Upload app.py

app.py

@@ -37,10 +37,10 @@ MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1216
 
 css = """
-#container {  margin: 0 auto; }
-#col-container { margin: 0 auto; }
-#result { max-width: 520px; max-height: 520px; width: 520px; height: 520px; align: center; }
-#model-info { text-align: center; }
+#container {  margin: 0 auto; !important; }
+#col-container { margin: 0 auto; !important; }
+#result { display: block; max-width: 520px; max-height: 520px; width: 520px; height: 520px; align: center; !important; }
+#model-info { text-align: center; !important; }
 """
 
 with gr.Blocks(css=css, fill_width=True, elem_id="container") as demo:
@@ -75,17 +75,16 @@ with gr.Blocks(css=css, fill_width=True, elem_id="container") as demo:
                     num_inference_steps = gr.Slider(label="Number of inference steps", minimum=1, maximum=100, step=1, value=28)
 
                 with gr.Row():
-                    with gr.Group():
-                        model_name = gr.Dropdown(label="Model", info="You can enter a huggingface model repo_id to want to use.", choices=get_diffusers_model_list(), value=get_diffusers_model_list()[0], allow_custom_value=True, interactive=True)
-                        model_info = gr.Markdown(elem_id="model-info")
-                        model_detail = gr.Checkbox(label="Show detail of model in list", value=False)
+                    model_name = gr.Dropdown(label="Model", info="You can enter a huggingface model repo_id to want to use.", choices=get_diffusers_model_list(), value=get_diffusers_model_list()[0], allow_custom_value=True, interactive=True)
+                    model_info = gr.Markdown(elem_id="model-info")
+                    model_detail = gr.Checkbox(label="Show detail of model in list", value=False)
 
                 with gr.Row():
                     sampler = gr.Dropdown(label="Sampler", choices=get_samplers(), value="Euler a")
                     vae_model = gr.Dropdown(label="VAE Model", choices=get_vaes(), value=get_vaes()[0])
 
-                with gr.Row():
-                    with gr.Accordion("LoRA", open=True, visible=True):
+                with gr.Accordion("LoRA", open=True, visible=True):
+                    with gr.Row():
                         with gr.Group():
                             lora1 = gr.Dropdown(label="LoRA 1", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True)
                             lora1_wt = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA 1: weight")
@@ -136,9 +135,9 @@ with gr.Blocks(css=css, fill_width=True, elem_id="container") as demo:
                     recom_prompt = gr.Checkbox(label="Recommended prompt", value=True)
                     quality_selector = gr.Radio(label="Quality Tag Presets", interactive=True, choices=list(preset_quality.keys()), value="None")
                     style_selector = gr.Radio(label="Style Presets", interactive=True, choices=list(preset_styles.keys()), value="None")
-
-                with gr.Row():
-                    with gr.Accordion("Translation Settings", open=False):
+                
+                with gr.Accordion("Translation Settings", open=False):
+                    with gr.Row():
                         chatbot = gr.Chatbot(likeable=False, render_markdown=False, visible=False) # component for auto-translation
                         chat_model = gr.Dropdown(choices=get_dolphin_models(), value=get_dolphin_models()[0][1], allow_custom_value=True, label="Model")
                         chat_model_info = gr.Markdown(value=get_dolphin_model_info(get_dolphin_models()[0][1]), label="Model info")