Hide LlaVa

gradio_demo.py

@@ -74,7 +74,6 @@ def update_seed(is_randomize_seed, seed):
 def check(input_image):
     if input_image is None:
         raise gr.Error("Please provide an image to restore.")
-    return [input_image, input_image, input_image]
 
 def reset_feedback():
     return 3, ''
@@ -241,10 +240,9 @@ else:
     title_md = """
     <h1><center>SUPIR Image Upscaler</center></h1>
     
-    <p>SUPIR is a practicing model scaling for photo-realistic image restoration. It is still a research project under tested and is not yet a stable commercial product.
+    <p>SUPIR is a practicing model scaling for photo-realistic image restoration. It is still a research project under tested and is not yet a stable commercial product. LLaVa is not available in this demo. If you want to auto-generate the description of your image, use another <a href="https://huggingface.co/spaces/MaziyarPanahi/llava-llama-3-8b">LLaVa space</a>.
     
     <a href="https://arxiv.org/abs/2401.13627">Paper</a> &emsp; <a href="http://supir.xpixel.group/">Project Page</a> &emsp; <a href="https://github.com/Fanghua-Yu/SUPIR/blob/master/assets/DemoGuide.png">How to play</a> &emsp; <a href="https://huggingface.co/blog/MonsterMMORPG/supir-sota-image-upscale-better-than-magnific-ai">Local Install Guide</a></p>
-    <p style="background-color: blue;">LLaVa is disabled.</p>
     """
 
 
@@ -277,7 +275,7 @@ with gr.Blocks(title="SUPIR") as interface:
             with gr.Accordion("Pre-denoising options", open=False):
                 gamma_correction = gr.Slider(label="Gamma Correction", minimum=0.1, maximum=2.0, value=1.0, step=0.1)
 
-            with gr.Accordion("LLaVA options", open=False):
+            with gr.Accordion("LLaVA options", open=False, visible=False):
                 temperature = gr.Slider(label="Temperature", info = "lower=Always similar, higher=More creative", minimum=0., maximum=1.0, value=0.2, step=0.1)
                 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)
@@ -338,11 +336,10 @@ with gr.Blocks(title="SUPIR") as interface:
                 result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery1")
             else:
                 result_gallery = ImageSlider(label='Output', show_label=False, elem_id="gallery1")
-            result_gallery2 = ImageSlider(label='Output', show_label=False, elem_id="gallery2")
             with gr.Row():
                 with gr.Column():
                     denoise_button = gr.Button(value="Pre-denoise")
-                with gr.Column():
+                with gr.Column(visible=False):
                     llave_button = gr.Button(value="Generate description by LlaVa (disabled)")
                 with gr.Column():
                     diffusion_button = gr.Button(value="🚀 Restore", variant = "primary")
@@ -363,7 +360,7 @@ with gr.Blocks(title="SUPIR") as interface:
     
     denoise_button.click(fn = check, inputs = [
         input_image
-    ], outputs = [result_gallery2], queue = False, show_progress = False).success(fn = stage1_process, inputs = [
+    ], outputs = [], queue = False, show_progress = False).success(fn = stage1_process, inputs = [
         input_image,
         gamma_correction
     ], outputs=[
@@ -372,7 +369,7 @@ with gr.Blocks(title="SUPIR") as interface:
 
     llave_button.click(fn = check, inputs = [
         denoise_image
-    ], outputs = [result_gallery2], queue = False, show_progress = False).success(fn = llave_process, inputs = [
+    ], outputs = [], queue = False, show_progress = False).success(fn = llave_process, inputs = [
         denoise_image,
         temperature,
         top_p,
@@ -388,7 +385,7 @@ with gr.Blocks(title="SUPIR") as interface:
         seed
     ], queue = False, show_progress = False).then(fn = check, inputs = [
         input_image
-    ], outputs = [result_gallery2], queue = False, show_progress = False).then(fn = reset_feedback, inputs = [], outputs = [
+    ], outputs = [], queue = False, show_progress = False).then(fn = reset_feedback, inputs = [], outputs = [
         fb_score,
         fb_text
     ], queue = False, show_progress = False).success(fn=stage2_process, inputs = [