All the results have the same size

gradio_demo.py

@@ -203,6 +203,9 @@ def stage2_process(
         for i, result in enumerate(results):
             Image.fromarray(result).save(f'./history/{event_id[:5]}/{event_id[5:]}/HQ_{i}.png')
 
+    # All the results have the same size
+    result_height, result_width, result_channel = np.array(results[0]).shape
+
     print('End stage2_process')
     end = time.time()
     secondes = int(end - start)
@@ -212,7 +215,7 @@ def stage2_process(
     minutes = minutes - (hours * 60)
     information = ("Restart the process to get another result. " if randomize_seed else "") + "The image(s) has(ve) been generated in " + ((str(hours) + " h, ") if hours != 0 else "") + ((str(minutes) + " min, ") if hours != 0 or minutes != 0 else "") + str(secondes) + " sec."
 
-    return [input_image] + results, [input_image] + results, gr.update(value = information, visible = True), event_id
+    return [noisy_image] + results, [noisy_image] + results, gr.update(value = information, visible = True), event_id
 
 def load_and_reset(param_setting):
     print('Start load_and_reset')
@@ -296,18 +299,17 @@ with gr.Blocks(title="SUPIR") as interface:
     """)
     gr.HTML(title_html)
 
-    with gr.Group():
-        input_image = gr.Image(label="Input", show_label=True, type="numpy", height=600, elem_id="image-input")
-        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",
-                              info="Help the AI to understand what the image represents",
-                              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, '
-                                    'hyper sharpness, perfect without deformations.',
-                              lines=3)
-        a_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.")
+    input_image = gr.Image(label="Input", show_label=True, type="numpy", height=600, elem_id="image-input")
+    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",
+                          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, '
+                                'hyper sharpness, perfect without deformations.',
+                          lines=3)
+    a_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.")
 
     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)