I think this question needs to be looked at on a case-by-case basis. In some simple graphics production tasks, AI drawing tools can replace part of the manual operation of designers, but in more complex graphic design, the professional skills of designers are still irreplaceable.
First of all, AI drawing tools such as Midjourney and Stable Diffusion have shown high efficiency and accuracy in some simple graphics production tasks, and can replace manual operations. For example, they can quickly generate some simple floor plans in a short period of time, such as network topology diagrams, flow charts, etc. Moreover, when these AI drawing tools generate images, they can use training data and algorithms to ensure the beauty and fidelity of the images, thereby producing high-quality images.
In some specific application scenarios, AI drawing tools can also replace part of the designer's work. For example, some website design platforms can use AI to automatically generate the layout and color matching of the website without manual adjustment by the designer. In addition, the design of some commercial advertisements or leaflets can also be automatically generated by AI drawing tools, reducing some basic workload.
Even in these cases, the role of the designer remains irreplaceable. Because the designer not only needs to consider the beauty and fidelity of the image, but also needs to consider the purpose and audience of the design, as well as the visual communication effect of the image. In some design tasks that need to interact with users, designers need to make adjustments and optimizations based on user feedback to meet user needs.
In addition, the capabilities of AI drawing tools are also limited. They can only generate images that already exist in the dataset, they cannot create brand new images, and they cannot handle some very individual design requirements. For these cases, the professional skills of the designer are still essential.
Therefore, although AI drawing tools such as Midjourney and Stable Diffusion can replace part of the work of designers in certain application scenarios, the professional skills of designers are still irreplaceable for more complex and personalized design tasks. At the same time, we should also see that with the continuous development of AI technology, AI drawing tools will become more and more popular. Designers also need to adapt to this trend and continuously improve their skills to maintain competitiveness.
As a designer, it is not necessary to master the use of Stable Diffusion and model training. It depends on personal career planning and development direction, but it is helpful for improving one's own skills and competitiveness. Mastering the use of AI drawing tools such as Stable Diffusion can allow designers to complete graphic design more efficiently, improve work efficiency, and reduce work costs. Understanding the use of these tools can also help designers better integrate with AI technology and develop more creative and competitive design works.
To use Stable Diffusion, you need to master some basic concepts and techniques. First, you need to understand the fundamentals of probabilistic models and probabilistic inference. Second, you need to master mathematical algorithms such as random walk and stochastic gradient descent. Finally, knowledge of the Python programming language and deep learning framework PyTorch is required.
In addition, I have prepared some training models and cloud server configuration materials to be continuously updated.
Using Stable Diffusion for model training and image generation requires high computing power and storage resources. Here are some suggested computer configurations:
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CPU: A computer with a multi-core processor such as Intel Core i7 or higher is recommended.
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GPU: Stable Diffusion uses a Graphics Processing Unit (GPU) for calculations, an Nvidia GPU is recommended, such as a GeForce RTX 3090 or higher.
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Memory: At least 16 GB of memory, 32 GB or higher recommended.
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Storage: It is recommended to use a high-speed solid-state drive (SSD) for training and generation to improve data read and write speed.
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Operating system: Stable Diffusion supports Windows, Linux, and macOS operating systems, and it is recommended to use the latest version.
Before using Stable Diffusion, model training is required. Training the Stable Diffusion model requires some preparatory work, including data preprocessing, model building, and hyperparameter adjustment. First, you need to prepare a training dataset, which can be any type of image dataset, such as human faces, natural scenery, animals, etc. Then, a Stable Diffusion model needs to be constructed, which includes a generator and a discriminator. The generator is responsible for generating images, and the discriminator is responsible for judging whether the generated images are real or not. Finally, hyperparameters need to be tuned, including learning rate, batch size, gradient clipping, etc. Tuning hyperparameters can improve the training performance and generation quality of the model.
Once the model training is complete, images can be generated or edited using Stable Diffusion. The method of generating the image is by inputting the noise vector to the generator, and then generating the image according to the probability model of Stable Diffusion. The method of editing the image is by modifying the input vector of the generator, and then generating the modified image according to the probability model of Stable Diffusion.
Mastering the use of Stable Diffusion and training models is a challenging task that requires related skills such as deep learning and Python programming. However, with the continuous development of AI technology, generative models based on probabilistic models such as Stable Diffusion will become a very promising technology.