convertodiffv2.py

import argparse
import os
import torch
from transformers import CLIPTextModel, CLIPTokenizer, CLIPTextConfig
from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNet2DConditionModel


# DiffUsers版StableDiffusionのモデルパラメータ
NUM_TRAIN_TIMESTEPS = 1000
BETA_START = 0.00085
BETA_END = 0.0120

UNET_PARAMS_MODEL_CHANNELS = 320
UNET_PARAMS_CHANNEL_MULT = [1, 2, 4, 4]
UNET_PARAMS_ATTENTION_RESOLUTIONS = [4, 2, 1]
UNET_PARAMS_IMAGE_SIZE = 64
UNET_PARAMS_IN_CHANNELS = 4
UNET_PARAMS_OUT_CHANNELS = 4
UNET_PARAMS_NUM_RES_BLOCKS = 2
UNET_PARAMS_CONTEXT_DIM = 768
UNET_PARAMS_NUM_HEADS = 8

VAE_PARAMS_Z_CHANNELS = 4
VAE_PARAMS_RESOLUTION = 768
VAE_PARAMS_IN_CHANNELS = 3
VAE_PARAMS_OUT_CH = 3
VAE_PARAMS_CH = 128
VAE_PARAMS_CH_MULT = [1, 2, 4, 4]
VAE_PARAMS_NUM_RES_BLOCKS = 2

# V2
V2_UNET_PARAMS_ATTENTION_HEAD_DIM = [5, 10, 20, 20]
V2_UNET_PARAMS_CONTEXT_DIM = 1024


# region StableDiffusion->Diffusersの変換コード
# convert_original_stable_diffusion_to_diffusers をコピーしている（ASL 2.0）


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]
    else:
      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 linear_transformer_to_conv(checkpoint):
  keys = list(checkpoint.keys())
  tf_keys = ["proj_in.weight", "proj_out.weight"]
  for key in keys:
    if ".".join(key.split(".")[-2:]) in tf_keys:
      if checkpoint[key].ndim == 2:
        checkpoint[key] = checkpoint[key].unsqueeze(2).unsqueeze(2)


def convert_ldm_unet_checkpoint(checkpoint, config):
    """
    Takes a state dict and a config, and returns a converted checkpoint.
    """

    # extract state_dict for UNet
    unet_state_dict = {}
    keys = list(checkpoint.keys())

    unet_key = "model.diffusion_model."

    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"] = unet_state_dict["time_embed.0.weight"]
    new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"]
    new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"]
    new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["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"
            )

        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]

            resnet_0_paths = renew_resnet_paths(resnets)
            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
            )

            output_block_list = {k: sorted(v) for k, v in output_block_list.items()}
            if ["conv.bias", "conv.weight"] in output_block_list.values():
                index = list(output_block_list.values()).index(["conv.bias", "conv.weight"])
                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 = []

            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_ldm_vae_checkpoint(checkpoint, config):
  # extract state dict for VAE
  vae_state_dict = {}
  vae_key = "first_stage_model."
  keys = list(checkpoint.keys())
  for key in keys:
    if key.startswith(vae_key):
      vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key)
  # if len(vae_state_dict) == 0:
  #   # 渡されたcheckpointは.ckptから読み込んだcheckpointではなくvaeのstate_dict
  #   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 create_unet_diffusers_config():
    """
    Creates a config for the diffusers based on the config of the LDM model.
    """
    #unet_params = original_config.model.params.unet_config.params

    #vae_params = original_config.model.params.first_stage_config.params.ddconfig

    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 resolution in UNET_PARAMS_ATTENTION_RESOLUTIONS 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 resolution in UNET_PARAMS_ATTENTION_RESOLUTIONS else "UpBlock2D"
        up_block_types.append(block_type)
        resolution //= 2

    class_embed_type = None
    projection_class_embeddings_input_dim = None

    config = dict(
        sample_size=UNET_PARAMS_IMAGE_SIZE,
        in_channels=UNET_PARAMS_IN_CHANNELS,
        out_channels=UNET_PARAMS_OUT_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_RES_BLOCKS,
        use_linear_projection=True,
        cross_attention_dim=V2_UNET_PARAMS_CONTEXT_DIM,
        attention_head_dim=V2_UNET_PARAMS_ATTENTION_HEAD_DIM,
      )

    return config


def create_vae_diffusers_config():
  """
  Creates a config for the diffusers based on the config of the LDM model.
  """
  # vae_params = original_config.model.params.first_stage_config.params.ddconfig
  # _ = original_config.model.params.first_stage_config.params.embed_dim
  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 = dict(
      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 convert_ldm_clip_checkpoint_v1(checkpoint):
  keys = list(checkpoint.keys())
  text_model_dict = {}
  for key in keys:
    if key.startswith("cond_stage_model.transformer"):
      text_model_dict[key[len("cond_stage_model.transformer."):]] = checkpoint[key]
  return text_model_dict


def convert_ldm_clip_checkpoint_v2(checkpoint, max_length):
  # 嫌になるくらい違うぞ！
  def convert_key(key):
    if not key.startswith("cond_stage_model"):
      return None

    # common conversion
    key = key.replace("cond_stage_model.model.transformer.", "text_model.encoder.")
    key = key.replace("cond_stage_model.model.", "text_model.")

    if "resblocks" in key:
      # resblocks conversion
      key = key.replace(".resblocks.", ".layers.")
      if ".ln_" in key:
        key = key.replace(".ln_", ".layer_norm")
      elif ".mlp." in key:
        key = key.replace(".c_fc.", ".fc1.")
        key = key.replace(".c_proj.", ".fc2.")
      elif '.attn.out_proj' in key:
        key = key.replace(".attn.out_proj.", ".self_attn.out_proj.")
      elif '.attn.in_proj' in key:
        key = None                  # 特殊なので後で処理する
      else:
        raise ValueError(f"unexpected key in SD: {key}")
    elif '.positional_embedding' in key:
      key = key.replace(".positional_embedding", ".embeddings.position_embedding.weight")
    elif '.text_projection' in key:
      key = None    # 使われない???
    elif '.logit_scale' in key:
      key = None    # 使われない???
    elif '.token_embedding' in key:
      key = key.replace(".token_embedding.weight", ".embeddings.token_embedding.weight")
    elif '.ln_final' in key:
      key = key.replace(".ln_final", ".final_layer_norm")
    return key

  keys = list(checkpoint.keys())
  new_sd = {}
  for key in keys:
    # remove resblocks 23
    if '.resblocks.23.' in key:
      continue
    new_key = convert_key(key)
    if new_key is None:
      continue
    new_sd[new_key] = checkpoint[key]

  # attnの変換
  for key in keys:
    if '.resblocks.23.' in key:
      continue
    if '.resblocks' in key and '.attn.in_proj_' in key:
      # 三つに分割
      values = torch.chunk(checkpoint[key], 3)

      key_suffix = ".weight" if "weight" in key else ".bias"
      key_pfx = key.replace("cond_stage_model.model.transformer.resblocks.", "text_model.encoder.layers.")
      key_pfx = key_pfx.replace("_weight", "")
      key_pfx = key_pfx.replace("_bias", "")
      key_pfx = key_pfx.replace(".attn.in_proj", ".self_attn.")
      new_sd[key_pfx + "q_proj" + key_suffix] = values[0]
      new_sd[key_pfx + "k_proj" + key_suffix] = values[1]
      new_sd[key_pfx + "v_proj" + key_suffix] = values[2]

  # position_idsの追加
  new_sd["text_model.embeddings.position_ids"] = torch.Tensor([list(range(max_length))]).to(torch.int64)
  return new_sd

# endregion


# region Diffusers->StableDiffusion の変換コード
# convert_diffusers_to_original_stable_diffusion をコピーしている（ASL 2.0）

def conv_transformer_to_linear(checkpoint):
  keys = list(checkpoint.keys())
  tf_keys = ["proj_in.weight", "proj_out.weight"]
  for key in keys:
    if ".".join(key.split(".")[-2:]) in tf_keys:
      if checkpoint[key].ndim > 2:
        checkpoint[key] = checkpoint[key][:, :, 0, 0]


def convert_unet_state_dict_to_sd(v2, unet_state_dict):
  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 = []
  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))

  # 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()}
  for sd_name, hf_name in unet_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()}

  if v2:
    conv_transformer_to_linear(new_state_dict)

  return new_state_dict


# ================#
# VAE Conversion #
# ================#

def reshape_weight_for_sd(w):
    # convert HF linear weights to SD conv2d weights
  return w.reshape(*w.shape, 1, 1)


def convert_vae_state_dict(vae_state_dict):
  vae_conversion_map = [
      # (stable-diffusion, HF Diffusers)
      ("nin_shortcut", "conv_shortcut"),
      ("norm_out", "conv_norm_out"),
      ("mid.attn_1.", "mid_block.attentions.0."),
  ]

  for i in range(4):
    # down_blocks have two resnets
    for j in range(2):
      hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}."
      sd_down_prefix = f"encoder.down.{i}.block.{j}."
      vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

    if i < 3:
      hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0."
      sd_downsample_prefix = f"down.{i}.downsample."
      vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

      hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
      sd_upsample_prefix = f"up.{3-i}.upsample."
      vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

    # up_blocks have three resnets
    # also, up blocks in hf are numbered in reverse from sd
    for j in range(3):
      hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
      sd_up_prefix = f"decoder.up.{3-i}.block.{j}."
      vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

  # this part accounts for mid blocks in both the encoder and the decoder
  for i in range(2):
    hf_mid_res_prefix = f"mid_block.resnets.{i}."
    sd_mid_res_prefix = f"mid.block_{i+1}."
    vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))

  vae_conversion_map_attn = [
      # (stable-diffusion, HF Diffusers)
      ("norm.", "group_norm."),
      ("q.", "query."),
      ("k.", "key."),
      ("v.", "value."),
      ("proj_out.", "proj_attn."),
  ]

  mapping = {k: k for k in vae_state_dict.keys()}
  for k, v in mapping.items():
    for sd_part, hf_part in vae_conversion_map:
      v = v.replace(hf_part, sd_part)
    mapping[k] = v
  for k, v in mapping.items():
    if "attentions" in k:
      for sd_part, hf_part in vae_conversion_map_attn:
        v = v.replace(hf_part, sd_part)
      mapping[k] = v
  new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}
  weights_to_convert = ["q", "k", "v", "proj_out"]

  for k, v in new_state_dict.items():
    for weight_name in weights_to_convert:
      if f"mid.attn_1.{weight_name}.weight" in k:
        new_state_dict[k] = reshape_weight_for_sd(v)

  return new_state_dict


# endregion


def load_checkpoint_with_text_encoder_conversion(ckpt_path):
  # text encoderの格納形式が違うモデルに対応する ('text_model'がない)
  TEXT_ENCODER_KEY_REPLACEMENTS = [
      ('cond_stage_model.transformer.embeddings.', 'cond_stage_model.transformer.text_model.embeddings.'),
      ('cond_stage_model.transformer.encoder.', 'cond_stage_model.transformer.text_model.encoder.'),
      ('cond_stage_model.transformer.final_layer_norm.', 'cond_stage_model.transformer.text_model.final_layer_norm.')
  ]
  
  if args.from_safetensors:
      from safetensors import safe_open

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

  #while "state_dict" in checkpoint:
  #    checkpoint = checkpoint["state_dict"]
  #else:
  #  state_dict = checkpoint

  key_reps = []
  for rep_from, rep_to in TEXT_ENCODER_KEY_REPLACEMENTS:
    for key in state_dict.keys():
      if key.startswith(rep_from):
        new_key = rep_to + key[len(rep_from):]
        key_reps.append((key, new_key))

  for key, new_key in key_reps:
    state_dict[new_key] = state_dict[key]
    del state_dict[key]

  return checkpoint


# TODO dtype指定の動作が怪しいので確認する text_encoderを指定形式で作れるか未確認
def load_models_from_stable_diffusion_checkpoint(v2, ckpt_path, dtype=None):
  
  checkpoint = load_checkpoint_with_text_encoder_conversion(ckpt_path)

  while "state_dict" in checkpoint:
      checkpoint = checkpoint["state_dict"]
  else:
    state_dict = checkpoint

  if dtype is not None:
    for k, v in state_dict.items():
      if type(v) is torch.Tensor:
        state_dict[k] = v.to(dtype)

  # Convert the UNet2DConditionModel model.
  unet_config = create_unet_diffusers_config()
  converted_unet_checkpoint = convert_ldm_unet_checkpoint(state_dict, unet_config)

  unet = UNet2DConditionModel(**unet_config)
  info = unet.load_state_dict(converted_unet_checkpoint)


  # Convert the VAE model.
  vae_config = create_vae_diffusers_config()
  converted_vae_checkpoint = convert_ldm_vae_checkpoint(state_dict, vae_config)

  vae = AutoencoderKL(**vae_config)
  info = vae.load_state_dict(converted_vae_checkpoint)


  # convert text_model
  if v2:
    converted_text_encoder_checkpoint = convert_ldm_clip_checkpoint_v2(state_dict, 77)
    cfg = CLIPTextConfig(
        vocab_size=49408,
        hidden_size=1024,
        intermediate_size=4096,
        num_hidden_layers=23,
        num_attention_heads=16,
        max_position_embeddings=77,
        hidden_act="gelu",
        layer_norm_eps=1e-05,
        dropout=0.0,
        attention_dropout=0.0,
        initializer_range=0.02,
        initializer_factor=1.0,
        pad_token_id=1,
        bos_token_id=0,
        eos_token_id=2,
        model_type="clip_text_model",
        projection_dim=512,
        torch_dtype="float32",
        transformers_version="4.25.0.dev0",
    )
    text_model = CLIPTextModel._from_config(cfg)
    info = text_model.load_state_dict(converted_text_encoder_checkpoint)
  else:
    converted_text_encoder_checkpoint = convert_ldm_clip_checkpoint_v1(state_dict)
    text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14")
    info = text_model.load_state_dict(converted_text_encoder_checkpoint)


  return text_model, vae, unet


def convert_text_encoder_state_dict_to_sd_v2(checkpoint, make_dummy_weights=False):
  def convert_key(key):
    # position_idsの除去
    if ".position_ids" in key:
      return None

    # common
    key = key.replace("text_model.encoder.", "transformer.")
    key = key.replace("text_model.", "")
    if "layers" in key:
      # resblocks conversion
      key = key.replace(".layers.", ".resblocks.")
      if ".layer_norm" in key:
        key = key.replace(".layer_norm", ".ln_")
      elif ".mlp." in key:
        key = key.replace(".fc1.", ".c_fc.")
        key = key.replace(".fc2.", ".c_proj.")
      elif '.self_attn.out_proj' in key:
        key = key.replace(".self_attn.out_proj.", ".attn.out_proj.")
      elif '.self_attn.' in key:
        key = None                  # 特殊なので後で処理する
      else:
        raise ValueError(f"unexpected key in DiffUsers model: {key}")
    elif '.position_embedding' in key:
      key = key.replace("embeddings.position_embedding.weight", "positional_embedding")
    elif '.token_embedding' in key:
      key = key.replace("embeddings.token_embedding.weight", "token_embedding.weight")
    elif 'final_layer_norm' in key:
      key = key.replace("final_layer_norm", "ln_final")
    return key

  keys = list(checkpoint.keys())
  new_sd = {}
  for key in keys:
    new_key = convert_key(key)
    if new_key is None:
      continue
    new_sd[new_key] = checkpoint[key]

  # attnの変換
  for key in keys:
    if 'layers' in key and 'q_proj' in key:
      # 三つを結合
      key_q = key
      key_k = key.replace("q_proj", "k_proj")
      key_v = key.replace("q_proj", "v_proj")

      value_q = checkpoint[key_q]
      value_k = checkpoint[key_k]
      value_v = checkpoint[key_v]
      value = torch.cat([value_q, value_k, value_v])

      new_key = key.replace("text_model.encoder.layers.", "transformer.resblocks.")
      new_key = new_key.replace(".self_attn.q_proj.", ".attn.in_proj_")
      new_sd[new_key] = value

  # 最後の層などを捏造するか
  if make_dummy_weights:

    keys = list(new_sd.keys())
    for key in keys:
      if key.startswith("transformer.resblocks.22."):
        new_sd[key.replace(".22.", ".23.")] = new_sd[key]

    # Diffusersに含まれない重みを作っておく
    new_sd['text_projection'] = torch.ones((1024, 1024), dtype=new_sd[keys[0]].dtype, device=new_sd[keys[0]].device)
    new_sd['logit_scale'] = torch.tensor(1)

  return new_sd


def save_stable_diffusion_checkpoint(v2, output_file, text_encoder, unet, ckpt_path, epochs, steps, save_dtype=None, vae=None):
  if ckpt_path is not None:
    # epoch/stepを参照する。またVAEがメモリ上にないときなど、もう一度VAEを含めて読み込む
    checkpoint = load_checkpoint_with_text_encoder_conversion(ckpt_path)
    state_dict = checkpoint["state_dict"]
    strict = True
  else:
    # 新しく作る
    checkpoint = {}
    state_dict = {}
    strict = False

  def update_sd(prefix, sd):
    for k, v in sd.items():
      key = prefix + k
      assert not strict or key in state_dict, f"Illegal key in save SD: {key}"
      if save_dtype is not None:
        v = v.detach().clone().to("cpu").to(save_dtype)
      state_dict[key] = v

  # Convert the UNet model
  unet_state_dict = convert_unet_state_dict_to_sd(v2, unet.state_dict())
  update_sd("model.diffusion_model.", unet_state_dict)

  # Convert the text encoder model
  if v2:
    make_dummy = ckpt_path is None                 # 参照元のcheckpointがない場合は最後の層を前の層から複製して作るなどダミーの重みを入れる
    text_enc_dict = convert_text_encoder_state_dict_to_sd_v2(text_encoder.state_dict(), make_dummy)
    update_sd("cond_stage_model.model.", text_enc_dict)
  else:
    text_enc_dict = text_encoder.state_dict()
    update_sd("cond_stage_model.transformer.", text_enc_dict)

  # Convert the VAE
  if vae is not None:
    vae_dict = convert_vae_state_dict(vae.state_dict())
    update_sd("first_stage_model.", vae_dict)

  # Put together new checkpoint
  key_count = len(state_dict.keys())
  new_ckpt = {'state_dict': state_dict}

  if 'epoch' in checkpoint:
    epochs += checkpoint['epoch']
  if 'global_step' in checkpoint:
    steps += checkpoint['global_step']

  new_ckpt['epoch'] = epochs
  new_ckpt['global_step'] = steps

  torch.save(new_ckpt, output_file)

  return key_count


def save_diffusers_checkpoint(v2, output_dir, text_encoder, unet, pretrained_model_name_or_path, vae=None):
  if vae is None:
    vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder="vae")
  pipeline = StableDiffusionPipeline(
      unet=unet,
      text_encoder=text_encoder,
      vae=vae,
      scheduler=DDIMScheduler.from_pretrained(pretrained_model_name_or_path, subfolder="scheduler"),
      tokenizer=CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="tokenizer"),
      safety_checker=None,
      feature_extractor=None,
  )
  pipeline.save_pretrained(output_dir)



def convert(args):
  print("Converting to Diffusers ...")
  load_dtype = torch.float16 if args.fp16 else None

  save_dtype = None
  if args.fp16:
    save_dtype = torch.float16
  elif args.bf16:
    save_dtype = torch.bfloat16
  elif args.float:
    save_dtype = torch.float

  is_load_ckpt = os.path.isfile(args.model_to_load)
  is_save_ckpt = len(os.path.splitext(args.model_to_save)[1]) > 0

  assert not is_load_ckpt or args.v1 != args.v2, f"v1 or v2 is required to load checkpoint / checkpointの読み込みにはv1/v2指定が必要です"
  assert is_save_ckpt or args.reference_model is not None, f"reference model is required to save as Diffusers / Diffusers形式での保存には参照モデルが必要です"

  # モデルを読み込む
  msg = "checkpoint" if is_load_ckpt else ("Diffusers" + (" as fp16" if args.fp16 else ""))


  if is_load_ckpt:
    v2_model = args.v2
    text_encoder, vae, unet = load_models_from_stable_diffusion_checkpoint(v2_model, args.model_to_load)
  else:
    pipe = StableDiffusionPipeline.from_pretrained(args.model_to_load, torch_dtype=load_dtype, tokenizer=None, safety_checker=None)
    text_encoder = pipe.text_encoder
    vae = pipe.vae
    unet = pipe.unet

    if args.v1 == args.v2:
      # 自動判定する
      v2_model = unet.config.cross_attention_dim == 1024
      #print("checking model version: model is " + ('v2' if v2_model else 'v1'))
    else:
      v2_model = args.v1

  # 変換して保存する
  msg = ("checkpoint" + ("" if save_dtype is None else f" in {save_dtype}")) if is_save_ckpt else "Diffusers"
  

  if is_save_ckpt:
    original_model = args.model_to_load if is_load_ckpt else None
    key_count = save_stable_diffusion_checkpoint(v2_model, args.model_to_save, text_encoder, unet,
                                                            original_model, args.epoch, args.global_step, save_dtype, vae)
    
  else:    
    save_diffusers_checkpoint(v2_model, args.model_to_save, text_encoder, unet, args.reference_model, vae)
    


if __name__ == '__main__':
  parser = argparse.ArgumentParser()
  parser.add_argument("--v1", action='store_true',
                      help='load v1.x model (v1 or v2 is required to load checkpoint) / 1.xのモデルを読み込む')
  parser.add_argument("--v2", action='store_true',
                      help='load v2.0 model (v1 or v2 is required to load checkpoint) / 2.0のモデルを読み込む')
  parser.add_argument("--fp16", action='store_true',
                      help='load as fp16 (Diffusers only) and save as fp16 (checkpoint only) / fp16形式で読み込み（Diffusers形式のみ対応）、保存する（checkpointのみ対応）')
  parser.add_argument("--bf16", action='store_true', help='save as bf16 (checkpoint only) / bf16形式で保存する（checkpointのみ対応）')
  parser.add_argument("--float", action='store_true',
                      help='save as float (checkpoint only) / float(float32)形式で保存する（checkpointのみ対応）')
  parser.add_argument("--epoch", type=int, default=0, help='epoch to write to checkpoint / checkpointに記録するepoch数の値')
  parser.add_argument("--global_step", type=int, default=0,
                      help='global_step to write to checkpoint / checkpointに記録するglobal_stepの値')
  parser.add_argument("--reference_model", type=str, default=None,
                      help="reference model for schduler/tokenizer, required in saving Diffusers, copy schduler/tokenizer from this / scheduler/tokenizerのコピー元のDiffusersモデル、Diffusers形式で保存するときに必要")

  parser.add_argument("model_to_load", type=str, default=None,
                      help="model to load: checkpoint file or Diffusers model's directory / 読み込むモデル、checkpointかDiffusers形式モデルのディレクトリ")
  parser.add_argument("model_to_save", type=str, default=None,
                      help="model to save: checkpoint (with extension) or Diffusers model's directory (without extension) / 変換後のモデル、拡張子がある場合はcheckpoint、ない場合はDiffusesモデルとして保存")
  parser.add_argument(
      "--from_safetensors",
      action="store_true",
      help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
  )
  args = parser.parse_args()
  convert(args)