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Hifi Inpaint

HiFi-Inpaint is a Python research implementation for reference-based inpainting that integrates products into masked human images while aiming to preserve product details. It includes inference code, training code and model weights.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Hifi Inpaint — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use HiFi-Inpaint when you want a code-based workflow for reference-guided product integration into masked human images, or when researching and training methods for this task.

Who it is for

  • Researchers studying reference-based image inpainting and human–product image generation.
  • Developers exploring product placement and scene completion workflows based on a reference image.
  • Teams creating human–product imagery for advertising, e-commerce or digital marketing.

Documented uses

  • Generate a human–product image by using a product reference to guide inpainting of a masked human image.
  • Run the released inference workflow with FLUX.1-dev and the HiFi-Inpaint LoRA weights.
  • Configure and launch training with image-path and caption records in the documented JSON format.
Before you choose

Requirements & limits

The repository is public and licensed under Apache-2.0. The documented workflow uses a Linux environment and recommends Conda with Python 3.11; inference requires the FLUX.1-dev base model and HiFi-Inpaint LoRA weights.

  • This is a repository-based research implementation, not a hosted editor; the README describes local environment setup and command-line workflows.
  • Inference depends on both FLUX.1-dev and the HiFi-Inpaint LoRA checkpoint.
  • Training data is described through a JSON record containing reference, ground-truth, masked-condition and mask paths, plus a caption.
  • The README documents single-GPU and eight-process torchrun training commands and Weights & Biases log tracking.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up and run the documented inference workflow

Generate an inpainted human–product image using a product reference and a masked human image.

Before you start

  • A Linux environment with Conda and Python 3.11, as recommended by the repository.
  • The FLUX.1-dev base model and HiFi-Inpaint LoRA weights.
  • The repository requirements installed in the Conda environment.

Steps

  1. Clone the repository, create and activate a clean Conda environment with Python 3.11, then install dependencies with the command below.

    Documented command
    pip install -r requirements.txt

  2. Download FLUX.1-dev and the HiFi-Inpaint LoRA weights.

  3. Set the FLUX_PATH and LORA_PATH values in run_inference.sh to the downloaded model locations.

  4. Run the command below.

    Documented command
    bash run_inference.sh

Expected outcome

The README says generated results are saved to `./output/`.

Guide limitations

  • The guide follows the repository's documented steps and does not establish execution success or hardware requirements.
  • The workflow requires the base model and LoRA checkpoint; the README does not specify hardware requirements.
Documentation 1 ↗
Source-based practical guide

Prepare the documented training inputs and launch training

Configure the released training code to use data in the stated JSON format and start a training run.

Before you start

  • The repository environment and training dependencies.
  • FLUX.1-dev and training data JSON file or files.
  • Each JSON entry needs ref_image_path, gt_image_path, condition_image_path, mask_path and caption fields.
  • A Weights & Biases API key for the documented training-log tracking.

Steps

  1. Prepare JSON entries with paths to the reference, ground-truth, masked-condition and mask images, plus a caption.

  2. Set flux_path and data_path in train/config/hifi_inpaint.yaml.

  3. Set WANDB_API_KEY before training.

  4. Start a single-GPU run with the command below, or use the documented multi-GPU command torchrun --nproc_per_node=8 -m src.train.train.

    Documented command
    bash train/scripts/train.sh

Expected outcome

The training script or multi-GPU command starts the repository's training workflow; training logs are tracked via Weights & Biases.

Guide limitations

  • The README provides the expected data fields and launch commands but does not specify hardware requirements or guarantee a successful run.
  • The stated dataset compatibility is with HP-Image-40K; other data may need conversion to the listed JSON format.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Hifi InpaintGenerate a human–product image by using a product reference to guide inpainting of a masked human image.LinuxThe repository is public and licensed under Apache-2.0. The documented workflow uses a Linux environment and recommends Conda with Python 3.11; inference requires the FLUX.1-dev base model and HiFi-Inpaint LoRA weights.
FASHN VTON v1.5Generate a try-on preview from a person image and a garment image.CLI, Linux, macOS, Self-hostedThe repository is public and states an Apache-2.0 license. Local setup requires downloading approximately 2 GB of model weights.
PosteromniRescale a poster to a target aspect ratio using a text prompt.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
Correr-Zhou/HiFi-Inpaint · Official homepage title · retained in its source language
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