Fix top-k/top-p

app.py

@@ -183,7 +183,7 @@ def generate_diffusion_text(input_ids):
     with torch.no_grad():
         input_tensor = torch.tensor([input_ids], dtype=torch.long).to(model.device)
         logits = model(input_ids=input_tensor)["logits"]
-        logits = filter_logits(logits, top_k=top_k, top_p=top_p) 
+        logits = filter_logits(logits, top_k=100, top_p=0.9) 
         logits = logits.clamp(min=-1e8, max=1e4)
         probs = torch.nn.functional.softmax(logits, dim=-1)[0]
         probs = torch.clamp(probs, min=1e-8, max=1.0)