# coding=utf-8

# Copyright 2023 The HuggingFace Inc. team.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

""" Conversion script for the Versatile Stable Diffusion checkpoints. """

import argparse

from argparse import Namespace

import torch

from transformers import (

CLIPImageProcessor,

CLIPTextModelWithProjection,

CLIPTokenizer,

CLIPVisionModelWithProjection,

)

from diffusers import (

AutoencoderKL,

DDIMScheduler,

DPMSolverMultistepScheduler,

EulerAncestralDiscreteScheduler,

EulerDiscreteScheduler,

LMSDiscreteScheduler,

PNDMScheduler,

UNet2DConditionModel,

VersatileDiffusionPipeline,

)

from diffusers.pipelines.versatile_diffusion.modeling_text_unet import UNetFlatConditionModel

SCHEDULER_CONFIG = Namespace(

**{

"beta_linear_start": 0.00085,

"beta_linear_end": 0.012,

"timesteps": 1000,

"scale_factor": 0.18215,

}

)

IMAGE_UNET_CONFIG = Namespace(

**{

"input_channels": 4,

"model_channels": 320,

"output_channels": 4,

"num_noattn_blocks": [2, 2, 2, 2],

"channel_mult": [1, 2, 4, 4],

"with_attn": [True, True, True, False],

"num_heads": 8,

"context_dim": 768,

"use_checkpoint": True,

}

)

TEXT_UNET_CONFIG = Namespace(

**{

"input_channels": 768,

"model_channels": 320,

"output_channels": 768,

"num_noattn_blocks": [2, 2, 2, 2],

"channel_mult": [1, 2, 4, 4],

"second_dim": [4, 4, 4, 4],

"with_attn": [True, True, True, False],

"num_heads": 8,

"context_dim": 768,

"use_checkpoint": True,

}

)

AUTOENCODER_CONFIG = Namespace(

**{

"double_z": True,

"z_channels": 4,

"resolution": 256,

"in_channels": 3,

"out_ch": 3,

"ch": 128,

"ch_mult": [1, 2, 4, 4],

"num_res_blocks": 2,

"attn_resolutions": [],

"dropout": 0.0,

}

)

def shave_segments(path, n_shave_prefix_segments=1):

"""

Removes segments. Positive values shave the first segments, negative shave the last segments.

"""

if n_shave_prefix_segments >= 0:

return ".".join(path.split(".")[n_shave_prefix_segments:])

else:

return ".".join(path.split(".")[:n_shave_prefix_segments])

def renew_resnet_paths(old_list, n_shave_prefix_segments=0):

"""

Updates paths inside resnets to the new naming scheme (local renaming)

"""

mapping = []

for old_item in old_list:

new_item = old_item.replace("in_layers.0", "norm1")

new_item = new_item.replace("in_layers.2", "conv1")

new_item = new_item.replace("out_layers.0", "norm2")

new_item = new_item.replace("out_layers.3", "conv2")

new_item = new_item.replace("emb_layers.1", "time_emb_proj")

new_item = new_item.replace("skip_connection", "conv_shortcut")

new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

mapping.append({"old": old_item, "new": new_item})

return mapping

def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):

"""

Updates paths inside resnets to the new naming scheme (local renaming)

"""

mapping = []

for old_item in old_list:

new_item = old_item

new_item = new_item.replace("nin_shortcut", "conv_shortcut")

new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

mapping.append({"old": old_item, "new": new_item})

return mapping

def renew_attention_paths(old_list, n_shave_prefix_segments=0):

"""

Updates paths inside attentions to the new naming scheme (local renaming)

"""

mapping = []

for old_item in old_list:

new_item = old_item

# new_item = new_item.replace('norm.weight', 'group_norm.weight')

# new_item = new_item.replace('norm.bias', 'group_norm.bias')

# new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')

# new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

# new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

mapping.append({"old": old_item, "new": new_item})

return mapping

def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):

"""

Updates paths inside attentions to the new naming scheme (local renaming)

"""

mapping = []

for old_item in old_list:

new_item = old_item

new_item = new_item.replace("norm.weight", "group_norm.weight")

new_item = new_item.replace("norm.bias", "group_norm.bias")

new_item = new_item.replace("q.weight", "query.weight")

new_item = new_item.replace("q.bias", "query.bias")

new_item = new_item.replace("k.weight", "key.weight")

new_item = new_item.replace("k.bias", "key.bias")

new_item = new_item.replace("v.weight", "value.weight")

new_item = new_item.replace("v.bias", "value.bias")

new_item = new_item.replace("proj_out.weight", "proj_attn.weight")

new_item = new_item.replace("proj_out.bias", "proj_attn.bias")

new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

mapping.append({"old": old_item, "new": new_item})

return mapping

def assign_to_checkpoint(

paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None

):

"""

This does the final conversion step: take locally converted weights and apply a global renaming

to them. It splits attention layers, and takes into account additional replacements

that may arise.

Assigns the weights to the new checkpoint.

"""

assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."

# Splits the attention layers into three variables.

if attention_paths_to_split is not None:

for path, path_map in attention_paths_to_split.items():

old_tensor = old_checkpoint[path]

channels = old_tensor.shape[0] // 3

target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)

num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3

old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])

query, key, value = old_tensor.split(channels // num_heads, dim=1)

checkpoint[path_map["query"]] = query.reshape(target_shape)

checkpoint[path_map["key"]] = key.reshape(target_shape)

checkpoint[path_map["value"]] = value.reshape(target_shape)

for path in paths:

new_path = path["new"]

# These have already been assigned

if attention_paths_to_split is not None and new_path in attention_paths_to_split:

continue

# Global renaming happens here

new_path = new_path.replace("middle_block.0", "mid_block.resnets.0")

new_path = new_path.replace("middle_block.1", "mid_block.attentions.0")

new_path = new_path.replace("middle_block.2", "mid_block.resnets.1")

if additional_replacements is not None:

for replacement in additional_replacements:

new_path = new_path.replace(replacement["old"], replacement["new"])

# proj_attn.weight has to be converted from conv 1D to linear

if "proj_attn.weight" in new_path:

checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0]

elif path["old"] in old_checkpoint:

checkpoint[new_path] = old_checkpoint[path["old"]]

def conv_attn_to_linear(checkpoint):

keys = list(checkpoint.keys())

attn_keys = ["query.weight", "key.weight", "value.weight"]

for key in keys:

if ".".join(key.split(".")[-2:]) in attn_keys:

if checkpoint[key].ndim > 2:

checkpoint[key] = checkpoint[key][:, :, 0, 0]

elif "proj_attn.weight" in key:

if checkpoint[key].ndim > 2:

checkpoint[key] = checkpoint[key][:, :, 0]

def create_image_unet_diffusers_config(unet_params):

"""

Creates a config for the diffusers based on the config of the VD model.

"""

block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult]

down_block_types = []

resolution = 1

for i in range(len(block_out_channels)):

block_type = "CrossAttnDownBlock2D" if unet_params.with_attn[i] else "DownBlock2D"

down_block_types.append(block_type)

if i != len(block_out_channels) - 1:

resolution *= 2

up_block_types = []

for i in range(len(block_out_channels)):

block_type = "CrossAttnUpBlock2D" if unet_params.with_attn[-i - 1] else "UpBlock2D"

up_block_types.append(block_type)

resolution //= 2

if not all(n == unet_params.num_noattn_blocks[0] for n in unet_params.num_noattn_blocks):

raise ValueError("Not all num_res_blocks are equal, which is not supported in this script.")

config = {

"sample_size": None,

"in_channels": unet_params.input_channels,

"out_channels": unet_params.output_channels,

"down_block_types": tuple(down_block_types),

"up_block_types": tuple(up_block_types),

"block_out_channels": tuple(block_out_channels),

"layers_per_block": unet_params.num_noattn_blocks[0],

"cross_attention_dim": unet_params.context_dim,

"attention_head_dim": unet_params.num_heads,

}

return config

def create_text_unet_diffusers_config(unet_params):

"""

Creates a config for the diffusers based on the config of the VD model.

"""

block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult]

down_block_types = []

resolution = 1

for i in range(len(block_out_channels)):

block_type = "CrossAttnDownBlockFlat" if unet_params.with_attn[i] else "DownBlockFlat"

down_block_types.append(block_type)

if i != len(block_out_channels) - 1:

resolution *= 2

up_block_types = []

for i in range(len(block_out_channels)):

block_type = "CrossAttnUpBlockFlat" if unet_params.with_attn[-i - 1] else "UpBlockFlat"

up_block_types.append(block_type)

resolution //= 2

if not all(n == unet_params.num_noattn_blocks[0] for n in unet_params.num_noattn_blocks):

raise ValueError("Not all num_res_blocks are equal, which is not supported in this script.")

config = {

"sample_size": None,

"in_channels": (unet_params.input_channels, 1, 1),

"out_channels": (unet_params.output_channels, 1, 1),

"down_block_types": tuple(down_block_types),

"up_block_types": tuple(up_block_types),

"block_out_channels": tuple(block_out_channels),

"layers_per_block": unet_params.num_noattn_blocks[0],

"cross_attention_dim": unet_params.context_dim,

"attention_head_dim": unet_params.num_heads,

}

return config

def create_vae_diffusers_config(vae_params):

"""

Creates a config for the diffusers based on the config of the VD model.

"""

block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult]

down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels)

up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels)

config = {

"sample_size": vae_params.resolution,

"in_channels": vae_params.in_channels,

"out_channels": vae_params.out_ch,

"down_block_types": tuple(down_block_types),

"up_block_types": tuple(up_block_types),

"block_out_channels": tuple(block_out_channels),

"latent_channels": vae_params.z_channels,

"layers_per_block": vae_params.num_res_blocks,

}

return config

def create_diffusers_scheduler(original_config):

schedular = DDIMScheduler(

num_train_timesteps=original_config.model.params.timesteps,

beta_start=original_config.model.params.linear_start,

beta_end=original_config.model.params.linear_end,

beta_schedule="scaled_linear",

)

return schedular

def convert_vd_unet_checkpoint(checkpoint, config, unet_key, extract_ema=False):

"""

Takes a state dict and a config, and returns a converted checkpoint.

"""

# extract state_dict for UNet

unet_state_dict = {}

keys = list(checkpoint.keys())

# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA

if sum(k.startswith("model_ema") for k in keys) > 100:

print("Checkpoint has both EMA and non-EMA weights.")

if extract_ema:

print(

"In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"

" weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."

)

for key in keys:

if key.startswith("model.diffusion_model"):

flat_ema_key = "model_ema." + "".join(key.split(".")[1:])

unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key)

else:

print(

"In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"

" weights (usually better for inference), please make sure to add the `--extract_ema` flag."

)

for key in keys:

if key.startswith(unet_key):

unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)

new_checkpoint = {}

new_checkpoint["time_embedding.linear_1.weight"] = checkpoint["model.diffusion_model.time_embed.0.weight"]

new_checkpoint["time_embedding.linear_1.bias"] = checkpoint["model.diffusion_model.time_embed.0.bias"]

new_checkpoint["time_embedding.linear_2.weight"] = checkpoint["model.diffusion_model.time_embed.2.weight"]

new_checkpoint["time_embedding.linear_2.bias"] = checkpoint["model.diffusion_model.time_embed.2.bias"]

new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"]

new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"]

new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"]

new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"]

new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"]

new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"]

# Retrieves the keys for the input blocks only

num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer})

input_blocks = {

layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]

for layer_id in range(num_input_blocks)

}

# Retrieves the keys for the middle blocks only

num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer})

middle_blocks = {

layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]

for layer_id in range(num_middle_blocks)

}

# Retrieves the keys for the output blocks only

num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer})

output_blocks = {

layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]

for layer_id in range(num_output_blocks)

}

for i in range(1, num_input_blocks):

block_id = (i - 1) // (config["layers_per_block"] + 1)

layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1)

resnets = [

key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key

]

attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]

if f"input_blocks.{i}.0.op.weight" in unet_state_dict:

new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop(

f"input_blocks.{i}.0.op.weight"

)

new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop(

f"input_blocks.{i}.0.op.bias"

)

elif f"input_blocks.{i}.0.weight" in unet_state_dict:

# text_unet uses linear layers in place of downsamplers

shape = unet_state_dict[f"input_blocks.{i}.0.weight"].shape

if shape[0] != shape[1]:

continue

new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.weight"] = unet_state_dict.pop(

f"input_blocks.{i}.0.weight"

)

new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.bias"] = unet_state_dict.pop(

f"input_blocks.{i}.0.bias"

)

paths = renew_resnet_paths(resnets)

meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}

assign_to_checkpoint(

paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config

)

if len(attentions):

paths = renew_attention_paths(attentions)

meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}

assign_to_checkpoint(

paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config

)

resnet_0 = middle_blocks[0]

attentions = middle_blocks[1]

resnet_1 = middle_blocks[2]

resnet_0_paths = renew_resnet_paths(resnet_0)

assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)

resnet_1_paths = renew_resnet_paths(resnet_1)

assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)

attentions_paths = renew_attention_paths(attentions)

meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}

assign_to_checkpoint(

attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config

)

for i in range(num_output_blocks):

block_id = i // (config["layers_per_block"] + 1)

layer_in_block_id = i % (config["layers_per_block"] + 1)

output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]

output_block_list = {}

for layer in output_block_layers:

layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1)

if layer_id in output_block_list:

output_block_list[layer_id].append(layer_name)

else:

output_block_list[layer_id] = [layer_name]

if len(output_block_list) > 1:

resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]

attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]

paths = renew_resnet_paths(resnets)

meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}

assign_to_checkpoint(

paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config

)

if ["conv.weight", "conv.bias"] in output_block_list.values():

index = list(output_block_list.values()).index(["conv.weight", "conv.bias"])

new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[

f"output_blocks.{i}.{index}.conv.weight"

]

new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[

f"output_blocks.{i}.{index}.conv.bias"

]

# Clear attentions as they have been attributed above.

if len(attentions) == 2:

attentions = []

elif f"output_blocks.{i}.1.weight" in unet_state_dict:

# text_unet uses linear layers in place of upsamplers

shape = unet_state_dict[f"output_blocks.{i}.1.weight"].shape

if shape[0] != shape[1]:

continue

new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.weight"] = unet_state_dict.pop(

f"output_blocks.{i}.1.weight"

)

new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.bias"] = unet_state_dict.pop(

f"output_blocks.{i}.1.bias"

)

# Clear attentions as they have been attributed above.

if len(attentions) == 2:

attentions = []

elif f"output_blocks.{i}.2.weight" in unet_state_dict:

# text_unet uses linear layers in place of upsamplers

shape = unet_state_dict[f"output_blocks.{i}.2.weight"].shape

if shape[0] != shape[1]:

continue

new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.weight"] = unet_state_dict.pop(

f"output_blocks.{i}.2.weight"

)

new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.bias"] = unet_state_dict.pop(

f"output_blocks.{i}.2.bias"

)

if len(attentions):

paths = renew_attention_paths(attentions)

meta_path = {

"old": f"output_blocks.{i}.1",

"new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",

}

assign_to_checkpoint(

paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config

)

else:

resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)

for path in resnet_0_paths:

old_path = ".".join(["output_blocks", str(i), path["old"]])

new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]])

new_checkpoint[new_path] = unet_state_dict[old_path]

return new_checkpoint

def convert_vd_vae_checkpoint(checkpoint, config):

# extract state dict for VAE

vae_state_dict = {}

keys = list(checkpoint.keys())

for key in keys:

vae_state_dict[key] = checkpoint.get(key)

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

if __name__ == "__main__":

parser = argparse.ArgumentParser()

parser.add_argument(

"--unet_checkpoint_path", default=None, type=str, required=False, help="Path to the checkpoint to convert."

)

parser.add_argument(

"--vae_checkpoint_path", default=None, type=str, required=False, help="Path to the checkpoint to convert."

)

parser.add_argument(

"--optimus_checkpoint_path", default=None, type=str, required=False, help="Path to the checkpoint to convert."

)

parser.add_argument(

"--scheduler_type",

default="pndm",

type=str,

help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']",

)

parser.add_argument(

"--extract_ema",

action="store_true",

help=(

"Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"

" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"

" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."

),

)

parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

args = parser.parse_args()

scheduler_config = SCHEDULER_CONFIG

num_train_timesteps = scheduler_config.timesteps

beta_start = scheduler_config.beta_linear_start

beta_end = scheduler_config.beta_linear_end

if args.scheduler_type == "pndm":

scheduler = PNDMScheduler(

beta_end=beta_end,

beta_schedule="scaled_linear",

beta_start=beta_start,

num_train_timesteps=num_train_timesteps,

skip_prk_steps=True,

steps_offset=1,

)

elif args.scheduler_type == "lms":

scheduler = LMSDiscreteScheduler(beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear")

elif args.scheduler_type == "euler":

scheduler = EulerDiscreteScheduler(beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear")

elif args.scheduler_type == "euler-ancestral":

scheduler = EulerAncestralDiscreteScheduler(

beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear"

)

elif args.scheduler_type == "dpm":

scheduler = DPMSolverMultistepScheduler(

beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear"

)

elif args.scheduler_type == "ddim":

scheduler = DDIMScheduler(

beta_start=beta_start,

beta_end=beta_end,

beta_schedule="scaled_linear",

clip_sample=False,

set_alpha_to_one=False,

steps_offset=1,

)

else:

raise ValueError(f"Scheduler of type {args.scheduler_type} doesn't exist!")

# Convert the UNet2DConditionModel models.

if args.unet_checkpoint_path is not None:

# image UNet

image_unet_config = create_image_unet_diffusers_config(IMAGE_UNET_CONFIG)

checkpoint = torch.load(args.unet_checkpoint_path)

converted_image_unet_checkpoint = convert_vd_unet_checkpoint(

checkpoint, image_unet_config, unet_key="model.diffusion_model.unet_image.", extract_ema=args.extract_ema

)

image_unet = UNet2DConditionModel(**image_unet_config)

image_unet.load_state_dict(converted_image_unet_checkpoint)

# text UNet

text_unet_config = create_text_unet_diffusers_config(TEXT_UNET_CONFIG)

converted_text_unet_checkpoint = convert_vd_unet_checkpoint(

checkpoint, text_unet_config, unet_key="model.diffusion_model.unet_text.", extract_ema=args.extract_ema

)

text_unet = UNetFlatConditionModel(**text_unet_config)

text_unet.load_state_dict(converted_text_unet_checkpoint)

# Convert the VAE model.

if args.vae_checkpoint_path is not None:

vae_config = create_vae_diffusers_config(AUTOENCODER_CONFIG)

checkpoint = torch.load(args.vae_checkpoint_path)

converted_vae_checkpoint = convert_vd_vae_checkpoint(checkpoint, vae_config)

vae = AutoencoderKL(**vae_config)

vae.load_state_dict(converted_vae_checkpoint)

tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")

image_feature_extractor = CLIPImageProcessor.from_pretrained("openai/clip-vit-large-patch14")

text_encoder = CLIPTextModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

pipe = VersatileDiffusionPipeline(

scheduler=scheduler,

tokenizer=tokenizer,

image_feature_extractor=image_feature_extractor,

text_encoder=text_encoder,

image_encoder=image_encoder,

image_unet=image_unet,

text_unet=text_unet,

vae=vae,

)

pipe.save_pretrained(args.dump_path)