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To fix a few formatting typos in Gradio-and-Wandb-Integration.md tutorial (#8563)
* A few typo fix in Gradio&WandB tutorial * Apply suggestions from code review Co-authored-by: Ali Abdalla <ali.abdalla@huggingface.co> --------- Co-authored-by: Ali Abdalla <ali.si3luwa@gmail.com> Co-authored-by: pngwn <hello@pngwn.io> Co-authored-by: Ali Abdalla <ali.abdalla@huggingface.co>
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Sadra Barikbin
GitHub
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@ -49,10 +49,9 @@ Let's get started!
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Install Gradio and Wandb at the top:
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```sh
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pip install gradio wandb
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```
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```sh
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pip install gradio wandb
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```
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3. Finetune StyleGAN and W&B experiment tracking
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@ -133,198 +132,113 @@ pip install gradio wandb
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out_table = wandb.Table(data=samples, columns=column_names)
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wandb.log({"Current Samples": out_table})
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```
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alpha = 1.0
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alpha = 1-alpha
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preserve_color = True
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num_iter = 100
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log_interval = 50
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samples = []
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column_names = ["Referece (y)", "Style Code(w)", "Real Face Image(x)"]
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wandb.init(project="JoJoGAN")
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config = wandb.config
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config.num_iter = num_iter
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config.preserve_color = preserve_color
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wandb.log(
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{"Style reference": [wandb.Image(transforms.ToPILImage()(target_im))]},
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step=0)
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# load discriminator for perceptual loss
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discriminator = Discriminator(1024, 2).eval().to(device)
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ckpt = torch.load('models/stylegan2-ffhq-config-f.pt', map_location=lambda storage, loc: storage)
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discriminator.load_state_dict(ckpt["d"], strict=False)
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# reset generator
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del generator
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generator = deepcopy(original_generator)
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g_optim = optim.Adam(generator.parameters(), lr=2e-3, betas=(0, 0.99))
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# Which layers to swap for generating a family of plausible real images -> fake image
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if preserve_color:
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id_swap = [9,11,15,16,17]
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else:
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id_swap = list(range(7, generator.n_latent))
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for idx in tqdm(range(num_iter)):
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mean_w = generator.get_latent(torch.randn([latents.size(0), latent_dim]).to(device)).unsqueeze(1).repeat(1, generator.n_latent, 1)
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in_latent = latents.clone()
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in_latent[:, id_swap] = alpha*latents[:, id_swap] + (1-alpha)*mean_w[:, id_swap]
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img = generator(in_latent, input_is_latent=True)
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with torch.no_grad():
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real_feat = discriminator(targets)
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fake_feat = discriminator(img)
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loss = sum([F.l1_loss(a, b) for a, b in zip(fake_feat, real_feat)])/len(fake_feat)
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wandb.log({"loss": loss}, step=idx)
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if idx % log_interval == 0:
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generator.eval()
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my_sample = generator(my_w, input_is_latent=True)
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generator.train()
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my_sample = transforms.ToPILImage()(utils.make_grid(my_sample, normalize=True, range=(-1, 1)))
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wandb.log(
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{"Current stylization": [wandb.Image(my_sample)]},
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step=idx)
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table_data = [
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wandb.Image(transforms.ToPILImage()(target_im)),
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wandb.Image(img),
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wandb.Image(my_sample),
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]
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samples.append(table_data)
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g_optim.zero_grad()
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loss.backward()
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g_optim.step()
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out_table = wandb.Table(data=samples, columns=column_names)
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wandb.log({"Current Samples": out_table})
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````
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4. Save, Download, and Load Model
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Here's how to save and download your model.
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```python
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from PIL import Image
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import torch
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torch.backends.cudnn.benchmark = True
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from torchvision import transforms, utils
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from util import *
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import math
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import random
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import numpy as np
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from torch import nn, autograd, optim
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from torch.nn import functional as F
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from tqdm import tqdm
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import lpips
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from model import *
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from e4e_projection import projection as e4e_projection
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from copy import deepcopy
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import imageio
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import os
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import sys
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import torchvision.transforms as transforms
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from argparse import Namespace
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from e4e.models.psp import pSp
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from util import *
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from huggingface_hub import hf_hub_download
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from google.colab import files
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torch.save({"g": generator.state_dict()}, "your-model-name.pt")
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files.download('your-model-name.pt')
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latent_dim = 512
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device="cuda"
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model_path_s = hf_hub_download(repo_id="akhaliq/jojogan-stylegan2-ffhq-config-f", filename="stylegan2-ffhq-config-f.pt")
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original_generator = Generator(1024, latent_dim, 8, 2).to(device)
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ckpt = torch.load(model_path_s, map_location=lambda storage, loc: storage)
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original_generator.load_state_dict(ckpt["g_ema"], strict=False)
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mean_latent = original_generator.mean_latent(10000)
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generator = deepcopy(original_generator)
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ckpt = torch.load("/content/JoJoGAN/your-model-name.pt", map_location=lambda storage, loc: storage)
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generator.load_state_dict(ckpt["g"], strict=False)
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generator.eval()
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plt.rcParams['figure.dpi'] = 150
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transform = transforms.Compose(
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[
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transforms.Resize((1024, 1024)),
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transforms.ToTensor(),
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transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
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]
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)
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def inference(img):
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img.save('out.jpg')
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aligned_face = align_face('out.jpg')
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my_w = e4e_projection(aligned_face, "out.pt", device).unsqueeze(0)
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with torch.no_grad():
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my_sample = generator(my_w, input_is_latent=True)
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npimage = my_sample[0].cpu().permute(1, 2, 0).detach().numpy()
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imageio.imwrite('filename.jpeg', npimage)
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return 'filename.jpeg'
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````
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```python
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from PIL import Image
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import torch
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torch.backends.cudnn.benchmark = True
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from torchvision import transforms, utils
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from util import *
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import math
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import random
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import numpy as np
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from torch import nn, autograd, optim
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from torch.nn import functional as F
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from tqdm import tqdm
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import lpips
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from model import *
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from e4e_projection import projection as e4e_projection
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from copy import deepcopy
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import imageio
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import os
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import sys
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import torchvision.transforms as transforms
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from argparse import Namespace
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from e4e.models.psp import pSp
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from util import *
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from huggingface_hub import hf_hub_download
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from google.colab import files
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torch.save({"g": generator.state_dict()}, "your-model-name.pt")
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files.download('your-model-name.pt')
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latent_dim = 512
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device="cuda"
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model_path_s = hf_hub_download(repo_id="akhaliq/jojogan-stylegan2-ffhq-config-f", filename="stylegan2-ffhq-config-f.pt")
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original_generator = Generator(1024, latent_dim, 8, 2).to(device)
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ckpt = torch.load(model_path_s, map_location=lambda storage, loc: storage)
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original_generator.load_state_dict(ckpt["g_ema"], strict=False)
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mean_latent = original_generator.mean_latent(10000)
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generator = deepcopy(original_generator)
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ckpt = torch.load("/content/JoJoGAN/your-model-name.pt", map_location=lambda storage, loc: storage)
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generator.load_state_dict(ckpt["g"], strict=False)
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generator.eval()
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plt.rcParams['figure.dpi'] = 150
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transform = transforms.Compose(
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[
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transforms.Resize((1024, 1024)),
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transforms.ToTensor(),
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transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
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]
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)
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def inference(img):
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img.save('out.jpg')
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aligned_face = align_face('out.jpg')
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my_w = e4e_projection(aligned_face, "out.pt", device).unsqueeze(0)
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with torch.no_grad():
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my_sample = generator(my_w, input_is_latent=True)
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npimage = my_sample[0].cpu().permute(1, 2, 0).detach().numpy()
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imageio.imwrite('filename.jpeg', npimage)
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return 'filename.jpeg'
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````
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5. Build a Gradio Demo
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```python
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import gradio as gr
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title = "JoJoGAN"
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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."
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demo = gr.Interface(
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inference,
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gr.Image(type="pil"),
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gr.Image(type="file"),
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title=title,
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description=description
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)
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demo.launch(share=True)
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```
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```python
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import gradio as gr
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title = "JoJoGAN"
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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."
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demo = gr.Interface(
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inference,
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gr.Image(type="pil"),
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gr.Image(type="file"),
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title=title,
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description=description
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)
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demo.launch(share=True)
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```
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6. Integrate Gradio into your W&B Dashboard
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The last step—integrating your Gradio demo with your W&B dashboard—is just one extra line:
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```python
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```python
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demo.integrate(wandb=wandb)
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```
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demo.integrate(wandb=wandb)
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```
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Once you call integrate, a demo will be created and you can integrate it into your dashboard or report.
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Once you call integrate, a demo will be created and you can integrate it into your dashboard or report
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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.:
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```html
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<gradio-app space="akhaliq/JoJoGAN"> </gradio-app>
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```
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Outside of W&B with Web components, using the `gradio-app` tags, anyone can embed Gradio demos on HF spaces directly into their blogs, websites, documentation, etc.:
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```html
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<gradio-app space="akhaliq/JoJoGAN"> </gradio-app>
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```
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7. (Optional) Embed W&B plots in your Gradio App
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@ -333,20 +247,19 @@ demo.integrate(wandb=wandb)
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The Report will need to be public and you will need to wrap the URL within an iFrame like this:
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```python
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import gradio as gr
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def wandb_report(url):
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iframe = f'<iframe src={url} style="border:none;height:1024px;width:100%">'
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return gr.HTML(iframe)
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with gr.Blocks() as demo:
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report_url = 'https://wandb.ai/_scott/pytorch-sweeps-demo/reports/loss-22-10-07-16-00-17---VmlldzoyNzU2NzAx'
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report = wandb_report(report_url)
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demo.launch(share=True)
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```
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```python
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import gradio as gr
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def wandb_report(url):
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iframe = f'<iframe src={url} style="border:none;height:1024px;width:100%">'
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return gr.HTML(iframe)
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with gr.Blocks() as demo:
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report_url = 'https://wandb.ai/_scott/pytorch-sweeps-demo/reports/loss-22-10-07-16-00-17---VmlldzoyNzU2NzAx'
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report = wandb_report(report_url)
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demo.launch(share=True)
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```
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## Conclusion
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