import torch
from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, PopBlock, DownSampler, UpSampler
class SDXLUNet(torch.nn.Module):
def __init__(self):
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.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1)
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),
# CrossAttnUpBlock2D
PopBlock(),
ResnetBlock(2560, 1280, 1280),
AttentionBlock(20, 64, 1280, 10, 2048),
PopBlock(),
ResnetBlock(2560, 1280, 1280),
AttentionBlock(20, 64, 1280, 10, 2048),
PopBlock(),
ResnetBlock(1920, 1280, 1280),
AttentionBlock(20, 64, 1280, 10, 2048),
UpSampler(1280),
# CrossAttnUpBlock2D
PopBlock(),
ResnetBlock(1920, 640, 1280),
AttentionBlock(10, 64, 640, 2, 2048),
PopBlock(),
ResnetBlock(1280, 640, 1280),
AttentionBlock(10, 64, 640, 2, 2048),
PopBlock(),
ResnetBlock(960, 640, 1280),
AttentionBlock(10, 64, 640, 2, 2048),
UpSampler(640),
# UpBlock2D
PopBlock(),
ResnetBlock(960, 320, 1280),
PopBlock(),
ResnetBlock(640, 320, 1280),
PopBlock(),
ResnetBlock(640, 320, 1280)
])
self.conv_norm_out = torch.nn.GroupNorm(num_channels=320, num_groups=32, eps=1e-5)
self.conv_act = torch.nn.SiLU()
self.conv_out = torch.nn.Conv2d(320, 4, kernel_size=3, padding=1)
def forward(
self,
sample, timestep, encoder_hidden_states, add_time_id, add_text_embeds,
tiled=False, tile_size=64, tile_stride=8, **kwargs
):
# 1. time
t_emb = self.time_proj(timestep[None]).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)
add_embeds = self.add_time_embedding(add_embeds)
time_emb = t_emb + add_embeds
# 2. pre-process
height, width = sample.shape[2], sample.shape[3]
hidden_states = self.conv_in(sample)
text_emb = encoder_hidden_states
res_stack = [hidden_states]
# 3. blocks
for i, block in enumerate(self.blocks):
hidden_states, time_emb, text_emb, res_stack = block(
hidden_states, time_emb, text_emb, res_stack,
tiled=tiled, tile_size=tile_size, tile_stride=tile_stride
)
# 4. output
hidden_states = self.conv_norm_out(hidden_states)
hidden_states = self.conv_act(hidden_states)
hidden_states = self.conv_out(hidden_states)
return hidden_states
def state_dict_converter(self):
return SDXLUNetStateDictConverter()
class SDXLUNetStateDictConverter:
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',
'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler',
'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler',
'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock'
]
# 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"]:
pass
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] 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:
raise ValueError(f"Unknown parameters: {name}")
rename_dict[name] = ".".join(names)
# Convert state_dict
state_dict_ = {}
for name, param in state_dict.items():
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):
rename_dict = {
"model.diffusion_model.input_blocks.0.0.bias": "conv_in.bias",
"model.diffusion_model.input_blocks.0.0.weight": "conv_in.weight",
"model.diffusion_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias",
"model.diffusion_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight",
"model.diffusion_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias",
"model.diffusion_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight",
"model.diffusion_model.input_blocks.1.0.in_layers.2.bias": "blocks.0.conv1.bias",
"model.diffusion_model.input_blocks.1.0.in_layers.2.weight": "blocks.0.conv1.weight",
"model.diffusion_model.input_blocks.1.0.out_layers.0.bias": "blocks.0.norm2.bias",
"model.diffusion_model.input_blocks.1.0.out_layers.0.weight": "blocks.0.norm2.weight",
"model.diffusion_model.input_blocks.1.0.out_layers.3.bias": "blocks.0.conv2.bias",
"model.diffusion_model.input_blocks.1.0.out_layers.3.weight": "blocks.0.conv2.weight",
"model.diffusion_model.input_blocks.2.0.emb_layers.1.bias": "blocks.2.time_emb_proj.bias",
"model.diffusion_model.input_blocks.2.0.emb_layers.1.weight": "blocks.2.time_emb_proj.weight",
"model.diffusion_model.input_blocks.2.0.in_layers.0.bias": "blocks.2.norm1.bias",
"model.diffusion_model.input_blocks.2.0.in_layers.0.weight": "blocks.2.norm1.weight",
"model.diffusion_model.input_blocks.2.0.in_layers.2.bias": "blocks.2.conv1.bias",
"model.diffusion_model.input_blocks.2.0.in_layers.2.weight": "blocks.2.conv1.weight",
"model.diffusion_model.input_blocks.2.0.out_layers.0.bias": "blocks.2.norm2.bias",
"model.diffusion_model.input_blocks.2.0.out_layers.0.weight": "blocks.2.norm2.weight",
"model.diffusion_model.input_blocks.2.0.out_layers.3.bias": "blocks.2.conv2.bias",
"model.diffusion_model.input_blocks.2.0.out_layers.3.weight": "blocks.2.conv2.weight",
"model.diffusion_model.input_blocks.3.0.op.bias": "blocks.4.conv.bias",
"model.diffusion_model.input_blocks.3.0.op.weight": "blocks.4.conv.weight",
"model.diffusion_model.input_blocks.4.0.emb_layers.1.bias": "blocks.6.time_emb_proj.bias",
"model.diffusion_model.input_blocks.4.0.emb_layers.1.weight": "blocks.6.time_emb_proj.weight",
"model.diffusion_model.input_blocks.4.0.in_layers.0.bias": "blocks.6.norm1.bias",
"model.diffusion_model.input_blocks.4.0.in_layers.0.weight": "blocks.6.norm1.weight",
"model.diffusion_model.input_blocks.4.0.in_layers.2.bias": "blocks.6.conv1.bias",
"model.diffusion_model.input_blocks.4.0.in_layers.2.weight": "blocks.6.conv1.weight",
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"model.diffusion_model.input_blocks.4.0.out_layers.3.bias": "blocks.6.conv2.bias",
"model.diffusion_model.input_blocks.4.0.out_layers.3.weight": "blocks.6.conv2.weight",
"model.diffusion_model.input_blocks.4.0.skip_connection.bias": "blocks.6.conv_shortcut.bias",
"model.diffusion_model.input_blocks.4.0.skip_connection.weight": "blocks.6.conv_shortcut.weight",
"model.diffusion_model.input_blocks.4.1.norm.bias": "blocks.7.norm.bias",
"model.diffusion_model.input_blocks.4.1.norm.weight": "blocks.7.norm.weight",
"model.diffusion_model.input_blocks.4.1.proj_in.bias": "blocks.7.proj_in.bias",
"model.diffusion_model.input_blocks.4.1.proj_in.weight": "blocks.7.proj_in.weight",
"model.diffusion_model.input_blocks.4.1.proj_out.bias": "blocks.7.proj_out.bias",
"model.diffusion_model.input_blocks.4.1.proj_out.weight": "blocks.7.proj_out.weight",
"model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_k.weight": "blocks.7.transformer_blocks.0.attn1.to_k.weight",
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}
state_dict_ = {}
for name in state_dict:
if name in rename_dict:
param = state_dict[name]
if ".proj_in." in name or ".proj_out." in name:
param = param.squeeze()
state_dict_[rename_dict[name]] = param
return state_dict_