RuntimeError: Mismatch in Input Channels for Convolution Layer

[norbob]

During model training, a runtime error occurs due to a channel mismatch in a convolutional layer:

RuntimeError: Given groups=1, weight of size [3072, 32, 2, 2], expected input[4, 33, 32, 32] to have 32 channels, but got 33 channels instead

This error indicates that the model expects input with 32 channels but receives input with 33 channels instead, causing the training process to fail.

Already Checked

The dataset consists of 512x512 images with 24-bit depth and has been verified to have 3 channels (RGB). All images meet these criteria.
The image sizes and channels were re-verified using a script, and they match the expected specifications.

Question

What steps can be taken to identify the root cause of this channel mismatch? Could this issue be related to model configuration or preprocessing steps? Any guidance on resolving this would be greatly appreciated.

[Passenger12138]

From the error message, it seems that the issue is caused by a mismatch in the model architecture. We can investigate this from the following two perspectives:

1. Model-related issue
   The error indicates that there might be a mismatch in the input layer. Specifically, the v1 version of CogVideo-i2v (patch_embed.proj) uses Conv2d, while the v1.5 version uses Linear2d. This error should not occur if you are using the v1.5 model. Please ensure that you are using the correct model from the Huggingface repository:
   https://huggingface.co/THUDM/CogVideoX1.5-5B-I2V.

2. Data-related issue
   It also seems that you might be using image data to fine-tune the i2v model. Please note that the i2v model is designed for video generation tasks, and it is not feasible to use image data for fine-tuning. Make sure that your dataset consists of video data.

Additionally, to better assist you in diagnosing the issue, could you please provide more error screenshots? This will help me pinpoint the exact cause of the problem.

For your information, if you are trying to perform text-to-video (T2V) tasks, I plan to release LoRA fine-tuning and full fine-tuning support for CogVideo 1.5 in the next version. This will allow you to fine-tune the model for T2V tasks effectively.

[wujiafu007]

Thank you for your response, the issue has been resolved.

[norbob]

Thanks for the response! I wasn’t aware that I can't use images. Is it generally the case that images cannot be used as a dataset for Loras in video models? I'm a newcomer, and it's really hard to grasp these connections right away.