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Why Amiga Coding Still Matters: A Look at the Impact on Modern Game Development?

  The computers were first created in the 1985 to 19940's as end of their life. The best way I could explain them is like across between Windows and Linux in the sense vety easy to do jobs with but still little bit hard to work. A lot of people loved how easy it was work doing complex this is why many software engineers started on it the following countrys had many use them often in  the past United Kingdom Canada New Zealand Australia Poland South Africa  A lot of people made many different things with it such as Videogames Music Softwares Many of the video games were way better than windows or Apple it was at the time best for gaming more less plus better that SNES or Mega drive No real mascot game existed but many good ones existed such as Lemmings a great puzzlegame got released many times. Toughbook  CF-19 Dune 2 could be considered one of the first games to  do command and conquer style of game very for getting resources or fighting the other army dur...

Create Python code to make bulk generate AI images

 To generate AI images in bulk, we can use a deep learning library like TensorFlow and a pre-trained image generation model like DALL-E or GPT-3's image generation API. Here's a general outline of the code:

  1. Install necessary libraries: TensorFlow, Pillow (Python Imaging Library)

  2. Load the pre-trained image generation model. For example, if using DALL-E, we can use the OpenAI API to generate images.

  3. Define a function that takes in a list of text prompts and generates an image for each prompt.

  4. The function should call the image generation model for each prompt and save the resulting image to a specified folder.

Here's the Python code that implements this:

python
import requests import json from PIL import Image # API key for OpenAI API_KEY = 'YOUR_API_KEY_HERE' # Function to generate image from text prompt def generate_image(prompt, model): url = 'https://api.openai.com/v1/images/generations' headers = { 'Content-Type': 'application/json', 'Authorization': f'Bearer {API_KEY}' } data = { 'model': model, 'prompt': prompt, 'num_images': 1, 'size': '512x512' } response = requests.post(url, headers=headers, data=json.dumps(data)) if response.status_code != 200: print('Error:', response.json()['error']) return None else: image_url = response.json()['data'][0]['url'] image_response = requests.get(image_url) image = Image.open(BytesIO(image_response.content)) return image # List of text prompts to generate images for prompts = ['cat', 'dog', 'tree'] # Model to use for image generation model = 'image-alpha-001' # Folder to save generated images folder_path = 'generated_images/' # Generate image for each prompt and save to folder for prompt in prompts: image = generate_image(prompt, model) if image is not None: file_name = prompt + '.jpg' image.save(folder_path + file_name)

https://www.theengineer.info/This code uses the OpenAI API to generate images based on text prompts, and saves the resulting images to a specified folder. You can modify the prompts, model, and folder_path variables to suit your needs. Note that you'll need an API key from OpenAI to use their image generation API.



Saysimport requests import json from PIL import Image # API key for OpenAI API_KEY = 'YOUR_API_KEY_HERE' # Function to generate image from text prompt def generate_image(prompt, model): url = 'https://api.openai.com/v1/images/generations' headers = { 'Content-Type': 'application/json', 'Authorization': f'Bearer {API_KEY}' } data = { 'model': model, 'prompt': prompt, 'num_images': 1, 'size': '512x512' } response = requests.post(url, headers=headers, data=json.dumps(data)) if response.status_code != 200: print('Error:', response.json()['error']) return None else: image_url = response.json()['data'][0]['url'] image_response = requests.get(image_url) image = Image.open(BytesIO(image_response.content)) return image # List of text prompts to generate images for prompts = ['cat', 'dog', 'tree'] # Model to use for image generation model = 'image-alpha-001' # Folder to save generated images folder_path = 'generated_images/' # Generate image for each prompt and save to folder for prompt in prompts: image = generate_image(prompt, model) if image is not None: file_name = prompt + '.jpg' image.save(folder_path + file_name)




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