min_size parameter

gradio_demo.py

@@ -79,7 +79,7 @@ def check(input_image):
 def reset_feedback():
     return 3, ''
 
-@spaces.GPU(duration=300)
+@spaces.GPU(duration=360)
 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=300)
+@spaces.GPU(duration=360)
 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=300)
+@spaces.GPU(duration=360)
 def stage2_process(
     noisy_image,
     denoise_image,
@@ -125,6 +125,7 @@ def stage2_process(
     a_prompt,
     n_prompt,
     num_samples,
+    min_size,
     downscale,
     upscale,
     edm_steps,
@@ -148,7 +149,6 @@ def stage2_process(
 ):
     start = time.time()
     print('Start stage2_process')
-    print(a_prompt)
     if torch.cuda.device_count() == 0:
         gr.Warning('Set this space to GPU config to make it work.')
         return None, None, None
@@ -175,7 +175,7 @@ def stage2_process(
         model.current_model = model_select
     input_image = HWC3(input_image)
     input_image = upscale_image(input_image, upscale, unit_resolution=32,
-                                min_size=32)
+                                min_size=min_size)
 
     LQ = np.array(input_image) / 255.0
     LQ = np.power(LQ, gamma_correction)
@@ -228,6 +228,7 @@ def stage2_process(
     ((str(hours) + " h, ") if hours != 0 else "") + \
     ((str(minutes) + " min, ") if hours != 0 or minutes != 0 else "") + \
     str(secondes) + " sec."
+    print(information)
 
     # Only one image can be shown in the slider
     return [noisy_image] + [results[0]], gr.update(format = output_format, value = [noisy_image] + results), gr.update(value = information, visible = True), event_id
@@ -291,7 +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 5min.
+    This demo can handle huge images but the process will be aborted if it lasts more than 5 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>
     """
@@ -359,6 +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)
         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():
@@ -449,6 +451,7 @@ with gr.Blocks(title="SUPIR") as interface:
         a_prompt,
         n_prompt,
         num_samples,
+        min_size,
         downscale,
         upscale,
         edm_steps,