InvokeAI/invokeai/backend/model_management/convert_ckpt_to_diffusers.py

# coding=utf-8
# Copyright 2022 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.
#
# Adapted for use as a module by Lincoln Stein <lstein@gmail.com>
# Original file at: https://github.com/huggingface/diffusers/blob/main/scripts/convert_ldm_original_checkpoint_to_diffusers.py
""" Conversion script for the LDM checkpoints. """

import re
import warnings
from pathlib import Path
from typing import Union
from packaging import version

import torch
from safetensors.torch import load_file

import invokeai.backend.util.logging as logger
from invokeai.app.services.config import InvokeAIAppConfig

from .model_manager import ModelManager
from picklescan.scanner import scan_file_path
from .models import BaseModelType, ModelVariantType

try:
    from omegaconf import OmegaConf
    from omegaconf.dictconfig import DictConfig
except ImportError:
    raise ImportError(
        "OmegaConf is required to convert the LDM checkpoints. Please install it with `pip install OmegaConf`."
    )

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    HeunDiscreteScheduler,
    LDMTextToImagePipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UniPCMultistepScheduler,
    StableDiffusionPipeline,
    UNet2DConditionModel,
)
from diffusers import logging as dlogging
from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import (
    LDMBertConfig,
    LDMBertModel,
)
from diffusers.pipelines.stable_diffusion.safety_checker import (
    StableDiffusionSafetyChecker,
)
from diffusers.utils import is_safetensors_available
import transformers
from transformers import (
    AutoFeatureExtractor,
    BertTokenizerFast,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
)

from ..stable_diffusion import StableDiffusionGeneratorPipeline

# TODO: redo in future
#CONVERT_MODEL_ROOT = InvokeAIAppConfig.get_config().models_path / "core" / "convert"
CONVERT_MODEL_ROOT = InvokeAIAppConfig.get_config().root_path / "models" / "core" / "convert"

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", "to_q.weight")
        new_item = new_item.replace("q.bias", "to_q.bias")

        new_item = new_item.replace("k.weight", "to_k.weight")
        new_item = new_item.replace("k.bias", "to_k.bias")

        new_item = new_item.replace("v.weight", "to_v.weight")
        new_item = new_item.replace("v.bias", "to_v.bias")

        new_item = new_item.replace("proj_out.weight", "to_out.0.weight")
        new_item = new_item.replace("proj_out.bias", "to_out.0.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,
    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."

    for path in paths:
        new_path = path["new"]

        # 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 = ["to_q.weight", "to_k.weight", "to_v.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 "to_out.0.weight" in key:
            if checkpoint[key].ndim > 2:
                checkpoint[key] = checkpoint[key][:, :, 0, 0]


def create_unet_diffusers_config(original_config, image_size: int):
    """
    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

    vae_scale_factor = 2 ** (len(vae_params.ch_mult) - 1)

    head_dim = unet_params.num_heads if "num_heads" in unet_params else None
    use_linear_projection = (
        unet_params.use_linear_in_transformer
        if "use_linear_in_transformer" in unet_params
        else False
    )
    if use_linear_projection:
        # stable diffusion 2-base-512 and 2-768
        if head_dim is None:
            head_dim = [5, 10, 20, 20]

    config = dict(
        sample_size=image_size // vae_scale_factor,
        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,
        cross_attention_dim=unet_params.context_dim,
        attention_head_dim=head_dim,
        use_linear_projection=use_linear_projection,
    )

    return config


def create_vae_diffusers_config(original_config, image_size: int):
    """
    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=image_size,
        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_schedular(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 create_ldm_bert_config(original_config):
    bert_params = original_config.model.params.cond_stage_config.params
    config = LDMBertConfig(
        d_model=bert_params.n_embed,
        encoder_layers=bert_params.n_layer,
        encoder_ffn_dim=bert_params.n_embed * 4,
    )
    return config


def convert_ldm_unet_checkpoint(checkpoint, config, path=None, 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())

    unet_key = "model.diffusion_model."
    # 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:
        logger.debug(f"Checkpoint {path} has both EMA and non-EMA weights.")
        if extract_ema:
            logger.debug("Extracting EMA weights (usually better for inference)")
            for key in keys:
                if key.startswith("model.diffusion_model"):
                    flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
                    flat_ema_key_alt = "model_ema." + "".join(key.split(".")[2:])
                    if flat_ema_key in checkpoint:
                        unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(
                            flat_ema_key
                        )
                    elif flat_ema_key_alt in checkpoint:
                        unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(
                            flat_ema_key_alt
                        )
                    else:
                        unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(
                            key
                        )
        else:
            logger.debug(
                "Extracting only the non-EMA weights (usually better for fine-tuning)"
            )

    for key in keys:
        if key.startswith("model.diffusion_model") and key in checkpoint:
            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. Works both with burnt-in
    # VAEs, and with standalone VAEs.

    # checkpoint can either be a all-in-one stable diffusion
    # model, or an isolated vae .ckpt. This tests for
    # a key that will be present in the all-in-one model
    # that isn't present in the isolated ckpt.
    probe_key = "first_stage_model.encoder.conv_in.weight"
    if probe_key in checkpoint:
        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)
    else:
        vae_state_dict = checkpoint
        
    new_checkpoint = convert_ldm_vae_state_dict(vae_state_dict, config)
    return new_checkpoint

def convert_ldm_vae_state_dict(vae_state_dict, config):
    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 convert_ldm_bert_checkpoint(checkpoint, config):
    def _copy_attn_layer(hf_attn_layer, pt_attn_layer):
        hf_attn_layer.q_proj.weight.data = pt_attn_layer.to_q.weight
        hf_attn_layer.k_proj.weight.data = pt_attn_layer.to_k.weight
        hf_attn_layer.v_proj.weight.data = pt_attn_layer.to_v.weight

        hf_attn_layer.out_proj.weight = pt_attn_layer.to_out.weight
        hf_attn_layer.out_proj.bias = pt_attn_layer.to_out.bias

    def _copy_linear(hf_linear, pt_linear):
        hf_linear.weight = pt_linear.weight
        hf_linear.bias = pt_linear.bias

    def _copy_layer(hf_layer, pt_layer):
        # copy layer norms
        _copy_linear(hf_layer.self_attn_layer_norm, pt_layer[0][0])
        _copy_linear(hf_layer.final_layer_norm, pt_layer[1][0])

        # copy attn
        _copy_attn_layer(hf_layer.self_attn, pt_layer[0][1])

        # copy MLP
        pt_mlp = pt_layer[1][1]
        _copy_linear(hf_layer.fc1, pt_mlp.net[0][0])
        _copy_linear(hf_layer.fc2, pt_mlp.net[2])

    def _copy_layers(hf_layers, pt_layers):
        for i, hf_layer in enumerate(hf_layers):
            if i != 0:
                i += i
            pt_layer = pt_layers[i : i + 2]
            _copy_layer(hf_layer, pt_layer)

    hf_model = LDMBertModel(config).eval()

    # copy  embeds
    hf_model.model.embed_tokens.weight = checkpoint.transformer.token_emb.weight
    hf_model.model.embed_positions.weight.data = (
        checkpoint.transformer.pos_emb.emb.weight
    )

    # copy layer norm
    _copy_linear(hf_model.model.layer_norm, checkpoint.transformer.norm)

    # copy hidden layers
    _copy_layers(hf_model.model.layers, checkpoint.transformer.attn_layers.layers)

    _copy_linear(hf_model.to_logits, checkpoint.transformer.to_logits)

    return hf_model


def convert_ldm_clip_checkpoint(checkpoint):
    text_model = CLIPTextModel.from_pretrained(CONVERT_MODEL_ROOT / 'clip-vit-large-patch14')
    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
            ]

    # transformers 4.31.0 and higher - this key no longer in state dict
    if version.parse(transformers.__version__) >= version.parse("4.31.0"):
        position_ids = text_model_dict.pop("text_model.embeddings.position_ids", None)
        text_model.load_state_dict(text_model_dict)
        if position_ids is not None:
            text_model.text_model.embeddings.position_ids.copy_(position_ids)

    # transformers 4.30.2 and lower - position_ids is part of state_dict
    else:
        text_model.load_state_dict(text_model_dict)        

    return text_model


textenc_conversion_lst = [
    (
        "cond_stage_model.model.positional_embedding",
        "text_model.embeddings.position_embedding.weight",
    ),
    (
        "cond_stage_model.model.token_embedding.weight",
        "text_model.embeddings.token_embedding.weight",
    ),
    ("cond_stage_model.model.ln_final.weight", "text_model.final_layer_norm.weight"),
    ("cond_stage_model.model.ln_final.bias", "text_model.final_layer_norm.bias"),
]
textenc_conversion_map = {x[0]: x[1] for x in textenc_conversion_lst}

textenc_transformer_conversion_lst = [
    # (stable-diffusion, HF Diffusers)
    ("resblocks.", "text_model.encoder.layers."),
    ("ln_1", "layer_norm1"),
    ("ln_2", "layer_norm2"),
    (".c_fc.", ".fc1."),
    (".c_proj.", ".fc2."),
    (".attn", ".self_attn"),
    ("ln_final.", "transformer.text_model.final_layer_norm."),
    (
        "token_embedding.weight",
        "transformer.text_model.embeddings.token_embedding.weight",
    ),
    (
        "positional_embedding",
        "transformer.text_model.embeddings.position_embedding.weight",
    ),
]
protected = {re.escape(x[0]): x[1] for x in textenc_transformer_conversion_lst}
textenc_pattern = re.compile("|".join(protected.keys()))


def convert_open_clip_checkpoint(checkpoint):
    text_model = CLIPTextModel.from_pretrained(
        CONVERT_MODEL_ROOT / 'stable-diffusion-2-clip',
        subfolder='text_encoder',
    )

    keys = list(checkpoint.keys())

    text_model_dict = {}

    if "cond_stage_model.model.text_projection" in keys:
        d_model = int(checkpoint["cond_stage_model.model.text_projection"].shape[0])
    elif "cond_stage_model.model.ln_final.bias" in keys:
        d_model = int(checkpoint["cond_stage_model.model.ln_final.bias"].shape[0])
    else:
        raise KeyError(
            'Expected key "cond_stage_model.model.text_projection" not found in model'
        )

    text_model_dict[
        "text_model.embeddings.position_ids"
    ] = text_model.text_model.embeddings.get_buffer("position_ids")

    for key in keys:
        if (
            "resblocks.23" in key
        ):  # Diffusers drops the final layer and only uses the penultimate layer
            continue
        if key in textenc_conversion_map:
            text_model_dict[textenc_conversion_map[key]] = checkpoint[key]
        if key.startswith("cond_stage_model.model.transformer."):
            new_key = key[len("cond_stage_model.model.transformer.") :]
            if new_key.endswith(".in_proj_weight"):
                new_key = new_key[: -len(".in_proj_weight")]
                new_key = textenc_pattern.sub(
                    lambda m: protected[re.escape(m.group(0))], new_key
                )
                text_model_dict[new_key + ".q_proj.weight"] = checkpoint[key][
                    :d_model, :
                ]
                text_model_dict[new_key + ".k_proj.weight"] = checkpoint[key][
                    d_model : d_model * 2, :
                ]
                text_model_dict[new_key + ".v_proj.weight"] = checkpoint[key][
                    d_model * 2 :, :
                ]
            elif new_key.endswith(".in_proj_bias"):
                new_key = new_key[: -len(".in_proj_bias")]
                new_key = textenc_pattern.sub(
                    lambda m: protected[re.escape(m.group(0))], new_key
                )
                text_model_dict[new_key + ".q_proj.bias"] = checkpoint[key][:d_model]
                text_model_dict[new_key + ".k_proj.bias"] = checkpoint[key][
                    d_model : d_model * 2
                ]
                text_model_dict[new_key + ".v_proj.bias"] = checkpoint[key][
                    d_model * 2 :
                ]
            else:
                new_key = textenc_pattern.sub(
                    lambda m: protected[re.escape(m.group(0))], new_key
                )

                text_model_dict[new_key] = checkpoint[key]

    # transformers 4.31.0 and higher - this key no longer in state dict
    if version.parse(transformers.__version__) >= version.parse("4.31.0"):
        position_ids = text_model_dict.pop("text_model.embeddings.position_ids", None)
        text_model.load_state_dict(text_model_dict)
        if position_ids is not None:
            text_model.text_model.embeddings.position_ids.copy_(position_ids)

    # transformers 4.30.2 and lower - position_ids is part of state_dict
    else:
        text_model.load_state_dict(text_model_dict)  

    return text_model

def replace_checkpoint_vae(checkpoint, vae_path: str):
    if vae_path.endswith(".safetensors"):
        vae_ckpt = load_file(vae_path)
    else:
        vae_ckpt = torch.load(vae_path, map_location="cpu")
    state_dict = vae_ckpt['state_dict'] if "state_dict" in vae_ckpt else vae_ckpt
    for vae_key in state_dict:
        new_key = f'first_stage_model.{vae_key}'
        checkpoint[new_key] = state_dict[vae_key]

def convert_ldm_vae_to_diffusers(checkpoint, vae_config: DictConfig, image_size: int) -> AutoencoderKL:
    vae_config = create_vae_diffusers_config(
        vae_config, image_size=image_size
    )

    converted_vae_checkpoint = convert_ldm_vae_checkpoint(
        checkpoint, vae_config
    )

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

def load_pipeline_from_original_stable_diffusion_ckpt(
    checkpoint_path: str,
    model_version: BaseModelType,
    model_variant: ModelVariantType,
    original_config_file: str,
    extract_ema: bool = True,
    precision: torch.dtype = torch.float32,
    scan_needed: bool = True,
) -> StableDiffusionPipeline:
    """
    Load a Stable Diffusion pipeline object from a CompVis-style `.ckpt`/`.safetensors` file and (ideally) a `.yaml`
    config file.

    Although many of the arguments can be automatically inferred, some of these rely on brittle checks against the
    global step count, which will likely fail for models that have undergone further fine-tuning. Therefore, it is
    recommended that you override the default values and/or supply an `original_config_file` wherever possible.

    :param checkpoint_path: Path to `.ckpt` file.
    :param original_config_file: Path to `.yaml` config file corresponding to the original architecture.
      If `None`, will be automatically inferred by looking for a key that only exists in SD2.0 models.
    :param scheduler_type: Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler",
     "euler-ancestral", "dpm", "ddim"]`. :param model_type: The pipeline type. `None` to automatically infer, or one of
     `["FrozenOpenCLIPEmbedder", "FrozenCLIPEmbedder"]`. :param extract_ema: Only relevant for
     checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights
     or not. Defaults to `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher
     quality images for inference. Non-EMA weights are usually better to continue fine-tuning.
    :param precision: precision to use - torch.float16, torch.float32 or torch.autocast
    """
    if not isinstance(checkpoint_path, Path):
        checkpoint_path = Path(checkpoint_path)

    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        verbosity = dlogging.get_verbosity()
        dlogging.set_verbosity_error()

        if checkpoint_path.suffix == ".safetensors":
            checkpoint = load_file(checkpoint_path)
        else:
            if scan_needed:
                # scan model
                scan_result = scan_file_path(checkpoint_path)
                if scan_result.infected_files != 0:
                    raise "The model {checkpoint_path} is potentially infected by malware. Aborting import."
            checkpoint = torch.load(checkpoint_path)

        # sometimes there is a state_dict key and sometimes not
        if "state_dict" in checkpoint:
            checkpoint = checkpoint["state_dict"]

        original_config = OmegaConf.load(original_config_file)

        if model_version == BaseModelType.StableDiffusion2 and original_config["model"]["params"]["parameterization"] == "v":
            prediction_type = "v_prediction"
            upcast_attention = True
            image_size = 768
        else:
            prediction_type = "epsilon"
            upcast_attention = False
            image_size = 512

        #
        # convert scheduler
        #

        num_train_timesteps = original_config.model.params.timesteps
        beta_start = original_config.model.params.linear_start
        beta_end = original_config.model.params.linear_end

        scheduler = PNDMScheduler(
            beta_end=beta_end,
            beta_schedule="scaled_linear",
            beta_start=beta_start,
            num_train_timesteps=num_train_timesteps,
            steps_offset=1,
            set_alpha_to_one=False,
            prediction_type=prediction_type,
            skip_prk_steps=True
        )
        # make sure scheduler works correctly with DDIM
        scheduler.register_to_config(clip_sample=False)

        #
        # convert unet
        #

        unet_config = create_unet_diffusers_config(
            original_config, image_size=image_size
        )
        unet_config["upcast_attention"] = upcast_attention
        unet = UNet2DConditionModel(**unet_config)

        converted_unet_checkpoint = convert_ldm_unet_checkpoint(
            checkpoint, unet_config, path=checkpoint_path, extract_ema=extract_ema
        )

        unet.load_state_dict(converted_unet_checkpoint)

        #
        # convert vae
        #

        vae = convert_ldm_vae_to_diffusers(
            checkpoint,
            original_config,
            image_size,
        )

        # Convert the text model.
        model_type = original_config.model.params.cond_stage_config.target.split(".")[-1]
        if model_type == "FrozenOpenCLIPEmbedder":
            text_model = convert_open_clip_checkpoint(checkpoint)
            tokenizer = CLIPTokenizer.from_pretrained(
                CONVERT_MODEL_ROOT / 'stable-diffusion-2-clip',
                subfolder='tokenizer',
            )
            pipe = StableDiffusionPipeline(
                vae=vae.to(precision),
                text_encoder=text_model.to(precision),
                tokenizer=tokenizer,
                unet=unet.to(precision),
                scheduler=scheduler,
                safety_checker=None,
                feature_extractor=None,
                requires_safety_checker=False,
            )

        elif model_type in ["FrozenCLIPEmbedder", "WeightedFrozenCLIPEmbedder"]:
            text_model = convert_ldm_clip_checkpoint(checkpoint)
            tokenizer = CLIPTokenizer.from_pretrained(CONVERT_MODEL_ROOT / 'clip-vit-large-patch14')
            safety_checker = StableDiffusionSafetyChecker.from_pretrained(CONVERT_MODEL_ROOT / 'stable-diffusion-safety-checker')
            feature_extractor = AutoFeatureExtractor.from_pretrained(CONVERT_MODEL_ROOT / 'stable-diffusion-safety-checker')
            pipe = StableDiffusionPipeline(
                vae=vae.to(precision),
                text_encoder=text_model.to(precision),
                tokenizer=tokenizer,
                unet=unet.to(precision),
                scheduler=scheduler,
                safety_checker=safety_checker.to(precision),
                feature_extractor=feature_extractor,
            )

        else:
            text_config = create_ldm_bert_config(original_config)
            text_model = convert_ldm_bert_checkpoint(checkpoint, text_config)
            tokenizer = BertTokenizerFast.from_pretrained(CONVERT_MODEL_ROOT / "bert-base-uncased")
            pipe = LDMTextToImagePipeline(
                vqvae=vae,
                bert=text_model,
                tokenizer=tokenizer,
                unet=unet,
                scheduler=scheduler,
            )
    dlogging.set_verbosity(verbosity)

    return pipe


def convert_ckpt_to_diffusers(
        checkpoint_path: Union[str, Path],
        dump_path: Union[str, Path],
        **kwargs,
):
    """
    Takes all the arguments of load_pipeline_from_original_stable_diffusion_ckpt(),
    and in addition a path-like object indicating the location of the desired diffusers
    model to be written.
    """
    pipe = load_pipeline_from_original_stable_diffusion_ckpt(checkpoint_path, **kwargs)

    pipe.save_pretrained(
        dump_path,
        safe_serialization=is_safetensors_available(),
    )