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InvokeAI/ldm/invoke/ckpt_to_diffuser.py
Lincoln Stein 4fa91724d9 fix conversion of checkpoints into incompatible diffusers models 
- The checkpoint conversion script was generating diffusers models
  with the safety checker set to null. This resulted in models
  that could not be merged with ones that have the safety checker
  activated.

- This PR fixes the issue by incorporating the safety checker into
  all 1.x-derived checkpoints, regardless of user's nsfw_checker setting.
2023-02-18 11:07:38 -05:00

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# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Adapted for use as a module by Lincoln Stein <lstein@gmail.com>
# Original file at: https://github.com/huggingface/diffusers/blob/main/scripts/convert_ldm_original_checkpoint_to_diffusers.py
""" Conversion script for the LDM checkpoints. """
import os
import re
import torch
import warnings
from pathlib import Path
from ldm.invoke.globals import (
Globals,
global_cache_dir,
global_config_dir,
)
from safetensors.torch import load_file
from typing import Union
try:
from omegaconf import OmegaConf
except ImportError:
raise ImportError(
"OmegaConf is required to convert the LDM checkpoints. Please install it with `pip install OmegaConf`."
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
LDMTextToImagePipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNet2DConditionModel,
logging as dlogging,
)
from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder, PaintByExamplePipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import is_safetensors_available
from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextModel, CLIPTokenizer, CLIPVisionConfig
from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline
def shave_segments(path, n_shave_prefix_segments=1):
"""
Removes segments. Positive values shave the first segments, negative shave the last segments.
"""
if n_shave_prefix_segments >= 0:
return ".".join(path.split(".")[n_shave_prefix_segments:])
else:
return ".".join(path.split(".")[:n_shave_prefix_segments])
def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside resnets to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item.replace("in_layers.0", "norm1")
new_item = new_item.replace("in_layers.2", "conv1")
new_item = new_item.replace("out_layers.0", "norm2")
new_item = new_item.replace("out_layers.3", "conv2")
new_item = new_item.replace("emb_layers.1", "time_emb_proj")
new_item = new_item.replace("skip_connection", "conv_shortcut")
new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside resnets to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item
new_item = new_item.replace("nin_shortcut", "conv_shortcut")
new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def renew_attention_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside attentions to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item
# new_item = new_item.replace('norm.weight', 'group_norm.weight')
# new_item = new_item.replace('norm.bias', 'group_norm.bias')
# new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
# new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
# new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside attentions to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item
new_item = new_item.replace("norm.weight", "group_norm.weight")
new_item = new_item.replace("norm.bias", "group_norm.bias")
new_item = new_item.replace("q.weight", "query.weight")
new_item = new_item.replace("q.bias", "query.bias")
new_item = new_item.replace("k.weight", "key.weight")
new_item = new_item.replace("k.bias", "key.bias")
new_item = new_item.replace("v.weight", "value.weight")
new_item = new_item.replace("v.bias", "value.bias")
new_item = new_item.replace("proj_out.weight", "proj_attn.weight")
new_item = new_item.replace("proj_out.bias", "proj_attn.bias")
new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def assign_to_checkpoint(
paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None
):
"""
This does the final conversion step: take locally converted weights and apply a global renaming
to them. It splits attention layers, and takes into account additional replacements
that may arise.
Assigns the weights to the new checkpoint.
"""
assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."
# Splits the attention layers into three variables.
if attention_paths_to_split is not None:
for path, path_map in attention_paths_to_split.items():
old_tensor = old_checkpoint[path]
channels = old_tensor.shape[0] // 3
target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)
num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3
old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
query, key, value = old_tensor.split(channels // num_heads, dim=1)
checkpoint[path_map["query"]] = query.reshape(target_shape)
checkpoint[path_map["key"]] = key.reshape(target_shape)
checkpoint[path_map["value"]] = value.reshape(target_shape)
for path in paths:
new_path = path["new"]
# These have already been assigned
if attention_paths_to_split is not None and new_path in attention_paths_to_split:
continue
# Global renaming happens here
new_path = new_path.replace("middle_block.0", "mid_block.resnets.0")
new_path = new_path.replace("middle_block.1", "mid_block.attentions.0")
new_path = new_path.replace("middle_block.2", "mid_block.resnets.1")
if additional_replacements is not None:
for replacement in additional_replacements:
new_path = new_path.replace(replacement["old"], replacement["new"])
# proj_attn.weight has to be converted from conv 1D to linear
if "proj_attn.weight" in new_path:
checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0]
else:
checkpoint[new_path] = old_checkpoint[path["old"]]
def conv_attn_to_linear(checkpoint):
keys = list(checkpoint.keys())
attn_keys = ["query.weight", "key.weight", "value.weight"]
for key in keys:
if ".".join(key.split(".")[-2:]) in attn_keys:
if checkpoint[key].ndim > 2:
checkpoint[key] = checkpoint[key][:, :, 0, 0]
elif "proj_attn.weight" in key:
if checkpoint[key].ndim > 2:
checkpoint[key] = checkpoint[key][:, :, 0]
def create_unet_diffusers_config(original_config, image_size: int):
"""
Creates a config for the diffusers based on the config of the LDM model.
"""
unet_params = original_config.model.params.unet_config.params
vae_params = original_config.model.params.first_stage_config.params.ddconfig
block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult]
down_block_types = []
resolution = 1
for i in range(len(block_out_channels)):
block_type = "CrossAttnDownBlock2D" if resolution in unet_params.attention_resolutions else "DownBlock2D"
down_block_types.append(block_type)
if i != len(block_out_channels) - 1:
resolution *= 2
up_block_types = []
for i in range(len(block_out_channels)):
block_type = "CrossAttnUpBlock2D" if resolution in unet_params.attention_resolutions else "UpBlock2D"
up_block_types.append(block_type)
resolution //= 2
vae_scale_factor = 2 ** (len(vae_params.ch_mult) - 1)
head_dim = unet_params.num_heads if "num_heads" in unet_params else None
use_linear_projection = (
unet_params.use_linear_in_transformer if "use_linear_in_transformer" in unet_params else False
)
if use_linear_projection:
# stable diffusion 2-base-512 and 2-768
if head_dim is None:
head_dim = [5, 10, 20, 20]
config = dict(
sample_size=image_size // vae_scale_factor,
in_channels=unet_params.in_channels,
out_channels=unet_params.out_channels,
down_block_types=tuple(down_block_types),
up_block_types=tuple(up_block_types),
block_out_channels=tuple(block_out_channels),
layers_per_block=unet_params.num_res_blocks,
cross_attention_dim=unet_params.context_dim,
attention_head_dim=head_dim,
use_linear_projection=use_linear_projection,
)
return config
def create_vae_diffusers_config(original_config, image_size: int):
"""
Creates a config for the diffusers based on the config of the LDM model.
"""
vae_params = original_config.model.params.first_stage_config.params.ddconfig
_ = original_config.model.params.first_stage_config.params.embed_dim
block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult]
down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels)
up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels)
config = dict(
sample_size=image_size,
in_channels=vae_params.in_channels,
out_channels=vae_params.out_ch,
down_block_types=tuple(down_block_types),
up_block_types=tuple(up_block_types),
block_out_channels=tuple(block_out_channels),
latent_channels=vae_params.z_channels,
layers_per_block=vae_params.num_res_blocks,
)
return config
def create_diffusers_schedular(original_config):
schedular = DDIMScheduler(
num_train_timesteps=original_config.model.params.timesteps,
beta_start=original_config.model.params.linear_start,
beta_end=original_config.model.params.linear_end,
beta_schedule="scaled_linear",
)
return schedular
def create_ldm_bert_config(original_config):
bert_params = original_config.model.params.cond_stage_config.params
config = LDMBertConfig(
d_model=bert_params.n_embed,
encoder_layers=bert_params.n_layer,
encoder_ffn_dim=bert_params.n_embed * 4,
)
return config
def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False):
"""
Takes a state dict and a config, and returns a converted checkpoint.
"""
# extract state_dict for UNet
unet_state_dict = {}
keys = list(checkpoint.keys())
unet_key = "model.diffusion_model."
# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
if sum(k.startswith("model_ema") for k in keys) > 100:
print(f" | Checkpoint {path} has both EMA and non-EMA weights.")
if extract_ema:
print(
' | Extracting EMA weights (usually better for inference)'
)
for key in keys:
if key.startswith("model.diffusion_model"):
flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key)
else:
print(
' | Extracting only the non-EMA weights (usually better for fine-tuning)'
)
for key in keys:
if key.startswith(unet_key):
unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)
new_checkpoint = {}
new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"]
new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"]
new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"]
new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"]
new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"]
new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"]
new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"]
new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"]
new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"]
new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"]
# Retrieves the keys for the input blocks only
num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer})
input_blocks = {
layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]
for layer_id in range(num_input_blocks)
}
# Retrieves the keys for the middle blocks only
num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer})
middle_blocks = {
layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
for layer_id in range(num_middle_blocks)
}
# Retrieves the keys for the output blocks only
num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer})
output_blocks = {
layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]
for layer_id in range(num_output_blocks)
}
for i in range(1, num_input_blocks):
block_id = (i - 1) // (config["layers_per_block"] + 1)
layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1)
resnets = [
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
]
attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
if f"input_blocks.{i}.0.op.weight" in unet_state_dict:
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop(
f"input_blocks.{i}.0.op.weight"
)
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop(
f"input_blocks.{i}.0.op.bias"
)
paths = renew_resnet_paths(resnets)
meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
assign_to_checkpoint(
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
if len(attentions):
paths = renew_attention_paths(attentions)
meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
assign_to_checkpoint(
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
resnet_0 = middle_blocks[0]
attentions = middle_blocks[1]
resnet_1 = middle_blocks[2]
resnet_0_paths = renew_resnet_paths(resnet_0)
assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)
resnet_1_paths = renew_resnet_paths(resnet_1)
assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)
attentions_paths = renew_attention_paths(attentions)
meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(
attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
for i in range(num_output_blocks):
block_id = i // (config["layers_per_block"] + 1)
layer_in_block_id = i % (config["layers_per_block"] + 1)
output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]
output_block_list = {}
for layer in output_block_layers:
layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1)
if layer_id in output_block_list:
output_block_list[layer_id].append(layer_name)
else:
output_block_list[layer_id] = [layer_name]
if len(output_block_list) > 1:
resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
resnet_0_paths = renew_resnet_paths(resnets)
paths = renew_resnet_paths(resnets)
meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
assign_to_checkpoint(
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
output_block_list = {k: sorted(v) for k, v in output_block_list.items()}
if ["conv.bias", "conv.weight"] in output_block_list.values():
index = list(output_block_list.values()).index(["conv.bias", "conv.weight"])
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",cache_dir=global_cache_dir('hub'))
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
textenc_conversion_lst = [
("cond_stage_model.model.positional_embedding", "text_model.embeddings.position_embedding.weight"),
("cond_stage_model.model.token_embedding.weight", "text_model.embeddings.token_embedding.weight"),
("cond_stage_model.model.ln_final.weight", "text_model.final_layer_norm.weight"),
("cond_stage_model.model.ln_final.bias", "text_model.final_layer_norm.bias"),
]
textenc_conversion_map = {x[0]: x[1] for x in textenc_conversion_lst}
textenc_transformer_conversion_lst = [
# (stable-diffusion, HF Diffusers)
("resblocks.", "text_model.encoder.layers."),
("ln_1", "layer_norm1"),
("ln_2", "layer_norm2"),
(".c_fc.", ".fc1."),
(".c_proj.", ".fc2."),
(".attn", ".self_attn"),
("ln_final.", "transformer.text_model.final_layer_norm."),
("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
]
protected = {re.escape(x[0]): x[1] for x in textenc_transformer_conversion_lst}
textenc_pattern = re.compile("|".join(protected.keys()))
def convert_paint_by_example_checkpoint(checkpoint):
cache_dir = global_cache_dir('hub')
config = CLIPVisionConfig.from_pretrained("openai/clip-vit-large-patch14",cache_dir=cache_dir)
model = PaintByExampleImageEncoder(config)
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]
# load clip vision
model.model.load_state_dict(text_model_dict)
# load mapper
keys_mapper = {
k[len("cond_stage_model.mapper.res") :]: v
for k, v in checkpoint.items()
if k.startswith("cond_stage_model.mapper")
}
MAPPING = {
"attn.c_qkv": ["attn1.to_q", "attn1.to_k", "attn1.to_v"],
"attn.c_proj": ["attn1.to_out.0"],
"ln_1": ["norm1"],
"ln_2": ["norm3"],
"mlp.c_fc": ["ff.net.0.proj"],
"mlp.c_proj": ["ff.net.2"],
}
mapped_weights = {}
for key, value in keys_mapper.items():
prefix = key[: len("blocks.i")]
suffix = key.split(prefix)[-1].split(".")[-1]
name = key.split(prefix)[-1].split(suffix)[0][1:-1]
mapped_names = MAPPING[name]
num_splits = len(mapped_names)
for i, mapped_name in enumerate(mapped_names):
new_name = ".".join([prefix, mapped_name, suffix])
shape = value.shape[0] // num_splits
mapped_weights[new_name] = value[i * shape : (i + 1) * shape]
model.mapper.load_state_dict(mapped_weights)
# load final layer norm
model.final_layer_norm.load_state_dict(
{
"bias": checkpoint["cond_stage_model.final_ln.bias"],
"weight": checkpoint["cond_stage_model.final_ln.weight"],
}
)
# load final proj
model.proj_out.load_state_dict(
{
"bias": checkpoint["proj_out.bias"],
"weight": checkpoint["proj_out.weight"],
}
)
# load uncond vector
model.uncond_vector.data = torch.nn.Parameter(checkpoint["learnable_vector"])
return model
def convert_open_clip_checkpoint(checkpoint):
cache_dir=global_cache_dir('hub')
text_model = CLIPTextModel.from_pretrained("stabilityai/stable-diffusion-2", subfolder="text_encoder", cache_dir=cache_dir)
keys = list(checkpoint.keys())
text_model_dict = {}
d_model = int(checkpoint["cond_stage_model.model.text_projection"].shape[0])
text_model_dict["text_model.embeddings.position_ids"] = text_model.text_model.embeddings.get_buffer("position_ids")
for key in keys:
if "resblocks.23" in key: # Diffusers drops the final layer and only uses the penultimate layer
continue
if key in textenc_conversion_map:
text_model_dict[textenc_conversion_map[key]] = checkpoint[key]
if key.startswith("cond_stage_model.model.transformer."):
new_key = key[len("cond_stage_model.model.transformer.") :]
if new_key.endswith(".in_proj_weight"):
new_key = new_key[: -len(".in_proj_weight")]
new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)
text_model_dict[new_key + ".q_proj.weight"] = checkpoint[key][:d_model, :]
text_model_dict[new_key + ".k_proj.weight"] = checkpoint[key][d_model : d_model * 2, :]
text_model_dict[new_key + ".v_proj.weight"] = checkpoint[key][d_model * 2 :, :]
elif new_key.endswith(".in_proj_bias"):
new_key = new_key[: -len(".in_proj_bias")]
new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)
text_model_dict[new_key + ".q_proj.bias"] = checkpoint[key][:d_model]
text_model_dict[new_key + ".k_proj.bias"] = checkpoint[key][d_model : d_model * 2]
text_model_dict[new_key + ".v_proj.bias"] = checkpoint[key][d_model * 2 :]
else:
new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)
text_model_dict[new_key] = checkpoint[key]
text_model.load_state_dict(text_model_dict)
return text_model
def load_pipeline_from_original_stable_diffusion_ckpt(
checkpoint_path:str,
original_config_file:str=None,
num_in_channels:int=None,
scheduler_type:str='pndm',
pipeline_type:str=None,
image_size:int=None,
prediction_type:str=None,
extract_ema:bool=True,
upcast_attn:bool=False,
vae:AutoencoderKL=None,
return_generator_pipeline:bool=False,
)->Union[StableDiffusionPipeline,StableDiffusionGeneratorPipeline]:
'''
Load a Stable Diffusion pipeline object from a CompVis-style `.ckpt`/`.safetensors` file and (ideally) a `.yaml`
config file.
Although many of the arguments can be automatically inferred, some of these rely on brittle checks against the
global step count, which will likely fail for models that have undergone further fine-tuning. Therefore, it is
recommended that you override the default values and/or supply an `original_config_file` wherever possible.
:param checkpoint_path: Path to `.ckpt` file.
:param original_config_file: Path to `.yaml` config file corresponding to the original architecture.
If `None`, will be automatically inferred by looking for a key that only exists in SD2.0 models.
:param image_size: The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Diffusion v2
Base. Use 768 for Stable Diffusion v2.
:param prediction_type: The prediction type that the model was trained on. Use `'epsilon'` for Stable Diffusion
v1.X and Stable Diffusion v2 Base. Use `'v-prediction'` for Stable Diffusion v2.
:param num_in_channels: The number of input channels. If `None` number of input channels will be automatically
inferred.
:param scheduler_type: Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler",
"euler-ancestral", "dpm", "ddim"]`. :param model_type: The pipeline type. `None` to automatically infer, or one of
`["FrozenOpenCLIPEmbedder", "FrozenCLIPEmbedder", "PaintByExample"]`. :param extract_ema: Only relevant for
checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights
or not. Defaults to `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher
quality images for inference. Non-EMA weights are usually better to continue fine-tuning.
:param upcast_attention: Whether the attention computation should always be upcasted. This is necessary when
running stable diffusion 2.1.
'''
with warnings.catch_warnings():
warnings.simplefilter('ignore')
verbosity = dlogging.get_verbosity()
dlogging.set_verbosity_error()
checkpoint = load_file(checkpoint_path) if Path(checkpoint_path).suffix == '.safetensors' else torch.load(checkpoint_path)
cache_dir = global_cache_dir('hub')
pipeline_class = StableDiffusionGeneratorPipeline if return_generator_pipeline else StableDiffusionPipeline
# Sometimes models don't have the global_step item
if "global_step" in checkpoint:
global_step = checkpoint["global_step"]
else:
print(" | global_step key not found in model")
global_step = None
# sometimes there is a state_dict key and sometimes not
if 'state_dict' in checkpoint:
checkpoint = checkpoint["state_dict"]
upcast_attention = False
if original_config_file is None:
key_name = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
if key_name in checkpoint and checkpoint[key_name].shape[-1] == 1024:
original_config_file = global_config_dir() / 'stable-diffusion' / 'v2-inference-v.yaml'
if global_step == 110000:
# v2.1 needs to upcast attention
upcast_attention = True
elif str(checkpoint_path).lower().find('inpaint') >= 0: # brittle - please pass original_config_file parameter!
print(f' | checkpoint has "inpaint" in name, assuming an inpainting model')
original_config_file = global_config_dir() / 'stable-diffusion' / 'v1-inpainting-inference.yaml'
else:
original_config_file = global_config_dir() / 'stable-diffusion' / 'v1-inference.yaml'
original_config = OmegaConf.load(original_config_file)
if num_in_channels is not None:
original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels
if (
"parameterization" in original_config["model"]["params"]
and original_config["model"]["params"]["parameterization"] == "v"
):
if prediction_type is None:
# NOTE: For stable diffusion 2 base it is recommended to pass `prediction_type=="epsilon"`
# as it relies on a brittle global step parameter here
prediction_type = "epsilon" if global_step == 875000 else "v_prediction"
if image_size is None:
# NOTE: For stable diffusion 2 base one has to pass `image_size==512`
# as it relies on a brittle global step parameter here
image_size = 512 if global_step == 875000 else 768
else:
if prediction_type is None:
prediction_type = "epsilon"
if image_size is None:
image_size = 512
num_train_timesteps = original_config.model.params.timesteps
beta_start = original_config.model.params.linear_start
beta_end = original_config.model.params.linear_end
scheduler = DDIMScheduler(
beta_end=beta_end,
beta_schedule="scaled_linear",
beta_start=beta_start,
num_train_timesteps=num_train_timesteps,
steps_offset=1,
clip_sample=False,
set_alpha_to_one=False,
prediction_type=prediction_type,
)
# make sure scheduler works correctly with DDIM
scheduler.register_to_config(clip_sample=False)
if scheduler_type == "pndm":
config = dict(scheduler.config)
config["skip_prk_steps"] = True
scheduler = PNDMScheduler.from_config(config)
elif scheduler_type == "lms":
scheduler = LMSDiscreteScheduler.from_config(scheduler.config)
elif scheduler_type == "heun":
scheduler = HeunDiscreteScheduler.from_config(scheduler.config)
elif scheduler_type == "euler":
scheduler = EulerDiscreteScheduler.from_config(scheduler.config)
elif scheduler_type == "euler-ancestral":
scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config)
elif scheduler_type == "dpm":
scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)
elif scheduler_type == "ddim":
scheduler = scheduler
else:
raise ValueError(f"Scheduler of type {scheduler_type} doesn't exist!")
# Convert the UNet2DConditionModel model.
unet_config = create_unet_diffusers_config(original_config, image_size=image_size)
unet_config["upcast_attention"] = upcast_attention
unet = UNet2DConditionModel(**unet_config)
converted_unet_checkpoint = convert_ldm_unet_checkpoint(
checkpoint, unet_config, path=checkpoint_path, extract_ema=extract_ema
)
unet.load_state_dict(converted_unet_checkpoint)
# Convert the VAE model, or use the one passed
if not vae:
print(' | Using checkpoint model\'s original VAE')
vae_config = create_vae_diffusers_config(original_config, image_size=image_size)
converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
vae = AutoencoderKL(**vae_config)
vae.load_state_dict(converted_vae_checkpoint)
else:
print(' | Using external VAE specified in config')
# Convert the text model.
model_type = pipeline_type
if model_type is None:
model_type = original_config.model.params.cond_stage_config.target.split(".")[-1]
if model_type == "FrozenOpenCLIPEmbedder":
text_model = convert_open_clip_checkpoint(checkpoint)
tokenizer = CLIPTokenizer.from_pretrained("stabilityai/stable-diffusion-2",
subfolder="tokenizer",
cache_dir=global_cache_dir('diffusers')
)
pipe = pipeline_class(
vae=vae,
text_encoder=text_model,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=None,
feature_extractor=None,
requires_safety_checker=False,
)
elif model_type == "PaintByExample":
vision_model = convert_paint_by_example_checkpoint(checkpoint)
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14",cache_dir=cache_dir)
feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker",cache_dir=cache_dir)
pipe = PaintByExamplePipeline(
vae=vae,
image_encoder=vision_model,
unet=unet,
scheduler=scheduler,
safety_checker=None,
feature_extractor=feature_extractor,
)
elif model_type in ['FrozenCLIPEmbedder','WeightedFrozenCLIPEmbedder']:
text_model = convert_ldm_clip_checkpoint(checkpoint)
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14",cache_dir=cache_dir)
safety_checker = StableDiffusionSafetyChecker.from_pretrained('CompVis/stable-diffusion-safety-checker',cache_dir=global_cache_dir("hub"))
feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker",cache_dir=cache_dir)
pipe = pipeline_class(
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",cache_dir=cache_dir)
pipe = LDMTextToImagePipeline(vqvae=vae, bert=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
dlogging.set_verbosity(verbosity)
return pipe
def convert_ckpt_to_diffuser(
checkpoint_path:Union[str,Path],
dump_path:Union[str,Path],
**kwargs,
):
'''
Takes all the arguments of load_pipeline_from_original_stable_diffusion_ckpt(),
and in addition a path-like object indicating the location of the desired diffusers
model to be written.
'''
pipe = load_pipeline_from_original_stable_diffusion_ckpt(
checkpoint_path,
**kwargs
)
pipe.save_pretrained(
dump_path,
safe_serialization=is_safetensors_available(),
)
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