Datasets:
jblitzar
/
github-python-corpus

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pre_transform: Optional[Callable] = None,
pre_filter: Optional[Callable] = None,
**kwargs) -> list[TAGDataset]:
if isinstance(names, str):
return [get_dataset(names, root, transform, pre_transform, pre_filter, **kwargs)]
else:
return [get_dataset(name, root, transform, pre_transform, pre_filter, **kwargs) for name in names]
def get_task(
name: str,
task_type: str = "default",
split: str = "train",
root: Optional[str] = None,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
pre_filter: Optional[Callable] = None,
**kwargs) -> BaseTask:
dataset = get_dataset(name, root, transform, pre_transform, pre_filter, **kwargs)
if task_type not in DATASET_INFOR_DICT[name]["task"].keys():
avaliable_tasks = ', '.join(list(DATASET_INFOR_DICT[name]["task"].keys()))
raise ValueError(f"The task type {task_type} is not supported for dataset {name}. "
f"The supported task types are {avaliable_tasks}")
return DATASET_INFOR_DICT[name]["task"][task_type](dataset, split, **kwargs)
def get_tasks(names: Union[str, list[str]],
task_types: Union[str, list[str]] = "default",
root: Optional[str] = None,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
pre_filter: Optional[Callable] = None,
**kwargs):
if isinstance(names, str):
names = [names]
if isinstance(task_types, str):
task_types = [task_types] * len(names)
assert len(names) == len(task_types)
return [get_task(name, task_type, root, transform, pre_transform, pre_filter, **kwargs) for name, task_type in
zip(names, task_types)]
def get_evaluator(name: str,
task_type: str = "default") -> tuple[str, Metric]:
task_type = "QA" if task_type == "QA" else "default"
if task_type not in DATASET_INFOR_DICT[name]["evaluation"].keys():
avaliable_evaluation = ', '.join(list(DATASET_INFOR_DICT[name]["evaluation"].keys()))
raise ValueError(f"The evaluation of task type {task_type} is not supported for dataset {name}. "
f"The supported task types are {avaliable_evaluation}")
metric_name, evaluator_args = DATASET_INFOR_DICT[name]["evaluation"][task_type]
return metric_name, Evaluator(**evaluator_args)
def get_evaluators(names: Union[str, list[str]], task_types: Union[str, list[str]] = "default") \
-> tuple[list[str], list[Metric]]:
if isinstance(names, str):
names = [names]
if isinstance(task_types, str):
task_types = [task_types] * len(names)
metric_names = []
evaluator_list = []
for name, task_type in zip(names, task_types):
metric_name, evaluator_func = get_evaluator(name, task_type)
metric_names.append(metric_name)
evaluator_list.append(evaluator_func)
return metric_names, evaluator_list
# <FILESEP>
import torch
import os
import sys
from torchvision import transforms
import comfy.model_management as mm
from comfy.utils import ProgressBar
import folder_paths
script_directory = os.path.dirname(os.path.abspath(__file__))
sys.path.append(script_directory)
from diffsynth import ModelManager, SVDVideoPipeline
class DownloadAndLoadDiffSynthExVideoSVD:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"diffsynth_model": (
[
'ECNU-CILab/ExVideo-SVD-128f-v1',
],
{
"default": 'ECNU-CILab/ExVideo-SVD-128f-v1'
}),
"svd_model": (folder_paths.get_filename_list("checkpoints"),),
},
}