Unleashing Real-Time Image Generation with Latent Consistency Models and Next.js

The world of generative AI is rapidly evolving, and Latent Consistency Models (LCMs) are emerging as a groundbreaking advancement, offering “synthesizing high-resolution images with few-step inference.” These models represent the next frontier after Latent Diffusion Models (LDMs), promising a significant leap in both speed and accessibility for AI-powered image generation.

[00:00:57.428] The ability of LCMs to achieve a “10 to 20x improvement in speed” for text-to-image generation and natural language to image generation is truly transformative. This speed boost opens up a vast array of new possibilities that were previously unattainable with earlier models.

[00:00:34.499] Fal.ai has embraced this potential by releasing an SDK that utilizes web sockets to connect to their API. This integration allows developers to build applications and create real-time image generation experiences within a remarkably short timeframe, estimated to be around “10 to 15 minutes,” a stark contrast to the days of manual, from-scratch model building.

[00:00:58.151] The impact of LCMs is already being felt across social media, with impressive examples showcasing their capabilities. One notable demonstration involves transforming a live video feed into an AI-generated image in real-time.

[00:01:01.221] In this specific example, a user is seen interacting with a real-time AI image generation system. As the user’s facial expression and pose change, the generated image on the screen dynamically updates, demonstrating the “real-time latent consistency models” in action. The transformation of a person’s face into that of Elon Musk, complete with “curly hair,” highlights the model’s versatility and responsiveness.

[00:01:14.161] Another compelling application showcased is a drawing app that leverages real-time AI. Here, a user sketches a simple palm tree on an iPad. As the drawing progresses, the AI “overlaid on top of the image” dynamically generates corresponding visual elements, creating an interactive and fluid creative process.

[00:01:31.080] Further demonstrating the power of this technology, a user is shown interacting with a drawing application powered by ExcaliDraw and SDXL-Turbo. By drawing on the canvas, they can “turn anything into AI in real-time,” with the generated output appearing dynamically on screen. The example shows the transformation of a rough sketch into a detailed image of a raccoon in real-time, a testament to the model’s ability to interpret and render complex prompts.

[00:01:43.256] In a gaming context, we see the application of AI for game canvas generation. As the user manipulates the view within the game, the AI dynamically generates and modifies the game environment in real-time, showcasing the potential for “interactive game creation.”

[00:01:55.498] The video also highlights a creative use of a browser extension that transforms a regular video into an AI-generated animation. By applying an “oops all flowers” mode, the original video content is reinterpreted into a visually rich, AI-generated animation, demonstrating the flexibility of these new models.

[00:02:08.103] This transition from video to video-to-video generation is particularly exciting. The example shows a real-time application where the input video is seamlessly transformed into a different style or aesthetic, all powered by AI. The prompt even mentions “James Bond OVER THE NETWORK,” hinting at the potential for real-time character or scene transformations.

[00:02:14.104] The underlying technology, @fal.ai/serverless-client, is showcased as a key enabler for these real-time applications. The video then delves into the practical steps of setting up a Next.js project to leverage these capabilities.

[00:02:31.923] The process begins with creating a new Next.js application using the command:

npx create-next-app falaiturbo

This command initiates the project setup, prompting the user to select desired configurations like TypeScript, ESLint, Tailwind CSS, and the src/ directory.

[00:03:11.104] Following the project setup, the necessary dependencies are installed, including @excalidraw/excalidraw and the @fal-ai/serverless-client package. These libraries are crucial for interacting with the Fal.ai API and integrating ExcaliDraw for canvas manipulation.

[00:03:46.756] The core of the application is built within the pages/index.tsx file. First, a use client directive is added to enable React Server Components. Next, the necessary imports are made, including useState from React, along with ExcaliDraw, exportToBlob, and serializeAsJSON from the ExcaliDraw library.

[00:04:16.326] The code then imports the Fal AI client and sets up essential state variables using useState. These include states for the user’s input prompt, the generated image, scene data, and loading status, which are all initialized to null or false.

[00:07:32.170] The fal.config() function is used to configure the client, specifying the proxyUrl to /api/fal/proxy. This setup allows the application to communicate with the Fal AI API through a local proxy endpoint.

[00:07:51.910] Crucially, a send function is defined, which utilizes fal.realtime.connect() to establish a connection to the desired model. In this tutorial, the 110602490-sdxl-turbo-realtime model is used, identified by its unique ID.

[00:08:31.000] The send function also incorporates a connectionKey and an onResult handler. The onResult handler is responsible for processing the response from the AI model. If an error occurs, it returns; otherwise, it updates the image state with the URL of the generated image.

[00:09:07.231] A helper function, getDataUrl, is defined to retrieve the data URL for the canvas. This function first checks if any elements exist in the appState. If so, it converts the ExcaliDraw scene into a blob using exportToBlob, then creates a data URL from that blob.

[00:09:54.310] An onChange handler is implemented for the ExcaliDraw component. This handler is triggered whenever the canvas content changes. It first serializes the new scene data and then checks if the newSceneData differs from the existing sceneData.

[00:10:24.937] If there’s a change, it updates the application state, sets the new scene data, fetches the data URL, and finally calls the send function to generate a new image based on the updated prompt and data URL.

[00:11:23.680] The code also includes a console.log('change') statement within the onChange handler to track when changes are detected. This helps in debugging and understanding the flow of data.

[00:14:44.439] When the application runs, a canvas appears, allowing users to draw directly. As the drawing is made, the AI model processes the input and generates corresponding images in real-time, demonstrating the seamless integration of LCMs into interactive applications. The console logs show the “change” events being triggered as the drawing occurs.

[00:15:40.011] The final code structure includes the ExcaliDraw component, which takes the api as a prop and uses it to manage the drawing state. The onChange handler is central to this process, orchestrating the data flow from user input to AI generation.

This tutorial provides a comprehensive guide to building a real-time image generation application using Latent Consistency Models, Next.js, and ExcaliDraw, showcasing the immense potential of these technologies for creative and interactive experiences.