753 lines
31 KiB
Python
753 lines
31 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 os
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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(
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"OmegaConf is required to convert the LDM checkpoints. Please install it with `pip install OmegaConf`."
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)
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from diffusers import (
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AutoencoderKL,
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DDIMScheduler,
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DPMSolverMultistepScheduler,
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EulerAncestralDiscreteScheduler,
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EulerDiscreteScheduler,
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LDMTextToImagePipeline,
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LMSDiscreteScheduler,
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PNDMScheduler,
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StableDiffusionPipeline,
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UNet2DConditionModel,
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)
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from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel
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from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
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from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextModel, CLIPTokenizer
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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(
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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_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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model_params = original_config.model.params
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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=model_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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_ = 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, path=None, extract_ema=False):
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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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keys = list(checkpoint.keys())
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unet_key = "model.diffusion_model."
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# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
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if sum(k.startswith("model_ema") for k in keys) > 100:
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print(f"Checkpoint {path} has both EMA and non-EMA weights.")
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if extract_ema:
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print(
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"In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
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" weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."
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)
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for key in keys:
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if key.startswith("model.diffusion_model"):
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flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
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unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key)
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else:
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print(
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"In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
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" weights (usually better for inference), please make sure to add the `--extract_ema` flag."
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)
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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 = {
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layer_id: [key for key in unet_state_dict 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 unet_state_dict if "middle_block" in layer})
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middle_blocks = {
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layer_id: [key for key in unet_state_dict 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 unet_state_dict if "output_blocks" in layer})
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output_blocks = {
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layer_id: [key for key in unet_state_dict 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["layers_per_block"] + 1)
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layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1)
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resnets = [
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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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]
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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(
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f"input_blocks.{i}.0.op.weight"
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)
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new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop(
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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"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
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assign_to_checkpoint(
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paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], 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 = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
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assign_to_checkpoint(
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paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], 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, unet_state_dict, 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, unet_state_dict, config=config)
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attentions_paths = renew_attention_paths(attentions)
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meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
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assign_to_checkpoint(
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attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], 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["layers_per_block"] + 1)
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layer_in_block_id = i % (config["layers_per_block"] + 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(
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paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
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)
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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"] = 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
|
|
|
|
|
|
def convert_ldm_clip_checkpoint(checkpoint):
|
|
text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14")
|
|
|
|
keys = list(checkpoint.keys())
|
|
|
|
text_model_dict = {}
|
|
|
|
for key in keys:
|
|
if key.startswith("cond_stage_model.transformer"):
|
|
text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[key]
|
|
|
|
text_model.load_state_dict(text_model_dict)
|
|
|
|
return text_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."
|
|
)
|
|
# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
|
|
parser.add_argument(
|
|
"--original_config_file",
|
|
default=None,
|
|
type=str,
|
|
help="The YAML config file corresponding to the original architecture.",
|
|
)
|
|
parser.add_argument(
|
|
"--scheduler_type",
|
|
default="pndm",
|
|
type=str,
|
|
help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancest', 'dpm']",
|
|
)
|
|
parser.add_argument(
|
|
"--extract_ema",
|
|
action="store_true",
|
|
help=(
|
|
"Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
|
|
" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
|
|
" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
|
|
),
|
|
)
|
|
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
|
|
|
|
args = parser.parse_args()
|
|
|
|
if args.original_config_file is None:
|
|
os.system(
|
|
"wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
|
|
)
|
|
args.original_config_file = "./v1-inference.yaml"
|
|
|
|
original_config = OmegaConf.load(args.original_config_file)
|
|
|
|
checkpoint = torch.load(args.checkpoint_path)
|
|
checkpoint = checkpoint["state_dict"]
|
|
|
|
num_train_timesteps = original_config.model.params.timesteps
|
|
beta_start = original_config.model.params.linear_start
|
|
beta_end = original_config.model.params.linear_end
|
|
if args.scheduler_type == "pndm":
|
|
scheduler = PNDMScheduler(
|
|
beta_end=beta_end,
|
|
beta_schedule="scaled_linear",
|
|
beta_start=beta_start,
|
|
num_train_timesteps=num_train_timesteps,
|
|
skip_prk_steps=True,
|
|
)
|
|
elif args.scheduler_type == "lms":
|
|
scheduler = LMSDiscreteScheduler(beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear")
|
|
elif args.scheduler_type == "euler":
|
|
scheduler = EulerDiscreteScheduler(beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear")
|
|
elif args.scheduler_type == "euler-ancestral":
|
|
scheduler = EulerAncestralDiscreteScheduler(
|
|
beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear"
|
|
)
|
|
elif args.scheduler_type == "dpm":
|
|
scheduler = DPMSolverMultistepScheduler(
|
|
beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear"
|
|
)
|
|
elif args.scheduler_type == "ddim":
|
|
scheduler = DDIMScheduler(
|
|
beta_start=beta_start,
|
|
beta_end=beta_end,
|
|
beta_schedule="scaled_linear",
|
|
clip_sample=False,
|
|
set_alpha_to_one=False,
|
|
)
|
|
else:
|
|
raise ValueError(f"Scheduler of type {args.scheduler_type} doesn't exist!")
|
|
|
|
# Convert the UNet2DConditionModel model.
|
|
unet_config = create_unet_diffusers_config(original_config)
|
|
converted_unet_checkpoint = convert_ldm_unet_checkpoint(
|
|
checkpoint, unet_config, path=args.checkpoint_path, extract_ema=args.extract_ema
|
|
)
|
|
|
|
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 = convert_ldm_clip_checkpoint(checkpoint)
|
|
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
|
|
safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")
|
|
feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker")
|
|
pipe = StableDiffusionPipeline(
|
|
vae=vae,
|
|
text_encoder=text_model,
|
|
tokenizer=tokenizer,
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
safety_checker=safety_checker,
|
|
feature_extractor=feature_extractor,
|
|
)
|
|
else:
|
|
text_config = create_ldm_bert_config(original_config)
|
|
text_model = convert_ldm_bert_checkpoint(checkpoint, text_config)
|
|
tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
|
|
pipe = LDMTextToImagePipeline(vqvae=vae, bert=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
|
|
|
|
pipe.save_pretrained(args.dump_path)
|