Related Spaces:

Gradio and W&B Integration

Introduction

In this Guide, we'll walk you through:

  • Introduction of Gradio, and Hugging Face Spaces, and Wandb
  • How to setup a Gradio demo using the Wandb integration for JoJoGAN
  • How to contribute your own Gradio demos after tracking your experiments on wandb to the Wandb organization on Hugging Face

Here's an example of an model trained and experiments tracked on wandb, try out the JoJoGAN demo below.

What is Wandb?

Weights and Biases (W&B) allows data scientists and machine learning scientists to track their machine learning experiments at every stage, from training to production. Any metric can be aggregated over samples and shown in panels in a customizable and searchable dashboard, like below:

Screen Shot 2022-08-01 at 5 54 59 PM

What are Hugging Face Spaces & Gradio?

Gradio

Gradio lets users demo their machine learning models as a web app, all in a few lines of Python. Gradio wraps any Python function (such as a machine learning model's inference function) into a user inferface and the demos can be launched inside jupyter notebooks, colab notebooks, as well as embedded in your own website and hosted on Hugging Face Spaces for free.

Get started here

Hugging Face Spaces

Hugging Face Spaces is a free hosting option for Gradio demos. Spaces comes with 3 SDK options: Gradio, Streamlit and Static HTML demos. Spaces can be public or private and the workflow is similar to github repos. There are over 2000+ spaces currently on Hugging Face. Learn more about spaces here.

Setting up a Gradio Demo for JoJoGAN

Now, let's walk you through how to do this on your own. We'll make the assumption that you're new to W&B and Gradio for the purposes of this tutorial.

Let's get started!

  1. Create a W&B account

    Follow these quick instructions to create your free account if you don’t have one already. It shouldn't take more than a couple minutes. Once you're done (or if you've already got an account), next, we'll run a quick colab.

  2. Open Colab Install Gradio and W&B

    We'll be following along with the colab provided in the JoJoGAN repo with some minor modifications to use Wandb and Gradio more effectively.

    Open In Colab

    Install Gradio and Wandb at the top:

    
    pip install gradio wandb
    
  3. Finetune StyleGAN and W&B experiment tracking

    This next step will open a W&B dashboard to track your experiments and a gradio panel showing pretrained models to choose from a drop down menu from a Gradio Demo hosted on Huggingface Spaces. Here's the code you need for that:

        
    alpha =  1.0 
    alpha = 1-alpha
    
    preserve_color = True 
    num_iter = 100 
    log_interval = 50 
    
    
    samples = []
    column_names = ["Referece (y)", "Style Code(w)", "Real Face Image(x)"]
    
    wandb.init(project="JoJoGAN")
    config = wandb.config
    config.num_iter = num_iter
    config.preserve_color = preserve_color
    wandb.log(
    {"Style reference": [wandb.Image(transforms.ToPILImage()(target_im))]},
    step=0)
    
    

    load discriminator for perceptual loss

    discriminator = Discriminator(1024, 2).eval().to(device) ckpt = torch.load('models/stylegan2-ffhq-config-f.pt', map_location=lambda storage, loc: storage) discriminator.load_state_dict(ckpt["d"], strict=False)

    reset generator

    del generator generator = deepcopy(original_generator) g_optim = optim.Adam(generator.parameters(), lr=2e-3, betas=(0, 0.99))

    Which layers to swap for generating a family of plausible real images -> fake image

    if preserve_color: id_swap = [9,11,15,16,17] else: id_swap = list(range(7, generator.n_latent)) for idx in tqdm(range(num_iter)): mean_w = generator.get_latent(torch.randn([latents.size(0), latent_dim]).to(device)).unsqueeze(1).repeat(1, generator.n_latent, 1) in_latent = latents.clone() in_latent[:, id_swap] = alpha*latents[:, id_swap] + (1-alpha)*mean_w[:, id_swap] img = generator(in_latent, input_is_latent=True) with torch.no_grad(): real_feat = discriminator(targets) fake_feat = discriminator(img) loss = sum([F.l1_loss(a, b) for a, b in zip(fake_feat, real_feat)])/len(fake_feat) wandb.log({"loss": loss}, step=idx) if idx % log_interval == 0: generator.eval() my_sample = generator(my_w, input_is_latent=True) generator.train() my_sample = transforms.ToPILImage()(utils.make_grid(my_sample, normalize=True, range=(-1, 1))) wandb.log( {"Current stylization": [wandb.Image(my_sample)]}, step=idx) table_data = [ wandb.Image(transforms.ToPILImage()(target_im)), wandb.Image(img), wandb.Image(my_sample), ] samples.append(table_data) g_optim.zero_grad() loss.backward() g_optim.step() out_table = wandb.Table(data=samples, columns=column_names) wandb.log({"Current Samples": out_table})
  4. Save, Download, and Load Model

    Here's how to save and download your model.

    
    from PIL import Image
    import torch
    torch.backends.cudnn.benchmark = True
    from torchvision import transforms, utils
    from util import *
    import math
    import random
    import numpy as np
    from torch import nn, autograd, optim
    from torch.nn import functional as F
    from tqdm import tqdm
    import lpips
    from model import *
    from e4e_projection import projection as e4e_projection
    
    from copy import deepcopy
    import imageio
    
    import os
    import sys
    import torchvision.transforms as transforms
    from argparse import Namespace
    from e4e.models.psp import pSp
    from util import *
    from huggingface_hub import hf_hub_download
    from google.colab import files
    
    torch.save({"g": generator.state_dict()}, "your-model-name.pt")
    
    files.download('your-model-name.pt') 
    
    latent_dim = 512
    device="cuda"
    model_path_s = hf_hub_download(repo_id="akhaliq/jojogan-stylegan2-ffhq-config-f", filename="stylegan2-ffhq-config-f.pt")
    original_generator = Generator(1024, latent_dim, 8, 2).to(device)
    ckpt = torch.load(model_path_s, map_location=lambda storage, loc: storage)
    original_generator.load_state_dict(ckpt["g_ema"], strict=False)
    mean_latent = original_generator.mean_latent(10000)
    
    generator = deepcopy(original_generator)
    
    ckpt = torch.load("/content/JoJoGAN/your-model-name.pt", map_location=lambda storage, loc: storage)
    generator.load_state_dict(ckpt["g"], strict=False)
    generator.eval()
    
    plt.rcParams['figure.dpi'] = 150
    
    
    
    transform = transforms.Compose(
        [
            transforms.Resize((1024, 1024)),
            transforms.ToTensor(),
            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
        ]
    )
    
    
    def inference(img):  
        img.save('out.jpg')  
        aligned_face = align_face('out.jpg')
    
        my_w = e4e_projection(aligned_face, "out.pt", device).unsqueeze(0)  
        with torch.no_grad():
            my_sample = generator(my_w, input_is_latent=True)
    
    
        npimage = my_sample[0].cpu().permute(1, 2, 0).detach().numpy()
        imageio.imwrite('filename.jpeg', npimage)
        return 'filename.jpeg'
    
  5. Build a Gradio Demo

    
    import gradio as gr
    
    title = "JoJoGAN"
    description = "Gradio Demo for JoJoGAN: One Shot Face Stylization. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
    
    demo = gr.Interface(
        inference, 
        gr.Image(type="pil"), 
        gr.Image(type="file"),
        title=title,
        description=description
    )
    
    demo.launch(share=True)
    
  6. Integrate Gradio into your W&B Dashboard

    The last step—integrating your Gradio demo with your W&B dashboard—is just one extra line:

    
    demo.integrate(wandb=wandb)
    

    Once you call integrate, a demo will be created and you can integrate it into your dashboard or report

    Outside of W&B with Web components, using the gradio-app tags allows anyone can embed Gradio demos on HF spaces directly into their blogs, websites, documentation, etc.:

        
    <gradio-app space="akhaliq/JoJoGAN"> <gradio-app>
    

Conclusion

We hope you enjoyed this brief demo of embedding a Gradio demo to a W&B report! Thanks for making it to the end. To recap:

  • Only one single reference image is needed for fine-tuning JoJoGAN which usually takes about 1 minute on a GPU in colab. After training, style can be applied to any input image. Read more in the paper.

  • W&B tracks experiments with just a few lines of code added to a colab and you can visualize, sort, and understand your experiments in a single, centralized dashboard.

  • Gradio, meanwhile, demos the model in a user friendly interface to share anywhere on the web.

How to contribute Gradio demos on HF spaces on the Wandb organization

  • Create an account on Hugging Face here.
  • Add Gradio Demo under your username, see this course for setting up Gradio Demo on Hugging Face.
  • Request to join wandb organization here.
  • Once approved transfer model from your username to Wandb organization