Create app.py

app.py

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+import warnings
+import argparse
+import os
+from PIL import Image
+import numpy as np
+import torch
+
+import stylegan2
+from stylegan2 import utils
+
+def generate_images(G, args):
+    latent_size, label_size = G.latent_size, G.label_size
+    device = torch.device(args.gpu[0] if args.gpu else 'cpu')
+    if device.index is not None:
+        torch.cuda.set_device(device.index)
+    G.to(device)
+    if args.truncation_psi != 1:
+        G.set_truncation(truncation_psi=args.truncation_psi)
+    if len(args.gpu) > 1:
+        warnings.warn(
+            'Noise can not be randomized based on the seed ' +
+            'when using more than 1 GPU device. Noise will ' +
+            'now be randomized from default random state.'
+        )
+        G.random_noise()
+        G = torch.nn.DataParallel(G, device_ids=args.gpu)
+    else:
+        noise_reference = G.static_noise()
+
+    def get_batch(seeds):
+        latents = []
+        labels = []
+        if len(args.gpu) <= 1:
+            noise_tensors = [[] for _ in noise_reference]
+        for seed in seeds:
+            rnd = np.random.RandomState(seed)
+            latents.append(torch.from_numpy(rnd.randn(latent_size)))
+            if len(args.gpu) <= 1:
+                for i, ref in enumerate(noise_reference):
+                    noise_tensors[i].append(
+                        torch.from_numpy(rnd.randn(*ref.size()[1:])))
+            if label_size:
+                labels.append(torch.tensor([rnd.randint(0, label_size)]))
+        latents = torch.stack(latents, dim=0).to(
+            device=device, dtype=torch.float32)
+        if labels:
+            labels = torch.cat(labels, dim=0).to(
+                device=device, dtype=torch.int64)
+        else:
+            labels = None
+        if len(args.gpu) <= 1:
+            noise_tensors = [
+                torch.stack(noise, dim=0).to(
+                    device=device, dtype=torch.float32)
+                for noise in noise_tensors
+            ]
+        else:
+            noise_tensors = None
+        return latents, labels, noise_tensors
+    return_images = []
+    for i in range(0, len(args.seeds), args.batch_size):
+        latents, labels, noise_tensors = get_batch(
+            args.seeds[i: i + args.batch_size])
+        if noise_tensors is not None:
+            G.static_noise(noise_tensors=noise_tensors)
+        with torch.no_grad():
+            generated = G(latents, labels=labels)
+        images = utils.tensor_to_PIL(
+            generated, pixel_min=args.pixel_min, pixel_max=args.pixel_max)
+        return_images.extend(images)
+    return return_images
+ 
+ 
+ #----------------------------------------------------------------------------
+
+def inference(seed):
+    
+    
+title = "TWDNEv3 CPU Generator"
+description = "Gradio Demo for TWDNEv3 CPU Generator (stylegan2_pytorch port). To use it, simply put your random seed."
+article = ""
+gr.Interface(inference, ["number"], gr.outputs.Image(type="pil"),title=title,description=description,article=article,allow_flagging=False,allow_screenshot=False).launch()