SS

app.py

@@ -131,10 +131,10 @@ num_inference_steps = st.slider('Number of Inference Steps:', min_value=5, max_v
 num_images_per_prompt = st.slider('Image Count to be Produced:', min_value=1, max_value=2, value=1)
 
 # use seed with torch generator
-torch.manual_seed(0)
+#torch.manual_seed(0)
 # seed
-seed = st.slider('Seed:', min_value=0, max_value=100, value=1)
-generator = [torch.Generator(device="cuda").manual_seed(seed) for i in range(num_images_per_prompt)]
+#seed = st.slider('Seed:', min_value=0, max_value=100, value=1)
+#generator = [torch.Generator(device="cuda").manual_seed(seed) for i in range(num_images_per_prompt)]
 
 #generator = torch.Generator(device="cuda").manual_seed(0)
 run_model_button = st.button("Run Model") 
@@ -193,7 +193,7 @@ if run_model_button == True and input_prompt is not None :
     output_height = 128
     output_width = 128
     
-    x5 = pipe(image=dif_image, mask_image=inverted_image, num_inference_steps=num_inference_steps, generator= generator,
+    x5 = pipe(image=dif_image, mask_image=inverted_image, num_inference_steps=num_inference_steps,  # , generator= generator
                 num_images_per_prompt=num_images_per_prompt, prompt=prompt, negative_prompt=input_negative_prompt,
                 height=output_height, width=output_width).images