InvokeAI/ldm/invoke/ckpt_to_diffuser.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 os
import re
import torch
import warnings
from pathlib import Path
from ldm.invoke.globals import (
    Globals,
    global_cache_dir,
    global_config_dir,
    )
from safetensors.torch import load_file
from typing import Union

try:
    from omegaconf import OmegaConf
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,
    StableDiffusionPipeline,
    UNet2DConditionModel,
    logging as dlogging,
)
from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder, PaintByExamplePipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import is_safetensors_available
from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextModel, CLIPTokenizer, CLIPVisionConfig

from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline

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 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:
        print(f"  | Checkpoint {path} has both EMA and non-EMA weights.")
        if extract_ema:
            print(
                '  | 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:])
                    unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key)
        else:
            print(
                '  | Extracting only the non-EMA weights (usually better for fine-tuning)'
            )

    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)

    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("openai/clip-vit-large-patch14",cache_dir=global_cache_dir('hub'))

    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]

    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_paint_by_example_checkpoint(checkpoint):
    cache_dir = global_cache_dir('hub')
    config = CLIPVisionConfig.from_pretrained("openai/clip-vit-large-patch14",cache_dir=cache_dir)
    model = PaintByExampleImageEncoder(config)

    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]

    # load clip vision
    model.model.load_state_dict(text_model_dict)

    # load mapper
    keys_mapper = {
        k[len("cond_stage_model.mapper.res") :]: v
        for k, v in checkpoint.items()
        if k.startswith("cond_stage_model.mapper")
    }

    MAPPING = {
        "attn.c_qkv": ["attn1.to_q", "attn1.to_k", "attn1.to_v"],
        "attn.c_proj": ["attn1.to_out.0"],
        "ln_1": ["norm1"],
        "ln_2": ["norm3"],
        "mlp.c_fc": ["ff.net.0.proj"],
        "mlp.c_proj": ["ff.net.2"],
    }

    mapped_weights = {}
    for key, value in keys_mapper.items():
        prefix = key[: len("blocks.i")]
        suffix = key.split(prefix)[-1].split(".")[-1]
        name = key.split(prefix)[-1].split(suffix)[0][1:-1]
        mapped_names = MAPPING[name]

        num_splits = len(mapped_names)
        for i, mapped_name in enumerate(mapped_names):
            new_name = ".".join([prefix, mapped_name, suffix])
            shape = value.shape[0] // num_splits
            mapped_weights[new_name] = value[i * shape : (i + 1) * shape]

    model.mapper.load_state_dict(mapped_weights)

    # load final layer norm
    model.final_layer_norm.load_state_dict(
        {
            "bias": checkpoint["cond_stage_model.final_ln.bias"],
            "weight": checkpoint["cond_stage_model.final_ln.weight"],
        }
    )

    # load final proj
    model.proj_out.load_state_dict(
        {
            "bias": checkpoint["proj_out.bias"],
            "weight": checkpoint["proj_out.weight"],
        }
    )

    # load uncond vector
    model.uncond_vector.data = torch.nn.Parameter(checkpoint["learnable_vector"])
    return model


def convert_open_clip_checkpoint(checkpoint):
    cache_dir=global_cache_dir('hub')
    text_model = CLIPTextModel.from_pretrained("stabilityai/stable-diffusion-2", subfolder="text_encoder", cache_dir=cache_dir)

    keys = list(checkpoint.keys())

    text_model_dict = {}

    d_model = int(checkpoint["cond_stage_model.model.text_projection"].shape[0])

    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]

    text_model.load_state_dict(text_model_dict)

    return text_model

def load_pipeline_from_original_stable_diffusion_ckpt(
        checkpoint_path:str,
        original_config_file:str=None,
        num_in_channels:int=None,
        scheduler_type:str='pndm',
        pipeline_type:str=None,
        image_size:int=None,
        prediction_type:str=None,
        extract_ema:bool=True,
        upcast_attn:bool=False,
        vae:AutoencoderKL=None,
        return_generator_pipeline:bool=False,
)->Union[StableDiffusionPipeline,StableDiffusionGeneratorPipeline]:
    '''
    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 image_size: The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Diffusion v2
      Base. Use 768 for Stable Diffusion v2.
    :param prediction_type: The prediction type that the model was trained on. Use `'epsilon'` for Stable Diffusion
     v1.X and Stable Diffusion v2 Base. Use `'v-prediction'` for Stable Diffusion v2.
    :param num_in_channels: The number of input channels. If `None` number of input channels will be automatically
    inferred. 
    :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", "PaintByExample"]`. :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 upcast_attention: Whether the attention computation should always be upcasted. This is necessary when
    running stable diffusion 2.1.
    '''

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

        checkpoint = load_file(checkpoint_path) if Path(checkpoint_path).suffix == '.safetensors' else torch.load(checkpoint_path)
        cache_dir = global_cache_dir('hub')
        pipeline_class = StableDiffusionGeneratorPipeline if return_generator_pipeline else StableDiffusionPipeline

        # Sometimes models don't have the global_step item
        if "global_step" in checkpoint:
            global_step = checkpoint["global_step"]
        else:
            print("  | global_step key not found in model")
            global_step = None

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

        upcast_attention = False
        if original_config_file is None:
            key_name = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"

            if key_name in checkpoint and checkpoint[key_name].shape[-1] == 1024:
                original_config_file = global_config_dir() / 'stable-diffusion' / 'v2-inference-v.yaml'

                if global_step == 110000:
                    # v2.1 needs to upcast attention
                    upcast_attention = True
            elif str(checkpoint_path).lower().find('inpaint') >= 0: # brittle - please pass original_config_file parameter!
                print(f'  | checkpoint has "inpaint" in name, assuming an inpainting model')
                original_config_file = global_config_dir() / 'stable-diffusion' / 'v1-inpainting-inference.yaml'
            else:
                original_config_file = global_config_dir() / 'stable-diffusion' / 'v1-inference.yaml'

        original_config = OmegaConf.load(original_config_file)

        if num_in_channels is not None:
            original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels

        if (
            "parameterization" in original_config["model"]["params"]
            and original_config["model"]["params"]["parameterization"] == "v"
        ):
            if prediction_type is None:
                # NOTE: For stable diffusion 2 base it is recommended to pass `prediction_type=="epsilon"`
                # as it relies on a brittle global step parameter here
                prediction_type = "epsilon" if global_step == 875000 else "v_prediction"
            if image_size is None:
                # NOTE: For stable diffusion 2 base one has to pass `image_size==512`
                # as it relies on a brittle global step parameter here
                image_size = 512 if global_step == 875000 else 768
        else:
            if prediction_type is None:
                prediction_type = "epsilon"
            if image_size is None:
                image_size = 512

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

        if scheduler_type == "pndm":
            config = dict(scheduler.config)
            config["skip_prk_steps"] = True
            scheduler = PNDMScheduler.from_config(config)
        elif scheduler_type == "lms":
            scheduler = LMSDiscreteScheduler.from_config(scheduler.config)
        elif scheduler_type == "heun":
            scheduler = HeunDiscreteScheduler.from_config(scheduler.config)
        elif scheduler_type == "euler":
            scheduler = EulerDiscreteScheduler.from_config(scheduler.config)
        elif scheduler_type == "euler-ancestral":
            scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config)
        elif scheduler_type == "dpm":
            scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)
        elif scheduler_type == "ddim":
            scheduler = scheduler
        else:
            raise ValueError(f"Scheduler of type {scheduler_type} doesn't exist!")

        # Convert the UNet2DConditionModel model.
        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 the VAE model, or use the one passed
        if not vae:
            print('  | Using checkpoint model\'s original VAE')
            vae_config = create_vae_diffusers_config(original_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)
        else:
            print('  | Using external VAE specified in config')

        # Convert the text model.
        model_type = pipeline_type
        if model_type is None:
            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("stabilityai/stable-diffusion-2",
                                                      subfolder="tokenizer",
                                                      cache_dir=global_cache_dir('diffusers')
                                                      )
            pipe = pipeline_class(
                vae=vae,
                text_encoder=text_model,
                tokenizer=tokenizer,
                unet=unet,
                scheduler=scheduler,
                safety_checker=None,
                feature_extractor=None,
                requires_safety_checker=False,
            )
        elif model_type == "PaintByExample":
            vision_model = convert_paint_by_example_checkpoint(checkpoint)
            tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14",cache_dir=cache_dir)
            feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker",cache_dir=cache_dir)
            pipe = PaintByExamplePipeline(
                vae=vae,
                image_encoder=vision_model,
                unet=unet,
                scheduler=scheduler,
                safety_checker=None,
                feature_extractor=feature_extractor,
            )
        elif model_type in ['FrozenCLIPEmbedder','WeightedFrozenCLIPEmbedder']:
            text_model = convert_ldm_clip_checkpoint(checkpoint)
            tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14",cache_dir=cache_dir)
            safety_checker = StableDiffusionSafetyChecker.from_pretrained('CompVis/stable-diffusion-safety-checker',cache_dir=global_cache_dir("hub"))
            feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker",cache_dir=cache_dir)
            pipe = pipeline_class(
                vae=vae,
                text_encoder=text_model,
                tokenizer=tokenizer,
                unet=unet,
                scheduler=scheduler,
                safety_checker=safety_checker,
                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("bert-base-uncased",cache_dir=cache_dir)
            pipe = LDMTextToImagePipeline(vqvae=vae, bert=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
    dlogging.set_verbosity(verbosity)

    return pipe

def convert_ckpt_to_diffuser(
        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(),
    )