imaginAIry/imaginairy/utils/model_manager.py

863 lines
28 KiB
Python

"""Classes and functions for managing AI models"""
import logging
import os
import sys
from functools import lru_cache
import torch
from omegaconf import OmegaConf
from safetensors.torch import load_file
from imaginairy import config as iconfig
from imaginairy.config import IMAGE_WEIGHTS_SHORT_NAMES, ModelArchitecture
from imaginairy.modules import attention
from imaginairy.modules.refiners_sd import (
SDXLAutoencoderSliced,
StableDiffusion_XL,
StableDiffusion_XL_Inpainting,
)
from imaginairy.utils import clear_gpu_cache, get_device, instantiate_from_config
from imaginairy.utils.downloads import (
HuggingFaceAuthorizationError,
download_huggingface_weights,
get_cached_url_path,
is_diffusers_repo_url,
normalize_diffusers_repo_url,
)
from imaginairy.utils.model_cache import memory_managed_model
from imaginairy.utils.named_resolutions import normalize_image_size
from imaginairy.utils.paths import PKG_ROOT
from imaginairy.vendored.refiners.foundationals.clip.text_encoder import (
CLIPTextEncoderL,
)
from imaginairy.vendored.refiners.foundationals.latent_diffusion import (
DoubleTextEncoder,
SD1UNet,
SDXLUNet,
)
from imaginairy.vendored.refiners.foundationals.latent_diffusion.model import (
LatentDiffusionModel,
)
from imaginairy.weight_management import translators
from imaginairy.weight_management.translators import (
DoubleTextEncoderTranslator,
diffusers_autoencoder_kl_to_refiners_translator,
diffusers_unet_sdxl_to_refiners_translator,
load_weight_map,
)
logger = logging.getLogger(__name__)
MOST_RECENTLY_LOADED_MODEL = None
def load_state_dict(weights_location, half_mode=False, device=None):
if device is None:
device = get_device()
if weights_location.startswith("http"):
ckpt_path = get_cached_url_path(weights_location, category="weights")
else:
ckpt_path = weights_location
logger.info(f"Loading model {ckpt_path} onto {get_device()} backend...")
state_dict = None
# weights_cache_key = (ckpt_path, half_mode)
# if weights_cache_key in GLOBAL_WEIGHTS_CACHE:
# return GLOBAL_WEIGHTS_CACHE.get(weights_cache_key)
try:
state_dict = load_tensors(ckpt_path, map_location="cpu")
except FileNotFoundError as e:
if e.errno == 2:
logger.error(
f'Error: "{ckpt_path}" not a valid path to model weights.\nPreconfigured models you can use: {IMAGE_WEIGHTS_SHORT_NAMES}.'
)
sys.exit(1)
raise
except RuntimeError as e:
err_str = str(e)
if (
"PytorchStreamReader failed reading zip archive" in err_str
and weights_location.startswith("http")
):
logger.warning("Corrupt checkpoint. deleting and re-downloading...")
os.remove(ckpt_path)
ckpt_path = get_cached_url_path(weights_location, category="weights")
state_dict = load_tensors(ckpt_path, map_location="cpu")
if state_dict is None:
raise
state_dict = state_dict.get("state_dict", state_dict)
if half_mode:
state_dict = {k: v.half() for k, v in state_dict.items()}
# change device
state_dict = {k: v.to(device) for k, v in state_dict.items()}
# GLOBAL_WEIGHTS_CACHE.set(weights_cache_key, state_dict)
return state_dict
def load_model_from_config(config, weights_location, half_mode=False):
model = instantiate_from_config(config.model)
base_model_dict = load_state_dict(weights_location, half_mode=half_mode)
model.init_from_state_dict(base_model_dict)
if half_mode:
model = model.half()
model.to(get_device())
model.eval()
return model
def get_diffusion_model(
weights_location=iconfig.DEFAULT_MODEL_WEIGHTS,
config_path="configs/stable-diffusion-v1.yaml",
control_weights_locations=None,
half_mode=None,
for_inpainting=False,
):
"""
Load a diffusion model.
Weights location may also be shortcut name, e.g. "SD-1.5"
"""
try:
return _get_diffusion_model(
weights_location,
config_path,
half_mode,
for_inpainting,
control_weights_locations=control_weights_locations,
)
except HuggingFaceAuthorizationError as e:
if for_inpainting:
logger.warning(
f"Failed to load inpainting model. Attempting to fall-back to standard model. {e!s}"
)
return _get_diffusion_model(
iconfig.DEFAULT_MODEL_WEIGHTS,
config_path,
half_mode,
for_inpainting=False,
control_weights_locations=control_weights_locations,
)
raise
def _get_diffusion_model(
weights_location=iconfig.DEFAULT_MODEL_WEIGHTS,
model_architecture="configs/stable-diffusion-v1.yaml",
half_mode=None,
for_inpainting=False,
control_weights_locations=None,
):
"""
Load a diffusion model.
Weights location may also be shortcut name, e.g. "SD-1.5"
"""
global MOST_RECENTLY_LOADED_MODEL
model_weights_config = resolve_model_weights_config(
model_weights=weights_location,
default_model_architecture=model_architecture,
for_inpainting=for_inpainting,
)
# some models need the attention calculated in float32
if model_weights_config is not None:
attention.ATTENTION_PRECISION_OVERRIDE = (
model_weights_config.forced_attn_precision
)
else:
attention.ATTENTION_PRECISION_OVERRIDE = "default"
diffusion_model = _load_diffusion_model(
config_path=model_weights_config.architecture.config_path,
weights_location=weights_location,
half_mode=half_mode,
)
MOST_RECENTLY_LOADED_MODEL = diffusion_model
if control_weights_locations:
controlnets = []
for control_weights_location in control_weights_locations:
controlnets.append(load_controlnet(control_weights_location, half_mode))
diffusion_model.set_control_models(controlnets)
return diffusion_model
def get_diffusion_model_refiners(
weights_config: iconfig.ModelWeightsConfig,
for_inpainting=False,
dtype=None,
) -> LatentDiffusionModel:
"""Load a diffusion model."""
sd = _get_diffusion_model_refiners(
weights_location=weights_config.weights_location,
architecture_alias=weights_config.architecture.primary_alias,
for_inpainting=for_inpainting,
dtype=dtype,
)
# ensures a "fresh" copy that doesn't have additional injected parts
sd = sd.structural_copy()
sd.set_self_attention_guidance(enable=True)
return sd
@lru_cache(maxsize=1)
def _get_diffusion_model_refiners(
weights_location: str,
architecture_alias: str,
for_inpainting: bool = False,
device=None,
dtype=torch.float16,
) -> LatentDiffusionModel:
"""
Load a diffusion model.
Weights location may also be shortcut name, e.g. "SD-1.5"
"""
global MOST_RECENTLY_LOADED_MODEL
_get_diffusion_model_refiners.cache_clear()
clear_gpu_cache()
architecture = iconfig.MODEL_ARCHITECTURE_LOOKUP[architecture_alias]
if architecture.primary_alias in ("sd15", "sd15inpaint"):
sd = load_sd15_pipeline(
weights_location=weights_location,
for_inpainting=for_inpainting,
device=device,
dtype=dtype,
)
elif architecture.primary_alias in ("sdxl", "sdxlinpaint"):
sd = load_sdxl_pipeline(
base_url=weights_location, device=device, for_inpainting=for_inpainting
)
else:
msg = f"Invalid architecture {architecture.primary_alias}"
raise ValueError(msg)
MOST_RECENTLY_LOADED_MODEL = sd
msg = (
"Pipeline loaded "
f"sd[dtype:{sd.dtype} device:{sd.device}] "
f"sd.unet[dtype:{sd.unet.dtype} device:{sd.unet.device}] "
f"sd.lda[dtype:{sd.lda.dtype} device:{sd.lda.device}]"
f"sd.clip_text_encoder[dtype:{sd.clip_text_encoder.dtype} device:{sd.clip_text_encoder.device}]"
)
logger.debug(msg)
return sd
# new
def load_sd15_pipeline(
weights_location: str,
for_inpainting: bool = False,
device=None,
dtype=torch.float16,
) -> LatentDiffusionModel:
"""
Load a diffusion model.
Weights location may also be shortcut name, e.g. "SD-1.5"
"""
from imaginairy.modules.refiners_sd import (
SD1AutoencoderSliced,
StableDiffusion_1,
StableDiffusion_1_Inpainting,
)
device = device or get_device()
if is_diffusers_repo_url(weights_location):
(
vae_weights,
unet_weights,
text_encoder_weights,
) = load_sd15_diffusers_weights(weights_location, device="cpu")
else:
(
vae_weights,
unet_weights,
text_encoder_weights,
) = load_stable_diffusion_compvis_weights(weights_location)
StableDiffusionCls: type[LatentDiffusionModel]
if for_inpainting:
unet = SD1UNet(in_channels=9, device="cpu", dtype=dtype)
StableDiffusionCls = StableDiffusion_1_Inpainting
else:
unet = SD1UNet(in_channels=4, device="cpu", dtype=dtype)
StableDiffusionCls = StableDiffusion_1
logger.debug(f"Using class {StableDiffusionCls.__name__}")
sd = StableDiffusionCls(
device=device, dtype=dtype, lda=SD1AutoencoderSliced(), unet=unet
)
logger.debug("Loading VAE")
sd.lda.load_state_dict(vae_weights, assign=True)
logger.debug("Loading text encoder")
sd.clip_text_encoder.load_state_dict(text_encoder_weights, assign=True)
logger.debug("Loading UNet")
sd.unet.load_state_dict(unet_weights, strict=False, assign=True)
logger.debug(f"'{weights_location}' Loaded")
sd.to(device=device, dtype=dtype)
return sd
def _get_sd15_diffusion_model_refiners_new(
weights_location: str,
for_inpainting: bool = False,
device=None,
dtype=torch.float16,
) -> LatentDiffusionModel:
"""
Load a diffusion model.
Weights location may also be shortcut name, e.g. "SD-1.5"
"""
from imaginairy.modules.refiners_sd import (
SD1AutoencoderSliced,
StableDiffusion_1,
StableDiffusion_1_Inpainting,
)
device = device or get_device()
if is_diffusers_repo_url(weights_location):
(
vae_weights,
unet_weights,
text_encoder_weights,
) = load_sd15_diffusers_weights(weights_location, device="cpu")
else:
(
vae_weights,
unet_weights,
text_encoder_weights,
) = load_stable_diffusion_compvis_weights(weights_location)
StableDiffusionCls: type[LatentDiffusionModel]
if for_inpainting:
unet = SD1UNet(in_channels=9, device="cpu", dtype=dtype)
StableDiffusionCls = StableDiffusion_1_Inpainting
else:
unet = SD1UNet(in_channels=4, device="cpu", dtype=dtype)
StableDiffusionCls = StableDiffusion_1
logger.debug("Loading UNet")
unet.load_state_dict(unet_weights, strict=False, assign=True)
del unet_weights
unet.to(device=device, dtype=dtype)
logger.debug("Loading VAE")
lda = SD1AutoencoderSliced(device=device, dtype=dtype)
lda.load_state_dict(vae_weights, assign=True)
del vae_weights
lda.to(device=device, dtype=dtype)
logger.debug("Loading text encoder")
clip_text_encoder = CLIPTextEncoderL()
clip_text_encoder.load_state_dict(text_encoder_weights, assign=True)
del text_encoder_weights
clip_text_encoder.to(device=device, dtype=dtype)
logger.debug(f"Using class {StableDiffusionCls.__name__}")
sd = StableDiffusionCls(device=None, dtype=dtype, lda=lda, unet=unet) # type: ignore
sd.to(device=device, dtype=dtype)
logger.debug(f"'{weights_location}' Loaded")
return sd
@memory_managed_model("stable-diffusion", memory_usage_mb=1951)
def _load_diffusion_model(config_path, weights_location, half_mode):
model_config = OmegaConf.load(f"{PKG_ROOT}/{config_path}")
# only run half-mode on cuda. run it by default
half_mode = half_mode is None and get_device() == "cuda"
model = load_model_from_config(
config=model_config,
weights_location=weights_location,
half_mode=half_mode,
)
return model
@memory_managed_model("controlnet")
def load_controlnet(control_weights_location, half_mode):
controlnet_state_dict = load_state_dict(
control_weights_location, half_mode=half_mode
)
controlnet_state_dict = {
k.replace("control_model.", ""): v for k, v in controlnet_state_dict.items()
}
control_stage_config = OmegaConf.load(f"{PKG_ROOT}/configs/control-net-v15.yaml")[
"model"
]["params"]["control_stage_config"]
controlnet = instantiate_from_config(control_stage_config)
controlnet.load_state_dict(controlnet_state_dict, assign=True)
controlnet.to(get_device())
return controlnet
def resolve_model_weights_config(
model_weights: str | iconfig.ModelWeightsConfig,
default_model_architecture: str | None = None,
for_inpainting: bool = False,
) -> iconfig.ModelWeightsConfig:
"""Resolve weight and config path if they happen to be shortcuts."""
if isinstance(model_weights, iconfig.ModelWeightsConfig):
return model_weights
if not isinstance(model_weights, str):
msg = f"Invalid model weights: {model_weights}"
raise ValueError(msg) # noqa
if default_model_architecture is not None and not isinstance(
default_model_architecture, str
):
msg = f"Invalid model architecture: {default_model_architecture}"
raise ValueError(msg)
if for_inpainting:
model_weights_config = iconfig.MODEL_WEIGHT_CONFIG_LOOKUP.get(
f"{model_weights.lower()}-inpaint", None
)
if model_weights_config:
return model_weights_config
model_weights_config = iconfig.MODEL_WEIGHT_CONFIG_LOOKUP.get(
model_weights.lower(), None
)
if model_weights_config:
return model_weights_config
if not default_model_architecture:
msg = "You must specify the model architecture when loading custom weights."
raise ValueError(msg)
default_model_architecture = default_model_architecture.lower()
model_architecture_config = None
if for_inpainting:
model_architecture_config = iconfig.MODEL_ARCHITECTURE_LOOKUP.get(
f"{default_model_architecture}-inpaint", None
)
if not model_architecture_config:
model_architecture_config = iconfig.MODEL_ARCHITECTURE_LOOKUP.get(
default_model_architecture, None
)
if model_architecture_config is None:
msg = f"Invalid model architecture: {default_model_architecture}"
raise ValueError(msg)
model_weights_config = iconfig.ModelWeightsConfig(
name="Custom Loaded",
aliases=[],
architecture=model_architecture_config,
weights_location=model_weights,
defaults={},
)
return model_weights_config
def get_model_default_image_size(model_architecture: str | ModelArchitecture | None):
if isinstance(model_architecture, str):
model_architecture = iconfig.MODEL_ARCHITECTURE_LOOKUP.get(
model_architecture, None
)
default_size = None
if model_architecture:
default_size = model_architecture.defaults.get("size")
if default_size is None:
default_size = 512
default_size = normalize_image_size(default_size)
return default_size
def get_current_diffusion_model():
return MOST_RECENTLY_LOADED_MODEL
def load_sd15_diffusers_weights(base_url: str, device=None):
from imaginairy.utils import get_device
from imaginairy.weight_management.conversion import cast_weights
from imaginairy.weight_management.utils import (
COMPONENT_NAMES,
FORMAT_NAMES,
MODEL_NAMES,
)
base_url = normalize_diffusers_repo_url(base_url)
if device is None:
device = get_device()
vae_weights_path = download_huggingface_weights(base_url=base_url, sub="vae")
vae_weights = open_weights(vae_weights_path, device=device)
vae_weights = cast_weights(
source_weights=vae_weights,
source_model_name=MODEL_NAMES.SD15,
source_component_name=COMPONENT_NAMES.VAE,
source_format=FORMAT_NAMES.DIFFUSERS,
dest_format=FORMAT_NAMES.REFINERS,
)
unet_weights_path = download_huggingface_weights(base_url=base_url, sub="unet")
unet_weights = open_weights(unet_weights_path, device=device)
unet_weights = cast_weights(
source_weights=unet_weights,
source_model_name=MODEL_NAMES.SD15,
source_component_name=COMPONENT_NAMES.UNET,
source_format=FORMAT_NAMES.DIFFUSERS,
dest_format=FORMAT_NAMES.REFINERS,
)
text_encoder_weights_path = download_huggingface_weights(
base_url=base_url, sub="text_encoder"
)
text_encoder_weights = open_weights(text_encoder_weights_path, device=device)
text_encoder_weights = cast_weights(
source_weights=text_encoder_weights,
source_model_name=MODEL_NAMES.SD15,
source_component_name=COMPONENT_NAMES.TEXT_ENCODER,
source_format=FORMAT_NAMES.DIFFUSERS,
dest_format=FORMAT_NAMES.REFINERS,
)
first_vae = next(iter(vae_weights.values()))
first_unet = next(iter(unet_weights.values()))
first_encoder = next(iter(text_encoder_weights.values()))
msg = (
f"vae weights. dtype: {first_vae.dtype} device: {first_vae.device}\n"
f"unet weights. dtype: {first_unet.dtype} device: {first_unet.device}\n"
f"text_encoder weights. dtype: {first_encoder.dtype} device: {first_encoder.device}\n"
)
logger.debug(msg)
return vae_weights, unet_weights, text_encoder_weights
def load_sdxl_pipeline_from_diffusers_weights(
base_url: str, for_inpainting=False, device=None, dtype=torch.float16
):
from imaginairy.utils import get_device
device = device or get_device()
base_url = normalize_diffusers_repo_url(base_url)
translator = translators.diffusers_autoencoder_kl_to_refiners_translator()
vae_weights_path = download_huggingface_weights(
base_url=base_url, sub="vae", prefer_fp16=False
)
logger.debug(f"vae: {vae_weights_path}")
vae_weights = translator.load_and_translate_weights(
source_path=vae_weights_path,
device="cpu",
)
lda = SDXLAutoencoderSliced(device="cpu", dtype=dtype)
lda.load_state_dict(vae_weights, assign=True)
del vae_weights
translator = translators.diffusers_unet_sdxl_to_refiners_translator()
unet_weights_path = download_huggingface_weights(
base_url=base_url, sub="unet", prefer_fp16=True
)
logger.debug(f"unet: {unet_weights_path}")
unet_weights = translator.load_and_translate_weights(
source_path=unet_weights_path,
device="cpu",
)
if for_inpainting:
unet = SDXLUNet(device="cpu", dtype=dtype, in_channels=9)
else:
unet = SDXLUNet(device="cpu", dtype=dtype, in_channels=4)
unet.load_state_dict(unet_weights, assign=True)
del unet_weights
text_encoder_1_path = download_huggingface_weights(
base_url=base_url, sub="text_encoder"
)
text_encoder_2_path = download_huggingface_weights(
base_url=base_url, sub="text_encoder_2"
)
logger.debug(f"text encoder 1: {text_encoder_1_path}")
logger.debug(f"text encoder 2: {text_encoder_2_path}")
text_encoder_weights = (
translators.DoubleTextEncoderTranslator().load_and_translate_weights(
text_encoder_l_weights_path=text_encoder_1_path,
text_encoder_g_weights_path=text_encoder_2_path,
device="cpu",
)
)
text_encoder = DoubleTextEncoder(device="cpu", dtype=torch.float32)
text_encoder.load_state_dict(text_encoder_weights, assign=True)
del text_encoder_weights
lda = lda.to(device=device, dtype=torch.float32)
unet = unet.to(device=device, dtype=dtype)
text_encoder = text_encoder.to(device=device, dtype=dtype)
if for_inpainting:
StableDiffusionCls = StableDiffusion_XL_Inpainting
else:
StableDiffusionCls = StableDiffusion_XL
sd = StableDiffusionCls(
device=device, dtype=None, lda=lda, unet=unet, clip_text_encoder=text_encoder
)
return sd
def load_sdxl_pipeline_from_compvis_weights(
base_url: str, for_inpainting=False, device=None, dtype=torch.float16
):
from imaginairy.utils import get_device
device = device or get_device()
unet_weights, vae_weights, text_encoder_weights = load_sdxl_compvis_weights(
base_url
)
lda = SDXLAutoencoderSliced(device="cpu", dtype=dtype)
lda.load_state_dict(vae_weights, assign=True)
del vae_weights
if for_inpainting:
unet = SDXLUNet(device="cpu", dtype=dtype, in_channels=9)
else:
unet = SDXLUNet(device="cpu", dtype=dtype, in_channels=4)
unet.load_state_dict(unet_weights, assign=True)
del unet_weights
text_encoder = DoubleTextEncoder(device="cpu", dtype=torch.float32)
text_encoder.load_state_dict(text_encoder_weights, assign=True)
del text_encoder_weights
lda = lda.to(device=device, dtype=torch.float32)
unet = unet.to(device=device)
text_encoder = text_encoder.to(device=device)
if for_inpainting:
StableDiffusionCls = StableDiffusion_XL_Inpainting
else:
StableDiffusionCls = StableDiffusion_XL
sd = StableDiffusionCls(
device=device, dtype=None, lda=lda, unet=unet, clip_text_encoder=text_encoder
)
return sd
def load_sdxl_pipeline(base_url, device=None, for_inpainting=False):
logger.info(f"Loading SDXL weights from {base_url}")
device = device or get_device()
with logger.timed_info(f"Loaded SDXL pipeline from {base_url}"):
if is_diffusers_repo_url(base_url):
sd = load_sdxl_pipeline_from_diffusers_weights(
base_url, for_inpainting=for_inpainting, device=device
)
else:
sd = load_sdxl_pipeline_from_compvis_weights(
base_url, for_inpainting=for_inpainting, device=device
)
return sd
def open_weights(filepath, device=None):
from imaginairy.utils import get_device
if device is None:
device = get_device()
if "safetensor" in filepath.lower():
from imaginairy.vendored.refiners.fluxion.utils import safe_open
with safe_open(path=filepath, framework="pytorch", device=device) as tensors:
state_dict = {
key: tensors.get_tensor(key)
for key in tensors.keys() # noqa
}
else:
import torch
state_dict = torch.load(filepath, map_location=device)
while "state_dict" in state_dict:
state_dict = state_dict["state_dict"]
return state_dict
def load_tensors(tensorfile, map_location=None):
if tensorfile == "empty":
# used for testing
return {}
if tensorfile.endswith((".ckpt", ".pth", ".bin")):
return torch.load(tensorfile, map_location=map_location)
if tensorfile.endswith(".safetensors"):
return load_file(tensorfile, device=map_location)
return load_file(tensorfile, device=map_location)
# raise ValueError(f"Unknown tensorfile type: {tensorfile}")
def load_stable_diffusion_compvis_weights(weights_url):
from imaginairy.utils import get_device
from imaginairy.weight_management.conversion import cast_weights
from imaginairy.weight_management.utils import (
COMPONENT_NAMES,
FORMAT_NAMES,
MODEL_NAMES,
)
weights_path = get_cached_url_path(weights_url, category="weights")
logger.info(f"Loading weights from {weights_path}")
state_dict = open_weights(weights_path, device=get_device())
text_encoder_prefix = "cond_stage_model."
cut_start = len(text_encoder_prefix)
text_encoder_state_dict = {
k[cut_start:]: v
for k, v in state_dict.items()
if k.startswith(text_encoder_prefix)
}
text_encoder_state_dict = cast_weights(
source_weights=text_encoder_state_dict,
source_model_name=MODEL_NAMES.SD15,
source_component_name=COMPONENT_NAMES.TEXT_ENCODER,
source_format=FORMAT_NAMES.COMPVIS,
dest_format=FORMAT_NAMES.DIFFUSERS,
)
text_encoder_state_dict = cast_weights(
source_weights=text_encoder_state_dict,
source_model_name=MODEL_NAMES.SD15,
source_component_name=COMPONENT_NAMES.TEXT_ENCODER,
source_format=FORMAT_NAMES.DIFFUSERS,
dest_format=FORMAT_NAMES.REFINERS,
)
vae_prefix = "first_stage_model."
cut_start = len(vae_prefix)
vae_state_dict = {
k[cut_start:]: v for k, v in state_dict.items() if k.startswith(vae_prefix)
}
vae_state_dict = cast_weights(
source_weights=vae_state_dict,
source_model_name=MODEL_NAMES.SD15,
source_component_name=COMPONENT_NAMES.VAE,
source_format=FORMAT_NAMES.COMPVIS,
dest_format=FORMAT_NAMES.DIFFUSERS,
)
vae_state_dict = cast_weights(
source_weights=vae_state_dict,
source_model_name=MODEL_NAMES.SD15,
source_component_name=COMPONENT_NAMES.VAE,
source_format=FORMAT_NAMES.DIFFUSERS,
dest_format=FORMAT_NAMES.REFINERS,
)
unet_prefix = "model."
cut_start = len(unet_prefix)
unet_state_dict = {
k[cut_start:]: v for k, v in state_dict.items() if k.startswith(unet_prefix)
}
unet_state_dict = cast_weights(
source_weights=unet_state_dict,
source_model_name=MODEL_NAMES.SD15,
source_component_name=COMPONENT_NAMES.UNET,
source_format=FORMAT_NAMES.COMPVIS,
dest_format=FORMAT_NAMES.DIFFUSERS,
)
unet_state_dict = cast_weights(
source_weights=unet_state_dict,
source_model_name=MODEL_NAMES.SD15,
source_component_name=COMPONENT_NAMES.UNET,
source_format=FORMAT_NAMES.DIFFUSERS,
dest_format=FORMAT_NAMES.REFINERS,
)
return vae_state_dict, unet_state_dict, text_encoder_state_dict
def load_sdxl_compvis_weights(url):
from safetensors import safe_open
weights_path = get_cached_url_path(url)
state_dict = {}
unet_state_dict = {}
vae_state_dict = {}
text_encoder_1_state_dict = {}
text_encoder_2_state_dict = {}
with safe_open(weights_path, framework="pt") as f:
for key in f.keys(): # noqa
if key.startswith("model.diffusion_model."):
unet_state_dict[key] = f.get_tensor(key)
elif key.startswith("first_stage_model"):
vae_state_dict[key] = f.get_tensor(key)
elif key.startswith("conditioner.embedders.0."):
text_encoder_1_state_dict[key] = f.get_tensor(key)
elif key.startswith("conditioner.embedders.1."):
text_encoder_2_state_dict[key] = f.get_tensor(key)
else:
state_dict[key] = f.get_tensor(key)
logger.warning(f"Unused key {key}")
unet_weightmap = load_weight_map("Compvis-UNet-SDXL-to-Diffusers")
vae_weightmap = load_weight_map("Compvis-Autoencoder-SDXL-to-Diffusers")
text_encoder_1_weightmap = load_weight_map("Compvis-TextEncoder-SDXL-to-Diffusers")
text_encoder_2_weightmap = load_weight_map(
"Compvis-OpenClipTextEncoder-SDXL-to-Diffusers"
)
diffusers_unet_state_dict = unet_weightmap.translate_weights(unet_state_dict)
refiners_unet_state_dict = (
diffusers_unet_sdxl_to_refiners_translator().translate_weights(
diffusers_unet_state_dict
)
)
diffusers_vae_state_dict = vae_weightmap.translate_weights(vae_state_dict)
refiners_vae_state_dict = (
diffusers_autoencoder_kl_to_refiners_translator().translate_weights(
diffusers_vae_state_dict
)
)
diffusers_text_encoder_1_state_dict = text_encoder_1_weightmap.translate_weights(
text_encoder_1_state_dict
)
for key in list(text_encoder_2_state_dict.keys()):
if key.endswith((".in_proj_bias", ".in_proj_weight")):
value = text_encoder_2_state_dict[key]
q, k, v = value.chunk(3, dim=0)
text_encoder_2_state_dict[f"{key}.0"] = q
text_encoder_2_state_dict[f"{key}.1"] = k
text_encoder_2_state_dict[f"{key}.2"] = v
del text_encoder_2_state_dict[key]
diffusers_text_encoder_2_state_dict = text_encoder_2_weightmap.translate_weights(
text_encoder_2_state_dict
)
refiners_text_encoder_weights = DoubleTextEncoderTranslator().translate_weights(
diffusers_text_encoder_1_state_dict, diffusers_text_encoder_2_state_dict
)
return (
refiners_unet_state_dict,
refiners_vae_state_dict,
refiners_text_encoder_weights,
)