基于回答提供图片的proment

现在我想给大家展示如何用Gemini处理图像和文本，然后得到文本响应。首先，我们需要从Vertex AI SDK导入一些额外的类。你已经知道generative_model了，对吧？我们还要添加image这个类。有了它，我们就能处理图像并把它们发送到Gemini API。另外，我们还要导入Part。这个很有用，特别是当我们想组合不同类型的内容时。比如说，我们想把文本和图像放在一起发送给Gemini API，Part就派上用场了。这样一来，我们就为处理更复杂的输入做好了准备。你觉得怎么样？这样解释是不是更容易理解了？接下来，我们可以导入我们的多模态模型。在这种情况下，我们使用的是Gemini 1.0 Pro Vision，它可以用于图像或视频等数据。接下来，我们需要准备一个提示和一张图片。我这里有一张吴恩达的很酷的照片，我们要让模型描述一下这张图里有什么。首先，我们要加载这张本地图片。然后，我们准备好提示。有意思的是，我发现把图片放在前面，提示放在后面，能得到更好的回答。所以我会把图片和提示组合在一起，按这个顺序发送给Gemini API。你知道吗？这个顺序其实挺重要的。我试过不同的组合，发现这样做效果最好。这就是为什么我们要把图片放在前面，然后才是文字提示。这样一来，我们就准备好让Gemini分析这张图片了。让我们看看图像和提示。所以这里你可以看到我们有一个吴恩达拿着锤子和电钻的图片（看到这我要笑死🤣）。我们要求Gemini描述这个图像中有什么。让我们调用API，看看它的响应。我把图像和提示都发送给Gemini，Gemini给我们一个响应，说图像显示一个男人拿着锤子和电钻。是的，它还注意到吴恩达在微笑。

流程：可以复制每一步，按照下面步骤跟gpt聊下去原理：把mj的官网的说明书喂给gpt，让它根据说明一步步的了解机制和结构，给出适合的提示词。tips：如果mj的官网说明更新了，大家可以自主替换也可以用这个方法去学习一些其他技能1——————————————————————————————————————我将使用一个Diffusion Model模型去生成一张图片或照片。现在我提供给你关于这个模型的资料，回答是否可以吗？2——————————————————————————————————————这是Midjourney的工作原理介绍:Midjourney is an Al image generation tool that takes inputs through text prompts and parameters and uses a Machine Learning(ML)algorithm trained on a large amount of image data to produce unique images.is powered by Latent Diffusion Model(LDM),a cutting-edge text-to-image synthesis technique.Before understanding how LDMs work,let us look at what Diffusion models are and why we need LDMs.Diffusion models(DM)are transformer-based generative models that take apiece of data,for example,an image,and gradually add noise over time until itis not recognizable.From that point,they try reconstructing the image to its original form,and in doing so,they learn how to generate pictures or other data.The issue with DMs is that the powerful ones often consume hundreds of GPU days,and inference is quite expensive due to sequential evaluations.To enable DM training on limited computational resources without compromising their quality as well as flexibility,DMs are applied in the latent space of powerful pre-trained autoencoders.

Training a diffusion model on such a representation makes it possible to achieve an optimal point between complexity reduction and detail preservation,significantly improving visual fidelity.Introducing a cross attention layer to the model architecture turns the diffusion model into a powerful and flexible generator for generally conditioned inputs such as text and bounding boxes,enabling high-resolution convolution-based synthesis.先不用多做回复，请问答是否收到即可？3——————————————————————————————————————VersionMidjourney routinely releases new model versions to improve efficiency,coherency,and quality.The latest model is the default,but other models can be used using the--version or--v parameter or by using the/settings command and selecting a model version.Different models excel at different types of images.Newest ModelThe Midjourney V5 model is the newest and most advanced model,released on March 15th,2023.To use this model,add the--v 5 parameter to the end of a prompt,or use the/settings command and select MJ Version 5.This model has very high Coherency,excels at interpreting natural language prompts,is higher resolution,and supports advanced features like repeating patterns with–tile.What's new with the V5 base model?-Much wider stylistic range and more responsive to prompting-Much higher image quality(2x resolution increase)improved dynamic range-More detailed images.Details more likely to be correct.Less unwanted text-Improved performance with image prompting-Supports--tile argument for seamless tiling(experimental)-Supports--ar aspect ratios greater than 2:1(experimental)-Supports--iw for weighing image prompts versus text promptsStyle and prompting for V5