final_app

app.py

@@ -139,7 +139,14 @@ def generate_image(pipe, seed_no, prompts, loss_type, loss_apply=False, progress
     latents = latents * scheduler.init_noise_sigma
 
     # Diffusion Process
-    for i, t in progress.tqdm(enumerate(scheduler.timesteps), total=len(scheduler.timesteps)):
+    timesteps = scheduler.timesteps
+    progress(0, desc="Generating")
+    
+    # Fixed loop - separate the progress tracking from the enumeration
+    for i in range(len(timesteps)):
+        progress((i + 1) / len(timesteps), desc=f"Diffusion step {i+1}/{len(timesteps)}")
+        t = timesteps[i]
+        
         # Process the latent model input
         latent_model_input = torch.cat([latents] * 2)
         sigma = scheduler.sigmas[i]