30min

gradio_demo.py

@@ -78,7 +78,7 @@ def check(input_image):
 def reset_feedback():
     return 3, ''
 
-@spaces.GPU(duration=300)
+@spaces.GPU(duration=1800)
 def stage1_process(input_image, gamma_correction):
     print('Start stage1_process')
     if torch.cuda.device_count() == 0:
@@ -100,7 +100,7 @@ def stage1_process(input_image, gamma_correction):
     print('End stage1_process')
     return LQ, gr.update(visible = True)
 
-@spaces.GPU(duration=300)
+@spaces.GPU(duration=1800)
 def llave_process(input_image, temperature, top_p, qs=None):
     print('Start llave_process')
     if torch.cuda.device_count() == 0:
@@ -116,7 +116,7 @@ def llave_process(input_image, temperature, top_p, qs=None):
     print('End llave_process')
     return captions[0]
 
-@spaces.GPU(duration=300)
+@spaces.GPU(duration=1800)
 def stage2_process(
     noisy_image,
     denoise_image,
@@ -321,7 +321,7 @@ with gr.Blocks(title="SUPIR") as interface:
     prompt = gr.Textbox(label="Image description for LlaVa", 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="Image description (optional)",
-                          info="Help the AI to understand what the image represents; describe as much as possible",
+                          info="Help the AI understand what the image represents; describe as much as possible",
                           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, '
@@ -357,7 +357,7 @@ with gr.Blocks(title="SUPIR") as interface:
                 model_select = gr.Radio(["v0-Q", "v0-F"], label="Model Selection", info="Q=Quality, F=Fidelity", value="v0-Q",
                                         interactive=True)
             with gr.Column():
-                color_fix_type = gr.Radio(["None", "AdaIn", "Wavelet"], label="Color-Fix Type", info="AdaIn=Adaptive Instance Normalization, Wavelet=For JPEG artifacts", value="Wavelet",
+                color_fix_type = gr.Radio(["None", "AdaIn", "Wavelet"], label="Color-Fix Type", info="AdaIn=Improve following a style, Wavelet=For JPEG artifacts", value="Wavelet",
                                           interactive=True)
         s_cfg = gr.Slider(label="Text Guidance Scale", info="lower=follow the image, higher=follow the prompt", minimum=1.0, maximum=15.0,
                           value=default_setting.s_cfg_Quality if torch.cuda.device_count() > 0 else 1.0, step=0.1)