Current State of Quantization Support

Oct 13, 2025 by sorusgentrification.com 65 views

Introduction

The advent of large-scale transformer models has revolutionized the field of natural language processing and computer vision. However, these models come with a significant computational cost, making them challenging to deploy on edge devices or in resource-constrained environments. To address this issue, quantization techniques have been developed to reduce the precision of model weights and activations, resulting in significant memory and computational savings. In this article, we will explore the current state of quantization support for WAN 2.1 and discuss the prospects of full Quanto support in the near future.

Current State of Quantization Support

Currently, we can quantize a WAN 2.1 transformer and store it for use. However, we cannot requantize the transformer for inference. This limitation is due to the absence of a specific class in the Diffusers library, which is responsible for requantizing the model. The relevant code snippet is available on the Diffusers GitHub repository:

https://github.com/huggingface/diffusers/tree/main/src/diffusers/quantizers

Relevant Code Snippet

The following code snippet demonstrates the attempt to requantize a WAN transformer:

print('Quantize transformer')
class QuantizedWanTransformer3DModel(QuantizedDiffusersModel):
    base_class = WanTransformer3DModel
transformer = QuantizedWanTransformer3DModel.from_pretrained(
    "./wan quantro T2V 14B Diffusers/basemodel/wantransformer3dmodel_qint8"
     ).to(dtype=dtype)

As we can see, the code attempts to create a new class QuantizedWanTransformer3DModel that inherits from QuantizedDiffusersModel. However, this approach fails due to the absence of the required class.

Prospects of Full Quanto Support

The absence of full Quanto support for WAN 2.1 is a significant limitation, and it is essential to understand the prospects of this feature becoming available in the near future. While the Diffusers library is actively maintained and updated, the development of new features and classes can be a time-consuming process.

However, there are several reasons to be optimistic about the prospects of full Quanto support:

Growing demand for edge AI: The increasing demand for edge AI applications, such as computer vision and natural language processing, has driven the development of more efficient and lightweight models. Quanto support for WAN 2.1 would enable the deployment of these models on edge devices, making them more accessible and convenient to use.
Advancements in quantization techniques: Recent advancements in quantization techniques have led to the development of more efficient and accurate methods for reducing the precision of model weights and activations. These advancements have the potential to improve the performance of Quanto support for WAN 2.1.
Community engagement: The Diffusers community is actively engaged in the development and maintenance of the library. This community-driven approach ensures that the library remains up-to-date and relevant to the needs of its users.

Timeline for Full Quanto Support

While it is difficult to predict an exact timeline for full Quanto support, we can make an educated estimate based on the current state of development and the prospects of the feature becoming available.

Short-term (next 6-12 months): In the short term, we can expect to see incremental updates to the Diffusers library, including improvements to the quantization support for WAN 2.1. These updates may include the development of new classes and methods for requantizing the model.
Medium-term (next 1-2 years): In the medium term, we can expect to see more significant updates to the Diffusers library, including the development of full Quanto support for WAN 2.1. This may involve the creation of new classes and methods for requantizing the model, as well as improvements to the overall performance and accuracy of the library.
Long-term (next 2-5 years): In the long term, we can expect to see continued advancements in the development of the Diffusers library, including the integration of new features and classes for requantizing the model. This may also involve the development of new quantization techniques and methods for improving the performance and accuracy of the library.

Conclusion

Q: What is the current state of quantization support for WAN 2.1?

A: Currently, we can quantize a WAN 2.1 transformer and store it for use. However, we cannot requantize the transformer for inference. This limitation is due to the absence of a specific class in the Diffusers library, which is responsible for requantizing the model.

Q: What is the reason for the absence of the required class?

A: The reason for the absence of the required class is that it has not been developed yet. The Diffusers library is actively maintained and updated, but the development of new features and classes can be a time-consuming process.

Q: What are the prospects of full Quanto support becoming available?

A: There are several reasons to be optimistic about the prospects of full Quanto support becoming available. The growing demand for edge AI, advancements in quantization techniques, and community engagement all contribute to the likelihood of this feature becoming available.

Q: What are the potential benefits of full Quanto support for WAN 2.1?

A: The potential benefits of full Quanto support for WAN 2.1 include:

Improved performance: Full Quanto support would enable the deployment of WAN 2.1 on edge devices, making it more accessible and convenient to use.
Reduced memory requirements: Quanto support would reduce the memory requirements of WAN 2.1, making it more suitable for deployment on resource-constrained devices.
Increased accuracy: Full Quanto support would enable the use of more accurate quantization techniques, resulting in improved performance and accuracy.

Q: What is the estimated timeline for full Quanto support?

A: While it is difficult to predict an exact timeline, we can make an educated estimate based on the current state of development and the prospects of the feature becoming available.

Short-term (next 6-12 months): In the short term, we can expect to see incremental updates to the Diffusers library, including improvements to the quantization support for WAN 2.1.
Medium-term (next 1-2 years): In the medium term, we can expect to see more significant updates to the Diffusers library, including the development of full Quanto support for WAN 2.1.
Long-term (next 2-5 years): In the long term, we can expect to see continued advancements in the development of the Diffusers library, including the integration of new features and classes for requantizing the model.

Q: How can I contribute to the development of full Quanto support?

A: There are several ways to contribute to the development of full Quanto support:

Report issues: If you encounter any issues with the current quantization support, please report them on the Diffusers GitHub repository.
Contribute code: If you have expertise in the area of quantization and would like to contribute code, please submit a pull request to the Diffusers GitHub repository.
Participate in discussions: Join the Diffusers community and participate in discussions about the development of full Quanto support.

Q: What are the next steps for full Quanto support?

A: The next steps for full Quanto support include:

Development of new classes and methods: The development of new classes and methods for requantizing the model is essential for full Quanto support.
Improvements to quantization techniques: Improvements to quantization techniques will enable the use of more accurate and efficient methods for reducing the precision of model weights and activations.
Integration with existing features: The integration of full Quanto support with existing features and classes in the Diffusers library will ensure seamless deployment and use of WAN 2.1 on edge devices.