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# Copyright (c) 2024-present, BAAI. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ------------------------------------------------------------------------
"""Pipeline utilities."""
from typing import List, Union
from diffusers.utils import BaseOutput
import numpy as np
import PIL.Image
import torch
class NOVAPipelineOutput(BaseOutput):
"""Output class for NOVA pipelines.
Args:
images (List[PIL.Image.Image] or np.ndarray)
List of PIL images or numpy array of shape `(batch_size, height, width, num_channels)`.
frames (np.ndarray)
List of video frames. The array shape is `(batch_size, num_frames, height, width, num_channels)`
""" # noqa
images: Union[List[PIL.Image.Image], np.ndarray]
frames: np.array
class URSAPipelineOutput(BaseOutput):
"""Output class for URSA pipelines.
Args:
images (List[PIL.Image.Image] or np.ndarray)
List of PIL images or numpy array of shape `(batch_size, height, width, num_channels)`.
frames (np.ndarray)
List of video frames. The array shape is `(batch_size, num_frames, height, width, num_channels)`
""" # noqa
images: Union[List[PIL.Image.Image], np.ndarray]
frames: np.array
class PipelineMixin(object):
"""Base class for diffusion pipeline."""
def register_module(self, model_or_path, name) -> torch.nn.Module:
"""Register pipeline component.
Args:
model_or_path (str or torch.nn.Module):
The model or path to model.
name (str):
The module name.
Returns:
torch.nn.Module: The registered module.
"""
model = model_or_path
if isinstance(model_or_path, str):
cls = self.__init__.__annotations__[name]
if hasattr(cls, "from_pretrained") and model_or_path:
model = cls.from_pretrained(model_or_path, torch_dtype=self.dtype)
model = model.to(self.device) if isinstance(model, torch.nn.Module) else model
model = cls()
self.register_to_config(**{name: model.__class__.__name__})
return model
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