The size of tensor a (145) must match the size of tensor b (150) at non-singleton dimension 3

Loading model /root/.cache/huggingface/hub/models--lllyasviel--control_v11f1e_sd15_tile/snapshots/3f877705c37010b7221c3d10743307d6b5b6efac/diffusion_pytorch_model.bin onto cuda backend...

  0%|          | 0/40 [00:00<?, ?it/s]

Traceback (most recent call last): File "/usr/local/bin/imagine", line 7, in imaginairy.cli.imagine.imagine_cmd() File "/usr/local/lib/python3.10/site-packages/click/core.py", line 1130, in call return self.main(*args, **kwargs) File "/usr/local/lib/python3.10/site-packages/click/core.py", line 1055, in main rv = self.invoke(ctx) File "/usr/local/lib/python3.10/site-packages/click/core.py", line 1404, in invoke return ctx.invoke(self.callback, **ctx.params) File "/usr/local/lib/python3.10/site-packages/click/core.py", line 760, in invoke return __callback(*args, **kwargs) File "/usr/local/lib/python3.10/site-packages/click/decorators.py", line 26, in new_func return f(get_current_context(), *args, **kwargs) File "/usr/local/lib/python3.10/contextlib.py", line 79, in inner return func(*args, **kwds) File "/usr/local/lib/python3.10/site-packages/imaginairy/cli/imagine.py", line 187, in imagine_cmd return _imagine_cmd( File "/usr/local/lib/python3.10/site-packages/imaginairy/cli/shared.py", line 220, in _imagine_cmd filenames = imagine_image_files( File "/usr/local/lib/python3.10/site-packages/imaginairy/api/generate.py", line 90, in imagine_image_files for result in imagine( File "/usr/local/lib/python3.10/site-packages/imaginairy/api/generate.py", line 237, in imagine result = generate_single_image( File "/usr/local/lib/python3.10/site-packages/imaginairy/api/generate_refiners.py", line 336, in generate_single_image x = sd( File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl return forward_call(*args, **kwargs) File "/usr/local/lib/python3.10/site-packages/imaginairy/vendored/refiners/foundationals/latent_diffusion/model.py", line 94, in forward unconditional_prediction, conditional_prediction = self.unet(latents).chunk(2) File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl return forward_call(*args, **kwargs) File "/usr/local/lib/python3.10/site-packages/imaginairy/vendored/refiners/fluxion/layers/chain.py", line 279, in forward result = self._call_layer(layer, name, *intermediate_args) File "/usr/local/lib/python3.10/site-packages/imaginairy/vendored/refiners/fluxion/layers/chain.py", line 273, in _call_layer raise ChainError(message) from None imaginairy.vendored.refiners.fluxion.layers.chain.ChainError:

RuntimeError: The size of tensor a (145) must match the size of tensor b (150) at non-singleton dimension 3

(CHAIN) ├── Conv2d(in_channels=4, out_channels=320, kernel_size=(3, 3), padding=(1, 1)) ├── >>> (RES) Residual() | SD1ControlnetAdapter.SD1ControlnetAdapter.SD1UNet.Controlnet_1.DownBlocks.Chain_1.Residual │ ├── UseContext(context=controlnet, key=condition_details) │ └── (CHAIN) ConditionEncoder() │ ├── (CHAIN) #1 ... │ ├── (CHAIN) #2 ... │ ├── (CHAIN) #3 ... │ ├── (CHAIN) #4 ... │ └── Conv2d(in_channels=256, out_channels=320, kernel_size=(3, 3), padding=(1, 1)) └── (PASS)

0: Tensor(shape=(2, 320, 99, 150), dtype=float16, device=cuda:0, min=-4.19, max=3.85, mean=0.01, std=0.40, norm=1238.00, grad=False)

(CHAIN) DownBlocks(in_channels=4) ├── >>> (CHAIN) | SD1ControlnetAdapter.SD1ControlnetAdapter.SD1UNet.Controlnet_1.DownBlocks.Chain_1 #1 │ ├── Conv2d(in_channels=4, out_channels=320, kernel_size=(3, 3), padding=(1, 1)) │ ├── (RES) Residual() │ │ ├── UseContext(context=controlnet, key=condition_details) │ │ └── (CHAIN) ConditionEncoder() ... │ └── (PASS) │ ├── Conv2d(in_channels=320, out_channels=320, kernel_size=(1, 1)) │ └── Lambda(_store_residual(x: torch.Tensor)) ├── (CHAIN) (x2) #2 │ ├── (SUM) ResidualBlock(in_channels=320, out_channels=320)

0: Tensor(shape=(2, 4, 99, 150), dtype=float16, device=cuda:0, min=-4.61, max=4.02, mean=-0.18, std=1.12, norm=389.50, grad=False)

(PASS) Controlnet(name=details) ├── (PASS) TimestepEncoder() │ ├── UseContext(context=diffusion, key=timestep) │ ├── (CHAIN) RangeEncoder(sinuosidal_embedding_dim=320, embedding_dim=1280) │ │ ├── Lambda(compute_sinuosoidal_embedding(x: jaxtyping.Int[Tensor, '*batch 1']) -> jaxtyping.Float[Tensor, '*batch 1 embedding_dim']) │ │ ├── Converter(set_device=False) │ │ ├── Linear(in_features=320, out_features=1280) #1 │ │ ├── SiLU() │ │ └── Linear(in_features=1280, out_features=1280) #2 │ └── SetContext(context=range_adapter, key=timestep_embedding_details) ├── Slicing(dim=1, end=4)

0: Tensor(shape=(2, 4, 99, 150), dtype=float16, device=cuda:0, min=-4.61, max=4.02, mean=-0.18, std=1.12, norm=389.50, grad=False)

(CHAIN) SD1UNet(in_channels=4) ├── >>> (PASS) Controlnet(name=details) | SD1ControlnetAdapter.SD1ControlnetAdapter.SD1UNet.Controlnet_1 #1 │ ├── (PASS) TimestepEncoder() │ │ ├── UseContext(context=diffusion, key=timestep) │ │ ├── (CHAIN) RangeEncoder(sinuosidal_embedding_dim=320, embedding_dim=1280) ... │ │ └── SetContext(context=range_adapter, key=timestep_embedding_details) │ ├── Slicing(dim=1, end=4) │ ├── (CHAIN) DownBlocks(in_channels=4) │ │ ├── (CHAIN) #1 ... │ │ ├── (CHAIN) (x2) #2 ... │ │ ├── (CHAIN) #3 ... 0: Tensor(shape=(2, 4, 99, 150), dtype=float16, device=cuda:0, min=-4.61, max=4.02, mean=-0.18, std=1.12, norm=389.50, grad=False)