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import logging
import math
import torch
from modules.Utilities import Latent
from modules.Device import Device
from modules.NeuralNetwork import unet
from modules.cond import cast, cond
from modules.sample import sampling
class BaseModel(torch.nn.Module):
"""#### Base class for models."""
def __init__(
self,
model_config: object,
model_type: sampling.ModelType = sampling.ModelType.EPS,
device: torch.device = None,
unet_model: object = unet.UNetModel1,
flux: bool = False,
):
"""#### Initialize the BaseModel class.
#### Args:
- `model_config` (object): The model configuration.
- `model_type` (sampling.ModelType, optional): The model type. Defaults to sampling.ModelType.EPS.
- `device` (torch.device, optional): The device to use. Defaults to None.
- `unet_model` (object, optional): The UNet model. Defaults to unet.UNetModel1.
"""
super().__init__()
unet_config = model_config.unet_config
self.latent_format = model_config.latent_format
self.model_config = model_config
self.manual_cast_dtype = model_config.manual_cast_dtype
self.device = device
if flux:
if not unet_config.get("disable_unet_model_creation", False):
operations = model_config.custom_operations
self.diffusion_model = unet_model(
**unet_config, device=device, operations=operations
)
logging.info(
"model weight dtype {}, manual cast: {}".format(
self.get_dtype(), self.manual_cast_dtype
)
)
else:
if not unet_config.get("disable_unet_model_creation", False):
if self.manual_cast_dtype is not None:
operations = cast.manual_cast
else:
operations = cast.disable_weight_init
self.diffusion_model = unet_model(
**unet_config, device=device, operations=operations
)
self.model_type = model_type
self.model_sampling = sampling.model_sampling(
model_config, model_type, flux=flux
)
self.adm_channels = unet_config.get("adm_in_channels", None)
if self.adm_channels is None:
self.adm_channels = 0
self.concat_keys = ()
logging.info("model_type {}".format(model_type.name))
logging.debug("adm {}".format(self.adm_channels))
self.memory_usage_factor = model_config.memory_usage_factor if flux else 2.0
def apply_model(
self,
x: torch.Tensor,
t: torch.Tensor,
c_concat: torch.Tensor = None,
c_crossattn: torch.Tensor = None,
control: torch.Tensor = None,
transformer_options: dict = {},
**kwargs,
) -> torch.Tensor:
"""#### Apply the model to the input tensor.
#### Args:
- `x` (torch.Tensor): The input tensor.
- `t` (torch.Tensor): The timestep tensor.
- `c_concat` (torch.Tensor, optional): The concatenated condition tensor. Defaults to None.
- `c_crossattn` (torch.Tensor, optional): The cross-attention condition tensor. Defaults to None.
- `control` (torch.Tensor, optional): The control tensor. Defaults to None.
- `transformer_options` (dict, optional): The transformer options. Defaults to {}.
- `**kwargs`: Additional keyword arguments.
#### Returns:
- `torch.Tensor`: The output tensor.
"""
sigma = t
xc = self.model_sampling.calculate_input(sigma, x)
# Optimize concatenation operation by avoiding unnecessary list creation
if c_concat is not None:
xc = torch.cat((xc, c_concat), dim=1)
# Determine dtype once to avoid repeated calls to get_dtype()
dtype = (
self.manual_cast_dtype
if self.manual_cast_dtype is not None
else self.get_dtype()
)
# Batch operations to reduce overhead
xc = xc.to(dtype)
t = self.model_sampling.timestep(t).float()
context = c_crossattn.to(dtype) if c_crossattn is not None else None
# Process extra conditions more efficiently
extra_conds = {}
for name, value in kwargs.items():
if hasattr(value, "dtype") and value.dtype not in (torch.int, torch.long):
extra_conds[name] = value.to(dtype)
else:
extra_conds[name] = value
# Run diffusion model and calculate denoised output
model_output = self.diffusion_model(
xc,
t,
context=context,
control=control,
transformer_options=transformer_options,
**extra_conds,
).float()
return self.model_sampling.calculate_denoised(sigma, model_output, x)
def get_dtype(self) -> torch.dtype:
"""#### Get the data type of the model.
#### Returns:
- `torch.dtype`: The data type.
"""
return self.diffusion_model.dtype
def encode_adm(self, **kwargs) -> None:
"""#### Encode the ADM.
#### Args:
- `**kwargs`: Additional keyword arguments.
#### Returns:
- `None`: The encoded ADM.
"""
return None
def extra_conds(self, **kwargs) -> dict:
"""#### Get the extra conditions.
#### Args:
- `**kwargs`: Additional keyword arguments.
#### Returns:
- `dict`: The extra conditions.
"""
out = {}
adm = self.encode_adm(**kwargs)
if adm is not None:
out["y"] = cond.CONDRegular(adm)
cross_attn = kwargs.get("cross_attn", None)
if cross_attn is not None:
out["c_crossattn"] = cond.CONDCrossAttn(cross_attn)
cross_attn_cnet = kwargs.get("cross_attn_controlnet", None)
if cross_attn_cnet is not None:
out["crossattn_controlnet"] = cond.CONDCrossAttn(cross_attn_cnet)
return out
def load_model_weights(self, sd: dict, unet_prefix: str = "") -> "BaseModel":
"""#### Load the model weights.
#### Args:
- `sd` (dict): The state dictionary.
- `unet_prefix` (str, optional): The UNet prefix. Defaults to "".
#### Returns:
- `BaseModel`: The model with loaded weights.
"""
to_load = {}
keys = list(sd.keys())
for k in keys:
if k.startswith(unet_prefix):
to_load[k[len(unet_prefix) :]] = sd.pop(k)
to_load = self.model_config.process_unet_state_dict(to_load)
m, u = self.diffusion_model.load_state_dict(to_load, strict=False)
if len(m) > 0:
logging.warning("unet missing: {}".format(m))
if len(u) > 0:
logging.warning("unet unexpected: {}".format(u))
del to_load
return self
def process_latent_in(self, latent: torch.Tensor) -> torch.Tensor:
"""#### Process the latent input.
#### Args:
- `latent` (torch.Tensor): The latent tensor.
#### Returns:
- `torch.Tensor`: The processed latent tensor.
"""
return self.latent_format.process_in(latent)
def process_latent_out(self, latent: torch.Tensor) -> torch.Tensor:
"""#### Process the latent output.
#### Args:
- `latent` (torch.Tensor): The latent tensor.
#### Returns:
- `torch.Tensor`: The processed latent tensor.
"""
return self.latent_format.process_out(latent)
def memory_required(self, input_shape: tuple) -> float:
"""#### Calculate the memory required for the model.
#### Args:
- `input_shape` (tuple): The input shape.
#### Returns:
- `float`: The memory required.
"""
dtype = self.get_dtype()
if self.manual_cast_dtype is not None:
dtype = self.manual_cast_dtype
# TODO: this needs to be tweaked
area = input_shape[0] * math.prod(input_shape[2:])
return (area * Device.dtype_size(dtype) * 0.01 * self.memory_usage_factor) * (
1024 * 1024
)
class BASE:
"""#### Base class for model configurations."""
unet_config = {}
unet_extra_config = {
"num_heads": -1,
"num_head_channels": 64,
}
required_keys = {}
clip_prefix = []
clip_vision_prefix = None
noise_aug_config = None
sampling_settings = {}
latent_format = Latent.LatentFormat
vae_key_prefix = ["first_stage_model."]
text_encoder_key_prefix = ["cond_stage_model."]
supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]
memory_usage_factor = 2.0
manual_cast_dtype = None
custom_operations = None
@classmethod
def matches(cls, unet_config: dict, state_dict: dict = None) -> bool:
"""#### Check if the UNet configuration matches.
#### Args:
- `unet_config` (dict): The UNet configuration.
- `state_dict` (dict, optional): The state dictionary. Defaults to None.
#### Returns:
- `bool`: Whether the configuration matches.
"""
for k in cls.unet_config:
if k not in unet_config or cls.unet_config[k] != unet_config[k]:
return False
if state_dict is not None:
for k in cls.required_keys:
if k not in state_dict:
return False
return True
def model_type(self, state_dict: dict, prefix: str = "") -> sampling.ModelType:
"""#### Get the model type.
#### Args:
- `state_dict` (dict): The state dictionary.
- `prefix` (str, optional): The prefix. Defaults to "".
#### Returns:
- `sampling.ModelType`: The model type.
"""
return sampling.ModelType.EPS
def inpaint_model(self) -> bool:
"""#### Check if the model is an inpaint model.
#### Returns:
- `bool`: Whether the model is an inpaint model.
"""
return self.unet_config["in_channels"] > 4
def __init__(self, unet_config: dict):
"""#### Initialize the BASE class.
#### Args:
- `unet_config` (dict): The UNet configuration.
"""
self.unet_config = unet_config.copy()
self.sampling_settings = self.sampling_settings.copy()
self.latent_format = self.latent_format()
for x in self.unet_extra_config:
self.unet_config[x] = self.unet_extra_config[x]
def get_model(
self, state_dict: dict, prefix: str = "", device: torch.device = None
) -> BaseModel:
"""#### Get the model.
#### Args:
- `state_dict` (dict): The state dictionary.
- `prefix` (str, optional): The prefix. Defaults to "".
- `device` (torch.device, optional): The device to use. Defaults to None.
#### Returns:
- `BaseModel`: The model.
"""
out = BaseModel(
self, model_type=self.model_type(state_dict, prefix), device=device
)
return out
def process_unet_state_dict(self, state_dict: dict) -> dict:
"""#### Process the UNet state dictionary.
#### Args:
- `state_dict` (dict): The state dictionary.
#### Returns:
- `dict`: The processed state dictionary.
"""
return state_dict
def process_vae_state_dict(self, state_dict: dict) -> dict:
"""#### Process the VAE state dictionary.
#### Args:
- `state_dict` (dict): The state dictionary.
#### Returns:
- `dict`: The processed state dictionary.
"""
return state_dict
def set_inference_dtype(
self, dtype: torch.dtype, manual_cast_dtype: torch.dtype
) -> None:
"""#### Set the inference data type.
#### Args:
- `dtype` (torch.dtype): The data type.
- `manual_cast_dtype` (torch.dtype): The manual cast data type.
"""
self.unet_config["dtype"] = dtype
self.manual_cast_dtype = manual_cast_dtype
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