import argparse

import os

import torch

from diffusers import (

CMStochasticIterativeScheduler,

ConsistencyModelPipeline,

UNet2DModel,

)

TEST_UNET_CONFIG = {

"sample_size": 32,

"in_channels": 3,

"out_channels": 3,

"layers_per_block": 2,

"num_class_embeds": 1000,

"block_out_channels": [32, 64],

"attention_head_dim": 8,

"down_block_types": [

"ResnetDownsampleBlock2D",

"AttnDownBlock2D",

],

"up_block_types": [

"AttnUpBlock2D",

"ResnetUpsampleBlock2D",

],

"resnet_time_scale_shift": "scale_shift",

"attn_norm_num_groups": 32,

"upsample_type": "resnet",

"downsample_type": "resnet",

}

IMAGENET_64_UNET_CONFIG = {

"sample_size": 64,

"in_channels": 3,

"out_channels": 3,

"layers_per_block": 3,

"num_class_embeds": 1000,

"block_out_channels": [192, 192 * 2, 192 * 3, 192 * 4],

"attention_head_dim": 64,

"down_block_types": [

"ResnetDownsampleBlock2D",

"AttnDownBlock2D",

"AttnDownBlock2D",

"AttnDownBlock2D",

],

"up_block_types": [

"AttnUpBlock2D",

"AttnUpBlock2D",

"AttnUpBlock2D",

"ResnetUpsampleBlock2D",

],

"resnet_time_scale_shift": "scale_shift",

"attn_norm_num_groups": 32,

"upsample_type": "resnet",

"downsample_type": "resnet",

}

LSUN_256_UNET_CONFIG = {

"sample_size": 256,

"in_channels": 3,

"out_channels": 3,

"layers_per_block": 2,

"num_class_embeds": None,

"block_out_channels": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],

"attention_head_dim": 64,

"down_block_types": [

"ResnetDownsampleBlock2D",

"ResnetDownsampleBlock2D",

"ResnetDownsampleBlock2D",

"AttnDownBlock2D",

"AttnDownBlock2D",

"AttnDownBlock2D",

],

"up_block_types": [

"AttnUpBlock2D",

"AttnUpBlock2D",

"AttnUpBlock2D",

"ResnetUpsampleBlock2D",

"ResnetUpsampleBlock2D",

"ResnetUpsampleBlock2D",

],

"resnet_time_scale_shift": "default",

"upsample_type": "resnet",

"downsample_type": "resnet",

}

CD_SCHEDULER_CONFIG = {

"num_train_timesteps": 40,

"sigma_min": 0.002,

"sigma_max": 80.0,

}

CT_IMAGENET_64_SCHEDULER_CONFIG = {

"num_train_timesteps": 201,

"sigma_min": 0.002,

"sigma_max": 80.0,

}

CT_LSUN_256_SCHEDULER_CONFIG = {

"num_train_timesteps": 151,

"sigma_min": 0.002,

"sigma_max": 80.0,

}

def str2bool(v):

"""

https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse

"""

if isinstance(v, bool):

return v

if v.lower() in ("yes", "true", "t", "y", "1"):

return True

elif v.lower() in ("no", "false", "f", "n", "0"):

return False

else:

raise argparse.ArgumentTypeError("boolean value expected")

def convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=False):

new_checkpoint[f"{new_prefix}.norm1.weight"] = checkpoint[f"{old_prefix}.in_layers.0.weight"]

new_checkpoint[f"{new_prefix}.norm1.bias"] = checkpoint[f"{old_prefix}.in_layers.0.bias"]

new_checkpoint[f"{new_prefix}.conv1.weight"] = checkpoint[f"{old_prefix}.in_layers.2.weight"]

new_checkpoint[f"{new_prefix}.conv1.bias"] = checkpoint[f"{old_prefix}.in_layers.2.bias"]

new_checkpoint[f"{new_prefix}.time_emb_proj.weight"] = checkpoint[f"{old_prefix}.emb_layers.1.weight"]

new_checkpoint[f"{new_prefix}.time_emb_proj.bias"] = checkpoint[f"{old_prefix}.emb_layers.1.bias"]

new_checkpoint[f"{new_prefix}.norm2.weight"] = checkpoint[f"{old_prefix}.out_layers.0.weight"]

new_checkpoint[f"{new_prefix}.norm2.bias"] = checkpoint[f"{old_prefix}.out_layers.0.bias"]

new_checkpoint[f"{new_prefix}.conv2.weight"] = checkpoint[f"{old_prefix}.out_layers.3.weight"]

new_checkpoint[f"{new_prefix}.conv2.bias"] = checkpoint[f"{old_prefix}.out_layers.3.bias"]

if has_skip:

new_checkpoint[f"{new_prefix}.conv_shortcut.weight"] = checkpoint[f"{old_prefix}.skip_connection.weight"]

new_checkpoint[f"{new_prefix}.conv_shortcut.bias"] = checkpoint[f"{old_prefix}.skip_connection.bias"]

return new_checkpoint

def convert_attention(checkpoint, new_checkpoint, old_prefix, new_prefix, attention_dim=None):

weight_q, weight_k, weight_v = checkpoint[f"{old_prefix}.qkv.weight"].chunk(3, dim=0)

bias_q, bias_k, bias_v = checkpoint[f"{old_prefix}.qkv.bias"].chunk(3, dim=0)

new_checkpoint[f"{new_prefix}.group_norm.weight"] = checkpoint[f"{old_prefix}.norm.weight"]

new_checkpoint[f"{new_prefix}.group_norm.bias"] = checkpoint[f"{old_prefix}.norm.bias"]

new_checkpoint[f"{new_prefix}.to_q.weight"] = weight_q.squeeze(-1).squeeze(-1)

new_checkpoint[f"{new_prefix}.to_q.bias"] = bias_q.squeeze(-1).squeeze(-1)

new_checkpoint[f"{new_prefix}.to_k.weight"] = weight_k.squeeze(-1).squeeze(-1)

new_checkpoint[f"{new_prefix}.to_k.bias"] = bias_k.squeeze(-1).squeeze(-1)

new_checkpoint[f"{new_prefix}.to_v.weight"] = weight_v.squeeze(-1).squeeze(-1)

new_checkpoint[f"{new_prefix}.to_v.bias"] = bias_v.squeeze(-1).squeeze(-1)

new_checkpoint[f"{new_prefix}.to_out.0.weight"] = (

checkpoint[f"{old_prefix}.proj_out.weight"].squeeze(-1).squeeze(-1)

)

new_checkpoint[f"{new_prefix}.to_out.0.bias"] = checkpoint[f"{old_prefix}.proj_out.bias"].squeeze(-1).squeeze(-1)

return new_checkpoint

def con_pt_to_diffuser(checkpoint_path: str, unet_config):

checkpoint = torch.load(checkpoint_path, map_location="cpu")

new_checkpoint = {}

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

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

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

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

if unet_config["num_class_embeds"] is not None:

new_checkpoint["class_embedding.weight"] = checkpoint["label_emb.weight"]

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

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

down_block_types = unet_config["down_block_types"]

layers_per_block = unet_config["layers_per_block"]

attention_head_dim = unet_config["attention_head_dim"]

channels_list = unet_config["block_out_channels"]

current_layer = 1

prev_channels = channels_list[0]

for i, layer_type in enumerate(down_block_types):

current_channels = channels_list[i]

downsample_block_has_skip = current_channels != prev_channels

if layer_type == "ResnetDownsampleBlock2D":

for j in range(layers_per_block):

new_prefix = f"down_blocks.{i}.resnets.{j}"

old_prefix = f"input_blocks.{current_layer}.0"

has_skip = True if j == 0 and downsample_block_has_skip else False

new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=has_skip)

current_layer += 1

elif layer_type == "AttnDownBlock2D":

for j in range(layers_per_block):

new_prefix = f"down_blocks.{i}.resnets.{j}"

old_prefix = f"input_blocks.{current_layer}.0"

has_skip = True if j == 0 and downsample_block_has_skip else False

new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=has_skip)

new_prefix = f"down_blocks.{i}.attentions.{j}"

old_prefix = f"input_blocks.{current_layer}.1"

new_checkpoint = convert_attention(

checkpoint, new_checkpoint, old_prefix, new_prefix, attention_head_dim

)

current_layer += 1

if i != len(down_block_types) - 1:

new_prefix = f"down_blocks.{i}.downsamplers.0"

old_prefix = f"input_blocks.{current_layer}.0"

new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix)

current_layer += 1

prev_channels = current_channels

# hardcoded the mid-block for now

new_prefix = "mid_block.resnets.0"

old_prefix = "middle_block.0"

new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix)

new_prefix = "mid_block.attentions.0"

old_prefix = "middle_block.1"

new_checkpoint = convert_attention(checkpoint, new_checkpoint, old_prefix, new_prefix, attention_head_dim)

new_prefix = "mid_block.resnets.1"

old_prefix = "middle_block.2"

new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix)

current_layer = 0

up_block_types = unet_config["up_block_types"]

for i, layer_type in enumerate(up_block_types):

if layer_type == "ResnetUpsampleBlock2D":

for j in range(layers_per_block + 1):

new_prefix = f"up_blocks.{i}.resnets.{j}"

old_prefix = f"output_blocks.{current_layer}.0"

new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=True)

current_layer += 1

if i != len(up_block_types) - 1:

new_prefix = f"up_blocks.{i}.upsamplers.0"

old_prefix = f"output_blocks.{current_layer-1}.1"

new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix)

elif layer_type == "AttnUpBlock2D":

for j in range(layers_per_block + 1):

new_prefix = f"up_blocks.{i}.resnets.{j}"

old_prefix = f"output_blocks.{current_layer}.0"

new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=True)

new_prefix = f"up_blocks.{i}.attentions.{j}"

old_prefix = f"output_blocks.{current_layer}.1"

new_checkpoint = convert_attention(

checkpoint, new_checkpoint, old_prefix, new_prefix, attention_head_dim

)

current_layer += 1

if i != len(up_block_types) - 1:

new_prefix = f"up_blocks.{i}.upsamplers.0"

old_prefix = f"output_blocks.{current_layer-1}.2"

new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix)

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

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

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

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

return new_checkpoint

if __name__ == "__main__":

parser = argparse.ArgumentParser()

parser.add_argument("--unet_path", default=None, type=str, required=True, help="Path to the unet.pt to convert.")

parser.add_argument(

"--dump_path", default=None, type=str, required=True, help="Path to output the converted UNet model."

)

parser.add_argument("--class_cond", default=True, type=str, help="Whether the model is class-conditional.")

args = parser.parse_args()

args.class_cond = str2bool(args.class_cond)

ckpt_name = os.path.basename(args.unet_path)

print(f"Checkpoint: {ckpt_name}")

# Get U-Net config

if "imagenet64" in ckpt_name:

unet_config = IMAGENET_64_UNET_CONFIG

elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):

unet_config = LSUN_256_UNET_CONFIG

elif "test" in ckpt_name:

unet_config = TEST_UNET_CONFIG

else:

raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.")

if not args.class_cond:

unet_config["num_class_embeds"] = None

converted_unet_ckpt = con_pt_to_diffuser(args.unet_path, unet_config)

image_unet = UNet2DModel(**unet_config)

image_unet.load_state_dict(converted_unet_ckpt)

# Get scheduler config

if "cd" in ckpt_name or "test" in ckpt_name:

scheduler_config = CD_SCHEDULER_CONFIG

elif "ct" in ckpt_name and "imagenet64" in ckpt_name:

scheduler_config = CT_IMAGENET_64_SCHEDULER_CONFIG

elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):

scheduler_config = CT_LSUN_256_SCHEDULER_CONFIG

else:

raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.")

cm_scheduler = CMStochasticIterativeScheduler(**scheduler_config)

consistency_model = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)

consistency_model.save_pretrained(args.dump_path)