Move options

gradio_demo.py

@@ -286,9 +286,17 @@ with gr.Blocks(title="SUPIR") as interface:
     gr.HTML(title_html)
 
     input_image = gr.Image(label="Input", show_label=True, type="numpy", height=600, elem_id="image-input")
-    prompt = gr.Textbox(label="Image description", value="", placeholder="A person, walking, in a town, Summer, photorealistic", lines=3)
+    prompt = gr.Textbox(label="Image description", value="", placeholder="A person, walking, in a town, Summer, photorealistic", lines=3, visible=False)
+        upscale = gr.Radio([1, 2, 3, 4, 5, 6, 7, 8], label="Upscale factor", info="Resolution x1 to x8", value=2, interactive=True)
+        a_prompt = gr.Textbox(label="Positive Prompt",
+                              info="Describe what the image represents",
+                              value='Cinematic, High Contrast, highly detailed, taken using a Canon EOS R '
+                                    'camera, hyper detailed photo - realistic maximum detail, 32k, Color '
+                                    'Grading, ultra HD, extreme meticulous detailing, skin pore detailing, '
+                                    'hyper sharpness, perfect without deformations.',
+                              lines=3)
 
-    with gr.Accordion("Pre-denoising", open=False):
+    with gr.Accordion("Pre-denoising (optional)", open=False):
         gamma_correction = gr.Slider(label="Gamma Correction", minimum=0.1, maximum=2.0, value=1.0, step=0.1)
         denoise_button = gr.Button(value="Pre-denoise")
         denoise_image = gr.Image(label="Denoised image", show_label=True, type="numpy", height=600, elem_id="image-s1")
@@ -299,25 +307,17 @@ with gr.Blocks(title="SUPIR") as interface:
         top_p = gr.Slider(label="Top P", info = "Percent of tokens shortlisted", minimum=0., maximum=1.0, value=0.7, step=0.1)
         qs = gr.Textbox(label="Question", info="Ask LLaVa what description you want", value="Describe the image and its style in a very detailed manner. The image is a realistic photography, not an art painting.", lines=3)
 
-    with gr.Accordion("Restoring options", open=False):
-        upscale = gr.Radio([1, 2, 3, 4, 5, 6, 7, 8], label="Upscale factor", info="Resolution x1 to x8", value=2, interactive=True)
-        a_prompt = gr.Textbox(label="Default Positive Prompt",
-                              info="Describe what the image represents",
-                              value='Cinematic, High Contrast, highly detailed, taken using a Canon EOS R '
-                                    'camera, hyper detailed photo - realistic maximum detail, 32k, Color '
-                                    'Grading, ultra HD, extreme meticulous detailing, skin pore detailing, '
-                                    'hyper sharpness, perfect without deformations.',
-                              lines=3)
-        n_prompt = gr.Textbox(label="Default Negative Prompt",
+    with gr.Accordion("Advanced options", open=False):
+        n_prompt = gr.Textbox(label="Negative Prompt",
                               info="List what the image does NOT represent",
                               value='painting, oil painting, illustration, drawing, art, sketch, anime, '
                                     'cartoon, CG Style, 3D render, unreal engine, blurring, bokeh, ugly, dirty, messy, '
                                     'worst quality, low quality, frames, watermark, signature, jpeg artifacts, '
                                     'deformed, lowres, over-smooth',
                               lines=3)
+        edm_steps = gr.Slider(label="Steps", info="lower=faster, higher=more details", minimum=1, maximum=200, value=default_setting.edm_steps if torch.cuda.device_count() > 0 else 1, step=1)
         num_samples = gr.Slider(label="Num Samples", info="Number of generated results; I discourage to increase because the process is limited to 4 min", minimum=1, maximum=4 if not args.use_image_slider else 1
                                 , value=1, step=1)
-        edm_steps = gr.Slider(label="Steps", info="lower=faster, higher=more details", minimum=1, maximum=200, value=default_setting.edm_steps if torch.cuda.device_count() > 0 else 1, step=1)
         with gr.Row():
             with gr.Column():
                 model_select = gr.Radio(["v0-Q", "v0-F"], label="Model Selection", info="Q=Quality, F=Fidelity", value="v0-Q",