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调用ios原生分享

要调用iOS原生分享功能，可以使用`UIActivityViewController`类来实现。下面是一个简单的示例代码：

```swift
import UIKit

class ViewController: UIViewController {

    @IBAction func shareButtonTapped(_ sender: UIButton) {
        let text = "Hello, World!"
        let image = UIImage(named: "exampleImage")
        let url = URL(string: "https://www.example.com")
        
        var itemsToShare = [Any]()
        if let text = text {
            itemsToShare.append(text)
        }
        if let image = image {
            itemsToShare.append(image)
        }
        if let url = url {
            itemsToShare.append(url)
        }
        
        let activityViewController = UIActivityViewController(activityItems: itemsToShare, applicationActivities: nil)
        activityViewController.popoverPresentationController?.sourceView = sender

        present(activityViewController, animated: true, completion: nil)
    }
}
```

在这个示例中，当分享按钮被点击时，会创建一个`UIActivityViewController`实例，将要分享的内容（文本、图片、链接）添加到`activityItems`数组中。然后，通过`present`方法将`activityViewController`显示出来。用户可以选择要使用的分享途径（如短信、邮件、社交媒体应用等）来分享内容。

已知 int x=3, y=10，求 x++ + ++y + y 等于多少

首先，在表达式 x++ + ++y + y 中，x++ 表示先使用 x 的值，然后再将 x 的值增加 1，而且 ++y 表示先将 y 的值增加 1，然后再使用 y 的值。

根据题目中给出的条件，我们有 x=3, y=10。

- 首先，x++ 表示先使用 x 的值，即先将 x 的值 3 加入表达式中，然后再将 x 的值增加 1，所以此时表达式变为 3 + ++y + y。
- 接着，++y 表示先将 y 的值增加 1，即将 y 的值 10 增加 1，变为 11，所以此时表达式变为 3 + 11 + y。
- 最后，y 表示使用 y 的值，即将 y 的值 11 加入表达式中。

将这些值带入表达式中，得到 3 + 11 + 11 = 25。

所以，x++ + ++y + y 等于 25。

以下程序片段的输出值是多少                     
int a[][] = new int[5][2];
for (int i = 0; i <5; i++) {
  a[i][0] = i;
  if (i >0) a[i][1] = a[i][0] + a[i-1][1];
}
System.out.println(a[4][1]);

首先，创建一个二维数组 a，其中有 5 行 2 列。

然后，通过 for 循环遍历数组的每一行，i 从 0 到 4。

在循环中，将 a[i][0] 的值设置为 i，也就是将行号赋值给第一列。

接着，使用 if 条件判断，当 i 大于 0 时，将 a[i][1] 的值设置为 a[i][0] 加上 a[i-1][1]。其中，a[i-1][1] 表示前一行的第二列的值。

最后，输出 a[4][1] 的值。

根据程序计算得到：

- 第 0 行的第一列：a[0][0] = 0
- 第 1 行的第一列：a[1][0] = 1
- 第 2 行的第一列：a[2][0] = 2
- 第 3 行的第一列：a[3][0] = 3
- 第 4 行的第一列：a[4][0] = 4

根据 if 条件判断：

- 第 1 行的第二列：a[1][1] = a[1][0] + a[0][1] = 1 + 0 = 1
- 第 2 行的第二列：a[2][1] = a[2][0] + a[1][1] = 2 + 1 = 3
- 第 3 行的第二列：a[3][1] = a[3][0] + a[2][1] = 3 + 3 = 6
- 第 4 行的第二列：a[4][1] = a[4][0] + a[3][1] = 4 + 6 = 10

因此，最后的输出值为 10。

关于接口和抽象类的描述，错误的是？
A.类可以实现很多个接口，但是只能继承一个抽象类
B.抽象类可以有构造方法，接口中不能有构造方法
C.抽象类中可以包含静态方法，接口中不能包含静态方法
D.一个类实现接口或继承抽象类时，都必须实现它们定义的所有方法

错误的描述是：A. 类可以实现很多个接口，但是只能继承一个抽象类。

实际上，类可以同时实现多个接口，也可以继承一个抽象类。所以类既可以实现多个接口，也可以继承一个抽象类。

以下哪个方法用于定义线程的执行体？
A.start()
B.init()
C.run()
D.execute()

答案是 C. run()

在Java中，用于定义线程的执行体的方法是 `run()`。当线程启动后，会调用 `run()` 方法来执行线程的任务代码。

选项 A 中的 `start()` 方法用于启动线程，会创建一个新的线程，并执行 `run()` 方法。

选项 B 中的 `init()` 方法并非用于定义线程的执行体，它是一个生命周期方法，在某些特殊的情况下才会使用到。

选项 D 中的 `execute()` 方法不是用于定义线程的执行体，它通常用于执行一些任务或者提交线程池执行。

所以，正确的选项是 C. run()。

以下关于List删除方法使用错误的是		
A.list循环外指定index删除：list.remove(i);
B.list循环外指定元素删除：list.remove(obj);
C.for(Object obj:list){list.remove(obj);}
D.for(Iterator it= list.iterator();it.hasNext();){it.next();it.remove();}

错误的使用是C. for(Object obj:list){list.remove(obj);}

在循环中使用 list.remove(obj) 方法删除元素会导致 ConcurrentModificationException 异常。这是因为在使用增强型 for 循环遍历 List 时，不能修改 List 的结构，否则会抛出该异常。

正确的做法是使用 Iterator 来遍历并删除 List 中的元素，类似于选项 D 中的代码：

```java
for (Iterator it = list.iterator(); it.hasNext();) {
    it.next();
    it.remove();
}
```

这样可以通过 Iterator 的 remove() 方法来安全地删除元素，而不会抛出异常。

所以，选项 C 的代码是错误的使用方法。

工程造价论文

工程造价是指工程项目的建设投资及相关费用。工程造价论文是指对工程造价相关问题进行研究和探讨的论文。下面是一些可能的工程造价论文题目：

1. 工程造价控制及优化研究
2. 工程造价预测模型的建立与应用
3. 工程变更对造价的影响研究
4. 工程造价与质量关系的研究
5. 建筑工程中人力资源成本的控制与管理
6. 工程绿色建造对造价的影响研究
7. 工程风险管理与造价控制研究
8. 建筑工程施工工序优化与造价控制研究
9. 工程材料采购成本控制研究
10. 工程造价管理软件的开发与应用

这些题目只是为您提供一些参考，您可以根据自己的兴趣和实际情况选择一个适合的题目进行研究。

What's this ( https://github.com/Stability-AI/stablediffusion ) about?

The link you provided is to a GitHub repository named "stablediffusion" owned by Stability AI. Unfortunately, without further information, it is difficult to determine the exact purpose and functionality of the project. However, the name "Stability AI" suggests that it might be related to artificial intelligence or machine learning. To get a better understanding, it is recommended to visit the repository and read its documentation, explore the source code, or refer to any provided README files or project descriptions.

 README.md
Stable Diffusion Version 2

t2i t2i t2i

This repository contains Stable Diffusion models trained from scratch and will be continuously updated with new checkpoints. The following list provides an overview of all currently available models. More coming soon.
News

March 24, 2023

Stable UnCLIP 2.1

    New stable diffusion finetune (Stable unCLIP 2.1, Hugging Face) at 768x768 resolution, based on SD2.1-768. This model allows for image variations and mixing operations as described in Hierarchical Text-Conditional Image Generation with CLIP Latents, and, thanks to its modularity, can be combined with other models such as KARLO. Comes in two variants: Stable unCLIP-L and Stable unCLIP-H, which are conditioned on CLIP ViT-L and ViT-H image embeddings, respectively. Instructions are available here.

    A public demo of SD-unCLIP is already available at clipdrop.co/stable-diffusion-reimagine

December 7, 2022

Version 2.1

    New stable diffusion model (Stable Diffusion 2.1-v, Hugging Face) at 768x768 resolution and (Stable Diffusion 2.1-base, HuggingFace) at 512x512 resolution, both based on the same number of parameters and architecture as 2.0 and fine-tuned on 2.0, on a less restrictive NSFW filtering of the LAION-5B dataset. Per default, the attention operation of the model is evaluated at full precision when xformers is not installed. To enable fp16 (which can cause numerical instabilities with the vanilla attention module on the v2.1 model) , run your script with ATTN_PRECISION=fp16 python <thescript.py>

November 24, 2022

Version 2.0

    New stable diffusion model (Stable Diffusion 2.0-v) at 768x768 resolution. Same number of parameters in the U-Net as 1.5, but uses OpenCLIP-ViT/H as the text encoder and is trained from scratch. SD 2.0-v is a so-called v-prediction model.

    The above model is finetuned from SD 2.0-base, which was trained as a standard noise-prediction model on 512x512 images and is also made available.

    Added a x4 upscaling latent text-guided diffusion model.

    New depth-guided stable diffusion model, finetuned from SD 2.0-base. The model is conditioned on monocular depth estimates inferred via MiDaS and can be used for structure-preserving img2img and shape-conditional synthesis.

    d2i

    A text-guided inpainting model, finetuned from SD 2.0-base.

We follow the original repository and provide basic inference scripts to sample from the models.

The original Stable Diffusion model was created in a collaboration with CompVis and RunwayML and builds upon the work:

High-Resolution Image Synthesis with Latent Diffusion Models
Robin Rombach*, Andreas Blattmann*, Dominik Lorenz, Patrick Esser, Björn Ommer
CVPR '22 Oral | GitHub | arXiv | Project page

and many others.

Stable Diffusion is a latent text-to-image diffusion model.
Requirements

You can update an existing latent diffusion environment by running

conda install pytorch==1.12.1 torchvision==0.13.1 -c pytorch
pip install transformers==4.19.2 diffusers invisible-watermark
pip install -e .

xformers efficient attention

For more efficiency and speed on GPUs, we highly recommended installing the xformers library.

Tested on A100 with CUDA 11.4. Installation needs a somewhat recent version of nvcc and gcc/g++, obtain those, e.g., via

export CUDA_HOME=/usr/local/cuda-11.4
conda install -c nvidia/label/cuda-11.4.0 cuda-nvcc
conda install -c conda-forge gcc
conda install -c conda-forge gxx_linux-64==9.5.0

Then, run the following (compiling takes up to 30 min).

cd ..
git clone https://github.com/facebookresearch/xformers.git
cd xformers
git submodule update --init --recursive
pip install -r requirements.txt
pip install -e .
cd ../stablediffusion

Upon successful installation, the code will automatically default to memory efficient attention for the self- and cross-attention layers in the U-Net and autoencoder.
General Disclaimer

Stable Diffusion models are general text-to-image diffusion models and therefore mirror biases and (mis-)conceptions that are present in their training data. Although efforts were made to reduce the inclusion of explicit pornographic material, we do not recommend using the provided weights for services or products without additional safety mechanisms and considerations. The weights are research artifacts and should be treated as such. Details on the training procedure and data, as well as the intended use of the model can be found in the corresponding model card. The weights are available via the StabilityAI organization at Hugging Face under the CreativeML Open RAIL++-M License.
Stable Diffusion v2

Stable Diffusion v2 refers to a specific configuration of the model architecture that uses a downsampling-factor 8 autoencoder with an 865M UNet and OpenCLIP ViT-H/14 text encoder for the diffusion model. The SD 2-v model produces 768x768 px outputs.

Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 DDIM sampling steps show the relative improvements of the checkpoints:

sd evaluation results
Text-to-Image

txt2img-stable2 txt2img-stable2

Stable Diffusion 2 is a latent diffusion model conditioned on the penultimate text embeddings of a CLIP ViT-H/14 text encoder. We provide a reference script for sampling.
Reference Sampling Script

This script incorporates an invisible watermarking of the outputs, to help viewers identify the images as machine-generated. We provide the configs for the SD2-v (768px) and SD2-base (512px) model.

First, download the weights for SD2.1-v and SD2.1-base.

To sample from the SD2.1-v model, run the following:

python scripts/txt2img.py --prompt "a professional photograph of an astronaut riding a horse" --ckpt <path/to/768model.ckpt/> --config configs/stable-diffusion/v2-inference-v.yaml --H 768 --W 768  

or try out the Web Demo: Hugging Face Spaces.

To sample from the base model, use

python scripts/txt2img.py --prompt "a professional photograph of an astronaut riding a horse" --ckpt <path/to/model.ckpt/> --config <path/to/config.yaml/>  

By default, this uses the DDIM sampler, and renders images of size 768x768 (which it was trained on) in 50 steps. Empirically, the v-models can be sampled with higher guidance scales.

Note: The inference config for all model versions is designed to be used with EMA-only checkpoints. For this reason use_ema=False is set in the configuration, otherwise the code will try to switch from non-EMA to EMA weights.
Enable Intel® Extension for PyTorch* optimizations in Text-to-Image script

If you're planning on running Text-to-Image on Intel® CPU, try to sample an image with TorchScript and Intel® Extension for PyTorch* optimizations. Intel® Extension for PyTorch* extends PyTorch by enabling up-to-date features optimizations for an extra performance boost on Intel® hardware. It can optimize memory layout of the operators to Channel Last memory format, which is generally beneficial for Intel CPUs, take advantage of the most advanced instruction set available on a machine, optimize operators and many more.

Prerequisites

Before running the script, make sure you have all needed libraries installed. (the optimization was checked on Ubuntu 20.04). Install jemalloc, numactl, Intel® OpenMP and Intel® Extension for PyTorch*.

apt-get install numactl libjemalloc-dev
pip install intel-openmp
pip install intel_extension_for_pytorch -f https://software.intel.com/ipex-whl-stable

To sample from the SD2.1-v model with TorchScript+IPEX optimizations, run the following. Remember to specify desired number of instances you want to run the program on (more).

MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance <number of an instance> --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt <path/to/768model.ckpt/> --config configs/stable-diffusion/intel/v2-inference-v-fp32.yaml  --H 768 --W 768 --precision full --device cpu --torchscript --ipex

To sample from the base model with IPEX optimizations, use

MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance <number of an instance> --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt <path/to/model.ckpt/> --config configs/stable-diffusion/intel/v2-inference-fp32.yaml  --n_samples 1 --n_iter 4 --precision full --device cpu --torchscript --ipex

If you're using a CPU that supports bfloat16, consider sample from the model with bfloat16 enabled for a performance boost, like so

# SD2.1-v
MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance <number of an instance> --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt <path/to/768model.ckpt/> --config configs/stable-diffusion/intel/v2-inference-v-bf16.yaml --H 768 --W 768 --precision full --device cpu --torchscript --ipex --bf16
# SD2.1-base
MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance <number of an instance> --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt <path/to/model.ckpt/> --config configs/stable-diffusion/intel/v2-inference-bf16.yaml --precision full --device cpu --torchscript --ipex --bf16

Image Modification with Stable Diffusion

depth2img-stable2
Depth-Conditional Stable Diffusion

To augment the well-established img2img functionality of Stable Diffusion, we provide a shape-preserving stable diffusion model.

Note that the original method for image modification introduces significant semantic changes w.r.t. the initial image. If that is not desired, download our depth-conditional stable diffusion model and the dpt_hybrid MiDaS model weights, place the latter in a folder midas_models and sample via

python scripts/gradio/depth2img.py configs/stable-diffusion/v2-midas-inference.yaml <path-to-ckpt>

or

streamlit run scripts/streamlit/depth2img.py configs/stable-diffusion/v2-midas-inference.yaml <path-to-ckpt>

This method can be used on the samples of the base model itself. For example, take this sample generated by an anonymous discord user. Using the gradio or streamlit script depth2img.py, the MiDaS model first infers a monocular depth estimate given this input, and the diffusion model is then conditioned on the (relative) depth output.

depth2image

This model is particularly useful for a photorealistic style; see the examples. For a maximum strength of 1.0, the model removes all pixel-based information and only relies on the text prompt and the inferred monocular depth estimate.

depth2img-stable3
Classic Img2Img

For running the "classic" img2img, use

python scripts/img2img.py --prompt "A fantasy landscape, trending on artstation" --init-img <path-to-img.jpg> --strength 0.8 --ckpt <path/to/model.ckpt>

and adapt the checkpoint and config paths accordingly.
Image Upscaling with Stable Diffusion

upscaling-x4 After downloading the weights, run

python scripts/gradio/superresolution.py configs/stable-diffusion/x4-upscaling.yaml <path-to-checkpoint>

or

streamlit run scripts/streamlit/superresolution.py -- configs/stable-diffusion/x4-upscaling.yaml <path-to-checkpoint>

for a Gradio or Streamlit demo of the text-guided x4 superresolution model.
This model can be used both on real inputs and on synthesized examples. For the latter, we recommend setting a higher noise_level, e.g. noise_level=100.
Image Inpainting with Stable Diffusion

inpainting-stable2

Download the SD 2.0-inpainting checkpoint and run

python scripts/gradio/inpainting.py configs/stable-diffusion/v2-inpainting-inference.yaml <path-to-checkpoint>

or

streamlit run scripts/streamlit/inpainting.py -- configs/stable-diffusion/v2-inpainting-inference.yaml <path-to-checkpoint>

for a Gradio or Streamlit demo of the inpainting model. This scripts adds invisible watermarking to the demo in the RunwayML repository, but both should work interchangeably with the checkpoints/configs.
Shout-Outs

    Thanks to Hugging Face and in particular Apolinário for support with our model releases!
    Stable Diffusion would not be possible without LAION and their efforts to create open, large-scale datasets.
    The DeepFloyd team at Stability AI, for creating the subset of LAION-5B dataset used to train the model.
    Stable Diffusion 2.0 uses OpenCLIP, trained by Romain Beaumont.
    Our codebase for the diffusion models builds heavily on OpenAI's ADM codebase and https://github.com/lucidrains/denoising-diffusion-pytorch. Thanks for open-sourcing!
    CompVis initial stable diffusion release
    Patrick's implementation of the streamlit demo for inpainting.
    img2img is an application of SDEdit by Chenlin Meng from the Stanford AI Lab.
    Kat's implementation of the PLMS sampler, and more.
    DPMSolver integration by Cheng Lu.
    Facebook's xformers for efficient attention computation.
    MiDaS for monocular depth estimation.

License

The code in this repository is released under the MIT License.

The weights are available via the StabilityAI organization at Hugging Face, and released under the CreativeML Open RAIL++-M License License.
BibTeX

@misc{rombach2021highresolution,
      title={High-Resolution Image Synthesis with Latent Diffusion Models}, 
      author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
      year={2021},
      eprint={2112.10752},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

The GitHub repository "stablediffusion" contains Stable Diffusion models and is continuously updated with new checkpoints. Stable Diffusion is a latent text-to-image diffusion model. The repository provides various models, including Stable unCLIP, stable diffusion models at different resolutions, depth-guided diffusion models, and text-guided inpainting models. The models are trained from scratch and can be used for tasks such as text-to-image synthesis, image modification, image upscaling, and image inpainting. The repository includes documentation, examples, and scripts for sampling and using the models. The models are designed to be used with specific requirements and dependencies mentioned in the README file. The code in the repository is released under the MIT License, while the weights are available under the CreativeML Open RAIL++-M License.

What are weights in this context?

In the context of machine learning, "weights" typically refers to the parameters of a trained model. These parameters are learned during the training process and capture the knowledge and patterns present in the training data. In the case of the "stablediffusion" repository, weights refer to the trained parameters of the Stable Diffusion models. These weights are numerical values that define the model's behavior and are used during the inference phase to generate or modify images based on given text prompts. The weights capture the learned relationships between text inputs and image outputs, allowing the model to generate realistic and relevant images based on the provided text descriptions.