Update app.py

app.py

@@ -60,7 +60,7 @@ optimize_pipeline_(pipe,
 )
 
 
-default_prompt_i2v = "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
+default_prompt_t2v = "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
 default_negative_prompt = "色调艳丽, 过曝, 静态, 细节模糊不清, 字幕, 风格, 作品, 画作, 画面, 静止, 整体发灰, 最差质量, 低质量, JPEG压缩残留, 丑陋的, 残缺的, 多余的手指, 画得不好的手部, 画得不好的脸部, 畸形的, 毁容的, 形态畸形的肢体, 手指融合, 静止不动的画面, 杂乱的背景, 三条腿, 背景人很多, 倒着走"
 
 
@@ -90,11 +90,11 @@ def generate_video(
     progress=gr.Progress(track_tqdm=True),
 ):
     """
-    Generate a video from an input image using the Wan 2.2 14B I2V model with Phantom LoRA.
+    Generate a video from a text prompt using the Wan 2.2 14B T2V model with Lightning LoRA.
     
-    This function takes an input image and generates a video animation based on the provided
-    prompt and parameters. It uses an FP8 qunatized Wan 2.2 14B Image-to-Video model in with Phantom LoRA
-    for fast generation in 6-8 steps.
+    This function takes an input prompt and generates a video animation based on the provided
+    prompt and parameters. It uses an FP8 qunatized Wan 2.2 14B Text-to-Video model with Lightning LoRA
+    for fast generation in 4-8 steps.
     
     Args:
         prompt (str): Text prompt describing the desired animation or motion.
@@ -153,18 +153,18 @@ def generate_video(
     return video_path, current_seed
 
 with gr.Blocks() as demo:
-    gr.Markdown("# Fast 6 steps Wan 2.2 I2V (14B) with Phantom LoRA")
-    gr.Markdown("run Wan 2.2 in just 6-8 steps, with [FusionX Phantom LoRA by DeeJayT](https://huggingface.co/vrgamedevgirl84/Wan14BT2VFusioniX/tree/main/FusionX_LoRa), compatible with 🧨 diffusers")
+    gr.Markdown("# Fast 4 steps Wan 2.2 T2V (14B) with Lightning LoRA")
+    gr.Markdown("run Wan 2.2 in just 4-8 steps, with [Wan 2.2 Lightning LoRA](https://huggingface.co/Kijai/WanVideo_comfy/tree/main/Wan22-Lightning), compatible with 🧨 diffusers")
     with gr.Row():
         with gr.Column():
-            prompt_input = gr.Textbox(label="Prompt", value=default_prompt_i2v)
+            prompt_input = gr.Textbox(label="Prompt", value=default_prompt_t2v)
             duration_seconds_input = gr.Slider(minimum=MIN_DURATION, maximum=MAX_DURATION, step=0.1, value=MAX_DURATION, label="Duration (seconds)", info=f"Clamped to model's {MIN_FRAMES_MODEL}-{MAX_FRAMES_MODEL} frames at {FIXED_FPS}fps.")
             
             with gr.Accordion("Advanced Settings", open=False):
                 negative_prompt_input = gr.Textbox(label="Negative Prompt", value=default_negative_prompt, lines=3)
                 seed_input = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=42, interactive=True)
                 randomize_seed_checkbox = gr.Checkbox(label="Randomize seed", value=True, interactive=True)
-                steps_slider = gr.Slider(minimum=1, maximum=30, step=1, value=6, label="Inference Steps") 
+                steps_slider = gr.Slider(minimum=1, maximum=30, step=1, value=4, label="Inference Steps") 
                 guidance_scale_input = gr.Slider(minimum=0.0, maximum=10.0, step=0.5, value=1, label="Guidance Scale - high noise stage")
                 guidance_scale_2_input = gr.Slider(minimum=0.0, maximum=10.0, step=0.5, value=3, label="Guidance Scale 2 - low noise stage")