pipeline_utils.py

# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
# Copyright (c) 2022, NVIDIA CORPORATION.  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.

import importlib
import inspect
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import torch

import PIL
from huggingface_hub import snapshot_download
from PIL import Image
from tqdm.auto import tqdm

from .configuration_utils import ConfigMixin
from .utils import DIFFUSERS_CACHE, BaseOutput, logging


INDEX_FILE = "diffusion_pytorch_model.bin"


logger = logging.get_logger(__name__)


LOADABLE_CLASSES = {
    "diffusers": {
        "ModelMixin": ["save_pretrained", "from_pretrained"],
        "SchedulerMixin": ["save_config", "from_config"],
        "DiffusionPipeline": ["save_pretrained", "from_pretrained"],
    },
    "transformers": {
        "PreTrainedTokenizer": ["save_pretrained", "from_pretrained"],
        "PreTrainedTokenizerFast": ["save_pretrained", "from_pretrained"],
        "PreTrainedModel": ["save_pretrained", "from_pretrained"],
        "FeatureExtractionMixin": ["save_pretrained", "from_pretrained"],
    },
}

ALL_IMPORTABLE_CLASSES = {}
for library in LOADABLE_CLASSES:
    ALL_IMPORTABLE_CLASSES.update(LOADABLE_CLASSES[library])


@dataclass
class ImagePipelineOutput(BaseOutput):
    """
    Output class for image pipelines.

    Args:
        images (`List[PIL.Image.Image]` or `np.ndarray`)
            List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
            num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
    """

    images: Union[List[PIL.Image.Image], np.ndarray]


class DiffusionPipeline(ConfigMixin):
    config_name = "model_index.json"

    def register_modules(self, **kwargs):
        # import it here to avoid circular import
        from diffusers import pipelines

        for name, module in kwargs.items():
            # retrive library
            library = module.__module__.split(".")[0]

            # check if the module is a pipeline module
            pipeline_dir = module.__module__.split(".")[-2]
            path = module.__module__.split(".")
            is_pipeline_module = pipeline_dir in path and hasattr(pipelines, pipeline_dir)

            # if library is not in LOADABLE_CLASSES, then it is a custom module.
            # Or if it's a pipeline module, then the module is inside the pipeline
            # folder so we set the library to module name.
            if library not in LOADABLE_CLASSES or is_pipeline_module:
                library = pipeline_dir

            # retrive class_name
            class_name = module.__class__.__name__

            register_dict = {name: (library, class_name)}

            # save model index config
            self.register_to_config(**register_dict)

            # set models
            setattr(self, name, module)

    def save_pretrained(self, save_directory: Union[str, os.PathLike]):
        self.save_config(save_directory)

        model_index_dict = dict(self.config)
        model_index_dict.pop("_class_name")
        model_index_dict.pop("_diffusers_version")
        model_index_dict.pop("_module", None)

        for pipeline_component_name in model_index_dict.keys():
            sub_model = getattr(self, pipeline_component_name)
            model_cls = sub_model.__class__

            save_method_name = None
            # search for the model's base class in LOADABLE_CLASSES
            for library_name, library_classes in LOADABLE_CLASSES.items():
                library = importlib.import_module(library_name)
                for base_class, save_load_methods in library_classes.items():
                    class_candidate = getattr(library, base_class)
                    if issubclass(model_cls, class_candidate):
                        # if we found a suitable base class in LOADABLE_CLASSES then grab its save method
                        save_method_name = save_load_methods[0]
                        break
                if save_method_name is not None:
                    break

            save_method = getattr(sub_model, save_method_name)
            save_method(os.path.join(save_directory, pipeline_component_name))

    def to(self, torch_device: Optional[Union[str, torch.device]] = None):
        if torch_device is None:
            return self

        module_names, _ = self.extract_init_dict(dict(self.config))
        for name in module_names.keys():
            module = getattr(self, name)
            if isinstance(module, torch.nn.Module):
                module.to(torch_device)
        return self

    @property
    def device(self) -> torch.device:
        module_names, _ = self.extract_init_dict(dict(self.config))
        for name in module_names.keys():
            module = getattr(self, name)
            if isinstance(module, torch.nn.Module):
                return module.device
        return torch.device("cpu")

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
        r"""
        Add docstrings
        """
        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
        resume_download = kwargs.pop("resume_download", False)
        proxies = kwargs.pop("proxies", None)
        local_files_only = kwargs.pop("local_files_only", False)
        use_auth_token = kwargs.pop("use_auth_token", None)
        revision = kwargs.pop("revision", None)
        torch_dtype = kwargs.pop("torch_dtype", None)

        # 1. Download the checkpoints and configs
        # use snapshot download here to get it working from from_pretrained
        if not os.path.isdir(pretrained_model_name_or_path):
            cached_folder = snapshot_download(
                pretrained_model_name_or_path,
                cache_dir=cache_dir,
                resume_download=resume_download,
                proxies=proxies,
                local_files_only=local_files_only,
                use_auth_token=use_auth_token,
                revision=revision,
            )
        else:
            cached_folder = pretrained_model_name_or_path

        config_dict = cls.get_config_dict(cached_folder)

        # 2. Load the pipeline class, if using custom module then load it from the hub
        # if we load from explicit class, let's use it
        if cls != DiffusionPipeline:
            pipeline_class = cls
        else:
            diffusers_module = importlib.import_module(cls.__module__.split(".")[0])
            pipeline_class = getattr(diffusers_module, config_dict["_class_name"])

        # some modules can be passed directly to the init
        # in this case they are already instantiated in `kwargs`
        # extract them here
        expected_modules = set(inspect.signature(pipeline_class.__init__).parameters.keys())
        passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}

        init_dict, _ = pipeline_class.extract_init_dict(config_dict, **kwargs)

        init_kwargs = {}

        # import it here to avoid circular import
        from diffusers import pipelines

        # 3. Load each module in the pipeline
        for name, (library_name, class_name) in init_dict.items():
            is_pipeline_module = hasattr(pipelines, library_name)
            loaded_sub_model = None

            # if the model is in a pipeline module, then we load it from the pipeline
            if name in passed_class_obj:
                # 1. check that passed_class_obj has correct parent class
                if not is_pipeline_module:
                    library = importlib.import_module(library_name)
                    class_obj = getattr(library, class_name)
                    importable_classes = LOADABLE_CLASSES[library_name]
                    class_candidates = {c: getattr(library, c) for c in importable_classes.keys()}

                    expected_class_obj = None
                    for class_name, class_candidate in class_candidates.items():
                        if issubclass(class_obj, class_candidate):
                            expected_class_obj = class_candidate

                    if not issubclass(passed_class_obj[name].__class__, expected_class_obj):
                        raise ValueError(
                            f"{passed_class_obj[name]} is of type: {type(passed_class_obj[name])}, but should be"
                            f" {expected_class_obj}"
                        )
                else:
                    logger.warn(
                        f"You have passed a non-standard module {passed_class_obj[name]}. We cannot verify whether it"
                        " has the correct type"
                    )

                # set passed class object
                loaded_sub_model = passed_class_obj[name]
            elif is_pipeline_module:
                pipeline_module = getattr(pipelines, library_name)
                class_obj = getattr(pipeline_module, class_name)
                importable_classes = ALL_IMPORTABLE_CLASSES
                class_candidates = {c: class_obj for c in importable_classes.keys()}
            else:
                # else we just import it from the library.
                library = importlib.import_module(library_name)
                class_obj = getattr(library, class_name)
                importable_classes = LOADABLE_CLASSES[library_name]
                class_candidates = {c: getattr(library, c) for c in importable_classes.keys()}

            if loaded_sub_model is None:
                load_method_name = None
                for class_name, class_candidate in class_candidates.items():
                    if issubclass(class_obj, class_candidate):
                        load_method_name = importable_classes[class_name][1]

                load_method = getattr(class_obj, load_method_name)

                loading_kwargs = {}
                if issubclass(class_obj, torch.nn.Module):
                    loading_kwargs["torch_dtype"] = torch_dtype

                # check if the module is in a subdirectory
                if os.path.isdir(os.path.join(cached_folder, name)):
                    loaded_sub_model = load_method(os.path.join(cached_folder, name), **loading_kwargs)
                else:
                    # else load from the root directory
                    loaded_sub_model = load_method(cached_folder, **loading_kwargs)

            init_kwargs[name] = loaded_sub_model  # UNet(...), # DiffusionSchedule(...)

        # 4. Instantiate the pipeline
        model = pipeline_class(**init_kwargs)
        return model

    @staticmethod
    def numpy_to_pil(images):
        """
        Convert a numpy image or a batch of images to a PIL image.
        """
        if images.ndim == 3:
            images = images[None, ...]
        images = (images * 255).round().astype("uint8")
        pil_images = [Image.fromarray(image) for image in images]

        return pil_images

    def progress_bar(self, iterable):
        if not hasattr(self, "_progress_bar_config"):
            self._progress_bar_config = {}
        elif not isinstance(self._progress_bar_config, dict):
            raise ValueError(
                f"`self._progress_bar_config` should be of type `dict`, but is {type(self._progress_bar_config)}."
            )

        return tqdm(iterable, **self._progress_bar_config)

    def set_progress_bar_config(self, **kwargs):
        self._progress_bar_config = kwargs