Stable Diffusion is challenging the dominance of other AI art tools like Dall-E and Midjourney
The emergence of text-to-image models is threatening the presence of human artists. The text-to-image generator revolution is in full swing with tools like OpenAI’s Dall-E.2, Google’s Imagen, and Midjourney, but the art pieces generated by these tools have sparked massive controversies and debates in the creative industry. These AI tools are basically intelligence systems and generative models extended on image captions to produce novel visual scenes. AI-based intelligent artistic systems can create images and videos to have a wide range of applications, from entertainment to education, with the potential to be used as accessible solutions to even people with physical disabilities. Recently, a new AI art tool has emerged, known as Stable Diffusion that is taking the internet by storm and is expected to bring out the ugly side of the technology with its unfiltered text-to-model algorithms.
So, how does the Stable Diffusion tool really works?
Stable Diffusion is an open-source AI art generator launched by Stability AI. The tool is based on Python, and its type is the transformer language model. It can basically work on any operating system that supports Cuda kernels. The aspect that scares experts most is its open-source image synthesis model, which allows anybody with a PC and a decent hi-tech GU to create a visually striking art piece with the help of texts. Besides, Stable Diffusion has also made its source code available, unlike other applications like Dall-E. However, its license forbids certain dangerous use-case scenarios to protect its users.
The rise of these types of AI tools has experts questioning the technology’s ethics. They feel that the model can be used to produce deepfakes and raise the issue of whether it is actually permissible to produce images using a model trained on a dataset that contains copyrighted content without the permission of the original creators. AI is definitely growing but developers should focus more on how end-users can deploy it for personal gains, avoiding them from making mistakes.
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