Great news! Guo Ailun has recovered well, and the Asian Games is expected to show its grace again.

Great news! Guo Ailun has recovered well, and the Asian Games is expected to show its grace again.

The fans ushered in great news! Guo Ailun is recovering well, and he is expected to show his outstanding strength again in the Asian Games. The resolution of the injury problem has cheered the fans, and his return will add a lot of strength and confidence to the China men’s basketball team. I expect him to shine again in the Asian Games and win honor for the China men’s basketball team. In the past few years, Guo Ailun has played an important role in the men’s basketball team in China. His outstanding performance and leadership style have made him an idol in the hearts of fans at home and abroad. However, injuries have been plaguing his career, which not only separated him from the team for a long time, but also worried the fans. Countless fans are looking forward to his recovery, hoping to enjoy his excellent performance again in the Asian Games.

In the process of recovery, Guo Ailun made great efforts and perseverance. He actively cooperated with the professional rehabilitation team to carry out rehabilitation training and constantly broke through himself in order to return to the stadium as soon as possible. And his firm will and unyielding fighting spirit also deeply infected the people around him. His teammates are full of expectations for his return, and the coaching staff is full of confidence in him. They believe that under the leadership of Guo Ailun, the China men’s basketball team will make some gains in the Asian Games. As the highest comprehensive sports meeting in Asia, the Asian Games is of great significance to the China men’s basketball team. This is not only a competitive game, but also an opportunity to show our strength. For Guo Ailun personally, the Asian Games is also a stage to prove himself again. He is eager to win honor for his country and bring joy to the fans with his own strength.

Therefore, he attaches no less importance to the Asian Games than other important competitions. Guo Ailun’s return will have a positive impact on the China men’s basketball team. As a leader, his play on the court and his influence off the court are irreplaceable. His excellent organizational skills and calm judgment made him the soul of the team. In addition, his personal ability is also quite outstanding. With superb skills and decisive attack, he can score key points for the team at critical moments. The fans expressed full expectations and appeals for Guo Ailun’s return. Numerous messages and comments have sprung up on social media, expecting him to shine again in the Asian Games.

Someone said: "I look forward to seeing Guo Ailun show his excellent state in the Asian Games, so that the whole world can know the strength of the China men’s basketball team!" Others wrote: "I wish Guo Ailun a smooth recovery and look forward to seeing him bring more victories to the China men’s basketball team!" The voice of the fans is undoubtedly the greatest support and encouragement for Guo Ailun. With the Asian Games coming, the news that Guo Ailun has recovered well is exciting. His return will add a lot of strength and confidence to the China men’s basketball team. I believe he will shine again in the Asian Games and win more honors for the China men’s basketball team! No matter how the game results, Guo Ailun will always be a hero in the hearts of fans, and his persistence and fighting spirit will inspire more young people to pursue their basketball dreams.

May he achieve excellent performance in the Asian Games and continue to make greater contributions to the basketball cause in China!

Premier League -18-year-old Ferguson double-sounded and three-smoked assists+Zhongzhu Brighton 3-1 saints locked in European qualification

Live on May 21st, in the 37th round of Premier League, Brighton played against Southampton at home. In the first half of this game, Ferguson, an 18-year-old teenager from Brighton, double-rang, sent three assists and a center post, and Brighton led 2-0 at half-time. In the second half, each team scored a goal, and Brighton finally beat Southampton 3-1 at home, which sealed the qualification for next season’s European War.

[Key Events of Competition]

In the 29th minute, Ivan Ferguson, an 18-year-old teenager from Brighton, broke into the restricted area and scored a low goal. Brighton 1-0 Southampton.

Goal playback

In the 40th minute of the game, Sanxun crossed the restricted area after breaking through the sidewalk, and young Ferguson scored twice again. Brighton 2-0 Southampton.

Goal playback

In the 58th minute of the game, Southampton pulled back a goal and El Yunusi headed the goal. Brighton 2-1 Southampton.

Goal playback

In the 69th minute of the game, Gross faced the defenders in the restricted area and volleyed once again to expand the score for Brighton. Brighton leads 3-1.

Goal playback

[Moment of Competition Focus]

In the 8 th minute of the game, Sanxun hit the door in the restricted area and missed slightly.

In the 21st minute of the game, Sanxun steals from the frontcourt and shoots the center post.

In the 62nd minute, Walcott scored, but the offside was cancelled.

In the end, Brighton beat Southampton 3-1. Lock in next season’s European qualification.

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Technical password of generated image AI

In the past few years, artificial intelligence (AI) has made great progress, and AI’s new products include AI image generator. This is a tool that can convert input statements into images. There are many AI tools for text-to-image conversion, but the most prominent ones are DALL-E 2, Stable Diffusion and Midjourney.

DALL-E 2 is developed by OpenAI and the project of chatgpt is complementary. It generates images through a paragraph of text description. Its GPT-3 converter model trained with more than 10 billion parameters can interpret natural language input and generate corresponding images.

DALL-E 2 mainly consists of two parts-converting user input into a representation of an image (called Prior), and then converting this representation into an actual photo (called Decoder).

The text and images used in it are embedded in another network called CLIP (Contrast Language-Image Pre-training), which is also developed by OpenAI. CLIP is a neural network that returns the best title for the input image. What it does is the opposite of what DALL-E 2 does-it converts images into text, while DALL-E 2 converts text into images. The purpose of introducing CLIP is to learn the connection between visual and text representation of objects.

DALL-E 2′ s job is to train two models. The first one is Prior, which accepts text labels and creates CLIP image embedding. The second is Decoder, which accepts CLIP image embedding and generates images. After the model training is completed, the reasoning process is as follows:

  • The input text is converted into CLIP text embedding using neural network.

  • Use Principal Component Analysis to reduce the dimension of text embedding.

  • Create an image embedding using text embedding.

  • After entering the Decoder step, the diffusion model is used to embed the image into an image.

  • The image is enlarged from 64×64 to 256×256, and finally enlarged to 1024×1024 by using convolutional neural network.

Stable Diffusion is a text-to-image model, which uses CLIP ViT-L/14 text encoder and can adjust the model through text prompts. It separates the imaging process into a "diffusion" process at runtime-starting from the noisy situation, gradually improving the image until there is no noise at all, and gradually approaching the provided text description.

Stable Diffusion is based on Latent Diffusion Model(LDM), which is a top-notch text-to-image synthesis technology. Before understanding the working principle of LDM, let’s look at what is diffusion model and why we need LDM.

Diffusion Models, DM) is a generation model based on Transformer, which samples a piece of data (such as an image) and gradually increases the noise over time until the data cannot be recognized. This model tries to return the image to its original form, and in the process, it learns how to generate pictures or other data.

The problem of DM is that powerful DM often consumes a lot of GPU resources, and the cost of reasoning is quite high due to Sequential Evaluations. In order to train DM on limited computing resources without affecting its quality and flexibility, Stable Diffusion applies DM to powerful Pre-trained Autoencoders.

On this premise, the diffusion model is trained, which makes it possible to achieve an optimal balance between reducing complexity and preserving data details, and significantly improves the visual reality. The cross attention layer is introduced into the model structure, which makes the diffusion model a powerful and flexible generator and realizes the high-resolution image generation based on convolution.

Midjourney is also a tool driven by artificial intelligence, which can generate images according to the user’s prompts. MidJourney is good at adapting to the actual artistic style and creating images with any combination of effects that users want. It is good at environmental effects, especially fantasy and science fiction scenes, which look like the artistic effects of games.

DALL-E 2 uses millions of image data for training, and its output results are more mature, which is very suitable for enterprises to use. When there are more than two characters, the image generated by DALL-E 2 is much better than that generated by Midjourney or Stable Diffusion.

Midjourney is a tool famous for its artistic style. Midjourney uses its Discord robot to send and receive requests for AI servers, and almost everything happens on Discord. The resulting image rarely looks like a photo, it seems to be more like a painting.

Stable Diffusion is an open source model that everyone can use. It has a good understanding of contemporary art images and can produce works of art full of details. However, it needs to explain the complex prompt. Stable Diffusion is more suitable for generating complex and creative illustrations. However, there are some shortcomings in creating general images.