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mediasynthesismuseum/pix2pix
pix2pix is a image-to-image model from mediasynthesismuseum. Use it when you need one image transformed into another. It is set up for pytorch. The card lists the license as bsd-3-clause.
The original Pix2Pix (Isola et al., CVPR 2017, Image-to-Image Translation with Conditional Adversarial Networks) pretrained U-Net generators, hosted for mediasynthesismuseum/pix2pix. These are the official berkeley Py…
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Updated Jul 6, 2026
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From the Hugging Face model README
The original Pix2Pix (Isola et al., CVPR 2017, Image-to-Image Translation with Conditional
Adversarial Networks) pretrained U-Net generators, hosted for
mediasynthesismuseum/pix2pix.
These are the official berkeley PyTorch weights (junyanz/pytorch-CycleGAN-and-pix2pix).
| File | Task | Input |
|---|---|---|
edges2shoes.pth | edges → shoe photo | draw an outline |
edges2handbags.pth | edges → handbag photo | draw an outline |
facades_label2photo.pth | label map → building facade | draw colored regions |
day2night.pth | daytime → nighttime photo | upload a photo |
map2sat.pth | map tile → satellite image | upload a map |
All are unet_256 generators: input_nc=output_nc=3, ngf=64, BatchNorm.
Resize to 256×256, normalize to [-1, 1] ((x/127.5) - 1), RGB; de-normalize the output the
same way. The UnetGenerator definition is included in this repo (pix2pix_net.py).
import torch
from huggingface_hub import hf_hub_download
from pix2pix_net import build_generator
G = build_generator()
sd = torch.load(hf_hub_download("mediasynthesismuseum/pix2pix", "edges2shoes.pth"),
map_location="cpu", weights_only=False)
G.load_state_dict(sd); G.eval()
Credit: Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros (pix2pix), and the pytorch-CycleGAN-and-pix2pix authors.