Update examples.

webui/tab_texture_synthesis.py

@@ -1,46 +1,46 @@
-import os
-from PIL import Image
-import gradio as gr
-
-
-def create_interface_texture_synthesis(runner):
-    with gr.Blocks():
-        with gr.Row():
-            gr.Markdown('1. Upload the texture image as input.\n'
-                        '2. (Optional) Customize the configurations below as needed.\n'
-                        '3. Cilck `Run` to start synthesis.')
-        
-        with gr.Row():
-            with gr.Column():
-                with gr.Row():
-                    texture_image = gr.Image(label='Input Texture Image', type='pil', interactive=True,
-                                           value=Image.open('examples/s1.jpg').convert('RGB') if os.path.exists('examples/s1.jpg') else None)
-            
-                run_button = gr.Button(value='Run')
-
-                with gr.Accordion('Options', open=True):
-                    height = gr.Number(label='Height', value=512, precision=0, minimum=2, maximum=4096)
-                    width = gr.Number(label='Width', value=1024, precision=0, minimum=2, maximum=4096)
-                    seed = gr.Number(label='Seed', value=2025, precision=0, minimum=0, maximum=2**31)
-                    num_steps = gr.Slider(label='Number of Steps', minimum=1, maximum=1000, value=200, step=1)
-                    iterations = gr.Slider(label='Iterations', minimum=0, maximum=10, value=2, step=1)
-                    lr = gr.Slider(label='Learning Rate', minimum=0.01, maximum=0.5, value=0.05, step=0.01)
-                    mixed_precision = gr.Radio(choices=['bf16', 'no'], value='bf16', label='Mixed Precision')
-                    num_images_per_prompt = gr.Slider(label='Num Images Per Prompt', minimum=1, maximum=10, value=1, step=1)
-                    
-                    base_model_list = ['stable-diffusion-v1-5/stable-diffusion-v1-5',]
-                    model = gr.Radio(choices=base_model_list, label='Select a Base Model', value='stable-diffusion-v1-5/stable-diffusion-v1-5')
-                    synthesis_way = gr.Radio(['Sampling', 'MultiDiffusion'], label='Synthesis Way', value='MultiDiffusion')
-
-            with gr.Column():
-                gr.Markdown('#### Output Image:\n')
-                result_gallery = gr.Gallery(label='Output', elem_id='gallery', columns=2, height='auto', preview=True)
-
-                gr.Examples(
-                    [[Image.open('./webui/images/42.jpg').convert('RGB'), 'MultiDiffusion', 512, 1024]],
-                    [texture_image, synthesis_way, height, width]
-                )
-        ips = [texture_image, height, width, seed, num_steps, iterations, lr, mixed_precision, num_images_per_prompt, synthesis_way,model]
-
-        run_button.click(fn=runner.run_texture_synthesis, inputs=ips, outputs=[result_gallery])
-
+import os
+from PIL import Image
+import gradio as gr
+
+
+def create_interface_texture_synthesis(runner):
+    with gr.Blocks():
+        with gr.Row():
+            gr.Markdown('1. Upload the texture image as input.\n'
+                        '2. (Optional) Customize the configurations below as needed.\n'
+                        '3. Cilck `Run` to start synthesis.')
+        
+        with gr.Row():
+            with gr.Column():
+                with gr.Row():
+                    texture_image = gr.Image(label='Input Texture Image', type='pil', interactive=True,
+                                           value=Image.open('examples/s1.jpg').convert('RGB') if os.path.exists('examples/s1.jpg') else None)
+            
+                run_button = gr.Button(value='Run')
+
+                with gr.Accordion('Options', open=True):
+                    height = gr.Number(label='Height', value=512, precision=0, minimum=2, maximum=4096)
+                    width = gr.Number(label='Width', value=1024, precision=0, minimum=2, maximum=4096)
+                    seed = gr.Number(label='Seed', value=2025, precision=0, minimum=0, maximum=2**31)
+                    num_steps = gr.Slider(label='Number of Steps', minimum=1, maximum=1000, value=200, step=1)
+                    iterations = gr.Slider(label='Iterations', minimum=0, maximum=10, value=2, step=1)
+                    lr = gr.Slider(label='Learning Rate', minimum=0.01, maximum=0.5, value=0.05, step=0.01)
+                    mixed_precision = gr.Radio(choices=['bf16', 'no'], value='bf16', label='Mixed Precision')
+                    num_images_per_prompt = gr.Slider(label='Num Images Per Prompt', minimum=1, maximum=10, value=1, step=1)
+                    
+                    base_model_list = ['stable-diffusion-v1-5/stable-diffusion-v1-5',]
+                    model = gr.Radio(choices=base_model_list, label='Select a Base Model', value='stable-diffusion-v1-5/stable-diffusion-v1-5')
+                    synthesis_way = gr.Radio(['Sampling', 'MultiDiffusion'], label='Synthesis Way', value='MultiDiffusion')
+
+            with gr.Column():
+                gr.Markdown('#### Output Image:\n')
+                result_gallery = gr.Gallery(label='Output', elem_id='gallery', columns=2, height='auto', preview=True)
+
+                gr.Examples(
+                    [[Image.open('./webui/images/42.jpg').convert('RGB'), 'Sampling', 512, 1024, 50]],
+                    [texture_image, synthesis_way, height, width, num_steps]
+                )
+        ips = [texture_image, height, width, seed, num_steps, iterations, lr, mixed_precision, num_images_per_prompt, synthesis_way,model]
+
+        run_button.click(fn=runner.run_texture_synthesis, inputs=ips, outputs=[result_gallery])
+