From Large Models to Mobile Magic: The Technology Powering the YouTube Real-Time Generative AI Effects

Effects are central to the appeal of YouTube Shorts, but to feel seamless they must run in real time inside the camera as creators record. That raises a challenge: how can capabilities from large generative AI models, such as cartoon style transfer, be delivered on a creator’s phone?

The solution is a pipeline that distills a large model’s capability into a much smaller network tailored to a single task. Narrowing the scope yields a compact, efficient model that runs on-device and processes video frame-by-frame. Using this approach, more than 20 real-time effects have launched for creators on Shorts. This report outlines the data curation, training process, and on‑device setup.

Data comes first

The foundation of this work is high‑quality data. A face dataset was assembled from properly licensed images and rigorously filtered to ensure diversity and uniform distribution across genders, ages, and skin tones measured by the Monk Skin Tone Scale, so effects perform reliably for everyone.

Teacher–student approach

The method centers on knowledge distillation via a teacher–student setup. The teacher is a large, pre‑trained generative model that can produce the desired visual effect but is too slow for real time. Early on, a custom‑trained StyleGAN2 model on the curated dataset powered facial effects and paired with tools such as StyleCLIP to manipulate facial features from text prompts. This provided a strong foundation. As the work progressed, the stack moved to more advanced generative models like Google DeepMind’s Imagen, boosting fidelity, diversity, artistic control, and the breadth of styles available for on‑device generative effects.

The student model is the network that ultimately runs on users’ devices, so it must be small, fast, and efficient. It employs a UNet‑based architecture suited to image‑to‑image tasks, using a MobileNet backbone as the encoder—known for mobile performance—and a decoder built with MobileNet blocks.

Distillation: iterative instruction

To reach production quality, a robust training methodology was developed to overcome synthetic data distillation’s pitfalls—namely artifacts and loss of high‑frequency detail. The approach leverages real‑world data to form image pairs and trains student models, enabling a more efficient hyperparameter search.

Training the smaller student model involves two main stages:

Data Generation: A large image corpus is processed through the teacher to produce thousands of “before and after” pairs. During generation, augmentations are applied—such as AR glasses, sunglasses, and occlusion with synthetic hands—and Pivotal Tuning Inversion is used to preserve user identity.

Student Training: The student is trained on these pairs using a combination of L1, LPIPS, Adaptive, and Adversarial losses to ensure outputs are numerically faithful and perceptually convincing. A neural architecture search then tunes parameters like depth multiplier and width multiplier to identify efficient architectures tailored to different use cases and effect types.

Key challenge: preserving user identity

Editing occurs in latent space, a compressed numerical representation where salient features are encoded. Converting raw pixels to this latent representation is “inversion.” In facial image‑to‑image models, preserving identity is difficult because the effect regenerates the entire frame. A naïve approach can distort crucial attributes—skin tone, glasses, clothing—so the result no longer resembles the person. This “inversion problem” arises when the model fails to faithfully represent a real face in latent space.

This is addressed with pivotal tuning inversion (PTI). In brief:

The original image is converted into an embedding using an encoder, and an initial inversion is generated with a generator. This first pass is typically close to the source but not identical; skin tone and fine facial details may differ.

The generator is fine‑tuned via PTI to preserve identity and details, yielding a new generator that performs better for the specific face and its embedding neighborhood.

The target effect is applied by editing the embedding, typically with a prepared vector created using techniques such as StyleCLIP.

The final image is produced by the fine‑tuned generator using the edited embedding, delivering the effect while keeping the face consistent.

On-device execution with MediaPipe from Google AI Edge

Once trained, the student model is integrated into a phone‑ready pipeline. The on‑device solution was built with MediaPipe, the open‑source framework for cross‑platform multimodal ML pipelines from Google AI Edge. The final inference flow operates as follows:

First, the MediaPipe Face Mesh module detects one or more faces in the video stream.

Because student models are sensitive to alignment, the system computes a stable, rotated face crop to maintain consistency.

The cropped image is converted to a tensor and passed to the lean student model.

The model renders the effect (for example, a smile or a cartoon style), then the result is warped back and seamlessly composited onto the original video frame in real time.

To feel responsive, experiences must run at 30 frames per second or more, so the pipeline must finish in under 33 milliseconds per frame. Reported inference latencies are about 6 ms on Pixel 8 Pro with Google Tensor G3 and 10.6 ms on iPhone 13 GPU. Significant optimization work—especially GPU acceleration—was invested to ensure smooth performance across a wide range of mobile devices.

The result: Enhanced mobile creativity

This technology has been a key part of YouTube Shorts since 2023, enabling popular releases such as expression‑based effects (e.g., Never blink), Halloween‑themed masks (e.g., Risen zombie), and immersive full‑frame effects (e.g., Toon 2). Together, these have broadened creative options for video creators on the platform.

By narrowing massive generative models to fit mobile constraints, the work pushes the boundaries of what is possible for real‑time, on‑device generative effects. Next steps include integrating newer models like Veo 3 and sharply reducing latency on entry‑level devices, further democratizing access to cutting‑edge generative AI in YouTube Shorts.