transform( task => '{ "task" : "text-generation", "model" : "gpt2-medium" }'::JSONB, inputs => ARRAY[ 'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone' ], args => '{ "do_sample" : true, "top_p" : 0.8 }'::JSONB )...
NLP的RLHF中训练的Reward model数据集相对更好收集,模型也容易拟合(两个文本选出一个更好的文本,争议没有那么大)。但文生图就难很多(图片维度更高,更重视生成细节),存在 反事实(artifacts)、不真实(implausibility)、图文不一致(misalignment with text descriptions)、美学质量差(low aesthetic quality)等问题,这篇...
我们的方法能够生成具有多个对象的复杂图像。 图3.5 Overview of image generation network f for generating images from scene graphs.[5] 6. Controllable text-to-image generation(Li B, el al, NeuralIPS 2019) Li B 等人[16]提出了一种可控的文本-图像生成对抗网络(ControlGAN),该网络既能有效地合成高质...
1、主要贡献 提出了一个Text2Image Transformer model:Muse。在Text2Image的SOTA方法中,Muse的速度比基于diffusion models和AutoRegressive model的方法更快,而且在性能上非常出色。 2、方法 图1,Muse Framework 图1展示了Muse的整体框架。与DALL·E 2和IMAGEN等Text2Image大模型类似,Muse采用”生成+超分“的级联方式获得...
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The impact of Sora in shaping video generation and its implications for various industries has been seen through factors like enhanced text-to-video capabilities and exploration of novel applications. According to AFP, the French video game giant Ubisoft hailed the tool as a "quantum leap forward"...
Generative Adversarial Networks (GANs) have shown success in text-to-image generation tasks. Most of the current methods use multi-stages to generate images, but the quality of the final images is largely dependent on the quality of the initial generated images, thus it is difficult to generate...
Recent advances in data-to-text generation have led to the use of large-scale datasets and neural network models which are trained end-to-end, without explicitly modeling what to say and in what order. In this work, we present a neural network architecture which incorporates content selection ...
has been upgraded again. It integrates with advanced text-to-image generation architectures, Transformer and VQGAN. At the same time, it gives free access to the open-source community for the checkpoints of Chinese text-to-image generation models with different parameters an...
Figure 3. Gender, race, and age distribution as interpreted by human annotators and automated face processing within the context of image generation for the prompt “person.” Our study also examines biases of similar representations across positive and negative personality traits, revealing...