mirror of
https://github.com/huggingface/diffusers.git
synced 2026-01-27 17:22:53 +03:00
371f765908
* fix: bias loading bug * fixes for SDXL * apply changes to the conversion script to match single_file_utils.py * do transpose to match the single file loading logic.
1388 lines
56 KiB
Python
1388 lines
56 KiB
Python
# coding=utf-8
|
|
# Copyright 2024 The HuggingFace Inc. team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
""" Conversion script for the Stable Diffusion checkpoints."""
|
|
|
|
import os
|
|
import re
|
|
from contextlib import nullcontext
|
|
from io import BytesIO
|
|
from urllib.parse import urlparse
|
|
|
|
import requests
|
|
import yaml
|
|
|
|
from ..models.modeling_utils import load_state_dict
|
|
from ..schedulers import (
|
|
DDIMScheduler,
|
|
DDPMScheduler,
|
|
DPMSolverMultistepScheduler,
|
|
EulerAncestralDiscreteScheduler,
|
|
EulerDiscreteScheduler,
|
|
HeunDiscreteScheduler,
|
|
LMSDiscreteScheduler,
|
|
PNDMScheduler,
|
|
)
|
|
from ..utils import is_accelerate_available, is_transformers_available, logging
|
|
from ..utils.hub_utils import _get_model_file
|
|
|
|
|
|
if is_transformers_available():
|
|
from transformers import (
|
|
CLIPTextConfig,
|
|
CLIPTextModel,
|
|
CLIPTextModelWithProjection,
|
|
CLIPTokenizer,
|
|
)
|
|
|
|
if is_accelerate_available():
|
|
from accelerate import init_empty_weights
|
|
from accelerate.utils import set_module_tensor_to_device
|
|
|
|
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
|
|
|
CONFIG_URLS = {
|
|
"v1": "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml",
|
|
"v2": "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml",
|
|
"xl": "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml",
|
|
"xl_refiner": "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_refiner.yaml",
|
|
"upscale": "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/x4-upscaling.yaml",
|
|
"controlnet": "https://raw.githubusercontent.com/lllyasviel/ControlNet/main/models/cldm_v15.yaml",
|
|
}
|
|
|
|
CHECKPOINT_KEY_NAMES = {
|
|
"v2": "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight",
|
|
"xl_base": "conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_proj.bias",
|
|
"xl_refiner": "conditioner.embedders.0.model.transformer.resblocks.9.mlp.c_proj.bias",
|
|
}
|
|
|
|
SCHEDULER_DEFAULT_CONFIG = {
|
|
"beta_schedule": "scaled_linear",
|
|
"beta_start": 0.00085,
|
|
"beta_end": 0.012,
|
|
"interpolation_type": "linear",
|
|
"num_train_timesteps": 1000,
|
|
"prediction_type": "epsilon",
|
|
"sample_max_value": 1.0,
|
|
"set_alpha_to_one": False,
|
|
"skip_prk_steps": True,
|
|
"steps_offset": 1,
|
|
"timestep_spacing": "leading",
|
|
}
|
|
|
|
DIFFUSERS_TO_LDM_MAPPING = {
|
|
"unet": {
|
|
"layers": {
|
|
"time_embedding.linear_1.weight": "time_embed.0.weight",
|
|
"time_embedding.linear_1.bias": "time_embed.0.bias",
|
|
"time_embedding.linear_2.weight": "time_embed.2.weight",
|
|
"time_embedding.linear_2.bias": "time_embed.2.bias",
|
|
"conv_in.weight": "input_blocks.0.0.weight",
|
|
"conv_in.bias": "input_blocks.0.0.bias",
|
|
"conv_norm_out.weight": "out.0.weight",
|
|
"conv_norm_out.bias": "out.0.bias",
|
|
"conv_out.weight": "out.2.weight",
|
|
"conv_out.bias": "out.2.bias",
|
|
},
|
|
"class_embed_type": {
|
|
"class_embedding.linear_1.weight": "label_emb.0.0.weight",
|
|
"class_embedding.linear_1.bias": "label_emb.0.0.bias",
|
|
"class_embedding.linear_2.weight": "label_emb.0.2.weight",
|
|
"class_embedding.linear_2.bias": "label_emb.0.2.bias",
|
|
},
|
|
"addition_embed_type": {
|
|
"add_embedding.linear_1.weight": "label_emb.0.0.weight",
|
|
"add_embedding.linear_1.bias": "label_emb.0.0.bias",
|
|
"add_embedding.linear_2.weight": "label_emb.0.2.weight",
|
|
"add_embedding.linear_2.bias": "label_emb.0.2.bias",
|
|
},
|
|
},
|
|
"controlnet": {
|
|
"layers": {
|
|
"time_embedding.linear_1.weight": "time_embed.0.weight",
|
|
"time_embedding.linear_1.bias": "time_embed.0.bias",
|
|
"time_embedding.linear_2.weight": "time_embed.2.weight",
|
|
"time_embedding.linear_2.bias": "time_embed.2.bias",
|
|
"conv_in.weight": "input_blocks.0.0.weight",
|
|
"conv_in.bias": "input_blocks.0.0.bias",
|
|
"controlnet_cond_embedding.conv_in.weight": "input_hint_block.0.weight",
|
|
"controlnet_cond_embedding.conv_in.bias": "input_hint_block.0.bias",
|
|
"controlnet_cond_embedding.conv_out.weight": "input_hint_block.14.weight",
|
|
"controlnet_cond_embedding.conv_out.bias": "input_hint_block.14.bias",
|
|
},
|
|
"class_embed_type": {
|
|
"class_embedding.linear_1.weight": "label_emb.0.0.weight",
|
|
"class_embedding.linear_1.bias": "label_emb.0.0.bias",
|
|
"class_embedding.linear_2.weight": "label_emb.0.2.weight",
|
|
"class_embedding.linear_2.bias": "label_emb.0.2.bias",
|
|
},
|
|
"addition_embed_type": {
|
|
"add_embedding.linear_1.weight": "label_emb.0.0.weight",
|
|
"add_embedding.linear_1.bias": "label_emb.0.0.bias",
|
|
"add_embedding.linear_2.weight": "label_emb.0.2.weight",
|
|
"add_embedding.linear_2.bias": "label_emb.0.2.bias",
|
|
},
|
|
},
|
|
"vae": {
|
|
"encoder.conv_in.weight": "encoder.conv_in.weight",
|
|
"encoder.conv_in.bias": "encoder.conv_in.bias",
|
|
"encoder.conv_out.weight": "encoder.conv_out.weight",
|
|
"encoder.conv_out.bias": "encoder.conv_out.bias",
|
|
"encoder.conv_norm_out.weight": "encoder.norm_out.weight",
|
|
"encoder.conv_norm_out.bias": "encoder.norm_out.bias",
|
|
"decoder.conv_in.weight": "decoder.conv_in.weight",
|
|
"decoder.conv_in.bias": "decoder.conv_in.bias",
|
|
"decoder.conv_out.weight": "decoder.conv_out.weight",
|
|
"decoder.conv_out.bias": "decoder.conv_out.bias",
|
|
"decoder.conv_norm_out.weight": "decoder.norm_out.weight",
|
|
"decoder.conv_norm_out.bias": "decoder.norm_out.bias",
|
|
"quant_conv.weight": "quant_conv.weight",
|
|
"quant_conv.bias": "quant_conv.bias",
|
|
"post_quant_conv.weight": "post_quant_conv.weight",
|
|
"post_quant_conv.bias": "post_quant_conv.bias",
|
|
},
|
|
"openclip": {
|
|
"layers": {
|
|
"text_model.embeddings.position_embedding.weight": "positional_embedding",
|
|
"text_model.embeddings.token_embedding.weight": "token_embedding.weight",
|
|
"text_model.final_layer_norm.weight": "ln_final.weight",
|
|
"text_model.final_layer_norm.bias": "ln_final.bias",
|
|
"text_projection.weight": "text_projection",
|
|
},
|
|
"transformer": {
|
|
"text_model.encoder.layers.": "resblocks.",
|
|
"layer_norm1": "ln_1",
|
|
"layer_norm2": "ln_2",
|
|
".fc1.": ".c_fc.",
|
|
".fc2.": ".c_proj.",
|
|
".self_attn": ".attn",
|
|
"transformer.text_model.final_layer_norm.": "ln_final.",
|
|
"transformer.text_model.embeddings.token_embedding.weight": "token_embedding.weight",
|
|
"transformer.text_model.embeddings.position_embedding.weight": "positional_embedding",
|
|
},
|
|
},
|
|
}
|
|
|
|
LDM_VAE_KEY = "first_stage_model."
|
|
LDM_UNET_KEY = "model.diffusion_model."
|
|
LDM_CONTROLNET_KEY = "control_model."
|
|
LDM_CLIP_PREFIX_TO_REMOVE = ["cond_stage_model.transformer.", "conditioner.embedders.0.transformer."]
|
|
LDM_OPEN_CLIP_TEXT_PROJECTION_DIM = 1024
|
|
|
|
SD_2_TEXT_ENCODER_KEYS_TO_IGNORE = [
|
|
"cond_stage_model.model.transformer.resblocks.23.attn.in_proj_bias",
|
|
"cond_stage_model.model.transformer.resblocks.23.attn.in_proj_weight",
|
|
"cond_stage_model.model.transformer.resblocks.23.attn.out_proj.bias",
|
|
"cond_stage_model.model.transformer.resblocks.23.attn.out_proj.weight",
|
|
"cond_stage_model.model.transformer.resblocks.23.ln_1.bias",
|
|
"cond_stage_model.model.transformer.resblocks.23.ln_1.weight",
|
|
"cond_stage_model.model.transformer.resblocks.23.ln_2.bias",
|
|
"cond_stage_model.model.transformer.resblocks.23.ln_2.weight",
|
|
"cond_stage_model.model.transformer.resblocks.23.mlp.c_fc.bias",
|
|
"cond_stage_model.model.transformer.resblocks.23.mlp.c_fc.weight",
|
|
"cond_stage_model.model.transformer.resblocks.23.mlp.c_proj.bias",
|
|
"cond_stage_model.model.transformer.resblocks.23.mlp.c_proj.weight",
|
|
"cond_stage_model.model.text_projection",
|
|
]
|
|
|
|
|
|
VALID_URL_PREFIXES = ["https://huggingface.co/", "huggingface.co/", "hf.co/", "https://hf.co/"]
|
|
|
|
|
|
def _extract_repo_id_and_weights_name(pretrained_model_name_or_path):
|
|
pattern = r"([^/]+)/([^/]+)/(?:blob/main/)?(.+)"
|
|
weights_name = None
|
|
repo_id = (None,)
|
|
for prefix in VALID_URL_PREFIXES:
|
|
pretrained_model_name_or_path = pretrained_model_name_or_path.replace(prefix, "")
|
|
match = re.match(pattern, pretrained_model_name_or_path)
|
|
if not match:
|
|
return repo_id, weights_name
|
|
|
|
repo_id = f"{match.group(1)}/{match.group(2)}"
|
|
weights_name = match.group(3)
|
|
|
|
return repo_id, weights_name
|
|
|
|
|
|
def fetch_ldm_config_and_checkpoint(
|
|
pretrained_model_link_or_path,
|
|
class_name,
|
|
original_config_file=None,
|
|
resume_download=False,
|
|
force_download=False,
|
|
proxies=None,
|
|
token=None,
|
|
cache_dir=None,
|
|
local_files_only=None,
|
|
revision=None,
|
|
use_safetensors=True,
|
|
):
|
|
file_extension = pretrained_model_link_or_path.rsplit(".", 1)[-1]
|
|
from_safetensors = file_extension == "safetensors"
|
|
|
|
if from_safetensors and use_safetensors is False:
|
|
raise ValueError("Make sure to install `safetensors` with `pip install safetensors`.")
|
|
|
|
if os.path.isfile(pretrained_model_link_or_path):
|
|
checkpoint = load_state_dict(pretrained_model_link_or_path)
|
|
|
|
else:
|
|
repo_id, weights_name = _extract_repo_id_and_weights_name(pretrained_model_link_or_path)
|
|
checkpoint_path = _get_model_file(
|
|
repo_id,
|
|
weights_name=weights_name,
|
|
force_download=force_download,
|
|
cache_dir=cache_dir,
|
|
resume_download=resume_download,
|
|
proxies=proxies,
|
|
local_files_only=local_files_only,
|
|
token=token,
|
|
revision=revision,
|
|
)
|
|
checkpoint = load_state_dict(checkpoint_path)
|
|
|
|
# some checkpoints contain the model state dict under a "state_dict" key
|
|
while "state_dict" in checkpoint:
|
|
checkpoint = checkpoint["state_dict"]
|
|
|
|
original_config = fetch_original_config(class_name, checkpoint, original_config_file)
|
|
|
|
return original_config, checkpoint
|
|
|
|
|
|
def infer_original_config_file(class_name, checkpoint):
|
|
if CHECKPOINT_KEY_NAMES["v2"] in checkpoint and checkpoint[CHECKPOINT_KEY_NAMES["v2"]].shape[-1] == 1024:
|
|
config_url = CONFIG_URLS["v2"]
|
|
|
|
elif CHECKPOINT_KEY_NAMES["xl_base"] in checkpoint:
|
|
config_url = CONFIG_URLS["xl"]
|
|
|
|
elif CHECKPOINT_KEY_NAMES["xl_refiner"] in checkpoint:
|
|
config_url = CONFIG_URLS["xl_refiner"]
|
|
|
|
elif class_name == "StableDiffusionUpscalePipeline":
|
|
config_url = CONFIG_URLS["upscale"]
|
|
|
|
elif class_name == "ControlNetModel":
|
|
config_url = CONFIG_URLS["controlnet"]
|
|
|
|
else:
|
|
config_url = CONFIG_URLS["v1"]
|
|
|
|
original_config_file = BytesIO(requests.get(config_url).content)
|
|
|
|
return original_config_file
|
|
|
|
|
|
def fetch_original_config(pipeline_class_name, checkpoint, original_config_file=None):
|
|
def is_valid_url(url):
|
|
result = urlparse(url)
|
|
if result.scheme and result.netloc:
|
|
return True
|
|
|
|
return False
|
|
|
|
if original_config_file is None:
|
|
original_config_file = infer_original_config_file(pipeline_class_name, checkpoint)
|
|
|
|
elif os.path.isfile(original_config_file):
|
|
with open(original_config_file, "r") as fp:
|
|
original_config_file = fp.read()
|
|
|
|
elif is_valid_url(original_config_file):
|
|
original_config_file = BytesIO(requests.get(original_config_file).content)
|
|
|
|
else:
|
|
raise ValueError("Invalid `original_config_file` provided. Please set it to a valid file path or URL.")
|
|
|
|
original_config = yaml.safe_load(original_config_file)
|
|
|
|
return original_config
|
|
|
|
|
|
def infer_model_type(original_config, model_type=None):
|
|
if model_type is not None:
|
|
return model_type
|
|
|
|
has_cond_stage_config = (
|
|
"cond_stage_config" in original_config["model"]["params"]
|
|
and original_config["model"]["params"]["cond_stage_config"] is not None
|
|
)
|
|
has_network_config = (
|
|
"network_config" in original_config["model"]["params"]
|
|
and original_config["model"]["params"]["network_config"] is not None
|
|
)
|
|
|
|
if has_cond_stage_config:
|
|
model_type = original_config["model"]["params"]["cond_stage_config"]["target"].split(".")[-1]
|
|
|
|
elif has_network_config:
|
|
context_dim = original_config["model"]["params"]["network_config"]["params"]["context_dim"]
|
|
if context_dim == 2048:
|
|
model_type = "SDXL"
|
|
else:
|
|
model_type = "SDXL-Refiner"
|
|
else:
|
|
raise ValueError("Unable to infer model type from config")
|
|
|
|
logger.debug(f"No `model_type` given, `model_type` inferred as: {model_type}")
|
|
|
|
return model_type
|
|
|
|
|
|
def get_default_scheduler_config():
|
|
return SCHEDULER_DEFAULT_CONFIG
|
|
|
|
|
|
def set_image_size(pipeline_class_name, original_config, checkpoint, image_size=None, model_type=None):
|
|
if image_size:
|
|
return image_size
|
|
|
|
global_step = checkpoint["global_step"] if "global_step" in checkpoint else None
|
|
model_type = infer_model_type(original_config, model_type)
|
|
|
|
if pipeline_class_name == "StableDiffusionUpscalePipeline":
|
|
image_size = original_config["model"]["params"]["unet_config"]["params"]["image_size"]
|
|
return image_size
|
|
|
|
elif model_type in ["SDXL", "SDXL-Refiner"]:
|
|
image_size = 1024
|
|
return image_size
|
|
|
|
elif (
|
|
"parameterization" in original_config["model"]["params"]
|
|
and original_config["model"]["params"]["parameterization"] == "v"
|
|
):
|
|
# 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
|
|
return image_size
|
|
|
|
else:
|
|
image_size = 512
|
|
return image_size
|
|
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.conv_attn_to_linear
|
|
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.
|
|
"""
|
|
if (
|
|
"unet_config" in original_config["model"]["params"]
|
|
and original_config["model"]["params"]["unet_config"] is not None
|
|
):
|
|
unet_params = original_config["model"]["params"]["unet_config"]["params"]
|
|
else:
|
|
unet_params = original_config["model"]["params"]["network_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
|
|
|
|
if unet_params["transformer_depth"] is not None:
|
|
transformer_layers_per_block = (
|
|
unet_params["transformer_depth"]
|
|
if isinstance(unet_params["transformer_depth"], int)
|
|
else list(unet_params["transformer_depth"])
|
|
)
|
|
else:
|
|
transformer_layers_per_block = 1
|
|
|
|
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_mult = unet_params["model_channels"] // unet_params["num_head_channels"]
|
|
head_dim = [head_dim_mult * c for c in list(unet_params["channel_mult"])]
|
|
|
|
class_embed_type = None
|
|
addition_embed_type = None
|
|
addition_time_embed_dim = None
|
|
projection_class_embeddings_input_dim = None
|
|
context_dim = None
|
|
|
|
if unet_params["context_dim"] is not None:
|
|
context_dim = (
|
|
unet_params["context_dim"]
|
|
if isinstance(unet_params["context_dim"], int)
|
|
else unet_params["context_dim"][0]
|
|
)
|
|
|
|
if "num_classes" in unet_params:
|
|
if unet_params["num_classes"] == "sequential":
|
|
if context_dim in [2048, 1280]:
|
|
# SDXL
|
|
addition_embed_type = "text_time"
|
|
addition_time_embed_dim = 256
|
|
else:
|
|
class_embed_type = "projection"
|
|
assert "adm_in_channels" in unet_params
|
|
projection_class_embeddings_input_dim = unet_params["adm_in_channels"]
|
|
|
|
config = {
|
|
"sample_size": image_size // vae_scale_factor,
|
|
"in_channels": unet_params["in_channels"],
|
|
"down_block_types": tuple(down_block_types),
|
|
"block_out_channels": tuple(block_out_channels),
|
|
"layers_per_block": unet_params["num_res_blocks"],
|
|
"cross_attention_dim": context_dim,
|
|
"attention_head_dim": head_dim,
|
|
"use_linear_projection": use_linear_projection,
|
|
"class_embed_type": class_embed_type,
|
|
"addition_embed_type": addition_embed_type,
|
|
"addition_time_embed_dim": addition_time_embed_dim,
|
|
"projection_class_embeddings_input_dim": projection_class_embeddings_input_dim,
|
|
"transformer_layers_per_block": transformer_layers_per_block,
|
|
}
|
|
|
|
if "disable_self_attentions" in unet_params:
|
|
config["only_cross_attention"] = unet_params["disable_self_attentions"]
|
|
|
|
if "num_classes" in unet_params and isinstance(unet_params["num_classes"], int):
|
|
config["num_class_embeds"] = unet_params["num_classes"]
|
|
|
|
config["out_channels"] = unet_params["out_channels"]
|
|
config["up_block_types"] = tuple(up_block_types)
|
|
|
|
return config
|
|
|
|
|
|
def create_controlnet_diffusers_config(original_config, image_size: int):
|
|
unet_params = original_config["model"]["params"]["control_stage_config"]["params"]
|
|
diffusers_unet_config = create_unet_diffusers_config(original_config, image_size=image_size)
|
|
|
|
controlnet_config = {
|
|
"conditioning_channels": unet_params["hint_channels"],
|
|
"in_channels": diffusers_unet_config["in_channels"],
|
|
"down_block_types": diffusers_unet_config["down_block_types"],
|
|
"block_out_channels": diffusers_unet_config["block_out_channels"],
|
|
"layers_per_block": diffusers_unet_config["layers_per_block"],
|
|
"cross_attention_dim": diffusers_unet_config["cross_attention_dim"],
|
|
"attention_head_dim": diffusers_unet_config["attention_head_dim"],
|
|
"use_linear_projection": diffusers_unet_config["use_linear_projection"],
|
|
"class_embed_type": diffusers_unet_config["class_embed_type"],
|
|
"addition_embed_type": diffusers_unet_config["addition_embed_type"],
|
|
"addition_time_embed_dim": diffusers_unet_config["addition_time_embed_dim"],
|
|
"projection_class_embeddings_input_dim": diffusers_unet_config["projection_class_embeddings_input_dim"],
|
|
"transformer_layers_per_block": diffusers_unet_config["transformer_layers_per_block"],
|
|
}
|
|
|
|
return controlnet_config
|
|
|
|
|
|
def create_vae_diffusers_config(original_config, image_size, scaling_factor=None):
|
|
"""
|
|
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"]
|
|
scaling_factor = scaling_factor or original_config["model"]["params"]["scale_factor"]
|
|
|
|
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 = {
|
|
"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"],
|
|
"scaling_factor": scaling_factor,
|
|
}
|
|
|
|
return config
|
|
|
|
|
|
def update_unet_resnet_ldm_to_diffusers(ldm_keys, new_checkpoint, checkpoint, mapping=None):
|
|
for ldm_key in ldm_keys:
|
|
diffusers_key = (
|
|
ldm_key.replace("in_layers.0", "norm1")
|
|
.replace("in_layers.2", "conv1")
|
|
.replace("out_layers.0", "norm2")
|
|
.replace("out_layers.3", "conv2")
|
|
.replace("emb_layers.1", "time_emb_proj")
|
|
.replace("skip_connection", "conv_shortcut")
|
|
)
|
|
if mapping:
|
|
diffusers_key = diffusers_key.replace(mapping["old"], mapping["new"])
|
|
new_checkpoint[diffusers_key] = checkpoint.pop(ldm_key)
|
|
|
|
|
|
def update_unet_attention_ldm_to_diffusers(ldm_keys, new_checkpoint, checkpoint, mapping):
|
|
for ldm_key in ldm_keys:
|
|
diffusers_key = ldm_key.replace(mapping["old"], mapping["new"])
|
|
new_checkpoint[diffusers_key] = checkpoint.pop(ldm_key)
|
|
|
|
|
|
def convert_ldm_unet_checkpoint(checkpoint, config, 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 = LDM_UNET_KEY
|
|
|
|
# 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 and extract_ema:
|
|
logger.warning("Checkpoint has both EMA and non-EMA weights.")
|
|
logger.warning(
|
|
"In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
|
|
" weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."
|
|
)
|
|
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:
|
|
if sum(k.startswith("model_ema") for k in keys) > 100:
|
|
logger.warning(
|
|
"In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
|
|
" weights (usually better for inference), please make sure to add the `--extract_ema` flag."
|
|
)
|
|
for key in keys:
|
|
if key.startswith(unet_key):
|
|
unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)
|
|
|
|
new_checkpoint = {}
|
|
ldm_unet_keys = DIFFUSERS_TO_LDM_MAPPING["unet"]["layers"]
|
|
for diffusers_key, ldm_key in ldm_unet_keys.items():
|
|
if ldm_key not in unet_state_dict:
|
|
continue
|
|
new_checkpoint[diffusers_key] = unet_state_dict[ldm_key]
|
|
|
|
if ("class_embed_type" in config) and (config["class_embed_type"] in ["timestep", "projection"]):
|
|
class_embed_keys = DIFFUSERS_TO_LDM_MAPPING["unet"]["class_embed_type"]
|
|
for diffusers_key, ldm_key in class_embed_keys.items():
|
|
new_checkpoint[diffusers_key] = unet_state_dict[ldm_key]
|
|
|
|
if ("addition_embed_type" in config) and (config["addition_embed_type"] == "text_time"):
|
|
addition_embed_keys = DIFFUSERS_TO_LDM_MAPPING["unet"]["addition_embed_type"]
|
|
for diffusers_key, ldm_key in addition_embed_keys.items():
|
|
new_checkpoint[diffusers_key] = unet_state_dict[ldm_key]
|
|
|
|
# Relevant to StableDiffusionUpscalePipeline
|
|
if "num_class_embeds" in config:
|
|
if (config["num_class_embeds"] is not None) and ("label_emb.weight" in unet_state_dict):
|
|
new_checkpoint["class_embedding.weight"] = unet_state_dict["label_emb.weight"]
|
|
|
|
# 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)
|
|
}
|
|
|
|
# Down 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
|
|
]
|
|
update_unet_resnet_ldm_to_diffusers(
|
|
resnets,
|
|
new_checkpoint,
|
|
unet_state_dict,
|
|
{"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"},
|
|
)
|
|
|
|
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"
|
|
)
|
|
|
|
attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
|
|
if attentions:
|
|
update_unet_attention_ldm_to_diffusers(
|
|
attentions,
|
|
new_checkpoint,
|
|
unet_state_dict,
|
|
{"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"},
|
|
)
|
|
|
|
# Mid blocks
|
|
resnet_0 = middle_blocks[0]
|
|
attentions = middle_blocks[1]
|
|
resnet_1 = middle_blocks[2]
|
|
|
|
update_unet_resnet_ldm_to_diffusers(
|
|
resnet_0, new_checkpoint, unet_state_dict, mapping={"old": "middle_block.0", "new": "mid_block.resnets.0"}
|
|
)
|
|
update_unet_resnet_ldm_to_diffusers(
|
|
resnet_1, new_checkpoint, unet_state_dict, mapping={"old": "middle_block.2", "new": "mid_block.resnets.1"}
|
|
)
|
|
update_unet_attention_ldm_to_diffusers(
|
|
attentions, new_checkpoint, unet_state_dict, mapping={"old": "middle_block.1", "new": "mid_block.attentions.0"}
|
|
)
|
|
|
|
# Up Blocks
|
|
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)
|
|
|
|
resnets = [
|
|
key for key in output_blocks[i] if f"output_blocks.{i}.0" in key and f"output_blocks.{i}.0.op" not in key
|
|
]
|
|
update_unet_resnet_ldm_to_diffusers(
|
|
resnets,
|
|
new_checkpoint,
|
|
unet_state_dict,
|
|
{"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"},
|
|
)
|
|
|
|
attentions = [
|
|
key for key in output_blocks[i] if f"output_blocks.{i}.1" in key and f"output_blocks.{i}.1.conv" not in key
|
|
]
|
|
if attentions:
|
|
update_unet_attention_ldm_to_diffusers(
|
|
attentions,
|
|
new_checkpoint,
|
|
unet_state_dict,
|
|
{"old": f"output_blocks.{i}.1", "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}"},
|
|
)
|
|
|
|
if f"output_blocks.{i}.1.conv.weight" in unet_state_dict:
|
|
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
|
|
f"output_blocks.{i}.1.conv.weight"
|
|
]
|
|
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
|
|
f"output_blocks.{i}.1.conv.bias"
|
|
]
|
|
if f"output_blocks.{i}.2.conv.weight" in unet_state_dict:
|
|
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
|
|
f"output_blocks.{i}.2.conv.weight"
|
|
]
|
|
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
|
|
f"output_blocks.{i}.2.conv.bias"
|
|
]
|
|
|
|
return new_checkpoint
|
|
|
|
|
|
def convert_controlnet_checkpoint(
|
|
checkpoint,
|
|
config,
|
|
):
|
|
# Some controlnet ckpt files are distributed independently from the rest of the
|
|
# model components i.e. https://huggingface.co/thibaud/controlnet-sd21/
|
|
if "time_embed.0.weight" in checkpoint:
|
|
controlnet_state_dict = checkpoint
|
|
|
|
else:
|
|
controlnet_state_dict = {}
|
|
keys = list(checkpoint.keys())
|
|
controlnet_key = LDM_CONTROLNET_KEY
|
|
for key in keys:
|
|
if key.startswith(controlnet_key):
|
|
controlnet_state_dict[key.replace(controlnet_key, "")] = checkpoint.pop(key)
|
|
|
|
new_checkpoint = {}
|
|
ldm_controlnet_keys = DIFFUSERS_TO_LDM_MAPPING["controlnet"]["layers"]
|
|
for diffusers_key, ldm_key in ldm_controlnet_keys.items():
|
|
if ldm_key not in controlnet_state_dict:
|
|
continue
|
|
new_checkpoint[diffusers_key] = controlnet_state_dict[ldm_key]
|
|
|
|
# Retrieves the keys for the input blocks only
|
|
num_input_blocks = len(
|
|
{".".join(layer.split(".")[:2]) for layer in controlnet_state_dict if "input_blocks" in layer}
|
|
)
|
|
input_blocks = {
|
|
layer_id: [key for key in controlnet_state_dict if f"input_blocks.{layer_id}" in key]
|
|
for layer_id in range(num_input_blocks)
|
|
}
|
|
|
|
# Down 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
|
|
]
|
|
update_unet_resnet_ldm_to_diffusers(
|
|
resnets,
|
|
new_checkpoint,
|
|
controlnet_state_dict,
|
|
{"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"},
|
|
)
|
|
|
|
if f"input_blocks.{i}.0.op.weight" in controlnet_state_dict:
|
|
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = controlnet_state_dict.pop(
|
|
f"input_blocks.{i}.0.op.weight"
|
|
)
|
|
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = controlnet_state_dict.pop(
|
|
f"input_blocks.{i}.0.op.bias"
|
|
)
|
|
|
|
attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
|
|
if attentions:
|
|
update_unet_attention_ldm_to_diffusers(
|
|
attentions,
|
|
new_checkpoint,
|
|
controlnet_state_dict,
|
|
{"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"},
|
|
)
|
|
|
|
# controlnet down blocks
|
|
for i in range(num_input_blocks):
|
|
new_checkpoint[f"controlnet_down_blocks.{i}.weight"] = controlnet_state_dict.pop(f"zero_convs.{i}.0.weight")
|
|
new_checkpoint[f"controlnet_down_blocks.{i}.bias"] = controlnet_state_dict.pop(f"zero_convs.{i}.0.bias")
|
|
|
|
# Retrieves the keys for the middle blocks only
|
|
num_middle_blocks = len(
|
|
{".".join(layer.split(".")[:2]) for layer in controlnet_state_dict if "middle_block" in layer}
|
|
)
|
|
middle_blocks = {
|
|
layer_id: [key for key in controlnet_state_dict if f"middle_block.{layer_id}" in key]
|
|
for layer_id in range(num_middle_blocks)
|
|
}
|
|
if middle_blocks:
|
|
resnet_0 = middle_blocks[0]
|
|
attentions = middle_blocks[1]
|
|
resnet_1 = middle_blocks[2]
|
|
|
|
update_unet_resnet_ldm_to_diffusers(
|
|
resnet_0,
|
|
new_checkpoint,
|
|
controlnet_state_dict,
|
|
mapping={"old": "middle_block.0", "new": "mid_block.resnets.0"},
|
|
)
|
|
update_unet_resnet_ldm_to_diffusers(
|
|
resnet_1,
|
|
new_checkpoint,
|
|
controlnet_state_dict,
|
|
mapping={"old": "middle_block.2", "new": "mid_block.resnets.1"},
|
|
)
|
|
update_unet_attention_ldm_to_diffusers(
|
|
attentions,
|
|
new_checkpoint,
|
|
controlnet_state_dict,
|
|
mapping={"old": "middle_block.1", "new": "mid_block.attentions.0"},
|
|
)
|
|
|
|
# mid block
|
|
new_checkpoint["controlnet_mid_block.weight"] = controlnet_state_dict.pop("middle_block_out.0.weight")
|
|
new_checkpoint["controlnet_mid_block.bias"] = controlnet_state_dict.pop("middle_block_out.0.bias")
|
|
|
|
# controlnet cond embedding blocks
|
|
cond_embedding_blocks = {
|
|
".".join(layer.split(".")[:2])
|
|
for layer in controlnet_state_dict
|
|
if "input_hint_block" in layer and ("input_hint_block.0" not in layer) and ("input_hint_block.14" not in layer)
|
|
}
|
|
num_cond_embedding_blocks = len(cond_embedding_blocks)
|
|
|
|
for idx in range(1, num_cond_embedding_blocks + 1):
|
|
diffusers_idx = idx - 1
|
|
cond_block_id = 2 * idx
|
|
|
|
new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_idx}.weight"] = controlnet_state_dict.pop(
|
|
f"input_hint_block.{cond_block_id}.weight"
|
|
)
|
|
new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_idx}.bias"] = controlnet_state_dict.pop(
|
|
f"input_hint_block.{cond_block_id}.bias"
|
|
)
|
|
|
|
return new_checkpoint
|
|
|
|
|
|
def create_diffusers_controlnet_model_from_ldm(
|
|
pipeline_class_name, original_config, checkpoint, upcast_attention=False, image_size=None
|
|
):
|
|
# import here to avoid circular imports
|
|
from ..models import ControlNetModel
|
|
|
|
image_size = set_image_size(pipeline_class_name, original_config, checkpoint, image_size=image_size)
|
|
|
|
diffusers_config = create_controlnet_diffusers_config(original_config, image_size=image_size)
|
|
diffusers_config["upcast_attention"] = upcast_attention
|
|
|
|
diffusers_format_controlnet_checkpoint = convert_controlnet_checkpoint(checkpoint, diffusers_config)
|
|
|
|
ctx = init_empty_weights if is_accelerate_available() else nullcontext
|
|
with ctx():
|
|
controlnet = ControlNetModel(**diffusers_config)
|
|
|
|
if is_accelerate_available():
|
|
for param_name, param in diffusers_format_controlnet_checkpoint.items():
|
|
set_module_tensor_to_device(controlnet, param_name, "cpu", value=param)
|
|
else:
|
|
controlnet.load_state_dict(diffusers_format_controlnet_checkpoint)
|
|
|
|
return {"controlnet": controlnet}
|
|
|
|
|
|
def update_vae_resnet_ldm_to_diffusers(keys, new_checkpoint, checkpoint, mapping):
|
|
for ldm_key in keys:
|
|
diffusers_key = ldm_key.replace(mapping["old"], mapping["new"]).replace("nin_shortcut", "conv_shortcut")
|
|
new_checkpoint[diffusers_key] = checkpoint.pop(ldm_key)
|
|
|
|
|
|
def update_vae_attentions_ldm_to_diffusers(keys, new_checkpoint, checkpoint, mapping):
|
|
for ldm_key in keys:
|
|
diffusers_key = (
|
|
ldm_key.replace(mapping["old"], mapping["new"])
|
|
.replace("norm.weight", "group_norm.weight")
|
|
.replace("norm.bias", "group_norm.bias")
|
|
.replace("q.weight", "to_q.weight")
|
|
.replace("q.bias", "to_q.bias")
|
|
.replace("k.weight", "to_k.weight")
|
|
.replace("k.bias", "to_k.bias")
|
|
.replace("v.weight", "to_v.weight")
|
|
.replace("v.bias", "to_v.bias")
|
|
.replace("proj_out.weight", "to_out.0.weight")
|
|
.replace("proj_out.bias", "to_out.0.bias")
|
|
)
|
|
new_checkpoint[diffusers_key] = checkpoint.pop(ldm_key)
|
|
|
|
# proj_attn.weight has to be converted from conv 1D to linear
|
|
shape = new_checkpoint[diffusers_key].shape
|
|
|
|
if len(shape) == 3:
|
|
new_checkpoint[diffusers_key] = new_checkpoint[diffusers_key][:, :, 0]
|
|
elif len(shape) == 4:
|
|
new_checkpoint[diffusers_key] = new_checkpoint[diffusers_key][:, :, 0, 0]
|
|
|
|
|
|
def convert_ldm_vae_checkpoint(checkpoint, config):
|
|
# extract state dict for VAE
|
|
# remove the LDM_VAE_KEY prefix from the ldm checkpoint keys so that it is easier to map them to diffusers keys
|
|
vae_state_dict = {}
|
|
keys = list(checkpoint.keys())
|
|
vae_key = LDM_VAE_KEY if any(k.startswith(LDM_VAE_KEY) for k in keys) else ""
|
|
for key in keys:
|
|
if key.startswith(vae_key):
|
|
vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key)
|
|
|
|
new_checkpoint = {}
|
|
vae_diffusers_ldm_map = DIFFUSERS_TO_LDM_MAPPING["vae"]
|
|
for diffusers_key, ldm_key in vae_diffusers_ldm_map.items():
|
|
if ldm_key not in vae_state_dict:
|
|
continue
|
|
new_checkpoint[diffusers_key] = vae_state_dict[ldm_key]
|
|
|
|
# Retrieves the keys for the encoder down blocks only
|
|
num_down_blocks = len(config["down_block_types"])
|
|
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)
|
|
}
|
|
|
|
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]
|
|
update_vae_resnet_ldm_to_diffusers(
|
|
resnets,
|
|
new_checkpoint,
|
|
vae_state_dict,
|
|
mapping={"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"},
|
|
)
|
|
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"
|
|
)
|
|
|
|
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]
|
|
update_vae_resnet_ldm_to_diffusers(
|
|
resnets,
|
|
new_checkpoint,
|
|
vae_state_dict,
|
|
mapping={"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"},
|
|
)
|
|
|
|
mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
|
|
update_vae_attentions_ldm_to_diffusers(
|
|
mid_attentions, new_checkpoint, vae_state_dict, mapping={"old": "mid.attn_1", "new": "mid_block.attentions.0"}
|
|
)
|
|
|
|
# Retrieves the keys for the decoder up blocks only
|
|
num_up_blocks = len(config["up_block_types"])
|
|
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_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
|
|
]
|
|
update_vae_resnet_ldm_to_diffusers(
|
|
resnets,
|
|
new_checkpoint,
|
|
vae_state_dict,
|
|
mapping={"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"},
|
|
)
|
|
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"
|
|
]
|
|
|
|
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]
|
|
update_vae_resnet_ldm_to_diffusers(
|
|
resnets,
|
|
new_checkpoint,
|
|
vae_state_dict,
|
|
mapping={"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"},
|
|
)
|
|
|
|
mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
|
|
update_vae_attentions_ldm_to_diffusers(
|
|
mid_attentions, new_checkpoint, vae_state_dict, mapping={"old": "mid.attn_1", "new": "mid_block.attentions.0"}
|
|
)
|
|
conv_attn_to_linear(new_checkpoint)
|
|
|
|
return new_checkpoint
|
|
|
|
|
|
def create_text_encoder_from_ldm_clip_checkpoint(config_name, checkpoint, local_files_only=False):
|
|
try:
|
|
config = CLIPTextConfig.from_pretrained(config_name, local_files_only=local_files_only)
|
|
except Exception:
|
|
raise ValueError(
|
|
f"With local_files_only set to {local_files_only}, you must first locally save the configuration in the following path: 'openai/clip-vit-large-patch14'."
|
|
)
|
|
|
|
ctx = init_empty_weights if is_accelerate_available() else nullcontext
|
|
with ctx():
|
|
text_model = CLIPTextModel(config)
|
|
|
|
keys = list(checkpoint.keys())
|
|
text_model_dict = {}
|
|
|
|
remove_prefixes = LDM_CLIP_PREFIX_TO_REMOVE
|
|
|
|
for key in keys:
|
|
for prefix in remove_prefixes:
|
|
if key.startswith(prefix):
|
|
diffusers_key = key.replace(prefix, "")
|
|
text_model_dict[diffusers_key] = checkpoint[key]
|
|
|
|
if is_accelerate_available():
|
|
for param_name, param in text_model_dict.items():
|
|
set_module_tensor_to_device(text_model, param_name, "cpu", value=param)
|
|
else:
|
|
if not (hasattr(text_model, "embeddings") and hasattr(text_model.embeddings.position_ids)):
|
|
text_model_dict.pop("text_model.embeddings.position_ids", None)
|
|
|
|
text_model.load_state_dict(text_model_dict)
|
|
|
|
return text_model
|
|
|
|
|
|
def create_text_encoder_from_open_clip_checkpoint(
|
|
config_name,
|
|
checkpoint,
|
|
prefix="cond_stage_model.model.",
|
|
has_projection=False,
|
|
local_files_only=False,
|
|
**config_kwargs,
|
|
):
|
|
try:
|
|
config = CLIPTextConfig.from_pretrained(config_name, **config_kwargs, local_files_only=local_files_only)
|
|
except Exception:
|
|
raise ValueError(
|
|
f"With local_files_only set to {local_files_only}, you must first locally save the configuration in the following path: '{config_name}'."
|
|
)
|
|
|
|
ctx = init_empty_weights if is_accelerate_available() else nullcontext
|
|
with ctx():
|
|
text_model = CLIPTextModelWithProjection(config) if has_projection else CLIPTextModel(config)
|
|
|
|
text_model_dict = {}
|
|
text_proj_key = prefix + "text_projection"
|
|
text_proj_dim = (
|
|
int(checkpoint[text_proj_key].shape[0]) if text_proj_key in checkpoint else LDM_OPEN_CLIP_TEXT_PROJECTION_DIM
|
|
)
|
|
text_model_dict["text_model.embeddings.position_ids"] = text_model.text_model.embeddings.get_buffer("position_ids")
|
|
|
|
keys = list(checkpoint.keys())
|
|
keys_to_ignore = SD_2_TEXT_ENCODER_KEYS_TO_IGNORE
|
|
|
|
openclip_diffusers_ldm_map = DIFFUSERS_TO_LDM_MAPPING["openclip"]["layers"]
|
|
for diffusers_key, ldm_key in openclip_diffusers_ldm_map.items():
|
|
ldm_key = prefix + ldm_key
|
|
if ldm_key not in checkpoint:
|
|
continue
|
|
if ldm_key in keys_to_ignore:
|
|
continue
|
|
if ldm_key.endswith("text_projection"):
|
|
text_model_dict[diffusers_key] = checkpoint[ldm_key].T.contiguous()
|
|
else:
|
|
text_model_dict[diffusers_key] = checkpoint[ldm_key]
|
|
|
|
for key in keys:
|
|
if key in keys_to_ignore:
|
|
continue
|
|
|
|
if not key.startswith(prefix + "transformer."):
|
|
continue
|
|
|
|
diffusers_key = key.replace(prefix + "transformer.", "")
|
|
transformer_diffusers_to_ldm_map = DIFFUSERS_TO_LDM_MAPPING["openclip"]["transformer"]
|
|
for new_key, old_key in transformer_diffusers_to_ldm_map.items():
|
|
diffusers_key = (
|
|
diffusers_key.replace(old_key, new_key).replace(".in_proj_weight", "").replace(".in_proj_bias", "")
|
|
)
|
|
|
|
if key.endswith(".in_proj_weight"):
|
|
weight_value = checkpoint[key]
|
|
|
|
text_model_dict[diffusers_key + ".q_proj.weight"] = weight_value[:text_proj_dim, :]
|
|
text_model_dict[diffusers_key + ".k_proj.weight"] = weight_value[text_proj_dim : text_proj_dim * 2, :]
|
|
text_model_dict[diffusers_key + ".v_proj.weight"] = weight_value[text_proj_dim * 2 :, :]
|
|
|
|
elif key.endswith(".in_proj_bias"):
|
|
weight_value = checkpoint[key]
|
|
text_model_dict[diffusers_key + ".q_proj.bias"] = weight_value[:text_proj_dim]
|
|
text_model_dict[diffusers_key + ".k_proj.bias"] = weight_value[text_proj_dim : text_proj_dim * 2]
|
|
text_model_dict[diffusers_key + ".v_proj.bias"] = weight_value[text_proj_dim * 2 :]
|
|
else:
|
|
text_model_dict[diffusers_key] = checkpoint[key]
|
|
|
|
if is_accelerate_available():
|
|
for param_name, param in text_model_dict.items():
|
|
set_module_tensor_to_device(text_model, param_name, "cpu", value=param)
|
|
|
|
else:
|
|
if not (hasattr(text_model, "embeddings") and hasattr(text_model.embeddings.position_ids)):
|
|
text_model_dict.pop("text_model.embeddings.position_ids", None)
|
|
|
|
text_model.load_state_dict(text_model_dict)
|
|
|
|
return text_model
|
|
|
|
|
|
def create_diffusers_unet_model_from_ldm(
|
|
pipeline_class_name,
|
|
original_config,
|
|
checkpoint,
|
|
num_in_channels=None,
|
|
upcast_attention=False,
|
|
extract_ema=False,
|
|
image_size=None,
|
|
):
|
|
from ..models import UNet2DConditionModel
|
|
|
|
if num_in_channels is None:
|
|
if pipeline_class_name in [
|
|
"StableDiffusionInpaintPipeline",
|
|
"StableDiffusionXLInpaintPipeline",
|
|
"StableDiffusionXLControlNetInpaintPipeline",
|
|
]:
|
|
num_in_channels = 9
|
|
|
|
elif pipeline_class_name == "StableDiffusionUpscalePipeline":
|
|
num_in_channels = 7
|
|
|
|
else:
|
|
num_in_channels = 4
|
|
|
|
image_size = set_image_size(pipeline_class_name, original_config, checkpoint, image_size=image_size)
|
|
unet_config = create_unet_diffusers_config(original_config, image_size=image_size)
|
|
unet_config["in_channels"] = num_in_channels
|
|
unet_config["upcast_attention"] = upcast_attention
|
|
|
|
diffusers_format_unet_checkpoint = convert_ldm_unet_checkpoint(checkpoint, unet_config, extract_ema=extract_ema)
|
|
ctx = init_empty_weights if is_accelerate_available() else nullcontext
|
|
with ctx():
|
|
unet = UNet2DConditionModel(**unet_config)
|
|
|
|
if is_accelerate_available():
|
|
for param_name, param in diffusers_format_unet_checkpoint.items():
|
|
set_module_tensor_to_device(unet, param_name, "cpu", value=param)
|
|
else:
|
|
unet.load_state_dict(diffusers_format_unet_checkpoint)
|
|
|
|
return {"unet": unet}
|
|
|
|
|
|
def create_diffusers_vae_model_from_ldm(
|
|
pipeline_class_name, original_config, checkpoint, image_size=None, scaling_factor=0.18125
|
|
):
|
|
# import here to avoid circular imports
|
|
from ..models import AutoencoderKL
|
|
|
|
image_size = set_image_size(pipeline_class_name, original_config, checkpoint, image_size=image_size)
|
|
|
|
vae_config = create_vae_diffusers_config(original_config, image_size=image_size, scaling_factor=scaling_factor)
|
|
diffusers_format_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
|
|
ctx = init_empty_weights if is_accelerate_available() else nullcontext
|
|
|
|
with ctx():
|
|
vae = AutoencoderKL(**vae_config)
|
|
|
|
if is_accelerate_available():
|
|
for param_name, param in diffusers_format_vae_checkpoint.items():
|
|
set_module_tensor_to_device(vae, param_name, "cpu", value=param)
|
|
else:
|
|
vae.load_state_dict(diffusers_format_vae_checkpoint)
|
|
|
|
return {"vae": vae}
|
|
|
|
|
|
def create_text_encoders_and_tokenizers_from_ldm(
|
|
original_config,
|
|
checkpoint,
|
|
model_type=None,
|
|
local_files_only=False,
|
|
):
|
|
model_type = infer_model_type(original_config, model_type=model_type)
|
|
|
|
if model_type == "FrozenOpenCLIPEmbedder":
|
|
config_name = "stabilityai/stable-diffusion-2"
|
|
config_kwargs = {"subfolder": "text_encoder"}
|
|
|
|
try:
|
|
text_encoder = create_text_encoder_from_open_clip_checkpoint(
|
|
config_name, checkpoint, local_files_only=local_files_only, **config_kwargs
|
|
)
|
|
tokenizer = CLIPTokenizer.from_pretrained(
|
|
config_name, subfolder="tokenizer", local_files_only=local_files_only
|
|
)
|
|
except Exception:
|
|
raise ValueError(
|
|
f"With local_files_only set to {local_files_only}, you must first locally save the text_encoder in the following path: '{config_name}'."
|
|
)
|
|
else:
|
|
return {"text_encoder": text_encoder, "tokenizer": tokenizer}
|
|
|
|
elif model_type == "FrozenCLIPEmbedder":
|
|
try:
|
|
config_name = "openai/clip-vit-large-patch14"
|
|
text_encoder = create_text_encoder_from_ldm_clip_checkpoint(
|
|
config_name, checkpoint, local_files_only=local_files_only
|
|
)
|
|
tokenizer = CLIPTokenizer.from_pretrained(config_name, local_files_only=local_files_only)
|
|
|
|
except Exception:
|
|
raise ValueError(
|
|
f"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: '{config_name}'."
|
|
)
|
|
else:
|
|
return {"text_encoder": text_encoder, "tokenizer": tokenizer}
|
|
|
|
elif model_type == "SDXL-Refiner":
|
|
config_name = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
|
|
config_kwargs = {"projection_dim": 1280}
|
|
prefix = "conditioner.embedders.0.model."
|
|
|
|
try:
|
|
tokenizer_2 = CLIPTokenizer.from_pretrained(config_name, pad_token="!", local_files_only=local_files_only)
|
|
text_encoder_2 = create_text_encoder_from_open_clip_checkpoint(
|
|
config_name,
|
|
checkpoint,
|
|
prefix=prefix,
|
|
has_projection=True,
|
|
local_files_only=local_files_only,
|
|
**config_kwargs,
|
|
)
|
|
except Exception:
|
|
raise ValueError(
|
|
f"With local_files_only set to {local_files_only}, you must first locally save the text_encoder_2 and tokenizer_2 in the following path: {config_name} with `pad_token` set to '!'."
|
|
)
|
|
|
|
else:
|
|
return {
|
|
"text_encoder": None,
|
|
"tokenizer": None,
|
|
"tokenizer_2": tokenizer_2,
|
|
"text_encoder_2": text_encoder_2,
|
|
}
|
|
|
|
elif model_type == "SDXL":
|
|
try:
|
|
config_name = "openai/clip-vit-large-patch14"
|
|
tokenizer = CLIPTokenizer.from_pretrained(config_name, local_files_only=local_files_only)
|
|
text_encoder = create_text_encoder_from_ldm_clip_checkpoint(
|
|
config_name, checkpoint, local_files_only=local_files_only
|
|
)
|
|
|
|
except Exception:
|
|
raise ValueError(
|
|
f"With local_files_only set to {local_files_only}, you must first locally save the text_encoder and tokenizer in the following path: 'openai/clip-vit-large-patch14'."
|
|
)
|
|
|
|
try:
|
|
config_name = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
|
|
config_kwargs = {"projection_dim": 1280}
|
|
prefix = "conditioner.embedders.1.model."
|
|
tokenizer_2 = CLIPTokenizer.from_pretrained(config_name, pad_token="!", local_files_only=local_files_only)
|
|
text_encoder_2 = create_text_encoder_from_open_clip_checkpoint(
|
|
config_name,
|
|
checkpoint,
|
|
prefix=prefix,
|
|
has_projection=True,
|
|
local_files_only=local_files_only,
|
|
**config_kwargs,
|
|
)
|
|
except Exception:
|
|
raise ValueError(
|
|
f"With local_files_only set to {local_files_only}, you must first locally save the text_encoder_2 and tokenizer_2 in the following path: {config_name} with `pad_token` set to '!'."
|
|
)
|
|
|
|
return {
|
|
"tokenizer": tokenizer,
|
|
"text_encoder": text_encoder,
|
|
"tokenizer_2": tokenizer_2,
|
|
"text_encoder_2": text_encoder_2,
|
|
}
|
|
|
|
return
|
|
|
|
|
|
def create_scheduler_from_ldm(
|
|
pipeline_class_name,
|
|
original_config,
|
|
checkpoint,
|
|
prediction_type=None,
|
|
scheduler_type="ddim",
|
|
model_type=None,
|
|
):
|
|
scheduler_config = get_default_scheduler_config()
|
|
model_type = infer_model_type(original_config, model_type=model_type)
|
|
|
|
global_step = checkpoint["global_step"] if "global_step" in checkpoint else None
|
|
|
|
num_train_timesteps = getattr(original_config["model"]["params"], "timesteps", None) or 1000
|
|
scheduler_config["num_train_timesteps"] = num_train_timesteps
|
|
|
|
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"
|
|
|
|
else:
|
|
prediction_type = prediction_type or "epsilon"
|
|
|
|
scheduler_config["prediction_type"] = prediction_type
|
|
|
|
if model_type in ["SDXL", "SDXL-Refiner"]:
|
|
scheduler_type = "euler"
|
|
|
|
else:
|
|
beta_start = original_config["model"]["params"].get("linear_start", 0.02)
|
|
beta_end = original_config["model"]["params"].get("linear_end", 0.085)
|
|
scheduler_config["beta_start"] = beta_start
|
|
scheduler_config["beta_end"] = beta_end
|
|
scheduler_config["beta_schedule"] = "scaled_linear"
|
|
scheduler_config["clip_sample"] = False
|
|
scheduler_config["set_alpha_to_one"] = False
|
|
|
|
if scheduler_type == "pndm":
|
|
scheduler_config["skip_prk_steps"] = True
|
|
scheduler = PNDMScheduler.from_config(scheduler_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 = DDIMScheduler.from_config(scheduler_config)
|
|
|
|
else:
|
|
raise ValueError(f"Scheduler of type {scheduler_type} doesn't exist!")
|
|
|
|
if pipeline_class_name == "StableDiffusionUpscalePipeline":
|
|
scheduler = DDIMScheduler.from_pretrained("stabilityai/stable-diffusion-x4-upscaler", subfolder="scheduler")
|
|
low_res_scheduler = DDPMScheduler.from_pretrained(
|
|
"stabilityai/stable-diffusion-x4-upscaler", subfolder="low_res_scheduler"
|
|
)
|
|
|
|
return {
|
|
"scheduler": scheduler,
|
|
"low_res_scheduler": low_res_scheduler,
|
|
}
|
|
|
|
return {"scheduler": scheduler}
|