Update app.py

app.py

@@ -159,45 +159,33 @@ def process(
 
     return preds
 
-MARKDOWN = \
-    """
-    ## Improving the Stability of Diffusion Models for Content Consistent Super-Resolution
-
-    [GitHub](https://github.com/csslc/CCSR) | [Paper](https://arxiv.org/pdf/2401.00877.pdf) | [Project Page](https://csslc.github.io/project-CCSR/)
-
-    If CCSR is helpful for you, please help star the GitHub Repo. Thanks!
-    """
-
 block = gr.Blocks().queue()
 with block:
     with gr.Row():
-        gr.Markdown(MARKDOWN)
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(source="upload", type="pil", label="Input Image")
-            run_button = gr.Button(label="Run")
-            with gr.Accordion("Options", open=True):
-                num_samples = gr.Slider(label="Number Of Samples", minimum=1, maximum=12, value=1, step=1, info="Number of output images to generate.")
-                sr_scale = gr.Dropdown(label="SR Scale", choices=["2x", "4x", "8x"], value="4x", info="Super-resolution scale factor.")
-                strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01, info="Strength of the control signal.")
-                positive_prompt = gr.Textbox(label="Positive Prompt", value="", info="Positive text prompt to guide the image generation.")
-                negative_prompt = gr.Textbox(
-                    label="Negative Prompt",
-                    value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
-                    info="Negative text prompt to avoid undesirable features."
-                )
-                cfg_scale = gr.Slider(label="Classifier Free Guidance Scale", minimum=0.1, maximum=30.0, value=1.0, step=0.1, info="Scale for classifier-free guidance.")
-                steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=45, step=1, info="Number of diffusion steps.")
-                use_color_fix = gr.Checkbox(label="Use Color Correction", value=True, info="Apply color correction to the output image.")
-                seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, value=231, info="Random seed for reproducibility. Set to -1 for a random seed.")
-                tile_diffusion = gr.Checkbox(label="Tile diffusion", value=False, info="Enable tiled diffusion for large images.")
-                tile_diffusion_size = gr.Slider(label="Tile diffusion size", minimum=512, maximum=1024, value=512, step=256, info="Size of each tile for tiled diffusion.")
-                tile_diffusion_stride = gr.Slider(label="Tile diffusion stride", minimum=256, maximum=512, value=256, step=128, info="Stride between tiles for tiled diffusion.")
-                tile_vae = gr.Checkbox(label="Tile VAE", value=True, info="Enable tiled VAE for large images.")
-                vae_encoder_tile_size = gr.Slider(label="Encoder tile size", minimum=512, maximum=5000, value=1024, step=256, info="Size of each tile for the VAE encoder.")
-                vae_decoder_tile_size = gr.Slider(label="Decoder tile size", minimum=64, maximum=512, value=224, step=128, info="Size of each tile for the VAE decoder.")
-        with gr.Column():
-            result_gallery = gr.Gallery(label="Output", show_label=False, elem_id="gallery").style(grid=2, height="auto")
+        input_image = gr.Image(type="pil", label="Input Image")
+        run_button = gr.Button(label="Run")
+        with gr.Accordion("Options", open=True):
+            num_samples = gr.Slider(label="Number Of Samples", minimum=1, maximum=12, value=1, step=1, info="Number of output images to generate.")
+            sr_scale = gr.Dropdown(label="SR Scale", choices=["2x", "4x", "8x"], value="4x", info="Super-resolution scale factor.")
+            strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01, info="Strength of the control signal.")
+            positive_prompt = gr.Textbox(label="Positive Prompt", value="", info="Positive text prompt to guide the image generation.")
+            negative_prompt = gr.Textbox(
+                label="Negative Prompt",
+                value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
+                info="Negative text prompt to avoid undesirable features."
+            )
+            cfg_scale = gr.Slider(label="Classifier Free Guidance Scale", minimum=0.1, maximum=30.0, value=1.0, step=0.1, info="Scale for classifier-free guidance.")
+            steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=45, step=1, info="Number of diffusion steps.")
+            use_color_fix = gr.Checkbox(label="Use Color Correction", value=True, info="Apply color correction to the output image.")
+            seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, value=231, info="Random seed for reproducibility. Set to -1 for a random seed.")
+            tile_diffusion = gr.Checkbox(label="Tile diffusion", value=False, info="Enable tiled diffusion for large images.")
+            tile_diffusion_size = gr.Slider(label="Tile diffusion size", minimum=512, maximum=1024, value=512, step=256, info="Size of each tile for tiled diffusion.")
+            tile_diffusion_stride = gr.Slider(label="Tile diffusion stride", minimum=256, maximum=512, value=256, step=128, info="Stride between tiles for tiled diffusion.")
+            tile_vae = gr.Checkbox(label="Tile VAE", value=True, info="Enable tiled VAE for large images.")
+            vae_encoder_tile_size = gr.Slider(label="Encoder tile size", minimum=512, maximum=5000, value=1024, step=256, info="Size of each tile for the VAE encoder.")
+            vae_decoder_tile_size = gr.Slider(label="Decoder tile size", minimum=64, maximum=512, value=224, step=128, info="Size of each tile for the VAE decoder.")
+    with gr.Column():
+        result_gallery = gr.Gallery(label="Output", show_label=False, elem_id="gallery").style(grid=2, height="auto")
 
     inputs = [
         input_image,