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import inspect
import weakref
import torch
from typing import TYPE_CHECKING
from toolkit.lora_special import LoRASpecialNetwork
from diffusers import FluxTransformer2DModel
# weakref
if TYPE_CHECKING:
from toolkit.stable_diffusion_model import StableDiffusion
from toolkit.config_modules import AdapterConfig, TrainConfig, ModelConfig
from toolkit.custom_adapter import CustomAdapter
# after each step we concat the control image with the latents
# latent_model_input = torch.cat([latents, control_image], dim=2)
# the x_embedder has a full rank lora to handle the additional channels
# this replaces the x_embedder with a full rank lora. on flux this is
# x_embedder(diffusers) or img_in(bfl)
# Flux
# img_in.lora_A.weight [128, 128]
# img_in.lora_B.bias [3 072]
# img_in.lora_B.weight [3 072, 128]
class ImgEmbedder(torch.nn.Module):
def __init__(
self,
adapter: 'ControlLoraAdapter',
orig_layer: torch.nn.Linear,
in_channels=64,
out_channels=3072
):
super().__init__()
# only do the weight for the new input. We combine with the original linear layer
init = torch.randn(out_channels, in_channels, device=orig_layer.weight.device, dtype=orig_layer.weight.dtype) * 0.01
self.weight = torch.nn.Parameter(init)
self.adapter_ref: weakref.ref = weakref.ref(adapter)
self.orig_layer_ref: weakref.ref = weakref.ref(orig_layer)
@classmethod
def from_model(
cls,
model: FluxTransformer2DModel,
adapter: 'ControlLoraAdapter',
num_control_images=1,
has_inpainting_input=False
):
if model.__class__.__name__ == 'FluxTransformer2DModel':
num_adapter_in_channels = model.x_embedder.in_features * num_control_images
if has_inpainting_input:
# inpainting has the mask before packing latents. it is normally 16 ch + 1ch mask
# packed it is 64ch + 4ch mask
# so we need to add 4 to the input channels
num_adapter_in_channels += 4
x_embedder: torch.nn.Linear = model.x_embedder
img_embedder = cls(
adapter,
orig_layer=x_embedder,
in_channels=num_adapter_in_channels,
out_channels=x_embedder.out_features,
)
# hijack the forward method
x_embedder._orig_ctrl_lora_forward = x_embedder.forward
x_embedder.forward = img_embedder.forward
# update the config of the transformer
model.config.in_channels = model.config.in_channels * (num_control_images + 1)
model.config["in_channels"] = model.config.in_channels
return img_embedder
else:
raise ValueError("Model not supported")
@property
def is_active(self):
return self.adapter_ref().is_active
def forward(self, x):
if not self.is_active:
# make sure lora is not active
if self.adapter_ref().control_lora is not None:
self.adapter_ref().control_lora.is_active = False
return self.orig_layer_ref()._orig_ctrl_lora_forward(x)
# make sure lora is active
if self.adapter_ref().control_lora is not None:
self.adapter_ref().control_lora.is_active = True
orig_device = x.device
orig_dtype = x.dtype
x = x.to(self.weight.device, dtype=self.weight.dtype)
orig_weight = self.orig_layer_ref().weight.data.detach()
orig_weight = orig_weight.to(self.weight.device, dtype=self.weight.dtype)
linear_weight = torch.cat([orig_weight, self.weight], dim=1)
bias = None
if self.orig_layer_ref().bias is not None:
bias = self.orig_layer_ref().bias.data.detach().to(self.weight.device, dtype=self.weight.dtype)
x = torch.nn.functional.linear(x, linear_weight, bias)
x = x.to(orig_device, dtype=orig_dtype)
return x
class ControlLoraAdapter(torch.nn.Module):
def __init__(
self,
adapter: 'CustomAdapter',
sd: 'StableDiffusion',
config: 'AdapterConfig',
train_config: 'TrainConfig'
):
super().__init__()
self.adapter_ref: weakref.ref = weakref.ref(adapter)
self.sd_ref = weakref.ref(sd)
self.model_config: ModelConfig = sd.model_config
self.network_config = config.lora_config
self.train_config = train_config
self.device_torch = sd.device_torch
self.control_lora = None
if self.network_config is not None:
network_kwargs = {} if self.network_config.network_kwargs is None else self.network_config.network_kwargs
if hasattr(sd, 'target_lora_modules'):
network_kwargs['target_lin_modules'] = self.sd.target_lora_modules
if 'ignore_if_contains' not in network_kwargs:
network_kwargs['ignore_if_contains'] = []
# always ignore x_embedder
network_kwargs['ignore_if_contains'].append('x_embedder')
self.control_lora = LoRASpecialNetwork(
text_encoder=sd.text_encoder,
unet=sd.unet,
lora_dim=self.network_config.linear,
multiplier=1.0,
alpha=self.network_config.linear_alpha,
train_unet=self.train_config.train_unet,
train_text_encoder=self.train_config.train_text_encoder,
conv_lora_dim=self.network_config.conv,
conv_alpha=self.network_config.conv_alpha,
is_sdxl=self.model_config.is_xl or self.model_config.is_ssd,
is_v2=self.model_config.is_v2,
is_v3=self.model_config.is_v3,
is_pixart=self.model_config.is_pixart,
is_auraflow=self.model_config.is_auraflow,
is_flux=self.model_config.is_flux,
is_lumina2=self.model_config.is_lumina2,
is_ssd=self.model_config.is_ssd,
is_vega=self.model_config.is_vega,
dropout=self.network_config.dropout,
use_text_encoder_1=self.model_config.use_text_encoder_1,
use_text_encoder_2=self.model_config.use_text_encoder_2,
use_bias=False,
is_lorm=False,
network_config=self.network_config,
network_type=self.network_config.type,
transformer_only=self.network_config.transformer_only,
is_transformer=sd.is_transformer,
base_model=sd,
**network_kwargs
)
self.control_lora.force_to(self.device_torch, dtype=torch.float32)
self.control_lora._update_torch_multiplier()
self.control_lora.apply_to(
sd.text_encoder,
sd.unet,
self.train_config.train_text_encoder,
self.train_config.train_unet
)
self.control_lora.can_merge_in = False
self.control_lora.prepare_grad_etc(sd.text_encoder, sd.unet)
if self.train_config.gradient_checkpointing:
self.control_lora.enable_gradient_checkpointing()
self.x_embedder = ImgEmbedder.from_model(
sd.unet,
self,
num_control_images=config.num_control_images,
has_inpainting_input=config.has_inpainting_input
)
self.x_embedder.to(self.device_torch)
def get_params(self):
if self.control_lora is not None:
config = {
'text_encoder_lr': self.train_config.lr,
'unet_lr': self.train_config.lr,
}
sig = inspect.signature(self.control_lora.prepare_optimizer_params)
if 'default_lr' in sig.parameters:
config['default_lr'] = self.train_config.lr
if 'learning_rate' in sig.parameters:
config['learning_rate'] = self.train_config.lr
params_net = self.control_lora.prepare_optimizer_params(
**config
)
# we want only tensors here
params = []
for p in params_net:
if isinstance(p, dict):
params += p["params"]
elif isinstance(p, torch.Tensor):
params.append(p)
elif isinstance(p, list):
params += p
else:
params = []
# make sure the embedder is float32
self.x_embedder.to(torch.float32)
params += list(self.x_embedder.parameters())
# we need to be able to yield from the list like yield from params
return params
def load_weights(self, state_dict, strict=True):
lora_sd = {}
img_embedder_sd = {}
for key, value in state_dict.items():
if "x_embedder" in key:
new_key = key.replace("transformer.x_embedder.", "")
img_embedder_sd[new_key] = value
else:
lora_sd[key] = value
# todo process state dict before loading
if self.control_lora is not None:
self.control_lora.load_weights(lora_sd)
# automatically upgrade the x imbedder if more dims are added
if self.x_embedder.weight.shape[1] > img_embedder_sd['weight'].shape[1]:
print("Upgrading x_embedder from {} to {}".format(
img_embedder_sd['weight'].shape[1],
self.x_embedder.weight.shape[1]
))
while img_embedder_sd['weight'].shape[1] < self.x_embedder.weight.shape[1]:
img_embedder_sd['weight'] = torch.cat([img_embedder_sd['weight'] ] * 2, dim=1)
if img_embedder_sd['weight'].shape[1] > self.x_embedder.weight.shape[1]:
img_embedder_sd['weight'] = img_embedder_sd['weight'][:, :self.x_embedder.weight.shape[1]]
self.x_embedder.load_state_dict(img_embedder_sd, strict=False)
def get_state_dict(self):
if self.control_lora is not None:
lora_sd = self.control_lora.get_state_dict(dtype=torch.float32)
else:
lora_sd = {}
# todo make sure we match loras elseware.
img_embedder_sd = self.x_embedder.state_dict()
for key, value in img_embedder_sd.items():
lora_sd[f"transformer.x_embedder.{key}"] = value
return lora_sd
@property
def is_active(self):
return self.adapter_ref().is_active
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