reduce-image-size

app.py

@@ -13,7 +13,7 @@ def create_pipeline(name):
         pipe.safety_checker = lambda images, clip_input: (images, False)
     else:
         pipe = OVStableDiffusionPipeline.from_pretrained(name, compile=False)
-        pipe.reshape(batch_size=1, height=512, width=512, num_images_per_prompt=1)
+        pipe.reshape(batch_size=1, height=256, width=256, num_images_per_prompt=1)
         pipe.compile()
     return pipe
 
@@ -33,10 +33,7 @@ def generate(prompt, option, seed):
     pipe = create_pipeline(pipes[option])
     set_seed(int(seed))
     start_time = time.time()
-    if "Torch" in option:
-        output = pipe(prompt, num_inference_steps=50, output_type="pil", height=512, width=512)
-    else:
-        output = pipe(prompt, num_inference_steps=50, output_type="pil")
+    output = pipe(prompt, num_inference_steps=50, output_type="pil", height=256, width=256)
     elapsed_time = time.time() - start_time
     return (output.images[0], "{:10.4f}".format(elapsed_time))
 
@@ -57,4 +54,4 @@ gr.Interface(
     description="This is the Optimum-based demo for NNCF-optimized Stable Diffusion pipeline trained on 'lambdalabs/pokemon-blip-captions' dataset and running with OpenVINO.\n"
                  "The pipeline is run using 8 vCPUs (4 cores) only.",
     theme="huggingface",
-).launch()
+).launch()