import torch
from .attention import Attention
from .sd_unet import ResnetBlock, UpSampler
from .tiler import TileWorker
class VAEAttentionBlock(torch.nn.Module):
def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, norm_num_groups=32, eps=1e-5):
super().__init__()
inner_dim = num_attention_heads * attention_head_dim
self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True)
self.transformer_blocks = torch.nn.ModuleList([
Attention(
inner_dim,
num_attention_heads,
attention_head_dim,
bias_q=True,
bias_kv=True,
bias_out=True
)
for d in range(num_layers)
])
def forward(self, hidden_states, time_emb, text_emb, res_stack):
batch, _, height, width = hidden_states.shape
residual = hidden_states
hidden_states = self.norm(hidden_states)
inner_dim = hidden_states.shape[1]
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
for block in self.transformer_blocks:
hidden_states = block(hidden_states)
hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
hidden_states = hidden_states + residual
return hidden_states, time_emb, text_emb, res_stack
class SDVAEDecoder(torch.nn.Module):
def __init__(self):
super().__init__()
self.scaling_factor = 0.18215
self.post_quant_conv = torch.nn.Conv2d(4, 4, kernel_size=1)
self.conv_in = torch.nn.Conv2d(4, 512, kernel_size=3, padding=1)
self.blocks = torch.nn.ModuleList([
# UNetMidBlock2D
ResnetBlock(512, 512, eps=1e-6),
VAEAttentionBlock(1, 512, 512, 1, eps=1e-6),
ResnetBlock(512, 512, eps=1e-6),
# UpDecoderBlock2D
ResnetBlock(512, 512, eps=1e-6),
ResnetBlock(512, 512, eps=1e-6),
ResnetBlock(512, 512, eps=1e-6),
UpSampler(512),
# UpDecoderBlock2D
ResnetBlock(512, 512, eps=1e-6),
ResnetBlock(512, 512, eps=1e-6),
ResnetBlock(512, 512, eps=1e-6),
UpSampler(512),
# UpDecoderBlock2D
ResnetBlock(512, 256, eps=1e-6),
ResnetBlock(256, 256, eps=1e-6),
ResnetBlock(256, 256, eps=1e-6),
UpSampler(256),
# UpDecoderBlock2D
ResnetBlock(256, 128, eps=1e-6),
ResnetBlock(128, 128, eps=1e-6),
ResnetBlock(128, 128, eps=1e-6),
])
self.conv_norm_out = torch.nn.GroupNorm(num_channels=128, num_groups=32, eps=1e-5)
self.conv_act = torch.nn.SiLU()
self.conv_out = torch.nn.Conv2d(128, 3, kernel_size=3, padding=1)
def tiled_forward(self, sample, tile_size=64, tile_stride=32):
hidden_states = TileWorker().tiled_forward(
lambda x: self.forward(x),
sample,
tile_size,
tile_stride,
tile_device=sample.device,
tile_dtype=sample.dtype
)
return hidden_states
def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs):
original_dtype = sample.dtype
sample = sample.to(dtype=next(iter(self.parameters())).dtype)
# For VAE Decoder, we do not need to apply the tiler on each layer.
if tiled:
return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride)
# 1. pre-process
sample = sample / self.scaling_factor
hidden_states = self.post_quant_conv(sample)
hidden_states = self.conv_in(hidden_states)
time_emb = None
text_emb = None
res_stack = None
# 2. 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)
# 3. output
hidden_states = self.conv_norm_out(hidden_states)
hidden_states = self.conv_act(hidden_states)
hidden_states = self.conv_out(hidden_states)
hidden_states = hidden_states.to(original_dtype)
return hidden_states
@staticmethod
def state_dict_converter():
return SDVAEDecoderStateDictConverter()
class SDVAEDecoderStateDictConverter:
def __init__(self):
pass
def from_diffusers(self, state_dict):
# architecture
block_types = [
'ResnetBlock', 'VAEAttentionBlock', 'ResnetBlock',
'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler',
'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler',
'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler',
'ResnetBlock', 'ResnetBlock', 'ResnetBlock'
]
# Rename each parameter
local_rename_dict = {
"post_quant_conv": "post_quant_conv",
"decoder.conv_in": "conv_in",
"decoder.mid_block.attentions.0.group_norm": "blocks.1.norm",
"decoder.mid_block.attentions.0.to_q": "blocks.1.transformer_blocks.0.to_q",
"decoder.mid_block.attentions.0.to_k": "blocks.1.transformer_blocks.0.to_k",
"decoder.mid_block.attentions.0.to_v": "blocks.1.transformer_blocks.0.to_v",
"decoder.mid_block.attentions.0.to_out.0": "blocks.1.transformer_blocks.0.to_out",
"decoder.mid_block.resnets.0.norm1": "blocks.0.norm1",
"decoder.mid_block.resnets.0.conv1": "blocks.0.conv1",
"decoder.mid_block.resnets.0.norm2": "blocks.0.norm2",
"decoder.mid_block.resnets.0.conv2": "blocks.0.conv2",
"decoder.mid_block.resnets.1.norm1": "blocks.2.norm1",
"decoder.mid_block.resnets.1.conv1": "blocks.2.conv1",
"decoder.mid_block.resnets.1.norm2": "blocks.2.norm2",
"decoder.mid_block.resnets.1.conv2": "blocks.2.conv2",
"decoder.conv_norm_out": "conv_norm_out",
"decoder.conv_out": "conv_out",
}
name_list = sorted([name for name in state_dict])
rename_dict = {}
block_id = {"ResnetBlock": 2, "DownSampler": 2, "UpSampler": 2}
last_block_type_with_id = {"ResnetBlock": "", "DownSampler": "", "UpSampler": ""}
for name in name_list:
names = name.split(".")
name_prefix = ".".join(names[:-1])
if name_prefix in local_rename_dict:
rename_dict[name] = local_rename_dict[name_prefix] + "." + names[-1]
elif name.startswith("decoder.up_blocks"):
block_type = {"resnets": "ResnetBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[3]]
block_type_with_id = ".".join(names[:5])
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[:5])
names = ["blocks", str(block_id[block_type])] + names[5:]
rename_dict[name] = ".".join(names)
# Convert state_dict
state_dict_ = {}
for name, param in state_dict.items():
if name in rename_dict:
state_dict_[rename_dict[name]] = param
return state_dict_
def from_civitai(self, state_dict):
rename_dict = {
"first_stage_model.decoder.conv_in.bias": "conv_in.bias",
"first_stage_model.decoder.conv_in.weight": "conv_in.weight",
"first_stage_model.decoder.conv_out.bias": "conv_out.bias",
"first_stage_model.decoder.conv_out.weight": "conv_out.weight",
"first_stage_model.decoder.mid.attn_1.k.bias": "blocks.1.transformer_blocks.0.to_k.bias",
"first_stage_model.decoder.mid.attn_1.k.weight": "blocks.1.transformer_blocks.0.to_k.weight",
"first_stage_model.decoder.mid.attn_1.norm.bias": "blocks.1.norm.bias",
"first_stage_model.decoder.mid.attn_1.norm.weight": "blocks.1.norm.weight",
"first_stage_model.decoder.mid.attn_1.proj_out.bias": "blocks.1.transformer_blocks.0.to_out.bias",
"first_stage_model.decoder.mid.attn_1.proj_out.weight": "blocks.1.transformer_blocks.0.to_out.weight",
"first_stage_model.decoder.mid.attn_1.q.bias": "blocks.1.transformer_blocks.0.to_q.bias",
"first_stage_model.decoder.mid.attn_1.q.weight": "blocks.1.transformer_blocks.0.to_q.weight",
"first_stage_model.decoder.mid.attn_1.v.bias": "blocks.1.transformer_blocks.0.to_v.bias",
"first_stage_model.decoder.mid.attn_1.v.weight": "blocks.1.transformer_blocks.0.to_v.weight",
"first_stage_model.decoder.mid.block_1.conv1.bias": "blocks.0.conv1.bias",
"first_stage_model.decoder.mid.block_1.conv1.weight": "blocks.0.conv1.weight",
"first_stage_model.decoder.mid.block_1.conv2.bias": "blocks.0.conv2.bias",
"first_stage_model.decoder.mid.block_1.conv2.weight": "blocks.0.conv2.weight",
"first_stage_model.decoder.mid.block_1.norm1.bias": "blocks.0.norm1.bias",
"first_stage_model.decoder.mid.block_1.norm1.weight": "blocks.0.norm1.weight",
"first_stage_model.decoder.mid.block_1.norm2.bias": "blocks.0.norm2.bias",
"first_stage_model.decoder.mid.block_1.norm2.weight": "blocks.0.norm2.weight",
"first_stage_model.decoder.mid.block_2.conv1.bias": "blocks.2.conv1.bias",
"first_stage_model.decoder.mid.block_2.conv1.weight": "blocks.2.conv1.weight",
"first_stage_model.decoder.mid.block_2.conv2.bias": "blocks.2.conv2.bias",
"first_stage_model.decoder.mid.block_2.conv2.weight": "blocks.2.conv2.weight",
"first_stage_model.decoder.mid.block_2.norm1.bias": "blocks.2.norm1.bias",
"first_stage_model.decoder.mid.block_2.norm1.weight": "blocks.2.norm1.weight",
"first_stage_model.decoder.mid.block_2.norm2.bias": "blocks.2.norm2.bias",
"first_stage_model.decoder.mid.block_2.norm2.weight": "blocks.2.norm2.weight",
"first_stage_model.decoder.norm_out.bias": "conv_norm_out.bias",
"first_stage_model.decoder.norm_out.weight": "conv_norm_out.weight",
"first_stage_model.decoder.up.0.block.0.conv1.bias": "blocks.15.conv1.bias",
"first_stage_model.decoder.up.0.block.0.conv1.weight": "blocks.15.conv1.weight",
"first_stage_model.decoder.up.0.block.0.conv2.bias": "blocks.15.conv2.bias",
"first_stage_model.decoder.up.0.block.0.conv2.weight": "blocks.15.conv2.weight",
"first_stage_model.decoder.up.0.block.0.nin_shortcut.bias": "blocks.15.conv_shortcut.bias",
"first_stage_model.decoder.up.0.block.0.nin_shortcut.weight": "blocks.15.conv_shortcut.weight",
"first_stage_model.decoder.up.0.block.0.norm1.bias": "blocks.15.norm1.bias",
"first_stage_model.decoder.up.0.block.0.norm1.weight": "blocks.15.norm1.weight",
"first_stage_model.decoder.up.0.block.0.norm2.bias": "blocks.15.norm2.bias",
"first_stage_model.decoder.up.0.block.0.norm2.weight": "blocks.15.norm2.weight",
"first_stage_model.decoder.up.0.block.1.conv1.bias": "blocks.16.conv1.bias",
"first_stage_model.decoder.up.0.block.1.conv1.weight": "blocks.16.conv1.weight",
"first_stage_model.decoder.up.0.block.1.conv2.bias": "blocks.16.conv2.bias",
"first_stage_model.decoder.up.0.block.1.conv2.weight": "blocks.16.conv2.weight",
"first_stage_model.decoder.up.0.block.1.norm1.bias": "blocks.16.norm1.bias",
"first_stage_model.decoder.up.0.block.1.norm1.weight": "blocks.16.norm1.weight",
"first_stage_model.decoder.up.0.block.1.norm2.bias": "blocks.16.norm2.bias",
"first_stage_model.decoder.up.0.block.1.norm2.weight": "blocks.16.norm2.weight",
"first_stage_model.decoder.up.0.block.2.conv1.bias": "blocks.17.conv1.bias",
"first_stage_model.decoder.up.0.block.2.conv1.weight": "blocks.17.conv1.weight",
"first_stage_model.decoder.up.0.block.2.conv2.bias": "blocks.17.conv2.bias",
"first_stage_model.decoder.up.0.block.2.conv2.weight": "blocks.17.conv2.weight",
"first_stage_model.decoder.up.0.block.2.norm1.bias": "blocks.17.norm1.bias",
"first_stage_model.decoder.up.0.block.2.norm1.weight": "blocks.17.norm1.weight",
"first_stage_model.decoder.up.0.block.2.norm2.bias": "blocks.17.norm2.bias",
"first_stage_model.decoder.up.0.block.2.norm2.weight": "blocks.17.norm2.weight",
"first_stage_model.decoder.up.1.block.0.conv1.bias": "blocks.11.conv1.bias",
"first_stage_model.decoder.up.1.block.0.conv1.weight": "blocks.11.conv1.weight",
"first_stage_model.decoder.up.1.block.0.conv2.bias": "blocks.11.conv2.bias",
"first_stage_model.decoder.up.1.block.0.conv2.weight": "blocks.11.conv2.weight",
"first_stage_model.decoder.up.1.block.0.nin_shortcut.bias": "blocks.11.conv_shortcut.bias",
"first_stage_model.decoder.up.1.block.0.nin_shortcut.weight": "blocks.11.conv_shortcut.weight",
"first_stage_model.decoder.up.1.block.0.norm1.bias": "blocks.11.norm1.bias",
"first_stage_model.decoder.up.1.block.0.norm1.weight": "blocks.11.norm1.weight",
"first_stage_model.decoder.up.1.block.0.norm2.bias": "blocks.11.norm2.bias",
"first_stage_model.decoder.up.1.block.0.norm2.weight": "blocks.11.norm2.weight",
"first_stage_model.decoder.up.1.block.1.conv1.bias": "blocks.12.conv1.bias",
"first_stage_model.decoder.up.1.block.1.conv1.weight": "blocks.12.conv1.weight",
"first_stage_model.decoder.up.1.block.1.conv2.bias": "blocks.12.conv2.bias",
"first_stage_model.decoder.up.1.block.1.conv2.weight": "blocks.12.conv2.weight",
"first_stage_model.decoder.up.1.block.1.norm1.bias": "blocks.12.norm1.bias",
"first_stage_model.decoder.up.1.block.1.norm1.weight": "blocks.12.norm1.weight",
"first_stage_model.decoder.up.1.block.1.norm2.bias": "blocks.12.norm2.bias",
"first_stage_model.decoder.up.1.block.1.norm2.weight": "blocks.12.norm2.weight",
"first_stage_model.decoder.up.1.block.2.conv1.bias": "blocks.13.conv1.bias",
"first_stage_model.decoder.up.1.block.2.conv1.weight": "blocks.13.conv1.weight",
"first_stage_model.decoder.up.1.block.2.conv2.bias": "blocks.13.conv2.bias",
"first_stage_model.decoder.up.1.block.2.conv2.weight": "blocks.13.conv2.weight",
"first_stage_model.decoder.up.1.block.2.norm1.bias": "blocks.13.norm1.bias",
"first_stage_model.decoder.up.1.block.2.norm1.weight": "blocks.13.norm1.weight",
"first_stage_model.decoder.up.1.block.2.norm2.bias": "blocks.13.norm2.bias",
"first_stage_model.decoder.up.1.block.2.norm2.weight": "blocks.13.norm2.weight",
"first_stage_model.decoder.up.1.upsample.conv.bias": "blocks.14.conv.bias",
"first_stage_model.decoder.up.1.upsample.conv.weight": "blocks.14.conv.weight",
"first_stage_model.decoder.up.2.block.0.conv1.bias": "blocks.7.conv1.bias",
"first_stage_model.decoder.up.2.block.0.conv1.weight": "blocks.7.conv1.weight",
"first_stage_model.decoder.up.2.block.0.conv2.bias": "blocks.7.conv2.bias",
"first_stage_model.decoder.up.2.block.0.conv2.weight": "blocks.7.conv2.weight",
"first_stage_model.decoder.up.2.block.0.norm1.bias": "blocks.7.norm1.bias",
"first_stage_model.decoder.up.2.block.0.norm1.weight": "blocks.7.norm1.weight",
"first_stage_model.decoder.up.2.block.0.norm2.bias": "blocks.7.norm2.bias",
"first_stage_model.decoder.up.2.block.0.norm2.weight": "blocks.7.norm2.weight",
"first_stage_model.decoder.up.2.block.1.conv1.bias": "blocks.8.conv1.bias",
"first_stage_model.decoder.up.2.block.1.conv1.weight": "blocks.8.conv1.weight",
"first_stage_model.decoder.up.2.block.1.conv2.bias": "blocks.8.conv2.bias",
"first_stage_model.decoder.up.2.block.1.conv2.weight": "blocks.8.conv2.weight",
"first_stage_model.decoder.up.2.block.1.norm1.bias": "blocks.8.norm1.bias",
"first_stage_model.decoder.up.2.block.1.norm1.weight": "blocks.8.norm1.weight",
"first_stage_model.decoder.up.2.block.1.norm2.bias": "blocks.8.norm2.bias",
"first_stage_model.decoder.up.2.block.1.norm2.weight": "blocks.8.norm2.weight",
"first_stage_model.decoder.up.2.block.2.conv1.bias": "blocks.9.conv1.bias",
"first_stage_model.decoder.up.2.block.2.conv1.weight": "blocks.9.conv1.weight",
"first_stage_model.decoder.up.2.block.2.conv2.bias": "blocks.9.conv2.bias",
"first_stage_model.decoder.up.2.block.2.conv2.weight": "blocks.9.conv2.weight",
"first_stage_model.decoder.up.2.block.2.norm1.bias": "blocks.9.norm1.bias",
"first_stage_model.decoder.up.2.block.2.norm1.weight": "blocks.9.norm1.weight",
"first_stage_model.decoder.up.2.block.2.norm2.bias": "blocks.9.norm2.bias",
"first_stage_model.decoder.up.2.block.2.norm2.weight": "blocks.9.norm2.weight",
"first_stage_model.decoder.up.2.upsample.conv.bias": "blocks.10.conv.bias",
"first_stage_model.decoder.up.2.upsample.conv.weight": "blocks.10.conv.weight",
"first_stage_model.decoder.up.3.block.0.conv1.bias": "blocks.3.conv1.bias",
"first_stage_model.decoder.up.3.block.0.conv1.weight": "blocks.3.conv1.weight",
"first_stage_model.decoder.up.3.block.0.conv2.bias": "blocks.3.conv2.bias",
"first_stage_model.decoder.up.3.block.0.conv2.weight": "blocks.3.conv2.weight",
"first_stage_model.decoder.up.3.block.0.norm1.bias": "blocks.3.norm1.bias",
"first_stage_model.decoder.up.3.block.0.norm1.weight": "blocks.3.norm1.weight",
"first_stage_model.decoder.up.3.block.0.norm2.bias": "blocks.3.norm2.bias",
"first_stage_model.decoder.up.3.block.0.norm2.weight": "blocks.3.norm2.weight",
"first_stage_model.decoder.up.3.block.1.conv1.bias": "blocks.4.conv1.bias",
"first_stage_model.decoder.up.3.block.1.conv1.weight": "blocks.4.conv1.weight",
"first_stage_model.decoder.up.3.block.1.conv2.bias": "blocks.4.conv2.bias",
"first_stage_model.decoder.up.3.block.1.conv2.weight": "blocks.4.conv2.weight",
"first_stage_model.decoder.up.3.block.1.norm1.bias": "blocks.4.norm1.bias",
"first_stage_model.decoder.up.3.block.1.norm1.weight": "blocks.4.norm1.weight",
"first_stage_model.decoder.up.3.block.1.norm2.bias": "blocks.4.norm2.bias",
"first_stage_model.decoder.up.3.block.1.norm2.weight": "blocks.4.norm2.weight",
"first_stage_model.decoder.up.3.block.2.conv1.bias": "blocks.5.conv1.bias",
"first_stage_model.decoder.up.3.block.2.conv1.weight": "blocks.5.conv1.weight",
"first_stage_model.decoder.up.3.block.2.conv2.bias": "blocks.5.conv2.bias",
"first_stage_model.decoder.up.3.block.2.conv2.weight": "blocks.5.conv2.weight",
"first_stage_model.decoder.up.3.block.2.norm1.bias": "blocks.5.norm1.bias",
"first_stage_model.decoder.up.3.block.2.norm1.weight": "blocks.5.norm1.weight",
"first_stage_model.decoder.up.3.block.2.norm2.bias": "blocks.5.norm2.bias",
"first_stage_model.decoder.up.3.block.2.norm2.weight": "blocks.5.norm2.weight",
"first_stage_model.decoder.up.3.upsample.conv.bias": "blocks.6.conv.bias",
"first_stage_model.decoder.up.3.upsample.conv.weight": "blocks.6.conv.weight",
"first_stage_model.post_quant_conv.bias": "post_quant_conv.bias",
"first_stage_model.post_quant_conv.weight": "post_quant_conv.weight",
}
state_dict_ = {}
for name in state_dict:
if name in rename_dict:
param = state_dict[name]
if "transformer_blocks" in rename_dict[name]:
param = param.squeeze()
state_dict_[rename_dict[name]] = param
return state_dict_