In recent years, diffusion models have shown great success in text-to-image generation, achieving high image quality, improved inference performance, and expanding our creative inspiration. Nevertheless, it is still challenging to efficiently control the generation, especially with conditions that are difficult to describe with text.
Today, we announce MediaPipe diffusion plugins, which enable controllable text-to-image generation to be run on-device. Expanding upon our prior work on GPU inference for on-device large generative models, we introduce new low-cost solutions for controllable text-to-image generation that can be plugged into existing diffusion models and their Low-Rank Adaptation (LoRA) variants.
Text-to-image generation with control plugins running on-device.
Background
With diffusion models, image generation is modeled as an iterative denoising process. Starting from a noise image, at each step, the diffusion model gradually denoises the image to reveal an image of the target concept. Research shows that leveraging language understanding via text prompts can greatly improve image generation. For text-to-image generation, the text embedding is connected to the model via cross-attention layers. Yet, some information is difficult to describe by text prompts, e.g., the position and pose of an object. To address this problem, researchers add additional models into the diffusion to inject control information from a condition image.
Common approaches for controlled text-to-image generation include Plug-and-Play, ControlNet, and T2I Adapter. Plug-and-Play applies a widely used denoising diffusion implicit model (DDIM) inversion approach that reverses the generation process starting from an input image to derive an initial noise input, and then employs a copy of the diffusion model (860M parameters for Stable Diffusion 1.5) to encode the condition from an input image. Plug-and-Play extracts spatial features with self-attention from the copied diffusion, and injects them into the text-to-image diffusion. ControlNet creates a trainable copy of the encoder of a diffusion model, which connects via a convolution layer with zero-initialized parameters to encode conditioning information that is conveyed to the decoder layers. However, as a result, the size is large, half that of the diffusion model (430M parameters for Stable Diffusion 1.5). T2I Adapter is a smaller network (77M parameters) and achieves similar effects in controllable generation. T2I Adapter only takes the condition image as input, and its output is shared across all diffusion iterations. Yet, the adapter model is not designed for portable devices.
The MediaPipe diffusion plugins
To make conditioned generation efficient, customizable, and scalable, we design the MediaPipe diffusion plugin as a separate network that is:
Plugable: It can be easily connected to a pre-trained base model.
Trained from scratch: It does not use pre-trained weights from the base model.
Portable: It runs outside the base model on mobile devices, with negligible cost compared to the base model inference.
Method
Parameter Size
Plugable
From Scratch
Portable
Plug-and-Play
860M*
✔️
❌
❌
ControlNet
430M*
✔️
❌
❌
T2I Adapter
77M
✔️
✔️
❌
MediaPipe Plugin
6M
✔️
✔️
✔️
Comparison of Plug-and-Play, ControlNet, T2I Adapter, and the MediaPipe diffusion plugin.
* The number varies depending on the particulars of the diffusion model.
The MediaPipe diffusion plugin is a portable on-device model for text-to-image generation. It extracts multiscale features from a conditioning image, which are added to the encoder of a diffusion model at corresponding levels. When connecting to a text-to-image diffusion model, the plugin model can provide an extra conditioning signal to the image generation. We design the plugin network to be a lightweight model with only 6M parameters. It uses depth-wise convolutions and inverted bottlenecks from MobileNetv2 for fast inference on mobile devices.
Overview of the MediaPipe diffusion model plugin. The plugin is a separate network, whose output can be plugged into a pre-trained text-to-image generation model. Features extracted by the plugin are applied to the associated downsampling layer of the diffusion model (blue).
Unlike ControlNet, we inject the same control features in all diffusion iterations. That is, we only run the plugin once for one image generation, which saves computation. We illustrate some intermediate results of a diffusion process below. The control is effective at every diffusion step and enables controlled generation even at early steps. More iterations improve the alignment of the image with the text prompt and generate more detail.
Illustration of the generation process using the MediaPipe diffusion plugin.
Examples
In this work, we developed plugins for a diffusion-based text-to-image generation model with MediaPipe Face Landmark, MediaPipe Holistic Landmark, depth maps, and Canny edge. For each task, we select about 100K images from a web-scale image-text dataset, and compute control signals using corresponding MediaPipe solutions. We use refined captions from PaLI for training the plugins.
Face Landmark
The MediaPipe Face Landmarker task computes 478 landmarks (with attention) of a human face. We use the drawing utils in MediaPipe to render a face, including face contour, mouth, eyes, eyebrows, and irises, with different colors. The following table shows randomly generated samples by conditioning on face mesh and prompts. As a comparison, both ControlNet and Plugin can control text-to-image generation with given conditions.
Face-landmark plugin for text-to-image generation, compared with ControlNet.
Holistic Landmark
MediaPipe Holistic Landmarker task includes landmarks of body pose, hands, and face mesh. Below, we generate various stylized images by conditioning on the holistic features.
Holistic-landmark plugin for text-to-image generation.
Depth
Depth-plugin for text-to-image generation.
Canny Edge
Canny-edge plugin for text-to-image generation.
Evaluation
We conduct a quantitative study of the face landmark plugin to demonstrate the model’s performance. The evaluation dataset contains 5K human images. We compare the generation quality as measured by the widely used metrics, Fréchet Inception Distance (FID) and CLIP scores. The base model is a pre-trained text-to-image diffusion model. We use Stable Diffusion v1.5 here.
As shown in the following table, both ControlNet and the MediaPipe diffusion plugin produce much better sample quality than the base model, in terms of FID and CLIP scores. Unlike ControlNet, which needs to run at every diffusion step, the MediaPipe plugin only runs once for each image generated. We measured the performance of the three models on a server machine (with Nvidia V100 GPU) and a mobile phone (Galaxy S23). On the server, we run all three models with 50 diffusion steps, and on mobile, we run 20 diffusion steps using the MediaPipe image generation app. Compared with ControlNet, the MediaPipe plugin shows a clear advantage in inference efficiency while preserving the sample quality.
Model
FID↓
CLIP↑
Inference Time (s)
Nvidia V100
Galaxy S23
Base
10.32
0.26
5.0
11.5
Base + ControlNet
6.51
0.31
7.4 (+48%)
18.2 (+58.3%)
Base + MediaPipe Plugin
6.50
0.30
5.0 (+0.2%)
11.8 (+2.6%)
Quantitative comparison on FID, CLIP, and inference time.
We test the performance of the plugin on a wide range of mobile devices from mid-tier to high-end. We list the results on some representative devices in the following table, covering both Android and iOS.
Device
Android
iOS
Pixel 4
Pixel 6
Pixel 7
Galaxy S23
iPhone 12 Pro
iPhone 13 Pro
Time (ms)
128
68
50
48
73
63
Inference time (ms) of the plugin on different mobile devices.
Conclusion
In this work, we present MediaPipe, a portable plugin for conditioned text-to-image generation. It injects features extracted from a condition image to a diffusion model, and consequently controls the image generation. Portable plugins can be connected to pre-trained diffusion models running on servers or devices. By running text-to-image generation and plugins fully on-device, we enable more flexible applications of generative AI.
Acknowledgments
We’d like to thank all team members who contributed to this work: Raman Sarokin and Juhyun Lee for the GPU inference solution; Khanh LeViet, Chuo-Ling Chang, Andrei Kulik, and Matthias Grundmann for leadership. Special thanks to Jiuqiang Tang, Joe Zou and Lu wang, who made this technology and all the demos running on-device.