Update app.py

app.py

@@ -121,7 +121,7 @@ def edit_inference(prompt, negative_prompt, guidance_scale, ddim_steps, seed, st
     global young
     global pointy
     global wavy
-    global large
+    global thick
     
     original_weights = network.proj.clone()
     
@@ -132,10 +132,10 @@ def edit_inference(prompt, negative_prompt, guidance_scale, ddim_steps, seed, st
     young_pad = torch.cat((young, padding), 1)
     pointy_pad = torch.cat((pointy, padding), 1)
     wavy_pad = torch.cat((wavy, padding), 1)
-    large_pad = torch.cat((large, padding), 1)
+    thick_pad = torch.cat((thick, padding), 1)
     
 
-    edited_weights = original_weights+a1*1e6*young_pad+a2*1e6*pointy_pad+a3*1e6*wavy_pad+a4*2e6*large_pad
+    edited_weights = original_weights+a1*1e6*young_pad+a2*1e6*pointy_pad+a3*1e6*wavy_pad+a4*2e6*thick_pad
    
     generator = generator.manual_seed(seed)
     latents = torch.randn(
@@ -200,7 +200,7 @@ def sample_then_run():
     negative_prompt = "low quality, blurry, unfinished, nudity, weapon"
     seed = 5
     cfg = 3.0
-    steps = 50
+    steps = 25
     image = inference( prompt, negative_prompt, cfg, steps, seed)
     torch.save(network.proj, "model.pt" )
     return image, "model.pt"
@@ -209,7 +209,7 @@ def sample_then_run():
 global young
 global pointy
 global wavy 
-global large
+global thick
 
 young = get_direction(df, "Young", pinverse, 1000, device)
 young = debias(young, "Male", df, pinverse, device)
@@ -235,20 +235,20 @@ wavy = debias(wavy, "Chubby", df, pinverse, device)
 wavy = debias(wavy, "Heavy_Makeup", df, pinverse, device)
 
 
-large = get_direction(df, "Bushy_Eyebrows", pinverse, 1000, device)
-large = debias(large, "Male", df, pinverse, device)
-large = debias(large, "Young", df, pinverse, device)
-large = debias(large, "Pointy_Nose", df, pinverse, device)
-large = debias(large, "Wavy_Hair", df, pinverse, device)
-large = debias(large, "Mustache", df, pinverse, device)
-large = debias(large, "No_Beard", df, pinverse, device)
-large = debias(large, "Sideburns", df, pinverse, device)
-large = debias(large, "Big_Nose", df, pinverse, device)
-large = debias(large, "Big_Lips", df, pinverse, device)
-large = debias(large, "Black_Hair", df, pinverse, device)
-large = debias(large, "Brown_Hair", df, pinverse, device)
-large = debias(large, "Pale_Skin", df, pinverse, device)
-large = debias(large, "Heavy_Makeup", df, pinverse, device)
+thick = get_direction(df, "Bushy_Eyebrows", pinverse, 1000, device)
+thick = debias(thick, "Male", df, pinverse, device)
+thick = debias(thick, "Young", df, pinverse, device)
+thick = debias(thick, "Pointy_Nose", df, pinverse, device)
+thick = debias(thick, "Wavy_Hair", df, pinverse, device)
+thick = debias(thick, "Mustache", df, pinverse, device)
+thick = debias(thick, "No_Beard", df, pinverse, device)
+thick = debias(thick, "Sideburns", df, pinverse, device)
+thick = debias(thick, "Big_Nose", df, pinverse, device)
+thick = debias(thick, "Big_Lips", df, pinverse, device)
+thick = debias(thick, "Black_Hair", df, pinverse, device)
+thick = debias(thick, "Brown_Hair", df, pinverse, device)
+thick = debias(thick, "Pale_Skin", df, pinverse, device)
+thick = debias(thick, "Heavy_Makeup", df, pinverse, device)
 
 
 
@@ -343,7 +343,7 @@ def run_inversion(dict, pcs, epochs, weight_decay,lr):
     negative_prompt = "low quality, blurry, unfinished, nudity"
     seed = 5
     cfg = 3.0
-    steps = 50
+    steps = 25
     image = inference( prompt, negative_prompt, cfg, steps, seed)
     torch.save(network.proj, "model.pt" )
     return image, "model.pt"
@@ -368,7 +368,7 @@ def file_upload(file):
     unet, _, _, _, _ = load_models(device)
 
 
-    network = LoRAw2w( proj, mean, std, v[:, :pcs], 
+    network = LoRAw2w( proj, mean, std, v[:, :10000], 
                         unet,
                         rank=1,
                         multiplier=1.0,
@@ -381,7 +381,7 @@ def file_upload(file):
     negative_prompt = "low quality, blurry, unfinished, nudity"
     seed = 5
     cfg = 3.0
-    steps = 50
+    steps = 25
     image = inference( prompt, negative_prompt, cfg, steps, seed)
     return image