import glob import os from typing import Dict, List, Union import safetensors.torch import torch from huggingface_hub import snapshot_download from huggingface_hub.utils import validate_hf_hub_args from diffusers import DiffusionPipeline, __version__ from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME from diffusers.utils import CONFIG_NAME, ONNX_WEIGHTS_NAME, WEIGHTS_NAME class CheckpointMergerPipeline(DiffusionPipeline): """ A class that supports merging diffusion models based on the discussion here: https://github.com/huggingface/diffusers/issues/877 Example usage:- pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="checkpoint_merger.py") merged_pipe = pipe.merge(["CompVis/stable-diffusion-v1-4","prompthero/openjourney"], interp = 'inv_sigmoid', alpha = 0.8, force = True) merged_pipe.to('cuda') prompt = "An astronaut riding a unicycle on Mars" results = merged_pipe(prompt) ## For more details, see the docstring for the merge method. """ def __init__(self): self.register_to_config() super().__init__() def _compare_model_configs(self, dict0, dict1): if dict0 == dict1: return True else: config0, meta_keys0 = self._remove_meta_keys(dict0) config1, meta_keys1 = self._remove_meta_keys(dict1) if config0 == config1: print(f"Warning !: Mismatch in keys {meta_keys0} and {meta_keys1}.") return True return False def _remove_meta_keys(self, config_dict: Dict): meta_keys = [] temp_dict = config_dict.copy() for key in config_dict.keys(): if key.startswith("_"): temp_dict.pop(key) meta_keys.append(key) return (temp_dict, meta_keys) @torch.no_grad() @validate_hf_hub_args def merge(self, pretrained_model_name_or_path_list: List[Union[str, os.PathLike]], **kwargs): """ Returns a new pipeline object of the class 'DiffusionPipeline' with the merged checkpoints(weights) of the models passed in the argument 'pretrained_model_name_or_path_list' as a list. Parameters: ----------- pretrained_model_name_or_path_list : A list of valid pretrained model names in the HuggingFace hub or paths to locally stored models in the HuggingFace format. **kwargs: Supports all the default DiffusionPipeline.get_config_dict kwargs viz.. cache_dir, resume_download, force_download, proxies, local_files_only, token, revision, torch_dtype, device_map. alpha - The interpolation parameter. Ranges from 0 to 1. It affects the ratio in which the checkpoints are merged. A 0.8 alpha would mean that the first model checkpoints would affect the final result far less than an alpha of 0.2 interp - The interpolation method to use for the merging. Supports "sigmoid", "inv_sigmoid", "add_diff" and None. Passing None uses the default interpolation which is weighted sum interpolation. For merging three checkpoints, only "add_diff" is supported. force - Whether to ignore mismatch in model_config.json for the current models. Defaults to False. variant - which variant of a pretrained model to load, e.g. "fp16" (None) """ # Default kwargs from DiffusionPipeline cache_dir = kwargs.pop("cache_dir", None) resume_download = kwargs.pop("resume_download", False) force_download = kwargs.pop("force_download", False) proxies = kwargs.pop("proxies", None) local_files_only = kwargs.pop("local_files_only", False) token = kwargs.pop("token", None) variant = kwargs.pop("variant", None) revision = kwargs.pop("revision", None) torch_dtype = kwargs.pop("torch_dtype", None) device_map = kwargs.pop("device_map", None) alpha = kwargs.pop("alpha", 0.5) interp = kwargs.pop("interp", None) print("Received list", pretrained_model_name_or_path_list) print(f"Combining with alpha={alpha}, interpolation mode={interp}") checkpoint_count = len(pretrained_model_name_or_path_list) # Ignore result from model_index_json comparison of the two checkpoints force = kwargs.pop("force", False) # If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now. if checkpoint_count > 3 or checkpoint_count < 2: raise ValueError( "Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being" " passed." ) print("Received the right number of checkpoints") # chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2] # chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None # Validate that the checkpoints can be merged # Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_' config_dicts = [] for pretrained_model_name_or_path in pretrained_model_name_or_path_list: config_dict = DiffusionPipeline.load_config( pretrained_model_name_or_path, cache_dir=cache_dir, resume_download=resume_download, force_download=force_download, proxies=proxies, local_files_only=local_files_only, token=token, revision=revision, ) config_dicts.append(config_dict) comparison_result = True for idx in range(1, len(config_dicts)): comparison_result &= self._compare_model_configs(config_dicts[idx - 1], config_dicts[idx]) if not force and comparison_result is False: raise ValueError("Incompatible checkpoints. Please check model_index.json for the models.") print("Compatible model_index.json files found") # Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files. cached_folders = [] for pretrained_model_name_or_path, config_dict in zip(pretrained_model_name_or_path_list, config_dicts): folder_names = [k for k in config_dict.keys() if not k.startswith("_")] allow_patterns = [os.path.join(k, "*") for k in folder_names] allow_patterns += [ WEIGHTS_NAME, SCHEDULER_CONFIG_NAME, CONFIG_NAME, ONNX_WEIGHTS_NAME, DiffusionPipeline.config_name, ] requested_pipeline_class = config_dict.get("_class_name") user_agent = {"diffusers": __version__, "pipeline_class": requested_pipeline_class} cached_folder = ( pretrained_model_name_or_path if os.path.isdir(pretrained_model_name_or_path) else snapshot_download( pretrained_model_name_or_path, cache_dir=cache_dir, resume_download=resume_download, proxies=proxies, local_files_only=local_files_only, token=token, revision=revision, allow_patterns=allow_patterns, user_agent=user_agent, ) ) print("Cached Folder", cached_folder) cached_folders.append(cached_folder) # Step 3:- # Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place final_pipe = DiffusionPipeline.from_pretrained( cached_folders[0], torch_dtype=torch_dtype, device_map=device_map, variant=variant, ) final_pipe.to(self.device) checkpoint_path_2 = None if len(cached_folders) > 2: checkpoint_path_2 = os.path.join(cached_folders[2]) if interp == "sigmoid": theta_func = CheckpointMergerPipeline.sigmoid elif interp == "inv_sigmoid": theta_func = CheckpointMergerPipeline.inv_sigmoid elif interp == "add_diff": theta_func = CheckpointMergerPipeline.add_difference else: theta_func = CheckpointMergerPipeline.weighted_sum # Find each module's state dict. for attr in final_pipe.config.keys(): if not attr.startswith("_"): checkpoint_path_1 = os.path.join(cached_folders[1], attr) if os.path.exists(checkpoint_path_1): files = [ *glob.glob(os.path.join(checkpoint_path_1, "*.safetensors")), *glob.glob(os.path.join(checkpoint_path_1, "*.bin")), ] checkpoint_path_1 = files[0] if len(files) > 0 else None if len(cached_folders) < 3: checkpoint_path_2 = None else: checkpoint_path_2 = os.path.join(cached_folders[2], attr) if os.path.exists(checkpoint_path_2): files = [ *glob.glob(os.path.join(checkpoint_path_2, "*.safetensors")), *glob.glob(os.path.join(checkpoint_path_2, "*.bin")), ] checkpoint_path_2 = files[0] if len(files) > 0 else None # For an attr if both checkpoint_path_1 and 2 are None, ignore. # If at least one is present, deal with it according to interp method, of course only if the state_dict keys match. if checkpoint_path_1 is None and checkpoint_path_2 is None: print(f"Skipping {attr}: not present in 2nd or 3d model") continue try: module = getattr(final_pipe, attr) if isinstance(module, bool): # ignore requires_safety_checker boolean continue theta_0 = getattr(module, "state_dict") theta_0 = theta_0() update_theta_0 = getattr(module, "load_state_dict") theta_1 = ( safetensors.torch.load_file(checkpoint_path_1) if (checkpoint_path_1.endswith(".safetensors")) else torch.load(checkpoint_path_1, map_location="cpu") ) theta_2 = None if checkpoint_path_2: theta_2 = ( safetensors.torch.load_file(checkpoint_path_2) if (checkpoint_path_2.endswith(".safetensors")) else torch.load(checkpoint_path_2, map_location="cpu") ) if not theta_0.keys() == theta_1.keys(): print(f"Skipping {attr}: key mismatch") continue if theta_2 and not theta_1.keys() == theta_2.keys(): print(f"Skipping {attr}:y mismatch") except Exception as e: print(f"Skipping {attr} do to an unexpected error: {str(e)}") continue print(f"MERGING {attr}") for key in theta_0.keys(): if theta_2: theta_0[key] = theta_func(theta_0[key], theta_1[key], theta_2[key], alpha) else: theta_0[key] = theta_func(theta_0[key], theta_1[key], None, alpha) del theta_1 del theta_2 update_theta_0(theta_0) del theta_0 return final_pipe @staticmethod def weighted_sum(theta0, theta1, theta2, alpha): return ((1 - alpha) * theta0) + (alpha * theta1) # Smoothstep (https://en.wikipedia.org/wiki/Smoothstep) @staticmethod def sigmoid(theta0, theta1, theta2, alpha): alpha = alpha * alpha * (3 - (2 * alpha)) return theta0 + ((theta1 - theta0) * alpha) # Inverse Smoothstep (https://en.wikipedia.org/wiki/Smoothstep) @staticmethod def inv_sigmoid(theta0, theta1, theta2, alpha): import math alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0) return theta0 + ((theta1 - theta0) * alpha) @staticmethod def add_difference(theta0, theta1, theta2, alpha): return theta0 + (theta1 - theta2) * (1.0 - alpha)