists():
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return None
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type = self.get_base_type()
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if type == BaseModelType.StableDiffusion2:
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checkpoint = self.checkpoint
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state_dict = self.checkpoint.get("state_dict") or checkpoint
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key_name = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
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if key_name in state_dict and state_dict[key_name].shape[-1] == 1024:
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if "global_step" in checkpoint:
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if checkpoint["global_step"] == 220000:
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return SchedulerPredictionType.Epsilon
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elif checkpoint["global_step"] == 110000:
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return SchedulerPredictionType.VPrediction
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if self.helper and self.checkpoint_path:
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if helper_guess := self.helper(self.checkpoint_path):
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return helper_guess
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return SchedulerPredictionType.VPrediction # a guess for sd2 ckpts
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elif type == BaseModelType.StableDiffusion1:
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if self.helper and self.checkpoint_path:
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if helper_guess := self.helper(self.checkpoint_path):
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return helper_guess
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return SchedulerPredictionType.Epsilon # a reasonable guess for sd1 ckpts
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else:
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return None
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class VaeCheckpointProbe(CheckpointProbeBase):
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def get_base_type(self) -> BaseModelType:
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# I can't find any standalone 2.X VAEs to test with!
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return BaseModelType.StableDiffusion1
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class LoRACheckpointProbe(CheckpointProbeBase):
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def get_format(self) -> str:
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return "lycoris"
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def get_base_type(self) -> BaseModelType:
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checkpoint = self.checkpoint
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token_vector_length = lora_token_vector_length(checkpoint)
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if token_vector_length == 768:
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return BaseModelType.StableDiffusion1
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elif token_vector_length == 1024:
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return BaseModelType.StableDiffusion2
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elif token_vector_length == 2048:
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return BaseModelType.StableDiffusionXL
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else:
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raise InvalidModelException(f"Unknown LoRA type: {self.checkpoint_path}")
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class TextualInversionCheckpointProbe(CheckpointProbeBase):
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def get_format(self) -> str:
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return None
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def get_base_type(self) -> BaseModelType:
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checkpoint = self.checkpoint
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if "string_to_token" in checkpoint:
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token_dim = list(checkpoint["string_to_param"].values())[0].shape[-1]
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elif "emb_params" in checkpoint:
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token_dim = checkpoint["emb_params"].shape[-1]
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else:
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token_dim = list(checkpoint.values())[0].shape[0]
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if token_dim == 768:
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return BaseModelType.StableDiffusion1
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elif token_dim == 1024:
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return BaseModelType.StableDiffusion2
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else:
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return None
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class ControlNetCheckpointProbe(CheckpointProbeBase):
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def get_base_type(self) -> BaseModelType:
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checkpoint = self.checkpoint
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for key_name in (
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"control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight",
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"input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight",
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):
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if key_name not in checkpoint:
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continue
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if checkpoint[key_name].shape[-1] == 768:
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return BaseModelType.StableDiffusion1
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elif checkpoint[key_name].shape[-1] == 1024:
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return BaseModelType.StableDiffusion2
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elif self.checkpoint_path and self.helper:
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return self.helper(self.checkpoint_path)
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raise InvalidModelException("Unable to determine base type for {self.checkpoint_path}")
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class IPAdapterCheckpointProbe(CheckpointProbeBase):
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def get_base_type(self) -> BaseModelType:
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raise NotImplementedError()
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class CLIPVisionCheckpointProbe(CheckpointProbeBase):
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def get_base_type(self) -> BaseModelType:
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raise NotImplementedError()
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class T2IAdapterCheckpointProbe(CheckpointProbeBase):
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def get_base_type(self) -> BaseModelType:
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raise NotImplementedError()
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########################################################
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# classes for probing folders
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#######################################################
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class FolderProbeBase(ProbeBase):
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def __init__(self, folder_path: Path, model: ModelMixin = None, helper: Callable = None): # not used
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self.model = model
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self.folder_path = folder_path
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def get_variant_type(self) -> ModelVariantType:
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return ModelVariantType.Normal
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def get_format(self) -> str:
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return "diffusers"
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class PipelineFolderProbe(FolderProbeBase):
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def get_base_type(self) -> BaseModelType:
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if self.model:
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unet_conf = self.model.unet.config
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else:
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with open(self.folder_path / "unet" / "config.json", "r") as file:
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unet_conf = json.load(file)
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if unet_conf["cross_attention_dim"] == 768:
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return BaseModelType.StableDiffusion1
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elif unet_conf["cross_attention_dim"] == 1024:
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return BaseModelType.StableDiffusion2
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elif unet_conf["cross_attention_dim"] == 1280:
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return BaseModelType.StableDiffusionXLRefiner
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elif unet_conf["cross_attention_dim"] == 2048:
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return BaseModelType.StableDiffusionXL
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else:
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raise InvalidModelException(f"Unknown base model for {self.folder_path}")
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def get_scheduler_prediction_type(self) -> SchedulerPredictionType:
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if self.model:
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scheduler_conf = self.model.scheduler.config
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else:
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with open(self.folder_path / "scheduler" / "scheduler_config.json", "r") as file:
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scheduler_conf = json.load(file)
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if scheduler_conf["prediction_type"] == "v_prediction":
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return SchedulerPredictionType.VPrediction
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elif scheduler_conf["prediction_type"] == "epsilon":
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return SchedulerPredictionType.Epsilon
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else:
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return None
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def get_variant_type(self) -> ModelVariantType:
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# This only works for pipelines! Any kind of
|
|
# exception results in our returning the
|
|
# "normal" variant type
|
|
try:
|
|
if self.model:
|
|
conf = self.model.unet.config
|
|
else:
|
|
config_file = self.folder_path / "unet" / "config.json"
|
|
with open(config_file, "r") as file:
|
|
conf = json.load(file)
|
|
|
|
in_channels = conf["in_channels"]
|
|
if in_channels == 9:
|
|
return ModelVariantType.Inpaint
|
|
elif in_channels == 5:
|
|
return ModelVariantType.Depth
|
|
elif in_channels == 4:
|
|
return ModelVariantType.Normal
|
|
except Exception:
|
|
pass
|
|
return ModelVariantType.Normal
|
|
|
|
|
|
class VaeFolderProbe(FolderProbeBase):
|
|
def get_base_type(self) -> BaseModelType:
|
|
if self._config_looks_like_sdxl():
|
|
return BaseModelType.StableDiffusionXL
|
|
elif self._name_looks_like_sdxl():
|
|
# but SD and SDXL VAE are the same shape (3-channel RGB to 4-channel float scaled down
|
|
# by a factor of 8), we can't necessarily tell them apart by config hyperparameters.
|
|
return BaseModelType.StableDiffusionXL
|
|
else:
|
|
return BaseModelType.StableDiffusion1
|
|
|
|
def _config_looks_like_sdxl(self) -> bool:
|
|
# config values that distinguish Stability's SD 1.x VAE from their SDXL VAE.
|
|
config_file = self.folder_path / "config.json"
|
|
if not config_file.exists():
|
|
raise InvalidModelException(f"Cannot determine base type for {self.folder_path}")
|
|
with open(config_file, "r") as file:
|
|
config = json.load(file)
|
|
return config.get("scaling_factor", 0) == 0.13025 and config.get("sample_size") in [512, 1024]
|
|
|
|
def _name_looks_like_sdxl(self) -> bool:
|
|
return bool(re.search(r"xl\b", self._guess_name(), re.IGNORECASE))
|
|
|
|
def _guess_name(self) -> str:
|
|
name = self.folder_path.name
|
|
if name == "vae":
|
|
name = self.folder_path.parent.name
|
|
return name
|
|
|
|
|
|
class TextualInversionFolderProbe(FolderProbeBase):
|
|
def get_format(self) -> str:
|
|
return None
|
|
|
|
def get_base_type(self) -> BaseModelType:
|
|
path = self.folder_path / "learned_embeds.bin"
|
|
if not path.exists():
|
|
return None
|
|
checkpoint = ModelProbe._scan_and_load_checkpoint(path)
|
|
return TextualInversionCheckpointProbe(None, checkpoint=checkpoint).get_base_type()
|
|
|
|
|
|
class ONNXFolderProbe(FolderProbeBase):
|
|
def get_format(self) -> str:
|
|
return "onnx"
|
|
|
|
def get_base_type(self) -> BaseModelType:
|
|
return BaseModelType.StableDiffusion1
|
|
|
|
def get_variant_type(self) -> ModelVariantType:
|
|
return ModelVariantType.Normal
|
|
|
|
|
|
class ControlNetFolderProbe(FolderProbeBase):
|
|
def get_base_type(self) -> BaseModelType:
|
|
config_file = self.folder_path / "config.json"
|
|
if not config_file.exists():
|
|
raise InvalidModelException(f"Cannot determine base type for {self.folder_path}")
|
|
with open(config_file, "r") as file:
|
|
config = json.load(file)
|
|
# no obvious way to distinguish between sd2-base and sd2-768
|
|
dimension = config["cross_attention_dim"]
|
|
base_model = (
|
|
BaseModelType.StableDiffusion1
|
|
if dimension == 768
|
|
else (
|
|
BaseModelType.StableDiffusion2
|
|
if dimension == 1024
|
|
else BaseModelType.StableDiffusionXL
|
|
if dimension == 2048
|
|
else None
|
|
)
|
|
)
|
|
if not base_model:
|
|
raise InvalidModelException(f"Unable to determine model base for {self.folder_path}")
|
|
return base_model
|
|
|
|
|
|
class LoRAFolderProbe(FolderProbeBase):
|
|
def get_base_type(self) -> BaseModelType:
|
|
model_file = None
|
|
for suffix in ["safetensors", "bin"]:
|
|
base_file = self.folder_path / f"pytorch_lora_weights.{suffix}"
|
|
if base_file.exists():
|
|
model_file = base_file
|
|
break
|
|
if not model_file:
|
|
raise InvalidModelException("Unknown LoRA format encountered")
|
|
return LoRACheckpointProbe(model_file, None).get_base_type()
|
|
|
|
|
|
class IPAdapterFolderProbe(FolderProbeBase):
|
|
def get_format(self) -> str:
|
|
return IPAdapterModelFormat.InvokeAI.value
|
|
|
|
def get_base_type(self) -> BaseModelType:
|
|
model_file = self.folder_path / "ip_adapter.bin"
|
|
if not model_file.exists():
|
|
raise InvalidModelException("Unknown IP-Adapter model format.")
|
|
|
|
state_dict = torch.load(model_file, map_location="cpu")
|
|
cross_attention_dim = state_dict["ip_adapter"]["1.to_k_ip.weight"].shape[-1]
|
|
if cross_attention_dim == 768:
|
|
return BaseModelType.StableDiffusion1
|
|
elif cross_attention_dim == 1024:
|
|
return BaseModelType.StableDiffusion2
|
|
elif cross_attention_dim == 2048:
|
|
return BaseModelType.StableDiffusionXL
|
|
else:
|
|
raise InvalidModelException(f"IP-Adapter had unexpected cross-attention dimension: {cross_attention_dim}.")
|
|
|
|
|
|
class CLIPVisionFolderProbe(FolderProbeBase):
|
|
def get_base_type(self) -> BaseModelType:
|
|
return BaseModelType.Any
|
|
|
|
|
|
class T2IAdapterFolderProbe(FolderProbeBase):
|
|
def get_base_type(self) -> BaseModelType:
|
|
config_file = self.folder_path / "config.json"
|
|
if not config_file.exists():
|
|
raise InvalidModelException(f"Cannot determine base type for {self.folder_path}")
|
|
with open(config_file, "r") as file:
|
|
config = json.load(file)
|
|
|
|
adapter_type = config.get("adapter_type", None)
|
|
if adapter_type == "full_adapter_xl":
|
|
return BaseModelType.StableDiffusionXL
|
|
elif adapter_type == "full_adapter" or "light_adapter":
|
|
# I haven't seen any T2I adapter models for SD2, so assume that this is an SD1 adapter.
|
|
return BaseModelType.StableDiffusion1
|
|
else:
|
|
raise InvalidModelException(
|
|
f"Unable to determine base model for '{self.folder_path}' (adapter_type = {adapter_type})."
|
|
)
|
|
|
|
|
|
############## register probe classes ######
|
|
ModelProbe.register_probe("diffusers", ModelType.Main, PipelineFolderProbe)
|
|
ModelProbe.register_probe("diffusers", ModelType.Vae, VaeFolderProbe)
|
|
ModelProbe.register_probe("diffusers", ModelType.Lora, LoRAFolderProbe)
|
|
ModelProbe.register_probe("diffusers", ModelType.TextualInversion, TextualInversionFolderProbe)
|
|
ModelProbe.register_probe("diffusers", ModelType.ControlNet, ControlNetFolderProbe)
|
|
ModelProbe.register_probe("diffusers", ModelType.IPAdapter, IPAdapterFolderProbe)
|
|
ModelProbe.register_probe("diffusers", ModelType.CLIPVision, CLIPVisionFolderProbe)
|
|
ModelProbe.register_probe("diffusers", ModelType.T2IAdapter, T2IAdapterFolderProbe)
|
|
|
|
ModelProbe.register_probe("checkpoint", ModelType.Main, PipelineCheckpointProbe)
|
|
ModelProbe.register_probe("checkpoint", ModelType.Vae, VaeCheckpointProbe)
|
|
ModelProbe.register_probe("checkpoint", ModelType.Lora, LoRACheckpointProbe)
|
|
ModelProbe.register_probe("checkpoint", ModelType.TextualInversion, TextualInversionCheckpointProbe)
|
|
ModelProbe.register_probe("checkpoint", ModelType.ControlNet, ControlNetCheckpointProbe)
|
|
ModelProbe.register_probe("checkpoint", ModelType.IPAdapter, IPAdapterCheckpointProbe)
|
|
ModelProbe.register_probe("checkpoint", ModelType.CLIPVision, CLIPVisionCheckpointProbe)
|
|
ModelProbe.register_probe("checkpoint", ModelType.T2IAdapter, T2IAdapterCheckpointProbe)
|
|
|
|
ModelProbe.register_probe("onnx", ModelType.ONNX, ONNXFolderProbe)
|