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Qing 2023-03-01 21:44:02 +08:00
parent 8e5e4892af
commit 66d3c6e322
7 changed files with 644 additions and 3 deletions
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12
lama_cleaner/app/src/components/Settings/ModelSettingBlock.tsx
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@ -230,6 +230,18 @@ function ModelSettingBlock() {
'https://ommer-lab.com/research/latent-diffusion-models/',
'https://github.com/CompVis/stable-diffusion'
)
case AIModel.ANYTHING4:
return renderModelDesc(
'andite/anything-v4.0',
'https://huggingface.co/andite/anything-v4.0',
'https://huggingface.co/andite/anything-v4.0'
)
case AIModel.REALISTIC_VISION_1_4:
return renderModelDesc(
'SG161222/Realistic_Vision_V1.4',
'https://huggingface.co/SG161222/Realistic_Vision_V1.4',
'https://huggingface.co/SG161222/Realistic_Vision_V1.4'
)
case AIModel.SD2:
return renderModelDesc(
'Stable Diffusion 2',

23
lama_cleaner/app/src/store/Atoms.tsx
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@ -10,6 +10,8 @@ export enum AIModel {
MAT = 'mat',
FCF = 'fcf',
SD15 = 'sd1.5',
ANYTHING4 = 'anything4',
REALISTIC_VISION_1_4 = 'realisticVision1.4',
SD2 = 'sd2',
CV2 = 'cv2',
Mange = 'manga',
@ -422,6 +424,20 @@ const defaultHDSettings: ModelsHDSettings = {
hdStrategyCropMargin: 128,
enabled: false,
},
[AIModel.ANYTHING4]: {
hdStrategy: HDStrategy.ORIGINAL,
hdStrategyResizeLimit: 768,
hdStrategyCropTrigerSize: 512,
hdStrategyCropMargin: 128,
enabled: false,
},
[AIModel.REALISTIC_VISION_1_4]: {
hdStrategy: HDStrategy.ORIGINAL,
hdStrategyResizeLimit: 768,
hdStrategyCropTrigerSize: 512,
hdStrategyCropMargin: 128,
enabled: false,
},
[AIModel.SD2]: {
hdStrategy: HDStrategy.ORIGINAL,
hdStrategyResizeLimit: 768,
@ -601,7 +617,12 @@ export const isSDState = selector({
key: 'isSD',
get: ({ get }) => {
const settings = get(settingState)
return settings.model === AIModel.SD15 || settings.model === AIModel.SD2
return (
settings.model === AIModel.SD15 ||
settings.model === AIModel.SD2 ||
settings.model === AIModel.ANYTHING4 ||
settings.model === AIModel.REALISTIC_VISION_1_4
)
},
})

4
lama_cleaner/const.py
View File

@ -3,6 +3,8 @@ import os
MPS_SUPPORT_MODELS = [
"instruct_pix2pix",
"sd1.5",
"anything4",
"realisticVision1.4",
"sd2",
"paint_by_example"
]
@ -15,6 +17,8 @@ AVAILABLE_MODELS = [
"mat",
"fcf",
"sd1.5",
"anything4",
"realisticVision1.4",
"cv2",
"manga",
"sd2",

12
lama_cleaner/model/sd.py
View File

@ -136,7 +136,7 @@ class SD(DiffusionInpaintModel):
callback=self.callback,
height=img_h,
width=img_w,
generator=torch.manual_seed(config.sd_seed)
generator=torch.manual_seed(config.sd_seed),
).images[0]
output = (output * 255).round().astype("uint8")
@ -163,6 +163,16 @@ class SD15(SD):
model_id_or_path = "runwayml/stable-diffusion-inpainting"
class Anything4(SD):
name = "anything4"
model_id_or_path = "Sanster/anything-4.0-inpainting"
class RealisticVision14(SD):
name = "realisticVision1.4"
model_id_or_path = "Sanster/Realistic_Vision_V1.4-inpainting"
class SD2(SD):
name = "sd2"
model_id_or_path = "stabilityai/stable-diffusion-2-inpainting"

4
lama_cleaner/model_manager.py
View File

@ -9,7 +9,7 @@ from lama_cleaner.model.manga import Manga
from lama_cleaner.model.mat import MAT
from lama_cleaner.model.paint_by_example import PaintByExample
from lama_cleaner.model.instruct_pix2pix import InstructPix2Pix
from lama_cleaner.model.sd import SD15, SD2
from lama_cleaner.model.sd import SD15, SD2, Anything4, RealisticVision14
from lama_cleaner.model.zits import ZITS
from lama_cleaner.model.opencv2 import OpenCV2
from lama_cleaner.schema import Config
@ -21,6 +21,8 @@ models = {
"mat": MAT,
"fcf": FcF,
"sd1.5": SD15,
Anything4.name: Anything4,
RealisticVision14.name: RealisticVision14,
"cv2": OpenCV2,
"manga": Manga,
"sd2": SD2,

231
scripts/convert_vae_pt_to_diffusers.py Normal file
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@ -0,0 +1,231 @@
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_vae_resnet_paths,
)
def custom_convert_ldm_vae_checkpoint(checkpoint, config):
vae_state_dict = checkpoint
new_checkpoint = {}
new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
new_checkpoint["encoder.conv_out.weight"] = vae_state_dict[
"encoder.conv_out.weight"
]
new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict[
"encoder.norm_out.weight"
]
new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict[
"encoder.norm_out.bias"
]
new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
new_checkpoint["decoder.conv_out.weight"] = vae_state_dict[
"decoder.conv_out.weight"
]
new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict[
"decoder.norm_out.weight"
]
new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict[
"decoder.norm_out.bias"
]
new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]
# Retrieves the keys for the encoder down blocks only
num_down_blocks = len(
{
".".join(layer.split(".")[:3])
for layer in vae_state_dict
if "encoder.down" in layer
}
)
down_blocks = {
layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key]
for layer_id in range(num_down_blocks)
}
# Retrieves the keys for the decoder up blocks only
num_up_blocks = len(
{
".".join(layer.split(".")[:3])
for layer in vae_state_dict
if "decoder.up" in layer
}
)
up_blocks = {
layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key]
for layer_id in range(num_up_blocks)
}
for i in range(num_down_blocks):
resnets = [
key
for key in down_blocks[i]
if f"down.{i}" in key and f"down.{i}.downsample" not in key
]
if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
new_checkpoint[
f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"
] = vae_state_dict.pop(f"encoder.down.{i}.downsample.conv.weight")
new_checkpoint[
f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"
] = vae_state_dict.pop(f"encoder.down.{i}.downsample.conv.bias")
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
assign_to_checkpoint(
paths,
new_checkpoint,
vae_state_dict,
additional_replacements=[meta_path],
config=config,
)
mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
num_mid_res_blocks = 2
for i in range(1, num_mid_res_blocks + 1):
resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
assign_to_checkpoint(
paths,
new_checkpoint,
vae_state_dict,
additional_replacements=[meta_path],
config=config,
)
mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
paths = renew_vae_attention_paths(mid_attentions)
meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(
paths,
new_checkpoint,
vae_state_dict,
additional_replacements=[meta_path],
config=config,
)
conv_attn_to_linear(new_checkpoint)
for i in range(num_up_blocks):
block_id = num_up_blocks - 1 - i
resnets = [
key
for key in up_blocks[block_id]
if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
]
if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
new_checkpoint[
f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"
] = vae_state_dict[f"decoder.up.{block_id}.upsample.conv.weight"]
new_checkpoint[
f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"
] = vae_state_dict[f"decoder.up.{block_id}.upsample.conv.bias"]
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
assign_to_checkpoint(
paths,
new_checkpoint,
vae_state_dict,
additional_replacements=[meta_path],
config=config,
)
mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
num_mid_res_blocks = 2
for i in range(1, num_mid_res_blocks + 1):
resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
assign_to_checkpoint(
paths,
new_checkpoint,
vae_state_dict,
additional_replacements=[meta_path],
config=config,
)
mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
paths = renew_vae_attention_paths(mid_attentions)
meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(
paths,
new_checkpoint,
vae_state_dict,
additional_replacements=[meta_path],
config=config,
)
conv_attn_to_linear(new_checkpoint)
return new_checkpoint
def vae_pt_to_vae_diffuser(
checkpoint_path: str,
output_path: str,
):
# Only support V1
r = requests.get(
" https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
)
io_obj = io.BytesIO(r.content)
original_config = OmegaConf.load(io_obj)
image_size = 512
device = "cuda" if torch.cuda.is_available() else "cpu"
checkpoint = torch.load(checkpoint_path, map_location=device)
# Convert the VAE model.
vae_config = create_vae_diffusers_config(original_config, image_size=image_size)
converted_vae_checkpoint = custom_convert_ldm_vae_checkpoint(
checkpoint["state_dict"], vae_config
)
vae = AutoencoderKL(**vae_config)
vae.load_state_dict(converted_vae_checkpoint)
vae.save_pretrained(output_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--vae_pt_path",
default="/Users/cwq/code/github/lama-cleaner/scripts/anything-v4.0.vae.pt",
type=str,
help="Path to the VAE.pt to convert.",
)
parser.add_argument(
"--dump_path",
default="diffusion_pytorch_model.bin",
type=str,
help="Path to the VAE.pt to convert.",
)
args = parser.parse_args()
vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)

361
scripts/tool.py Normal file
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@ -0,0 +1,361 @@
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 = list(
(
*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 = list(
(
*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")
)
if attr in ['vae', 'text_encoder']:
print(f"Direct use theta1 {attr}: {checkpoint_path_1}")
update_theta_0(theta_1)
del theta_1
del theta_0
continue
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):
# theta0 + (theta1 - theta2) * (1.0 - alpha)
diff = (theta1 - theta2) * (1.0 - alpha)
# print(f"theta0.shape: {theta0.shape}, diff shape: {diff.shape}")
# theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)
if theta0.shape != diff.shape:
theta0[:, 0:4, :, :] = theta0[:, 0:4, :, :] + diff
else:
theta0 = theta0 + diff
return theta0
pipe = CheckpointMergerPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
merged_pipe = pipe.merge(
[
"runwayml/stable-diffusion-inpainting",
#"SG161222/Realistic_Vision_V1.4",
"dreamlike-art/dreamlike-diffusion-1.0",
"runwayml/stable-diffusion-v1-5",
],
force=True,
interp="add_diff",
alpha=0,
)
merged_pipe = merged_pipe.to(torch.float16)
merged_pipe.save_pretrained("dreamlike-diffusion-1.0-inpainting", safe_serialization=True)