change path and name of app.py

gradio_app.py → app.py

@@ -1,77 +1,77 @@
-import argparse
-import torch
-import gradio as gr
-from torchvision import transforms
-from runner import MaskGIT
-import numpy as np
-import random
-import torchvision.utils as vutils
-
-
-class Args(argparse.Namespace):
-    data_folder = ""
-    vqgan_folder = r"C:\Users\vbesnier\Experiment\VQGAN"
-    writer_log = ""
-    data = ""
-    mask_value = 1024
-    seed = 1
-    channel = 3
-    num_workers = 0
-    iter = 0
-    global_epoch = 0
-    lr = 1e-4
-    drop_label = 0.1
-    resume = True
-    device = "cpu"
-    print(device)
-    debug = True
-    test_only = False
-    is_master = True
-    is_multi_gpus = False
-    vit_size = "base"
-    vit_folder = r"C:\Users\vbesnier\Experiment\MaskGIT\current.pth"
-    img_size = 256
-    patch_size = 256 // 16
-
-
-def set_seed(seed):
-    if seed > 0:
-        torch.manual_seed(seed)
-        torch.cuda.manual_seed(seed)
-        np.random.seed(seed)
-        random.seed(seed)
-        torch.backends.cudnn.enable = False
-        torch.backends.cudnn.deterministic = True
-
-args = Args()
-maskgit = MaskGIT(args)
-
-
-# Function to perform image synthesis
-def synthesize_image(cls, sm_temp=1, w=3, r_temp=4.5, step=8, seed=1, nb_img=1):
-    # Perform image synthesis using your model
-    set_seed(seed)
-    with torch.no_grad():
-        labels = [cls] * nb_img
-        labels = torch.LongTensor(labels).to(args.device)
-        gen_sample = maskgit.sample(nb_sample=labels.size(0), labels=labels, sm_temp=sm_temp, w=w,
-                                    randomize="linear", r_temp=r_temp, sched_mode="arccos",
-                                    step=step)[0]
-
-    # Post-process the output image (adjust based on your needs)
-    output_image = transforms.ToPILImage()(vutils.make_grid(gen_sample, nrow=2, padding=0, normalize=True))
-
-    return output_image
-
-
-# Gradio Interface
-app = gr.Interface(
-    fn=synthesize_image,
-    inputs=[gr.Number(31), gr.Number(1.3), gr.Number(25), gr.Number(4.5), gr.Number(16),
-            gr.Slider(0, 1000, 60), gr.Number(1, maximum=4)],
-    outputs=gr.Image(),
-    title="Image Synthesis using MaskGIT",
-)
-
-# Launch the Gradio app
-app.launch(share=True)
+import argparse
+import torch
+import gradio as gr
+from torchvision import transforms
+from runner import MaskGIT
+import numpy as np
+import random
+import torchvision.utils as vutils
+
+
+class Args(argparse.Namespace):
+    data_folder = ""
+    vqgan_folder = "pretrained_maskgit/VQGAN"
+    writer_log = ""
+    data = ""
+    mask_value = 1024
+    seed = 1
+    channel = 3
+    num_workers = 0
+    iter = 0
+    global_epoch = 0
+    lr = 1e-4
+    drop_label = 0.1
+    resume = True
+    device = "cpu"
+    print(device)
+    debug = True
+    test_only = False
+    is_master = True
+    is_multi_gpus = False
+    vit_size = "base"
+    vit_folder = "pretrained_maskgit/MaskGIT/MaskGIT_ImageNet_256.pth"
+    img_size = 256
+    patch_size = 256 // 16
+
+
+def set_seed(seed):
+    if seed > 0:
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed(seed)
+        np.random.seed(seed)
+        random.seed(seed)
+        torch.backends.cudnn.enable = False
+        torch.backends.cudnn.deterministic = True
+
+args = Args()
+maskgit = MaskGIT(args)
+
+
+# Function to perform image synthesis
+def synthesize_image(cls, sm_temp=1, w=3, r_temp=4.5, step=8, seed=1, nb_img=1):
+    # Perform image synthesis using your model
+    set_seed(seed)
+    with torch.no_grad():
+        labels = [cls] * nb_img
+        labels = torch.LongTensor(labels).to(args.device)
+        gen_sample = maskgit.sample(nb_sample=labels.size(0), labels=labels, sm_temp=sm_temp, w=w,
+                                    randomize="linear", r_temp=r_temp, sched_mode="arccos",
+                                    step=step)[0]
+
+    # Post-process the output image (adjust based on your needs)
+    output_image = transforms.ToPILImage()(vutils.make_grid(gen_sample, nrow=2, padding=0, normalize=True))
+
+    return output_image
+
+
+# Gradio Interface
+app = gr.Interface(
+    fn=synthesize_image,
+    inputs=[gr.Number(31), gr.Number(1.3), gr.Number(25), gr.Number(4.5), gr.Number(16),
+            gr.Slider(0, 1000, 60), gr.Number(1, maximum=4)],
+    outputs=gr.Image(),
+    title="Image Synthesis using MaskGIT",
+)
+
+# Launch the Gradio app
+app.launch()