find some pretrained model loss and hard code error. #3
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It's path of bert-base-uncased |
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Good project, I want to know what is the different between with StoryDiffusion.
env: windows 11 x64, Python3.10.11 + torch 2.1.0+ cu11.8, prepared all stable-diffusion-v1-5/sd-vae-ft-mse/IP-Adapter checkpoints.
error message in the terminal windows:
D:\AITest\AutoStudio\model\pipeline_stable_diffusion.py:41: FutureWarning: Importing
DiffusionPipelineorImagePipelineOutputfrom diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.from diffusers.pipeline_utils import DiffusionPipeline
Using box scale: (512, 512)
D:\AITest\AutoStudio\DETECT_SAM/Grounding-DINO\groundingdino\models\GroundingDINO\ms_deform_attn.py:31: UserWarning: Failed to load custom C++ ops. Running on CPU mode Only!
warnings.warn("Failed to load custom C++ ops. Running on CPU mode Only!")
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ D:\AITest\AutoStudio\run_me.py:31 in │
│ │
│ 28 │
│ 29 from model.unet_2d_condition import UNet2DConditionModel │
│ 30 from model.utils import show_boxes, show_image, get_global_prompt │
│ ❱ 31 from model.autostudio import AUTOSTUDIO, AUTOSTUDIOPlus, AUTOSTUDIOXL, AUTOSTUDIOXLPlus │
│ 32 │
│ 33 from detectSam import EFFICIENT_SAM_MODEL, GROUNDING_DINO_MODEL │
│ 34 │
│ │
│ D:\AITest\AutoStudio\model\autostudio.py:16 in │
│ │
│ 13 │
│ 14 from PIL import Image │
│ 15 from typing import List │
│ ❱ 16 from detectSam import process_image │
│ 17 from diffusers.pipelines.controlnet import MultiControlNetModel │
│ 18 from safetensors import safe_open │
│ 19 from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection │
│ │
│ D:\AITest\AutoStudio/DETECT_SAM\detectSam.py:36 in │
│ │
│ 33 │ RESULTS = "results" │
│ 34 │ │
│ 35 │ DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") │
│ ❱ 36 │ EFFICIENT_SAM_MODEL = load(device=DEVICE) │
│ 37 │ GROUNDING_DINO_MODEL = Model(f"{dpath}/Grounding-DINO/groundingdino/config/Grounding │
│ 38 │ │
│ 39 │ BOUNDING_BOX_ANNOTATOR = sv.BoundingBoxAnnotator() │
│ │
│ D:\AITest\AutoStudio/DETECT_SAM\efficient_sam.py:14 in load │
│ │
│ 11 │
│ 12 def load(device: torch.device) -> torch.jit.ScriptModule: │
│ 13 │ if device.type == "cuda": │
│ ❱ 14 │ │ model = torch.jit.load(GPU_EFFICIENT_SAM_CHECKPOINT) │
│ 15 │ else: │
│ 16 │ │ model = torch.jit.load(CPU_EFFICIENT_SAM_CHECKPOINT) │
│ 17 │ model.eval() │
│ │
│ D:\AITest\AutoStudio\python310\lib\site-packages\torch\jit_serialization.py:152 in load │
│ │
│ 149 │ │
│ 150 │ if isinstance(f, str): │
│ 151 │ │ if not os.path.exists(f): # type: ignore[type-var] │
│ ❱ 152 │ │ │ raise ValueError(f"The provided filename {f} does not exist") # type: ignor │
│ 153 │ │ if os.path.isdir(f): │
│ 154 │ │ │ raise ValueError(f"The provided filename {f} is a directory") # type: ignor │
│ 155 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
ValueError: The provided filename D:\AITest\AutoStudio\DETECT_SAM/pretrain/efficient_sam_s_gpu.jit does not exist
Press any key to continue . . .
I find and download the efficient_sam_s_gpu.jit from "https://huggingface.co/merve/EfficientSAM" to put it above directory, and go ahead.
2.------
D:\AITest\AutoStudio\model\pipeline_stable_diffusion.py:41: FutureWarning: Importing
DiffusionPipelineorImagePipelineOutputfrom diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.from diffusers.pipeline_utils import DiffusionPipeline
Using box scale: (512, 512)
D:\AITest\AutoStudio\DETECT_SAM/Grounding-DINO\groundingdino\models\GroundingDINO\ms_deform_attn.py:31: UserWarning: Failed to load custom C++ ops. Running on CPU mode Only!
warnings.warn("Failed to load custom C++ ops. Running on CPU mode Only!")
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ D:\AITest\AutoStudio\python310\lib\site-packages\transformers\configuration_utils.py:629 in │
│ _get_config_dict │
│ │
│ 626 │ │ │ │
│ 627 │ │ │ try: │
│ 628 │ │ │ │ # Load from local folder or from cache or download from model Hub and ca │
│ ❱ 629 │ │ │ │ resolved_config_file = cached_file( │
│ 630 │ │ │ │ │ pretrained_model_name_or_path, │
│ 631 │ │ │ │ │ configuration_file, │
│ 632 │ │ │ │ │ cache_dir=cache_dir, │
│ │
│ D:\AITest\AutoStudio\python310\lib\site-packages\transformers\utils\hub.py:417 in cached_file │
│ │
│ 414 │ user_agent = http_user_agent(user_agent) │
│ 415 │ try: │
│ 416 │ │ # Load from URL or cache if already cached │
│ ❱ 417 │ │ resolved_file = hf_hub_download( │
│ 418 │ │ │ path_or_repo_id, │
│ 419 │ │ │ filename, │
│ 420 │ │ │ subfolder=None if len(subfolder) == 0 else subfolder, │
│ │
│ D:\AITest\AutoStudio\python310\lib\site-packages\huggingface_hub\utils_validators.py:110 in │
│ inner_fn │
│ │
│ 107 │ │ │ kwargs.items(), # Kwargs values │
│ 108 │ │ ): │
│ 109 │ │ │ if arg_name in ["repo_id", "from_id", "to_id"]: │
│ ❱ 110 │ │ │ │ validate_repo_id(arg_value) │
│ 111 │ │ │ │
│ 112 │ │ │ elif arg_name == "token" and arg_value is not None: │
│ 113 │ │ │ │ has_token = True │
│ │
│ D:\AITest\AutoStudio\python310\lib\site-packages\huggingface_hub\utils_validators.py:158 in │
│ validate_repo_id │
│ │
│ 155 │ │ raise HFValidationError(f"Repo id must be a string, not {type(repo_id)}: '{repo │
│ 156 │ │
│ 157 │ if repo_id.count("/") > 1: │
│ ❱ 158 │ │ raise HFValidationError( │
│ 159 │ │ │ "Repo id must be in the form 'repo_name' or 'namespace/repo_name':" │
│ 160 │ │ │ f" '{repo_id}'. Use
repo_typeargument if needed." ││ 161 │ │ ) │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name':
'/data2/chengjunhao/THEATERGEN/pretrained_models/dino_bert'. Use
repo_typeargument if needed.During handling of the above exception, another exception occurred:
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ D:\AITest\AutoStudio\run_me.py:31 in │
│ │
│ 28 │
│ 29 from model.unet_2d_condition import UNet2DConditionModel │
│ 30 from model.utils import show_boxes, show_image, get_global_prompt │
│ ❱ 31 from model.autostudio import AUTOSTUDIO, AUTOSTUDIOPlus, AUTOSTUDIOXL, AUTOSTUDIOXLPlus │
│ 32 │
│ 33 from detectSam import EFFICIENT_SAM_MODEL, GROUNDING_DINO_MODEL │
│ 34 │
│ │
│ D:\AITest\AutoStudio\model\autostudio.py:16 in │
│ │
│ 13 │
│ 14 from PIL import Image │
│ 15 from typing import List │
│ ❱ 16 from detectSam import process_image │
│ 17 from diffusers.pipelines.controlnet import MultiControlNetModel │
│ 18 from safetensors import safe_open │
│ 19 from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection │
│ │
│ D:\AITest\AutoStudio/DETECT_SAM\detectSam.py:37 in │
│ │
│ 34 │ │
│ 35 │ DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") │
│ 36 │ EFFICIENT_SAM_MODEL = load(device=DEVICE) │
│ ❱ 37 │ GROUNDING_DINO_MODEL = Model(f"{dpath}/Grounding-DINO/groundingdino/config/Grounding │
│ 38 │ │
│ 39 │ BOUNDING_BOX_ANNOTATOR = sv.BoundingBoxAnnotator() │
│ 40 │ MASK_ANNOTATOR = sv.MaskAnnotator() │
│ │
│ D:\AITest\AutoStudio\DETECT_SAM/Grounding-DINO\groundingdino\util\inference.py:142 in init │
│ │
│ 139 │ │ model_checkpoint_path: str, │
│ 140 │ │ device: str = "cuda" │
│ 141 │ ): │
│ ❱ 142 │ │ self.model = load_model( │
│ 143 │ │ │ model_config_path=model_config_path, │
│ 144 │ │ │ model_checkpoint_path=model_checkpoint_path, │
│ 145 │ │ │ device=device │
│ │
│ D:\AITest\AutoStudio\DETECT_SAM/Grounding-DINO\groundingdino\util\inference.py:40 in load_model │
│ │
│ 37 def load_model(model_config_path: str, model_checkpoint_path: str, device: str = "cuda") │
│ 38 │ args = SLConfig.fromfile(model_config_path) │
│ 39 │ args.device = device │
│ ❱ 40 │ model = build_model(args) │
│ 41 │ checkpoint = torch.load(model_checkpoint_path, map_location="cpu") │
│ 42 │ model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False) │
│ 43 │ model.eval() │
│ │
│ D:\AITest\AutoStudio\DETECT_SAM/Grounding-DINO\groundingdino\models_init_.py:17 in │
│ build_model │
│ │
│ 14 │ │
│ 15 │ assert args.modelname in MODULE_BUILD_FUNCS.module_dict │
│ 16 │ build_func = MODULE_BUILD_FUNCS.get(args.modelname) │
│ ❱ 17 │ model = build_func(args) │
│ 18 │ return model │
│ 19 │
│ │
│ D:\AITest\AutoStudio\DETECT_SAM/Grounding-DINO\groundingdino\models\GroundingDINO\groundingdino. │
│ py:395 in build_groundingdino │
│ │
│ 392 │ dec_pred_bbox_embed_share = args.dec_pred_bbox_embed_share │
│ 393 │ sub_sentence_present = args.sub_sentence_present │
│ 394 │ │
│ ❱ 395 │ model = GroundingDINO( │
│ 396 │ │ backbone, │
│ 397 │ │ transformer, │
│ 398 │ │ num_queries=args.num_queries, │
│ │
│ D:\AITest\AutoStudio\DETECT_SAM/Grounding-DINO\groundingdino\models\GroundingDINO\groundingdino. │
│ py:115 in init │
│ │
│ 112 │ │ │
│ 113 │ │ # bert │
│ 114 │ │ self.tokenizer = get_tokenlizer.get_tokenlizer(text_encoder_type) │
│ ❱ 115 │ │ self.bert = get_tokenlizer.get_pretrained_language_model(text_encoder_type) │
│ 116 │ │ self.bert.pooler.dense.weight.requires_grad(False) │
│ 117 │ │ self.bert.pooler.dense.bias.requires_grad_(False) │
│ 118 │ │ self.bert = BertModelWarper(bert_model=self.bert) │
│ │
│ D:\AITest\AutoStudio\DETECT_SAM/Grounding-DINO\groundingdino\util\get_tokenlizer.py:25 in │
│ get_pretrained_language_model │
│ │
│ 22 │
│ 23 def get_pretrained_language_model(text_encoder_type): │
│ 24 │ if text_encoder_type == "bert-base-uncased" or (os.path.isdir(text_encoder_type) and │
│ ❱ 25 │ │ return BertModel.from_pretrained("/data2/chengjunhao/THEATERGEN/pretrained_model │
│ 26 │ if text_encoder_type == "roberta-base": │
│ 27 │ │ return RobertaModel.from_pretrained(text_encoder_type) │
│ 28 │
│ │
│ D:\AITest\AutoStudio\python310\lib\site-packages\transformers\modeling_utils.py:2251 in │
│ from_pretrained │
│ │
│ 2248 │ │ # Load config if we don't provide a configuration │
│ 2249 │ │ if not isinstance(config, PretrainedConfig): │
│ 2250 │ │ │ config_path = config if config is not None else pretrained_model_name_or_pat │
│ ❱ 2251 │ │ │ config, model_kwargs = cls.config_class.from_pretrained( │
│ 2252 │ │ │ │ config_path, │
│ 2253 │ │ │ │ cache_dir=cache_dir, │
│ 2254 │ │ │ │ return_unused_kwargs=True, │
│ │
│ D:\AITest\AutoStudio\python310\lib\site-packages\transformers\configuration_utils.py:547 in │
│ from_pretrained │
│ │
│ 544 │ │ assert config.output_attentions == True │
│ 545 │ │ assert unused_kwargs == {"foo": False} │
│ 546 │ │ ```""" │
│ ❱ 547 │ │ config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwarg │
│ 548 │ │ if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["m │
│ 549 │ │ │ logger.warning( │
│ 550 │ │ │ │ f"You are using a model of type {config_dict['model_type']} to instantia │
│ │
│ D:\AITest\AutoStudio\python310\lib\site-packages\transformers\configuration_utils.py:574 in │
│ get_config_dict │
│ │
│ 571 │ │ """ │
│ 572 │ │ original_kwargs = copy.deepcopy(kwargs) │
│ 573 │ │ # Get config dict associated with the base config file │
│ ❱ 574 │ │ config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwar │
│ 575 │ │ if "_commit_hash" in config_dict: │
│ 576 │ │ │ original_kwargs["_commit_hash"] = config_dict["_commit_hash"] │
│ 577 │
│ │
│ D:\AITest\AutoStudio\python310\lib\site-packages\transformers\configuration_utils.py:650 in │
│ _get_config_dict │
│ │
│ 647 │ │ │ │ raise │
│ 648 │ │ │ except Exception: │
│ 649 │ │ │ │ # For any other exception, we throw a generic error. │
│ ❱ 650 │ │ │ │ raise EnvironmentError( │
│ 651 │ │ │ │ │ f"Can't load the configuration of '{pretrained_model_name_or_path}'. │
│ 652 │ │ │ │ │ " from 'https://huggingface.co/models', make sure you don't have a l │
│ 653 │ │ │ │ │ f" name. Otherwise, make sure '{pretrained_model_name_or_path}' is t │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
OSError: Can't load the configuration of '/data2/chengjunhao/THEATERGEN/pretrained_models/dino_bert'. If you were
trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name.
Otherwise, make sure '/data2/chengjunhao/THEATERGEN/pretrained_models/dino_bert' is the correct path to a directory
containing a config.json file
Press any key to continue . . .
I check the "https://github.com/IDEA-Research/GroundingDINO" and "https://github.com/donahowe/Theatergen", but can't find your pretrained model "dino_bert".
Please help me to check and analayze the error message, thanks!
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