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import os
from typing import Type, Callable, TypeVar, Dict, Any
import torch
import diffusers
from transformers.models.clip.modeling_clip import CLIPTextModel, CLIPTextModelWithProjection
class ENVStore:
__DESERIALIZER: Dict[Type, Callable[[str,], Any]] = {
bool: lambda x: bool(int(x)),
int: int,
str: lambda x: x,
}
__SERIALIZER: Dict[Type, Callable[[Any,], str]] = {
bool: lambda x: str(int(x)),
int: str,
str: lambda x: x,
}
def __getattr__(self, name: str):
value = os.environ.get(f"SDNEXT_OLIVE_{name}", None)
if value is None:
return
ty = self.__class__.__annotations__[name]
deserialize = self.__DESERIALIZER[ty]
return deserialize(value)
def __setattr__(self, name: str, value) -> None:
if name not in self.__class__.__annotations__:
return
ty = self.__class__.__annotations__[name]
serialize = self.__SERIALIZER[ty]
os.environ[f"SDNEXT_OLIVE_{name}"] = serialize(value)
def __delattr__(self, name: str) -> None:
if name not in self.__class__.__annotations__:
return
key = f"SDNEXT_OLIVE_{name}"
if key not in os.environ:
return
os.environ.pop(key)
class OliveOptimizerConfig(ENVStore):
from_diffusers_cache: bool
is_sdxl: bool
vae: str
vae_sdxl_fp16_fix: bool
width: int
height: int
batch_size: int
cross_attention_dim: int
time_ids_size: int
config = OliveOptimizerConfig()
def get_variant():
from modules.shared import opts
if opts.diffusers_model_load_variant == 'default':
from modules import devices
if devices.dtype == torch.float16:
return 'fp16'
return None
elif opts.diffusers_model_load_variant == 'fp32':
return None
else:
return opts.diffusers_model_load_variant
def get_loader_arguments(no_variant: bool = False):
kwargs = {}
if config.from_diffusers_cache:
from modules.shared import opts
kwargs["cache_dir"] = opts.diffusers_dir
if not no_variant:
kwargs["variant"] = get_variant()
return kwargs
T = TypeVar("T")
def from_pretrained(cls: Type[T], pretrained_model_name_or_path: os.PathLike, *args, no_variant: bool = False, **kwargs) -> T:
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
if pretrained_model_name_or_path.endswith(".onnx"):
cls = diffusers.OnnxRuntimeModel
pretrained_model_name_or_path = os.path.dirname(pretrained_model_name_or_path)
return cls.from_pretrained(pretrained_model_name_or_path, *args, **kwargs, **get_loader_arguments(no_variant))
# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# --------------------------------------------------------------------------
# Helper latency-only dataloader that creates random tensors with no label
class RandomDataLoader:
def __init__(self, create_inputs_func, batchsize, torch_dtype):
self.create_input_func = create_inputs_func
self.batchsize = batchsize
self.torch_dtype = torch_dtype
def __getitem__(self, idx):
label = None
return self.create_input_func(self.batchsize, self.torch_dtype), label
# -----------------------------------------------------------------------------
# TEXT ENCODER
# -----------------------------------------------------------------------------
def text_encoder_inputs(batchsize, torch_dtype):
input_ids = torch.zeros((config.batch_size, 77), dtype=torch_dtype)
return {
"input_ids": input_ids,
"output_hidden_states": True,
} if config.is_sdxl else input_ids
def text_encoder_load(model_name):
model = from_pretrained(CLIPTextModel, model_name, subfolder="text_encoder")
return model
def text_encoder_conversion_inputs(model):
return text_encoder_inputs(1, torch.int32)
def text_encoder_data_loader(data_dir, batchsize, *_, **__):
return RandomDataLoader(text_encoder_inputs, config.batch_size, torch.int32)
# -----------------------------------------------------------------------------
# TEXT ENCODER 2
# -----------------------------------------------------------------------------
def text_encoder_2_inputs(batchsize, torch_dtype):
return {
"input_ids": torch.zeros((config.batch_size, 77), dtype=torch_dtype),
"output_hidden_states": True,
}
def text_encoder_2_load(model_name):
model = from_pretrained(CLIPTextModelWithProjection, model_name, subfolder="text_encoder_2")
return model
def text_encoder_2_conversion_inputs(model):
return text_encoder_2_inputs(1, torch.int64)
def text_encoder_2_data_loader(data_dir, batchsize, *_, **__):
return RandomDataLoader(text_encoder_2_inputs, config.batch_size, torch.int64)
# -----------------------------------------------------------------------------
# UNET
# -----------------------------------------------------------------------------
def unet_inputs(batchsize, torch_dtype, is_conversion_inputs=False):
if config.is_sdxl:
inputs = {
"sample": torch.rand((2 * config.batch_size, 4, config.height // 8, config.width // 8), dtype=torch_dtype),
"timestep": torch.rand((1,), dtype=torch_dtype),
"encoder_hidden_states": torch.rand((2 * config.batch_size, 77, config.cross_attention_dim), dtype=torch_dtype),
}
if is_conversion_inputs:
inputs["additional_inputs"] = {
"added_cond_kwargs": {
"text_embeds": torch.rand((2 * config.batch_size, 1280), dtype=torch_dtype),
"time_ids": torch.rand((2 * config.batch_size, config.time_ids_size), dtype=torch_dtype),
}
}
else:
inputs["text_embeds"] = torch.rand((2 * config.batch_size, 1280), dtype=torch_dtype)
inputs["time_ids"] = torch.rand((2 * config.batch_size, config.time_ids_size), dtype=torch_dtype)
else:
inputs = {
"sample": torch.rand((config.batch_size, 4, config.height // 8, config.width // 8), dtype=torch_dtype),
"timestep": torch.rand((config.batch_size,), dtype=torch_dtype),
"encoder_hidden_states": torch.rand((config.batch_size, 77, config.cross_attention_dim), dtype=torch_dtype),
}
# use as kwargs since they won't be in the correct position if passed along with the tuple of inputs
kwargs = {
"return_dict": False,
}
if is_conversion_inputs:
inputs["additional_inputs"] = {
**kwargs,
"added_cond_kwargs": {
"text_embeds": torch.rand((1, 1280), dtype=torch_dtype),
"time_ids": torch.rand((1, 5), dtype=torch_dtype),
},
}
else:
inputs.update(kwargs)
inputs["onnx::Concat_4"] = torch.rand((1, 1280), dtype=torch_dtype)
inputs["onnx::Shape_5"] = torch.rand((1, 5), dtype=torch_dtype)
return inputs
def unet_load(model_name):
model = from_pretrained(diffusers.UNet2DConditionModel, model_name, subfolder="unet")
return model
def unet_conversion_inputs(model):
return tuple(unet_inputs(1, torch.float32, True).values())
def unet_data_loader(data_dir, batchsize, *_, **__):
return RandomDataLoader(unet_inputs, config.batch_size, torch.float16)
# -----------------------------------------------------------------------------
# VAE ENCODER
# -----------------------------------------------------------------------------
def vae_encoder_inputs(batchsize, torch_dtype):
return {
"sample": torch.rand((config.batch_size, 3, config.height, config.width), dtype=torch_dtype),
"return_dict": False,
}
def vae_encoder_load(model_name):
subfolder = "vae_encoder" if os.path.isdir(os.path.join(model_name, "vae_encoder")) else "vae"
if config.vae_sdxl_fp16_fix:
model_name = "madebyollin/sdxl-vae-fp16-fix"
subfolder = ""
if config.vae is None:
model = from_pretrained(diffusers.AutoencoderKL, model_name, subfolder=subfolder, no_variant=config.vae_sdxl_fp16_fix)
else:
model = diffusers.AutoencoderKL.from_single_file(config.vae)
model.forward = lambda sample, return_dict: model.encode(sample, return_dict)[0].sample()
return model
def vae_encoder_conversion_inputs(model):
return tuple(vae_encoder_inputs(1, torch.float32).values())
def vae_encoder_data_loader(data_dir, batchsize, *_, **__):
return RandomDataLoader(vae_encoder_inputs, config.batch_size, torch.float16)
# -----------------------------------------------------------------------------
# VAE DECODER
# -----------------------------------------------------------------------------
def vae_decoder_inputs(batchsize, torch_dtype):
return {
"latent_sample": torch.rand((config.batch_size, 4, config.height // 8, config.width // 8), dtype=torch_dtype),
"return_dict": False,
}
def vae_decoder_load(model_name):
subfolder = "vae_decoder" if os.path.isdir(os.path.join(model_name, "vae_decoder")) else "vae"
if config.vae_sdxl_fp16_fix:
model_name = "madebyollin/sdxl-vae-fp16-fix"
subfolder = ""
if config.vae is None:
model = from_pretrained(diffusers.AutoencoderKL, model_name, subfolder=subfolder, no_variant=config.vae_sdxl_fp16_fix)
else:
model = diffusers.AutoencoderKL.from_single_file(config.vae)
model.forward = model.decode
return model
def vae_decoder_conversion_inputs(model):
return tuple(vae_decoder_inputs(1, torch.float32).values())
def vae_decoder_data_loader(data_dir, batchsize, *_, **__):
return RandomDataLoader(vae_decoder_inputs, config.batch_size, torch.float16)
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