Efficient Scaling of Diffusion Transformer Models for Diverse Image Generation Tasks

Conceitos Básicos

A novel parameter-efficient scaling strategy, DiffScaler, that enables a single pre-trained diffusion transformer model to perform diverse image generation tasks across multiple datasets and conditions with minimal additional training.

Resumo

The paper proposes DiffScaler, a method to efficiently scale pre-trained diffusion transformer models to perform diverse image generation tasks. The key insights are:

1. DiffScaler introduces a lightweight "Affiner" block that can be plugged into each trainable layer of the diffusion model. The Affiner block learns task-specific scaling and shifting of the weights, as well as additional task-specific subspaces, allowing the model to adapt to new datasets and conditions with minimal additional parameters.

2. Experiments show that transformer-based diffusion models adapt better to smaller datasets compared to CNN-based models when performing parameter-efficient fine-tuning. DiffScaler enables a single transformer-based diffusion model to generate high-quality images across multiple unconditional datasets (e.g., FFHQ, Flowers, CUB-200, Caltech-101) and conditional tasks (e.g., depth maps, segmentation maps, canny edges) with just 0.5-0.9% of the total model parameters.

3. DiffScaler outperforms existing parameter-efficient fine-tuning methods like DiffFit and LORA, while achieving comparable performance to full fine-tuning, demonstrating its effectiveness in scaling diffusion models to diverse tasks.

4. The authors also show that DiffScaler can be used to enable a single text-conditioned diffusion model to perform multiple spatial conditioning tasks simultaneously, without the need for separate encoders or zero-initialized convolutional layers as in ControlNet.

Estatísticas

"A single pre-trained diffusion model can be scaled to perform diverse image generation tasks across multiple datasets and conditions with minimal additional training."
"Transformer-based diffusion models adapt better to smaller datasets compared to CNN-based models when performing parameter-efficient fine-tuning."
"DiffScaler achieves comparable performance to full fine-tuning while using only 0.5-0.9% of the total model parameters."

Citações

"DiffScaler enables a single pre-trained diffusion model to scale across multiple datasets swiftly, allowing for the completion of diverse generative tasks using just one model."
"We show that using our proposed method, a single pre-trained model can scale up to perform these conditional and unconditional tasks, respectively, with minimal parameter tuning while performing as close as fine-tuning an entire diffusion model for that particular task."

Principais Insights Extraídos De

Diffscaler: Enhancing the Generative Prowess of Diffusion Transformers

by Nithin Gopal... às arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09976.pdf

Perguntas Mais Profundas

How can DiffScaler be extended to enable a single diffusion model to perform both unconditional and conditional image generation tasks simultaneously?

DiffScaler can be extended to enable a single diffusion model to perform both unconditional and conditional image generation tasks simultaneously by incorporating task-specific transformations at each layer. This involves training a minimal amount of parameters to adapt to different tasks. For unconditional image generation, the model can be trained on diverse datasets like Faces, Flowers, Birds, and more. For conditional image generation, the model can be conditioned on various factors like segmentation maps, depth maps, hed maps, and canny edge maps. By adding new parameters for each dataset or task and training them individually while retaining the weights, biases, and normalization of the original network, DiffScaler allows a single diffusion model to scale efficiently across multiple tasks. Additionally, the model can be trained in parallel with different tasks or datasets, saving training time and resources.

What are the potential limitations of DiffScaler, and how can it be further improved to handle more diverse and challenging image generation tasks?

One potential limitation of DiffScaler could be the need for careful parameter tuning and balancing to ensure optimal performance across multiple tasks. To further improve DiffScaler for handling more diverse and challenging image generation tasks, several enhancements can be considered:

Dynamic Parameter Adjustment: Implementing a mechanism to dynamically adjust the parameters based on the complexity of the task or dataset could enhance adaptability and performance.

Regularization Techniques: Incorporating regularization techniques such as dropout or weight decay can help prevent overfitting and improve generalization to diverse datasets.

Advanced Architectures: Exploring more advanced network architectures, such as attention mechanisms or hierarchical structures, could enhance the model's ability to capture intricate patterns and relationships in the data.

Transfer Learning Strategies: Leveraging transfer learning strategies from pre-trained models or utilizing meta-learning approaches can help the model adapt more effectively to new tasks or datasets.

Data Augmentation: Introducing data augmentation techniques to increase the diversity of the training data can improve the model's robustness and ability to handle a wider range of image generation tasks.

Could the principles behind DiffScaler be applied to other types of generative models, such as GANs or VAEs, to enable efficient scaling and multitasking capabilities?

Yes, the principles behind DiffScaler can be applied to other types of generative models, such as GANs or VAEs, to enable efficient scaling and multitasking capabilities. By introducing lightweight blocks at each layer to learn task-specific transformations and adapt to new tasks, similar to how DiffScaler operates for diffusion models, GANs or VAEs can be enhanced for multitasking and efficient scaling. This approach would involve training minimal additional parameters for each new task while retaining the core model architecture. Additionally, techniques like transfer learning, regularization, and data augmentation can be applied to improve the performance and versatility of GANs or VAEs across diverse image generation tasks.