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#
# 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.
import os
from collections import defaultdict
from contextlib import nullcontext
from pathlib import Path
from typing import Callable
import safetensors
import torch
import torch.nn.functional as F
from huggingface_hub.utils import validate_hf_hub_args
from ..models.embeddings import (
ImageProjection,
IPAdapterFaceIDImageProjection,
IPAdapterFaceIDPlusImageProjection,
IPAdapterFullImageProjection,
IPAdapterPlusImageProjection,
MultiIPAdapterImageProjection,
)
from ..models.model_loading_utils import load_model_dict_into_meta
from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT, load_state_dict
from ..utils import (
_get_model_file,
is_accelerate_available,
is_torch_version,
logging,
)
from ..utils.torch_utils import empty_device_cache
from .lora_base import _func_optionally_disable_offloading
from .lora_pipeline import LORA_WEIGHT_NAME, LORA_WEIGHT_NAME_SAFE, TEXT_ENCODER_NAME, UNET_NAME
from .utils import AttnProcsLayers
logger = logging.get_logger(__name__)
CUSTOM_DIFFUSION_WEIGHT_NAME = "pytorch_custom_diffusion_weights.bin"
CUSTOM_DIFFUSION_WEIGHT_NAME_SAFE = "pytorch_custom_diffusion_weights.safetensors"
class UNet2DConditionLoadersMixin:
"""
Load LoRA layers into a [`UNet2DCondtionModel`].
"""
text_encoder_name = TEXT_ENCODER_NAME
unet_name = UNET_NAME
@validate_hf_hub_args
def load_attn_procs(self, pretrained_model_name_or_path_or_dict: str | dict[str, torch.Tensor], **kwargs):
r"""
Load pretrained Custom Diffusion attention processor layers into [`UNet2DConditionModel`]. Attention processor
layers have to be defined in
[`attention_processor.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py)
and be a `torch.nn.Module` class. To load LoRA layers, use [`~loaders.PeftAdapterMixin.load_lora_adapter`]
instead.
Parameters:
pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
Can be either:
- A string, the model id (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
the Hub.
- A path to a directory (for example `./my_model_directory`) containing the model weights saved
with [`ModelMixin.save_pretrained`].
- A [torch state
dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
cache_dir (`str | os.PathLike`, *optional*):
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
is not used.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
proxies (`dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
local_files_only (`bool`, *optional*, defaults to `False`):
Whether to only load local model weights and configuration files or not. If set to `True`, the model
won't be downloaded from the Hub.
token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
`diffusers-cli login` (stored in `~/.huggingface`) is used.
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
allowed by Git.
subfolder (`str`, *optional*, defaults to `""`):
The subfolder location of a model file within a larger model repository on the Hub or locally.
weight_name (`str`, *optional*, defaults to None):
Name of the serialized state dict file.
Example:
```py
import torch
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
torch_dtype=torch.float16,
).to("cuda")
pipeline.unet.load_attn_procs("path-to-save-model", weight_name="pytorch_custom_diffusion_weights.bin")
```
"""
from ..hooks.group_offloading import _maybe_remove_and_reapply_group_offloading
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwargs.pop("local_files_only", None)
token = kwargs.pop("token", None)
revision = kwargs.pop("revision", None)
subfolder = kwargs.pop("subfolder", None)
weight_name = kwargs.pop("weight_name", None)
use_safetensors = kwargs.pop("use_safetensors", None)
_pipeline = kwargs.pop("_pipeline", None)
allow_pickle = False
if use_safetensors is None:
use_safetensors = True
allow_pickle = True
user_agent = {"file_type": "attn_procs_weights", "framework": "pytorch"}
model_file = None
if not isinstance(pretrained_model_name_or_path_or_dict, dict):
# Let's first try to load .safetensors weights
if (use_safetensors and weight_name is None) or (
weight_name is not None and weight_name.endswith(".safetensors")
):
try:
model_file = _get_model_file(
pretrained_model_name_or_path_or_dict,
weights_name=weight_name or LORA_WEIGHT_NAME_SAFE,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
local_files_only=local_files_only,
token=token,
revision=revision,
subfolder=subfolder,
user_agent=user_agent,
)
state_dict = safetensors.torch.load_file(model_file, device="cpu")
except IOError as e:
if not allow_pickle:
raise e
# try loading non-safetensors weights
pass
if model_file is None:
model_file = _get_model_file(
pretrained_model_name_or_path_or_dict,
weights_name=weight_name or LORA_WEIGHT_NAME,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
local_files_only=local_files_only,
token=token,
revision=revision,
subfolder=subfolder,
user_agent=user_agent,
)
state_dict = load_state_dict(model_file)
else:
state_dict = pretrained_model_name_or_path_or_dict
is_custom_diffusion = any("custom_diffusion" in k for k in state_dict.keys())
if not is_custom_diffusion:
raise ValueError(
f"{model_file} does not seem to be in the correct format expected by Custom Diffusion training."
)
attn_processors = self._process_custom_diffusion(state_dict=state_dict)
# <Unsafe code
# We can be sure that the following works as it just sets attention processors and puts all in the same dtype.
# Now we remove any existing hooks to `_pipeline`.
is_model_cpu_offload = False
is_sequential_cpu_offload = False
is_group_offload = False
if _pipeline is not None:
is_model_cpu_offload, is_sequential_cpu_offload, is_group_offload = self._optionally_disable_offloading(
_pipeline=_pipeline
)
self.set_attn_processor(attn_processors)
self.to(dtype=self.dtype, device=self.device)
# Offload back.
if is_model_cpu_offload:
_pipeline.enable_model_cpu_offload()
elif is_sequential_cpu_offload:
_pipeline.enable_sequential_cpu_offload()
elif is_group_offload:
for component in _pipeline.components.values():
if isinstance(component, torch.nn.Module):
_maybe_remove_and_reapply_group_offloading(component)
# Unsafe code />
def _process_custom_diffusion(self, state_dict):
from ..models.attention_processor import CustomDiffusionAttnProcessor
attn_processors = {}
custom_diffusion_grouped_dict = defaultdict(dict)
for key, value in state_dict.items():
if len(value) == 0:
custom_diffusion_grouped_dict[key] = {}
else:
if "to_out" in key:
attn_processor_key, sub_key = ".".join(key.split(".")[:-3]), ".".join(key.split(".")[-3:])
else:
attn_processor_key, sub_key = ".".join(key.split(".")[:-2]), ".".join(key.split(".")[-2:])
custom_diffusion_grouped_dict[attn_processor_key][sub_key] = value
for key, value_dict in custom_diffusion_grouped_dict.items():
if len(value_dict) == 0:
attn_processors[key] = CustomDiffusionAttnProcessor(
train_kv=False, train_q_out=False, hidden_size=None, cross_attention_dim=None
)
else:
cross_attention_dim = value_dict["to_k_custom_diffusion.weight"].shape[1]
hidden_size = value_dict["to_k_custom_diffusion.weight"].shape[0]
train_q_out = True if "to_q_custom_diffusion.weight" in value_dict else False
attn_processors[key] = CustomDiffusionAttnProcessor(
train_kv=True,
train_q_out=train_q_out,
hidden_size=hidden_size,
cross_attention_dim=cross_attention_dim,
)
attn_processors[key].load_state_dict(value_dict)
return attn_processors
@classmethod
# Copied from diffusers.loaders.lora_base.LoraBaseMixin._optionally_disable_offloading
def _optionally_disable_offloading(cls, _pipeline):
return _func_optionally_disable_offloading(_pipeline=_pipeline)
def save_attn_procs(
self,
save_directory: str | os.PathLike,
is_main_process: bool = True,
weight_name: str = None,
save_function: Callable = None,
safe_serialization: bool = True,
**kwargs,
):
r"""
Save Custom Diffusion attention processor layers to a directory so that it can be reloaded with the
[`~loaders.UNet2DConditionLoadersMixin.load_attn_procs`] method. To save LoRA layers, use
[`~loaders.PeftAdapterMixin.save_lora_adapter`] instead.
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to save an attention processor to (will be created if it doesn't exist).
is_main_process (`bool`, *optional*, defaults to `True`):
Whether the process calling this is the main process or not. Useful during distributed training and you
need to call this function on all processes. In this case, set `is_main_process=True` only on the main
process to avoid race conditions.
save_function (`Callable`):
The function to use to save the state dictionary. Useful during distributed training when you need to
replace `torch.save` with another method. Can be configured with the environment variable
`DIFFUSERS_SAVE_MODE`.
safe_serialization (`bool`, *optional*, defaults to `True`):
Whether to save the model using `safetensors` or with `pickle`.
Example:
```py
import torch
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
torch_dtype=torch.float16,
).to("cuda")
pipeline.unet.load_attn_procs("path-to-save-model", weight_name="pytorch_custom_diffusion_weights.bin")
pipeline.unet.save_attn_procs("path-to-save-model", weight_name="pytorch_custom_diffusion_weights.bin")
```
"""
from ..models.attention_processor import (
CustomDiffusionAttnProcessor,
CustomDiffusionAttnProcessor2_0,
CustomDiffusionXFormersAttnProcessor,
)
if os.path.isfile(save_directory):
logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
return
is_custom_diffusion = any(
isinstance(
x,
(CustomDiffusionAttnProcessor, CustomDiffusionAttnProcessor2_0, CustomDiffusionXFormersAttnProcessor),
)
for (_, x) in self.attn_processors.items()
)
if not is_custom_diffusion:
raise ValueError(
"`save_attn_procs()` only supports saving Custom Diffusion attention processors. Please use "
"`save_lora_adapter()` to save LoRA layers."
)
state_dict = self._get_custom_diffusion_state_dict()
if save_function is None and safe_serialization:
# safetensors does not support saving dicts with non-tensor values
empty_state_dict = {k: v for k, v in state_dict.items() if not isinstance(v, torch.Tensor)}
if len(empty_state_dict) > 0:
logger.warning(
f"Safetensors does not support saving dicts with non-tensor values. "
f"The following keys will be ignored: {empty_state_dict.keys()}"
)
state_dict = {k: v for k, v in state_dict.items() if isinstance(v, torch.Tensor)}
if save_function is None:
if safe_serialization:
def save_function(weights, filename):
return safetensors.torch.save_file(weights, filename, metadata={"format": "pt"})
else:
save_function = torch.save
os.makedirs(save_directory, exist_ok=True)
if weight_name is None:
if safe_serialization:
weight_name = CUSTOM_DIFFUSION_WEIGHT_NAME_SAFE
else:
weight_name = CUSTOM_DIFFUSION_WEIGHT_NAME
# Save the model
save_path = Path(save_directory, weight_name).as_posix()
save_function(state_dict, save_path)
logger.info(f"Model weights saved in {save_path}")
def _get_custom_diffusion_state_dict(self):
from ..models.attention_processor import (
CustomDiffusionAttnProcessor,
CustomDiffusionAttnProcessor2_0,
CustomDiffusionXFormersAttnProcessor,
)
model_to_save = AttnProcsLayers(
{
y: x
for (y, x) in self.attn_processors.items()
if isinstance(
x,
(
CustomDiffusionAttnProcessor,
CustomDiffusionAttnProcessor2_0,
CustomDiffusionXFormersAttnProcessor,
),
)
}
)
state_dict = model_to_save.state_dict()
for name, attn in self.attn_processors.items():
if len(attn.state_dict()) == 0:
state_dict[name] = {}
return state_dict
def _convert_ip_adapter_image_proj_to_diffusers(self, state_dict, low_cpu_mem_usage=_LOW_CPU_MEM_USAGE_DEFAULT):
if low_cpu_mem_usage:
if is_accelerate_available():
from accelerate import init_empty_weights
else:
low_cpu_mem_usage = False
logger.warning(
"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
" environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
" `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
" install accelerate\n```\n."
)
if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
raise NotImplementedError(
"Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
" `low_cpu_mem_usage=False`."
)
updated_state_dict = {}
image_projection = None
init_context = init_empty_weights if low_cpu_mem_usage else nullcontext
if "proj.weight" in state_dict:
# IP-Adapter
num_image_text_embeds = 4
clip_embeddings_dim = state_dict["proj.weight"].shape[-1]
cross_attention_dim = state_dict["proj.weight"].shape[0] // 4
with init_context():
image_projection = ImageProjection(
cross_attention_dim=cross_attention_dim,
image_embed_dim=clip_embeddings_dim,
num_image_text_embeds=num_image_text_embeds,
)
for key, value in state_dict.items():
diffusers_name = key.replace("proj", "image_embeds")
updated_state_dict[diffusers_name] = value
elif "proj.3.weight" in state_dict:
# IP-Adapter Full
clip_embeddings_dim = state_dict["proj.0.weight"].shape[0]
cross_attention_dim = state_dict["proj.3.weight"].shape[0]
with init_context():
image_projection = IPAdapterFullImageProjection(
cross_attention_dim=cross_attention_dim, image_embed_dim=clip_embeddings_dim
)
for key, value in state_dict.items():
diffusers_name = key.replace("proj.0", "ff.net.0.proj")
diffusers_name = diffusers_name.replace("proj.2", "ff.net.2")
diffusers_name = diffusers_name.replace("proj.3", "norm")
updated_state_dict[diffusers_name] = value
elif "perceiver_resampler.proj_in.weight" in state_dict:
# IP-Adapter Face ID Plus
id_embeddings_dim = state_dict["proj.0.weight"].shape[1]
embed_dims = state_dict["perceiver_resampler.proj_in.weight"].shape[0]
hidden_dims = state_dict["perceiver_resampler.proj_in.weight"].shape[1]
output_dims = state_dict["perceiver_resampler.proj_out.weight"].shape[0]
heads = state_dict["perceiver_resampler.layers.0.0.to_q.weight"].shape[0] // 64
with init_context():
image_projection = IPAdapterFaceIDPlusImageProjection(
embed_dims=embed_dims,
output_dims=output_dims,
hidden_dims=hidden_dims,
heads=heads,
id_embeddings_dim=id_embeddings_dim,
)
for key, value in state_dict.items():
diffusers_name = key.replace("perceiver_resampler.", "")
diffusers_name = diffusers_name.replace("0.to", "attn.to")
diffusers_name = diffusers_name.replace("0.1.0.", "0.ff.0.")
diffusers_name = diffusers_name.replace("0.1.1.weight", "0.ff.1.net.0.proj.weight")
diffusers_name = diffusers_name.replace("0.1.3.weight", "0.ff.1.net.2.weight")
diffusers_name = diffusers_name.replace("1.1.0.", "1.ff.0.")
diffusers_name = diffusers_name.replace("1.1.1.weight", "1.ff.1.net.0.proj.weight")
diffusers_name = diffusers_name.replace("1.1.3.weight", "1.ff.1.net.2.weight")
diffusers_name = diffusers_name.replace("2.1.0.", "2.ff.0.")
diffusers_name = diffusers_name.replace("2.1.1.weight", "2.ff.1.net.0.proj.weight")
diffusers_name = diffusers_name.replace("2.1.3.weight", "2.ff.1.net.2.weight")
diffusers_name = diffusers_name.replace("3.1.0.", "3.ff.0.")
diffusers_name = diffusers_name.replace("3.1.1.weight", "3.ff.1.net.0.proj.weight")
diffusers_name = diffusers_name.replace("3.1.3.weight", "3.ff.1.net.2.weight")
diffusers_name = diffusers_name.replace("layers.0.0", "layers.0.ln0")
diffusers_name = diffusers_name.replace("layers.0.1", "layers.0.ln1")
diffusers_name = diffusers_name.replace("layers.1.0", "layers.1.ln0")
diffusers_name = diffusers_name.replace("layers.1.1", "layers.1.ln1")
diffusers_name = diffusers_name.replace("layers.2.0", "layers.2.ln0")
diffusers_name = diffusers_name.replace("layers.2.1", "layers.2.ln1")
diffusers_name = diffusers_name.replace("layers.3.0", "layers.3.ln0")
diffusers_name = diffusers_name.replace("layers.3.1", "layers.3.ln1")
if "norm1" in diffusers_name:
updated_state_dict[diffusers_name.replace("0.norm1", "0")] = value
elif "norm2" in diffusers_name:
updated_state_dict[diffusers_name.replace("0.norm2", "1")] = value
elif "to_kv" in diffusers_name:
v_chunk = value.chunk(2, dim=0)
updated_state_dict[diffusers_name.replace("to_kv", "to_k")] = v_chunk[0]
updated_state_dict[diffusers_name.replace("to_kv", "to_v")] = v_chunk[1]
elif "to_out" in diffusers_name:
updated_state_dict[diffusers_name.replace("to_out", "to_out.0")] = value
elif "proj.0.weight" == diffusers_name:
updated_state_dict["proj.net.0.proj.weight"] = value
elif "proj.0.bias" == diffusers_name:
updated_state_dict["proj.net.0.proj.bias"] = value
elif "proj.2.weight" == diffusers_name:
updated_state_dict["proj.net.2.weight"] = value
elif "proj.2.bias" == diffusers_name:
updated_state_dict["proj.net.2.bias"] = value
else:
updated_state_dict[diffusers_name] = value
elif "norm.weight" in state_dict:
# IP-Adapter Face ID
id_embeddings_dim_in = state_dict["proj.0.weight"].shape[1]
id_embeddings_dim_out = state_dict["proj.0.weight"].shape[0]
multiplier = id_embeddings_dim_out // id_embeddings_dim_in
norm_layer = "norm.weight"
cross_attention_dim = state_dict[norm_layer].shape[0]
num_tokens = state_dict["proj.2.weight"].shape[0] // cross_attention_dim
with init_context():
image_projection = IPAdapterFaceIDImageProjection(
cross_attention_dim=cross_attention_dim,
image_embed_dim=id_embeddings_dim_in,
mult=multiplier,
num_tokens=num_tokens,
)
for key, value in state_dict.items():
diffusers_name = key.replace("proj.0", "ff.net.0.proj")
diffusers_name = diffusers_name.replace("proj.2", "ff.net.2")
updated_state_dict[diffusers_name] = value
else:
# IP-Adapter Plus
num_image_text_embeds = state_dict["latents"].shape[1]
embed_dims = state_dict["proj_in.weight"].shape[1]
output_dims = state_dict["proj_out.weight"].shape[0]
hidden_dims = state_dict["latents"].shape[2]
attn_key_present = any("attn" in k for k in state_dict)
heads = (
state_dict["layers.0.attn.to_q.weight"].shape[0] // 64
if attn_key_present
else state_dict["layers.0.0.to_q.weight"].shape[0] // 64
)
with init_context():
image_projection = IPAdapterPlusImageProjection(
embed_dims=embed_dims,
output_dims=output_dims,
hidden_dims=hidden_dims,
heads=heads,
num_queries=num_image_text_embeds,
)
for key, value in state_dict.items():
diffusers_name = key.replace("0.to", "2.to")
diffusers_name = diffusers_name.replace("0.0.norm1", "0.ln0")
diffusers_name = diffusers_name.replace("0.0.norm2", "0.ln1")
diffusers_name = diffusers_name.replace("1.0.norm1", "1.ln0")
diffusers_name = diffusers_name.replace("1.0.norm2", "1.ln1")
diffusers_name = diffusers_name.replace("2.0.norm1", "2.ln0")
diffusers_name = diffusers_name.replace("2.0.norm2", "2.ln1")
diffusers_name = diffusers_name.replace("3.0.norm1", "3.ln0")
diffusers_name = diffusers_name.replace("3.0.norm2", "3.ln1")
if "to_kv" in diffusers_name:
parts = diffusers_name.split(".")
parts[2] = "attn"
diffusers_name = ".".join(parts)
v_chunk = value.chunk(2, dim=0)
updated_state_dict[diffusers_name.replace("to_kv", "to_k")] = v_chunk[0]
updated_state_dict[diffusers_name.replace("to_kv", "to_v")] = v_chunk[1]
elif "to_q" in diffusers_name:
parts = diffusers_name.split(".")
parts[2] = "attn"
diffusers_name = ".".join(parts)
updated_state_dict[diffusers_name] = value
elif "to_out" in diffusers_name:
parts = diffusers_name.split(".")
parts[2] = "attn"
diffusers_name = ".".join(parts)
updated_state_dict[diffusers_name.replace("to_out", "to_out.0")] = value
else:
diffusers_name = diffusers_name.replace("0.1.0", "0.ff.0")
diffusers_name = diffusers_name.replace("0.1.1", "0.ff.1.net.0.proj")
diffusers_name = diffusers_name.replace("0.1.3", "0.ff.1.net.2")
diffusers_name = diffusers_name.replace("1.1.0", "1.ff.0")
diffusers_name = diffusers_name.replace("1.1.1", "1.ff.1.net.0.proj")
diffusers_name = diffusers_name.replace("1.1.3", "1.ff.1.net.2")
diffusers_name = diffusers_name.replace("2.1.0", "2.ff.0")
diffusers_name = diffusers_name.replace("2.1.1", "2.ff.1.net.0.proj")
diffusers_name = diffusers_name.replace("2.1.3", "2.ff.1.net.2")
diffusers_name = diffusers_name.replace("3.1.0", "3.ff.0")
diffusers_name = diffusers_name.replace("3.1.1", "3.ff.1.net.0.proj")
diffusers_name = diffusers_name.replace("3.1.3", "3.ff.1.net.2")
updated_state_dict[diffusers_name] = value
if not low_cpu_mem_usage:
image_projection.load_state_dict(updated_state_dict, strict=True)
else:
device_map = {"": self.device}
load_model_dict_into_meta(image_projection, updated_state_dict, device_map=device_map, dtype=self.dtype)
empty_device_cache()
return image_projection
def _convert_ip_adapter_attn_to_diffusers(self, state_dicts, low_cpu_mem_usage=_LOW_CPU_MEM_USAGE_DEFAULT):
from ..models.attention_processor import (
IPAdapterAttnProcessor,
IPAdapterAttnProcessor2_0,
IPAdapterXFormersAttnProcessor,
)
if low_cpu_mem_usage:
if is_accelerate_available():
from accelerate import init_empty_weights
else:
low_cpu_mem_usage = False
logger.warning(
"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
" environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
" `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
" install accelerate\n```\n."
)
if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
raise NotImplementedError(
"Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
" `low_cpu_mem_usage=False`."
)
# set ip-adapter cross-attention processors & load state_dict
attn_procs = {}
key_id = 1
init_context = init_empty_weights if low_cpu_mem_usage else nullcontext
for name in self.attn_processors.keys():
cross_attention_dim = None if name.endswith("attn1.processor") else self.config.cross_attention_dim
if name.startswith("mid_block"):
hidden_size = self.config.block_out_channels[-1]
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
hidden_size = list(reversed(self.config.block_out_channels))[block_id]
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = self.config.block_out_channels[block_id]
if cross_attention_dim is None or "motion_modules" in name:
attn_processor_class = self.attn_processors[name].__class__
attn_procs[name] = attn_processor_class()
else:
if "XFormers" in str(self.attn_processors[name].__class__):
attn_processor_class = IPAdapterXFormersAttnProcessor
else:
attn_processor_class = (
IPAdapterAttnProcessor2_0
if hasattr(F, "scaled_dot_product_attention")
else IPAdapterAttnProcessor
)
num_image_text_embeds = []
for state_dict in state_dicts:
if "proj.weight" in state_dict["image_proj"]:
# IP-Adapter
num_image_text_embeds += [4]
elif "proj.3.weight" in state_dict["image_proj"]:
# IP-Adapter Full Face
num_image_text_embeds += [257] # 256 CLIP tokens + 1 CLS token
elif "perceiver_resampler.proj_in.weight" in state_dict["image_proj"]:
# IP-Adapter Face ID Plus
num_image_text_embeds += [4]
elif "norm.weight" in state_dict["image_proj"]:
# IP-Adapter Face ID
num_image_text_embeds += [4]
else:
# IP-Adapter Plus
num_image_text_embeds += [state_dict["image_proj"]["latents"].shape[1]]
with init_context():
attn_procs[name] = attn_processor_class(
hidden_size=hidden_size,
cross_attention_dim=cross_attention_dim,
scale=1.0,
num_tokens=num_image_text_embeds,
)
value_dict = {}
for i, state_dict in enumerate(state_dicts):
value_dict.update({f"to_k_ip.{i}.weight": state_dict["ip_adapter"][f"{key_id}.to_k_ip.weight"]})
value_dict.update({f"to_v_ip.{i}.weight": state_dict["ip_adapter"][f"{key_id}.to_v_ip.weight"]})
if not low_cpu_mem_usage:
attn_procs[name].load_state_dict(value_dict)
else:
device = next(iter(value_dict.values())).device
dtype = next(iter(value_dict.values())).dtype
device_map = {"": device}
load_model_dict_into_meta(attn_procs[name], value_dict, device_map=device_map, dtype=dtype)
key_id += 2
empty_device_cache()
return attn_procs
def _load_ip_adapter_weights(self, state_dicts, low_cpu_mem_usage=_LOW_CPU_MEM_USAGE_DEFAULT):
if not isinstance(state_dicts, list):
state_dicts = [state_dicts]
# Kolors Unet already has a `encoder_hid_proj`
if (
self.encoder_hid_proj is not None
and self.config.encoder_hid_dim_type == "text_proj"
and not hasattr(self, "text_encoder_hid_proj")
):
self.text_encoder_hid_proj = self.encoder_hid_proj
# Set encoder_hid_proj after loading ip_adapter weights,
# because `IPAdapterPlusImageProjection` also has `attn_processors`.
self.encoder_hid_proj = None
attn_procs = self._convert_ip_adapter_attn_to_diffusers(state_dicts, low_cpu_mem_usage=low_cpu_mem_usage)
self.set_attn_processor(attn_procs)
# convert IP-Adapter Image Projection layers to diffusers
image_projection_layers = []
for state_dict in state_dicts:
image_projection_layer = self._convert_ip_adapter_image_proj_to_diffusers(
state_dict["image_proj"], low_cpu_mem_usage=low_cpu_mem_usage
)
image_projection_layers.append(image_projection_layer)
self.encoder_hid_proj = MultiIPAdapterImageProjection(image_projection_layers)
self.config.encoder_hid_dim_type = "ip_image_proj"
self.to(dtype=self.dtype, device=self.device)
def _load_ip_adapter_loras(self, state_dicts):
lora_dicts = {}
for key_id, name in enumerate(self.attn_processors.keys()):
for i, state_dict in enumerate(state_dicts):
if f"{key_id}.to_k_lora.down.weight" in state_dict["ip_adapter"]:
if i not in lora_dicts:
lora_dicts[i] = {}
lora_dicts[i].update(
{
f"unet.{name}.to_k_lora.down.weight": state_dict["ip_adapter"][
f"{key_id}.to_k_lora.down.weight"
]
}
)
lora_dicts[i].update(
{
f"unet.{name}.to_q_lora.down.weight": state_dict["ip_adapter"][
f"{key_id}.to_q_lora.down.weight"
]
}
)
lora_dicts[i].update(
{
f"unet.{name}.to_v_lora.down.weight": state_dict["ip_adapter"][
f"{key_id}.to_v_lora.down.weight"
]
}
)
lora_dicts[i].update(
{
f"unet.{name}.to_out_lora.down.weight": state_dict["ip_adapter"][
f"{key_id}.to_out_lora.down.weight"
]
}
)
lora_dicts[i].update(
{f"unet.{name}.to_k_lora.up.weight": state_dict["ip_adapter"][f"{key_id}.to_k_lora.up.weight"]}
)
lora_dicts[i].update(
{f"unet.{name}.to_q_lora.up.weight": state_dict["ip_adapter"][f"{key_id}.to_q_lora.up.weight"]}
)
lora_dicts[i].update(
{f"unet.{name}.to_v_lora.up.weight": state_dict["ip_adapter"][f"{key_id}.to_v_lora.up.weight"]}
)
lora_dicts[i].update(
{
f"unet.{name}.to_out_lora.up.weight": state_dict["ip_adapter"][
f"{key_id}.to_out_lora.up.weight"
]
}
)
return lora_dicts
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