Update app.py

app.py

@@ -10,7 +10,7 @@ import numpy as np
 import gradio as gr
 import spaces
 
-model_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+model_id = "Wan-AI/Wan2.1-T2V-14B-Diffusers"
 vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
 pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
 flow_shift = 5.0  # 5.0 for 720P, 3.0 for 480P
@@ -37,13 +37,9 @@ iface = gr.Interface(
     fn=generate,
     inputs=[
         gr.Textbox(label="Input prompt"),
-        # gr.Slider(label="Width", minimum=256, maximum=2048, step=8, value=1024),
-        # gr.Slider(label="Height", minimum=256, maximum=2048, step=8, value=1024),
-        # gr.Textbox(label="Lora ID", placeholder="Optional"),
-        # gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Lora Scale", value=1)
     ],
     additional_inputs = [
-        gr.Textbox(label="Negative prompt", value = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"),
+        gr.Textbox(label="Negative prompt", value = ""),
         gr.Slider(label="Width", minimum=480, maximum=1280, step=8, value=1280),
         gr.Slider(label="Height", minimum=480, maximum=1280, step=8, value=720),
         gr.Slider(minimum=20, maximum=100, step=1, label="Inference Steps", value=50)