mirror of
https://github.com/modelscope/DiffSynth-Studio.git
synced 2026-03-18 22:08:13 +00:00
1090 lines
96 KiB
Python
1090 lines
96 KiB
Python
import torch, math
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from .attention import Attention
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from .tiler import Tiler
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class Timesteps(torch.nn.Module):
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def __init__(self, num_channels):
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super().__init__()
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self.num_channels = num_channels
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def forward(self, timesteps):
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half_dim = self.num_channels // 2
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exponent = -math.log(10000) * torch.arange(start=0, end=half_dim, dtype=torch.float32, device=timesteps.device) / half_dim
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timesteps = timesteps.unsqueeze(-1)
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emb = timesteps.float() * torch.exp(exponent)
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emb = torch.cat([torch.cos(emb), torch.sin(emb)], dim=-1)
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return emb
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class GEGLU(torch.nn.Module):
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def __init__(self, dim_in, dim_out):
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super().__init__()
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self.proj = torch.nn.Linear(dim_in, dim_out * 2)
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def forward(self, hidden_states):
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hidden_states, gate = self.proj(hidden_states).chunk(2, dim=-1)
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return hidden_states * torch.nn.functional.gelu(gate)
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class BasicTransformerBlock(torch.nn.Module):
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def __init__(self, dim, num_attention_heads, attention_head_dim, cross_attention_dim):
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super().__init__()
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# 1. Self-Attn
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self.norm1 = torch.nn.LayerNorm(dim, elementwise_affine=True)
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self.attn1 = Attention(q_dim=dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True)
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# 2. Cross-Attn
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self.norm2 = torch.nn.LayerNorm(dim, elementwise_affine=True)
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self.attn2 = Attention(q_dim=dim, kv_dim=cross_attention_dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True)
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# 3. Feed-forward
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self.norm3 = torch.nn.LayerNorm(dim, elementwise_affine=True)
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self.act_fn = GEGLU(dim, dim * 4)
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self.ff = torch.nn.Linear(dim * 4, dim)
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def forward(self, hidden_states, encoder_hidden_states):
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# 1. Self-Attention
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norm_hidden_states = self.norm1(hidden_states)
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attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None,)
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hidden_states = attn_output + hidden_states
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# 2. Cross-Attention
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norm_hidden_states = self.norm2(hidden_states)
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attn_output = self.attn2(norm_hidden_states, encoder_hidden_states=encoder_hidden_states)
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hidden_states = attn_output + hidden_states
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# 3. Feed-forward
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norm_hidden_states = self.norm3(hidden_states)
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ff_output = self.act_fn(norm_hidden_states)
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ff_output = self.ff(ff_output)
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hidden_states = ff_output + hidden_states
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return hidden_states
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class DownSampler(torch.nn.Module):
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def __init__(self, channels, padding=1, extra_padding=False):
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super().__init__()
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self.conv = torch.nn.Conv2d(channels, channels, 3, stride=2, padding=padding)
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self.extra_padding = extra_padding
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def forward(self, hidden_states, time_emb, text_emb, res_stack):
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if self.extra_padding:
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hidden_states = torch.nn.functional.pad(hidden_states, (0, 1, 0, 1), mode="constant", value=0)
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hidden_states = self.conv(hidden_states)
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return hidden_states, time_emb, text_emb, res_stack
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class UpSampler(torch.nn.Module):
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def __init__(self, channels):
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super().__init__()
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self.conv = torch.nn.Conv2d(channels, channels, 3, padding=1)
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def forward(self, hidden_states, time_emb, text_emb, res_stack):
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hidden_states = torch.nn.functional.interpolate(hidden_states, scale_factor=2.0, mode="nearest")
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hidden_states = self.conv(hidden_states)
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return hidden_states, time_emb, text_emb, res_stack
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class ResnetBlock(torch.nn.Module):
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def __init__(self, in_channels, out_channels, temb_channels=None, groups=32, eps=1e-5):
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super().__init__()
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self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
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self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
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if temb_channels is not None:
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self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels)
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self.norm2 = torch.nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True)
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self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
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self.nonlinearity = torch.nn.SiLU()
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self.conv_shortcut = None
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if in_channels != out_channels:
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self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=True)
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def forward(self, hidden_states, time_emb, text_emb, res_stack):
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x = hidden_states
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x = self.norm1(x)
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x = self.nonlinearity(x)
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x = self.conv1(x)
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if time_emb is not None:
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emb = self.nonlinearity(time_emb)
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emb = self.time_emb_proj(emb)[:, :, None, None]
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x = x + emb
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x = self.norm2(x)
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x = self.nonlinearity(x)
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x = self.conv2(x)
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if self.conv_shortcut is not None:
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hidden_states = self.conv_shortcut(hidden_states)
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hidden_states = hidden_states + x
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return hidden_states, time_emb, text_emb, res_stack
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class AttentionBlock(torch.nn.Module):
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def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, cross_attention_dim=None, norm_num_groups=32, eps=1e-5):
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super().__init__()
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inner_dim = num_attention_heads * attention_head_dim
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self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True)
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self.proj_in = torch.nn.Linear(in_channels, inner_dim)
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self.transformer_blocks = torch.nn.ModuleList([
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BasicTransformerBlock(
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inner_dim,
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num_attention_heads,
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attention_head_dim,
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cross_attention_dim=cross_attention_dim
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)
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for d in range(num_layers)
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])
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self.proj_out = torch.nn.Linear(inner_dim, in_channels)
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def forward(self, hidden_states, time_emb, text_emb, res_stack):
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batch, _, height, width = hidden_states.shape
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residual = hidden_states
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hidden_states = self.norm(hidden_states)
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inner_dim = hidden_states.shape[1]
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hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
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hidden_states = self.proj_in(hidden_states)
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for block in self.transformer_blocks:
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hidden_states = block(
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hidden_states,
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encoder_hidden_states=text_emb
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)
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hidden_states = self.proj_out(hidden_states)
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hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
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hidden_states = hidden_states + residual
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return hidden_states, time_emb, text_emb, res_stack
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class PushBlock(torch.nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, hidden_states, time_emb, text_emb, res_stack):
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res_stack.append(hidden_states)
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return hidden_states, time_emb, text_emb, res_stack
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class PopBlock(torch.nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, hidden_states, time_emb, text_emb, res_stack):
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res_hidden_states = res_stack.pop()
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hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
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return hidden_states, time_emb, text_emb, res_stack
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class SDUNet(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.time_proj = Timesteps(320)
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self.time_embedding = torch.nn.Sequential(
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torch.nn.Linear(320, 1280),
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torch.nn.SiLU(),
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torch.nn.Linear(1280, 1280)
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)
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self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1)
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self.blocks = torch.nn.ModuleList([
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# CrossAttnDownBlock2D
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ResnetBlock(320, 320, 1280),
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AttentionBlock(8, 40, 320, 1, 768, eps=1e-6),
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PushBlock(),
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ResnetBlock(320, 320, 1280),
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AttentionBlock(8, 40, 320, 1, 768, eps=1e-6),
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PushBlock(),
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DownSampler(320),
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PushBlock(),
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# CrossAttnDownBlock2D
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ResnetBlock(320, 640, 1280),
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AttentionBlock(8, 80, 640, 1, 768, eps=1e-6),
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PushBlock(),
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ResnetBlock(640, 640, 1280),
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AttentionBlock(8, 80, 640, 1, 768, eps=1e-6),
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PushBlock(),
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DownSampler(640),
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PushBlock(),
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# CrossAttnDownBlock2D
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ResnetBlock(640, 1280, 1280),
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AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6),
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PushBlock(),
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ResnetBlock(1280, 1280, 1280),
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AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6),
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PushBlock(),
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DownSampler(1280),
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PushBlock(),
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# DownBlock2D
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ResnetBlock(1280, 1280, 1280),
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PushBlock(),
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ResnetBlock(1280, 1280, 1280),
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PushBlock(),
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# UNetMidBlock2DCrossAttn
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ResnetBlock(1280, 1280, 1280),
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AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6),
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ResnetBlock(1280, 1280, 1280),
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# UpBlock2D
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PopBlock(),
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ResnetBlock(2560, 1280, 1280),
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PopBlock(),
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ResnetBlock(2560, 1280, 1280),
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PopBlock(),
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ResnetBlock(2560, 1280, 1280),
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UpSampler(1280),
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# CrossAttnUpBlock2D
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PopBlock(),
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ResnetBlock(2560, 1280, 1280),
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AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6),
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PopBlock(),
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ResnetBlock(2560, 1280, 1280),
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AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6),
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PopBlock(),
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ResnetBlock(1920, 1280, 1280),
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AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6),
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UpSampler(1280),
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# CrossAttnUpBlock2D
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PopBlock(),
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ResnetBlock(1920, 640, 1280),
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AttentionBlock(8, 80, 640, 1, 768, eps=1e-6),
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PopBlock(),
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ResnetBlock(1280, 640, 1280),
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AttentionBlock(8, 80, 640, 1, 768, eps=1e-6),
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PopBlock(),
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ResnetBlock(960, 640, 1280),
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AttentionBlock(8, 80, 640, 1, 768, eps=1e-6),
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UpSampler(640),
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# CrossAttnUpBlock2D
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PopBlock(),
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ResnetBlock(960, 320, 1280),
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AttentionBlock(8, 40, 320, 1, 768, eps=1e-6),
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PopBlock(),
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ResnetBlock(640, 320, 1280),
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AttentionBlock(8, 40, 320, 1, 768, eps=1e-6),
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PopBlock(),
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ResnetBlock(640, 320, 1280),
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AttentionBlock(8, 40, 320, 1, 768, eps=1e-6),
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])
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self.conv_norm_out = torch.nn.GroupNorm(num_channels=320, num_groups=32, eps=1e-5)
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self.conv_act = torch.nn.SiLU()
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self.conv_out = torch.nn.Conv2d(320, 4, kernel_size=3, padding=1)
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def forward(self, sample, timestep, encoder_hidden_states, tiled=False, tile_size=64, tile_stride=8, **kwargs):
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# 1. time
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time_emb = self.time_proj(timestep[None]).to(sample.dtype)
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time_emb = self.time_embedding(time_emb)
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time_emb = time_emb.repeat(sample.shape[0], 1)
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# 2. pre-process
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height, width = sample.shape[2], sample.shape[3]
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hidden_states = self.conv_in(sample)
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text_emb = encoder_hidden_states
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res_stack = [hidden_states]
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# 3. blocks
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for i, block in enumerate(self.blocks):
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if tiled:
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hidden_states, time_emb, text_emb, res_stack = self.tiled_inference(
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block, hidden_states, time_emb, text_emb, res_stack,
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height, width, tile_size, tile_stride
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)
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else:
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hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
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# 4. output
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hidden_states = self.conv_norm_out(hidden_states)
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hidden_states = self.conv_act(hidden_states)
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hidden_states = self.conv_out(hidden_states)
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return hidden_states
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def tiled_inference(self, block, hidden_states, time_emb, text_emb, res_stack, height, width, tile_size, tile_stride):
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if block.__class__.__name__ in ["ResnetBlock", "AttentionBlock", "DownSampler", "UpSampler"]:
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batch_size, inter_channel, inter_height, inter_width = hidden_states.shape
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resize_scale = inter_height / height
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hidden_states = Tiler()(
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lambda x: block(x, time_emb, text_emb, res_stack)[0],
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hidden_states,
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int(tile_size * resize_scale),
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int(tile_stride * resize_scale),
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inter_device=hidden_states.device,
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inter_dtype=hidden_states.dtype
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)
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else:
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hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
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return hidden_states, time_emb, text_emb, res_stack
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def state_dict_converter(self):
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return SDUNetStateDictConverter()
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class SDUNetStateDictConverter:
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def __init__(self):
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pass
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def from_diffusers(self, state_dict):
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# architecture
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block_types = [
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'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock',
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'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock',
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'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock',
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'ResnetBlock', 'PushBlock', 'ResnetBlock', 'PushBlock',
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'ResnetBlock', 'AttentionBlock', 'ResnetBlock',
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'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'UpSampler',
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'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler',
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'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler',
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'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock'
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]
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# Rename each parameter
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name_list = sorted([name for name in state_dict])
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rename_dict = {}
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block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1}
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last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""}
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for name in name_list:
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names = name.split(".")
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if names[0] in ["conv_in", "conv_norm_out", "conv_out"]:
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pass
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elif names[0] in ["time_embedding", "add_embedding"]:
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if names[0] == "add_embedding":
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names[0] = "add_time_embedding"
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names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]]
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elif names[0] in ["down_blocks", "mid_block", "up_blocks"]:
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if names[0] == "mid_block":
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names.insert(1, "0")
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block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]]
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block_type_with_id = ".".join(names[:4])
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if block_type_with_id != last_block_type_with_id[block_type]:
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block_id[block_type] += 1
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last_block_type_with_id[block_type] = block_type_with_id
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while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type:
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block_id[block_type] += 1
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block_type_with_id = ".".join(names[:4])
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names = ["blocks", str(block_id[block_type])] + names[4:]
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if "ff" in names:
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ff_index = names.index("ff")
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component = ".".join(names[ff_index:ff_index+3])
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component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component]
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names = names[:ff_index] + [component] + names[ff_index+3:]
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if "to_out" in names:
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names.pop(names.index("to_out") + 1)
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else:
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raise ValueError(f"Unknown parameters: {name}")
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rename_dict[name] = ".".join(names)
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# Convert state_dict
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state_dict_ = {}
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for name, param in state_dict.items():
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if ".proj_in." in name or ".proj_out." in name:
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param = param.squeeze()
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state_dict_[rename_dict[name]] = param
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return state_dict_
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def from_civitai(self, state_dict):
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rename_dict = {
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"model.diffusion_model.input_blocks.0.0.bias": "conv_in.bias",
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"model.diffusion_model.input_blocks.0.0.weight": "conv_in.weight",
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"model.diffusion_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias",
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"model.diffusion_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight",
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"model.diffusion_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias",
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"model.diffusion_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight",
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"model.diffusion_model.input_blocks.1.0.in_layers.2.bias": "blocks.0.conv1.bias",
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"model.diffusion_model.input_blocks.1.0.in_layers.2.weight": "blocks.0.conv1.weight",
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"model.diffusion_model.input_blocks.1.0.out_layers.0.bias": "blocks.0.norm2.bias",
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"model.diffusion_model.input_blocks.1.0.out_layers.0.weight": "blocks.0.norm2.weight",
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"model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn1.to_v.weight": "blocks.60.transformer_blocks.0.attn1.to_v.weight",
|
|
"model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn2.to_k.weight": "blocks.60.transformer_blocks.0.attn2.to_k.weight",
|
|
"model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.60.transformer_blocks.0.attn2.to_out.bias",
|
|
"model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.60.transformer_blocks.0.attn2.to_out.weight",
|
|
"model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn2.to_q.weight": "blocks.60.transformer_blocks.0.attn2.to_q.weight",
|
|
"model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn2.to_v.weight": "blocks.60.transformer_blocks.0.attn2.to_v.weight",
|
|
"model.diffusion_model.output_blocks.9.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.60.transformer_blocks.0.act_fn.proj.bias",
|
|
"model.diffusion_model.output_blocks.9.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.60.transformer_blocks.0.act_fn.proj.weight",
|
|
"model.diffusion_model.output_blocks.9.1.transformer_blocks.0.ff.net.2.bias": "blocks.60.transformer_blocks.0.ff.bias",
|
|
"model.diffusion_model.output_blocks.9.1.transformer_blocks.0.ff.net.2.weight": "blocks.60.transformer_blocks.0.ff.weight",
|
|
"model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm1.bias": "blocks.60.transformer_blocks.0.norm1.bias",
|
|
"model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm1.weight": "blocks.60.transformer_blocks.0.norm1.weight",
|
|
"model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm2.bias": "blocks.60.transformer_blocks.0.norm2.bias",
|
|
"model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm2.weight": "blocks.60.transformer_blocks.0.norm2.weight",
|
|
"model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm3.bias": "blocks.60.transformer_blocks.0.norm3.bias",
|
|
"model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm3.weight": "blocks.60.transformer_blocks.0.norm3.weight",
|
|
"model.diffusion_model.time_embed.0.bias": "time_embedding.0.bias",
|
|
"model.diffusion_model.time_embed.0.weight": "time_embedding.0.weight",
|
|
"model.diffusion_model.time_embed.2.bias": "time_embedding.2.bias",
|
|
"model.diffusion_model.time_embed.2.weight": "time_embedding.2.weight",
|
|
}
|
|
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_ |