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