denoise_image = gr.Image(label="Denoised image", show_label=True, type="numpy", sources=[], height=600, elem_id="image-s1")

gradio_demo.py

@@ -527,12 +527,13 @@ with gr.Blocks(title="SUPIR") as interface:
         prompt = gr.Textbox(label="Image description", info="Help the AI understand what the image represents; describe as much as possible; I advise you to write in English as other languages may not be handled", value="", placeholder="A person, walking, in a town, Summer, photorealistic", lines=3)
         prompt_hint = gr.HTML("You can use a <a href='"'https://huggingface.co/spaces/MaziyarPanahi/llava-llama-3-8b'"'>LlaVa space</a> to auto-generate the description of your image.")
         upscale = gr.Radio([["x1", 1], ["x2", 2], ["x3", 3], ["x4", 4], ["x5", 5], ["x6", 6], ["x7", 7], ["x8", 8]], label="Upscale factor", info="Resolution x1 to x8", value=2, interactive=True)
+        allocation = gr.Radio([["1 min", 1], ["2 min", 2], ["3 min", 3], ["4 min", 4], ["5 min", 5], ["6 min", 6], ["7 min", 7], ["8 min", 8], ["9 min", 9]], label="GPU allocation time", info="lower=May abort run, higher=Time penalty for next runs", value=6, interactive=True)
         output_format = gr.Radio([["*.png", "png"], ["*.webp", "webp"], ["*.jpeg", "jpeg"], ["*.gif", "gif"], ["*.bmp", "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", 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_image = gr.Image(label="Denoised image", show_label=True, type="numpy", sources=[], 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)
 
     with gr.Accordion("LLaVA options", open=False, visible=False):
@@ -567,7 +568,6 @@ with gr.Blocks(title="SUPIR") as interface:
             with gr.Column():
                 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)
-        allocation = gr.Radio([["1 min", 1], ["2 min", 2], ["3 min", 3], ["4 min", 4], ["5 min", 5], ["6 min", 6], ["7 min", 7], ["8 min", 8], ["9 min", 9]], label="GPU allocation time", info="lower=May abort run, higher=Time penalty for next runs", value=6, 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)
         s_stage2 = gr.Slider(label="Restoring Guidance Strength", minimum=0., maximum=1., value=1., step=0.05)