Spaces:
Running
on
Zero
Running
on
Zero
File size: 8,323 Bytes
703e263 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 |
from .sd_unet import SDUNet, Attention, GEGLU
import torch
from einops import rearrange, repeat
class TemporalTransformerBlock(torch.nn.Module):
def __init__(self, dim, num_attention_heads, attention_head_dim, max_position_embeddings=32):
super().__init__()
# 1. Self-Attn
self.pe1 = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, dim))
self.norm1 = torch.nn.LayerNorm(dim, elementwise_affine=True)
self.attn1 = Attention(q_dim=dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True)
# 2. Cross-Attn
self.pe2 = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, dim))
self.norm2 = torch.nn.LayerNorm(dim, elementwise_affine=True)
self.attn2 = Attention(q_dim=dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True)
# 3. Feed-forward
self.norm3 = torch.nn.LayerNorm(dim, elementwise_affine=True)
self.act_fn = GEGLU(dim, dim * 4)
self.ff = torch.nn.Linear(dim * 4, dim)
def forward(self, hidden_states, batch_size=1):
# 1. Self-Attention
norm_hidden_states = self.norm1(hidden_states)
norm_hidden_states = rearrange(norm_hidden_states, "(b f) h c -> (b h) f c", b=batch_size)
attn_output = self.attn1(norm_hidden_states + self.pe1[:, :norm_hidden_states.shape[1]])
attn_output = rearrange(attn_output, "(b h) f c -> (b f) h c", b=batch_size)
hidden_states = attn_output + hidden_states
# 2. Cross-Attention
norm_hidden_states = self.norm2(hidden_states)
norm_hidden_states = rearrange(norm_hidden_states, "(b f) h c -> (b h) f c", b=batch_size)
attn_output = self.attn2(norm_hidden_states + self.pe2[:, :norm_hidden_states.shape[1]])
attn_output = rearrange(attn_output, "(b h) f c -> (b f) h c", b=batch_size)
hidden_states = attn_output + hidden_states
# 3. Feed-forward
norm_hidden_states = self.norm3(hidden_states)
ff_output = self.act_fn(norm_hidden_states)
ff_output = self.ff(ff_output)
hidden_states = ff_output + hidden_states
return hidden_states
class TemporalBlock(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.proj_in = torch.nn.Linear(in_channels, inner_dim)
self.transformer_blocks = torch.nn.ModuleList([
TemporalTransformerBlock(
inner_dim,
num_attention_heads,
attention_head_dim
)
for d in range(num_layers)
])
self.proj_out = torch.nn.Linear(inner_dim, in_channels)
def forward(self, hidden_states, time_emb, text_emb, res_stack, batch_size=1):
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)
hidden_states = self.proj_in(hidden_states)
for block in self.transformer_blocks:
hidden_states = block(
hidden_states,
batch_size=batch_size
)
hidden_states = self.proj_out(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 SDMotionModel(torch.nn.Module):
def __init__(self):
super().__init__()
self.motion_modules = torch.nn.ModuleList([
TemporalBlock(8, 40, 320, eps=1e-6),
TemporalBlock(8, 40, 320, eps=1e-6),
TemporalBlock(8, 80, 640, eps=1e-6),
TemporalBlock(8, 80, 640, eps=1e-6),
TemporalBlock(8, 160, 1280, eps=1e-6),
TemporalBlock(8, 160, 1280, eps=1e-6),
TemporalBlock(8, 160, 1280, eps=1e-6),
TemporalBlock(8, 160, 1280, eps=1e-6),
TemporalBlock(8, 160, 1280, eps=1e-6),
TemporalBlock(8, 160, 1280, eps=1e-6),
TemporalBlock(8, 160, 1280, eps=1e-6),
TemporalBlock(8, 160, 1280, eps=1e-6),
TemporalBlock(8, 160, 1280, eps=1e-6),
TemporalBlock(8, 160, 1280, eps=1e-6),
TemporalBlock(8, 160, 1280, eps=1e-6),
TemporalBlock(8, 80, 640, eps=1e-6),
TemporalBlock(8, 80, 640, eps=1e-6),
TemporalBlock(8, 80, 640, eps=1e-6),
TemporalBlock(8, 40, 320, eps=1e-6),
TemporalBlock(8, 40, 320, eps=1e-6),
TemporalBlock(8, 40, 320, eps=1e-6),
])
self.call_block_id = {
1: 0,
4: 1,
9: 2,
12: 3,
17: 4,
20: 5,
24: 6,
26: 7,
29: 8,
32: 9,
34: 10,
36: 11,
40: 12,
43: 13,
46: 14,
50: 15,
53: 16,
56: 17,
60: 18,
63: 19,
66: 20
}
def forward(self):
pass
@staticmethod
def state_dict_converter():
return SDMotionModelStateDictConverter()
class SDMotionModelStateDictConverter:
def __init__(self):
pass
def from_diffusers(self, state_dict):
rename_dict = {
"norm": "norm",
"proj_in": "proj_in",
"transformer_blocks.0.attention_blocks.0.to_q": "transformer_blocks.0.attn1.to_q",
"transformer_blocks.0.attention_blocks.0.to_k": "transformer_blocks.0.attn1.to_k",
"transformer_blocks.0.attention_blocks.0.to_v": "transformer_blocks.0.attn1.to_v",
"transformer_blocks.0.attention_blocks.0.to_out.0": "transformer_blocks.0.attn1.to_out",
"transformer_blocks.0.attention_blocks.0.pos_encoder": "transformer_blocks.0.pe1",
"transformer_blocks.0.attention_blocks.1.to_q": "transformer_blocks.0.attn2.to_q",
"transformer_blocks.0.attention_blocks.1.to_k": "transformer_blocks.0.attn2.to_k",
"transformer_blocks.0.attention_blocks.1.to_v": "transformer_blocks.0.attn2.to_v",
"transformer_blocks.0.attention_blocks.1.to_out.0": "transformer_blocks.0.attn2.to_out",
"transformer_blocks.0.attention_blocks.1.pos_encoder": "transformer_blocks.0.pe2",
"transformer_blocks.0.norms.0": "transformer_blocks.0.norm1",
"transformer_blocks.0.norms.1": "transformer_blocks.0.norm2",
"transformer_blocks.0.ff.net.0.proj": "transformer_blocks.0.act_fn.proj",
"transformer_blocks.0.ff.net.2": "transformer_blocks.0.ff",
"transformer_blocks.0.ff_norm": "transformer_blocks.0.norm3",
"proj_out": "proj_out",
}
name_list = sorted([i for i in state_dict if i.startswith("down_blocks.")])
name_list += sorted([i for i in state_dict if i.startswith("mid_block.")])
name_list += sorted([i for i in state_dict if i.startswith("up_blocks.")])
state_dict_ = {}
last_prefix, module_id = "", -1
for name in name_list:
names = name.split(".")
prefix_index = names.index("temporal_transformer") + 1
prefix = ".".join(names[:prefix_index])
if prefix != last_prefix:
last_prefix = prefix
module_id += 1
middle_name = ".".join(names[prefix_index:-1])
suffix = names[-1]
if "pos_encoder" in names:
rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name]])
else:
rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name], suffix])
state_dict_[rename] = state_dict[name]
return state_dict_
def from_civitai(self, state_dict):
return self.from_diffusers(state_dict)
|