新增推薦參數 Presets，提供多種預設設定以簡化用戶操作；新增對應的解析函式以支援選擇預設

app.py

@@ -118,6 +118,35 @@ def caption_image(image: Image.Image, max_len: int = 50):
     caption = BLIP_PROCESSOR.decode(out[0], skip_special_tokens=True)
     return caption
 
+# -----------------------------
+# 推薦參數 Presets（新增）
+# -----------------------------
+# 說明：
+# - CPU_Quick：在 CPU 上較快出圖（較小解析度、較少步數）
+# - Balanced：一般情境（SDXL sweet-spot）
+# - HQ_GPU：偏重品質（建議 GPU），CPU 可能很慢
+PRESETS = {
+    "CPU_Quick": {
+        "steps": 16, "guidance": 6.5, "scheduler": "Euler A",
+        "use_karras": True, "width": 640, "height": 896,
+    },
+    "Balanced": {
+        "steps": 20, "guidance": 7.0, "scheduler": "DPM++ 2M (DPMSolverMultistep)",
+        "use_karras": True, "width": 768, "height": 768,
+    },
+    "HQ_GPU": {
+        "steps": 28, "guidance": 7.5, "scheduler": "DPM++ 2M (DPMSolverMultistep)",
+        "use_karras": True, "width": 1024, "height": 1024,
+    },
+}
+
+def resolve_preset(name: str):
+    p = PRESETS.get(name, PRESETS["Balanced"])
+    return (
+        int(p["steps"]), float(p["guidance"]), p["scheduler"], bool(p["use_karras"]),
+        int(p["width"]), int(p["height"]), p["neg"]
+    )
+
 # -----------------------------
 # Gradio 介面
 # -----------------------------
@@ -148,6 +177,11 @@ with gr.Blocks(title="NoobAI-XL + BLIP", theme=gr.themes.Soft()) as demo:
                     use_karras = gr.Checkbox(value=True, label="使用 Karras Sigmas")
                     width      = gr.Slider(256, 1536, value=768, step=8, label="寬度 Width")
                     height     = gr.Slider(256, 1536, value=768, step=8, label="高度 Height")
+                    ti_preset = gr.Dropdown(
+                        choices=list(PRESETS.keys()),
+                        value="Balanced",
+                        label="推薦參數 Preset"
+                    )
                     run_btn    = gr.Button("圖片生成", variant="primary")
 
                 with gr.Column(scale=1):
@@ -183,6 +217,16 @@ with gr.Blocks(title="NoobAI-XL + BLIP", theme=gr.themes.Soft()) as demo:
                 [out_img, status]
             )
 
+            def _apply_ti_preset(name):
+                steps, guidance, sched, karras, w, h, neg = resolve_preset(name)
+                return steps, guidance, sched, karras, w, h, neg
+
+            ti_preset.change(
+                _apply_ti_preset,
+                inputs=[ti_preset],
+                outputs=[steps, guidance, scheduler_name, use_karras, width, height, neg_prompt]
+            )
+
         # Tab 2: Image -> Caption
         with gr.TabItem("Image → Caption"):
             with gr.Row():