333 lines
14 KiB
Python
333 lines
14 KiB
Python
# 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 json
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import torch
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from diffusers import VQModel, DDPMScheduler, UNet2DModel, LDMPipeline
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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_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 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.resnets.0')
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new_path = new_path.replace('middle_block.1', 'mid.attentions.0')
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new_path = new_path.replace('middle_block.2', 'mid.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 convert_ldm_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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new_checkpoint = {}
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new_checkpoint['time_embedding.linear_1.weight'] = checkpoint['time_embed.0.weight']
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new_checkpoint['time_embedding.linear_1.bias'] = checkpoint['time_embed.0.bias']
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new_checkpoint['time_embedding.linear_2.weight'] = checkpoint['time_embed.2.weight']
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new_checkpoint['time_embedding.linear_2.bias'] = checkpoint['time_embed.2.bias']
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new_checkpoint['conv_in.weight'] = checkpoint['input_blocks.0.0.weight']
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new_checkpoint['conv_in.bias'] = checkpoint['input_blocks.0.0.bias']
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new_checkpoint['conv_norm_out.weight'] = checkpoint['out.0.weight']
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new_checkpoint['conv_norm_out.bias'] = checkpoint['out.0.bias']
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new_checkpoint['conv_out.weight'] = checkpoint['out.2.weight']
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new_checkpoint['conv_out.bias'] = checkpoint['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 checkpoint if 'input_blocks' in layer})
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input_blocks = {layer_id: [key for key in checkpoint 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 checkpoint if 'middle_block' in layer})
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middle_blocks = {layer_id: [key for key in checkpoint 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 checkpoint if 'output_blocks' in layer})
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output_blocks = {layer_id: [key for key in checkpoint 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['num_res_blocks'] + 1)
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layer_in_block_id = (i - 1) % (config['num_res_blocks'] + 1)
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resnets = [key for key in input_blocks[i] if f'input_blocks.{i}.0' 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 checkpoint:
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new_checkpoint[f'downsample_blocks.{block_id}.downsamplers.0.conv.weight'] = checkpoint[f'input_blocks.{i}.0.op.weight']
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new_checkpoint[f'downsample_blocks.{block_id}.downsamplers.0.conv.bias'] = checkpoint[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'downsample_blocks.{block_id}.resnets.{layer_in_block_id}'}
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resnet_op = {'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
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assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[meta_path, resnet_op], config=config)
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if len(attentions):
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paths = renew_attention_paths(attentions)
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meta_path = {'old': f'input_blocks.{i}.1', 'new': f'downsample_blocks.{block_id}.attentions.{layer_in_block_id}'}
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to_split = {
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f'input_blocks.{i}.1.qkv.bias': {
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'key': f'downsample_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
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'query': f'downsample_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
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'value': f'downsample_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
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},
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f'input_blocks.{i}.1.qkv.weight': {
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'key': f'downsample_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
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'query': f'downsample_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
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'value': f'downsample_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
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},
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}
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assign_to_checkpoint(
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paths,
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new_checkpoint,
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checkpoint,
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additional_replacements=[meta_path],
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attention_paths_to_split=to_split,
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config=config
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)
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resnet_0 = middle_blocks[0]
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attentions = middle_blocks[1]
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resnet_1 = middle_blocks[2]
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resnet_0_paths = renew_resnet_paths(resnet_0)
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assign_to_checkpoint(resnet_0_paths, new_checkpoint, checkpoint, config=config)
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resnet_1_paths = renew_resnet_paths(resnet_1)
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assign_to_checkpoint(resnet_1_paths, new_checkpoint, checkpoint, config=config)
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attentions_paths = renew_attention_paths(attentions)
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to_split = {
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'middle_block.1.qkv.bias': {
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'key': 'mid_block.attentions.0.key.bias',
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'query': 'mid_block.attentions.0.query.bias',
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'value': 'mid_block.attentions.0.value.bias',
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},
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'middle_block.1.qkv.weight': {
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'key': 'mid_block.attentions.0.key.weight',
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'query': 'mid_block.attentions.0.query.weight',
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'value': 'mid_block.attentions.0.value.weight',
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},
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}
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assign_to_checkpoint(attentions_paths, new_checkpoint, checkpoint, attention_paths_to_split=to_split, config=config)
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for i in range(num_output_blocks):
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block_id = i // (config['num_res_blocks'] + 1)
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layer_in_block_id = i % (config['num_res_blocks'] + 1)
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output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]
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output_block_list = {}
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for layer in output_block_layers:
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layer_id, layer_name = layer.split('.')[0], shave_segments(layer, 1)
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if layer_id in output_block_list:
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output_block_list[layer_id].append(layer_name)
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else:
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output_block_list[layer_id] = [layer_name]
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if len(output_block_list) > 1:
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resnets = [key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
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attentions = [key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]
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resnet_0_paths = renew_resnet_paths(resnets)
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paths = renew_resnet_paths(resnets)
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meta_path = {'old': f'output_blocks.{i}.0', 'new': f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
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assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[meta_path], config=config)
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if ['conv.weight', 'conv.bias'] in output_block_list.values():
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index = list(output_block_list.values()).index(['conv.weight', 'conv.bias'])
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new_checkpoint[f'up_blocks.{block_id}.upsamplers.0.conv.weight'] = checkpoint[f'output_blocks.{i}.{index}.conv.weight']
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new_checkpoint[f'up_blocks.{block_id}.upsamplers.0.conv.bias'] = checkpoint[f'output_blocks.{i}.{index}.conv.bias']
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# Clear attentions as they have been attributed above.
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if len(attentions) == 2:
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attentions = []
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if len(attentions):
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paths = renew_attention_paths(attentions)
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meta_path = {
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'old': f'output_blocks.{i}.1',
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'new': f'up_blocks.{block_id}.attentions.{layer_in_block_id}'
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}
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to_split = {
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f'output_blocks.{i}.1.qkv.bias': {
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'key': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
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'query': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
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'value': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
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},
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f'output_blocks.{i}.1.qkv.weight': {
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'key': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
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'query': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
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'value': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
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},
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}
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assign_to_checkpoint(
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paths,
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new_checkpoint,
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checkpoint,
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additional_replacements=[meta_path],
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attention_paths_to_split=to_split if any('qkv' in key for key in attentions) else None,
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config=config,
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)
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else:
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resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)
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for path in resnet_0_paths:
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old_path = '.'.join(['output_blocks', str(i), path['old']])
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new_path = '.'.join(['up_blocks', str(block_id), 'resnets', str(layer_in_block_id), path['new']])
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new_checkpoint[new_path] = checkpoint[old_path]
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return new_checkpoint
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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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"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
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)
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parser.add_argument(
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"--config_file",
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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(
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"--dump_path", default=None, type=str, required=True, help="Path to the output model."
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)
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args = parser.parse_args()
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checkpoint = torch.load(args.checkpoint_path)
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with open(args.config_file) as f:
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config = json.loads(f.read())
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converted_checkpoint = convert_ldm_checkpoint(checkpoint, config)
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if "ldm" in config:
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del config["ldm"]
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model = UNet2DModel(**config)
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model.load_state_dict(converted_checkpoint)
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try:
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scheduler = DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
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vqvae = VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))
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pipe = LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
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pipe.save_pretrained(args.dump_path)
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except:
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model.save_pretrained(args.dump_path)
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