601 lines
26 KiB
Python
601 lines
26 KiB
Python
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# coding=utf-8
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# Copyright 2022 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 torch
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try:
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from omegaconf import OmegaConf
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except ImportError:
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raise ImportError("OmegaConf is required to convert the LDM checkpoints. Please install it with `pip install OmegaConf`.")
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from transformers import BertTokenizerFast, CLIPTokenizer, CLIPTextModel
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from diffusers import LDMTextToImagePipeline, AutoencoderKL, UNet2DConditionModel, DDIMScheduler
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from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import LDMBertModel, LDMBertConfig
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def shave_segments(path, n_shave_prefix_segments=1):
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"""
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Removes segments. Positive values shave the first segments, negative shave the last segments.
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"""
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if n_shave_prefix_segments >= 0:
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return '.'.join(path.split('.')[n_shave_prefix_segments:])
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else:
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return '.'.join(path.split('.')[:n_shave_prefix_segments])
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def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
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"""
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Updates paths inside resnets to the new naming scheme (local renaming)
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"""
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mapping = []
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for old_item in old_list:
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new_item = old_item.replace('in_layers.0', 'norm1')
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new_item = new_item.replace('in_layers.2', 'conv1')
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new_item = new_item.replace('out_layers.0', 'norm2')
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new_item = new_item.replace('out_layers.3', 'conv2')
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new_item = new_item.replace('emb_layers.1', 'time_emb_proj')
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new_item = new_item.replace('skip_connection', 'conv_shortcut')
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new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
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mapping.append({'old': old_item, 'new': new_item})
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return mapping
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def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):
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"""
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Updates paths inside resnets to the new naming scheme (local renaming)
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"""
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mapping = []
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for old_item in old_list:
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new_item = old_item
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new_item = new_item.replace('nin_shortcut', 'conv_shortcut')
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new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
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mapping.append({'old': old_item, 'new': new_item})
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return mapping
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def renew_attention_paths(old_list, n_shave_prefix_segments=0):
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"""
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Updates paths inside attentions to the new naming scheme (local renaming)
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"""
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mapping = []
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for old_item in old_list:
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new_item = old_item
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# new_item = new_item.replace('norm.weight', 'group_norm.weight')
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# new_item = new_item.replace('norm.bias', 'group_norm.bias')
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# new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
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# new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
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# new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
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mapping.append({'old': old_item, 'new': new_item})
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return mapping
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def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
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"""
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Updates paths inside attentions to the new naming scheme (local renaming)
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"""
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mapping = []
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for old_item in old_list:
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new_item = old_item
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new_item = new_item.replace('norm.weight', 'group_norm.weight')
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new_item = new_item.replace('norm.bias', 'group_norm.bias')
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new_item = new_item.replace('q.weight', 'query.weight')
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new_item = new_item.replace('q.bias', 'query.bias')
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new_item = new_item.replace('k.weight', 'key.weight')
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new_item = new_item.replace('k.bias', 'key.bias')
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new_item = new_item.replace('v.weight', 'value.weight')
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new_item = new_item.replace('v.bias', 'value.bias')
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new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
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new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
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new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
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mapping.append({'old': old_item, 'new': new_item})
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return mapping
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def assign_to_checkpoint(paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None):
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"""
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This does the final conversion step: take locally converted weights and apply a global renaming
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to them. It splits attention layers, and takes into account additional replacements
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that may arise.
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Assigns the weights to the new checkpoint.
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"""
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assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."
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# Splits the attention layers into three variables.
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if attention_paths_to_split is not None:
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for path, path_map in attention_paths_to_split.items():
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old_tensor = old_checkpoint[path]
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channels = old_tensor.shape[0] // 3
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target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)
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num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3
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old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
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query, key, value = old_tensor.split(channels // num_heads, dim=1)
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checkpoint[path_map['query']] = query.reshape(target_shape)
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checkpoint[path_map['key']] = key.reshape(target_shape)
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checkpoint[path_map['value']] = value.reshape(target_shape)
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for path in paths:
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new_path = path['new']
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# These have already been assigned
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if attention_paths_to_split is not None and new_path in attention_paths_to_split:
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continue
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# Global renaming happens here
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new_path = new_path.replace('middle_block.0', 'mid_block.resnets.0')
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new_path = new_path.replace('middle_block.1', 'mid_block.attentions.0')
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new_path = new_path.replace('middle_block.2', 'mid_block.resnets.1')
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if additional_replacements is not None:
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for replacement in additional_replacements:
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new_path = new_path.replace(replacement['old'], replacement['new'])
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# proj_attn.weight has to be converted from conv 1D to linear
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if "proj_attn.weight" in new_path:
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checkpoint[new_path] = old_checkpoint[path['old']][:, :, 0]
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else:
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checkpoint[new_path] = old_checkpoint[path['old']]
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def conv_attn_to_linear(checkpoint):
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keys = list(checkpoint.keys())
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attn_keys = ["query.weight", "key.weight", "value.weight"]
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for key in keys:
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if ".".join(key.split(".")[-2:]) in attn_keys:
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if checkpoint[key].ndim > 2:
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checkpoint[key] = checkpoint[key][:, :, 0, 0]
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elif "proj_attn.weight" in key:
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if checkpoint[key].ndim > 2:
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checkpoint[key] = checkpoint[key][:, :, 0]
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def create_unet_diffusers_config(original_config):
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"""
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Creates a config for the diffusers based on the config of the LDM model.
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"""
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unet_params = original_config.model.params.unet_config.params
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block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult]
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down_block_types = []
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resolution = 1
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for i in range(len(block_out_channels)):
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block_type = "CrossAttnDownBlock2D" if resolution in unet_params.attention_resolutions else "DownBlock2D"
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down_block_types.append(block_type)
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if i != len(block_out_channels) - 1:
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resolution *= 2
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up_block_types = []
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for i in range(len(block_out_channels)):
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block_type = "CrossAttnUpBlock2D" if resolution in unet_params.attention_resolutions else "UpBlock2D"
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up_block_types.append(block_type)
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resolution //= 2
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config = dict(
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sample_size=unet_params.image_size,
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in_channels=unet_params.in_channels,
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out_channels=unet_params.out_channels,
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down_block_types=tuple(down_block_types),
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up_block_types=tuple(up_block_types),
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block_out_channels=tuple(block_out_channels),
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layers_per_block=unet_params.num_res_blocks,
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cross_attention_dim=unet_params.context_dim,
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attention_head_dim=unet_params.num_heads,
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)
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return config
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def create_vae_diffusers_config(original_config):
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"""
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Creates a config for the diffusers based on the config of the LDM model.
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"""
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vae_params = original_config.model.params.first_stage_config.params.ddconfig
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latent_channles = original_config.model.params.first_stage_config.params.embed_dim
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block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult]
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down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels)
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up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels)
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config = dict(
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sample_size=vae_params.resolution,
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in_channels=vae_params.in_channels,
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out_channels=vae_params.out_ch,
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down_block_types=tuple(down_block_types),
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up_block_types=tuple(up_block_types),
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block_out_channels=tuple(block_out_channels),
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latent_channels=vae_params.z_channels,
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layers_per_block=vae_params.num_res_blocks,
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)
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return config
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def create_diffusers_schedular(original_config):
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schedular = DDIMScheduler(
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num_train_timesteps=original_config.model.params.timesteps,
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beta_start=original_config.model.params.linear_start,
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beta_end=original_config.model.params.linear_end,
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beta_schedule="scaled_linear",
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)
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return schedular
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def create_ldm_bert_config(original_config):
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bert_params = original_config.model.parms.cond_stage_config.params
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config = LDMBertConfig(
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d_model=bert_params.n_embed,
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encoder_layers=bert_params.n_layer,
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encoder_ffn_dim=bert_params.n_embed * 4,
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)
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return config
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def convert_ldm_unet_checkpoint(checkpoint, config):
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"""
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Takes a state dict and a config, and returns a converted checkpoint.
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"""
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# extract state_dict for UNet
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unet_state_dict = {}
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unet_key = "model.diffusion_model."
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keys = list(checkpoint.keys())
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for key in keys:
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if key.startswith(unet_key):
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unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)
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new_checkpoint = {}
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new_checkpoint['time_embedding.linear_1.weight'] = unet_state_dict['time_embed.0.weight']
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new_checkpoint['time_embedding.linear_1.bias'] = unet_state_dict['time_embed.0.bias']
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new_checkpoint['time_embedding.linear_2.weight'] = unet_state_dict['time_embed.2.weight']
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new_checkpoint['time_embedding.linear_2.bias'] = unet_state_dict['time_embed.2.bias']
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new_checkpoint['conv_in.weight'] = unet_state_dict['input_blocks.0.0.weight']
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new_checkpoint['conv_in.bias'] = unet_state_dict['input_blocks.0.0.bias']
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new_checkpoint['conv_norm_out.weight'] = unet_state_dict['out.0.weight']
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new_checkpoint['conv_norm_out.bias'] = unet_state_dict['out.0.bias']
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new_checkpoint['conv_out.weight'] = unet_state_dict['out.2.weight']
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new_checkpoint['conv_out.bias'] = unet_state_dict['out.2.bias']
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# Retrieves the keys for the input blocks only
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num_input_blocks = len({'.'.join(layer.split('.')[:2]) for layer in unet_state_dict if 'input_blocks' in layer})
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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)}
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# Retrieves the keys for the middle blocks only
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num_middle_blocks = len({'.'.join(layer.split('.')[:2]) for layer in unet_state_dict if 'middle_block' in layer})
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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)}
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# Retrieves the keys for the output blocks only
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num_output_blocks = len({'.'.join(layer.split('.')[:2]) for layer in unet_state_dict if 'output_blocks' in layer})
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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)}
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for i in range(1, num_input_blocks):
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block_id = (i - 1) // (config['layers_per_block'] + 1)
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layer_in_block_id = (i - 1) % (config['layers_per_block'] + 1)
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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]
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attentions = [key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]
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if f'input_blocks.{i}.0.op.weight' in unet_state_dict:
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new_checkpoint[f'down_blocks.{block_id}.downsamplers.0.conv.weight'] = unet_state_dict.pop(f'input_blocks.{i}.0.op.weight')
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new_checkpoint[f'down_blocks.{block_id}.downsamplers.0.conv.bias'] = unet_state_dict.pop(f'input_blocks.{i}.0.op.bias')
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paths = renew_resnet_paths(resnets)
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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)
|
||
|
|
|
||
|
|
if ['conv.weight', 'conv.bias'] in output_block_list.values():
|
||
|
|
index = list(output_block_list.values()).index(['conv.weight', 'conv.bias'])
|
||
|
|
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
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
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(
|
||
|
|
"--original_config_file",
|
||
|
|
default=None,
|
||
|
|
type=str,
|
||
|
|
required=True,
|
||
|
|
help="The YAML config file corresponding to the original architecture.",
|
||
|
|
)
|
||
|
|
|
||
|
|
parser.add_argument(
|
||
|
|
"--dump_path", default=None, type=str, required=True, help="Path to the output model."
|
||
|
|
)
|
||
|
|
|
||
|
|
args = parser.parse_args()
|
||
|
|
|
||
|
|
original_config = OmegaConf.load(args.original_config_file)
|
||
|
|
|
||
|
|
checkpoint = torch.load(args.checkpoint_path)["state_dict"]
|
||
|
|
|
||
|
|
# Convert the UNet2DConditionModel model.
|
||
|
|
unet_config = create_unet_diffusers_config(original_config)
|
||
|
|
converted_unet_checkpoint = convert_ldm_unet_checkpoint(checkpoint, unet_config)
|
||
|
|
|
||
|
|
unet = UNet2DConditionModel(**unet_config)
|
||
|
|
unet.load_state_dict(converted_unet_checkpoint)
|
||
|
|
|
||
|
|
# Convert the VAE model.
|
||
|
|
vae_config = create_vae_diffusers_config(original_config)
|
||
|
|
converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
|
||
|
|
|
||
|
|
vae = AutoencoderKL(**vae_config)
|
||
|
|
vae.load_state_dict(converted_vae_checkpoint)
|
||
|
|
|
||
|
|
# Convert the text model.
|
||
|
|
text_model_type = original_config.model.params.cond_stage_config.target.split(".")[-1]
|
||
|
|
if text_model_type == "FrozenCLIPEmbedder":
|
||
|
|
text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14")
|
||
|
|
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
|
||
|
|
else:
|
||
|
|
# TODO: update the convert function to use the state_dict without the model instance.
|
||
|
|
text_config = create_ldm_bert_config(original_config)
|
||
|
|
text_model = convert_ldm_bert_checkpoint(checkpoint, text_config)
|
||
|
|
tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
|
||
|
|
|
||
|
|
scheduler = create_diffusers_schedular(original_config)
|
||
|
|
pipe = LDMTextToImagePipeline(vqvae=vae, bert=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
|
||
|
|
pipe.save_pretrained(args.dump_path)
|
||
|
|
|