min_size=32

gradio_demo.py

@@ -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=1024)
+                                min_size=32)
 
     LQ = np.array(input_image) / 255.0
     LQ = np.power(LQ, gamma_correction)
@@ -327,7 +327,7 @@ with gr.Blocks(title="SUPIR") as interface:
     with gr.Group():
         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)",
+        a_prompt = gr.Textbox(label="Image description",
                               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 '
@@ -338,7 +338,7 @@ with gr.Blocks(title="SUPIR") as interface:
         output_format = gr.Radio(["png", "webp", "jpeg", "gif", "bmp"], label="Image format for result", info="File extention", value="png", interactive=True)
 
     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)
+        gamma_correction = gr.Slider(label="Gamma Correction", info = "lower=lighter, higher=darker", 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")
         denoise_information = gr.HTML(value="If present, the denoised image will be used for the restoration instead of the input image.", visible=False)