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Diffchecker Desktop
가장 안전하게 Diffchecker를 사용하는 방법. 데스크톱 앱을 사용하면 비교 데이터가 외부로 전송되지 않습니다!
데스크톱 앱 받기
AnimateDiff-XL
생성일
3년 전
비교 결과 만료 없음
초기화
내보내기
공유
설명
104 삭제
행
총
삭제
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총
삭제
이 기능을 계속 사용하려면 업그레이드해 주세요
Diff
checker
Pro
요금제 보기
484 행
복사
159 추가
행
총
추가
글자
총
추가
이 기능을 계속 사용하려면 업그레이드해 주세요
Diff
checker
Pro
요금제 보기
493 행
복사
복사
복사됨
복사
복사됨
UNet
Mo
tionModel(
UNet
3DCondi
tionModel(
(conv_in): Conv2d(4, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv_in): Conv2d(4, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_proj): Timesteps()
(time_proj): Timesteps()
(time_embedding): TimestepEmbedding(
(time_embedding): TimestepEmbedding(
(linear_1): LoRACompatibleLinear(in_features=320, out_features=1280, bias=True)
(linear_1): LoRACompatibleLinear(in_features=320, out_features=1280, bias=True)
(act): SiLU()
(act): SiLU()
(linear_2): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(linear_2): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
)
)
(add_time_proj): Timesteps()
(add_time_proj): Timesteps()
(add_embedding): TimestepEmbedding(
(add_embedding): TimestepEmbedding(
(linear_1): LoRACompatibleLinear(in_features=2816, out_features=1280, bias=True)
(linear_1): LoRACompatibleLinear(in_features=2816, out_features=1280, bias=True)
(act): SiLU()
(act): SiLU()
(linear_2): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(linear_2): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
)
)
(down_blocks): ModuleList(
(down_blocks): ModuleList(
복사
복사됨
복사
복사됨
(0): DownBlock
Motion
(
(0): DownBlock
3D
(
(resnets): ModuleList(
(resnets): ModuleList(
(0-1): 2 x ResnetBlock2D(
(0-1): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 320, eps=1e-05, affine=True)
(norm1): GroupNorm(32, 320, eps=1e-05, affine=True)
(conv1): LoRACompatibleConv(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv1): LoRACompatibleConv(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=320, bias=True)
(time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=320, bias=True)
(norm2): GroupNorm(32, 320, eps=1e-05, affine=True)
(norm2): GroupNorm(32, 320, eps=1e-05, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): LoRACompatibleConv(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(nonlinearity): SiLU()
)
)
)
)
(motion_modules): ModuleList(
(motion_modules): ModuleList(
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(0-1): 2 x
TransformerTemporalModel(
(0-1): 2 x
VanillaTemporalModule(
(norm): GroupNorm(32, 320, eps=1e-06, affine=True)
(temporal_transformer): TemporalTransformer3DModel(
(proj_in): Linear(in_features=320, out_features=320, bias=True)
(norm): GroupNorm(32, 320, eps=1e-06, affine=True)
(transformer_blocks): ModuleList(
(proj_in): Linear(in_features=320, out_features=320, bias=True)
(0):
Basic
TransformerBlock(
(transformer_blocks): ModuleList(
(pos_embed): SinusoidalPositionalEmbedding()
(0):
Temporal
TransformerBlock(
(norm1): LayerNorm((320,), eps=1e-05, elementwise_affine=True)
(attention_blocks): ModuleList(
(attn1): Attention(
(0-1): 2 x TemporalSelfAttention(
(to_q): LoRACompatibleLinear(in_features=320, out_features=320, bias=False)
(to_q): LoRACompatibleLinear(in_features=320, out_features=320, bias=False)
(to_k): LoRACompatibleLinear(in_features=320, out_features=320, bias=False)
(to_k): LoRACompatibleLinear(in_features=320, out_features=320, bias=False)
(to_v): LoRACompatibleLinear(in_features=320, out_features=320, bias=False)
(to_v): LoRACompatibleLinear(in_features=320, out_features=320, bias=False)
(to_out): ModuleList(
(to_out): ModuleList(
(0): LoRACompatibleLinear(in_features=320, out_features=320, bias=True)
(0): LoRACompatibleLinear(in_features=320, out_features=320, bias=True)
(1): Dropout(p=0.0, inplace=False)
(1): Dropout(p=0.0, inplace=False)
)
(pos_encoder): PositionalEncoding(
(dropout): Dropout(p=0.0, inplace=False)
)
)
)
)
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)
(norms): ModuleList(
(
norm2):
LayerNorm((320,), eps=1e-05, elementwise_affine=True)
(
0-1): 2 x
LayerNorm((320,), eps=1e-05, elementwise_affine=True)
(attn2): Attention(
(to_q): LoRACompatibleLinear(in_features=320, out_features=320, bias=False)
(to_k): LoRACompatibleLinear(in_features=320, out_features=320, bias=False)
(to_v): LoRACompatibleLinear(in_features=320, out_features=320, bias=False)
(to_out): ModuleList(
(0): LoRACompatibleLinear(in_features=320, out_features=320, bias=True)
(1): Dropout(p=0.0, inplace=False)
)
)
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)
(ff): FeedForward(
(norm3): LayerNorm((320,), eps=1e-05, elementwise_affine=True)
(net): ModuleList(
(ff): FeedForward(
(0): GEGLU(
(net): ModuleList(
(proj): LoRACompatibleLinear(in_features=320, out_features=2560, bias=True)
(0): GEGLU(
)
(proj): LoRACompatibleLinear(in_features=320, out_features=2560, bias=True)
(1): Dropout(p=0.0, inplace=False)
(2): LoRACompatibleLinear(in_features=1280, out_features=320, bias=True)
)
)
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(1): Dropout(p=0.0, inplace=False)
(2): LoRACompatibleLinear(in_features=1280, out_features=320, bias=True)
)
)
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(ff_norm): LayerNorm((320,), eps=1e-05, elementwise_affine=True)
)
)
)
)
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복사됨
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복사됨
(proj_out): Linear(in_features=320, out_features=320, bias=True)
)
)
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(proj_out): Linear(in_features=320, out_features=320, bias=True)
)
)
)
)
(downsamplers): ModuleList(
(downsamplers): ModuleList(
(0): Downsample2D(
(0): Downsample2D(
(conv): LoRACompatibleConv(320, 320, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(conv): LoRACompatibleConv(320, 320, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
)
)
)
)
)
)
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(1): CrossAttnDownBlock
Motion
(
(1): CrossAttnDownBlock
3D
(
(attentions): ModuleList(
(attentions): ModuleList(
(0-1): 2 x Transformer2DModel(
(0-1): 2 x Transformer2DModel(
(norm): GroupNorm(32, 640, eps=1e-06, affine=True)
(norm): GroupNorm(32, 640, eps=1e-06, affine=True)
(proj_in): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
(proj_in): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
(transformer_blocks): ModuleList(
(transformer_blocks): ModuleList(
(0-1): 2 x BasicTransformerBlock(
(0-1): 2 x BasicTransformerBlock(
(norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
(norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
(attn1): Attention(
(attn1): Attention(
(to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_k): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_k): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_v): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_v): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_out): ModuleList(
(to_out): ModuleList(
(0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
(0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
(1): Dropout(p=0.0, inplace=False)
(1): Dropout(p=0.0, inplace=False)
)
)
)
)
(norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
(attn2): Attention(
(attn2): Attention(
(to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_k): LoRACompatibleLinear(in_features=2048, out_features=640, bias=False)
(to_k): LoRACompatibleLinear(in_features=2048, out_features=640, bias=False)
(to_v): LoRACompatibleLinear(in_features=2048, out_features=640, bias=False)
(to_v): LoRACompatibleLinear(in_features=2048, out_features=640, bias=False)
(to_out): ModuleList(
(to_out): ModuleList(
(0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
(0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
(1): Dropout(p=0.0, inplace=False)
(1): Dropout(p=0.0, inplace=False)
)
)
)
)
(norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
(norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
(ff): FeedForward(
(ff): FeedForward(
(net): ModuleList(
(net): ModuleList(
(0): GEGLU(
(0): GEGLU(
(proj): LoRACompatibleLinear(in_features=640, out_features=5120, bias=True)
(proj): LoRACompatibleLinear(in_features=640, out_features=5120, bias=True)
)
)
(1): Dropout(p=0.0, inplace=False)
(1): Dropout(p=0.0, inplace=False)
(2): LoRACompatibleLinear(in_features=2560, out_features=640, bias=True)
(2): LoRACompatibleLinear(in_features=2560, out_features=640, bias=True)
)
)
)
)
)
)
)
)
(proj_out): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
(proj_out): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
)
)
)
)
(resnets): ModuleList(
(resnets): ModuleList(
(0): ResnetBlock2D(
(0): ResnetBlock2D(
(norm1): GroupNorm(32, 320, eps=1e-05, affine=True)
(norm1): GroupNorm(32, 320, eps=1e-05, affine=True)
(conv1): LoRACompatibleConv(320, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv1): LoRACompatibleConv(320, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=640, bias=True)
(time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=640, bias=True)
(norm2): GroupNorm(32, 640, eps=1e-05, affine=True)
(norm2): GroupNorm(32, 640, eps=1e-05, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(nonlinearity): SiLU()
(conv_shortcut): LoRACompatibleConv(320, 640, kernel_size=(1, 1), stride=(1, 1))
(conv_shortcut): LoRACompatibleConv(320, 640, kernel_size=(1, 1), stride=(1, 1))
)
)
(1): ResnetBlock2D(
(1): ResnetBlock2D(
(norm1): GroupNorm(32, 640, eps=1e-05, affine=True)
(norm1): GroupNorm(32, 640, eps=1e-05, affine=True)
(conv1): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv1): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=640, bias=True)
(time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=640, bias=True)
(norm2): GroupNorm(32, 640, eps=1e-05, affine=True)
(norm2): GroupNorm(32, 640, eps=1e-05, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(nonlinearity): SiLU()
)
)
)
)
(motion_modules): ModuleList(
(motion_modules): ModuleList(
복사
복사됨
복사
복사됨
(0-1): 2 x
TransformerTemporalModel(
(0-1): 2 x
VanillaTemporalModule(
(norm): GroupNorm(32, 640, eps=1e-06, affine=True)
(temporal_transformer): TemporalTransformer3DModel(
(proj_in): Linear(in_features=640, out_features=640, bias=True)
(norm): GroupNorm(32, 640, eps=1e-06, affine=True)
(transformer_blocks): ModuleList(
(proj_in): Linear(in_features=640, out_features=640, bias=True)
(0):
Basic
TransformerBlock(
(transformer_blocks): ModuleList(
(pos_embed): SinusoidalPositionalEmbedding()
(0):
Temporal
TransformerBlock(
(norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
(attention_blocks): ModuleList(
(attn1): Attention(
(0-1): 2 x TemporalSelfAttention(
(to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_k): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_k): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_v): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_v): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_out): ModuleList(
(to_out): ModuleList(
(0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
(0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
(1): Dropout(p=0.0, inplace=False)
(1): Dropout(p=0.0, inplace=False)
)
(pos_encoder): PositionalEncoding(
(dropout): Dropout(p=0.0, inplace=False)
)
)
)
)
복사
복사됨
복사
복사됨
)
(norms): ModuleList(
(
norm2):
LayerNorm((640,), eps=1e-05, elementwise_affine=True)
(
0-1): 2 x
LayerNorm((640,), eps=1e-05, elementwise_affine=True)
(attn2): Attention(
(to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_k): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_v): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_out): ModuleList(
(0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
(1): Dropout(p=0.0, inplace=False)
)
)
복사
복사됨
복사
복사됨
)
(ff): FeedForward(
(norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
(net): ModuleList(
(ff): FeedForward(
(0): GEGLU(
(net): ModuleList(
(proj): LoRACompatibleLinear(in_features=640, out_features=5120, bias=True)
(0): GEGLU(
)
(proj): LoRACompatibleLinear(in_features=640, out_features=5120, bias=True)
(1): Dropout(p=0.0, inplace=False)
(2): LoRACompatibleLinear(in_features=2560, out_features=640, bias=True)
)
)
복사
복사됨
복사
복사됨
(1): Dropout(p=0.0, inplace=False)
(2): LoRACompatibleLinear(in_features=2560, out_features=640, bias=True)
)
)
복사
복사됨
복사
복사됨
(ff_norm): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
)
)
)
)
복사
복사됨
복사
복사됨
(proj_out): Linear(in_features=640, out_features=640, bias=True)
)
)
복사
복사됨
복사
복사됨
(proj_out): Linear(in_features=640, out_features=640, bias=True)
)
)
)
)
(downsamplers): ModuleList(
(downsamplers): ModuleList(
(0): Downsample2D(
(0): Downsample2D(
(conv): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(conv): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
)
)
)
)
)
)
복사
복사됨
복사
복사됨
(2): CrossAttnDownBlock
Motion
(
(2): CrossAttnDownBlock
3D
(
(attentions): ModuleList(
(attentions): ModuleList(
(0-1): 2 x Transformer2DModel(
(0-1): 2 x Transformer2DModel(
(norm): GroupNorm(32, 1280, eps=1e-06, affine=True)
(norm): GroupNorm(32, 1280, eps=1e-06, affine=True)
(proj_in): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(proj_in): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(transformer_blocks): ModuleList(
(transformer_blocks): ModuleList(
(0-9): 10 x BasicTransformerBlock(
(0-9): 10 x BasicTransformerBlock(
(norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(attn1): Attention(
(attn1): Attention(
(to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_k): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_k): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_v): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_v): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_out): ModuleList(
(to_out): ModuleList(
(0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(1): Dropout(p=0.0, inplace=False)
(1): Dropout(p=0.0, inplace=False)
)
)
)
)
(norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(attn2): Attention(
(attn2): Attention(
(to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_k): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False)
(to_k): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False)
(to_v): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False)
(to_v): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False)
(to_out): ModuleList(
(to_out): ModuleList(
(0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(1): Dropout(p=0.0, inplace=False)
(1): Dropout(p=0.0, inplace=False)
)
)
)
)
(norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(ff): FeedForward(
(ff): FeedForward(
(net): ModuleList(
(net): ModuleList(
(0): GEGLU(
(0): GEGLU(
(proj): LoRACompatibleLinear(in_features=1280, out_features=10240, bias=True)
(proj): LoRACompatibleLinear(in_features=1280, out_features=10240, bias=True)
)
)
(1): Dropout(p=0.0, inplace=False)
(1): Dropout(p=0.0, inplace=False)
(2): LoRACompatibleLinear(in_features=5120, out_features=1280, bias=True)
(2): LoRACompatibleLinear(in_features=5120, out_features=1280, bias=True)
)
)
)
)
)
)
)
)
(proj_out): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(proj_out): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
)
)
)
)
(resnets): ModuleList(
(resnets): ModuleList(
(0): ResnetBlock2D(
(0): ResnetBlock2D(
(norm1): GroupNorm(32, 640, eps=1e-05, affine=True)
(norm1): GroupNorm(32, 640, eps=1e-05, affine=True)
(conv1): LoRACompatibleConv(640, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv1): LoRACompatibleConv(640, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)
(norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(nonlinearity): SiLU()
(conv_shortcut): LoRACompatibleConv(640, 1280, kernel_size=(1, 1), stride=(1, 1))
(conv_shortcut): LoRACompatibleConv(640, 1280, kernel_size=(1, 1), stride=(1, 1))
)
)
(1): ResnetBlock2D(
(1): ResnetBlock2D(
(norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)
(norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)
(conv1): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv1): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)
(norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(nonlinearity): SiLU()
)
)
)
)
(motion_modules): ModuleList(
(motion_modules): ModuleList(
복사
복사됨
복사
복사됨
(0-1): 2 x
TransformerTemporalModel(
(0-1): 2 x
VanillaTemporalModule(
(norm): GroupNorm(32, 1280, eps=1e-06, affine=True)
(temporal_transformer): TemporalTransformer3DModel(
(proj_in): Linear(in_features=1280, out_features=1280, bias=True)
(norm): GroupNorm(32, 1280, eps=1e-06, affine=True)
(transformer_blocks): ModuleList(
(proj_in): Linear(in_features=1280, out_features=1280, bias=True)
(0):
Basic
TransformerBlock(
(transformer_blocks): ModuleList(
(pos_embed): SinusoidalPositionalEmbedding()
(0):
Temporal
TransformerBlock(
(norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(attention_blocks): ModuleList(
(attn1): Attention(
(0-1): 2 x TemporalSelfAttention(
(to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_k): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_k): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_v): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_v): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_out): ModuleList(
(to_out): ModuleList(
(0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(1): Dropout(p=0.0, inplace=False)
(1): Dropout(p=0.0, inplace=False)
)
(pos_encoder): PositionalEncoding(
(dropout): Dropout(p=0.0, inplace=False)
)
)
)
)
복사
복사됨
복사
복사됨
)
(norms): ModuleList(
(
norm2):
LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(
0-1): 2 x
LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(attn2): Attention(
(to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_k): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_v): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_out): ModuleList(
(0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(1): Dropout(p=0.0, inplace=False)
)
)
복사
복사됨
복사
복사됨
)
(ff): FeedForward(
(norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(net): ModuleList(
(ff): FeedForward(
(0): GEGLU(
(net): ModuleList(
(proj): LoRACompatibleLinear(in_features=1280, out_features=10240, bias=True)
(0): GEGLU(
)
(proj): LoRACompatibleLinear(in_features=1280, out_features=10240, bias=True)
(1): Dropout(p=0.0, inplace=False)
(2): LoRACompatibleLinear(in_features=5120, out_features=1280, bias=True)
)
)
복사
복사됨
복사
복사됨
(1): Dropout(p=0.0, inplace=False)
(2): LoRACompatibleLinear(in_features=5120, out_features=1280, bias=True)
)
)
복사
복사됨
복사
복사됨
(ff_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
)
)
)
)
복사
복사됨
복사
복사됨
(proj_out): Linear(in_features=1280, out_features=1280, bias=True)
)
)
복사
복사됨
복사
복사됨
(proj_out): Linear(in_features=1280, out_features=1280, bias=True)
)
)
)
)
)
)
)
)
(up_blocks): ModuleList(
(up_blocks): ModuleList(
복사
복사됨
복사
복사됨
(0): CrossAttnUpBlock
Motion
(
(0): CrossAttnUpBlock
3D
(
(attentions): ModuleList(
(attentions): ModuleList(
(0-2): 3 x Transformer2DModel(
(0-2): 3 x Transformer2DModel(
(norm): GroupNorm(32, 1280, eps=1e-06, affine=True)
(norm): GroupNorm(32, 1280, eps=1e-06, affine=True)
(proj_in): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(proj_in): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(transformer_blocks): ModuleList(
(transformer_blocks): ModuleList(
(0-9): 10 x BasicTransformerBlock(
(0-9): 10 x BasicTransformerBlock(
(norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(attn1): Attention(
(attn1): Attention(
(to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_k): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_k): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_v): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_v): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_out): ModuleList(
(to_out): ModuleList(
(0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(1): Dropout(p=0.0, inplace=False)
(1): Dropout(p=0.0, inplace=False)
)
)
)
)
(norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(attn2): Attention(
(attn2): Attention(
(to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_k): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False)
(to_k): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False)
(to_v): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False)
(to_v): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False)
(to_out): ModuleList(
(to_out): ModuleList(
(0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(1): Dropout(p=0.0, inplace=False)
(1): Dropout(p=0.0, inplace=False)
)
)
)
)
(norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(ff): FeedForward(
(ff): FeedForward(
(net): ModuleList(
(net): ModuleList(
(0): GEGLU(
(0): GEGLU(
(proj): LoRACompatibleLinear(in_features=1280, out_features=10240, bias=True)
(proj): LoRACompatibleLinear(in_features=1280, out_features=10240, bias=True)
)
)
(1): Dropout(p=0.0, inplace=False)
(1): Dropout(p=0.0, inplace=False)
(2): LoRACompatibleLinear(in_features=5120, out_features=1280, bias=True)
(2): LoRACompatibleLinear(in_features=5120, out_features=1280, bias=True)
)
)
)
)
)
)
)
)
(proj_out): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(proj_out): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
)
)
)
)
(resnets): ModuleList(
(resnets): ModuleList(
(0-1): 2 x ResnetBlock2D(
(0-1): 2 x ResnetBlock2D(
(norm1): GroupNorm(32, 2560, eps=1e-05, affine=True)
(norm1): GroupNorm(32, 2560, eps=1e-05, affine=True)
(conv1): LoRACompatibleConv(2560, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv1): LoRACompatibleConv(2560, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)
(norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(nonlinearity): SiLU()
(conv_shortcut): LoRACompatibleConv(2560, 1280, kernel_size=(1, 1), stride=(1, 1))
(conv_shortcut): LoRACompatibleConv(2560, 1280, kernel_size=(1, 1), stride=(1, 1))
)
)
(2): ResnetBlock2D(
(2): ResnetBlock2D(
(norm1): GroupNorm(32, 1920, eps=1e-05, affine=True)
(norm1): GroupNorm(32, 1920, eps=1e-05, affine=True)
(conv1): LoRACompatibleConv(1920, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv1): LoRACompatibleConv(1920, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)
(norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(nonlinearity): SiLU()
(conv_shortcut): LoRACompatibleConv(1920, 1280, kernel_size=(1, 1), stride=(1, 1))
(conv_shortcut): LoRACompatibleConv(1920, 1280, kernel_size=(1, 1), stride=(1, 1))
)
)
)
)
(motion_modules): ModuleList(
(motion_modules): ModuleList(
복사
복사됨
복사
복사됨
(0-2): 3 x
TransformerTemporalModel(
(0-2): 3 x
VanillaTemporalModule(
(norm): GroupNorm(32, 1280, eps=1e-06, affine=True)
(temporal_transformer): TemporalTransformer3DModel(
(proj_in): Linear(in_features=1280, out_features=1280, bias=True)
(norm): GroupNorm(32, 1280, eps=1e-06, affine=True)
(transformer_blocks): ModuleList(
(proj_in): Linear(in_features=1280, out_features=1280, bias=True)
(0):
Basic
TransformerBlock(
(transformer_blocks): ModuleList(
(pos_embed): SinusoidalPositionalEmbedding()
(0):
Temporal
TransformerBlock(
(norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(attention_blocks): ModuleList(
(attn1): Attention(
(0-1): 2 x TemporalSelfAttention(
(to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_k): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_k): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_v): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_v): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_out): ModuleList(
(to_out): ModuleList(
(0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(1): Dropout(p=0.0, inplace=False)
(1): Dropout(p=0.0, inplace=False)
)
(pos_encoder): PositionalEncoding(
(dropout): Dropout(p=0.0, inplace=False)
)
)
)
)
복사
복사됨
복사
복사됨
)
(norms): ModuleList(
(
norm2):
LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(
0-1): 2 x
LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(attn2): Attention(
(to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_k): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_v): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
(to_out): ModuleList(
(0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
(1): Dropout(p=0.0, inplace=False)
)
)
복사
복사됨
복사
복사됨
)
(ff): FeedForward(
(norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
(net): ModuleList(
(ff): FeedForward(
(0): GEGLU(
(net): ModuleList(
(proj): LoRACompatibleLinear(in_features=1280, out_features=10240, bias=True)
(0): GEGLU(
)
(proj): LoRACompatibleLinear(in_features=1280, out_features=10240, bias=True)
(1): Dropout(p=0.0, inplace=False)
(2): LoRACompatibleLinear(in_features=5120, out_features=1280, bias=True)
)
)
복사
복사됨
복사
복사됨
(1): Dropout(p=0.0, inplace=False)
(2): LoRACompatibleLinear(in_features=5120, out_features=1280, bias=True)
)
)
복사
복사됨
복사
복사됨
(ff_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
)
)
)
)
복사
복사됨
복사
복사됨
(proj_out): Linear(in_features=1280, out_features=1280, bias=True)
)
)
복사
복사됨
복사
복사됨
(proj_out): Linear(in_features=1280, out_features=1280, bias=True)
)
)
)
)
(upsamplers): ModuleList(
(upsamplers): ModuleList(
(0): Upsample2D(
(0): Upsample2D(
(conv): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
)
)
)
)
복사
복사됨
복사
복사됨
(1): CrossAttnUpBlock
Motion
(
(1): CrossAttnUpBlock
3D
(
(attentions): ModuleList(
(attentions): ModuleList(
(0-2): 3 x Transformer2DModel(
(0-2): 3 x Transformer2DModel(
(norm): GroupNorm(32, 640, eps=1e-06, affine=True)
(norm): GroupNorm(32, 640, eps=1e-06, affine=True)
(proj_in): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
(proj_in): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
(transformer_blocks): ModuleList(
(transformer_blocks): ModuleList(
(0-1): 2 x BasicTransformerBlock(
(0-1): 2 x BasicTransformerBlock(
(norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
(norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
(attn1): Attention(
(attn1): Attention(
(to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_k): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_k): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_v): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_v): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_out): ModuleList(
(to_out): ModuleList(
(0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
(0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
(1): Dropout(p=0.0, inplace=False)
(1): Dropout(p=0.0, inplace=False)
)
)
)
)
(norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
(attn2): Attention(
(attn2): Attention(
(to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_k): LoRACompatibleLinear(in_features=2048, out_features=640, bias=False)
(to_k): LoRACompatibleLinear(in_features=2048, out_features=640, bias=False)
(to_v): LoRACompatibleLinear(in_features=2048, out_features=640, bias=False)
(to_v): LoRACompatibleLinear(in_features=2048, out_features=640, bias=False)
(to_out): ModuleList(
(to_out): ModuleList(
(0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
(0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
(1): Dropout(p=0.0, inplace=False)
(1): Dropout(p=0.0, inplace=False)
)
)
)
)
(norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
(norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
(ff): FeedForward(
(ff): FeedForward(
(net): ModuleList(
(net): ModuleList(
(0): GEGLU(
(0): GEGLU(
(proj): LoRACompatibleLinear(in_features=640, out_features=5120, bias=True)
(proj): LoRACompatibleLinear(in_features=640, out_features=5120, bias=True)
)
)
(1): Dropout(p=0.0, inplace=False)
(1): Dropout(p=0.0, inplace=False)
(2): LoRACompatibleLinear(in_features=2560, out_features=640, bias=True)
(2): LoRACompatibleLinear(in_features=2560, out_features=640, bias=True)
)
)
)
)
)
)
)
)
(proj_out): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
(proj_out): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
)
)
)
)
(resnets): ModuleList(
(resnets): ModuleList(
(0): ResnetBlock2D(
(0): ResnetBlock2D(
(norm1): GroupNorm(32, 1920, eps=1e-05, affine=True)
(norm1): GroupNorm(32, 1920, eps=1e-05, affine=True)
(conv1): LoRACompatibleConv(1920, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv1): LoRACompatibleConv(1920, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=640, bias=True)
(time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=640, bias=True)
(norm2): GroupNorm(32, 640, eps=1e-05, affine=True)
(norm2): GroupNorm(32, 640, eps=1e-05, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(nonlinearity): SiLU()
(conv_shortcut): LoRACompatibleConv(1920, 640, kernel_size=(1, 1), stride=(1, 1))
(conv_shortcut): LoRACompatibleConv(1920, 640, kernel_size=(1, 1), stride=(1, 1))
)
)
(1): ResnetBlock2D(
(1): ResnetBlock2D(
(norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)
(norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)
(conv1): LoRACompatibleConv(1280, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv1): LoRACompatibleConv(1280, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=640, bias=True)
(time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=640, bias=True)
(norm2): GroupNorm(32, 640, eps=1e-05, affine=True)
(norm2): GroupNorm(32, 640, eps=1e-05, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(nonlinearity): SiLU()
(conv_shortcut): LoRACompatibleConv(1280, 640, kernel_size=(1, 1), stride=(1, 1))
(conv_shortcut): LoRACompatibleConv(1280, 640, kernel_size=(1, 1), stride=(1, 1))
)
)
(2): ResnetBlock2D(
(2): ResnetBlock2D(
(norm1): GroupNorm(32, 960, eps=1e-05, affine=True)
(norm1): GroupNorm(32, 960, eps=1e-05, affine=True)
(conv1): LoRACompatibleConv(960, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv1): LoRACompatibleConv(960, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=640, bias=True)
(time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=640, bias=True)
(norm2): GroupNorm(32, 640, eps=1e-05, affine=True)
(norm2): GroupNorm(32, 640, eps=1e-05, affine=True)
(dropout): Dropout(p=0.0, inplace=False)
(dropout): Dropout(p=0.0, inplace=False)
(conv2): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(nonlinearity): SiLU()
(nonlinearity): SiLU()
(conv_shortcut): LoRACompatibleConv(960, 640, kernel_size=(1, 1), stride=(1, 1))
(conv_shortcut): LoRACompatibleConv(960, 640, kernel_size=(1, 1), stride=(1, 1))
)
)
)
)
(motion_modules): ModuleList(
(motion_modules): ModuleList(
복사
복사됨
복사
복사됨
(0-2): 3 x
TransformerTemporalModel(
(0-2): 3 x
VanillaTemporalModule(
(norm): GroupNorm(32, 640, eps=1e-06, affine=True)
(temporal_transformer): TemporalTransformer3DModel(
(proj_in): Linea
(norm): GroupNorm(32, 640, eps=1e-06, affine=True)
(proj_in): Linea
r(in_features=640, out_features=640, bias=True)
(transformer_blocks): ModuleList(
(0): TemporalTransformerBlock(
(attention_blocks): ModuleList(
(0-1): 2 x TemporalSelfAttention(
(to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_k): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_v): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
(to_out): ModuleList(
(0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
(1): Dropout(p=0.0, inplace=False)
)
(pos_encoder): PositionalEncoding(
(dropout): Dropout(p=0.0, inplace=False)
)
)
저장된 비교 결과
원본
파일 열기
UNetMotionModel( (conv_in): Conv2d(4, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_proj): Timesteps() (time_embedding): TimestepEmbedding( (linear_1): LoRACompatibleLinear(in_features=320, out_features=1280, bias=True) (act): SiLU() (linear_2): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) ) (add_time_proj): Timesteps() (add_embedding): TimestepEmbedding( (linear_1): LoRACompatibleLinear(in_features=2816, out_features=1280, bias=True) (act): SiLU() (linear_2): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) ) (down_blocks): ModuleList( (0): DownBlockMotion( (resnets): ModuleList( (0-1): 2 x ResnetBlock2D( (norm1): GroupNorm(32, 320, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=320, bias=True) (norm2): GroupNorm(32, 320, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() ) ) (motion_modules): ModuleList( (0-1): 2 x TransformerTemporalModel( (norm): GroupNorm(32, 320, eps=1e-06, affine=True) (proj_in): Linear(in_features=320, out_features=320, bias=True) (transformer_blocks): ModuleList( (0): BasicTransformerBlock( (pos_embed): SinusoidalPositionalEmbedding() (norm1): LayerNorm((320,), eps=1e-05, elementwise_affine=True) (attn1): Attention( (to_q): LoRACompatibleLinear(in_features=320, out_features=320, bias=False) (to_k): LoRACompatibleLinear(in_features=320, out_features=320, bias=False) (to_v): LoRACompatibleLinear(in_features=320, out_features=320, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=320, out_features=320, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm2): LayerNorm((320,), eps=1e-05, elementwise_affine=True) (attn2): Attention( (to_q): LoRACompatibleLinear(in_features=320, out_features=320, bias=False) (to_k): LoRACompatibleLinear(in_features=320, out_features=320, bias=False) (to_v): LoRACompatibleLinear(in_features=320, out_features=320, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=320, out_features=320, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm3): LayerNorm((320,), eps=1e-05, elementwise_affine=True) (ff): FeedForward( (net): ModuleList( (0): GEGLU( (proj): LoRACompatibleLinear(in_features=320, out_features=2560, bias=True) ) (1): Dropout(p=0.0, inplace=False) (2): LoRACompatibleLinear(in_features=1280, out_features=320, bias=True) ) ) ) ) (proj_out): Linear(in_features=320, out_features=320, bias=True) ) ) (downsamplers): ModuleList( (0): Downsample2D( (conv): LoRACompatibleConv(320, 320, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) ) ) ) (1): CrossAttnDownBlockMotion( (attentions): ModuleList( (0-1): 2 x Transformer2DModel( (norm): GroupNorm(32, 640, eps=1e-06, affine=True) (proj_in): LoRACompatibleLinear(in_features=640, out_features=640, bias=True) (transformer_blocks): ModuleList( (0-1): 2 x BasicTransformerBlock( (norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True) (attn1): Attention( (to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_k): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_v): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True) (attn2): Attention( (to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_k): LoRACompatibleLinear(in_features=2048, out_features=640, bias=False) (to_v): LoRACompatibleLinear(in_features=2048, out_features=640, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True) (ff): FeedForward( (net): ModuleList( (0): GEGLU( (proj): LoRACompatibleLinear(in_features=640, out_features=5120, bias=True) ) (1): Dropout(p=0.0, inplace=False) (2): LoRACompatibleLinear(in_features=2560, out_features=640, bias=True) ) ) ) ) (proj_out): LoRACompatibleLinear(in_features=640, out_features=640, bias=True) ) ) (resnets): ModuleList( (0): ResnetBlock2D( (norm1): GroupNorm(32, 320, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(320, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=640, bias=True) (norm2): GroupNorm(32, 640, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() (conv_shortcut): LoRACompatibleConv(320, 640, kernel_size=(1, 1), stride=(1, 1)) ) (1): ResnetBlock2D( (norm1): GroupNorm(32, 640, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=640, bias=True) (norm2): GroupNorm(32, 640, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() ) ) (motion_modules): ModuleList( (0-1): 2 x TransformerTemporalModel( (norm): GroupNorm(32, 640, eps=1e-06, affine=True) (proj_in): Linear(in_features=640, out_features=640, bias=True) (transformer_blocks): ModuleList( (0): BasicTransformerBlock( (pos_embed): SinusoidalPositionalEmbedding() (norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True) (attn1): Attention( (to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_k): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_v): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True) (attn2): Attention( (to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_k): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_v): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True) (ff): FeedForward( (net): ModuleList( (0): GEGLU( (proj): LoRACompatibleLinear(in_features=640, out_features=5120, bias=True) ) (1): Dropout(p=0.0, inplace=False) (2): LoRACompatibleLinear(in_features=2560, out_features=640, bias=True) ) ) ) ) (proj_out): Linear(in_features=640, out_features=640, bias=True) ) ) (downsamplers): ModuleList( (0): Downsample2D( (conv): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) ) ) ) (2): CrossAttnDownBlockMotion( (attentions): ModuleList( (0-1): 2 x Transformer2DModel( (norm): GroupNorm(32, 1280, eps=1e-06, affine=True) (proj_in): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (transformer_blocks): ModuleList( (0-9): 10 x BasicTransformerBlock( (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) (attn1): Attention( (to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_k): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_v): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) (attn2): Attention( (to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_k): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False) (to_v): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) (ff): FeedForward( (net): ModuleList( (0): GEGLU( (proj): LoRACompatibleLinear(in_features=1280, out_features=10240, bias=True) ) (1): Dropout(p=0.0, inplace=False) (2): LoRACompatibleLinear(in_features=5120, out_features=1280, bias=True) ) ) ) ) (proj_out): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) ) ) (resnets): ModuleList( (0): ResnetBlock2D( (norm1): GroupNorm(32, 640, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(640, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() (conv_shortcut): LoRACompatibleConv(640, 1280, kernel_size=(1, 1), stride=(1, 1)) ) (1): ResnetBlock2D( (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() ) ) (motion_modules): ModuleList( (0-1): 2 x TransformerTemporalModel( (norm): GroupNorm(32, 1280, eps=1e-06, affine=True) (proj_in): Linear(in_features=1280, out_features=1280, bias=True) (transformer_blocks): ModuleList( (0): BasicTransformerBlock( (pos_embed): SinusoidalPositionalEmbedding() (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) (attn1): Attention( (to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_k): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_v): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) (attn2): Attention( (to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_k): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_v): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) (ff): FeedForward( (net): ModuleList( (0): GEGLU( (proj): LoRACompatibleLinear(in_features=1280, out_features=10240, bias=True) ) (1): Dropout(p=0.0, inplace=False) (2): LoRACompatibleLinear(in_features=5120, out_features=1280, bias=True) ) ) ) ) (proj_out): Linear(in_features=1280, out_features=1280, bias=True) ) ) ) ) (up_blocks): ModuleList( (0): CrossAttnUpBlockMotion( (attentions): ModuleList( (0-2): 3 x Transformer2DModel( (norm): GroupNorm(32, 1280, eps=1e-06, affine=True) (proj_in): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (transformer_blocks): ModuleList( (0-9): 10 x BasicTransformerBlock( (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) (attn1): Attention( (to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_k): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_v): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) (attn2): Attention( (to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_k): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False) (to_v): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) (ff): FeedForward( (net): ModuleList( (0): GEGLU( (proj): LoRACompatibleLinear(in_features=1280, out_features=10240, bias=True) ) (1): Dropout(p=0.0, inplace=False) (2): LoRACompatibleLinear(in_features=5120, out_features=1280, bias=True) ) ) ) ) (proj_out): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) ) ) (resnets): ModuleList( (0-1): 2 x ResnetBlock2D( (norm1): GroupNorm(32, 2560, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(2560, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() (conv_shortcut): LoRACompatibleConv(2560, 1280, kernel_size=(1, 1), stride=(1, 1)) ) (2): ResnetBlock2D( (norm1): GroupNorm(32, 1920, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(1920, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() (conv_shortcut): LoRACompatibleConv(1920, 1280, kernel_size=(1, 1), stride=(1, 1)) ) ) (motion_modules): ModuleList( (0-2): 3 x TransformerTemporalModel( (norm): GroupNorm(32, 1280, eps=1e-06, affine=True) (proj_in): Linear(in_features=1280, out_features=1280, bias=True) (transformer_blocks): ModuleList( (0): BasicTransformerBlock( (pos_embed): SinusoidalPositionalEmbedding() (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) (attn1): Attention( (to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_k): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_v): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) (attn2): Attention( (to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_k): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_v): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) (ff): FeedForward( (net): ModuleList( (0): GEGLU( (proj): LoRACompatibleLinear(in_features=1280, out_features=10240, bias=True) ) (1): Dropout(p=0.0, inplace=False) (2): LoRACompatibleLinear(in_features=5120, out_features=1280, bias=True) ) ) ) ) (proj_out): Linear(in_features=1280, out_features=1280, bias=True) ) ) (upsamplers): ModuleList( (0): Upsample2D( (conv): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) ) ) (1): CrossAttnUpBlockMotion( (attentions): ModuleList( (0-2): 3 x Transformer2DModel( (norm): GroupNorm(32, 640, eps=1e-06, affine=True) (proj_in): LoRACompatibleLinear(in_features=640, out_features=640, bias=True) (transformer_blocks): ModuleList( (0-1): 2 x BasicTransformerBlock( (norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True) (attn1): Attention( (to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_k): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_v): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True) (attn2): Attention( (to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_k): LoRACompatibleLinear(in_features=2048, out_features=640, bias=False) (to_v): LoRACompatibleLinear(in_features=2048, out_features=640, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True) (ff): FeedForward( (net): ModuleList( (0): GEGLU( (proj): LoRACompatibleLinear(in_features=640, out_features=5120, bias=True) ) (1): Dropout(p=0.0, inplace=False) (2): LoRACompatibleLinear(in_features=2560, out_features=640, bias=True) ) ) ) ) (proj_out): LoRACompatibleLinear(in_features=640, out_features=640, bias=True) ) ) (resnets): ModuleList( (0): ResnetBlock2D( (norm1): GroupNorm(32, 1920, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(1920, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=640, bias=True) (norm2): GroupNorm(32, 640, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() (conv_shortcut): LoRACompatibleConv(1920, 640, kernel_size=(1, 1), stride=(1, 1)) ) (1): ResnetBlock2D( (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(1280, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=640, bias=True) (norm2): GroupNorm(32, 640, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() (conv_shortcut): LoRACompatibleConv(1280, 640, kernel_size=(1, 1), stride=(1, 1)) ) (2): ResnetBlock2D( (norm1): GroupNorm(32, 960, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(960, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=640, bias=True) (norm2): GroupNorm(32, 640, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() (conv_shortcut): LoRACompatibleConv(960, 640, kernel_size=(1, 1), stride=(1, 1)) ) ) (motion_modules): ModuleList( (0-2): 3 x TransformerTemporalModel( (norm): GroupNorm(32, 640, eps=1e-06, affine=True) (proj_in): Linear(in_features=640, out_features=640, bias=True) (transformer_blocks): ModuleList( (0): BasicTransformerBlock( (pos_embed): SinusoidalPositionalEmbedding() (norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True) (attn1): Attention( (to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_k): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_v): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True) (attn2): Attention( (to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_k): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_v): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True) (ff): FeedForward( (net): ModuleList( (0): GEGLU( (proj): LoRACompatibleLinear(in_features=640, out_features=5120, bias=True) ) (1): Dropout(p=0.0, inplace=False) (2): LoRACompatibleLinear(in_features=2560, out_features=640, bias=True) ) ) ) ) (proj_out): Linear(in_features=640, out_features=640, bias=True) ) ) (upsamplers): ModuleList( (0): Upsample2D( (conv): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) ) ) (2): UpBlockMotion( (resnets): ModuleList( (0): ResnetBlock2D( (norm1): GroupNorm(32, 960, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(960, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=320, bias=True) (norm2): GroupNorm(32, 320, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() (conv_shortcut): LoRACompatibleConv(960, 320, kernel_size=(1, 1), stride=(1, 1)) ) (1-2): 2 x ResnetBlock2D( (norm1): GroupNorm(32, 640, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(640, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=320, bias=True) (norm2): GroupNorm(32, 320, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() (conv_shortcut): LoRACompatibleConv(640, 320, kernel_size=(1, 1), stride=(1, 1)) ) ) (motion_modules): ModuleList( (0-2): 3 x TransformerTemporalModel( (norm): GroupNorm(32, 320, eps=1e-06, affine=True) (proj_in): Linear(in_features=320, out_features=320, bias=True) (transformer_blocks): ModuleList( (0): BasicTransformerBlock( (pos_embed): SinusoidalPositionalEmbedding() (norm1): LayerNorm((320,), eps=1e-05, elementwise_affine=True) (attn1): Attention( (to_q): LoRACompatibleLinear(in_features=320, out_features=320, bias=False) (to_k): LoRACompatibleLinear(in_features=320, out_features=320, bias=False) (to_v): LoRACompatibleLinear(in_features=320, out_features=320, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=320, out_features=320, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm2): LayerNorm((320,), eps=1e-05, elementwise_affine=True) (attn2): Attention( (to_q): LoRACompatibleLinear(in_features=320, out_features=320, bias=False) (to_k): LoRACompatibleLinear(in_features=320, out_features=320, bias=False) (to_v): LoRACompatibleLinear(in_features=320, out_features=320, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=320, out_features=320, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm3): LayerNorm((320,), eps=1e-05, elementwise_affine=True) (ff): FeedForward( (net): ModuleList( (0): GEGLU( (proj): LoRACompatibleLinear(in_features=320, out_features=2560, bias=True) ) (1): Dropout(p=0.0, inplace=False) (2): LoRACompatibleLinear(in_features=1280, out_features=320, bias=True) ) ) ) ) (proj_out): Linear(in_features=320, out_features=320, bias=True) ) ) ) ) (mid_block): UNetMidBlock2DCrossAttn( (attentions): ModuleList( (0): Transformer2DModel( (norm): GroupNorm(32, 1280, eps=1e-06, affine=True) (proj_in): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (transformer_blocks): ModuleList( (0-9): 10 x BasicTransformerBlock( (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) (attn1): Attention( (to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_k): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_v): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) (attn2): Attention( (to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_k): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False) (to_v): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) (ff): FeedForward( (net): ModuleList( (0): GEGLU( (proj): LoRACompatibleLinear(in_features=1280, out_features=10240, bias=True) ) (1): Dropout(p=0.0, inplace=False) (2): LoRACompatibleLinear(in_features=5120, out_features=1280, bias=True) ) ) ) ) (proj_out): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) ) ) (resnets): ModuleList( (0-1): 2 x ResnetBlock2D( (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() ) ) ) (conv_norm_out): GroupNorm(32, 320, eps=1e-05, affine=True) (conv_act): SiLU() (conv_out): Conv2d(320, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) )
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파일 열기
UNet3DConditionModel( (conv_in): Conv2d(4, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_proj): Timesteps() (time_embedding): TimestepEmbedding( (linear_1): LoRACompatibleLinear(in_features=320, out_features=1280, bias=True) (act): SiLU() (linear_2): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) ) (add_time_proj): Timesteps() (add_embedding): TimestepEmbedding( (linear_1): LoRACompatibleLinear(in_features=2816, out_features=1280, bias=True) (act): SiLU() (linear_2): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) ) (down_blocks): ModuleList( (0): DownBlock3D( (resnets): ModuleList( (0-1): 2 x ResnetBlock2D( (norm1): GroupNorm(32, 320, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=320, bias=True) (norm2): GroupNorm(32, 320, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() ) ) (motion_modules): ModuleList( (0-1): 2 x VanillaTemporalModule( (temporal_transformer): TemporalTransformer3DModel( (norm): GroupNorm(32, 320, eps=1e-06, affine=True) (proj_in): Linear(in_features=320, out_features=320, bias=True) (transformer_blocks): ModuleList( (0): TemporalTransformerBlock( (attention_blocks): ModuleList( (0-1): 2 x TemporalSelfAttention( (to_q): LoRACompatibleLinear(in_features=320, out_features=320, bias=False) (to_k): LoRACompatibleLinear(in_features=320, out_features=320, bias=False) (to_v): LoRACompatibleLinear(in_features=320, out_features=320, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=320, out_features=320, bias=True) (1): Dropout(p=0.0, inplace=False) ) (pos_encoder): PositionalEncoding( (dropout): Dropout(p=0.0, inplace=False) ) ) ) (norms): ModuleList( (0-1): 2 x LayerNorm((320,), eps=1e-05, elementwise_affine=True) ) (ff): FeedForward( (net): ModuleList( (0): GEGLU( (proj): LoRACompatibleLinear(in_features=320, out_features=2560, bias=True) ) (1): Dropout(p=0.0, inplace=False) (2): LoRACompatibleLinear(in_features=1280, out_features=320, bias=True) ) ) (ff_norm): LayerNorm((320,), eps=1e-05, elementwise_affine=True) ) ) (proj_out): Linear(in_features=320, out_features=320, bias=True) ) ) ) (downsamplers): ModuleList( (0): Downsample2D( (conv): LoRACompatibleConv(320, 320, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) ) ) ) (1): CrossAttnDownBlock3D( (attentions): ModuleList( (0-1): 2 x Transformer2DModel( (norm): GroupNorm(32, 640, eps=1e-06, affine=True) (proj_in): LoRACompatibleLinear(in_features=640, out_features=640, bias=True) (transformer_blocks): ModuleList( (0-1): 2 x BasicTransformerBlock( (norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True) (attn1): Attention( (to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_k): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_v): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True) (attn2): Attention( (to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_k): LoRACompatibleLinear(in_features=2048, out_features=640, bias=False) (to_v): LoRACompatibleLinear(in_features=2048, out_features=640, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True) (ff): FeedForward( (net): ModuleList( (0): GEGLU( (proj): LoRACompatibleLinear(in_features=640, out_features=5120, bias=True) ) (1): Dropout(p=0.0, inplace=False) (2): LoRACompatibleLinear(in_features=2560, out_features=640, bias=True) ) ) ) ) (proj_out): LoRACompatibleLinear(in_features=640, out_features=640, bias=True) ) ) (resnets): ModuleList( (0): ResnetBlock2D( (norm1): GroupNorm(32, 320, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(320, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=640, bias=True) (norm2): GroupNorm(32, 640, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() (conv_shortcut): LoRACompatibleConv(320, 640, kernel_size=(1, 1), stride=(1, 1)) ) (1): ResnetBlock2D( (norm1): GroupNorm(32, 640, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=640, bias=True) (norm2): GroupNorm(32, 640, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() ) ) (motion_modules): ModuleList( (0-1): 2 x VanillaTemporalModule( (temporal_transformer): TemporalTransformer3DModel( (norm): GroupNorm(32, 640, eps=1e-06, affine=True) (proj_in): Linear(in_features=640, out_features=640, bias=True) (transformer_blocks): ModuleList( (0): TemporalTransformerBlock( (attention_blocks): ModuleList( (0-1): 2 x TemporalSelfAttention( (to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_k): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_v): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True) (1): Dropout(p=0.0, inplace=False) ) (pos_encoder): PositionalEncoding( (dropout): Dropout(p=0.0, inplace=False) ) ) ) (norms): ModuleList( (0-1): 2 x LayerNorm((640,), eps=1e-05, elementwise_affine=True) ) (ff): FeedForward( (net): ModuleList( (0): GEGLU( (proj): LoRACompatibleLinear(in_features=640, out_features=5120, bias=True) ) (1): Dropout(p=0.0, inplace=False) (2): LoRACompatibleLinear(in_features=2560, out_features=640, bias=True) ) ) (ff_norm): LayerNorm((640,), eps=1e-05, elementwise_affine=True) ) ) (proj_out): Linear(in_features=640, out_features=640, bias=True) ) ) ) (downsamplers): ModuleList( (0): Downsample2D( (conv): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) ) ) ) (2): CrossAttnDownBlock3D( (attentions): ModuleList( (0-1): 2 x Transformer2DModel( (norm): GroupNorm(32, 1280, eps=1e-06, affine=True) (proj_in): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (transformer_blocks): ModuleList( (0-9): 10 x BasicTransformerBlock( (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) (attn1): Attention( (to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_k): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_v): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) (attn2): Attention( (to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_k): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False) (to_v): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) (ff): FeedForward( (net): ModuleList( (0): GEGLU( (proj): LoRACompatibleLinear(in_features=1280, out_features=10240, bias=True) ) (1): Dropout(p=0.0, inplace=False) (2): LoRACompatibleLinear(in_features=5120, out_features=1280, bias=True) ) ) ) ) (proj_out): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) ) ) (resnets): ModuleList( (0): ResnetBlock2D( (norm1): GroupNorm(32, 640, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(640, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() (conv_shortcut): LoRACompatibleConv(640, 1280, kernel_size=(1, 1), stride=(1, 1)) ) (1): ResnetBlock2D( (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() ) ) (motion_modules): ModuleList( (0-1): 2 x VanillaTemporalModule( (temporal_transformer): TemporalTransformer3DModel( (norm): GroupNorm(32, 1280, eps=1e-06, affine=True) (proj_in): Linear(in_features=1280, out_features=1280, bias=True) (transformer_blocks): ModuleList( (0): TemporalTransformerBlock( (attention_blocks): ModuleList( (0-1): 2 x TemporalSelfAttention( (to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_k): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_v): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (1): Dropout(p=0.0, inplace=False) ) (pos_encoder): PositionalEncoding( (dropout): Dropout(p=0.0, inplace=False) ) ) ) (norms): ModuleList( (0-1): 2 x LayerNorm((1280,), eps=1e-05, elementwise_affine=True) ) (ff): FeedForward( (net): ModuleList( (0): GEGLU( (proj): LoRACompatibleLinear(in_features=1280, out_features=10240, bias=True) ) (1): Dropout(p=0.0, inplace=False) (2): LoRACompatibleLinear(in_features=5120, out_features=1280, bias=True) ) ) (ff_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) ) ) (proj_out): Linear(in_features=1280, out_features=1280, bias=True) ) ) ) ) ) (up_blocks): ModuleList( (0): CrossAttnUpBlock3D( (attentions): ModuleList( (0-2): 3 x Transformer2DModel( (norm): GroupNorm(32, 1280, eps=1e-06, affine=True) (proj_in): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (transformer_blocks): ModuleList( (0-9): 10 x BasicTransformerBlock( (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) (attn1): Attention( (to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_k): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_v): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) (attn2): Attention( (to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_k): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False) (to_v): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) (ff): FeedForward( (net): ModuleList( (0): GEGLU( (proj): LoRACompatibleLinear(in_features=1280, out_features=10240, bias=True) ) (1): Dropout(p=0.0, inplace=False) (2): LoRACompatibleLinear(in_features=5120, out_features=1280, bias=True) ) ) ) ) (proj_out): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) ) ) (resnets): ModuleList( (0-1): 2 x ResnetBlock2D( (norm1): GroupNorm(32, 2560, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(2560, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() (conv_shortcut): LoRACompatibleConv(2560, 1280, kernel_size=(1, 1), stride=(1, 1)) ) (2): ResnetBlock2D( (norm1): GroupNorm(32, 1920, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(1920, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() (conv_shortcut): LoRACompatibleConv(1920, 1280, kernel_size=(1, 1), stride=(1, 1)) ) ) (motion_modules): ModuleList( (0-2): 3 x VanillaTemporalModule( (temporal_transformer): TemporalTransformer3DModel( (norm): GroupNorm(32, 1280, eps=1e-06, affine=True) (proj_in): Linear(in_features=1280, out_features=1280, bias=True) (transformer_blocks): ModuleList( (0): TemporalTransformerBlock( (attention_blocks): ModuleList( (0-1): 2 x TemporalSelfAttention( (to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_k): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_v): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (1): Dropout(p=0.0, inplace=False) ) (pos_encoder): PositionalEncoding( (dropout): Dropout(p=0.0, inplace=False) ) ) ) (norms): ModuleList( (0-1): 2 x LayerNorm((1280,), eps=1e-05, elementwise_affine=True) ) (ff): FeedForward( (net): ModuleList( (0): GEGLU( (proj): LoRACompatibleLinear(in_features=1280, out_features=10240, bias=True) ) (1): Dropout(p=0.0, inplace=False) (2): LoRACompatibleLinear(in_features=5120, out_features=1280, bias=True) ) ) (ff_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) ) ) (proj_out): Linear(in_features=1280, out_features=1280, bias=True) ) ) ) (upsamplers): ModuleList( (0): Upsample2D( (conv): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) ) ) (1): CrossAttnUpBlock3D( (attentions): ModuleList( (0-2): 3 x Transformer2DModel( (norm): GroupNorm(32, 640, eps=1e-06, affine=True) (proj_in): LoRACompatibleLinear(in_features=640, out_features=640, bias=True) (transformer_blocks): ModuleList( (0-1): 2 x BasicTransformerBlock( (norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True) (attn1): Attention( (to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_k): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_v): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True) (attn2): Attention( (to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_k): LoRACompatibleLinear(in_features=2048, out_features=640, bias=False) (to_v): LoRACompatibleLinear(in_features=2048, out_features=640, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True) (ff): FeedForward( (net): ModuleList( (0): GEGLU( (proj): LoRACompatibleLinear(in_features=640, out_features=5120, bias=True) ) (1): Dropout(p=0.0, inplace=False) (2): LoRACompatibleLinear(in_features=2560, out_features=640, bias=True) ) ) ) ) (proj_out): LoRACompatibleLinear(in_features=640, out_features=640, bias=True) ) ) (resnets): ModuleList( (0): ResnetBlock2D( (norm1): GroupNorm(32, 1920, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(1920, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=640, bias=True) (norm2): GroupNorm(32, 640, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() (conv_shortcut): LoRACompatibleConv(1920, 640, kernel_size=(1, 1), stride=(1, 1)) ) (1): ResnetBlock2D( (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(1280, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=640, bias=True) (norm2): GroupNorm(32, 640, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() (conv_shortcut): LoRACompatibleConv(1280, 640, kernel_size=(1, 1), stride=(1, 1)) ) (2): ResnetBlock2D( (norm1): GroupNorm(32, 960, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(960, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=640, bias=True) (norm2): GroupNorm(32, 640, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() (conv_shortcut): LoRACompatibleConv(960, 640, kernel_size=(1, 1), stride=(1, 1)) ) ) (motion_modules): ModuleList( (0-2): 3 x VanillaTemporalModule( (temporal_transformer): TemporalTransformer3DModel( (norm): GroupNorm(32, 640, eps=1e-06, affine=True) (proj_in): Linear(in_features=640, out_features=640, bias=True) (transformer_blocks): ModuleList( (0): TemporalTransformerBlock( (attention_blocks): ModuleList( (0-1): 2 x TemporalSelfAttention( (to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_k): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_v): LoRACompatibleLinear(in_features=640, out_features=640, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True) (1): Dropout(p=0.0, inplace=False) ) (pos_encoder): PositionalEncoding( (dropout): Dropout(p=0.0, inplace=False) ) ) ) (norms): ModuleList( (0-1): 2 x LayerNorm((640,), eps=1e-05, elementwise_affine=True) ) (ff): FeedForward( (net): ModuleList( (0): GEGLU( (proj): LoRACompatibleLinear(in_features=640, out_features=5120, bias=True) ) (1): Dropout(p=0.0, inplace=False) (2): LoRACompatibleLinear(in_features=2560, out_features=640, bias=True) ) ) (ff_norm): LayerNorm((640,), eps=1e-05, elementwise_affine=True) ) ) (proj_out): Linear(in_features=640, out_features=640, bias=True) ) ) ) (upsamplers): ModuleList( (0): Upsample2D( (conv): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) ) ) (2): UpBlock3D( (resnets): ModuleList( (0): ResnetBlock2D( (norm1): GroupNorm(32, 960, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(960, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=320, bias=True) (norm2): GroupNorm(32, 320, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() (conv_shortcut): LoRACompatibleConv(960, 320, kernel_size=(1, 1), stride=(1, 1)) ) (1-2): 2 x ResnetBlock2D( (norm1): GroupNorm(32, 640, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(640, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=320, bias=True) (norm2): GroupNorm(32, 320, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() (conv_shortcut): LoRACompatibleConv(640, 320, kernel_size=(1, 1), stride=(1, 1)) ) ) (motion_modules): ModuleList( (0-2): 3 x VanillaTemporalModule( (temporal_transformer): TemporalTransformer3DModel( (norm): GroupNorm(32, 320, eps=1e-06, affine=True) (proj_in): Linear(in_features=320, out_features=320, bias=True) (transformer_blocks): ModuleList( (0): TemporalTransformerBlock( (attention_blocks): ModuleList( (0-1): 2 x TemporalSelfAttention( (to_q): LoRACompatibleLinear(in_features=320, out_features=320, bias=False) (to_k): LoRACompatibleLinear(in_features=320, out_features=320, bias=False) (to_v): LoRACompatibleLinear(in_features=320, out_features=320, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=320, out_features=320, bias=True) (1): Dropout(p=0.0, inplace=False) ) (pos_encoder): PositionalEncoding( (dropout): Dropout(p=0.0, inplace=False) ) ) ) (norms): ModuleList( (0-1): 2 x LayerNorm((320,), eps=1e-05, elementwise_affine=True) ) (ff): FeedForward( (net): ModuleList( (0): GEGLU( (proj): LoRACompatibleLinear(in_features=320, out_features=2560, bias=True) ) (1): Dropout(p=0.0, inplace=False) (2): LoRACompatibleLinear(in_features=1280, out_features=320, bias=True) ) ) (ff_norm): LayerNorm((320,), eps=1e-05, elementwise_affine=True) ) ) (proj_out): Linear(in_features=320, out_features=320, bias=True) ) ) ) ) ) (mid_block): UNetMidBlock3DCrossAttn( (attentions): ModuleList( (0): Transformer2DModel( (norm): GroupNorm(32, 1280, eps=1e-06, affine=True) (proj_in): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (transformer_blocks): ModuleList( (0-9): 10 x BasicTransformerBlock( (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) (attn1): Attention( (to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_k): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_v): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) (attn2): Attention( (to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False) (to_k): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False) (to_v): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False) (to_out): ModuleList( (0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (1): Dropout(p=0.0, inplace=False) ) ) (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True) (ff): FeedForward( (net): ModuleList( (0): GEGLU( (proj): LoRACompatibleLinear(in_features=1280, out_features=10240, bias=True) ) (1): Dropout(p=0.0, inplace=False) (2): LoRACompatibleLinear(in_features=5120, out_features=1280, bias=True) ) ) ) ) (proj_out): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) ) ) (resnets): ModuleList( (0-1): 2 x ResnetBlock2D( (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True) (conv1): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True) (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True) (dropout): Dropout(p=0.0, inplace=False) (conv2): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (nonlinearity): SiLU() ) ) (motion_modules): ModuleList( (0): None ) ) (conv_norm_out): GroupNorm(32, 320, eps=1e-05, affine=True) (conv_act): SiLU() (conv_out): Conv2d(320, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) )
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