Update app.py

app.py

@@ -57,7 +57,7 @@ pipe = AutoPipelineForText2Image.from_pretrained(
 #     return width, height
 
 @spaces.GPU
-def infer(edit_images, prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
+def infer(edit_images, prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5,control_strength=0.5, control_stop=0.33, num_inference_steps=50, progress=gr.Progress(track_tqdm=True)):
     image = edit_images["background"].convert("RGB")
     # width, height = calculate_optimal_dimensions(image)
     mask = edit_images["layers"][0].convert("RGB")
@@ -65,13 +65,13 @@ def infer(edit_images, prompt, seed=42, randomize_seed=False, width=1024, height
         seed = random.randint(0, MAX_SEED)
     image = pipe(
         prompt=prompt,
-        # image=image,
-        # mask_image=mask,
         inpaint_image=image,
         inpaint_mask=mask,
         height=height,
         width=width,
         guidance_scale=guidance_scale,
+        control_strength=control_strength, 
+        control_stop=control_stop,
         num_inference_steps=num_inference_steps,
         generator=torch.Generator("cpu").manual_seed(seed)
     ).images[0]
@@ -137,12 +137,13 @@ with gr.Blocks(css=css) as demo:
             with gr.Row():
 
                 guidance_scale = gr.Slider(0.0, 20.0, value=3.5, step=0.1, label="Guidance Scale")
-  
+                control_strength = gr.Slider(0.0, 1.0, value=0.5, step=0.05, label="Control Strength")
+                control_stop = gr.Slider(0.0, 1.0, value=0.33, step=0.05, label="Control Stop")
                 num_inference_steps = gr.Slider(1, 100, value=50, step=1, label="Inference Steps")
 
     run_button.click(
         fn = infer,
-        inputs = [edit_image, prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        inputs = [edit_image, prompt, seed, randomize_seed, width, height, guidance_scale, control_strength, control_stop,  num_inference_steps],
         outputs = [result, seed]
     )