Update app.py

app.py

@@ -295,6 +295,70 @@ def _apply_preset(name: str):
     p = PRESETS.get(name, PRESETS["Default"])
     return p["steps"], p["mu"], p["std"]
 
+DEFAULT_CAPTION = {
+    "high_level_description": "A clean poster announcing a small experimental image generation lab.",
+    "style_description": {
+        "aesthetics": "minimal, precise, modern graphic design, generous whitespace",
+        "lighting": "even soft studio lighting",
+        "medium": "graphic_design",
+        "art_style": "flat vector poster, crisp sans-serif typography",
+        "color_palette": ["#F9FAFB", "#111827", "#2563EB", "#F97316"],
+    },
+    "compositional_deconstruction": {
+        "background": "A warm off-white poster background with a subtle paper texture.",
+        "elements": [
+            {
+                "type": "text",
+                "bbox": [250, 130, 430, 870],
+                "text": "IDEOGRAM 4",
+                "desc": "Large bold black uppercase title text centered near the upper half.",
+                "color_palette": ["#111827"],
+            },
+            {
+                "type": "text",
+                "bbox": [470, 240, 580, 760],
+                "text": "JSON LAB",
+                "desc": "Medium blue uppercase subtitle text centered under the main title.",
+                "color_palette": ["#2563EB"],
+            },
+            {
+                "type": "obj",
+                "bbox": [660, 330, 760, 670],
+                "desc": "A thin orange rounded rectangle outline used as a design accent.",
+                "color_palette": ["#F97316"],
+            },
+        ],
+    },
+}
+
+
+def dumps_caption(caption):
+    return json.dumps(caption, ensure_ascii=False, separators=(",", ":"), indent=2)
+
+
+def normalize_caption(raw_caption):
+    try:
+        caption = json.loads(raw_caption, strict=False)
+    except Exception as e:
+        raise gr.Error(f"JSON parse error: {e}") from e
+
+    if not isinstance(caption, dict):
+        raise gr.Error("Top-level JSON must be an object.")
+
+    if "compositional_deconstruction" not in caption:
+        gr.Warning("compositional_deconstruction is missing. The model accepts any string, but this is outside the usual Ideogram 4 caption format.")
+
+    return json.dumps(caption, ensure_ascii=False, separators=(",", ":")), caption
+
+
+def build_preset(mode, main_guidance=DEFAULT_MAIN_GUIDANCE, final_guidance=DEFAULT_FINAL_GUIDANCE):
+    preset = dict(MODES.get(mode, MODES["Default · 20 steps"]))
+    steps = int(preset.pop("num_inference_steps"))
+    final_steps = min(int(preset.pop("final_guidance_steps")), steps)
+    main_steps = steps - final_steps
+    guidance_schedule = (float(main_guidance),) * main_steps + (float(final_guidance),) * final_steps
+    preset.update(num_inference_steps=steps, guidance_schedule=guidance_schedule)
+    return preset
 
 with gr.Blocks(title=APP_TITLE) as demo:
     lora_note = (