7 min

gradio_demo.py

@@ -79,7 +79,7 @@ def check(input_image):
 def reset_feedback():
     return 3, ''
 
-@spaces.GPU(duration=360)
+@spaces.GPU(duration=420)
 def stage1_process(input_image, gamma_correction):
     print('Start stage1_process')
     if torch.cuda.device_count() == 0:
@@ -101,7 +101,7 @@ def stage1_process(input_image, gamma_correction):
     print('End stage1_process')
     return LQ, gr.update(visible = True)
 
-@spaces.GPU(duration=360)
+@spaces.GPU(duration=420)
 def llave_process(input_image, temperature, top_p, qs=None):
     print('Start llave_process')
     if torch.cuda.device_count() == 0:
@@ -117,7 +117,7 @@ def llave_process(input_image, temperature, top_p, qs=None):
     print('End llave_process')
     return captions[0]
 
-@spaces.GPU(duration=360)
+@spaces.GPU(duration=420)
 def stage2_process(
     noisy_image,
     denoise_image,
@@ -292,7 +292,7 @@ title_html = """
     LlaVa is not integrated in this demo. The content added by SUPIR is imagination, not real-world information.
     The aim of SUPIR is the beauty and the illustration.
     Most of the processes only last few minutes.
-    This demo can handle huge images but the process will be aborted if it lasts more than 5 min.
+    This demo can handle huge images but the process will be aborted if it lasts more than 7 min.
     
     <p><center><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></center></p>
     """
@@ -360,7 +360,7 @@ with gr.Blocks(title="SUPIR") as interface:
         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", minimum=1, maximum=4 if not args.use_image_slider else 1
                                 , value=1, step=1)
-        min_size = gr.Slider(label="Minimum size", info="Minimum height, minimum width", minimum=32, value=1024, step=32)
+        min_size = gr.Slider(label="Minimum size", info="Minimum height, minimum width of the result", minimum=32, maximum=4096, value=1024, step=32)
         downscale = gr.Radio([1, 2, 3, 4, 5, 6, 7, 8], label="Pre-downscale factor", info="Reducing blurred image reduce the process time", value=1, interactive=True)
         with gr.Row():
             with gr.Column():