ip_adapter.py

# Copyright 2024 The HuggingFace Team. 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
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from pathlib import Path
from typing import Dict, List, Optional, Union

import torch
from huggingface_hub.utils import validate_hf_hub_args
from safetensors import safe_open

from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT, load_state_dict
from ..utils import (
    USE_PEFT_BACKEND,
    _get_model_file,
    is_accelerate_available,
    is_torch_version,
    is_transformers_available,
    logging,
)
from .unet_loader_utils import _maybe_expand_lora_scales


if is_transformers_available():
    from transformers import (
        CLIPImageProcessor,
        CLIPVisionModelWithProjection,
    )

    from ..models.attention_processor import (
        IPAdapterAttnProcessor,
        IPAdapterAttnProcessor2_0,
    )

logger = logging.get_logger(__name__)


class IPAdapterMixin:
    """Mixin for handling IP Adapters."""

    @validate_hf_hub_args
    def load_ip_adapter(
        self,
        pretrained_model_name_or_path_or_dict: Union[str, List[str], Dict[str, torch.Tensor]],
        subfolder: Union[str, List[str]],
        weight_name: Union[str, List[str]],
        image_encoder_folder: Optional[str] = "image_encoder",
        **kwargs,
    ):
        """
        Parameters:
            pretrained_model_name_or_path_or_dict (`str` or `List[str]` or `os.PathLike` or `List[os.PathLike]` or `dict` or `List[dict]`):
                Can be either:

                    - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
                      the Hub.
                    - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
                      with [`ModelMixin.save_pretrained`].
                    - A [torch state
                      dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
            subfolder (`str` or `List[str]`):
                The subfolder location of a model file within a larger model repository on the Hub or locally. If a
                list is passed, it should have the same length as `weight_name`.
            weight_name (`str` or `List[str]`):
                The name of the weight file to load. If a list is passed, it should have the same length as
                `weight_name`.
            image_encoder_folder (`str`, *optional*, defaults to `image_encoder`):
                The subfolder location of the image encoder within a larger model repository on the Hub or locally.
                Pass `None` to not load the image encoder. If the image encoder is located in a folder inside
                `subfolder`, you only need to pass the name of the folder that contains image encoder weights, e.g.
                `image_encoder_folder="image_encoder"`. If the image encoder is located in a folder other than
                `subfolder`, you should pass the path to the folder that contains image encoder weights, for example,
                `image_encoder_folder="different_subfolder/image_encoder"`.
            cache_dir (`Union[str, os.PathLike]`, *optional*):
                Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
                is not used.
            force_download (`bool`, *optional*, defaults to `False`):
                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
                cached versions if they exist.
            resume_download (`bool`, *optional*, defaults to `False`):
                Whether or not to resume downloading the model weights and configuration files. If set to `False`, any
                incompletely downloaded files are deleted.
            proxies (`Dict[str, str]`, *optional*):
                A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
            local_files_only (`bool`, *optional*, defaults to `False`):
                Whether to only load local model weights and configuration files or not. If set to `True`, the model
                won't be downloaded from the Hub.
            token (`str` or *bool*, *optional*):
                The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
                `diffusers-cli login` (stored in `~/.huggingface`) is used.
            revision (`str`, *optional*, defaults to `"main"`):
                The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
                allowed by Git.
            low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
                Speed up model loading only loading the pretrained weights and not initializing the weights. This also
                tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
                Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
                argument to `True` will raise an error.
        """

        # handle the list inputs for multiple IP Adapters
        if not isinstance(weight_name, list):
            weight_name = [weight_name]

        if not isinstance(pretrained_model_name_or_path_or_dict, list):
            pretrained_model_name_or_path_or_dict = [pretrained_model_name_or_path_or_dict]
        if len(pretrained_model_name_or_path_or_dict) == 1:
            pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict * len(weight_name)

        if not isinstance(subfolder, list):
            subfolder = [subfolder]
        if len(subfolder) == 1:
            subfolder = subfolder * len(weight_name)

        if len(weight_name) != len(pretrained_model_name_or_path_or_dict):
            raise ValueError("`weight_name` and `pretrained_model_name_or_path_or_dict` must have the same length.")

        if len(weight_name) != len(subfolder):
            raise ValueError("`weight_name` and `subfolder` must have the same length.")

        # Load the main state dict first.
        cache_dir = kwargs.pop("cache_dir", None)
        force_download = kwargs.pop("force_download", False)
        resume_download = kwargs.pop("resume_download", False)
        proxies = kwargs.pop("proxies", None)
        local_files_only = kwargs.pop("local_files_only", None)
        token = kwargs.pop("token", None)
        revision = kwargs.pop("revision", None)
        low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)

        if low_cpu_mem_usage and not is_accelerate_available():
            low_cpu_mem_usage = False
            logger.warning(
                "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
                " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
                " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
                " install accelerate\n```\n."
            )

        if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
            raise NotImplementedError(
                "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
                " `low_cpu_mem_usage=False`."
            )

        user_agent = {
            "file_type": "attn_procs_weights",
            "framework": "pytorch",
        }
        state_dicts = []
        for pretrained_model_name_or_path_or_dict, weight_name, subfolder in zip(
            pretrained_model_name_or_path_or_dict, weight_name, subfolder
        ):
            if not isinstance(pretrained_model_name_or_path_or_dict, dict):
                model_file = _get_model_file(
                    pretrained_model_name_or_path_or_dict,
                    weights_name=weight_name,
                    cache_dir=cache_dir,
                    force_download=force_download,
                    resume_download=resume_download,
                    proxies=proxies,
                    local_files_only=local_files_only,
                    token=token,
                    revision=revision,
                    subfolder=subfolder,
                    user_agent=user_agent,
                )
                if weight_name.endswith(".safetensors"):
                    state_dict = {"image_proj": {}, "ip_adapter": {}}
                    with safe_open(model_file, framework="pt", device="cpu") as f:
                        for key in f.keys():
                            if key.startswith("image_proj."):
                                state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key)
                            elif key.startswith("ip_adapter."):
                                state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key)
                else:
                    state_dict = load_state_dict(model_file)
            else:
                state_dict = pretrained_model_name_or_path_or_dict

            keys = list(state_dict.keys())
            if keys != ["image_proj", "ip_adapter"]:
                raise ValueError("Required keys are (`image_proj` and `ip_adapter`) missing from the state dict.")

            state_dicts.append(state_dict)

            # load CLIP image encoder here if it has not been registered to the pipeline yet
            if hasattr(self, "image_encoder") and getattr(self, "image_encoder", None) is None:
                if image_encoder_folder is not None:
                    if not isinstance(pretrained_model_name_or_path_or_dict, dict):
                        logger.info(f"loading image_encoder from {pretrained_model_name_or_path_or_dict}")
                        if image_encoder_folder.count("/") == 0:
                            image_encoder_subfolder = Path(subfolder, image_encoder_folder).as_posix()
                        else:
                            image_encoder_subfolder = Path(image_encoder_folder).as_posix()

                        image_encoder = CLIPVisionModelWithProjection.from_pretrained(
                            pretrained_model_name_or_path_or_dict,
                            subfolder=image_encoder_subfolder,
                            low_cpu_mem_usage=low_cpu_mem_usage,
                        ).to(self.device, dtype=self.dtype)
                        self.register_modules(image_encoder=image_encoder)
                    else:
                        raise ValueError(
                            "`image_encoder` cannot be loaded because `pretrained_model_name_or_path_or_dict` is a state dict."
                        )
                else:
                    logger.warning(
                        "image_encoder is not loaded since `image_encoder_folder=None` passed. You will not be able to use `ip_adapter_image` when calling the pipeline with IP-Adapter."
                        "Use `ip_adapter_image_embeds` to pass pre-generated image embedding instead."
                    )

            # create feature extractor if it has not been registered to the pipeline yet
            if hasattr(self, "feature_extractor") and getattr(self, "feature_extractor", None) is None:
                feature_extractor = CLIPImageProcessor()
                self.register_modules(feature_extractor=feature_extractor)

        # load ip-adapter into unet
        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
        unet._load_ip_adapter_weights(state_dicts, low_cpu_mem_usage=low_cpu_mem_usage)

        extra_loras = unet._load_ip_adapter_loras(state_dicts)
        if extra_loras != {}:
            if not USE_PEFT_BACKEND:
                logger.warning("PEFT backend is required to load these weights.")
            else:
                # apply the IP Adapter Face ID LoRA weights
                peft_config = getattr(unet, "peft_config", {})
                for k, lora in extra_loras.items():
                    if f"faceid_{k}" not in peft_config:
                        self.load_lora_weights(lora, adapter_name=f"faceid_{k}")
                        self.set_adapters([f"faceid_{k}"], adapter_weights=[1.0])

    def set_ip_adapter_scale(self, scale):
        """
        Set IP-Adapter scales per-transformer block. Input `scale` could be a single config or a list of configs for
        granular control over each IP-Adapter behavior. A config can be a float or a dictionary.

        Example:

        ```py
        # To use original IP-Adapter
        scale = 1.0
        pipeline.set_ip_adapter_scale(scale)

        # To use style block only
        scale = {
            "up": {"block_0": [0.0, 1.0, 0.0]},
        }
        pipeline.set_ip_adapter_scale(scale)

        # To use style+layout blocks
        scale = {
            "down": {"block_2": [0.0, 1.0]},
            "up": {"block_0": [0.0, 1.0, 0.0]},
        }
        pipeline.set_ip_adapter_scale(scale)

        # To use style and layout from 2 reference images
        scales = [{"down": {"block_2": [0.0, 1.0]}}, {"up": {"block_0": [0.0, 1.0, 0.0]}}]
        pipeline.set_ip_adapter_scale(scales)
        ```
        """
        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
        if not isinstance(scale, list):
            scale = [scale]
        scale_configs = _maybe_expand_lora_scales(unet, scale, default_scale=0.0)

        for attn_name, attn_processor in unet.attn_processors.items():
            if isinstance(attn_processor, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0)):
                if len(scale_configs) != len(attn_processor.scale):
                    raise ValueError(
                        f"Cannot assign {len(scale_configs)} scale_configs to "
                        f"{len(attn_processor.scale)} IP-Adapter."
                    )
                elif len(scale_configs) == 1:
                    scale_configs = scale_configs * len(attn_processor.scale)
                for i, scale_config in enumerate(scale_configs):
                    if isinstance(scale_config, dict):
                        for k, s in scale_config.items():
                            if attn_name.startswith(k):
                                attn_processor.scale[i] = s
                    else:
                        attn_processor.scale[i] = scale_config

    def unload_ip_adapter(self):
        """
        Unloads the IP Adapter weights

        Examples:

        ```python
        >>> # Assuming `pipeline` is already loaded with the IP Adapter weights.
        >>> pipeline.unload_ip_adapter()
        >>> ...
        ```
        """
        # remove CLIP image encoder
        if hasattr(self, "image_encoder") and getattr(self, "image_encoder", None) is not None:
            self.image_encoder = None
            self.register_to_config(image_encoder=[None, None])

        # remove feature extractor only when safety_checker is None as safety_checker uses
        # the feature_extractor later
        if not hasattr(self, "safety_checker"):
            if hasattr(self, "feature_extractor") and getattr(self, "feature_extractor", None) is not None:
                self.feature_extractor = None
                self.register_to_config(feature_extractor=[None, None])

        # remove hidden encoder
        self.unet.encoder_hid_proj = None
        self.config.encoder_hid_dim_type = None

        # restore original Unet attention processors layers
        self.unet.set_default_attn_processor()