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| 1 | +# Copyright 2023 The HuggingFace Team. All rights reserved. |
| 2 | +# |
| 3 | +# Licensed under the Apache License, Version 2.0 (the "License"); |
| 4 | +# you may not use this file except in compliance with the License. |
| 5 | +# You may obtain a copy of the License at |
| 6 | +# |
| 7 | +# http://www.apache.org/licenses/LICENSE-2.0 |
| 8 | +# |
| 9 | +# Unless required by applicable law or agreed to in writing, software |
| 10 | +# distributed under the License is distributed on an "AS IS" BASIS, |
| 11 | +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 12 | +# See the License for the specific language governing permissions and |
| 13 | +# limitations under the License. |
| 14 | +import os |
| 15 | +from typing import Dict, Union |
| 16 | + |
| 17 | +import torch |
| 18 | +from safetensors import safe_open |
| 19 | + |
| 20 | +from ..utils import ( |
| 21 | + DIFFUSERS_CACHE, |
| 22 | + HF_HUB_OFFLINE, |
| 23 | + _get_model_file, |
| 24 | + is_transformers_available, |
| 25 | + logging, |
| 26 | +) |
| 27 | + |
| 28 | + |
| 29 | +if is_transformers_available(): |
| 30 | + from transformers import ( |
| 31 | + CLIPImageProcessor, |
| 32 | + CLIPVisionModelWithProjection, |
| 33 | + ) |
| 34 | + |
| 35 | + from ..models.attention_processor import ( |
| 36 | + IPAdapterAttnProcessor, |
| 37 | + IPAdapterAttnProcessor2_0, |
| 38 | + ) |
| 39 | + |
| 40 | +logger = logging.get_logger(__name__) |
| 41 | + |
| 42 | + |
| 43 | +class IPAdapterMixin: |
| 44 | + """Mixin for handling IP Adapters.""" |
| 45 | + |
| 46 | + def load_ip_adapter( |
| 47 | + self, |
| 48 | + pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], |
| 49 | + subfolder: str, |
| 50 | + weight_name: str, |
| 51 | + **kwargs, |
| 52 | + ): |
| 53 | + """ |
| 54 | + Parameters: |
| 55 | + pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`): |
| 56 | + Can be either: |
| 57 | +
|
| 58 | + - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on |
| 59 | + the Hub. |
| 60 | + - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved |
| 61 | + with [`ModelMixin.save_pretrained`]. |
| 62 | + - A [torch state |
| 63 | + dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict). |
| 64 | +
|
| 65 | + cache_dir (`Union[str, os.PathLike]`, *optional*): |
| 66 | + Path to a directory where a downloaded pretrained model configuration is cached if the standard cache |
| 67 | + is not used. |
| 68 | + force_download (`bool`, *optional*, defaults to `False`): |
| 69 | + Whether or not to force the (re-)download of the model weights and configuration files, overriding the |
| 70 | + cached versions if they exist. |
| 71 | + resume_download (`bool`, *optional*, defaults to `False`): |
| 72 | + Whether or not to resume downloading the model weights and configuration files. If set to `False`, any |
| 73 | + incompletely downloaded files are deleted. |
| 74 | + proxies (`Dict[str, str]`, *optional*): |
| 75 | + A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128', |
| 76 | + 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. |
| 77 | + local_files_only (`bool`, *optional*, defaults to `False`): |
| 78 | + Whether to only load local model weights and configuration files or not. If set to `True`, the model |
| 79 | + won't be downloaded from the Hub. |
| 80 | + use_auth_token (`str` or *bool*, *optional*): |
| 81 | + The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from |
| 82 | + `diffusers-cli login` (stored in `~/.huggingface`) is used. |
| 83 | + revision (`str`, *optional*, defaults to `"main"`): |
| 84 | + The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier |
| 85 | + allowed by Git. |
| 86 | + subfolder (`str`, *optional*, defaults to `""`): |
| 87 | + The subfolder location of a model file within a larger model repository on the Hub or locally. |
| 88 | + """ |
| 89 | + |
| 90 | + # Load the main state dict first. |
| 91 | + cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE) |
| 92 | + force_download = kwargs.pop("force_download", False) |
| 93 | + resume_download = kwargs.pop("resume_download", False) |
| 94 | + proxies = kwargs.pop("proxies", None) |
| 95 | + local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE) |
| 96 | + use_auth_token = kwargs.pop("use_auth_token", None) |
| 97 | + revision = kwargs.pop("revision", None) |
| 98 | + |
| 99 | + user_agent = { |
| 100 | + "file_type": "attn_procs_weights", |
| 101 | + "framework": "pytorch", |
| 102 | + } |
| 103 | + |
| 104 | + if not isinstance(pretrained_model_name_or_path_or_dict, dict): |
| 105 | + model_file = _get_model_file( |
| 106 | + pretrained_model_name_or_path_or_dict, |
| 107 | + weights_name=weight_name, |
| 108 | + cache_dir=cache_dir, |
| 109 | + force_download=force_download, |
| 110 | + resume_download=resume_download, |
| 111 | + proxies=proxies, |
| 112 | + local_files_only=local_files_only, |
| 113 | + use_auth_token=use_auth_token, |
| 114 | + revision=revision, |
| 115 | + subfolder=subfolder, |
| 116 | + user_agent=user_agent, |
| 117 | + ) |
| 118 | + if weight_name.endswith(".safetensors"): |
| 119 | + state_dict = {"image_proj": {}, "ip_adapter": {}} |
| 120 | + with safe_open(model_file, framework="pt", device="cpu") as f: |
| 121 | + for key in f.keys(): |
| 122 | + if key.startswith("image_proj."): |
| 123 | + state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key) |
| 124 | + elif key.startswith("ip_adapter."): |
| 125 | + state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key) |
| 126 | + else: |
| 127 | + state_dict = torch.load(model_file, map_location="cpu") |
| 128 | + else: |
| 129 | + state_dict = pretrained_model_name_or_path_or_dict |
| 130 | + |
| 131 | + keys = list(state_dict.keys()) |
| 132 | + if keys != ["image_proj", "ip_adapter"]: |
| 133 | + raise ValueError("Required keys are (`image_proj` and `ip_adapter`) missing from the state dict.") |
| 134 | + |
| 135 | + # load CLIP image encoer here if it has not been registered to the pipeline yet |
| 136 | + if hasattr(self, "image_encoder") and getattr(self, "image_encoder", None) is None: |
| 137 | + if not isinstance(pretrained_model_name_or_path_or_dict, dict): |
| 138 | + logger.info(f"loading image_encoder from {pretrained_model_name_or_path_or_dict}") |
| 139 | + image_encoder = CLIPVisionModelWithProjection.from_pretrained( |
| 140 | + pretrained_model_name_or_path_or_dict, |
| 141 | + subfolder=os.path.join(subfolder, "image_encoder"), |
| 142 | + ).to(self.device, dtype=self.dtype) |
| 143 | + self.image_encoder = image_encoder |
| 144 | + else: |
| 145 | + raise ValueError("`image_encoder` cannot be None when using IP Adapters.") |
| 146 | + |
| 147 | + # create feature extractor if it has not been registered to the pipeline yet |
| 148 | + if hasattr(self, "feature_extractor") and getattr(self, "feature_extractor", None) is None: |
| 149 | + self.feature_extractor = CLIPImageProcessor() |
| 150 | + |
| 151 | + # load ip-adapter into unet |
| 152 | + self.unet._load_ip_adapter_weights(state_dict) |
| 153 | + |
| 154 | + def set_ip_adapter_scale(self, scale): |
| 155 | + for attn_processor in self.unet.attn_processors.values(): |
| 156 | + if isinstance(attn_processor, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0)): |
| 157 | + attn_processor.scale = scale |
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