Minor finalization changes.

app.py

@@ -110,17 +110,19 @@ default_negative_prompt = "Static image, no motion, blurred details, overexposed
 
 # --- LoRA Preset Helper Functions ---
 def parse_lset_prompt(lset_prompt):
-    """Parses a .lset prompt, resolving variables with their defaults."""
+    """Parses a .lset prompt, resolving variables and highlighting them."""
     # Find all variable declarations like ! {Subject}="woman"
     variables = dict(re.findall(r'! \{(\w+)\}="([^"]+)"', lset_prompt))
     
     # Remove the declaration lines to get the clean prompt template
     prompt_template = re.sub(r'! \{\w+\}="[^"]+"\n?', '', lset_prompt).strip()
     
-    # Replace placeholders with their default values
+    # Replace placeholders with their default values, highlighted with markdown
     resolved_prompt = prompt_template
     for key, value in variables.items():
-        resolved_prompt = resolved_prompt.replace(f"{{{key}}}", value)
+        # Highlight the default value to indicate it's a replaceable variable
+        highlighted_value = f"__{value}__"
+        resolved_prompt = resolved_prompt.replace(f"{{{key}}}", highlighted_value)
         
     return resolved_prompt
 
@@ -152,8 +154,9 @@ def handle_lora_selection_change(preset_name, current_prompt):
             return gr.update()
 
         resolved_prompt = parse_lset_prompt(lset_prompt_raw)
-        new_prompt = f"{current_prompt} {resolved_prompt}".strip()
-        gr.Info(f"✅ Appended prompt from '{lset_filename}'")
+        # Append with newlines for separation. If current prompt is empty, strip() handles it.
+        new_prompt = f"{current_prompt}\n\n{resolved_prompt}".strip()
+        gr.Info(f"✅ Appended prompt from '{lset_filename}'. Replace highlighted text like __this__.")
         return gr.update(value=new_prompt)
     except Exception as e:
         # This should be less common now, but good to keep for safety.
@@ -333,7 +336,17 @@ def generate_i2v_video(input_image, prompt, height, width,
 with gr.Blocks() as demo:
     with gr.Column(elem_classes=["main-container"]):
         i2v_aspect_ratio = gr.State(value=DEFAULT_W_SLIDER_VALUE / DEFAULT_H_SLIDER_VALUE)
-        gr.Markdown("# ⚡ FusionX Enhanced Wan 2.1 Video Suite")
+        gr.Markdown("# Wan 2.1 Video Suite with Dynamic LoRA Presets")
+        gr.Markdown(
+            """
+            Welcome! This space allows you to generate videos from images using the powerful Wan 2.1 model, enhanced with dynamic LoRA presets.
+            
+            **How to use:**
+            1.  Start in the **Image-to-Video** tab and upload your starting image.
+            2.  Select a **LoRA Preset** from the dropdown to apply a unique style and automatically add a suggested prompt.
+            3.  Customize the prompt, adjust settings like duration and resolution, and click **Generate I2V**!
+            """
+        )
         
         with gr.Tabs(elem_classes=["gr-tabs"]):
             # --- Image-to-Video Tab ---
@@ -345,6 +358,7 @@ with gr.Blocks() as demo:
                             label="🖼️ Input Image (auto-resizes H/W sliders)",
                             elem_classes=["image-upload"]
                         )
+                        i2v_preset_name = gr.Dropdown(label="🎨 LoRA Preset", choices=available_i2v_presets, value="None", info="Select a preset to apply a LoRA and a suggested prompt.", interactive=len(available_i2v_presets) > 1)
                         i2v_prompt = gr.Textbox(
                             label="✏️ Prompt",
                             value=default_prompt_i2v, lines=3
@@ -359,7 +373,6 @@ with gr.Blocks() as demo:
                             i2v_neg_prompt = gr.Textbox(label="❌ Negative Prompt", value=default_negative_prompt, lines=4)
                             i2v_seed = gr.Slider(label="🎲 Seed", minimum=0, maximum=MAX_SEED, step=1, value=42, interactive=True)
                             i2v_rand_seed = gr.Checkbox(label="🔀 Randomize seed", value=True, interactive=True)
-                            i2v_preset_name = gr.Dropdown(label="🎨 LoRA Preset", choices=available_i2v_presets, value="None", info="Select a preset to apply a LoRA and a suggested prompt.", interactive=len(available_i2v_presets) > 1)
                             i2v_lora_weight = gr.Slider(label="💪 LoRA Weight", minimum=0.0, maximum=2.0, step=0.1, value=0.8, interactive=True)
                             with gr.Row():
                                 i2v_height = gr.Slider(minimum=SLIDER_MIN_H, maximum=SLIDER_MAX_H, step=MOD_VALUE, value=DEFAULT_H_SLIDER_VALUE, label=f"📏 Height ({MOD_VALUE}px steps)")