readme: ---
  tags:
  - huggan
  - gan
  datasets:
  - huggan/night2day
  # See a list of available tags here:
  # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12
  # task: unconditional-image-generation or conditional-image-generation or image-to-image
  license: mit
  
  ---
  
  # MyModelName
  
  ## Model description
  
  [Pix2pix Model](https://arxiv.org/abs/1611.07004) is a conditional adversarial networks, a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.
  
  ## Intended uses & limitations:
  Used for reconstruction of images from edges
  
  #### How to use
  
  ```python
  from torchvision.transforms import Compose, Resize, ToTensor, Normalize
  from PIL import Image
  from torchvision.utils import save_image
  import cv2
  from huggan.pytorch.pix2pix.modeling_pix2pix import GeneratorUNet
  
  transform = Compose(
      [
          Resize((256, 256), Image.BICUBIC),
          ToTensor(),
          Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
      ]
  )
  model = GeneratorUNet.from_pretrained('huggan/pix2pix-night2day')
  
  def predict_fn(img):
      inp = transform(img).unsqueeze(0)
      out = model(inp)
      save_image(out, 'out.png', normalize=True)
      return 'out.png'
  
  predict_fn(img)
  ```
  
  #### Limitations and bias
  * Gives unrealistic colors in the image
  * Gives Blurry image sometimes
  
  ## Training data
  * [night2day](https://huggingface.co/datasets/huggan/night2day)
  
  ## Training procedure
  ```
  # clone the repository
  git clone https://github.com/huggingface/community-events.git
  pip install .
  
  # change directory
  cd community-events/huggan/pytorch/pix2pix/
  
  # define config
  accelerate config
  
  # launch training with required parameters
  accelerate launch train.py --checkpoint_interval 5 --dataset huggan/night2day --push_to_hub --model_name pix2pix-night2day --batch_size 128 --n_epochs 50
  ```
  
  ## Generated Images
  
  Here,
  * First Image Row: Input Image
  * Second Image Row: Generated Image
  * Third Image Row: Target Image
  
  ![image1](7000.png)
  ![image2](6500.png)
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @article{pix2pix2017,
    title={Image-to-Image Translation with Conditional Adversarial Networks},
    author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},
    journal={CVPR},
    year={2017}
  }
  ```