Update app.py

app.py

@@ -57,10 +57,10 @@ def generate_image(category, img1, img2, img3, height, width, img_guidance_scale
     wears = predict_clothing(input_images[1:])
     if len(wears)==1:
         dress = wears[0]
-        text = f"""The {category} in <img><|image_1|></img> wearing {dress} in <img><|image_2|></img>."""
+        text = f"""A {category} wearing {dress}, with a completely white background. The {category} is <img><|image_1|></img>. The {dress} is in <img><|image_2|></img>."""
     elif len(wears)==2:
         topwear, bottomwear = wears[0], wears[1]
-        text = f"""The {category} in <img><|image_1|></img> wearing {topwear} in <img><|image_2|></img> and {bottomwear} in <img><|image_3|></img>."""
+        text = f"""A {category} wearing {topwear} and {bottomwear}, with a completely white background. The {category} is <img><|image_1|></img>. The {topwear} is in <img><|image_2|></img>. The {bottomwear} is in <img><|image_3|></img>.""
     else:
         input_images = None
 
@@ -94,7 +94,7 @@ def get_example():
     ]
     return case
 
-def run_for_examples(img1, img2, img3, height, width, img_guidance_scale, seed, max_input_image_size, randomize_seed, use_input_image_size_as_output,):    
+def run_for_examples(img1, img2, img3, height, width, img_guidance_scale, seed, max_input_image_size, randomize_seed, use_input_image_size_as_output,guidance_scale):    
     # Check the internal configuration of the function
     inference_steps = 50
     separate_cfg_infer = True
@@ -102,7 +102,7 @@ def run_for_examples(img1, img2, img3, height, width, img_guidance_scale, seed,
 
     text = "According to the following examples, generate an output for the input.\nInput: <img><|image_1|></img>\nOutput: <img><|image_2|></img>\n\nInput: <img><|image_3|></img>\nOutput:"
     return generate_image(img1, img2, img3, height, width, img_guidance_scale, inference_steps, seed, separate_cfg_infer,
-                          offload_model, use_input_image_size_as_output, max_input_image_size, randomize_seed)
+                          offload_model, use_input_image_size_as_output, max_input_image_size, randomize_seed, guidance_scale)
 
 
 description = """
@@ -143,8 +143,6 @@ with gr.Blocks() as demo:
             height_input = gr.Slider(label="Height", minimum=128, maximum=1024, value=512, step=16)
             width_input = gr.Slider(label="Width", minimum=128, maximum=1024, value=512, step=16)
     
-            # guidance_scale_input = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=5.0, value=2.5, step=0.1)
-    
             num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=128, value=32, step=1)
     
             seed_input = gr.Slider(label="Seed", minimum=0, maximum=2147483647, value=42, step=1)
@@ -152,6 +150,8 @@ with gr.Blocks() as demo:
 
         with gr.Column():
             max_input_image_size = gr.Slider(label="max_input_image_size", minimum=128, maximum=2048, value=1024, step=16)
+
+            guidance_scale_input = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=5.0, value=2.5, step=0.1)
             
             img_guidance_scale_input = gr.Slider(label="img_guidance_scale", minimum=1.0, maximum=2.0, value=1.6, step=0.1)
             
@@ -176,7 +176,7 @@ with gr.Blocks() as demo:
     generate_button.click(
         generate_image,
         inputs=[category, image_input_1, image_input_2, image_input_3, height_input, width_input, img_guidance_scale_input, num_inference_steps,
-                seed_input, separate_cfg_infer, offload_model, use_input_image_size_as_output, max_input_image_size, randomize_seed,],
+                seed_input, separate_cfg_infer, offload_model, use_input_image_size_as_output, max_input_image_size, randomize_seed, guidance_scale_input],
         outputs=output_image,
     )
 
@@ -184,7 +184,7 @@ with gr.Blocks() as demo:
         examples=get_example(),
         fn=run_for_examples,
         inputs=[image_input_1, image_input_2, image_input_3, height_input, width_input, img_guidance_scale_input, seed_input,
-                max_input_image_size, randomize_seed, use_input_image_size_as_output],
+                max_input_image_size, randomize_seed, use_input_image_size_as_output, guidance_scale_input],
         outputs=output_image,
     )