pytorch 2.14.1+cpu:  z sdmulnode_mul"MulJ3 namespace&: __main__.Decode/mul: aten.mul.TensorJH pkg.torch.onnx.class_hierarchy&['__main__.Decode', 'aten.mul.Tensor']J pkg.torch.onnx.fx_nodeg%mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%z, %b_sd), kwargs = {})J) pkg.torch.onnx.name_scopes ['', 'mul']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1)  mul muadd_5 node_add_5"AddJ5 namespace(: __main__.Decode/add_5: aten.add.TensorJH pkg.torch.onnx.class_hierarchy&['__main__.Decode', 'aten.add.Tensor']J pkg.torch.onnx.fx_nodek%add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %b_mu), kwargs = {})J+ pkg.torch.onnx.name_scopes ['', 'add_5']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) add_5 vae.dec.0.weight vae.dec.0.biasconv2d node_conv2d"Conv* group* pads@@@@* auto_pad"NOTSET* strides@@* dilations@@* kernel_shape@@J namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.0: torch.nn.modules.conv.Conv2d/conv2d: aten.conv2d.defaultJ pkg.torch.onnx.class_hierarchys['__main__.Decode', 'torch.nn.modules.container.Sequential', 'torch.nn.modules.conv.Conv2d', 'aten.conv2d.default']J pkg.torch.onnx.fx_node%conv2d : [num_users=2] = call_function[target=torch.ops.aten.conv2d.default](args = (%add_5, %p_vae_dec_0_weight, %p_vae_dec_0_bias, [1, 1], [1, 1]), kwargs = {})JD pkg.torch.onnx.name_scopes&['', 'vae.dec', 'vae.dec.0', 'conv2d']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/conv.py", line 565, in forward return self._conv_forward(input, self.weight, self.bias) conv2d val_4val_5node_Reshape_5"Reshape* allowzeroJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.1: aurora_pipeline.VRes/vae.dec.1.n1: torch.nn.modules.normalization.GroupNorm/group_norm: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d, 8, %p_vae_dec_1_n1_weight, %p_vae_dec_1_n1_bias, 1e-05, False), kwargs = {})JX pkg.torch.onnx.name_scopes:['', 'vae.dec', 'vae.dec.1', 'vae.dec.1.n1', 'group_norm']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_5 val_8 val_11val_12node_InstanceNormalization_12"InstanceNormalization* epsilon'7J namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.1: aurora_pipeline.VRes/vae.dec.1.n1: torch.nn.modules.normalization.GroupNorm/group_norm: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d, 8, %p_vae_dec_1_n1_weight, %p_vae_dec_1_n1_bias, 1e-05, False), kwargs = {})JX pkg.torch.onnx.name_scopes:['', 'vae.dec', 'vae.dec.1', 'vae.dec.1.n1', 'group_norm']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) conv2dval_13 node_Shape_13"Shape* startJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.1: aurora_pipeline.VRes/vae.dec.1.n1: torch.nn.modules.normalization.GroupNorm/group_norm: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d, 8, %p_vae_dec_1_n1_weight, %p_vae_dec_1_n1_bias, 1e-05, False), kwargs = {})JX pkg.torch.onnx.name_scopes:['', 'vae.dec', 'vae.dec.1', 'vae.dec.1.n1', 'group_norm']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_12 val_13val_14node_Reshape_14"Reshape* allowzeroJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.1: aurora_pipeline.VRes/vae.dec.1.n1: torch.nn.modules.normalization.GroupNorm/group_norm: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d, 8, %p_vae_dec_1_n1_weight, %p_vae_dec_1_n1_bias, 1e-05, False), kwargs = {})JX pkg.torch.onnx.name_scopes:['', 'vae.dec', 'vae.dec.1', 'vae.dec.1.n1', 'group_norm']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_14 val_21val_22 node_Mul_22"MulJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.1: aurora_pipeline.VRes/vae.dec.1.n1: torch.nn.modules.normalization.GroupNorm/group_norm: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d, 8, %p_vae_dec_1_n1_weight, %p_vae_dec_1_n1_bias, 1e-05, False), kwargs = {})JX pkg.torch.onnx.name_scopes:['', 'vae.dec', 'vae.dec.1', 'vae.dec.1.n1', 'group_norm']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_22 val_23 group_normnode_group_norm"AddJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.1: aurora_pipeline.VRes/vae.dec.1.n1: torch.nn.modules.normalization.GroupNorm/group_norm: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d, 8, %p_vae_dec_1_n1_weight, %p_vae_dec_1_n1_bias, 1e-05, False), kwargs = {})JX pkg.torch.onnx.name_scopes:['', 'vae.dec', 'vae.dec.1', 'vae.dec.1.n1', 'group_norm']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps)  group_normval_24node_Sigmoid_24"SigmoidJ namespacex: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.1: aurora_pipeline.VRes/silu: aten.silu.defaultJ pkg.torch.onnx.class_hierarchyi['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'aten.silu.default']J pkg.torch.onnx.fx_nodem%silu : [num_users=1] = call_function[target=torch.ops.aten.silu.default](args = (%group_norm,), kwargs = {})JB pkg.torch.onnx.name_scopes$['', 'vae.dec', 'vae.dec.1', 'silu']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h  group_norm val_24silu node_silu"MulJ namespacex: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.1: aurora_pipeline.VRes/silu: aten.silu.defaultJ pkg.torch.onnx.class_hierarchyi['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'aten.silu.default']J pkg.torch.onnx.fx_nodem%silu : [num_users=1] = call_function[target=torch.ops.aten.silu.default](args = (%group_norm,), kwargs = {})JB pkg.torch.onnx.name_scopes$['', 'vae.dec', 'vae.dec.1', 'silu']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h silu vae.dec.1.c1.weight vae.dec.1.c1.biasconv2d_1 node_conv2d_1"Conv* group* pads@@@@* auto_pad"NOTSET* strides@@* dilations@@* kernel_shape@@J namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.1: aurora_pipeline.VRes/vae.dec.1.c1: torch.nn.modules.conv.Conv2d/conv2d_1: aten.conv2d.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.conv.Conv2d', 'aten.conv2d.default']J pkg.torch.onnx.fx_node%conv2d_1 : [num_users=1] = call_function[target=torch.ops.aten.conv2d.default](args = (%silu, %p_vae_dec_1_c1_weight, %p_vae_dec_1_c1_bias, [1, 1], [1, 1]), kwargs = {})JV pkg.torch.onnx.name_scopes8['', 'vae.dec', 'vae.dec.1', 'vae.dec.1.c1', 'conv2d_1']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/conv.py", line 565, in forward return self._conv_forward(input, self.weight, self.bias) conv2d_1 val_4val_29node_Reshape_29"Reshape* allowzeroJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.1: aurora_pipeline.VRes/vae.dec.1.n2: torch.nn.modules.normalization.GroupNorm/group_norm_1: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_1 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_1, 8, %p_vae_dec_1_n2_weight, %p_vae_dec_1_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.1', 'vae.dec.1.n2', 'group_norm_1']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_29 val_8 val_11val_36node_InstanceNormalization_36"InstanceNormalization* epsilon'7J namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.1: aurora_pipeline.VRes/vae.dec.1.n2: torch.nn.modules.normalization.GroupNorm/group_norm_1: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_1 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_1, 8, %p_vae_dec_1_n2_weight, %p_vae_dec_1_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.1', 'vae.dec.1.n2', 'group_norm_1']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) conv2d_1val_37 node_Shape_37"Shape* startJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.1: aurora_pipeline.VRes/vae.dec.1.n2: torch.nn.modules.normalization.GroupNorm/group_norm_1: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_1 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_1, 8, %p_vae_dec_1_n2_weight, %p_vae_dec_1_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.1', 'vae.dec.1.n2', 'group_norm_1']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_36 val_37val_38node_Reshape_38"Reshape* allowzeroJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.1: aurora_pipeline.VRes/vae.dec.1.n2: torch.nn.modules.normalization.GroupNorm/group_norm_1: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_1 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_1, 8, %p_vae_dec_1_n2_weight, %p_vae_dec_1_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.1', 'vae.dec.1.n2', 'group_norm_1']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_38 val_44val_45 node_Mul_45"MulJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.1: aurora_pipeline.VRes/vae.dec.1.n2: torch.nn.modules.normalization.GroupNorm/group_norm_1: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_1 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_1, 8, %p_vae_dec_1_n2_weight, %p_vae_dec_1_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.1', 'vae.dec.1.n2', 'group_norm_1']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_45 val_46 group_norm_1node_group_norm_1"AddJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.1: aurora_pipeline.VRes/vae.dec.1.n2: torch.nn.modules.normalization.GroupNorm/group_norm_1: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_1 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_1, 8, %p_vae_dec_1_n2_weight, %p_vae_dec_1_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.1', 'vae.dec.1.n2', 'group_norm_1']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps)  group_norm_1val_47node_Sigmoid_47"SigmoidJ namespacez: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.1: aurora_pipeline.VRes/silu_1: aten.silu.defaultJ pkg.torch.onnx.class_hierarchyi['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'aten.silu.default']J pkg.torch.onnx.fx_nodeq%silu_1 : [num_users=1] = call_function[target=torch.ops.aten.silu.default](args = (%group_norm_1,), kwargs = {})JD pkg.torch.onnx.name_scopes&['', 'vae.dec', 'vae.dec.1', 'silu_1']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h  group_norm_1 val_47silu_1 node_silu_1"MulJ namespacez: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.1: aurora_pipeline.VRes/silu_1: aten.silu.defaultJ pkg.torch.onnx.class_hierarchyi['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'aten.silu.default']J pkg.torch.onnx.fx_nodeq%silu_1 : [num_users=1] = call_function[target=torch.ops.aten.silu.default](args = (%group_norm_1,), kwargs = {})JD pkg.torch.onnx.name_scopes&['', 'vae.dec', 'vae.dec.1', 'silu_1']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h silu_1 vae.dec.1.c2.weight vae.dec.1.c2.biasconv2d_2 node_conv2d_2"Conv* group* pads@@@@* auto_pad"NOTSET* strides@@* dilations@@* kernel_shape@@J namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.1: aurora_pipeline.VRes/vae.dec.1.c2: torch.nn.modules.conv.Conv2d/conv2d_2: aten.conv2d.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.conv.Conv2d', 'aten.conv2d.default']J pkg.torch.onnx.fx_node%conv2d_2 : [num_users=1] = call_function[target=torch.ops.aten.conv2d.default](args = (%silu_1, %p_vae_dec_1_c2_weight, %p_vae_dec_1_c2_bias, [1, 1], [1, 1]), kwargs = {})JV pkg.torch.onnx.name_scopes8['', 'vae.dec', 'vae.dec.1', 'vae.dec.1.c2', 'conv2d_2']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/conv.py", line 565, in forward return self._conv_forward(input, self.weight, self.bias)  conv2d conv2d_2add_46 node_add_46"AddJ namespacex: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.1: aurora_pipeline.VRes/add_46: aten.add.TensorJ pkg.torch.onnx.class_hierarchyg['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'aten.add.Tensor']J pkg.torch.onnx.fx_nodes%add_46 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%conv2d, %conv2d_2), kwargs = {})JD pkg.torch.onnx.name_scopes&['', 'vae.dec', 'vae.dec.1', 'add_46']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h add_46 val_48upsample_nearest2dnode_upsample_nearest2d"Resize*& keep_aspect_ratio_policy"stretch* antialias* extrapolation_value* exclude_outside* nearest_mode"floor*/ coordinate_transformation_mode" asymmetric* cubic_coeff_a@* mode"nearestJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.2: torch.nn.modules.upsampling.Upsample/upsample_nearest2d: aten.upsample_nearest2d.vecJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'torch.nn.modules.upsampling.Upsample', 'aten.upsample_nearest2d.vec']J pkg.torch.onnx.fx_node%upsample_nearest2d : [num_users=1] = call_function[target=torch.ops.aten.upsample_nearest2d.vec](args = (%add_46, None, [2.0, 2.0]), kwargs = {})JP pkg.torch.onnx.name_scopes2['', 'vae.dec', 'vae.dec.2', 'upsample_nearest2d']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/upsampling.py", line 174, in forward return F.interpolate( upsample_nearest2d vae.dec.3.weight vae.dec.3.biasconv2d_3 node_conv2d_3"Conv* group* pads@@@@* auto_pad"NOTSET* strides@@* dilations@@* kernel_shape@@J namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.3: torch.nn.modules.conv.Conv2d/conv2d_3: aten.conv2d.defaultJ pkg.torch.onnx.class_hierarchys['__main__.Decode', 'torch.nn.modules.container.Sequential', 'torch.nn.modules.conv.Conv2d', 'aten.conv2d.default']J pkg.torch.onnx.fx_node%conv2d_3 : [num_users=2] = call_function[target=torch.ops.aten.conv2d.default](args = (%upsample_nearest2d, %p_vae_dec_3_weight, %p_vae_dec_3_bias, [1, 1], [1, 1]), kwargs = {})JF pkg.torch.onnx.name_scopes(['', 'vae.dec', 'vae.dec.3', 'conv2d_3']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/conv.py", line 565, in forward return self._conv_forward(input, self.weight, self.bias) conv2d_3 val_4val_53node_Reshape_53"Reshape* allowzeroJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.4: aurora_pipeline.VRes/vae.dec.4.n1: torch.nn.modules.normalization.GroupNorm/group_norm_2: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_2 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_3, 8, %p_vae_dec_4_n1_weight, %p_vae_dec_4_n1_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.4', 'vae.dec.4.n1', 'group_norm_2']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_53 val_8 val_11val_60node_InstanceNormalization_60"InstanceNormalization* epsilon'7J namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.4: aurora_pipeline.VRes/vae.dec.4.n1: torch.nn.modules.normalization.GroupNorm/group_norm_2: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_2 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_3, 8, %p_vae_dec_4_n1_weight, %p_vae_dec_4_n1_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.4', 'vae.dec.4.n1', 'group_norm_2']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) conv2d_3val_61 node_Shape_61"Shape* startJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.4: aurora_pipeline.VRes/vae.dec.4.n1: torch.nn.modules.normalization.GroupNorm/group_norm_2: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_2 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_3, 8, %p_vae_dec_4_n1_weight, %p_vae_dec_4_n1_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.4', 'vae.dec.4.n1', 'group_norm_2']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_60 val_61val_62node_Reshape_62"Reshape* allowzeroJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.4: aurora_pipeline.VRes/vae.dec.4.n1: torch.nn.modules.normalization.GroupNorm/group_norm_2: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_2 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_3, 8, %p_vae_dec_4_n1_weight, %p_vae_dec_4_n1_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.4', 'vae.dec.4.n1', 'group_norm_2']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_62 val_68val_69 node_Mul_69"MulJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.4: aurora_pipeline.VRes/vae.dec.4.n1: torch.nn.modules.normalization.GroupNorm/group_norm_2: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_2 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_3, 8, %p_vae_dec_4_n1_weight, %p_vae_dec_4_n1_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.4', 'vae.dec.4.n1', 'group_norm_2']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_69 val_70 group_norm_2node_group_norm_2"AddJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.4: aurora_pipeline.VRes/vae.dec.4.n1: torch.nn.modules.normalization.GroupNorm/group_norm_2: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_2 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_3, 8, %p_vae_dec_4_n1_weight, %p_vae_dec_4_n1_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.4', 'vae.dec.4.n1', 'group_norm_2']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps)  group_norm_2val_71node_Sigmoid_71"SigmoidJ namespacez: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.4: aurora_pipeline.VRes/silu_2: aten.silu.defaultJ pkg.torch.onnx.class_hierarchyi['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'aten.silu.default']J pkg.torch.onnx.fx_nodeq%silu_2 : [num_users=1] = call_function[target=torch.ops.aten.silu.default](args = (%group_norm_2,), kwargs = {})JD pkg.torch.onnx.name_scopes&['', 'vae.dec', 'vae.dec.4', 'silu_2']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h  group_norm_2 val_71silu_2 node_silu_2"MulJ namespacez: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.4: aurora_pipeline.VRes/silu_2: aten.silu.defaultJ pkg.torch.onnx.class_hierarchyi['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'aten.silu.default']J pkg.torch.onnx.fx_nodeq%silu_2 : [num_users=1] = call_function[target=torch.ops.aten.silu.default](args = (%group_norm_2,), kwargs = {})JD pkg.torch.onnx.name_scopes&['', 'vae.dec', 'vae.dec.4', 'silu_2']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h silu_2 vae.dec.4.c1.weight vae.dec.4.c1.biasconv2d_4 node_conv2d_4"Conv* group* pads@@@@* auto_pad"NOTSET* strides@@* dilations@@* kernel_shape@@J namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.4: aurora_pipeline.VRes/vae.dec.4.c1: torch.nn.modules.conv.Conv2d/conv2d_4: aten.conv2d.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.conv.Conv2d', 'aten.conv2d.default']J pkg.torch.onnx.fx_node%conv2d_4 : [num_users=1] = call_function[target=torch.ops.aten.conv2d.default](args = (%silu_2, %p_vae_dec_4_c1_weight, %p_vae_dec_4_c1_bias, [1, 1], [1, 1]), kwargs = {})JV pkg.torch.onnx.name_scopes8['', 'vae.dec', 'vae.dec.4', 'vae.dec.4.c1', 'conv2d_4']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/conv.py", line 565, in forward return self._conv_forward(input, self.weight, self.bias) conv2d_4 val_4val_76node_Reshape_76"Reshape* allowzeroJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.4: aurora_pipeline.VRes/vae.dec.4.n2: torch.nn.modules.normalization.GroupNorm/group_norm_3: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_3 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_4, 8, %p_vae_dec_4_n2_weight, %p_vae_dec_4_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.4', 'vae.dec.4.n2', 'group_norm_3']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_76 val_8 val_11val_83node_InstanceNormalization_83"InstanceNormalization* epsilon'7J namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.4: aurora_pipeline.VRes/vae.dec.4.n2: torch.nn.modules.normalization.GroupNorm/group_norm_3: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_3 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_4, 8, %p_vae_dec_4_n2_weight, %p_vae_dec_4_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.4', 'vae.dec.4.n2', 'group_norm_3']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) conv2d_4val_84 node_Shape_84"Shape* startJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.4: aurora_pipeline.VRes/vae.dec.4.n2: torch.nn.modules.normalization.GroupNorm/group_norm_3: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_3 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_4, 8, %p_vae_dec_4_n2_weight, %p_vae_dec_4_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.4', 'vae.dec.4.n2', 'group_norm_3']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_83 val_84val_85node_Reshape_85"Reshape* allowzeroJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.4: aurora_pipeline.VRes/vae.dec.4.n2: torch.nn.modules.normalization.GroupNorm/group_norm_3: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_3 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_4, 8, %p_vae_dec_4_n2_weight, %p_vae_dec_4_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.4', 'vae.dec.4.n2', 'group_norm_3']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_85 val_91val_92 node_Mul_92"MulJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.4: aurora_pipeline.VRes/vae.dec.4.n2: torch.nn.modules.normalization.GroupNorm/group_norm_3: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_3 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_4, 8, %p_vae_dec_4_n2_weight, %p_vae_dec_4_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.4', 'vae.dec.4.n2', 'group_norm_3']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_92 val_93 group_norm_3node_group_norm_3"AddJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.4: aurora_pipeline.VRes/vae.dec.4.n2: torch.nn.modules.normalization.GroupNorm/group_norm_3: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_3 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_4, 8, %p_vae_dec_4_n2_weight, %p_vae_dec_4_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.4', 'vae.dec.4.n2', 'group_norm_3']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps)  group_norm_3val_94node_Sigmoid_94"SigmoidJ namespacez: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.4: aurora_pipeline.VRes/silu_3: aten.silu.defaultJ pkg.torch.onnx.class_hierarchyi['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'aten.silu.default']J pkg.torch.onnx.fx_nodeq%silu_3 : [num_users=1] = call_function[target=torch.ops.aten.silu.default](args = (%group_norm_3,), kwargs = {})JD pkg.torch.onnx.name_scopes&['', 'vae.dec', 'vae.dec.4', 'silu_3']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h  group_norm_3 val_94silu_3 node_silu_3"MulJ namespacez: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.4: aurora_pipeline.VRes/silu_3: aten.silu.defaultJ pkg.torch.onnx.class_hierarchyi['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'aten.silu.default']J pkg.torch.onnx.fx_nodeq%silu_3 : [num_users=1] = call_function[target=torch.ops.aten.silu.default](args = (%group_norm_3,), kwargs = {})JD pkg.torch.onnx.name_scopes&['', 'vae.dec', 'vae.dec.4', 'silu_3']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h silu_3 vae.dec.4.c2.weight vae.dec.4.c2.biasconv2d_5 node_conv2d_5"Conv* group* pads@@@@* auto_pad"NOTSET* strides@@* dilations@@* kernel_shape@@J namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.4: aurora_pipeline.VRes/vae.dec.4.c2: torch.nn.modules.conv.Conv2d/conv2d_5: aten.conv2d.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.conv.Conv2d', 'aten.conv2d.default']J pkg.torch.onnx.fx_node%conv2d_5 : [num_users=1] = call_function[target=torch.ops.aten.conv2d.default](args = (%silu_3, %p_vae_dec_4_c2_weight, %p_vae_dec_4_c2_bias, [1, 1], [1, 1]), kwargs = {})JV pkg.torch.onnx.name_scopes8['', 'vae.dec', 'vae.dec.4', 'vae.dec.4.c2', 'conv2d_5']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/conv.py", line 565, in forward return self._conv_forward(input, self.weight, self.bias)  conv2d_3 conv2d_5add_92 node_add_92"AddJ namespacex: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.4: aurora_pipeline.VRes/add_92: aten.add.TensorJ pkg.torch.onnx.class_hierarchyg['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'aten.add.Tensor']J pkg.torch.onnx.fx_nodeu%add_92 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%conv2d_3, %conv2d_5), kwargs = {})JD pkg.torch.onnx.name_scopes&['', 'vae.dec', 'vae.dec.4', 'add_92']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h add_92 val_4val_99node_Reshape_99"Reshape* allowzeroJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.5: aurora_pipeline.VRes/vae.dec.5.n1: torch.nn.modules.normalization.GroupNorm/group_norm_4: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_4 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%add_92, 8, %p_vae_dec_5_n1_weight, %p_vae_dec_5_n1_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.5', 'vae.dec.5.n1', 'group_norm_4']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_99 val_8 val_11val_106node_InstanceNormalization_106"InstanceNormalization* epsilon'7J namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.5: aurora_pipeline.VRes/vae.dec.5.n1: torch.nn.modules.normalization.GroupNorm/group_norm_4: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_4 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%add_92, 8, %p_vae_dec_5_n1_weight, %p_vae_dec_5_n1_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.5', 'vae.dec.5.n1', 'group_norm_4']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) add_92val_107node_Shape_107"Shape* startJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.5: aurora_pipeline.VRes/vae.dec.5.n1: torch.nn.modules.normalization.GroupNorm/group_norm_4: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_4 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%add_92, 8, %p_vae_dec_5_n1_weight, %p_vae_dec_5_n1_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.5', 'vae.dec.5.n1', 'group_norm_4']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_106 val_107val_108node_Reshape_108"Reshape* allowzeroJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.5: aurora_pipeline.VRes/vae.dec.5.n1: torch.nn.modules.normalization.GroupNorm/group_norm_4: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_4 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%add_92, 8, %p_vae_dec_5_n1_weight, %p_vae_dec_5_n1_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.5', 'vae.dec.5.n1', 'group_norm_4']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_108 val_114val_115 node_Mul_115"MulJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.5: aurora_pipeline.VRes/vae.dec.5.n1: torch.nn.modules.normalization.GroupNorm/group_norm_4: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_4 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%add_92, 8, %p_vae_dec_5_n1_weight, %p_vae_dec_5_n1_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.5', 'vae.dec.5.n1', 'group_norm_4']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_115 val_116 group_norm_4node_group_norm_4"AddJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.5: aurora_pipeline.VRes/vae.dec.5.n1: torch.nn.modules.normalization.GroupNorm/group_norm_4: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_4 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%add_92, 8, %p_vae_dec_5_n1_weight, %p_vae_dec_5_n1_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.5', 'vae.dec.5.n1', 'group_norm_4']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps)  group_norm_4val_117node_Sigmoid_117"SigmoidJ namespacez: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.5: aurora_pipeline.VRes/silu_4: aten.silu.defaultJ pkg.torch.onnx.class_hierarchyi['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'aten.silu.default']J pkg.torch.onnx.fx_nodeq%silu_4 : [num_users=1] = call_function[target=torch.ops.aten.silu.default](args = (%group_norm_4,), kwargs = {})JD pkg.torch.onnx.name_scopes&['', 'vae.dec', 'vae.dec.5', 'silu_4']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h  group_norm_4 val_117silu_4 node_silu_4"MulJ namespacez: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.5: aurora_pipeline.VRes/silu_4: aten.silu.defaultJ pkg.torch.onnx.class_hierarchyi['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'aten.silu.default']J pkg.torch.onnx.fx_nodeq%silu_4 : [num_users=1] = call_function[target=torch.ops.aten.silu.default](args = (%group_norm_4,), kwargs = {})JD pkg.torch.onnx.name_scopes&['', 'vae.dec', 'vae.dec.5', 'silu_4']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h silu_4 vae.dec.5.c1.weight vae.dec.5.c1.biasconv2d_6 node_conv2d_6"Conv* group* pads@@@@* auto_pad"NOTSET* strides@@* dilations@@* kernel_shape@@J namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.5: aurora_pipeline.VRes/vae.dec.5.c1: torch.nn.modules.conv.Conv2d/conv2d_6: aten.conv2d.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.conv.Conv2d', 'aten.conv2d.default']J pkg.torch.onnx.fx_node%conv2d_6 : [num_users=1] = call_function[target=torch.ops.aten.conv2d.default](args = (%silu_4, %p_vae_dec_5_c1_weight, %p_vae_dec_5_c1_bias, [1, 1], [1, 1]), kwargs = {})JV pkg.torch.onnx.name_scopes8['', 'vae.dec', 'vae.dec.5', 'vae.dec.5.c1', 'conv2d_6']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/conv.py", line 565, in forward return self._conv_forward(input, self.weight, self.bias) conv2d_6 val_4val_122node_Reshape_122"Reshape* allowzeroJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.5: aurora_pipeline.VRes/vae.dec.5.n2: torch.nn.modules.normalization.GroupNorm/group_norm_5: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_5 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_6, 8, %p_vae_dec_5_n2_weight, %p_vae_dec_5_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.5', 'vae.dec.5.n2', 'group_norm_5']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_122 val_8 val_11val_129node_InstanceNormalization_129"InstanceNormalization* epsilon'7J namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.5: aurora_pipeline.VRes/vae.dec.5.n2: torch.nn.modules.normalization.GroupNorm/group_norm_5: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_5 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_6, 8, %p_vae_dec_5_n2_weight, %p_vae_dec_5_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.5', 'vae.dec.5.n2', 'group_norm_5']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) conv2d_6val_130node_Shape_130"Shape* startJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.5: aurora_pipeline.VRes/vae.dec.5.n2: torch.nn.modules.normalization.GroupNorm/group_norm_5: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_5 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_6, 8, %p_vae_dec_5_n2_weight, %p_vae_dec_5_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.5', 'vae.dec.5.n2', 'group_norm_5']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_129 val_130val_131node_Reshape_131"Reshape* allowzeroJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.5: aurora_pipeline.VRes/vae.dec.5.n2: torch.nn.modules.normalization.GroupNorm/group_norm_5: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_5 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_6, 8, %p_vae_dec_5_n2_weight, %p_vae_dec_5_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.5', 'vae.dec.5.n2', 'group_norm_5']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_131 val_137val_138 node_Mul_138"MulJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.5: aurora_pipeline.VRes/vae.dec.5.n2: torch.nn.modules.normalization.GroupNorm/group_norm_5: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_5 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_6, 8, %p_vae_dec_5_n2_weight, %p_vae_dec_5_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.5', 'vae.dec.5.n2', 'group_norm_5']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_138 val_139 group_norm_5node_group_norm_5"AddJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.5: aurora_pipeline.VRes/vae.dec.5.n2: torch.nn.modules.normalization.GroupNorm/group_norm_5: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_5 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_6, 8, %p_vae_dec_5_n2_weight, %p_vae_dec_5_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.5', 'vae.dec.5.n2', 'group_norm_5']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps)  group_norm_5val_140node_Sigmoid_140"SigmoidJ namespacez: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.5: aurora_pipeline.VRes/silu_5: aten.silu.defaultJ pkg.torch.onnx.class_hierarchyi['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'aten.silu.default']J pkg.torch.onnx.fx_nodeq%silu_5 : [num_users=1] = call_function[target=torch.ops.aten.silu.default](args = (%group_norm_5,), kwargs = {})JD pkg.torch.onnx.name_scopes&['', 'vae.dec', 'vae.dec.5', 'silu_5']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h  group_norm_5 val_140silu_5 node_silu_5"MulJ namespacez: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.5: aurora_pipeline.VRes/silu_5: aten.silu.defaultJ pkg.torch.onnx.class_hierarchyi['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'aten.silu.default']J pkg.torch.onnx.fx_nodeq%silu_5 : [num_users=1] = call_function[target=torch.ops.aten.silu.default](args = (%group_norm_5,), kwargs = {})JD pkg.torch.onnx.name_scopes&['', 'vae.dec', 'vae.dec.5', 'silu_5']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h silu_5 vae.dec.5.c2.weight vae.dec.5.c2.biasconv2d_7 node_conv2d_7"Conv* group* pads@@@@* auto_pad"NOTSET* strides@@* dilations@@* kernel_shape@@J namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.5: aurora_pipeline.VRes/vae.dec.5.c2: torch.nn.modules.conv.Conv2d/conv2d_7: aten.conv2d.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.conv.Conv2d', 'aten.conv2d.default']J pkg.torch.onnx.fx_node%conv2d_7 : [num_users=1] = call_function[target=torch.ops.aten.conv2d.default](args = (%silu_5, %p_vae_dec_5_c2_weight, %p_vae_dec_5_c2_bias, [1, 1], [1, 1]), kwargs = {})JV pkg.torch.onnx.name_scopes8['', 'vae.dec', 'vae.dec.5', 'vae.dec.5.c2', 'conv2d_7']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/conv.py", line 565, in forward return self._conv_forward(input, self.weight, self.bias)  add_92 conv2d_7add_128 node_add_128"AddJ namespacey: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.5: aurora_pipeline.VRes/add_128: aten.add.TensorJ pkg.torch.onnx.class_hierarchyg['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'aten.add.Tensor']J pkg.torch.onnx.fx_nodet%add_128 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_92, %conv2d_7), kwargs = {})JE pkg.torch.onnx.name_scopes'['', 'vae.dec', 'vae.dec.5', 'add_128']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h add_128 val_48upsample_nearest2d_1node_upsample_nearest2d_1"Resize*& keep_aspect_ratio_policy"stretch* antialias* extrapolation_value* exclude_outside* nearest_mode"floor*/ coordinate_transformation_mode" asymmetric* cubic_coeff_a@* mode"nearestJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.6: torch.nn.modules.upsampling.Upsample/upsample_nearest2d_1: aten.upsample_nearest2d.vecJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'torch.nn.modules.upsampling.Upsample', 'aten.upsample_nearest2d.vec']J pkg.torch.onnx.fx_node%upsample_nearest2d_1 : [num_users=1] = call_function[target=torch.ops.aten.upsample_nearest2d.vec](args = (%add_128, None, [2.0, 2.0]), kwargs = {})JR pkg.torch.onnx.name_scopes4['', 'vae.dec', 'vae.dec.6', 'upsample_nearest2d_1']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/upsampling.py", line 174, in forward return F.interpolate( upsample_nearest2d_1 vae.dec.7.weight vae.dec.7.biasconv2d_8 node_conv2d_8"Conv* group* pads@@@@* auto_pad"NOTSET* strides@@* dilations@@* kernel_shape@@J namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.7: torch.nn.modules.conv.Conv2d/conv2d_8: aten.conv2d.defaultJ pkg.torch.onnx.class_hierarchys['__main__.Decode', 'torch.nn.modules.container.Sequential', 'torch.nn.modules.conv.Conv2d', 'aten.conv2d.default']J pkg.torch.onnx.fx_node%conv2d_8 : [num_users=2] = call_function[target=torch.ops.aten.conv2d.default](args = (%upsample_nearest2d_1, %p_vae_dec_7_weight, %p_vae_dec_7_bias, [1, 1], [1, 1]), kwargs = {})JF pkg.torch.onnx.name_scopes(['', 'vae.dec', 'vae.dec.7', 'conv2d_8']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/conv.py", line 565, in forward return self._conv_forward(input, self.weight, self.bias) conv2d_8 val_4val_146node_Reshape_146"Reshape* allowzeroJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.8: aurora_pipeline.VRes/vae.dec.8.n1: torch.nn.modules.normalization.GroupNorm/group_norm_6: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_6 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_8, 8, %p_vae_dec_8_n1_weight, %p_vae_dec_8_n1_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.8', 'vae.dec.8.n1', 'group_norm_6']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_146 val_8 val_11val_153node_InstanceNormalization_153"InstanceNormalization* epsilon'7J namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.8: aurora_pipeline.VRes/vae.dec.8.n1: torch.nn.modules.normalization.GroupNorm/group_norm_6: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_6 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_8, 8, %p_vae_dec_8_n1_weight, %p_vae_dec_8_n1_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.8', 'vae.dec.8.n1', 'group_norm_6']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) conv2d_8val_154node_Shape_154"Shape* startJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.8: aurora_pipeline.VRes/vae.dec.8.n1: torch.nn.modules.normalization.GroupNorm/group_norm_6: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_6 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_8, 8, %p_vae_dec_8_n1_weight, %p_vae_dec_8_n1_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.8', 'vae.dec.8.n1', 'group_norm_6']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_153 val_154val_155node_Reshape_155"Reshape* allowzeroJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.8: aurora_pipeline.VRes/vae.dec.8.n1: torch.nn.modules.normalization.GroupNorm/group_norm_6: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_6 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_8, 8, %p_vae_dec_8_n1_weight, %p_vae_dec_8_n1_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.8', 'vae.dec.8.n1', 'group_norm_6']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_155 val_161val_162 node_Mul_162"MulJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.8: aurora_pipeline.VRes/vae.dec.8.n1: torch.nn.modules.normalization.GroupNorm/group_norm_6: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_6 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_8, 8, %p_vae_dec_8_n1_weight, %p_vae_dec_8_n1_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.8', 'vae.dec.8.n1', 'group_norm_6']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_162 val_163 group_norm_6node_group_norm_6"AddJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.8: aurora_pipeline.VRes/vae.dec.8.n1: torch.nn.modules.normalization.GroupNorm/group_norm_6: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_6 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_8, 8, %p_vae_dec_8_n1_weight, %p_vae_dec_8_n1_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.8', 'vae.dec.8.n1', 'group_norm_6']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps)  group_norm_6val_164node_Sigmoid_164"SigmoidJ namespacez: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.8: aurora_pipeline.VRes/silu_6: aten.silu.defaultJ pkg.torch.onnx.class_hierarchyi['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'aten.silu.default']J pkg.torch.onnx.fx_nodeq%silu_6 : [num_users=1] = call_function[target=torch.ops.aten.silu.default](args = (%group_norm_6,), kwargs = {})JD pkg.torch.onnx.name_scopes&['', 'vae.dec', 'vae.dec.8', 'silu_6']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h  group_norm_6 val_164silu_6 node_silu_6"MulJ namespacez: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.8: aurora_pipeline.VRes/silu_6: aten.silu.defaultJ pkg.torch.onnx.class_hierarchyi['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'aten.silu.default']J pkg.torch.onnx.fx_nodeq%silu_6 : [num_users=1] = call_function[target=torch.ops.aten.silu.default](args = (%group_norm_6,), kwargs = {})JD pkg.torch.onnx.name_scopes&['', 'vae.dec', 'vae.dec.8', 'silu_6']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h silu_6 vae.dec.8.c1.weight vae.dec.8.c1.biasconv2d_9 node_conv2d_9"Conv* group* pads@@@@* auto_pad"NOTSET* strides@@* dilations@@* kernel_shape@@J namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.8: aurora_pipeline.VRes/vae.dec.8.c1: torch.nn.modules.conv.Conv2d/conv2d_9: aten.conv2d.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.conv.Conv2d', 'aten.conv2d.default']J pkg.torch.onnx.fx_node%conv2d_9 : [num_users=1] = call_function[target=torch.ops.aten.conv2d.default](args = (%silu_6, %p_vae_dec_8_c1_weight, %p_vae_dec_8_c1_bias, [1, 1], [1, 1]), kwargs = {})JV pkg.torch.onnx.name_scopes8['', 'vae.dec', 'vae.dec.8', 'vae.dec.8.c1', 'conv2d_9']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/conv.py", line 565, in forward return self._conv_forward(input, self.weight, self.bias) conv2d_9 val_4val_169node_Reshape_169"Reshape* allowzeroJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.8: aurora_pipeline.VRes/vae.dec.8.n2: torch.nn.modules.normalization.GroupNorm/group_norm_7: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_7 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_9, 8, %p_vae_dec_8_n2_weight, %p_vae_dec_8_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.8', 'vae.dec.8.n2', 'group_norm_7']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_169 val_8 val_11val_176node_InstanceNormalization_176"InstanceNormalization* epsilon'7J namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.8: aurora_pipeline.VRes/vae.dec.8.n2: torch.nn.modules.normalization.GroupNorm/group_norm_7: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_7 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_9, 8, %p_vae_dec_8_n2_weight, %p_vae_dec_8_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.8', 'vae.dec.8.n2', 'group_norm_7']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) conv2d_9val_177node_Shape_177"Shape* startJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.8: aurora_pipeline.VRes/vae.dec.8.n2: torch.nn.modules.normalization.GroupNorm/group_norm_7: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_7 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_9, 8, %p_vae_dec_8_n2_weight, %p_vae_dec_8_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.8', 'vae.dec.8.n2', 'group_norm_7']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_176 val_177val_178node_Reshape_178"Reshape* allowzeroJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.8: aurora_pipeline.VRes/vae.dec.8.n2: torch.nn.modules.normalization.GroupNorm/group_norm_7: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_7 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_9, 8, %p_vae_dec_8_n2_weight, %p_vae_dec_8_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.8', 'vae.dec.8.n2', 'group_norm_7']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_178 val_184val_185 node_Mul_185"MulJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.8: aurora_pipeline.VRes/vae.dec.8.n2: torch.nn.modules.normalization.GroupNorm/group_norm_7: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_7 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_9, 8, %p_vae_dec_8_n2_weight, %p_vae_dec_8_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.8', 'vae.dec.8.n2', 'group_norm_7']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_185 val_186 group_norm_7node_group_norm_7"AddJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.8: aurora_pipeline.VRes/vae.dec.8.n2: torch.nn.modules.normalization.GroupNorm/group_norm_7: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_7 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_9, 8, %p_vae_dec_8_n2_weight, %p_vae_dec_8_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.8', 'vae.dec.8.n2', 'group_norm_7']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps)  group_norm_7val_187node_Sigmoid_187"SigmoidJ namespacez: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.8: aurora_pipeline.VRes/silu_7: aten.silu.defaultJ pkg.torch.onnx.class_hierarchyi['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'aten.silu.default']J pkg.torch.onnx.fx_nodeq%silu_7 : [num_users=1] = call_function[target=torch.ops.aten.silu.default](args = (%group_norm_7,), kwargs = {})JD pkg.torch.onnx.name_scopes&['', 'vae.dec', 'vae.dec.8', 'silu_7']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h  group_norm_7 val_187silu_7 node_silu_7"MulJ namespacez: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.8: aurora_pipeline.VRes/silu_7: aten.silu.defaultJ pkg.torch.onnx.class_hierarchyi['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'aten.silu.default']J pkg.torch.onnx.fx_nodeq%silu_7 : [num_users=1] = call_function[target=torch.ops.aten.silu.default](args = (%group_norm_7,), kwargs = {})JD pkg.torch.onnx.name_scopes&['', 'vae.dec', 'vae.dec.8', 'silu_7']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h silu_7 vae.dec.8.c2.weight vae.dec.8.c2.bias conv2d_10node_conv2d_10"Conv* group* pads@@@@* auto_pad"NOTSET* strides@@* dilations@@* kernel_shape@@J namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.8: aurora_pipeline.VRes/vae.dec.8.c2: torch.nn.modules.conv.Conv2d/conv2d_10: aten.conv2d.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.conv.Conv2d', 'aten.conv2d.default']J pkg.torch.onnx.fx_node%conv2d_10 : [num_users=1] = call_function[target=torch.ops.aten.conv2d.default](args = (%silu_7, %p_vae_dec_8_c2_weight, %p_vae_dec_8_c2_bias, [1, 1], [1, 1]), kwargs = {})JW pkg.torch.onnx.name_scopes9['', 'vae.dec', 'vae.dec.8', 'vae.dec.8.c2', 'conv2d_10']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/conv.py", line 565, in forward return self._conv_forward(input, self.weight, self.bias)  conv2d_8 conv2d_10add_174 node_add_174"AddJ namespacey: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.8: aurora_pipeline.VRes/add_174: aten.add.TensorJ pkg.torch.onnx.class_hierarchyg['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'aten.add.Tensor']J pkg.torch.onnx.fx_nodew%add_174 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%conv2d_8, %conv2d_10), kwargs = {})JE pkg.torch.onnx.name_scopes'['', 'vae.dec', 'vae.dec.8', 'add_174']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h add_174 val_4val_192node_Reshape_192"Reshape* allowzeroJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.9: aurora_pipeline.VRes/vae.dec.9.n1: torch.nn.modules.normalization.GroupNorm/group_norm_8: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_8 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%add_174, 8, %p_vae_dec_9_n1_weight, %p_vae_dec_9_n1_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.9', 'vae.dec.9.n1', 'group_norm_8']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_192 val_8 val_11val_199node_InstanceNormalization_199"InstanceNormalization* epsilon'7J namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.9: aurora_pipeline.VRes/vae.dec.9.n1: torch.nn.modules.normalization.GroupNorm/group_norm_8: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_8 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%add_174, 8, %p_vae_dec_9_n1_weight, %p_vae_dec_9_n1_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.9', 'vae.dec.9.n1', 'group_norm_8']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) add_174val_200node_Shape_200"Shape* startJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.9: aurora_pipeline.VRes/vae.dec.9.n1: torch.nn.modules.normalization.GroupNorm/group_norm_8: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_8 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%add_174, 8, %p_vae_dec_9_n1_weight, %p_vae_dec_9_n1_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.9', 'vae.dec.9.n1', 'group_norm_8']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_199 val_200val_201node_Reshape_201"Reshape* allowzeroJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.9: aurora_pipeline.VRes/vae.dec.9.n1: torch.nn.modules.normalization.GroupNorm/group_norm_8: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_8 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%add_174, 8, %p_vae_dec_9_n1_weight, %p_vae_dec_9_n1_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.9', 'vae.dec.9.n1', 'group_norm_8']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_201 val_207val_208 node_Mul_208"MulJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.9: aurora_pipeline.VRes/vae.dec.9.n1: torch.nn.modules.normalization.GroupNorm/group_norm_8: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_8 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%add_174, 8, %p_vae_dec_9_n1_weight, %p_vae_dec_9_n1_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.9', 'vae.dec.9.n1', 'group_norm_8']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_208 val_209 group_norm_8node_group_norm_8"AddJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.9: aurora_pipeline.VRes/vae.dec.9.n1: torch.nn.modules.normalization.GroupNorm/group_norm_8: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_8 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%add_174, 8, %p_vae_dec_9_n1_weight, %p_vae_dec_9_n1_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.9', 'vae.dec.9.n1', 'group_norm_8']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps)  group_norm_8val_210node_Sigmoid_210"SigmoidJ namespacez: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.9: aurora_pipeline.VRes/silu_8: aten.silu.defaultJ pkg.torch.onnx.class_hierarchyi['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'aten.silu.default']J pkg.torch.onnx.fx_nodeq%silu_8 : [num_users=1] = call_function[target=torch.ops.aten.silu.default](args = (%group_norm_8,), kwargs = {})JD pkg.torch.onnx.name_scopes&['', 'vae.dec', 'vae.dec.9', 'silu_8']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h  group_norm_8 val_210silu_8 node_silu_8"MulJ namespacez: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.9: aurora_pipeline.VRes/silu_8: aten.silu.defaultJ pkg.torch.onnx.class_hierarchyi['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'aten.silu.default']J pkg.torch.onnx.fx_nodeq%silu_8 : [num_users=1] = call_function[target=torch.ops.aten.silu.default](args = (%group_norm_8,), kwargs = {})JD pkg.torch.onnx.name_scopes&['', 'vae.dec', 'vae.dec.9', 'silu_8']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h silu_8 vae.dec.9.c1.weight vae.dec.9.c1.bias conv2d_11node_conv2d_11"Conv* group* pads@@@@* auto_pad"NOTSET* strides@@* dilations@@* kernel_shape@@J namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.9: aurora_pipeline.VRes/vae.dec.9.c1: torch.nn.modules.conv.Conv2d/conv2d_11: aten.conv2d.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.conv.Conv2d', 'aten.conv2d.default']J pkg.torch.onnx.fx_node%conv2d_11 : [num_users=1] = call_function[target=torch.ops.aten.conv2d.default](args = (%silu_8, %p_vae_dec_9_c1_weight, %p_vae_dec_9_c1_bias, [1, 1], [1, 1]), kwargs = {})JW pkg.torch.onnx.name_scopes9['', 'vae.dec', 'vae.dec.9', 'vae.dec.9.c1', 'conv2d_11']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/conv.py", line 565, in forward return self._conv_forward(input, self.weight, self.bias) conv2d_11 val_4val_215node_Reshape_215"Reshape* allowzeroJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.9: aurora_pipeline.VRes/vae.dec.9.n2: torch.nn.modules.normalization.GroupNorm/group_norm_9: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_9 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_11, 8, %p_vae_dec_9_n2_weight, %p_vae_dec_9_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.9', 'vae.dec.9.n2', 'group_norm_9']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_215 val_8 val_11val_222node_InstanceNormalization_222"InstanceNormalization* epsilon'7J namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.9: aurora_pipeline.VRes/vae.dec.9.n2: torch.nn.modules.normalization.GroupNorm/group_norm_9: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_9 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_11, 8, %p_vae_dec_9_n2_weight, %p_vae_dec_9_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.9', 'vae.dec.9.n2', 'group_norm_9']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) conv2d_11val_223node_Shape_223"Shape* startJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.9: aurora_pipeline.VRes/vae.dec.9.n2: torch.nn.modules.normalization.GroupNorm/group_norm_9: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_9 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_11, 8, %p_vae_dec_9_n2_weight, %p_vae_dec_9_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.9', 'vae.dec.9.n2', 'group_norm_9']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_222 val_223val_224node_Reshape_224"Reshape* allowzeroJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.9: aurora_pipeline.VRes/vae.dec.9.n2: torch.nn.modules.normalization.GroupNorm/group_norm_9: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_9 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_11, 8, %p_vae_dec_9_n2_weight, %p_vae_dec_9_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.9', 'vae.dec.9.n2', 'group_norm_9']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_224 val_230val_231 node_Mul_231"MulJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.9: aurora_pipeline.VRes/vae.dec.9.n2: torch.nn.modules.normalization.GroupNorm/group_norm_9: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_9 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_11, 8, %p_vae_dec_9_n2_weight, %p_vae_dec_9_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.9', 'vae.dec.9.n2', 'group_norm_9']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_231 val_232 group_norm_9node_group_norm_9"AddJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.9: aurora_pipeline.VRes/vae.dec.9.n2: torch.nn.modules.normalization.GroupNorm/group_norm_9: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_9 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%conv2d_11, 8, %p_vae_dec_9_n2_weight, %p_vae_dec_9_n2_bias, 1e-05, False), kwargs = {})JZ pkg.torch.onnx.name_scopes<['', 'vae.dec', 'vae.dec.9', 'vae.dec.9.n2', 'group_norm_9']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps)  group_norm_9val_233node_Sigmoid_233"SigmoidJ namespacez: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.9: aurora_pipeline.VRes/silu_9: aten.silu.defaultJ pkg.torch.onnx.class_hierarchyi['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'aten.silu.default']J pkg.torch.onnx.fx_nodeq%silu_9 : [num_users=1] = call_function[target=torch.ops.aten.silu.default](args = (%group_norm_9,), kwargs = {})JD pkg.torch.onnx.name_scopes&['', 'vae.dec', 'vae.dec.9', 'silu_9']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h  group_norm_9 val_233silu_9 node_silu_9"MulJ namespacez: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.9: aurora_pipeline.VRes/silu_9: aten.silu.defaultJ pkg.torch.onnx.class_hierarchyi['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'aten.silu.default']J pkg.torch.onnx.fx_nodeq%silu_9 : [num_users=1] = call_function[target=torch.ops.aten.silu.default](args = (%group_norm_9,), kwargs = {})JD pkg.torch.onnx.name_scopes&['', 'vae.dec', 'vae.dec.9', 'silu_9']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h silu_9 vae.dec.9.c2.weight vae.dec.9.c2.bias conv2d_12node_conv2d_12"Conv* group* pads@@@@* auto_pad"NOTSET* strides@@* dilations@@* kernel_shape@@J namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.9: aurora_pipeline.VRes/vae.dec.9.c2: torch.nn.modules.conv.Conv2d/conv2d_12: aten.conv2d.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'torch.nn.modules.conv.Conv2d', 'aten.conv2d.default']J pkg.torch.onnx.fx_node%conv2d_12 : [num_users=1] = call_function[target=torch.ops.aten.conv2d.default](args = (%silu_9, %p_vae_dec_9_c2_weight, %p_vae_dec_9_c2_bias, [1, 1], [1, 1]), kwargs = {})JW pkg.torch.onnx.name_scopes9['', 'vae.dec', 'vae.dec.9', 'vae.dec.9.c2', 'conv2d_12']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/conv.py", line 565, in forward return self._conv_forward(input, self.weight, self.bias)  add_174 conv2d_12add_210 node_add_210"AddJ namespacey: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.9: aurora_pipeline.VRes/add_210: aten.add.TensorJ pkg.torch.onnx.class_hierarchyg['__main__.Decode', 'torch.nn.modules.container.Sequential', 'aurora_pipeline.VRes', 'aten.add.Tensor']J pkg.torch.onnx.fx_nodev%add_210 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_174, %conv2d_12), kwargs = {})JE pkg.torch.onnx.name_scopes'['', 'vae.dec', 'vae.dec.9', 'add_210']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/direct-img/AuroraIMG-6M/.hf/pipeline.py", line 40, in forward h = s.c1(F.silu(s.n1(x))); h = s.c2(F.silu(s.n2(h))); return x + h add_210 val_4val_238node_Reshape_238"Reshape* allowzeroJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.10: torch.nn.modules.normalization.GroupNorm/group_norm_10: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_10 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%add_210, 8, %p_vae_dec_10_weight, %p_vae_dec_10_bias, 1e-05, False), kwargs = {})JL pkg.torch.onnx.name_scopes.['', 'vae.dec', 'vae.dec.10', 'group_norm_10']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_238 val_8 val_11val_245node_InstanceNormalization_245"InstanceNormalization* epsilon'7J namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.10: torch.nn.modules.normalization.GroupNorm/group_norm_10: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_10 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%add_210, 8, %p_vae_dec_10_weight, %p_vae_dec_10_bias, 1e-05, False), kwargs = {})JL pkg.torch.onnx.name_scopes.['', 'vae.dec', 'vae.dec.10', 'group_norm_10']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) add_210val_246node_Shape_246"Shape* startJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.10: torch.nn.modules.normalization.GroupNorm/group_norm_10: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_10 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%add_210, 8, %p_vae_dec_10_weight, %p_vae_dec_10_bias, 1e-05, False), kwargs = {})JL pkg.torch.onnx.name_scopes.['', 'vae.dec', 'vae.dec.10', 'group_norm_10']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_245 val_246val_247node_Reshape_247"Reshape* allowzeroJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.10: torch.nn.modules.normalization.GroupNorm/group_norm_10: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_10 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%add_210, 8, %p_vae_dec_10_weight, %p_vae_dec_10_bias, 1e-05, False), kwargs = {})JL pkg.torch.onnx.name_scopes.['', 'vae.dec', 'vae.dec.10', 'group_norm_10']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_247 val_253val_254 node_Mul_254"MulJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.10: torch.nn.modules.normalization.GroupNorm/group_norm_10: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_10 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%add_210, 8, %p_vae_dec_10_weight, %p_vae_dec_10_bias, 1e-05, False), kwargs = {})JL pkg.torch.onnx.name_scopes.['', 'vae.dec', 'vae.dec.10', 'group_norm_10']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) val_254 val_255 group_norm_10node_group_norm_10"AddJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.10: torch.nn.modules.normalization.GroupNorm/group_norm_10: aten.group_norm.defaultJ pkg.torch.onnx.class_hierarchy['__main__.Decode', 'torch.nn.modules.container.Sequential', 'torch.nn.modules.normalization.GroupNorm', 'aten.group_norm.default']J pkg.torch.onnx.fx_node%group_norm_10 : [num_users=1] = call_function[target=torch.ops.aten.group_norm.default](args = (%add_210, 8, %p_vae_dec_10_weight, %p_vae_dec_10_bias, 1e-05, False), kwargs = {})JL pkg.torch.onnx.name_scopes.['', 'vae.dec', 'vae.dec.10', 'group_norm_10']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 334, in forward return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps)  group_norm_10val_256node_Sigmoid_256"SigmoidJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.11: torch.nn.modules.activation.SiLU/silu_10: aten.silu.defaultJ pkg.torch.onnx.class_hierarchyu['__main__.Decode', 'torch.nn.modules.container.Sequential', 'torch.nn.modules.activation.SiLU', 'aten.silu.default']J pkg.torch.onnx.fx_nodes%silu_10 : [num_users=1] = call_function[target=torch.ops.aten.silu.default](args = (%group_norm_10,), kwargs = {})JF pkg.torch.onnx.name_scopes(['', 'vae.dec', 'vae.dec.11', 'silu_10']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/activation.py", line 476, in forward return F.silu(input, inplace=self.inplace)  group_norm_10 val_256silu_10 node_silu_10"MulJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.11: torch.nn.modules.activation.SiLU/silu_10: aten.silu.defaultJ pkg.torch.onnx.class_hierarchyu['__main__.Decode', 'torch.nn.modules.container.Sequential', 'torch.nn.modules.activation.SiLU', 'aten.silu.default']J pkg.torch.onnx.fx_nodes%silu_10 : [num_users=1] = call_function[target=torch.ops.aten.silu.default](args = (%group_norm_10,), kwargs = {})JF pkg.torch.onnx.name_scopes(['', 'vae.dec', 'vae.dec.11', 'silu_10']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/activation.py", line 476, in forward return F.silu(input, inplace=self.inplace) silu_10 vae.dec.12.weight vae.dec.12.bias conv2d_13node_conv2d_13"Conv* group* pads@@@@* auto_pad"NOTSET* strides@@* dilations@@* kernel_shape@@J namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.12: torch.nn.modules.conv.Conv2d/conv2d_13: aten.conv2d.defaultJ pkg.torch.onnx.class_hierarchys['__main__.Decode', 'torch.nn.modules.container.Sequential', 'torch.nn.modules.conv.Conv2d', 'aten.conv2d.default']J pkg.torch.onnx.fx_node%conv2d_13 : [num_users=1] = call_function[target=torch.ops.aten.conv2d.default](args = (%silu_10, %p_vae_dec_12_weight, %p_vae_dec_12_bias, [1, 1], [1, 1]), kwargs = {})JH pkg.torch.onnx.name_scopes*['', 'vae.dec', 'vae.dec.12', 'conv2d_13']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/conv.py", line 565, in forward return self._conv_forward(input, self.weight, self.bias)  conv2d_13tanh node_tanh"TanhJ namespace: __main__.Decode/vae.dec: torch.nn.modules.container.Sequential/vae.dec.13: torch.nn.modules.activation.Tanh/tanh: aten.tanh.defaultJ pkg.torch.onnx.class_hierarchyu['__main__.Decode', 'torch.nn.modules.container.Sequential', 'torch.nn.modules.activation.Tanh', 'aten.tanh.default']J pkg.torch.onnx.fx_nodel%tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%conv2d_13,), kwargs = {})JC pkg.torch.onnx.name_scopes%['', 'vae.dec', 'vae.dec.13', 'tanh']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/container.py", line 259, in forward input = module(input) File "/home/renox/workspace/aurora-test/.venv/lib/python3.12/site-packages/torch/nn/modules/activation.py", line 433, in forward return torch.tanh(input)  tanh scalar_tensor_defaultdivnode_div"DivJ3 namespace&: __main__.Decode/div: aten.div.TensorJH pkg.torch.onnx.class_hierarchy&['__main__.Decode', 'aten.div.Tensor']J pkg.torch.onnx.fx_node{%div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%tanh, %scalar_tensor_default), kwargs = {})J) pkg.torch.onnx.name_scopes ['', 'div']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1)  div val_258add_241 node_add_241"AddJ7 namespace*: __main__.Decode/add_241: aten.add.TensorJH pkg.torch.onnx.class_hierarchy&['__main__.Decode', 'aten.add.Tensor']J pkg.torch.onnx.fx_nodek%add_241 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, 0.5), kwargs = {})J- pkg.torch.onnx.name_scopes['', 'add_241']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1)  add_241 add_241_min add_241_maximage node_Clip_310"ClipJ7 namespace*: __main__.Decode/clamp: aten.clamp.TensorJ5 !pkg.onnxscript.rewriter.rule_nameFuseMaxMinToClipJJ pkg.torch.onnx.class_hierarchy(['__main__.Decode', 'aten.clamp.Tensor']J pkg.torch.onnx.fx_node%clamp : [num_users=1] = call_function[target=torch.ops.aten.clamp.Tensor](args = (%add_241, %scalar_tensor_default_1, %scalar_tensor_default_2), kwargs = {})J+ pkg.torch.onnx.name_scopes ['', 'clamp']J pkg.torch.onnx.stack_traceFile "/home/renox/workspace/direct-img/AuroraIMG-6M/scripts/export_onnx.py", line 42, in forward def forward(s, z): return (s.vae.decode(z * s.sd + s.mu) / 2 + 0.5).clamp(0, 1) main_graph*Bvae.dec.0.weightJѼ=ν }=[DhE=L=D1<}=z0<]=sPM⼹>buN =eQ̽[pCw=-=ѺI7u8bq=>F=׍z=tm>.==Sbj 8|Ȃ=,v(!x4^Mh=JDW9Q=- ;IQOGҀ<\㼝 ;5=ǽ#B==~A=ʣ+=>=>eYR߼h"`=l׿r< 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