# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
# *Only* converts the UNet, VAE, and Text Encoder.
# Does not convert optimizer state or any other thing.

# Originally written by jachiam at https://gist.github.com/jachiam/8a5c0b607e38fcc585168b90c686eb05
# modified by 1lint to support controlnet conversion

import argparse
import torch
from safetensors import safe_open
from safetensors.torch import save_file
from pathlib import Path

# =================#
# UNet Conversion #
# =================#

unet_conversion_map = [
    # (stable-diffusion, HF Diffusers)
    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
    ("input_blocks.0.0.weight", "conv_in.weight"),
    ("input_blocks.0.0.bias", "conv_in.bias"),
    ("out.0.weight", "conv_norm_out.weight"),
    ("out.0.bias", "conv_norm_out.bias"),
    ("out.2.weight", "conv_out.weight"),
    ("out.2.bias", "conv_out.bias"),
]

unet_conversion_map_resnet = [
    # (stable-diffusion, HF Diffusers)
    ("in_layers.0", "norm1"),
    ("in_layers.2", "conv1"),
    ("out_layers.0", "norm2"),
    ("out_layers.3", "conv2"),
    ("emb_layers.1", "time_emb_proj"),
    ("skip_connection", "conv_shortcut"),
]

unet_conversion_map_layer = []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
    # loop over downblocks/upblocks

    for j in range(2):
        # loop over resnets/attentions for downblocks
        hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
        sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
        unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

        if i < 3:
            # no attention layers in down_blocks.3
            hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
            sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
            unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

    for j in range(3):
        # loop over resnets/attentions for upblocks
        hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
        sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
        unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

        if i > 0:
            # no attention layers in up_blocks.0
            hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
            sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."
            unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

    if i < 3:
        # no downsample in down_blocks.3
        hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
        sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
        unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

        # no upsample in up_blocks.3
        hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
        sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
        unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

hf_mid_atn_prefix = "mid_block.attentions.0."
sd_mid_atn_prefix = "middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
    hf_mid_res_prefix = f"mid_block.resnets.{j}."
    sd_mid_res_prefix = f"middle_block.{2*j}."
    unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))


def convert_unet_state_dict(unet_state_dict, is_controlnet=True):
    # buyer beware: this is a *brittle* function,
    # and correct output requires that all of these pieces interact in
    # the exact order in which I have arranged them.
    mapping = {k: k for k in unet_state_dict.keys()}

    conversion_map = unet_conversion_map
    if is_controlnet:
        # remove output blocks from conversion mapping since controlnet doesn't have them
        conversion_map = unet_conversion_map[:6]

        for k, v in mapping.items():
            # convert controlnet zero convolution keys
            if "controlnet_down_blocks" in v:
                new_key = v.replace("controlnet_down_blocks", "zero_convs")
                new_key = ".0.".join(new_key.rsplit(".", 1))
                mapping[k] = new_key

        mapping["controlnet_mid_block.bias"] = "middle_block_out.0.bias"
        mapping["controlnet_mid_block.weight"] = "middle_block_out.0.weight"

        if "controlnet_cond_embedding.conv_in.weight" in mapping:
            mapping[
                "controlnet_cond_embedding.conv_in.weight"
            ] = "input_hint_block.0.weight"
            mapping[
                "controlnet_cond_embedding.conv_in.bias"
            ] = "input_hint_block.0.bias"

            for i in range(6):
                mapping[
                    f"controlnet_cond_embedding.blocks.{i}.weight"
                ] = f"input_hint_block.{2*(i+1)}.weight"
                mapping[
                    f"controlnet_cond_embedding.blocks.{i}.bias"
                ] = f"input_hint_block.{2*(i+1)}.bias"

            mapping[
                "controlnet_cond_embedding.conv_out.weight"
            ] = "input_hint_block.14.weight"
            mapping[
                "controlnet_cond_embedding.conv_out.bias"
            ] = "input_hint_block.14.bias"

    for sd_name, hf_name in conversion_map:
        mapping[hf_name] = sd_name
    for k, v in mapping.items():
        if "resnets" in k:
            for sd_part, hf_part in unet_conversion_map_resnet:
                v = v.replace(hf_part, sd_part)
            mapping[k] = v
    for k, v in mapping.items():
        for sd_part, hf_part in unet_conversion_map_layer:
            v = v.replace(hf_part, sd_part)
        mapping[k] = v

    new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}
    return new_state_dict


def load_state_dict(state_dict_path):
    file_ext = state_dict_path.rsplit(".", 1)[-1]

    if file_ext == "safetensors":
        state_dict = {}
        with safe_open(state_dict_path, framework="pt", device="cpu") as f:
            for key in f.keys():
                state_dict[key] = f.get_tensor(key)
    else:
        state_dict = torch.load(state_dict_path, map_location="cpu")

    return state_dict


if __name__ == "__main__":
    parser = argparse.ArgumentParser()

    parser.add_argument(
        "--model_path",
        default=None,
        type=str,
        required=True,
        help="Path to the model to convert.",
    )
    parser.add_argument(
        "--checkpoint_path",
        default=None,
        type=str,
        required=True,
        help="Path to the output model.",
    )
    parser.add_argument(
        "--half", action="store_true", help="Save weights in half precision."
    )
    parser.add_argument(
        "--is_controlnet",
        action="store_true",
        help="Whether conversion is for controlnet or standard sd unet",
    )
    parser.add_argument(
        "--to_safetensors",
        action="store_true",
        help="Whether to save state dict in safetensors format",
    )

    args = parser.parse_args()

    assert args.model_path is not None, "Must provide a model path!"

    assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

    unet_state_dict = load_state_dict(args.model_path)

    # Convert the UNet model
    unet_state_dict = convert_unet_state_dict(
        unet_state_dict, is_controlnet=args.is_controlnet
    )

    if args.half:
        unet_state_dict = {k: v.half() for k, v in unet_state_dict.items()}

    Path(args.checkpoint_path).parent.mkdir(parents=True, exist_ok=True)

    if args.to_safetensors:
        save_file(unet_state_dict, args.checkpoint_path)
    else:
        torch.save(unet_state_dict, args.checkpoint_path)

    print(
        f"Converted {Path(args.model_path)} to original SD format at {Path(args.checkpoint_path)}"
    )