mirror of
https://github.com/sd-webui/stable-diffusion-webui.git
synced 2024-12-14 23:02:00 +03:00
0d734219a8
Fixed the script that convert ckpt models to diffusers not working with some models if they had invalid or broken keys, these keys are now ignored so we can still convert the model.
754 lines
31 KiB
Python
754 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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unet_params = original_config.model.params.unet_config.params
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block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult]
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down_block_types = []
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resolution = 1
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for i in range(len(block_out_channels)):
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block_type = "CrossAttnDownBlock2D" if resolution in unet_params.attention_resolutions else "DownBlock2D"
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down_block_types.append(block_type)
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if i != len(block_out_channels) - 1:
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resolution *= 2
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up_block_types = []
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for i in range(len(block_out_channels)):
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block_type = "CrossAttnUpBlock2D" if resolution in unet_params.attention_resolutions else "UpBlock2D"
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up_block_types.append(block_type)
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resolution //= 2
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config = dict(
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sample_size=unet_params.image_size,
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in_channels=unet_params.in_channels,
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out_channels=unet_params.out_channels,
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down_block_types=tuple(down_block_types),
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up_block_types=tuple(up_block_types),
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block_out_channels=tuple(block_out_channels),
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layers_per_block=unet_params.num_res_blocks,
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cross_attention_dim=unet_params.context_dim,
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attention_head_dim=unet_params.num_heads,
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)
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return config
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def create_vae_diffusers_config(original_config):
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"""
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Creates a config for the diffusers based on the config of the LDM model.
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"""
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vae_params = original_config.model.params.first_stage_config.params.ddconfig
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_ = 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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)
|
|
|
|
if ["conv.weight", "conv.bias"] in output_block_list.values():
|
|
index = list(output_block_list.values()).index(["conv.weight", "conv.bias"])
|
|
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
|
|
f"output_blocks.{i}.{index}.conv.weight"
|
|
]
|
|
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
|
|
f"output_blocks.{i}.{index}.conv.bias"
|
|
]
|
|
|
|
# Clear attentions as they have been attributed above.
|
|
if len(attentions) == 2:
|
|
attentions = []
|
|
|
|
if len(attentions):
|
|
paths = renew_attention_paths(attentions)
|
|
meta_path = {
|
|
"old": f"output_blocks.{i}.1",
|
|
"new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
|
|
}
|
|
assign_to_checkpoint(
|
|
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
|
|
)
|
|
else:
|
|
resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)
|
|
for path in resnet_0_paths:
|
|
old_path = ".".join(["output_blocks", str(i), path["old"]])
|
|
new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]])
|
|
|
|
new_checkpoint[new_path] = unet_state_dict[old_path]
|
|
|
|
return new_checkpoint
|
|
|
|
|
|
def convert_ldm_vae_checkpoint(checkpoint, config):
|
|
# extract state dict for VAE
|
|
vae_state_dict = {}
|
|
vae_key = "first_stage_model."
|
|
keys = list(checkpoint.keys())
|
|
for key in keys:
|
|
if key.startswith(vae_key):
|
|
vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key)
|
|
|
|
new_checkpoint = {}
|
|
|
|
new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
|
|
new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
|
|
new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"]
|
|
new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
|
|
new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"]
|
|
new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"]
|
|
|
|
new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
|
|
new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
|
|
new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"]
|
|
new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
|
|
new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"]
|
|
new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"]
|
|
|
|
new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
|
|
new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
|
|
new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
|
|
new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]
|
|
|
|
# Retrieves the keys for the encoder down blocks only
|
|
num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer})
|
|
down_blocks = {
|
|
layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
|
|
}
|
|
|
|
# Retrieves the keys for the decoder up blocks only
|
|
num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer})
|
|
up_blocks = {
|
|
layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)
|
|
}
|
|
|
|
for i in range(num_down_blocks):
|
|
resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
|
|
|
|
if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
|
|
new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
|
|
f"encoder.down.{i}.downsample.conv.weight"
|
|
)
|
|
new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop(
|
|
f"encoder.down.{i}.downsample.conv.bias"
|
|
)
|
|
|
|
paths = renew_vae_resnet_paths(resnets)
|
|
meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
|
|
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
|
|
|
|
mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
|
|
num_mid_res_blocks = 2
|
|
for i in range(1, num_mid_res_blocks + 1):
|
|
resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]
|
|
|
|
paths = renew_vae_resnet_paths(resnets)
|
|
meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
|
|
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
|
|
|
|
mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
|
|
paths = renew_vae_attention_paths(mid_attentions)
|
|
meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
|
|
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
|
|
conv_attn_to_linear(new_checkpoint)
|
|
|
|
for i in range(num_up_blocks):
|
|
block_id = num_up_blocks - 1 - i
|
|
resnets = [
|
|
key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
|
|
]
|
|
|
|
if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
|
|
new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[
|
|
f"decoder.up.{block_id}.upsample.conv.weight"
|
|
]
|
|
new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[
|
|
f"decoder.up.{block_id}.upsample.conv.bias"
|
|
]
|
|
|
|
paths = renew_vae_resnet_paths(resnets)
|
|
meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
|
|
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
|
|
|
|
mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
|
|
num_mid_res_blocks = 2
|
|
for i in range(1, num_mid_res_blocks + 1):
|
|
resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]
|
|
|
|
paths = renew_vae_resnet_paths(resnets)
|
|
meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
|
|
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
|
|
|
|
mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
|
|
paths = renew_vae_attention_paths(mid_attentions)
|
|
meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
|
|
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
|
|
conv_attn_to_linear(new_checkpoint)
|
|
return new_checkpoint
|
|
|
|
|
|
def convert_ldm_bert_checkpoint(checkpoint, config):
|
|
def _copy_attn_layer(hf_attn_layer, pt_attn_layer):
|
|
hf_attn_layer.q_proj.weight.data = pt_attn_layer.to_q.weight
|
|
hf_attn_layer.k_proj.weight.data = pt_attn_layer.to_k.weight
|
|
hf_attn_layer.v_proj.weight.data = pt_attn_layer.to_v.weight
|
|
|
|
hf_attn_layer.out_proj.weight = pt_attn_layer.to_out.weight
|
|
hf_attn_layer.out_proj.bias = pt_attn_layer.to_out.bias
|
|
|
|
def _copy_linear(hf_linear, pt_linear):
|
|
hf_linear.weight = pt_linear.weight
|
|
hf_linear.bias = pt_linear.bias
|
|
|
|
def _copy_layer(hf_layer, pt_layer):
|
|
# copy layer norms
|
|
_copy_linear(hf_layer.self_attn_layer_norm, pt_layer[0][0])
|
|
_copy_linear(hf_layer.final_layer_norm, pt_layer[1][0])
|
|
|
|
# copy attn
|
|
_copy_attn_layer(hf_layer.self_attn, pt_layer[0][1])
|
|
|
|
# copy MLP
|
|
pt_mlp = pt_layer[1][1]
|
|
_copy_linear(hf_layer.fc1, pt_mlp.net[0][0])
|
|
_copy_linear(hf_layer.fc2, pt_mlp.net[2])
|
|
|
|
def _copy_layers(hf_layers, pt_layers):
|
|
for i, hf_layer in enumerate(hf_layers):
|
|
if i != 0:
|
|
i += i
|
|
pt_layer = pt_layers[i : i + 2]
|
|
_copy_layer(hf_layer, pt_layer)
|
|
|
|
hf_model = LDMBertModel(config).eval()
|
|
|
|
# copy embeds
|
|
hf_model.model.embed_tokens.weight = checkpoint.transformer.token_emb.weight
|
|
hf_model.model.embed_positions.weight.data = checkpoint.transformer.pos_emb.emb.weight
|
|
|
|
# copy layer norm
|
|
_copy_linear(hf_model.model.layer_norm, checkpoint.transformer.norm)
|
|
|
|
# copy hidden layers
|
|
_copy_layers(hf_model.model.layers, checkpoint.transformer.attn_layers.layers)
|
|
|
|
_copy_linear(hf_model.to_logits, checkpoint.transformer.to_logits)
|
|
|
|
return hf_model
|
|
|
|
|
|
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]
|
|
|
|
try:
|
|
text_model.load_state_dict(text_model_dict)
|
|
except RuntimeError:
|
|
pass
|
|
|
|
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) |