114 lines
3.8 KiB
Python
114 lines
3.8 KiB
Python
# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Conversion script for the LDM checkpoints. """
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import argparse
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import json
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import os
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import torch
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from transformers.file_utils import has_file
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from diffusers import UNet2DConditionModel, UNet2DModel
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do_only_config = False
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do_only_weights = True
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do_only_renaming = False
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--repo_path",
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default=None,
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type=str,
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required=True,
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help="The config json file corresponding to the architecture.",
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)
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parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
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args = parser.parse_args()
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config_parameters_to_change = {
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"image_size": "sample_size",
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"num_res_blocks": "layers_per_block",
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"block_channels": "block_out_channels",
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"down_blocks": "down_block_types",
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"up_blocks": "up_block_types",
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"downscale_freq_shift": "freq_shift",
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"resnet_num_groups": "norm_num_groups",
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"resnet_act_fn": "act_fn",
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"resnet_eps": "norm_eps",
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"num_head_channels": "attention_head_dim",
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}
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key_parameters_to_change = {
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"time_steps": "time_proj",
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"mid": "mid_block",
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"downsample_blocks": "down_blocks",
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"upsample_blocks": "up_blocks",
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}
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subfolder = "" if has_file(args.repo_path, "config.json") else "unet"
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with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader:
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text = reader.read()
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config = json.loads(text)
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if do_only_config:
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for key in config_parameters_to_change.keys():
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config.pop(key, None)
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if has_file(args.repo_path, "config.json"):
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model = UNet2DModel(**config)
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else:
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class_name = UNet2DConditionModel if "ldm-text2im-large-256" in args.repo_path else UNet2DModel
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model = class_name(**config)
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if do_only_config:
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model.save_config(os.path.join(args.repo_path, subfolder))
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config = dict(model.config)
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if do_only_renaming:
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for key, value in config_parameters_to_change.items():
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if key in config:
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config[value] = config[key]
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del config[key]
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config["down_block_types"] = [k.replace("UNetRes", "") for k in config["down_block_types"]]
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config["up_block_types"] = [k.replace("UNetRes", "") for k in config["up_block_types"]]
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if do_only_weights:
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state_dict = torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))
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new_state_dict = {}
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for param_key, param_value in state_dict.items():
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if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"):
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continue
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has_changed = False
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for key, new_key in key_parameters_to_change.items():
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if not has_changed and param_key.split(".")[0] == key:
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new_state_dict[".".join([new_key] + param_key.split(".")[1:])] = param_value
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has_changed = True
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if not has_changed:
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new_state_dict[param_key] = param_value
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model.load_state_dict(new_state_dict)
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model.save_pretrained(os.path.join(args.repo_path, subfolder))
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