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import torch, math
from einops import rearrange, repeat
from .sd_unet import Timesteps, PushBlock, PopBlock, Attention, GEGLU, ResnetBlock, AttentionBlock, DownSampler, UpSampler
class TemporalResnetBlock(torch.nn.Module):
def __init__(self, in_channels, out_channels, temb_channels=None, groups=32, eps=1e-5):
super().__init__()
self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
self.conv1 = torch.nn.Conv3d(in_channels, out_channels, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0))
if temb_channels is not None:
self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels)
self.norm2 = torch.nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True)
self.conv2 = torch.nn.Conv3d(out_channels, out_channels, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0))
self.nonlinearity = torch.nn.SiLU()
self.conv_shortcut = None
if in_channels != out_channels:
self.conv_shortcut = torch.nn.Conv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=True)
def forward(self, hidden_states, time_emb, text_emb, res_stack):
x = rearrange(hidden_states, "f c h w -> 1 c f h w")
x = self.norm1(x)
x = self.nonlinearity(x)
x = self.conv1(x)
if time_emb is not None:
emb = self.nonlinearity(time_emb)
emb = self.time_emb_proj(emb)
emb = repeat(emb, "b c -> b c f 1 1", f=hidden_states.shape[0])
x = x + emb
x = self.norm2(x)
x = self.nonlinearity(x)
x = self.conv2(x)
if self.conv_shortcut is not None:
hidden_states = self.conv_shortcut(hidden_states)
x = rearrange(x[0], "c f h w -> f c h w")
hidden_states = hidden_states + x
return hidden_states, time_emb, text_emb, res_stack
def get_timestep_embedding(
timesteps: torch.Tensor,
embedding_dim: int,
flip_sin_to_cos: bool = False,
downscale_freq_shift: float = 1,
scale: float = 1,
max_period: int = 10000,
):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
:param timesteps: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the
embeddings. :return: an [N x dim] Tensor of positional embeddings.
"""
assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
half_dim = embedding_dim // 2
exponent = -math.log(max_period) * torch.arange(
start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
)
exponent = exponent / (half_dim - downscale_freq_shift)
emb = torch.exp(exponent)
emb = timesteps[:, None].float() * emb[None, :]
# scale embeddings
emb = scale * emb
# concat sine and cosine embeddings
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
# flip sine and cosine embeddings
if flip_sin_to_cos:
emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
# zero pad
if embedding_dim % 2 == 1:
emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
return emb
class TemporalTimesteps(torch.nn.Module):
def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float):
super().__init__()
self.num_channels = num_channels
self.flip_sin_to_cos = flip_sin_to_cos
self.downscale_freq_shift = downscale_freq_shift
def forward(self, timesteps):
t_emb = get_timestep_embedding(
timesteps,
self.num_channels,
flip_sin_to_cos=self.flip_sin_to_cos,
downscale_freq_shift=self.downscale_freq_shift,
)
return t_emb
class TemporalAttentionBlock(torch.nn.Module):
def __init__(self, num_attention_heads, attention_head_dim, in_channels, cross_attention_dim=None):
super().__init__()
self.positional_embedding = TemporalTimesteps(in_channels, True, 0)
self.positional_embedding_proj = torch.nn.Sequential(
torch.nn.Linear(in_channels, in_channels * 4),
torch.nn.SiLU(),
torch.nn.Linear(in_channels * 4, in_channels)
)
self.norm_in = torch.nn.LayerNorm(in_channels)
self.act_fn_in = GEGLU(in_channels, in_channels * 4)
self.ff_in = torch.nn.Linear(in_channels * 4, in_channels)
self.norm1 = torch.nn.LayerNorm(in_channels)
self.attn1 = Attention(
q_dim=in_channels,
num_heads=num_attention_heads,
head_dim=attention_head_dim,
bias_out=True
)
self.norm2 = torch.nn.LayerNorm(in_channels)
self.attn2 = Attention(
q_dim=in_channels,
kv_dim=cross_attention_dim,
num_heads=num_attention_heads,
head_dim=attention_head_dim,
bias_out=True
)
self.norm_out = torch.nn.LayerNorm(in_channels)
self.act_fn_out = GEGLU(in_channels, in_channels * 4)
self.ff_out = torch.nn.Linear(in_channels * 4, in_channels)
def forward(self, hidden_states, time_emb, text_emb, res_stack):
batch, inner_dim, height, width = hidden_states.shape
pos_emb = torch.arange(batch)
pos_emb = self.positional_embedding(pos_emb).to(dtype=hidden_states.dtype, device=hidden_states.device)
pos_emb = self.positional_embedding_proj(pos_emb)[None, :, :]
hidden_states = hidden_states.permute(2, 3, 0, 1).reshape(height * width, batch, inner_dim)
hidden_states = hidden_states + pos_emb
residual = hidden_states
hidden_states = self.norm_in(hidden_states)
hidden_states = self.act_fn_in(hidden_states)
hidden_states = self.ff_in(hidden_states)
hidden_states = hidden_states + residual
norm_hidden_states = self.norm1(hidden_states)
attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None)
hidden_states = attn_output + hidden_states
norm_hidden_states = self.norm2(hidden_states)
attn_output = self.attn2(norm_hidden_states, encoder_hidden_states=text_emb.repeat(height * width, 1))
hidden_states = attn_output + hidden_states
residual = hidden_states
hidden_states = self.norm_out(hidden_states)
hidden_states = self.act_fn_out(hidden_states)
hidden_states = self.ff_out(hidden_states)
hidden_states = hidden_states + residual
hidden_states = hidden_states.reshape(height, width, batch, inner_dim).permute(2, 3, 0, 1)
return hidden_states, time_emb, text_emb, res_stack
class PopMixBlock(torch.nn.Module):
def __init__(self, in_channels=None):
super().__init__()
self.mix_factor = torch.nn.Parameter(torch.Tensor([0.5]))
self.need_proj = in_channels is not None
if self.need_proj:
self.proj = torch.nn.Linear(in_channels, in_channels)
def forward(self, hidden_states, time_emb, text_emb, res_stack):
res_hidden_states = res_stack.pop()
alpha = torch.sigmoid(self.mix_factor)
hidden_states = alpha * res_hidden_states + (1 - alpha) * hidden_states
if self.need_proj:
hidden_states = hidden_states.permute(0, 2, 3, 1)
hidden_states = self.proj(hidden_states)
hidden_states = hidden_states.permute(0, 3, 1, 2)
res_hidden_states = res_stack.pop()
hidden_states = hidden_states + res_hidden_states
return hidden_states, time_emb, text_emb, res_stack
class SVDUNet(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(768, 1280),
torch.nn.SiLU(),
torch.nn.Linear(1280, 1280)
)
self.conv_in = torch.nn.Conv2d(8, 320, kernel_size=3, padding=1)
self.blocks = torch.nn.ModuleList([
# CrossAttnDownBlockSpatioTemporal
ResnetBlock(320, 320, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6), PopMixBlock(), PushBlock(),
AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024), PopMixBlock(320), PushBlock(),
ResnetBlock(320, 320, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6), PopMixBlock(), PushBlock(),
AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024), PopMixBlock(320), PushBlock(),
DownSampler(320), PushBlock(),
# CrossAttnDownBlockSpatioTemporal
ResnetBlock(320, 640, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6), PopMixBlock(), PushBlock(),
AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024), PopMixBlock(640), PushBlock(),
ResnetBlock(640, 640, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6), PopMixBlock(), PushBlock(),
AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024), PopMixBlock(640), PushBlock(),
DownSampler(640), PushBlock(),
# CrossAttnDownBlockSpatioTemporal
ResnetBlock(640, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(), PushBlock(),
AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024), PopMixBlock(1280), PushBlock(),
ResnetBlock(1280, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(), PushBlock(),
AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024), PopMixBlock(1280), PushBlock(),
DownSampler(1280), PushBlock(),
# DownBlockSpatioTemporal
ResnetBlock(1280, 1280, 1280, eps=1e-5), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), PushBlock(),
ResnetBlock(1280, 1280, 1280, eps=1e-5), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), PushBlock(),
# UNetMidBlockSpatioTemporal
ResnetBlock(1280, 1280, 1280, eps=1e-5), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(), PushBlock(),
AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024), PopMixBlock(1280),
ResnetBlock(1280, 1280, 1280, eps=1e-5), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(),
# UpBlockSpatioTemporal
PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(),
PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(),
PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(),
UpSampler(1280),
# CrossAttnUpBlockSpatioTemporal
PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(), PushBlock(),
AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024), PopMixBlock(1280),
PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(), PushBlock(),
AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024), PopMixBlock(1280),
PopBlock(), ResnetBlock(1920, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(), PushBlock(),
AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024), PopMixBlock(1280),
UpSampler(1280),
# CrossAttnUpBlockSpatioTemporal
PopBlock(), ResnetBlock(1920, 640, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6), PopMixBlock(), PushBlock(),
AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024), PopMixBlock(640),
PopBlock(), ResnetBlock(1280, 640, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6), PopMixBlock(), PushBlock(),
AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024), PopMixBlock(640),
PopBlock(), ResnetBlock(960, 640, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6), PopMixBlock(), PushBlock(),
AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024), PopMixBlock(640),
UpSampler(640),
# CrossAttnUpBlockSpatioTemporal
PopBlock(), ResnetBlock(960, 320, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6), PopMixBlock(), PushBlock(),
AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024), PopMixBlock(320),
PopBlock(), ResnetBlock(640, 320, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6), PopMixBlock(), PushBlock(),
AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024), PopMixBlock(320),
PopBlock(), ResnetBlock(640, 320, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6), PopMixBlock(), PushBlock(),
AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024), PopMixBlock(320),
])
self.conv_norm_out = torch.nn.GroupNorm(32, 320, eps=1e-05, affine=True)
self.conv_act = torch.nn.SiLU()
self.conv_out = torch.nn.Conv2d(320, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
def forward(self, sample, timestep, encoder_hidden_states, add_time_id, **kwargs):
# 1. time
t_emb = self.time_proj(timestep[None]).to(sample.dtype)
t_emb = self.time_embedding(t_emb)
add_embeds = self.add_time_proj(add_time_id.flatten()).to(sample.dtype)
add_embeds = add_embeds.reshape((-1, 768))
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)
# 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 SVDUNetStateDictConverter()
class SVDUNetStateDictConverter:
def __init__(self):
pass
def get_block_name(self, names):
if names[0] in ["down_blocks", "mid_block", "up_blocks"]:
if names[4] in ["norm", "proj_in"]:
return ".".join(names[:4] + ["transformer_blocks"])
elif names[4] in ["time_pos_embed"]:
return ".".join(names[:4] + ["temporal_transformer_blocks"])
elif names[4] in ["proj_out"]:
return ".".join(names[:4] + ["time_mixer"])
else:
return ".".join(names[:5])
return ""
def from_diffusers(self, state_dict):
rename_dict = {
"time_embedding.linear_1": "time_embedding.0",
"time_embedding.linear_2": "time_embedding.2",
"add_embedding.linear_1": "add_time_embedding.0",
"add_embedding.linear_2": "add_time_embedding.2",
"conv_in": "conv_in",
"conv_norm_out": "conv_norm_out",
"conv_out": "conv_out",
}
blocks_rename_dict = [
"down_blocks.0.resnets.0.spatial_res_block", None, "down_blocks.0.resnets.0.temporal_res_block", "down_blocks.0.resnets.0.time_mixer", None,
"down_blocks.0.attentions.0.transformer_blocks", None, "down_blocks.0.attentions.0.temporal_transformer_blocks", "down_blocks.0.attentions.0.time_mixer", None,
"down_blocks.0.resnets.1.spatial_res_block", None, "down_blocks.0.resnets.1.temporal_res_block", "down_blocks.0.resnets.1.time_mixer", None,
"down_blocks.0.attentions.1.transformer_blocks", None, "down_blocks.0.attentions.1.temporal_transformer_blocks", "down_blocks.0.attentions.1.time_mixer", None,
"down_blocks.0.downsamplers.0.conv", None,
"down_blocks.1.resnets.0.spatial_res_block", None, "down_blocks.1.resnets.0.temporal_res_block", "down_blocks.1.resnets.0.time_mixer", None,
"down_blocks.1.attentions.0.transformer_blocks", None, "down_blocks.1.attentions.0.temporal_transformer_blocks", "down_blocks.1.attentions.0.time_mixer", None,
"down_blocks.1.resnets.1.spatial_res_block", None, "down_blocks.1.resnets.1.temporal_res_block", "down_blocks.1.resnets.1.time_mixer", None,
"down_blocks.1.attentions.1.transformer_blocks", None, "down_blocks.1.attentions.1.temporal_transformer_blocks", "down_blocks.1.attentions.1.time_mixer", None,
"down_blocks.1.downsamplers.0.conv", None,
"down_blocks.2.resnets.0.spatial_res_block", None, "down_blocks.2.resnets.0.temporal_res_block", "down_blocks.2.resnets.0.time_mixer", None,
"down_blocks.2.attentions.0.transformer_blocks", None, "down_blocks.2.attentions.0.temporal_transformer_blocks", "down_blocks.2.attentions.0.time_mixer", None,
"down_blocks.2.resnets.1.spatial_res_block", None, "down_blocks.2.resnets.1.temporal_res_block", "down_blocks.2.resnets.1.time_mixer", None,
"down_blocks.2.attentions.1.transformer_blocks", None, "down_blocks.2.attentions.1.temporal_transformer_blocks", "down_blocks.2.attentions.1.time_mixer", None,
"down_blocks.2.downsamplers.0.conv", None,
"down_blocks.3.resnets.0.spatial_res_block", None, "down_blocks.3.resnets.0.temporal_res_block", "down_blocks.3.resnets.0.time_mixer", None,
"down_blocks.3.resnets.1.spatial_res_block", None, "down_blocks.3.resnets.1.temporal_res_block", "down_blocks.3.resnets.1.time_mixer", None,
"mid_block.mid_block.resnets.0.spatial_res_block", None, "mid_block.mid_block.resnets.0.temporal_res_block", "mid_block.mid_block.resnets.0.time_mixer", None,
"mid_block.mid_block.attentions.0.transformer_blocks", None, "mid_block.mid_block.attentions.0.temporal_transformer_blocks", "mid_block.mid_block.attentions.0.time_mixer",
"mid_block.mid_block.resnets.1.spatial_res_block", None, "mid_block.mid_block.resnets.1.temporal_res_block", "mid_block.mid_block.resnets.1.time_mixer",
None, "up_blocks.0.resnets.0.spatial_res_block", None, "up_blocks.0.resnets.0.temporal_res_block", "up_blocks.0.resnets.0.time_mixer",
None, "up_blocks.0.resnets.1.spatial_res_block", None, "up_blocks.0.resnets.1.temporal_res_block", "up_blocks.0.resnets.1.time_mixer",
None, "up_blocks.0.resnets.2.spatial_res_block", None, "up_blocks.0.resnets.2.temporal_res_block", "up_blocks.0.resnets.2.time_mixer",
"up_blocks.0.upsamplers.0.conv",
None, "up_blocks.1.resnets.0.spatial_res_block", None, "up_blocks.1.resnets.0.temporal_res_block", "up_blocks.1.resnets.0.time_mixer", None,
"up_blocks.1.attentions.0.transformer_blocks", None, "up_blocks.1.attentions.0.temporal_transformer_blocks", "up_blocks.1.attentions.0.time_mixer",
None, "up_blocks.1.resnets.1.spatial_res_block", None, "up_blocks.1.resnets.1.temporal_res_block", "up_blocks.1.resnets.1.time_mixer", None,
"up_blocks.1.attentions.1.transformer_blocks", None, "up_blocks.1.attentions.1.temporal_transformer_blocks", "up_blocks.1.attentions.1.time_mixer",
None, "up_blocks.1.resnets.2.spatial_res_block", None, "up_blocks.1.resnets.2.temporal_res_block", "up_blocks.1.resnets.2.time_mixer", None,
"up_blocks.1.attentions.2.transformer_blocks", None, "up_blocks.1.attentions.2.temporal_transformer_blocks", "up_blocks.1.attentions.2.time_mixer",
"up_blocks.1.upsamplers.0.conv",
None, "up_blocks.2.resnets.0.spatial_res_block", None, "up_blocks.2.resnets.0.temporal_res_block", "up_blocks.2.resnets.0.time_mixer", None,
"up_blocks.2.attentions.0.transformer_blocks", None, "up_blocks.2.attentions.0.temporal_transformer_blocks", "up_blocks.2.attentions.0.time_mixer",
None, "up_blocks.2.resnets.1.spatial_res_block", None, "up_blocks.2.resnets.1.temporal_res_block", "up_blocks.2.resnets.1.time_mixer", None,
"up_blocks.2.attentions.1.transformer_blocks", None, "up_blocks.2.attentions.1.temporal_transformer_blocks", "up_blocks.2.attentions.1.time_mixer",
None, "up_blocks.2.resnets.2.spatial_res_block", None, "up_blocks.2.resnets.2.temporal_res_block", "up_blocks.2.resnets.2.time_mixer", None,
"up_blocks.2.attentions.2.transformer_blocks", None, "up_blocks.2.attentions.2.temporal_transformer_blocks", "up_blocks.2.attentions.2.time_mixer",
"up_blocks.2.upsamplers.0.conv",
None, "up_blocks.3.resnets.0.spatial_res_block", None, "up_blocks.3.resnets.0.temporal_res_block", "up_blocks.3.resnets.0.time_mixer", None,
"up_blocks.3.attentions.0.transformer_blocks", None, "up_blocks.3.attentions.0.temporal_transformer_blocks", "up_blocks.3.attentions.0.time_mixer",
None, "up_blocks.3.resnets.1.spatial_res_block", None, "up_blocks.3.resnets.1.temporal_res_block", "up_blocks.3.resnets.1.time_mixer", None,
"up_blocks.3.attentions.1.transformer_blocks", None, "up_blocks.3.attentions.1.temporal_transformer_blocks", "up_blocks.3.attentions.1.time_mixer",
None, "up_blocks.3.resnets.2.spatial_res_block", None, "up_blocks.3.resnets.2.temporal_res_block", "up_blocks.3.resnets.2.time_mixer", None,
"up_blocks.3.attentions.2.transformer_blocks", None, "up_blocks.3.attentions.2.temporal_transformer_blocks", "up_blocks.3.attentions.2.time_mixer",
]
blocks_rename_dict = {i:j for j,i in enumerate(blocks_rename_dict) if i is not None}
state_dict_ = {}
for name, param in sorted(state_dict.items()):
names = name.split(".")
if names[0] == "mid_block":
names = ["mid_block"] + names
if names[-1] in ["weight", "bias"]:
name_prefix = ".".join(names[:-1])
if name_prefix in rename_dict:
state_dict_[rename_dict[name_prefix] + "." + names[-1]] = param
else:
block_name = self.get_block_name(names)
if "resnets" in block_name and block_name in blocks_rename_dict:
rename = ".".join(["blocks", str(blocks_rename_dict[block_name])] + names[5:])
state_dict_[rename] = param
elif ("downsamplers" in block_name or "upsamplers" in block_name) and block_name in blocks_rename_dict:
rename = ".".join(["blocks", str(blocks_rename_dict[block_name])] + names[-2:])
state_dict_[rename] = param
elif "attentions" in block_name and block_name in blocks_rename_dict:
attention_id = names[5]
if "transformer_blocks" in names:
suffix_dict = {
"attn1.to_out.0": "attn1.to_out",
"attn2.to_out.0": "attn2.to_out",
"ff.net.0.proj": "act_fn.proj",
"ff.net.2": "ff",
}
suffix = ".".join(names[6:-1])
suffix = suffix_dict.get(suffix, suffix)
rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), "transformer_blocks", attention_id, suffix, names[-1]])
elif "temporal_transformer_blocks" in names:
suffix_dict = {
"attn1.to_out.0": "attn1.to_out",
"attn2.to_out.0": "attn2.to_out",
"ff_in.net.0.proj": "act_fn_in.proj",
"ff_in.net.2": "ff_in",
"ff.net.0.proj": "act_fn_out.proj",
"ff.net.2": "ff_out",
"norm3": "norm_out",
}
suffix = ".".join(names[6:-1])
suffix = suffix_dict.get(suffix, suffix)
rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), suffix, names[-1]])
elif "time_mixer" in block_name:
rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), "proj", names[-1]])
else:
suffix_dict = {
"linear_1": "positional_embedding_proj.0",
"linear_2": "positional_embedding_proj.2",
}
suffix = names[-2]
suffix = suffix_dict.get(suffix, suffix)
rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), suffix, names[-1]])
state_dict_[rename] = param
else:
print(name)
else:
block_name = self.get_block_name(names)
if len(block_name)>0 and block_name in blocks_rename_dict:
rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), names[-1]])
state_dict_[rename] = param
return state_dict_
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