Update app.py

app.py

@@ -64,12 +64,6 @@ def infer(
     lora_scale=0.5
     ):
     generator = torch.Generator(device).manual_seed(seed)
-
-    print(prompt)
-    print(type(prompt))
-
-    print(negative_prompt)
-    print(type(negative_prompt))
     
     if model_id != model_id_default:
         pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype).to(device)
@@ -95,37 +89,111 @@ def infer(
     
     return pipe(**params).images[0]
 
-with gr.Blocks() as demo:
-    with gr.Column():
-        gr.Markdown("# DEMO Text-to-Image")
-        model_id = gr.Textbox(label="Model ID", value=model_id_default)
-        prompt = gr.Textbox(label="Prompt")
-        negative_prompt = gr.Textbox(label="Negative prompt")
-        seed = gr.Number(label="Seed", value=42)
-        guidance_scale = gr.Slider(label="Guidance scale", minimum=0.0, maximum=10.0, value=7.0)
-        lora_scale = gr.Slider(label="LoRA scale", minimum=0.0, maximum=1.0, value=0.5)
-        num_inference_steps = gr.Slider(label="Number of inference steps", minimum=1, maximum=50, value=20)
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 640px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(" # DEMO Text-to-Image")
         
+        with gr.Row():
+            model_id = gr.Textbox(
+                label="Model ID",
+                max_lines=1,
+                placeholder="Enter model id like 'CompVis/stable-diffusion-v1-4'",
+                value=model_id_default
+            )
+
+        prompt = gr.Textbox(
+            label="Prompt",
+            max_lines=1,
+            placeholder="Enter your prompt",
+        )
+
+        negative_prompt = gr.Textbox(
+            label="Negative prompt",
+            max_lines=1,
+            placeholder="Enter a negative prompt",
+        )
+
+        with gr.Row():
+            seed = gr.Number(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=42,
+            )
+
+        with gr.Row():
+            guidance_scale = gr.Slider(
+                label="Guidance scale",
+                minimum=0.0,
+                maximum=10.0,
+                step=0.1,
+                value=7.0,
+            )
+
+        with gr.Row():
+            lora_scale = gr.Slider(
+                label="LoRA scale",
+                minimum=0.0,
+                maximum=1.0,
+                step=0.1,
+                value=0.5,
+            )
+
+        with gr.Row():
+            num_inference_steps = gr.Slider(
+                label="Number of inference steps",
+                minimum=1,
+                maximum=50,
+                step=1,
+                value=20,
+            )
+
         with gr.Accordion("Optional Settings", open=False):
-            width = gr.Slider(label="Width", minimum=256, maximum=1024, value=512, step=32)
-            height = gr.Slider(label="Height", minimum=256, maximum=1024, value=512, step=32)
-        
-        run_button = gr.Button("Run")
-        result = gr.Image(label="Result")
+            with gr.Row():
+                width = gr.Slider(
+                    label="Width",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=512,
+                )
+            
+            with gr.Row():
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=512,
+                )
+
+        run_button = gr.Button("Run", scale=1, variant="primary")
+        result = gr.Image(label="Result", show_label=False)
     
-    run_button.click(
-        fn=infer, 
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn=infer,
         inputs=[
-            prompt, 
-            negative_prompt, 
-            width, 
-            height, 
-            num_inference_steps, 
-            model_id, seed, 
-            guidance_scale, 
-            lora_scale
-        ], 
-        outputs=result)
+            prompt,
+            negative_prompt,
+            width,
+            height,
+            num_inference_steps,
+            model_id,
+            seed,
+            guidance_scale,
+            lora_scale,
+        ],
+        outputs=[result],
+    )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()