convert_ddpm_original_checkpoint_to_diffusers.py

import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNet2DModel, VQModel


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):
    mapping = []
    for old_item in old_list:
        new_item = old_item
        new_item = new_item.replace("block.", "resnets.")
        new_item = new_item.replace("conv_shorcut", "conv1")
        new_item = new_item.replace("nin_shortcut", "conv_shortcut")
        new_item = new_item.replace("temb_proj", "time_emb_proj")

        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, in_mid=False):
    mapping = []
    for old_item in old_list:
        new_item = old_item

        # In `model.mid`, the layer is called `attn`.
        if not in_mid:
            new_item = new_item.replace("attn", "attentions")
        new_item = new_item.replace(".k.", ".key.")
        new_item = new_item.replace(".v.", ".value.")
        new_item = new_item.replace(".q.", ".query.")

        new_item = new_item.replace("proj_out", "proj_attn")
        new_item = new_item.replace("norm", "group_norm")

        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
):
    assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."

    if attention_paths_to_split is not None:
        if config is None:
            raise ValueError("Please specify the config if setting 'attention_paths_to_split' to 'True'.")

        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.get("num_head_channels", 1) // 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).squeeze()
            checkpoint[path_map["key"]] = key.reshape(target_shape).squeeze()
            checkpoint[path_map["value"]] = value.reshape(target_shape).squeeze()

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

        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
            continue

        new_path = new_path.replace("down.", "down_blocks.")
        new_path = new_path.replace("up.", "up_blocks.")

        if additional_replacements is not None:
            for replacement in additional_replacements:
                new_path = new_path.replace(replacement["old"], replacement["new"])

        if "attentions" in new_path:
            checkpoint[new_path] = old_checkpoint[path["old"]].squeeze()
        else:
            checkpoint[new_path] = old_checkpoint[path["old"]]


def convert_ddpm_checkpoint(checkpoint, config):
    """
    Takes a state dict and a config, and returns a converted checkpoint.
    """
    new_checkpoint = {}

    new_checkpoint["time_embedding.linear_1.weight"] = checkpoint["temb.dense.0.weight"]
    new_checkpoint["time_embedding.linear_1.bias"] = checkpoint["temb.dense.0.bias"]
    new_checkpoint["time_embedding.linear_2.weight"] = checkpoint["temb.dense.1.weight"]
    new_checkpoint["time_embedding.linear_2.bias"] = checkpoint["temb.dense.1.bias"]

    new_checkpoint["conv_norm_out.weight"] = checkpoint["norm_out.weight"]
    new_checkpoint["conv_norm_out.bias"] = checkpoint["norm_out.bias"]

    new_checkpoint["conv_in.weight"] = checkpoint["conv_in.weight"]
    new_checkpoint["conv_in.bias"] = checkpoint["conv_in.bias"]
    new_checkpoint["conv_out.weight"] = checkpoint["conv_out.weight"]
    new_checkpoint["conv_out.bias"] = checkpoint["conv_out.bias"]

    num_down_blocks = len({".".join(layer.split(".")[:2]) for layer in checkpoint if "down" in layer})
    down_blocks = {
        layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
    }

    num_up_blocks = len({".".join(layer.split(".")[:2]) for layer in checkpoint if "up" in layer})
    up_blocks = {layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)}

    for i in range(num_down_blocks):
        block_id = (i - 1) // (config["layers_per_block"] + 1)

        if any("downsample" in layer for layer in down_blocks[i]):
            new_checkpoint[f"down_blocks.{i}.downsamplers.0.conv.weight"] = checkpoint[
                f"down.{i}.downsample.op.weight"
            ]
            new_checkpoint[f"down_blocks.{i}.downsamplers.0.conv.bias"] = checkpoint[f"down.{i}.downsample.op.bias"]
        #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
        #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

        if any("block" in layer for layer in down_blocks[i]):
            num_blocks = len(
                {".".join(shave_segments(layer, 2).split(".")[:2]) for layer in down_blocks[i] if "block" in layer}
            )
            blocks = {
                layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
                for layer_id in range(num_blocks)
            }

            if num_blocks > 0:
                for j in range(config["layers_per_block"]):
                    paths = renew_resnet_paths(blocks[j])
                    assign_to_checkpoint(paths, new_checkpoint, checkpoint)

        if any("attn" in layer for layer in down_blocks[i]):
            num_attn = len(
                {".".join(shave_segments(layer, 2).split(".")[:2]) for layer in down_blocks[i] if "attn" in layer}
            )
            attns = {
                layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
                for layer_id in range(num_blocks)
            }

            if num_attn > 0:
                for j in range(config["layers_per_block"]):
                    paths = renew_attention_paths(attns[j])
                    assign_to_checkpoint(paths, new_checkpoint, checkpoint, config=config)

    mid_block_1_layers = [key for key in checkpoint if "mid.block_1" in key]
    mid_block_2_layers = [key for key in checkpoint if "mid.block_2" in key]
    mid_attn_1_layers = [key for key in checkpoint if "mid.attn_1" in key]

    # Mid new 2
    paths = renew_resnet_paths(mid_block_1_layers)
    assign_to_checkpoint(
        paths,
        new_checkpoint,
        checkpoint,
        additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}],
    )

    paths = renew_resnet_paths(mid_block_2_layers)
    assign_to_checkpoint(
        paths,
        new_checkpoint,
        checkpoint,
        additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}],
    )

    paths = renew_attention_paths(mid_attn_1_layers, in_mid=True)
    assign_to_checkpoint(
        paths,
        new_checkpoint,
        checkpoint,
        additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}],
    )

    for i in range(num_up_blocks):
        block_id = num_up_blocks - 1 - i

        if any("upsample" in layer for layer in up_blocks[i]):
            new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = checkpoint[
                f"up.{i}.upsample.conv.weight"
            ]
            new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = checkpoint[f"up.{i}.upsample.conv.bias"]

        if any("block" in layer for layer in up_blocks[i]):
            num_blocks = len(
                {".".join(shave_segments(layer, 2).split(".")[:2]) for layer in up_blocks[i] if "block" in layer}
            )
            blocks = {
                layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(num_blocks)
            }

            if num_blocks > 0:
                for j in range(config["layers_per_block"] + 1):
                    replace_indices = {"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"}
                    paths = renew_resnet_paths(blocks[j])
                    assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices])

        if any("attn" in layer for layer in up_blocks[i]):
            num_attn = len(
                {".".join(shave_segments(layer, 2).split(".")[:2]) for layer in up_blocks[i] if "attn" in layer}
            )
            attns = {
                layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(num_blocks)
            }

            if num_attn > 0:
                for j in range(config["layers_per_block"] + 1):
                    replace_indices = {"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"}
                    paths = renew_attention_paths(attns[j])
                    assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices])

    new_checkpoint = {k.replace("mid_new_2", "mid_block"): v for k, v in new_checkpoint.items()}
    return new_checkpoint


def convert_vq_autoenc_checkpoint(checkpoint, config):
    """
    Takes a state dict and a config, and returns a converted checkpoint.
    """
    new_checkpoint = {}

    new_checkpoint["encoder.conv_norm_out.weight"] = checkpoint["encoder.norm_out.weight"]
    new_checkpoint["encoder.conv_norm_out.bias"] = checkpoint["encoder.norm_out.bias"]

    new_checkpoint["encoder.conv_in.weight"] = checkpoint["encoder.conv_in.weight"]
    new_checkpoint["encoder.conv_in.bias"] = checkpoint["encoder.conv_in.bias"]
    new_checkpoint["encoder.conv_out.weight"] = checkpoint["encoder.conv_out.weight"]
    new_checkpoint["encoder.conv_out.bias"] = checkpoint["encoder.conv_out.bias"]

    new_checkpoint["decoder.conv_norm_out.weight"] = checkpoint["decoder.norm_out.weight"]
    new_checkpoint["decoder.conv_norm_out.bias"] = checkpoint["decoder.norm_out.bias"]

    new_checkpoint["decoder.conv_in.weight"] = checkpoint["decoder.conv_in.weight"]
    new_checkpoint["decoder.conv_in.bias"] = checkpoint["decoder.conv_in.bias"]
    new_checkpoint["decoder.conv_out.weight"] = checkpoint["decoder.conv_out.weight"]
    new_checkpoint["decoder.conv_out.bias"] = checkpoint["decoder.conv_out.bias"]

    num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in checkpoint if "down" in layer})
    down_blocks = {
        layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
    }

    num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in checkpoint if "up" in layer})
    up_blocks = {layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)}

    for i in range(num_down_blocks):
        block_id = (i - 1) // (config["layers_per_block"] + 1)

        if any("downsample" in layer for layer in down_blocks[i]):
            new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = checkpoint[
                f"encoder.down.{i}.downsample.conv.weight"
            ]
            new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = checkpoint[
                f"encoder.down.{i}.downsample.conv.bias"
            ]

        if any("block" in layer for layer in down_blocks[i]):
            num_blocks = len(
                {".".join(shave_segments(layer, 3).split(".")[:3]) for layer in down_blocks[i] if "block" in layer}
            )
            blocks = {
                layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
                for layer_id in range(num_blocks)
            }

            if num_blocks > 0:
                for j in range(config["layers_per_block"]):
                    paths = renew_resnet_paths(blocks[j])
                    assign_to_checkpoint(paths, new_checkpoint, checkpoint)

        if any("attn" in layer for layer in down_blocks[i]):
            num_attn = len(
                {".".join(shave_segments(layer, 3).split(".")[:3]) for layer in down_blocks[i] if "attn" in layer}
            )
            attns = {
                layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
                for layer_id in range(num_blocks)
            }

            if num_attn > 0:
                for j in range(config["layers_per_block"]):
                    paths = renew_attention_paths(attns[j])
                    assign_to_checkpoint(paths, new_checkpoint, checkpoint, config=config)

    mid_block_1_layers = [key for key in checkpoint if "mid.block_1" in key]
    mid_block_2_layers = [key for key in checkpoint if "mid.block_2" in key]
    mid_attn_1_layers = [key for key in checkpoint if "mid.attn_1" in key]

    # Mid new 2
    paths = renew_resnet_paths(mid_block_1_layers)
    assign_to_checkpoint(
        paths,
        new_checkpoint,
        checkpoint,
        additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}],
    )

    paths = renew_resnet_paths(mid_block_2_layers)
    assign_to_checkpoint(
        paths,
        new_checkpoint,
        checkpoint,
        additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}],
    )

    paths = renew_attention_paths(mid_attn_1_layers, in_mid=True)
    assign_to_checkpoint(
        paths,
        new_checkpoint,
        checkpoint,
        additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}],
    )

    for i in range(num_up_blocks):
        block_id = num_up_blocks - 1 - i

        if any("upsample" in layer for layer in up_blocks[i]):
            new_checkpoint[f"decoder.up_blocks.{block_id}.upsamplers.0.conv.weight"] = checkpoint[
                f"decoder.up.{i}.upsample.conv.weight"
            ]
            new_checkpoint[f"decoder.up_blocks.{block_id}.upsamplers.0.conv.bias"] = checkpoint[
                f"decoder.up.{i}.upsample.conv.bias"
            ]

        if any("block" in layer for layer in up_blocks[i]):
            num_blocks = len(
                {".".join(shave_segments(layer, 3).split(".")[:3]) for layer in up_blocks[i] if "block" in layer}
            )
            blocks = {
                layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(num_blocks)
            }

            if num_blocks > 0:
                for j in range(config["layers_per_block"] + 1):
                    replace_indices = {"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"}
                    paths = renew_resnet_paths(blocks[j])
                    assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices])

        if any("attn" in layer for layer in up_blocks[i]):
            num_attn = len(
                {".".join(shave_segments(layer, 3).split(".")[:3]) for layer in up_blocks[i] if "attn" in layer}
            )
            attns = {
                layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(num_blocks)
            }

            if num_attn > 0:
                for j in range(config["layers_per_block"] + 1):
                    replace_indices = {"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"}
                    paths = renew_attention_paths(attns[j])
                    assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices])

    new_checkpoint = {k.replace("mid_new_2", "mid_block"): v for k, v in new_checkpoint.items()}
    new_checkpoint["quant_conv.weight"] = checkpoint["quant_conv.weight"]
    new_checkpoint["quant_conv.bias"] = checkpoint["quant_conv.bias"]
    if "quantize.embedding.weight" in checkpoint:
        new_checkpoint["quantize.embedding.weight"] = checkpoint["quantize.embedding.weight"]
    new_checkpoint["post_quant_conv.weight"] = checkpoint["post_quant_conv.weight"]
    new_checkpoint["post_quant_conv.bias"] = checkpoint["post_quant_conv.bias"]

    return new_checkpoint


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

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

    parser.add_argument(
        "--config_file",
        default=None,
        type=str,
        required=True,
        help="The config json file corresponding to the architecture.",
    )

    parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

    args = parser.parse_args()
    checkpoint = torch.load(args.checkpoint_path)

    with open(args.config_file) as f:
        config = json.loads(f.read())

    # unet case
    key_prefix_set = set(key.split(".")[0] for key in checkpoint.keys())
    if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
        converted_checkpoint = convert_vq_autoenc_checkpoint(checkpoint, config)
    else:
        converted_checkpoint = convert_ddpm_checkpoint(checkpoint, config)

    if "ddpm" in config:
        del config["ddpm"]

    if config["_class_name"] == "VQModel":
        model = VQModel(**config)
        model.load_state_dict(converted_checkpoint)
        model.save_pretrained(args.dump_path)
    elif config["_class_name"] == "AutoencoderKL":
        model = AutoencoderKL(**config)
        model.load_state_dict(converted_checkpoint)
        model.save_pretrained(args.dump_path)
    else:
        model = UNet2DModel(**config)
        model.load_state_dict(converted_checkpoint)

        scheduler = DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

        pipe = DDPMPipeline(unet=model, scheduler=scheduler)
        pipe.save_pretrained(args.dump_path)