Update app.py

app.py

@@ -4,30 +4,29 @@ from PIL import Image
 
 from lambda_diffusers import StableDiffusionImageEmbedPipeline
 
-def main(
-    input_im,
-    scale=3.0,
-    n_samples=2,
-    steps=25,
-    seed=0,
-    ):
+def ask(input_im, scale, n_samples, steps, seed):
     
-    generator = torch.Generator(device=device).manual_seed(int(seed))
-
     images_list = pipe(
         n_samples*[input_im],
         guidance_scale=scale,
         num_inference_steps=steps,
         generator=generator,
         )
-
-    images = []
     for i, image in enumerate(images_list["sample"]):
         if(images_list["nsfw_content_detected"][i]):
             safe_image = Image.open(r"unsafe.png")
             images.append(safe_image)
         else:
             images.append(image)
+
+def main(input_im, scale, n_samples, steps, seed):
+    
+    generator = torch.Generator(device=device).manual_seed(int(seed))
+
+    images = []
+    ask(input_im, scale, n_samples, steps, seed)
+    ask(input_im, scale, n_samples, steps, seed)
+    
     return images
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -40,7 +39,7 @@ pipe = pipe.to(device)
 inputs = [
     gr.Image(),
     gr.Slider(0, 25, value=3, step=1, label="Guidance scale"),
-    gr.Slider(1, 2, value=1, step=1, label="Number images"),
+    gr.Slider(1, 2, value=2, step=1, label="Number images"),
     gr.Slider(5, 50, value=25, step=5, label="Steps"),
     gr.Number(0, labal="Seed", precision=0)
 ]