| import os |
| from typing import Any, Callable, Dict, List, Optional, Tuple, Union |
|
|
| import torch |
| from torch import nn |
|
|
| from ...models.controlnet import ControlNetModel, ControlNetOutput |
| from ...models.modeling_utils import ModelMixin |
| from ...utils import logging |
|
|
|
|
| logger = logging.get_logger(__name__) |
|
|
|
|
| class MultiControlNetModel(ModelMixin): |
| r""" |
| Multiple `ControlNetModel` wrapper class for Multi-ControlNet |
| |
| This module is a wrapper for multiple instances of the `ControlNetModel`. The `forward()` API is designed to be |
| compatible with `ControlNetModel`. |
| |
| Args: |
| controlnets (`List[ControlNetModel]`): |
| Provides additional conditioning to the unet during the denoising process. You must set multiple |
| `ControlNetModel` as a list. |
| """ |
|
|
| def __init__(self, controlnets: Union[List[ControlNetModel], Tuple[ControlNetModel]]): |
| super().__init__() |
| self.nets = nn.ModuleList(controlnets) |
|
|
| def forward( |
| self, |
| sample: torch.Tensor, |
| timestep: Union[torch.Tensor, float, int], |
| encoder_hidden_states: torch.Tensor, |
| controlnet_cond: List[torch.tensor], |
| conditioning_scale: List[float], |
| class_labels: Optional[torch.Tensor] = None, |
| timestep_cond: Optional[torch.Tensor] = None, |
| attention_mask: Optional[torch.Tensor] = None, |
| added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, |
| cross_attention_kwargs: Optional[Dict[str, Any]] = None, |
| guess_mode: bool = False, |
| return_dict: bool = True, |
| ) -> Union[ControlNetOutput, Tuple]: |
| for i, (image, scale, controlnet) in enumerate(zip(controlnet_cond, conditioning_scale, self.nets)): |
| down_samples, mid_sample = controlnet( |
| sample=sample, |
| timestep=timestep, |
| encoder_hidden_states=encoder_hidden_states, |
| controlnet_cond=image, |
| conditioning_scale=scale, |
| class_labels=class_labels, |
| timestep_cond=timestep_cond, |
| attention_mask=attention_mask, |
| added_cond_kwargs=added_cond_kwargs, |
| cross_attention_kwargs=cross_attention_kwargs, |
| guess_mode=guess_mode, |
| return_dict=return_dict, |
| ) |
|
|
| |
| if i == 0: |
| down_block_res_samples, mid_block_res_sample = down_samples, mid_sample |
| else: |
| down_block_res_samples = [ |
| samples_prev + samples_curr |
| for samples_prev, samples_curr in zip(down_block_res_samples, down_samples) |
| ] |
| mid_block_res_sample += mid_sample |
|
|
| return down_block_res_samples, mid_block_res_sample |
|
|
| def save_pretrained( |
| self, |
| save_directory: Union[str, os.PathLike], |
| is_main_process: bool = True, |
| save_function: Callable = None, |
| safe_serialization: bool = True, |
| variant: Optional[str] = None, |
| ): |
| """ |
| Save a model and its configuration file to a directory, so that it can be re-loaded using the |
| `[`~pipelines.controlnet.MultiControlNetModel.from_pretrained`]` class method. |
| |
| Arguments: |
| save_directory (`str` or `os.PathLike`): |
| Directory to which to save. Will be created if it doesn't exist. |
| is_main_process (`bool`, *optional*, defaults to `True`): |
| Whether the process calling this is the main process or not. Useful when in distributed training like |
| TPUs and need to call this function on all processes. In this case, set `is_main_process=True` only on |
| the main process to avoid race conditions. |
| save_function (`Callable`): |
| The function to use to save the state dictionary. Useful on distributed training like TPUs when one |
| need to replace `torch.save` by another method. Can be configured with the environment variable |
| `DIFFUSERS_SAVE_MODE`. |
| safe_serialization (`bool`, *optional*, defaults to `True`): |
| Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`). |
| variant (`str`, *optional*): |
| If specified, weights are saved in the format pytorch_model.<variant>.bin. |
| """ |
| for idx, controlnet in enumerate(self.nets): |
| suffix = "" if idx == 0 else f"_{idx}" |
| controlnet.save_pretrained( |
| save_directory + suffix, |
| is_main_process=is_main_process, |
| save_function=save_function, |
| safe_serialization=safe_serialization, |
| variant=variant, |
| ) |
|
|
| @classmethod |
| def from_pretrained(cls, pretrained_model_path: Optional[Union[str, os.PathLike]], **kwargs): |
| r""" |
| Instantiate a pretrained MultiControlNet model from multiple pre-trained controlnet models. |
| |
| The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train |
| the model, you should first set it back in training mode with `model.train()`. |
| |
| The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come |
| pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning |
| task. |
| |
| The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those |
| weights are discarded. |
| |
| Parameters: |
| pretrained_model_path (`os.PathLike`): |
| A path to a *directory* containing model weights saved using |
| [`~diffusers.pipelines.controlnet.MultiControlNetModel.save_pretrained`], e.g., |
| `./my_model_directory/controlnet`. |
| torch_dtype (`str` or `torch.dtype`, *optional*): |
| Override the default `torch.dtype` and load the model under this dtype. If `"auto"` is passed the dtype |
| will be automatically derived from the model's weights. |
| output_loading_info(`bool`, *optional*, defaults to `False`): |
| Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. |
| device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*): |
| A map that specifies where each submodule should go. It doesn't need to be refined to each |
| parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the |
| same device. |
| |
| To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For |
| more information about each option see [designing a device |
| map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map). |
| max_memory (`Dict`, *optional*): |
| A dictionary device identifier to maximum memory. Will default to the maximum memory available for each |
| GPU and the available CPU RAM if unset. |
| low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`): |
| Speed up model loading by not initializing the weights and only loading the pre-trained weights. This |
| also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the |
| model. This is only supported when torch version >= 1.9.0. If you are using an older version of torch, |
| setting this argument to `True` will raise an error. |
| variant (`str`, *optional*): |
| If specified load weights from `variant` filename, *e.g.* pytorch_model.<variant>.bin. `variant` is |
| ignored when using `from_flax`. |
| use_safetensors (`bool`, *optional*, defaults to `None`): |
| If set to `None`, the `safetensors` weights will be downloaded if they're available **and** if the |
| `safetensors` library is installed. If set to `True`, the model will be forcibly loaded from |
| `safetensors` weights. If set to `False`, loading will *not* use `safetensors`. |
| """ |
| idx = 0 |
| controlnets = [] |
|
|
| |
| |
| |
| model_path_to_load = pretrained_model_path |
| while os.path.isdir(model_path_to_load): |
| controlnet = ControlNetModel.from_pretrained(model_path_to_load, **kwargs) |
| controlnets.append(controlnet) |
|
|
| idx += 1 |
| model_path_to_load = pretrained_model_path + f"_{idx}" |
|
|
| logger.info(f"{len(controlnets)} controlnets loaded from {pretrained_model_path}.") |
|
|
| if len(controlnets) == 0: |
| raise ValueError( |
| f"No ControlNets found under {os.path.dirname(pretrained_model_path)}. Expected at least {pretrained_model_path + '_0'}." |
| ) |
|
|
| return cls(controlnets) |
|
|
