set max randomness to 0 for writer/char blends

.gitignore

@@ -5,6 +5,7 @@ data/writers/*
 *.gif
 results/
 samples/
+__pycache__/
 
 !/data/writers/5
 !/data/writers/14

app.py

@@ -15,10 +15,7 @@ num_samples = 10
 net = SynthesisNetwork(weight_dim=256, num_layers=3).to(device)
 
 if not torch.cuda.is_available():
-    try: # retrained model also contains loss in dict 
-        net.load_state_dict(torch.load('./model/250000.pt', map_location=torch.device(device))["model_state_dict"])
-    except:
-        net.load_state_dict(torch.load('./model/250000.pt', map_location=torch.device(device)))
+    net.load_state_dict(torch.load('./model/250000.pt', map_location=torch.device(device))["model_state_dict"])
     
 
 dl = DataLoader(num_writer=1, num_samples=10, divider=5.0, datadir='./data/writers')
@@ -39,7 +36,8 @@ def interpolate_writers(target_word, weight):
     return image
 
 def choose_blend_writers(writer1, writer2):
-    id1, id2 = int(writer1.split(" ")[1]), int(writer1.split(" ")[1])
+    net.clamp_mdn = 0
+    id1, id2 = int(writer1.split(" ")[1]), int(writer2.split(" ")[1])
     all_loaded_data.clear()
     for writer_id in [id1, id2]:
         loaded_data = dl.next_batch(TYPE='TRAIN', uid=writer_id, tids=list(range(num_samples)))
@@ -54,16 +52,10 @@ def choose_writer(writ, c1, c2, c3, c4, val):
     all_loaded_data.append(loaded_data)
     return char_grid(c1, c2, c3, c4, val)
 '''
-# for character grrid
-def choose_grid_chars(c1, c2, c3, c4):
-      return gr.Button.update(value=f"Blend {c1}, {c2}, {c3}, and {c4}!")
-
-def char_grid(c1, c2, c3, c4):
-    image = convenience.sample_character_grid([c1, c2, c3, c4], 5, net, [default_loaded_data], device).convert("RGB")
-    return image
 
 # for character blend
 def interpolate_chars(c1, c2, weight):
+    net.clamp_mdn = 0
     image = convenience.sample_blended_chars([1 - weight, weight], [c1, c2], net, [default_loaded_data], device).convert("RGB")
     return image