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import glob
import os
from typing import Dict, List, Union
import torch
from diffusers.utils import is_safetensors_available
if is_safetensors_available():
import safetensors.torch
from huggingface_hub import snapshot_download
from diffusers import DiffusionPipeline, __version__
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME
class CheckpointMergerPipeline(DiffusionPipeline):
"""
A class that 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()
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, use_auth_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.
"""
# Default kwargs from DiffusionPipeline
cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
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)
use_auth_token = kwargs.pop("use_auth_token", 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 comparision 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,
use_auth_token=use_auth_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(config_dicts[0], config_dicts[1])
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,
use_auth_token=use_auth_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
)
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 atleast 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 (is_safetensors_available() and 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 (is_safetensors_available() and 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)
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