Spaces:
Running
on
Zero
Running
on
Zero
| # coding=utf-8 | |
| # Copyright 2023 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 torch | |
| from diffusers import UNet3DConditionModel | |
| 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 | |
| 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 | |
| weight = old_checkpoint[path["old"]] | |
| names = ["proj_attn.weight"] | |
| names_2 = ["proj_out.weight", "proj_in.weight"] | |
| if any(k in new_path for k in names): | |
| checkpoint[new_path] = weight[:, :, 0] | |
| elif any(k in new_path for k in names_2) and len(weight.shape) > 2 and ".attentions." not in new_path: | |
| checkpoint[new_path] = weight[:, :, 0] | |
| else: | |
| checkpoint[new_path] = weight | |
| 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 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_temp_conv_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: | |
| mapping.append({"old": old_item, "new": old_item}) | |
| return mapping | |
| 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) | |
| if "temopral_conv" not in old_item: | |
| mapping.append({"old": old_item, "new": new_item}) | |
| return mapping | |
| 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 and extract_ema: | |
| print(f"Checkpoint {path} has both EMA and non-EMA weights.") | |
| print( | |
| "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" | |
| " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." | |
| ) | |
| 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: | |
| if sum(k.startswith("model_ema") for k in keys) > 100: | |
| print( | |
| "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" | |
| " weights (usually better for inference), please make sure to add the `--extract_ema` flag." | |
| ) | |
| for key in keys: | |
| 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"] | |
| if config["class_embed_type"] is None: | |
| # No parameters to port | |
| ... | |
| elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection": | |
| new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"] | |
| new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"] | |
| new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"] | |
| new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"] | |
| else: | |
| raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}") | |
| 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"] | |
| first_temp_attention = [v for v in unet_state_dict if v.startswith("input_blocks.0.1")] | |
| paths = renew_attention_paths(first_temp_attention) | |
| meta_path = {"old": "input_blocks.0.1", "new": "transformer_in"} | |
| assign_to_checkpoint(paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config) | |
| 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] | |
| temp_attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.2" in key] | |
| if f"input_blocks.{i}.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}.op.weight" | |
| ) | |
| new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( | |
| f"input_blocks.{i}.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 | |
| ) | |
| temporal_convs = [key for key in resnets if "temopral_conv" in key] | |
| paths = renew_temp_conv_paths(temporal_convs) | |
| meta_path = { | |
| "old": f"input_blocks.{i}.0.temopral_conv", | |
| "new": f"down_blocks.{block_id}.temp_convs.{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 | |
| ) | |
| if len(temp_attentions): | |
| paths = renew_attention_paths(temp_attentions) | |
| meta_path = { | |
| "old": f"input_blocks.{i}.2", | |
| "new": f"down_blocks.{block_id}.temp_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] | |
| temporal_convs_0 = [key for key in resnet_0 if "temopral_conv" in key] | |
| attentions = middle_blocks[1] | |
| temp_attentions = middle_blocks[2] | |
| resnet_1 = middle_blocks[3] | |
| temporal_convs_1 = [key for key in resnet_1 if "temopral_conv" in key] | |
| resnet_0_paths = renew_resnet_paths(resnet_0) | |
| meta_path = {"old": "middle_block.0", "new": "mid_block.resnets.0"} | |
| assign_to_checkpoint( | |
| resnet_0_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path] | |
| ) | |
| temp_conv_0_paths = renew_temp_conv_paths(temporal_convs_0) | |
| meta_path = {"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"} | |
| assign_to_checkpoint( | |
| temp_conv_0_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path] | |
| ) | |
| resnet_1_paths = renew_resnet_paths(resnet_1) | |
| meta_path = {"old": "middle_block.3", "new": "mid_block.resnets.1"} | |
| assign_to_checkpoint( | |
| resnet_1_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path] | |
| ) | |
| temp_conv_1_paths = renew_temp_conv_paths(temporal_convs_1) | |
| meta_path = {"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"} | |
| assign_to_checkpoint( | |
| temp_conv_1_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path] | |
| ) | |
| 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 | |
| ) | |
| temp_attentions_paths = renew_attention_paths(temp_attentions) | |
| meta_path = {"old": "middle_block.2", "new": "mid_block.temp_attentions.0"} | |
| assign_to_checkpoint( | |
| temp_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] | |
| temp_attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.2" 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 | |
| ) | |
| temporal_convs = [key for key in resnets if "temopral_conv" in key] | |
| paths = renew_temp_conv_paths(temporal_convs) | |
| meta_path = { | |
| "old": f"output_blocks.{i}.0.temopral_conv", | |
| "new": f"up_blocks.{block_id}.temp_convs.{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 | |
| ) | |
| if len(temp_attentions): | |
| paths = renew_attention_paths(temp_attentions) | |
| meta_path = { | |
| "old": f"output_blocks.{i}.2", | |
| "new": f"up_blocks.{block_id}.temp_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] | |
| temopral_conv_paths = [l for l in output_block_layers if "temopral_conv" in l] | |
| for path in temopral_conv_paths: | |
| pruned_path = path.split("temopral_conv.")[-1] | |
| old_path = ".".join(["output_blocks", str(i), str(block_id), "temopral_conv", pruned_path]) | |
| new_path = ".".join(["up_blocks", str(block_id), "temp_convs", str(layer_in_block_id), pruned_path]) | |
| new_checkpoint[new_path] = unet_state_dict[old_path] | |
| 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("--dump_path", default=None, type=str, required=True, help="Path to the output model.") | |
| args = parser.parse_args() | |
| unet_checkpoint = torch.load(args.checkpoint_path, map_location="cpu") | |
| unet = UNet3DConditionModel() | |
| converted_ckpt = convert_ldm_unet_checkpoint(unet_checkpoint, unet.config) | |
| diff_0 = set(unet.state_dict().keys()) - set(converted_ckpt.keys()) | |
| diff_1 = set(converted_ckpt.keys()) - set(unet.state_dict().keys()) | |
| assert len(diff_0) == len(diff_1) == 0, "Converted weights don't match" | |
| # load state_dict | |
| unet.load_state_dict(converted_ckpt) | |
| unet.save_pretrained(args.dump_path) | |
| # -- finish converting the unet -- | |