update

app.py

@@ -188,12 +188,12 @@ def generate(
     model.to(device)
     if device == "cuda":
         with torch.no_grad(), autocast("cuda"), model.ema_scope():
-            return run(model, input_image, input_image_copy, width, height, instruction, steps)
+            return run(model, input_image, input_image_copy, width, height, instruction, steps, seed)
     else:
-        return run(model, input_image, input_image_copy, width, height, instruction, steps)
+        return run(model, input_image, input_image_copy, width, height, instruction, steps, seed)
 
 
-def run(model, input_image, input_image_copy, width, height, instruction, steps): 
+def run(model, input_image, input_image_copy, width, height, instruction, steps, seed): 
         cond = {}
         cond["c_crossattn"] = [model.get_learned_conditioning([instruction]).to(model.device)]
         input_image = 2 * torch.tensor(np.array(input_image)).float() / 255 - 1