Fix Wan2.2-TI2V-5B-Diffusers ValueError Decoder Bias Shape Mismatch

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Hey guys! Running into a frustrating ValueError when trying to load Wan2.2-TI2V-5B-Diffusers? Specifically, are you seeing an error message like "Cannot load because decoder.conv_in.bias expected shape torch.Size([640]), but got torch.Size([1024])"? Don't worry, you're not alone, and we're going to break down what's happening and how to fix it. This article dives deep into this error, providing a comprehensive guide to help you resolve it. We'll explore the root cause of the issue and present practical solutions to get your video generation pipeline back on track. Let's get this sorted!

Understanding the Error: Shape Mismatch

This error essentially boils down to a shape mismatch between what the model expects and what it's actually receiving. Specifically, the decoder.conv_in.bias tensor within the AutoencoderKLWan component of the Wan2.2-TI2V-5B-Diffusers model is expecting a shape of torch.Size([640]), but it's receiving a tensor with a shape of torch.Size([1024]). This kind of issue often arises from inconsistencies between the model's configuration and the actual weights being loaded. Understanding the root cause is crucial for implementing an effective fix.

Digging Deeper into the Components

To grasp the error better, let's break down the key components involved:

  • AutoencoderKLWan: This is a Variational Autoencoder (VAE) specifically designed for the Wan-Video models. VAEs are used to compress and decompress data, in this case, video frames. The decoder part of the VAE is responsible for reconstructing the video frames from a compressed latent representation. The decoder's structure, including its convolutional layers and bias terms, must match the expected input shape. The conv_in layer is the initial convolutional layer in the decoder, and its bias term is a learnable parameter that helps shift the output of the layer. The shape of this bias term corresponds to the number of output channels in the convolutional layer.
  • Wan2.2-TI2V-5B-Diffusers: This is the main diffusion model for text-to-video generation. It utilizes the VAE (AutoencoderKLWan) to encode and decode video frames and a diffusion process to generate new frames based on text prompts.
  • torch.Size([640]) vs. torch.Size([1024]): The error message indicates that the model expects the bias tensor to have 640 elements but is receiving one with 1024 elements. This discrepancy suggests an incompatibility in the model's architecture or the weights being loaded.

Possible Causes of the Mismatch

Several factors can contribute to this shape mismatch error:

  1. Incorrect Model Weights: The most common cause is loading weights from a different version of the model or a model with a different configuration. If the weights are not specifically designed for the Wan2.2-TI2V-5B-Diffusers architecture, they may have incompatible tensor shapes.
  2. Diffusers Library Version: Incompatibilities between the diffusers library version and the model architecture can also lead to this error. The library might expect a specific structure or naming convention for the model components, and if there's a mismatch, shape errors can occur.
  3. Custom Model Modifications: If you've made custom modifications to the model architecture, such as changing the number of channels in a convolutional layer, it can lead to shape mismatches. Ensure that any modifications are consistent and compatible with the rest of the model.
  4. Corrupted Model Files: In rare cases, the model files themselves might be corrupted during download or storage, leading to inconsistencies in the tensor shapes. This is less common but should be considered if other solutions don't work.

Troubleshooting Steps and Solutions

Now that we understand the error and its potential causes, let's walk through the solutions to resolve the ValueError. We'll cover everything from checking your environment setup to ensuring you're using the correct model weights. Follow these steps to get your video generation pipeline back on track!

1. Verify Diffusers Library Version

One of the first things you should check is your diffusers library version. Incompatible versions can lead to unexpected errors, especially shape mismatches. The user in the original problem reported using version 0.34.0. While this version might work, it's often best practice to use the latest stable version or the version recommended for the specific model you're using. You can also ensure compatibility by matching the diffusers version with the one used when the model was initially released or tested.

Solution:

  • Upgrade Diffusers: Try upgrading to the latest stable version of the diffusers library. You can do this using pip:

    pip install --upgrade diffusers

  • Specific Version: Alternatively, try installing a specific version known to be compatible with the model. Check the model's documentation or repository for recommended versions.

    pip install diffusers==<compatible_version>

2. Check PyTorch Version

The PyTorch version is also a critical factor. The user reported using torch==2.7.1, which seems unusual as the latest stable PyTorch version is in the 2.x series (e.g., 2.2.x). Using an outdated or incorrect version of PyTorch can lead to various compatibility issues, including shape mismatches. Ensure you are using a PyTorch version that is compatible with both the diffusers library and the model you are trying to load. Compatibility information is usually available in the documentation for diffusers or the model repository.

Solution:

  • Upgrade PyTorch: Update PyTorch to a stable version. It's best to align with the PyTorch versions tested and recommended by the diffusers library.

    pip install torch torchvision torchaudio --upgrade

    If you have CUDA enabled, make sure to install the CUDA-compatible version:

    pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

3. Ensure Correct Model Loading

Make sure you're loading the model correctly and specifying the correct subfolders. The error occurs when loading the vae, so pay close attention to how you load the AutoencoderKLWan.

Solution:

  • Verify Subfolder Path: Double-check that the subfolder argument in from_pretrained is correct. It should be `