What are the limitations and things that diffusion models are bad at?
Best insight from top research papers
Diffusion models face limitations in handling discrete data like languages due to the challenge of Gaussian noise not effectively corrupting discrete inputs . Additionally, existing diffusion models may suffer from instability and restricted representation abilities, hindering accurate modeling of complex user interactions, especially noisy interactions caused by various factors . Moreover, diffusion models encounter issues such as slow inference, high memory consumption, and computation intensity during noise estimation, impeding their efficient adoption for tasks like image synthesis . These limitations highlight areas where diffusion models struggle, emphasizing the need for further research and development to address these challenges effectively.
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| Papers (5) | Insight |
|---|---|
| Diffusion models face challenges in slow inference, high memory usage, and computation intensity due to noise estimation, hindering efficient adoption without proper quantization methods. | |
| Diffusion models excel in denoising user interactions, overcoming limitations of GANs and VAEs. They address challenges like high resource costs and temporal shifts in recommender systems. | |
3 Citations | Diffusion models struggle with discrete data due to issues with Gaussian noise and instability in high-dimensional spaces. The proposed Masked-Diffuse LM addresses these limitations effectively. |
10 Apr 2023 | Diffusion models struggle with discrete data due to Gaussian noise limitations and instability in high-dimensional spaces. They are inefficient for textual data generation. |
15 May 2023 | Diffusion models struggle with generating very bright and dark samples due to flawed noise schedules, preventing extreme brightness variation. Proposed fixes align training and inference for improved sample fidelity. |
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