Final result

app.py

@@ -43,7 +43,7 @@ def generate_images(G, args):
             if len(args.gpu) <= 1:
                 for i, ref in enumerate(noise_reference):
                     noise_tensors[i].append(
-                        torch.from_numpy(rnd.standard_normal(tuple([*ref.size()[1:]]))) #?
+                        torch.from_numpy(rnd.standard_normal(tuple([*ref.size()[1:]])))
                     )
             if label_size:
                 labels.append(torch.tensor([rnd.integers(0, label_size)]))
@@ -79,13 +79,13 @@ def generate_images(G, args):
  
  #----------------------------------------------------------------------------
 
-def inference(seed):
+def inference(seed, truncation_psi):
     G = stylegan2.models.load(hf_hub_download("hr16/Gwern-TWDNEv3-pytorch_ckpt", "Gs.pth", use_auth_token=os.environ['MODEL_READING_TOKEN']))
     G.eval()
     return generate_images(
         G, 
         SimpleNamespace(**{
-            'truncation_psi': 0.7, #It seems like 0.7 will give the best result for this model.
+            'truncation_psi': truncation_psi,
             'seeds': [seed],
             'batch_size': 1,
             'pixel_min': -1,
@@ -95,6 +95,6 @@ def inference(seed):
     )[0]
     
 title = "TWDNEv3 CPU Generator"
-description = "Gradio Demo for TWDNEv3 CPU Generator (stylegan2_pytorch port). To use it, simply put a any-bit-size unsigned integer seed."
+description = "Gradio Demo for TWDNEv3 CPU Generator (stylegan2_pytorch port)"
 article = ""
-gr.Interface(inference, [gr.Number(precision=0)], gr.outputs.Image(type="pil"),title=title,description=description,article=article,allow_flagging=False,allow_screenshot=False).launch()
+gr.Interface(inference, [gr.Number(precision=0, label="Seed (any-bit-size unsigned int)"), gr.Slider(0, 2, step=0.1, default=0.7, label='Truncation psi (aka creative level, between 0 and 2)')], gr.outputs.Image(type="pil"),title=title,description=description,article=article,allow_flagging=False,allow_screenshot=False).launch()