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import inspect
import weakref
import torch
from typing import TYPE_CHECKING, Tuple
from toolkit.lora_special import LoRASpecialNetwork
from diffusers import FluxTransformer2DModel
from diffusers.models.embeddings import (
CombinedTimestepTextProjEmbeddings,
CombinedTimestepGuidanceTextProjEmbeddings,
)
from functools import partial
if TYPE_CHECKING:
from toolkit.stable_diffusion_model import StableDiffusion
from toolkit.config_modules import AdapterConfig, TrainConfig, ModelConfig
from toolkit.custom_adapter import CustomAdapter
from extensions_built_in.diffusion_models.omnigen2.src.models.transformers import OmniGen2Transformer2DModel
def mean_flow_time_text_embed_forward(
self: CombinedTimestepTextProjEmbeddings, timestep, pooled_projection
):
mean_flow_adapter: "MeanFlowAdapter" = self.mean_flow_adapter_ref()
# make zero timestep ending if none is passed
if mean_flow_adapter.is_active and timestep.shape[0] == pooled_projection.shape[0]:
timestep = torch.cat(
[timestep, torch.zeros_like(timestep)], dim=0
) # timestep - 0 (final timestep) == same as start timestep
timesteps_proj = self.time_proj(timestep)
timesteps_emb = self.timestep_embedder(
timesteps_proj.to(dtype=pooled_projection.dtype)
) # (N, D)
# mean flow stuff
if mean_flow_adapter.is_active:
# todo make sure that timesteps is batched correctly, I think diffusers expects non batched timesteps
orig_dtype = timesteps_emb.dtype
timesteps_emb = timesteps_emb.to(torch.float32)
timesteps_emb_start, timesteps_emb_end = timesteps_emb.chunk(2, dim=0)
timesteps_emb = mean_flow_adapter.mean_flow_timestep_embedder(
torch.cat([timesteps_emb_start, timesteps_emb_end], dim=-1)
)
timesteps_emb = timesteps_emb.to(orig_dtype)
pooled_projections = self.text_embedder(pooled_projection)
conditioning = timesteps_emb + pooled_projections
return conditioning
def mean_flow_time_text_guidance_embed_forward(
self: CombinedTimestepGuidanceTextProjEmbeddings,
timestep,
guidance,
pooled_projection,
):
mean_flow_adapter: "MeanFlowAdapter" = self.mean_flow_adapter_ref()
# make zero timestep ending if none is passed
if mean_flow_adapter.is_active and timestep.shape[0] == pooled_projection.shape[0]:
timestep = torch.cat(
[timestep, torch.ones_like(timestep)], dim=0
) # timestep - 0 (final timestep) == same as start timestep
timesteps_proj = self.time_proj(timestep)
timesteps_emb = self.timestep_embedder(
timesteps_proj.to(dtype=pooled_projection.dtype)
) # (N, D)
guidance_proj = self.time_proj(guidance)
guidance_emb = self.guidance_embedder(
guidance_proj.to(dtype=pooled_projection.dtype)
) # (N, D)
# mean flow stuff
if mean_flow_adapter.is_active:
# todo make sure that timesteps is batched correctly, I think diffusers expects non batched timesteps
orig_dtype = timesteps_emb.dtype
timesteps_emb = timesteps_emb.to(torch.float32)
timesteps_emb_start, timesteps_emb_end = timesteps_emb.chunk(2, dim=0)
timesteps_emb = mean_flow_adapter.mean_flow_timestep_embedder(
torch.cat([timesteps_emb_start, timesteps_emb_end], dim=-1)
)
timesteps_emb = timesteps_emb.to(orig_dtype)
time_guidance_emb = timesteps_emb + guidance_emb
pooled_projections = self.text_embedder(pooled_projection)
conditioning = time_guidance_emb + pooled_projections
return conditioning
def convert_flux_to_mean_flow(
transformer: FluxTransformer2DModel,
):
if isinstance(transformer.time_text_embed, CombinedTimestepTextProjEmbeddings):
transformer.time_text_embed.forward = partial(
mean_flow_time_text_embed_forward, transformer.time_text_embed
)
elif isinstance(
transformer.time_text_embed, CombinedTimestepGuidanceTextProjEmbeddings
):
transformer.time_text_embed.forward = partial(
mean_flow_time_text_guidance_embed_forward, transformer.time_text_embed
)
else:
raise ValueError(
"Unsupported time_text_embed type: {}".format(
type(transformer.time_text_embed)
)
)
def mean_flow_omnigen2_time_text_embed_forward(
self, timestep: torch.Tensor, text_hidden_states: torch.Tensor, dtype: torch.dtype
) -> Tuple[torch.Tensor, torch.Tensor]:
mean_flow_adapter: "MeanFlowAdapter" = self.mean_flow_adapter_ref()
if mean_flow_adapter.is_active and timestep.shape[0] == text_hidden_states.shape[0]:
timestep = torch.cat(
[timestep, torch.ones_like(timestep)], dim=0 # omnigen does reverse timesteps
)
timestep_proj = self.time_proj(timestep).to(dtype=dtype)
time_embed = self.timestep_embedder(timestep_proj)
# mean flow stuff
if mean_flow_adapter.is_active:
# todo make sure that timesteps is batched correctly, I think diffusers expects non batched timesteps
orig_dtype = time_embed.dtype
time_embed = time_embed.to(torch.float32)
time_embed_start, time_embed_end = time_embed.chunk(2, dim=0)
time_embed = mean_flow_adapter.mean_flow_timestep_embedder(
torch.cat([time_embed_start, time_embed_end], dim=-1)
)
time_embed = time_embed.to(orig_dtype)
caption_embed = self.caption_embedder(text_hidden_states)
return time_embed, caption_embed
def convert_omnigen2_to_mean_flow(
transformer: 'OmniGen2Transformer2DModel',
):
transformer.time_caption_embed.forward = partial(
mean_flow_omnigen2_time_text_embed_forward, transformer.time_caption_embed
)
class MeanFlowAdapter(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.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"] = sd.target_lora_modules
if "ignore_if_contains" not in network_kwargs:
network_kwargs["ignore_if_contains"] = []
self.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.lora.force_to(self.device_torch, dtype=torch.float32)
self.lora._update_torch_multiplier()
self.lora.apply_to(
sd.text_encoder,
sd.unet,
self.train_config.train_text_encoder,
self.train_config.train_unet,
)
self.lora.can_merge_in = False
self.lora.prepare_grad_etc(sd.text_encoder, sd.unet)
if self.train_config.gradient_checkpointing:
self.lora.enable_gradient_checkpointing()
emb_dim = None
if self.model_config.arch in ["flux", "flex2", "flex2"]:
transformer: FluxTransformer2DModel = sd.unet
emb_dim = (
transformer.config.num_attention_heads
* transformer.config.attention_head_dim
)
convert_flux_to_mean_flow(transformer)
elif self.model_config.arch in ["omnigen2"]:
transformer: 'OmniGen2Transformer2DModel' = sd.unet
emb_dim = (
1024
)
convert_omnigen2_to_mean_flow(transformer)
else:
raise ValueError(f"Unsupported architecture: {self.model_config.arch}")
self.mean_flow_timestep_embedder = torch.nn.Linear(
emb_dim * 2,
emb_dim,
)
# make the model function as before adding this adapter by initializing the weights
with torch.no_grad():
self.mean_flow_timestep_embedder.weight.zero_()
self.mean_flow_timestep_embedder.weight[:, :emb_dim] = torch.eye(emb_dim)
self.mean_flow_timestep_embedder.bias.zero_()
self.mean_flow_timestep_embedder.to(self.device_torch)
# add our adapter as a weak ref
if self.model_config.arch in ["flux", "flex2", "flex2"]:
sd.unet.time_text_embed.mean_flow_adapter_ref = weakref.ref(self)
elif self.model_config.arch in ["omnigen2"]:
sd.unet.time_caption_embed.mean_flow_adapter_ref = weakref.ref(self)
def get_params(self):
if self.lora is not None:
config = {
"text_encoder_lr": self.train_config.lr,
"unet_lr": self.train_config.lr,
}
sig = inspect.signature(self.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.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.mean_flow_timestep_embedder.to(torch.float32)
self.mean_flow_timestep_embedder.requires_grad = True
self.mean_flow_timestep_embedder.train()
params += list(self.mean_flow_timestep_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 = {}
mean_flow_embedder_sd = {}
for key, value in state_dict.items():
if "mean_flow_timestep_embedder" in key:
new_key = key.replace("transformer.mean_flow_timestep_embedder.", "")
mean_flow_embedder_sd[new_key] = value
else:
lora_sd[key] = value
# todo process state dict before loading for models that need it
if self.lora is not None:
self.lora.load_weights(lora_sd)
self.mean_flow_timestep_embedder.load_state_dict(
mean_flow_embedder_sd, strict=False
)
def get_state_dict(self):
if self.lora is not None:
lora_sd = self.lora.get_state_dict(dtype=torch.float32)
else:
lora_sd = {}
# todo make sure we match loras elseware.
mean_flow_embedder_sd = self.mean_flow_timestep_embedder.state_dict()
for key, value in mean_flow_embedder_sd.items():
lora_sd[f"transformer.mean_flow_timestep_embedder.{key}"] = value
return lora_sd
@property
def is_active(self):
return self.adapter_ref().is_active
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