Model reference · open weights

LaMa-Dilated

Image qualcomm Image edit 1 build Open weights 1k dl/mo

LaMa-Dilated is an open-weight image model from qualcomm.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • LaMa-Dilated is an image-to-image model developed by Qualcomm for erasing and in-painting parts of input images.
  • It features a 512x512 input resolution, 45.6 million parameters, and a float model size of 174 MB.
  • The model is licensed under Apache-2.0 and is optimized for deployment on Qualcomm devices using ONNX, QNN_DLC, or TFLITE runtimes.

Summary of the qualcomm/LaMa-Dilated model card, 2026-10-01

What it is

Released byqualcomm
Released2024-02-25

From the model card

What qualcomm says about LaMa-Dilated

Read the model card

LaMa-Dilated is a machine learning model that allows to erase and in-paint part of given input image.

This is based on the implementation of LaMa-Dilated found here. This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the Qualcomm® AI Hub Models library to export with custom configurations. More details on model performance across various devices, can be found here.

Qualcomm AI Hub Models uses Qualcomm AI Hub Workbench to compile, profile, and evaluate this model. Sign up to run these models on a hosted Qualcomm® device.

Getting Started

There are two ways to deploy this model on your device:

Option 1: Download Pre-Exported Models

Below are pre-exported model assets ready for deployment.

RuntimePrecisionChipsetSDK VersionsDownload
ONNXfloatUniversalQAIRT 2.45, ONNX Runtime 1.27.1Download
QNN_DLCfloatUniversalQAIRT 2.45Download
TFLITEfloatUniversalQAIRT 2.45Download

For more device-specific assets and performance metrics, visit LaMa-Dilated on Qualcomm® AI Hub.

Option 2: Export with Custom Configurations

Use the Qualcomm® AI Hub Models Python library to compile and export the model with your own:

  • Custom weights (e.g., fine-tuned checkpoints)
  • Custom input shapes
  • Target device and runtime configurations

This option is ideal if you need to customize the model beyond the default configuration provided here.

See our repository for LaMa-Dilated on GitHub for usage instructions.

Model Details

Model Type: Model_use_case.image_editing

Model Stats:

  • Input resolution: 512x512
  • Model checkpoint: Dilated CelebAHQ
  • Model size (float): 174 MB
  • Number of parameters: 45.6M

Performance Summary

ModelRuntimePrecisionChipsetInference Time (ms)Peak Memory Range (MB)Primary Compute Unit
LaMa-DilatedONNXfloatSnapdragon® X2 Elite36.378 ms8 - 8 MBNPU
LaMa-DilatedONNXfloatSnapdragon® X Elite77.556 ms90 - 90 MBNPU
LaMa-DilatedONNXfloatSnapdragon® 8 Gen 3 Mobile54.56 ms0 - 479 MBNPU
LaMa-DilatedONNXfloatSnapdragon® 8 Gen 1 Mobile133.618 ms2 - 527 MBNPU
LaMa-DilatedONNXfloatQualcomm® Dragonwing™ IQ-8275123.774 ms6 - 14 MBNPU
LaMa-DilatedONNXfloatQualcomm® Dragonwing™ QCS8550 (Proxy)77.985 ms0 - 94 MBNPU
LaMa-DilatedONNXfloatQualcomm® QCS8450133.618 ms2 - 527 MBNPU
LaMa-DilatedONNXfloatQualcomm® Dragonwing™ IQ-9075108.956 ms6 - 13 MBNPU
LaMa-DilatedONNXfloatQualcomm® Dragonwing™ IQ-X718177.556 ms90 - 90 MBNPU
LaMa-DilatedONNXfloatQualcomm® Dragonwing™ Q-875043.695 ms7 - 437 MBNPU
LaMa-DilatedONNXfloatSnapdragon® 8 Elite Mobile43.695 ms7 - 437 MBNPU
LaMa-DilatedONNXfloatSnapdragon® 8 Elite Gen 5 Mobile34.287 ms5 - 404 MBNPU
LaMa-DilatedQNN_DLCfloatSnapdragon® X2 Elite34.369 ms4 - 4 MBNPU
LaMa-DilatedQNN_DLCfloatSnapdragon® X Elite75.421 ms4 - 4 MBNPU
LaMa-DilatedQNN_DLCfloatSnapdragon® 8 Gen 3 Mobile52.33 ms0 - 454 MBNPU
LaMa-DilatedQNN_DLCfloatSnapdragon® 8 Gen 1 Mobile133.148 ms4 - 409 MBNPU
LaMa-DilatedQNN_DLCfloatQualcomm® Dragonwing™ IQ-8275120.514 ms4 - 13 MBNPU
LaMa-DilatedQNN_DLCfloatQualcomm® Dragonwing™ QCS8550 (Proxy)74.969 ms4 - 6 MBNPU
LaMa-DilatedQNN_DLCfloatQualcomm® SA8775P106.893 ms1 - 364 MBNPU
LaMa-DilatedQNN_DLCfloatQualcomm® SA8650P106.893 ms1 - 364 MBNPU
LaMa-DilatedQNN_DLCfloatQualcomm® SA8255P106.893 ms1 - 364 MBNPU
LaMa-DilatedQNN_DLCfloatQualcomm® QCS8450133.148 ms4 - 409 MBNPU
LaMa-DilatedQNN_DLCfloatQualcomm® Dragonwing™ IQ-9075106.306 ms4 - 13 MBNPU
LaMa-DilatedQNN_DLCfloatQualcomm® Dragonwing™ IQ-X718175.421 ms4 - 4 MBNPU
LaMa-DilatedQNN_DLCfloatQualcomm® Dragonwing™ Q-875040.187 ms1 - 367 MBNPU
LaMa-DilatedQNN_DLCfloatQualcomm® SA7255P403.099 ms1 - 364 MBNPU
LaMa-DilatedQNN_DLCfloatQualcomm® SA8295P109.591 ms1 - 300 MBNPU
LaMa-DilatedQNN_DLCfloatSnapdragon® 8 Elite Mobile40.187 ms1 - 367 MBNPU
LaMa-DilatedQNN_DLCfloatSnapdragon® 8 Elite Gen 5 Mobile32.351 ms4 - 397 MBNPU
LaMa-DilatedTFLITEfloatSnapdragon® 8 Gen 3 Mobile52.831 ms2 - 538 MBNPU
LaMa-DilatedTFLITEfloatSnapdragon® 8 Gen 1 Mobile133.005 ms4 - 462 MBNPU
LaMa-DilatedTFLITEfloatQualcomm® Dragonwing™ IQ-8275121.318 ms3 - 106 MBNPU
LaMa-DilatedTFLITEfloatQualcomm® Dragonwing™ QCS8550 (Proxy)74.971 ms1 - 293 MBNPU
LaMa-DilatedTFLITEfloatQualcomm® SA8775P106.491 ms3 - 380 MBNPU
LaMa-DilatedTFLITEfloatQualcomm® SA8650P106.491 ms3 - 380 MBNPU

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Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

How it works

How image models work

Text promptwhat to makeText encoderunderstands itDiffusion stepsdenoise to pixelsImagePNG / JPEGA diffusion model starts from noise and denoises it, guided by your prompt, into a finished image.

Running it yourself

Run it on a rented GPU

Rent a machine by the hour. How to run this model is on its model card.

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