Gemini 2.0 blew me away - The future of Multimodal Model

Key Concepts

• Gemini 2.0 Experimental Model: Google's multimodal model supporting both image understanding and generation.
• Image Generation & Editing: Using Gemini 2.0 to create and modify images based on text prompts.
• Multimodal Model: An AI model that can process and generate different types of data, such as text and images.
• Replicate: A platform for hosting and deploying AI models, including image and video generation models.
• 1 2.1: An image-to-video model hosted on Replicate, used to generate videos from images.
• Streamlit: A Python framework for building interactive web applications.
• API (Application Programming Interface): A set of rules and specifications that software programs can follow to communicate with each other.

Gemini 2.0 Experimental Model: Overview and Capabilities

• Introduction: Google released Gemini 2.0, a multimodal model capable of both understanding and generating images.
• Image Understanding and Generation: Users can upload an image and provide a text prompt, and the model will respond with both text and a generated image.
• Examples:
  • Combining images: Uploading an image of a model and an image of clothes to create a new image.
  • Image extraction: Extracting a passport photo from an image with high fidelity.
  • Animation generation: Generating multiple images in a row to create frames for an animation or GIF.
• API Availability and Cost: The experimental model is available via API and is significantly cheaper (96% cheaper) than OpenAI's GPT-4o and Cloud.
• Potential Applications: AI-native Photoshop, GIF makers, and other applications where users can interact with the AI to update images or generate animations.

Testing Gemini 2.0's Image Generation Capabilities

• Example Use Cases:
  • Image editing: Adding chocolate toppings to an image of ice cream.
  • Visual story generation: Generating a story with an image for each scene.
• Personal Tests:
  • Changing a flag: Uploading an image of a man with a flag and changing the flag to the USA flag. The result was "extremely good," with minor facial changes.
  • Sketch to 3D render: Converting a sketch into a 3D render with a colorful style.
  • GIF generation: Generating five frames of a 2D pixel game of a dragon monster. The results were "awesome," with some inconsistencies in the last two images.
• Observations:
  • High-quality image generation on the first shot.
  • Quality decreases with more turns in the conversation (prompting).
  • Good at maintaining character consistency across different images.
• Conclusion: Gemini 2.0 is impressive and enables more people to do image editing jobs, potentially leading to new types of Photoshop or Canva experiences.

Building a Prototype with Gemini 2.0 API

• Project Setup:
  • Creating a new project in Cursor.
  • Adding an .env file to store the Gemini API key (obtained from Google AI Studio).
  • Creating a gemini_experimental.py file.
• Code Implementation:
  • Importing necessary libraries.
  • Creating a Gemini client.
  • Creating a user prompt message (using types.Part).
  • Passing the conversation history to the Gemini 2.0 model.
  • Configuring the response modality to be both text and image.
  • Handling different response types (text or image).
  • Saving the generated image to a file.
• Testing Image Generation:
  • Running the script to generate an image of a cat.
• Passing an Image to Gemini:
  • Adding a types.Part from bytes with the image data.
  • Updating the prompt to modify the cat's hair color to red.
  • Running the script again to see the updated image.

Turning Images into Videos with 1 2.1

• Introduction to Replicate: Replicate is a model marketplace with various AI models for image generation, video generation, and large language models.
• Using the 1 2.1 Model:
  • Creating a Replicate account.
  • Selecting the "1 2.1 480p" model.
  • Copying the Python code example from the API page.
• Modifying the Code:
  • Changing the code to read an image from the local disk instead of a URL.
  • Creating a function to open a local image, pass it to the Replicate model, and save the video.
• Testing the Video Generation:
  • Running the function with the generated cat photo and a prompt ("cat is looking around").
  • Ensuring Replicate is installed (pip install replicate).
  • Verifying the generation of a 5-second video.

Building a Web Application with Streamlit

• Creating a General Function for Gemini Response:
  • Detecting if it's the first message from the user.
  • Attaching the uploaded image as part of the content.
  • Appending messages and generating a response.
• Creating a Function for Video Generation:
  • Calling the Replicate model to generate a video.
  • Returning the video path when ready.
• Creating Helper Functions:
  • Resetting the video state.
• Utility Functions (in utility.py):
  • Saving binary files.
  • Processing uploaded images.
  • Checking if images are duplicated.
• Building the GUI with Streamlit:
  • Importing necessary packages and libraries.
  • Setting a title for the app.
  • Defining a list of states to track messages, uploaded images, Gemini-returned images, and 1 2.1-returned videos.
  • Creating a sidebar for users to upload images.
  • Displaying the list of images and updating the state.
  • Creating two tabs:
    • Chat Experience: Allowing users to chat with Gemini to iterate on the image. Displaying chat history and handling the "Send" message logic.
    • Video Generation: Displaying the images returned by Gemini for users to select. Allowing users to select an image and generate a video. Displaying the generated video on the screen.
• Running the Application:
  • Using streamlit run app.py to start the web app.
• Testing the Web App:
  • Uploading an image of a bracelet.
  • Prompting Gemini to generate a product shot of a hand wearing the bracelet.
  • Prompting Gemini to change the hand to a black man's hand.
  • Generating a video from one of the generated images with the prompt "a p shot at showcasing the bracelet."

Conclusion and Call to Action

• Summary: The video demonstrates how to use the Gemini 2.0 API to build a prototype application that combines image generation, editing, and video creation.
• AI Builder Club Community: Invitation to join the AI Builder Club Community for in-depth API usage, step-by-step rebuilding of the example, tips and tricks for building AI applications, and weekly live coding sessions.
• Replicate Credits: Offer of $100 free Replicate credits for the first 1,000 AI Builder Club members.
• Community Benefits: Access to a community of top AI builders for asking questions and sharing learnings.
• Link in Description: Link to join the AI Builder Club Community provided in the video description.