In this video, we'll explore vision and diffusion models to analyze and modify images from tweets. We'll process image URLs from tweets, describe and extract entities with a vision-to-language model, and generate or edit images using diffusion models. Let’s break down the steps for this tutorial.
Demo notebook: https://github.com/xbwei/data-analysis-with-generative-ai/blob/main/Twitter-Image-Classification-Recreation-Editing.ipynb
Step 1: Prepare Data
Collect Tweet Data: We'll work with a dataset of tweets, specifically focusing on those with images. For efficiency, we’ll use a smaller sample (around 500 tweets), selecting only those containing image URLs.
Identify Image URLs: In Twitter data, image URLs are stored under entities.url. We’ll extract these URLs, focusing on the 150x150-pixel versions for faster processing.
To learn more about collecting Twitter data, please check out our online course, Introduction to Database and Data Collection.
Step 2: Set Up the Environment
Set Up Credentials: Securely store API keys (for AWS, MongoDB, and OpenAI) in AWS Secrets Manager for safe access.
Install Required Libraries: Use pymongo to manage the MongoDB database and openai for accessing vision and diffusion models.
Load Libraries: Import essential Python libraries and retrieve stored API credentials to connect to the necessary services.
Step 3: Extract Image URLs
Filter Tweets with Images: Extract tweets containing image URLs, store their IDs, and URLs in a list for easy access.
Verify Images: Check that each URL is accessible. Some may be unavailable if tweets were deleted after data collection.
Step 4: Define Utility Functions
Retrieve Image from URL: Define a function to pull images from URLs.
Display Images: Use matplotlib to visualize images in the notebook.
Convert Image to Bytes: Convert images to byte format, which OpenAI models require, and save them as PNGs.
Step 5: Use Vision-to-Language Models
Analyze Images: Use OpenAI’s vision-to-language model to analyze each image, generating descriptions and identifying entities (e.g., people, places).
Store Analysis Results: Save each image’s description and entities as JSON documents for further use.
View Results: Load the data into MongoDB to enable easy visualization of entities (such as charts) through MongoDB’s natural language charting tools.
Step 6: Generate Images with Diffusion Models
Understand Diffusion Model Options: OpenAI offers two diffusion models, DALL-E 2 and DALL-E 3. DALL-E 2 can create variations of images or edit existing images.
Generate Images from Prompts: Create images based on text prompts (e.g., “a woman handling voting equipment”) using DALL-E 2 for a demonstration.
Create Variants of Existing Images: Upload an original image, then use DALL-E 2 to generate a modified version without a prompt.
Step 7: Edit Images with Diffusion Models
Apply Edits with Prompts and Masks: To modify a specific part of an image, upload the image, define a region to edit with a mask, and specify changes through a prompt.
Create a Mask for Image Edits: Use a Python library like torch and DeepLab V2 model to create masks for segmentation. ChatGPT can assist with generating code for this if needed.
Perform the Edit: Provide the masked region and prompt to DALL-E 2 for customized edits based on instructions (e.g., changing colors or adding details).
Responsible AI Consideration
When generating images from text or editing existing images, it’s important to avoid creating content that could be harmful or invade someone's privacy. AI should not be used to generate images that could reveal private information or misrepresent individuals.
AI companies implement automated content moderation systems to detect and filter harmful content before it's generated or shared. For example, OpenAI’s DALL·E includes features that automatically detect and prevent the generation of certain types of harmful content, such as explicit images or violent depictions. It also has capabilities to ensure that users can distinguish between AI-generated content and real images.
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