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import torch
from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, DownSampler
from .sdxl_unet import SDXLUNet
from .tiler import TileWorker
from .sd_controlnet import ControlNetConditioningLayer
from collections import OrderedDict
class QuickGELU(torch.nn.Module):
def forward(self, x: torch.Tensor):
return x * torch.sigmoid(1.702 * x)
class ResidualAttentionBlock(torch.nn.Module):
def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
super().__init__()
self.attn = torch.nn.MultiheadAttention(d_model, n_head)
self.ln_1 = torch.nn.LayerNorm(d_model)
self.mlp = torch.nn.Sequential(OrderedDict([
("c_fc", torch.nn.Linear(d_model, d_model * 4)),
("gelu", QuickGELU()),
("c_proj", torch.nn.Linear(d_model * 4, d_model))
]))
self.ln_2 = torch.nn.LayerNorm(d_model)
self.attn_mask = attn_mask
def attention(self, x: torch.Tensor):
self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
def forward(self, x: torch.Tensor):
x = x + self.attention(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
class SDXLControlNetUnion(torch.nn.Module):
def __init__(self, global_pool=False):
super().__init__()
self.time_proj = Timesteps(320)
self.time_embedding = torch.nn.Sequential(
torch.nn.Linear(320, 1280),
torch.nn.SiLU(),
torch.nn.Linear(1280, 1280)
)
self.add_time_proj = Timesteps(256)
self.add_time_embedding = torch.nn.Sequential(
torch.nn.Linear(2816, 1280),
torch.nn.SiLU(),
torch.nn.Linear(1280, 1280)
)
self.control_type_proj = Timesteps(256)
self.control_type_embedding = torch.nn.Sequential(
torch.nn.Linear(256 * 8, 1280),
torch.nn.SiLU(),
torch.nn.Linear(1280, 1280)
)
self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1)
self.controlnet_conv_in = ControlNetConditioningLayer(channels=(3, 16, 32, 96, 256, 320))
self.controlnet_transformer = ResidualAttentionBlock(320, 8)
self.task_embedding = torch.nn.Parameter(torch.randn(8, 320))
self.spatial_ch_projs = torch.nn.Linear(320, 320)
self.blocks = torch.nn.ModuleList([
# DownBlock2D
ResnetBlock(320, 320, 1280),
PushBlock(),
ResnetBlock(320, 320, 1280),
PushBlock(),
DownSampler(320),
PushBlock(),
# CrossAttnDownBlock2D
ResnetBlock(320, 640, 1280),
AttentionBlock(10, 64, 640, 2, 2048),
PushBlock(),
ResnetBlock(640, 640, 1280),
AttentionBlock(10, 64, 640, 2, 2048),
PushBlock(),
DownSampler(640),
PushBlock(),
# CrossAttnDownBlock2D
ResnetBlock(640, 1280, 1280),
AttentionBlock(20, 64, 1280, 10, 2048),
PushBlock(),
ResnetBlock(1280, 1280, 1280),
AttentionBlock(20, 64, 1280, 10, 2048),
PushBlock(),
# UNetMidBlock2DCrossAttn
ResnetBlock(1280, 1280, 1280),
AttentionBlock(20, 64, 1280, 10, 2048),
ResnetBlock(1280, 1280, 1280),
PushBlock()
])
self.controlnet_blocks = torch.nn.ModuleList([
torch.nn.Conv2d(320, 320, kernel_size=(1, 1)),
torch.nn.Conv2d(320, 320, kernel_size=(1, 1)),
torch.nn.Conv2d(320, 320, kernel_size=(1, 1)),
torch.nn.Conv2d(320, 320, kernel_size=(1, 1)),
torch.nn.Conv2d(640, 640, kernel_size=(1, 1)),
torch.nn.Conv2d(640, 640, kernel_size=(1, 1)),
torch.nn.Conv2d(640, 640, kernel_size=(1, 1)),
torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)),
torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)),
torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)),
])
self.global_pool = global_pool
# 0 -- openpose
# 1 -- depth
# 2 -- hed/pidi/scribble/ted
# 3 -- canny/lineart/anime_lineart/mlsd
# 4 -- normal
# 5 -- segment
# 6 -- tile
# 7 -- repaint
self.task_id = {
"openpose": 0,
"depth": 1,
"softedge": 2,
"canny": 3,
"lineart": 3,
"lineart_anime": 3,
"tile": 6,
"inpaint": 7
}
def fuse_condition_to_input(self, hidden_states, task_id, conditioning):
controlnet_cond = self.controlnet_conv_in(conditioning)
feat_seq = torch.mean(controlnet_cond, dim=(2, 3))
feat_seq = feat_seq + self.task_embedding[task_id]
x = torch.stack([feat_seq, torch.mean(hidden_states, dim=(2, 3))], dim=1)
x = self.controlnet_transformer(x)
alpha = self.spatial_ch_projs(x[:,0]).unsqueeze(-1).unsqueeze(-1)
controlnet_cond_fuser = controlnet_cond + alpha
hidden_states = hidden_states + controlnet_cond_fuser
return hidden_states
def forward(
self,
sample, timestep, encoder_hidden_states,
conditioning, processor_id, add_time_id, add_text_embeds,
tiled=False, tile_size=64, tile_stride=32,
unet:SDXLUNet=None,
**kwargs
):
task_id = self.task_id[processor_id]
# 1. time
t_emb = self.time_proj(timestep).to(sample.dtype)
t_emb = self.time_embedding(t_emb)
time_embeds = self.add_time_proj(add_time_id)
time_embeds = time_embeds.reshape((add_text_embeds.shape[0], -1))
add_embeds = torch.concat([add_text_embeds, time_embeds], dim=-1)
add_embeds = add_embeds.to(sample.dtype)
if unet is not None and unet.is_kolors:
add_embeds = unet.add_time_embedding(add_embeds)
else:
add_embeds = self.add_time_embedding(add_embeds)
control_type = torch.zeros((sample.shape[0], 8), dtype=sample.dtype, device=sample.device)
control_type[:, task_id] = 1
control_embeds = self.control_type_proj(control_type.flatten())
control_embeds = control_embeds.reshape((sample.shape[0], -1))
control_embeds = control_embeds.to(sample.dtype)
control_embeds = self.control_type_embedding(control_embeds)
time_emb = t_emb + add_embeds + control_embeds
# 2. pre-process
height, width = sample.shape[2], sample.shape[3]
hidden_states = self.conv_in(sample)
hidden_states = self.fuse_condition_to_input(hidden_states, task_id, conditioning)
text_emb = encoder_hidden_states
if unet is not None and unet.is_kolors:
text_emb = unet.text_intermediate_proj(text_emb)
res_stack = [hidden_states]
# 3. blocks
for i, block in enumerate(self.blocks):
if tiled and not isinstance(block, PushBlock):
_, _, inter_height, _ = hidden_states.shape
resize_scale = inter_height / height
hidden_states = TileWorker().tiled_forward(
lambda x: block(x, time_emb, text_emb, res_stack)[0],
hidden_states,
int(tile_size * resize_scale),
int(tile_stride * resize_scale),
tile_device=hidden_states.device,
tile_dtype=hidden_states.dtype
)
else:
hidden_states, _, _, _ = block(hidden_states, time_emb, text_emb, res_stack)
# 4. ControlNet blocks
controlnet_res_stack = [block(res) for block, res in zip(self.controlnet_blocks, res_stack)]
# pool
if self.global_pool:
controlnet_res_stack = [res.mean(dim=(2, 3), keepdim=True) for res in controlnet_res_stack]
return controlnet_res_stack
@staticmethod
def state_dict_converter():
return SDXLControlNetUnionStateDictConverter()
class SDXLControlNetUnionStateDictConverter:
def __init__(self):
pass
def from_diffusers(self, state_dict):
# architecture
block_types = [
"ResnetBlock", "PushBlock", "ResnetBlock", "PushBlock", "DownSampler", "PushBlock",
"ResnetBlock", "AttentionBlock", "PushBlock", "ResnetBlock", "AttentionBlock", "PushBlock", "DownSampler", "PushBlock",
"ResnetBlock", "AttentionBlock", "PushBlock", "ResnetBlock", "AttentionBlock", "PushBlock",
"ResnetBlock", "AttentionBlock", "ResnetBlock", "PushBlock"
]
# controlnet_rename_dict
controlnet_rename_dict = {
"controlnet_cond_embedding.conv_in.weight": "controlnet_conv_in.blocks.0.weight",
"controlnet_cond_embedding.conv_in.bias": "controlnet_conv_in.blocks.0.bias",
"controlnet_cond_embedding.blocks.0.weight": "controlnet_conv_in.blocks.2.weight",
"controlnet_cond_embedding.blocks.0.bias": "controlnet_conv_in.blocks.2.bias",
"controlnet_cond_embedding.blocks.1.weight": "controlnet_conv_in.blocks.4.weight",
"controlnet_cond_embedding.blocks.1.bias": "controlnet_conv_in.blocks.4.bias",
"controlnet_cond_embedding.blocks.2.weight": "controlnet_conv_in.blocks.6.weight",
"controlnet_cond_embedding.blocks.2.bias": "controlnet_conv_in.blocks.6.bias",
"controlnet_cond_embedding.blocks.3.weight": "controlnet_conv_in.blocks.8.weight",
"controlnet_cond_embedding.blocks.3.bias": "controlnet_conv_in.blocks.8.bias",
"controlnet_cond_embedding.blocks.4.weight": "controlnet_conv_in.blocks.10.weight",
"controlnet_cond_embedding.blocks.4.bias": "controlnet_conv_in.blocks.10.bias",
"controlnet_cond_embedding.blocks.5.weight": "controlnet_conv_in.blocks.12.weight",
"controlnet_cond_embedding.blocks.5.bias": "controlnet_conv_in.blocks.12.bias",
"controlnet_cond_embedding.conv_out.weight": "controlnet_conv_in.blocks.14.weight",
"controlnet_cond_embedding.conv_out.bias": "controlnet_conv_in.blocks.14.bias",
"control_add_embedding.linear_1.weight": "control_type_embedding.0.weight",
"control_add_embedding.linear_1.bias": "control_type_embedding.0.bias",
"control_add_embedding.linear_2.weight": "control_type_embedding.2.weight",
"control_add_embedding.linear_2.bias": "control_type_embedding.2.bias",
}
# Rename each parameter
name_list = sorted([name for name in state_dict])
rename_dict = {}
block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1}
last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""}
for name in name_list:
names = name.split(".")
if names[0] in ["conv_in", "conv_norm_out", "conv_out", "task_embedding", "spatial_ch_projs"]:
pass
elif name in controlnet_rename_dict:
names = controlnet_rename_dict[name].split(".")
elif names[0] == "controlnet_down_blocks":
names[0] = "controlnet_blocks"
elif names[0] == "controlnet_mid_block":
names = ["controlnet_blocks", "9", names[-1]]
elif names[0] in ["time_embedding", "add_embedding"]:
if names[0] == "add_embedding":
names[0] = "add_time_embedding"
names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]]
elif names[0] == "control_add_embedding":
names[0] = "control_type_embedding"
elif names[0] == "transformer_layes":
names[0] = "controlnet_transformer"
names.pop(1)
elif names[0] in ["down_blocks", "mid_block", "up_blocks"]:
if names[0] == "mid_block":
names.insert(1, "0")
block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]]
block_type_with_id = ".".join(names[:4])
if block_type_with_id != last_block_type_with_id[block_type]:
block_id[block_type] += 1
last_block_type_with_id[block_type] = block_type_with_id
while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type:
block_id[block_type] += 1
block_type_with_id = ".".join(names[:4])
names = ["blocks", str(block_id[block_type])] + names[4:]
if "ff" in names:
ff_index = names.index("ff")
component = ".".join(names[ff_index:ff_index+3])
component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component]
names = names[:ff_index] + [component] + names[ff_index+3:]
if "to_out" in names:
names.pop(names.index("to_out") + 1)
else:
print(name, state_dict[name].shape)
# raise ValueError(f"Unknown parameters: {name}")
rename_dict[name] = ".".join(names)
# Convert state_dict
state_dict_ = {}
for name, param in state_dict.items():
if name not in rename_dict:
continue
if ".proj_in." in name or ".proj_out." in name:
param = param.squeeze()
state_dict_[rename_dict[name]] = param
return state_dict_
def from_civitai(self, state_dict):
return self.from_diffusers(state_dict) |