some more cleaning

scripts/change_naming_configs_and_checkpoints.py

@@ -0,0 +1,112 @@
+# 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.
+""" Conversion script for the LDM checkpoints. """
+
+import argparse
+import os
+import json
+import torch
+from diffusers import UNet2DModel, UNet2DConditionModel
+from transformers.file_utils import has_file
+
+do_only_config = False
+do_only_weights = True
+do_only_renaming = False
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--repo_path",
+        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()
+
+    config_parameters_to_change = {
+        "image_size": "sample_size",
+        "num_res_blocks": "layers_per_block",
+        "block_channels": "block_out_channels",
+        "down_blocks": "down_block_types",
+        "up_blocks": "up_block_types",
+        "downscale_freq_shift": "freq_shift",
+        "resnet_num_groups": "norm_num_groups",
+        "resnet_act_fn": "act_fn",
+        "resnet_eps": "norm_eps",
+        "num_head_channels": "attention_head_dim",
+    }
+
+    key_parameters_to_change = {
+        "time_steps": "time_proj",
+        "mid": "mid_block",
+        "downsample_blocks": "down_blocks",
+        "upsample_blocks": "up_blocks",
+    }
+
+    subfolder = "" if has_file(args.repo_path, "config.json") else "unet"
+
+    with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader:
+        text = reader.read()
+        config = json.loads(text)
+
+    if do_only_config:
+        for key in config_parameters_to_change.keys():
+            config.pop(key, None)
+
+    if has_file(args.repo_path, "config.json"):
+        model = UNet2DModel(**config)
+    else:
+        class_name = UNet2DConditionModel if "ldm-text2im-large-256" in args.repo_path else UNet2DModel
+        model = class_name(**config)
+
+    if do_only_config:
+        model.save_config(os.path.join(args.repo_path, subfolder))
+
+    config = dict(model.config)
+
+    if do_only_renaming:
+        for key, value in config_parameters_to_change.items():
+            if key in config:
+                config[value] = config[key]
+                del config[key]
+
+        config["down_block_types"] = [k.replace("UNetRes", "") for k in config["down_block_types"]]
+        config["up_block_types"] = [k.replace("UNetRes", "") for k in config["up_block_types"]]
+
+    if do_only_weights:
+        state_dict = torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))
+
+        new_state_dict = {}
+        for param_key, param_value in state_dict.items():
+            if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"):
+                continue
+            has_changed = False
+            for key, new_key in key_parameters_to_change.items():
+                if not has_changed and param_key.split(".")[0] == key:
+                    new_state_dict[".".join([new_key] + param_key.split(".")[1:])] = param_value
+                    has_changed = True
+            if not has_changed:
+                new_state_dict[param_key] = param_value
+
+        model.load_state_dict(new_state_dict)
+        model.save_pretrained(os.path.join(args.repo_path, subfolder))

src/diffusers/configuration_utils.py

@@ -48,6 +48,7 @@ class ConfigMixin:
 
     """
     config_name = None
+    ignore_for_config = []
 
     def register_to_config(self, **kwargs):
         if self.config_name is None:
@@ -212,6 +213,9 @@ def extract_init_dict(cls, config_dict, **kwargs):
         # remove general kwargs if present in dict
         if "kwargs" in expected_keys:
             expected_keys.remove("kwargs")
+        # remove keys to be ignored
+        if len(cls.ignore_for_config) > 0:
+            expected_keys = expected_keys - set(cls.ignore_for_config)
         init_dict = {}
         for key in expected_keys:
             if key in kwargs: