Researchers have developed a new text-to-image generative model called PIXART-α that offers high-quality picture generation while reducing resource usage. They propose three main designs, including decomposition of the training plan and using cross-attention modules. Their model significantly lowers training costs and saves money compared to other models, making it more accessible for researchers and businesses. PIXART-α delivers better picture quality and semantic alignment, and its training efficiency outperforms other state-of-the-art models.
A Revolutionary Image Generation Model: PIXART-α
A new era of photorealistic image synthesis has arrived with the development of text-to-image (T2I) generative models like DALLE 2, Imagen, and Stable Diffusion. These models have influenced various applications such as picture editing, video production, and 3D asset creation. However, the high computational requirements and environmental impact of training these models have posed significant challenges.
Researchers from Huawei Noah’s Ark Lab, Dalian University of Technology, HKU, and HKUST have introduced PIXART-α, a groundbreaking solution that drastically reduces the computing resources needed for training while maintaining competitive image generation quality. They propose three main designs to achieve this:
1. Decomposition of the training plan
They break down the text-to-image production problem into three subtasks: learning the distribution of pixels in natural pictures, learning text-image alignment, and improving the aesthetic appeal of images. By using a low-cost class-condition model for initialization and employing a training paradigm of pretraining and fine-tuning, they significantly lower the learning cost for the first subtask and increase training effectiveness.
2. A productive T2I transformer
They utilize cross-attention modules to inject text conditions and simplify the computationally demanding class-condition branch. They also present a reparameterization method that allows the modified text-to-image model to import parameters from the original class condition model. This approach leverages ImageNet’s knowledge of natural picture distribution to provide an acceptable initialization and speed up training.
3. High-quality information
Existing text-image pair datasets have significant flaws, such as a lack of informative content and severe long-tail effects. To overcome these issues, the researchers propose an autolabeling pipeline using advanced vision-language models to produce captions on a dataset called SAM. This dataset provides high information density and improves text-image alignment learning. The efficient training of PIXART-α requires only 675 A100 GPU days and $26,000, significantly reducing training data volume and time compared to other models.
PIXART-α not only delivers superior picture quality and semantic alignment compared to current state-of-the-art models but also demonstrates its advantage in semantic control. By making high-quality T2I models more accessible and affordable, this research aims to empower independent academics and companies in their AI endeavors.
For more details, you can check out the paper and project.
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