318 lines
14 KiB
Python
318 lines
14 KiB
Python
# 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 json
|
|
import torch
|
|
|
|
|
|
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_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)
|
|
|
|
mapping.append({'old': old_item, 'new': new_item})
|
|
|
|
return mapping
|
|
|
|
|
|
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 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
|
|
|
|
# Global renaming happens here
|
|
new_path = new_path.replace('middle_block.0', 'mid.resnets.0')
|
|
new_path = new_path.replace('middle_block.1', 'mid.attentions.0')
|
|
new_path = new_path.replace('middle_block.2', 'mid.resnets.1')
|
|
|
|
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
|
|
if "proj_attn.weight" in new_path:
|
|
checkpoint[new_path] = old_checkpoint[path['old']][:, :, 0]
|
|
else:
|
|
checkpoint[new_path] = old_checkpoint[path['old']]
|
|
|
|
|
|
def convert_ldm_checkpoint(checkpoint, config):
|
|
"""
|
|
Takes a state dict and the path to
|
|
"""
|
|
new_checkpoint = {}
|
|
|
|
new_checkpoint['time_embedding.linear_1.weight'] = checkpoint['time_embed.0.weight']
|
|
new_checkpoint['time_embedding.linear_1.bias'] = checkpoint['time_embed.0.bias']
|
|
new_checkpoint['time_embedding.linear_2.weight'] = checkpoint['time_embed.2.weight']
|
|
new_checkpoint['time_embedding.linear_2.bias'] = checkpoint['time_embed.2.bias']
|
|
|
|
new_checkpoint['conv_in.weight'] = checkpoint['input_blocks.0.0.weight']
|
|
new_checkpoint['conv_in.bias'] = checkpoint['input_blocks.0.0.bias']
|
|
|
|
new_checkpoint['conv_norm_out.weight'] = checkpoint['out.0.weight']
|
|
new_checkpoint['conv_norm_out.bias'] = checkpoint['out.0.bias']
|
|
new_checkpoint['conv_out.weight'] = checkpoint['out.2.weight']
|
|
new_checkpoint['conv_out.bias'] = checkpoint['out.2.bias']
|
|
|
|
# Retrieves the keys for the input blocks only
|
|
num_input_blocks = len({shave_segments(layer, -2) for layer in checkpoint if 'input_blocks' in layer})
|
|
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)}
|
|
|
|
# Retrieves the keys for the middle blocks only
|
|
num_middle_blocks = len({shave_segments(layer, -2) for layer in checkpoint if 'middle_block' in layer})
|
|
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)}
|
|
|
|
# Retrieves the keys for the output blocks only
|
|
num_output_blocks = len({shave_segments(layer, -2) for layer in checkpoint if 'output_blocks' in layer})
|
|
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)}
|
|
|
|
for i in range(1, num_input_blocks):
|
|
block_id = (i - 1) // (config['num_res_blocks'] + 1)
|
|
layer_in_block_id = (i - 1) % (config['num_res_blocks'] + 1)
|
|
|
|
resnets = [key for key in input_blocks[i] if f'input_blocks.{i}.0' in key]
|
|
attentions = [key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]
|
|
|
|
if f'input_blocks.{i}.0.op.weight' in checkpoint:
|
|
new_checkpoint[f'downsample_blocks.{block_id}.downsamplers.0.conv.weight'] = checkpoint[f'input_blocks.{i}.0.op.weight']
|
|
new_checkpoint[f'downsample_blocks.{block_id}.downsamplers.0.conv.bias'] = checkpoint[f'input_blocks.{i}.0.op.bias']
|
|
|
|
paths = renew_resnet_paths(resnets)
|
|
meta_path = {'old': f'input_blocks.{i}.0', 'new': f'downsample_blocks.{block_id}.resnets.{layer_in_block_id}'}
|
|
resnet_op = {'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
|
|
assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[meta_path, resnet_op], config=config)
|
|
|
|
if len(attentions):
|
|
paths = renew_attention_paths(attentions)
|
|
meta_path = {'old': f'input_blocks.{i}.1', 'new': f'downsample_blocks.{block_id}.attentions.{layer_in_block_id}'}
|
|
to_split = {
|
|
f'input_blocks.{i}.1.qkv.bias': {
|
|
'key': f'downsample_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
|
|
'query': f'downsample_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
|
|
'value': f'downsample_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
|
|
},
|
|
f'input_blocks.{i}.1.qkv.weight': {
|
|
'key': f'downsample_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
|
|
'query': f'downsample_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
|
|
'value': f'downsample_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
|
|
},
|
|
}
|
|
assign_to_checkpoint(
|
|
paths,
|
|
new_checkpoint,
|
|
checkpoint,
|
|
additional_replacements=[meta_path],
|
|
attention_paths_to_split=to_split,
|
|
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, checkpoint, config=config)
|
|
|
|
resnet_1_paths = renew_resnet_paths(resnet_1)
|
|
assign_to_checkpoint(resnet_1_paths, new_checkpoint, checkpoint, config=config)
|
|
|
|
attentions_paths = renew_attention_paths(attentions)
|
|
to_split = {
|
|
'middle_block.1.qkv.bias': {
|
|
'key': 'mid.attentions.0.key.bias',
|
|
'query': 'mid.attentions.0.query.bias',
|
|
'value': 'mid.attentions.0.value.bias',
|
|
},
|
|
'middle_block.1.qkv.weight': {
|
|
'key': 'mid.attentions.0.key.weight',
|
|
'query': 'mid.attentions.0.query.weight',
|
|
'value': 'mid.attentions.0.value.weight',
|
|
},
|
|
}
|
|
assign_to_checkpoint(attentions_paths, new_checkpoint, checkpoint, attention_paths_to_split=to_split, config=config)
|
|
|
|
for i in range(num_output_blocks):
|
|
block_id = i // (config['num_res_blocks'] + 1)
|
|
layer_in_block_id = i % (config['num_res_blocks'] + 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'upsample_blocks.{block_id}.resnets.{layer_in_block_id}'}
|
|
assign_to_checkpoint(paths, new_checkpoint, checkpoint, 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'upsample_blocks.{block_id}.upsamplers.0.conv.weight'] = checkpoint[f'output_blocks.{i}.{index}.conv.weight']
|
|
new_checkpoint[f'upsample_blocks.{block_id}.upsamplers.0.conv.bias'] = checkpoint[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'upsample_blocks.{block_id}.attentions.{layer_in_block_id}'
|
|
}
|
|
to_split = {
|
|
f'output_blocks.{i}.1.qkv.bias': {
|
|
'key': f'upsample_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
|
|
'query': f'upsample_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
|
|
'value': f'upsample_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
|
|
},
|
|
f'output_blocks.{i}.1.qkv.weight': {
|
|
'key': f'upsample_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
|
|
'query': f'upsample_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
|
|
'value': f'upsample_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
|
|
},
|
|
}
|
|
assign_to_checkpoint(
|
|
paths,
|
|
new_checkpoint,
|
|
checkpoint,
|
|
additional_replacements=[meta_path],
|
|
attention_paths_to_split=to_split if any('qkv' in key for key in attentions) else None,
|
|
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(['upsample_blocks', str(block_id), 'resnets', str(layer_in_block_id), path['new']])
|
|
|
|
new_checkpoint[new_path] = checkpoint[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(
|
|
"--config_file",
|
|
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()
|
|
|
|
checkpoint = torch.load(args.checkpoint_path)
|
|
|
|
with open(args.config_file) as f:
|
|
config = json.loads(f.read())
|
|
|
|
converted_checkpoint = convert_ldm_checkpoint(checkpoint, config)
|
|
torch.save(checkpoint, args.dump_path)
|