Create app.py

app.py

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+import os
+import gradio as gr
+import plotly.graph_objects as go
+import sys
+import torch
+from huggingface_hub import hf_hub_download
+import numpy as np
+import random
+
+os.system("https://github.com/Zhengxinyang/SDF-StyleGAN.git")
+sys.path.append("SDF-StyleGAN")
+
+#Codes reference : https://github.com/Zhengxinyang/SDF-StyleGAN
+
+from utils.utils import noise, evaluate_in_chunks, scale_to_unit_sphere, volume_noise, process_sdf, linear_slerp
+from network.model import StyleGAN2_3D
+
+
+cars=hf_hub_download("SerdarHelli/SDF-StyleGAN-3D", filename="cars.ckpt",revision="main")
+
+
+["Car","Airplane","Chair","Rifle","Table"]
+
+#default model
+device='cuda' if torch.cuda.is_available() else 'cpu'
+if device=="cuda":
+  model = StyleGAN2_3D.load_from_checkpoint(cars).cuda(0)
+else:
+  model = StyleGAN2_3D.load_from_checkpoint(cars)
+model.eval()
+
+
+models={"Car":cars,
+        "Airplane":"./planes.ckpt"
+        "Chair":"./chairs.ckpt",
+        "Rifle":"./rifles.ckpt",
+        "Table":"./tables.ckpt"
+}
+
+
+def seed_all(seed):
+
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+
+
+def change_model(ckpt_path):
+    global model
+    if device=="cuda":
+      model = StyleGAN2_3D.load_from_checkpoint(cars).cuda(0)
+    else:
+      model = StyleGAN2_3D.load_from_checkpoint(cars)
+    model.eval()
+
+
+def predict(seed,trunc_psi):
+  if seed==None:
+    seed=777 
+  seed_all(seed)
+  if trunc_psi==None:
+    trunc_psi=1
+
+  z = noise(100000, model.latent_dim, device=model.device)
+  samples = evaluate_in_chunks(1000, model.SE, z)
+  model.av = torch.mean(samples, dim=0, keepdim=True)
+
+  mesh = model.generate_mesh(
+      ema=True, mc_vol_size=64, level=-0.015, trunc_psi=trunc_psi)
+  mesh = scale_to_unit_sphere(mesh)
+  mesh.export("/content/asdads.obj")
+  x=np.asarray(mesh.vertices).T[0]
+  y=np.asarray(mesh.vertices).T[1]
+  z=np.asarray(mesh.vertices).T[2]
+
+  i=np.asarray(mesh.faces).T[0]
+  j=np.asarray(mesh.faces).T[1]
+  k=np.asarray(mesh.faces).T[2]
+
+  return x,y,z,i,j,k
+
+def generate(seed,model,trunc_psi):
+
+    global model
+    change_model(models[model])
+    x,y,z,i,j,k=predict(seed,trunc_psi)
+
+
+    fig = go.Figure(go.Mesh3d(x=x, y=y, z=z, 
+                    i=i, j=j, k=k, 
+                    colorscale="Viridis",
+                  colorbar_len=0.75,
+                  flatshading=True,
+                  lighting=dict(ambient=0.5,
+                                diffuse=1,
+                                fresnel=4,        
+                                specular=0.5,
+                                roughness=0.05,
+                                facenormalsepsilon=0,
+                                vertexnormalsepsilon=0),
+                  lightposition=dict(x=100,
+                                    y=100,
+                                    z=1000)))
+    return fig
+    
+markdown=f'''
+  # SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generation
+
+  
+  [The space demo for the SGP 2022 paper "SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generation".](https://arxiv.org/abs/2206.12055)
+  
+  [For the official implementation.](https://github.com/Zhengxinyang/SDF-StyleGAN)
+  ### Future Work based on interest
+  - Adding new models for new type objects
+  - New Customization 
+  
+  
+  It is running on {device}
+  
+'''
+with gr.Blocks() as demo:
+    with gr.Column():
+        with gr.Row():
+            gr.Markdown(markdown)
+        with gr.Row():
+            seed = gr.Slider( minimum=0, maximum=2**16,label='Seed')
+            model=gr.Dropdown(choices=["Car","Airplane","Chair","Rifle","Table"],label="Choose Model Type")
+            trunc_psi = gr.Slider( minimum=0, maximum=2,label='Truncate PSI')
+
+        btn = gr.Button(value="Generate")
+        mesh = gr.Plot()
+    demo.load(generate, [seed,model,trunc_psi], mesh)
+    btn.click(generate, [seed,model,trunc_psi], mesh)
+
+demo.launch(debug=True)