Upgrade to WAMU_v3 + default 4 steps (faster)

app.py

@@ -234,9 +234,7 @@ def interpolate_bits(frames_np, multiplier=2, scale=1.0):
 
 ORG_NAME = "TestOrganizationPleaseIgnore"
 # MODEL_ID = "Wan-AI/Wan2.2-I2V-A14B-Diffusers"
-MODEL_ID = os.getenv("REPO_ID") or random.choice(
-    list(list_models(author=ORG_NAME, filter='diffusers:WanImageToVideoPipeline'))
-).modelId
+MODEL_ID = os.getenv("REPO_ID") or "TestOrganizationPleaseIgnore/WAMU_v3_WAN2.2_I2V_LIGHTNING"
 CACHE_DIR = os.path.expanduser("~/.cache/huggingface/")
 
 LORA_MODELS = [
@@ -662,7 +660,7 @@ with gr.Blocks(delete_cache=(3600, 10800)) as demo:
                 quality_slider = gr.Slider(minimum=1, maximum=10, step=1, value=6, label="Video Quality", info="If set to 10, the generated video may be too large and won't play in the Gradio preview.")
                 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", info="4 steps = fastest (Lightning default). Raise to 6-8 for slightly more detail.")
                 guidance_scale_input = gr.Slider(minimum=0.0, maximum=10.0, step=0.5, value=1, label="Guidance Scale - high noise stage", info="Values above 1 increase GPU usage and may take longer to process.")
                 guidance_scale_2_input = gr.Slider(minimum=0.0, maximum=10.0, step=0.5, value=1, label="Guidance Scale 2 - low noise stage")
                 scheduler_dropdown = gr.Dropdown(