Lora inference problem

[zacaikido]

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When trying to run inference using --lora_path parameter, getting :

LoRA rank 64 is too large. setting to: 4
list index out of range
Couldn't inject LoRA's due to an error.

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Traceback (most recent call last):
File "/content/drive/MyDrive/Text-To-Video-Finetuning/inference.py", line 194, in <module>
videos = inference(**args)
File "/usr/local/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/content/drive/MyDrive/Text-To-Video-Finetuning/inference.py", line 141, in inference
videos = pipeline(
File "/usr/local/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py", line 646, in __call__
noise_pred = self.unet(
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/content/drive/MyDrive/Text-To-Video-Finetuning/models/unet_3d_condition.py", line 399, in forward
emb = self.time_embedding(t_emb, timestep_cond)
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/diffusers/models/embeddings.py", line 192, in forward
sample = self.linear_1(sample)
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/content/drive/MyDrive/Text-To-Video-Finetuning/utils/lora.py", line 60, in forward
+ self.dropout(self.lora_up(self.selector(self.lora_down(input))))
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/linear.py", line 114, in forward
return F.linear(input, self.weight, self.bias)
RuntimeError: mat1 and mat2 shapes cannot be multiplied (6x320 and 1280x16)

I'm running it on a Colab

[ExponentialML]

Hello. Is this a LoRA trained using this finetuning repository or are you using another one?

[ChawDoe]

I had the same problem using the latest version of this repo.

[JCBrouwer]

If either of you @ChawDoe @zacaikido could provide a LoRa checkpoint, the settings it was trained with, and the settings you're running inference with, I can take a look.

[zacaikido]

Pointing to a folder with only the xxx_text_encoder.pt in it will solve the problem. Seems to be a problem with the xxx_unet.pt or multiple files situation?

[howardgriffin]

Same promblem. 'mat1 and mat2 shapes cannot be multiplied (24576x256 and 512x512)'

[fairleehu]

me too, RuntimeError: mat1 and mat2 shapes cannot be multiplied (12288x256 and 512x512)

[zideliu]

When trying to run inference using --lora_path parameter, getting :

LoRA rank 64 is too large. setting to: 4
list index out of range
Couldn't inject LoRA's due to an error.

0%|          | 0/50 [00:00<?, ?it/s]
0%|          | 0/50 [00:00<?, ?it/s]
Traceback (most recent call last):
File "/content/drive/MyDrive/Text-To-Video-Finetuning/inference.py", line 194, in <module>
videos = inference(**args)
File "/usr/local/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/content/drive/MyDrive/Text-To-Video-Finetuning/inference.py", line 141, in inference
videos = pipeline(
File "/usr/local/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py", line 646, in __call__
noise_pred = self.unet(
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/content/drive/MyDrive/Text-To-Video-Finetuning/models/unet_3d_condition.py", line 399, in forward
emb = self.time_embedding(t_emb, timestep_cond)
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/diffusers/models/embeddings.py", line 192, in forward
sample = self.linear_1(sample)
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/content/drive/MyDrive/Text-To-Video-Finetuning/utils/lora.py", line 60, in forward
+ self.dropout(self.lora_up(self.selector(self.lora_down(input))))
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/linear.py", line 114, in forward
return F.linear(input, self.weight, self.bias)
RuntimeError: mat1 and mat2 shapes cannot be multiplied (6x320 and 1280x16)

I'm running it on a Colab

in /utils/lora.pyhere,

And here