Update app.py

app.py

@@ -121,14 +121,14 @@ lazy_realesrgan_x4 = LazyRealESRGAN(device, scale=4)
 
 @timer_func
 def resize_and_upscale(input_image, resolution):
-    scale = 2 if resolution <= 2048 else 4
+    scale = 5 if resolution <= 2048 else 4
     input_image = input_image.convert("RGB")
     W, H = input_image.size
     k = float(resolution) / min(H, W)
     H = int(round(H * k / 64.0)) * 64
     W = int(round(W * k / 64.0)) * 64
     img = input_image.resize((W, H), resample=Image.LANCZOS)
-    if scale == 2:
+    if scale == 5:
         img = lazy_realesrgan_x2.predict(img)
     else:
         img = lazy_realesrgan_x4.predict(img)
@@ -164,7 +164,7 @@ def gradio_process_image(input_image, resolution, num_inference_steps, strength,
     
     condition_image = prepare_image(input_image, resolution, hdr)
     
-    prompt = "masterpiece, best quality, highres"
+    prompt = "masterpiece, best quality, highres, family guy, 2d animation, family guy tv series animation"
     negative_prompt = "low quality, normal quality, ugly, blurry, blur, lowres, bad anatomy, bad hands, cropped, worst quality, verybadimagenegative_v1.3, JuggernautNegative-neg"
     
     options = {
@@ -207,11 +207,11 @@ with gr.Blocks() as demo:
         with gr.Column():
             output_slider = ImageSlider(label="Before / After", type="numpy")
     with gr.Accordion("Advanced Options", open=False):
-        resolution = gr.Slider(minimum=256, maximum=2048, value=512, step=256, label="Resolution")
-        num_inference_steps = gr.Slider(minimum=1, maximum=50, value=20, step=1, label="Number of Inference Steps")
-        strength = gr.Slider(minimum=0, maximum=1, value=0.4, step=0.01, label="Strength")
+        resolution = gr.Slider(minimum=256, maximum=2048, value=1920, step=256, label="Resolution")
+        num_inference_steps = gr.Slider(minimum=1, maximum=50, value=49, step=1, label="Number of Inference Steps")
+        strength = gr.Slider(minimum=0, maximum=1, value=0.15, step=0.01, label="Strength")
         hdr = gr.Slider(minimum=0, maximum=1, value=0, step=0.1, label="HDR Effect")
-        guidance_scale = gr.Slider(minimum=0, maximum=20, value=3, step=0.5, label="Guidance Scale")
+        guidance_scale = gr.Slider(minimum=0, maximum=20, value=6, step=0.5, label="Guidance Scale")
 
     run_button.click(fn=gradio_process_image, 
                      inputs=[input_image, resolution, num_inference_steps, strength, hdr, guidance_scale],