pytorch-image-translation-models 0.4.1

A PyTorch library for multi-modal image translation with diffusion bridges, GANs, and transformer backbones.

pytorch-image-translation-models

A PyTorch library for multi-modal image translation with diffusion bridges, GANs, and transformer backbones.

Installation

Install from PyPI

pip install pytorch-image-translation-models

Install from source

pip install -e .

With optional dependencies:

# With training extras (accelerate, peft, datasets, tensorboard)
pip install -e ".[training]"

# With metrics extras (torchmetrics, lpips, torch-fidelity, scipy)
pip install -e ".[metrics]"

# Everything
pip install -e ".[all]"

Note: PyTorch is listed as a dependency but you may want to install a specific CUDA build first. See PyTorch — Get Started for details.

Quick Start

Examples default to device="cuda". If your environment is CPU-only, replace "cuda" with "cpu".

from PIL import Image

# Baseline method (UNSB)
from src.pipelines.unsb import UNSBPipeline
unsb = UNSBPipeline.from_pretrained(
    "path/to/UNSB-ckpt/horse2zebra",  # https://huggingface.co/BiliSakura/UNSB-ckpt
    subfolder="generator",
    scheduler_num_timesteps=5,
    scheduler_tau=0.01,
)
unsb.to("cuda")
unsb_out = unsb(source_image=source, output_type="pil")
unsb_out.images[0].save("unsb_output.png")

# Community method (DiffuseIT) - text/image-guided diffusion translation
from examples.community.diffuseit import load_diffuseit_community_pipeline

pipe = load_diffuseit_community_pipeline(
    "/root/worksapce/models/BiliSakura/DiffuseIT-ckpt/imagenet256-uncond",
    diffuseit_src_path="projects/DiffuseIT",
)
pipe.to("cuda")
out = pipe(
    source_image=source,
    prompt="Black Leopard",
    source="Lion",
    use_range_restart=True,
    use_noise_aug_all=True,
    output_type="pil",
)
out.images[0].save("diffuseit_output.png")

# Community method (E3Diff)
from examples.community.e3diff import E3DiffPipeline
e3diff = E3DiffPipeline.from_pretrained("path/to/E3Diff-ckpt/SEN12")
e3diff.to("cuda")
community_out = e3diff(source_image=source, num_inference_steps=50, output_type="pil")
community_out.images[0].save("e3diff_output.png")

Documentation

All information regarding per-method checkpoint folder conventions required by from_pretrained(...), as well as comprehensive package documentation, is integrated below.

Doc	Description
Checkpoint layouts	Provides detailed checkpoint folder structures, naming conventions, and requirements for each pipeline and the from_pretrained(...) API.
Features	Documents supported models, schedulers, pipelines, data types, training methods, and evaluation metrics.
Metrics README	One-stop usage for paired/unpaired metrics and custom HuggingFace/local checkpoints.
Datasets	Common image-to-image translation datasets (pix2pix, CycleGAN) with paper and download links.
Examples	Extended usage patterns and code snippets for pipelines such as I2SB, DDBM, UNSB, and Local Diffusion.
Package structure	Overview of the codebase organization, modules, and directories.
Credits	Citations for reference papers and third-party contributions.

Credits

This repository/package is primarily built upon 4th-MAVIC-T by the EarthBridge Team:

• Zheyuan Chen — [e-mail]
• Yuanshen Guan — [e-mail]

License

MIT