359 lines
15 KiB
Python
359 lines
15 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 DDPMScheduler, LDMPipeline, UNet2DModel, VQModel
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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(
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paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None
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):
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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 = {
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layer_id: [key for key in checkpoint if f"input_blocks.{layer_id}" in key]
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for layer_id in range(num_input_blocks)
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}
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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 = {
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layer_id: [key for key in checkpoint if f"middle_block.{layer_id}" in key]
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for layer_id in range(num_middle_blocks)
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}
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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 = {
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layer_id: [key for key in checkpoint if f"output_blocks.{layer_id}" in key]
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for layer_id in range(num_output_blocks)
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}
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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[
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f"input_blocks.{i}.0.op.weight"
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]
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new_checkpoint[f"downsample_blocks.{block_id}.downsamplers.0.conv.bias"] = checkpoint[
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f"input_blocks.{i}.0.op.bias"
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]
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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(
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paths, new_checkpoint, checkpoint, additional_replacements=[meta_path, resnet_op], config=config
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)
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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"input_blocks.{i}.1",
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"new": f"downsample_blocks.{block_id}.attentions.{layer_in_block_id}",
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}
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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(
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attentions_paths, new_checkpoint, checkpoint, attention_paths_to_split=to_split, config=config
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)
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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[
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f"output_blocks.{i}.{index}.conv.weight"
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]
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new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = checkpoint[
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f"output_blocks.{i}.{index}.conv.bias"
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]
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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("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
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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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