Create app_14bt2v.py

app_14bt2v.py

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+import spaces
+import os
+import gradio as gr
+import torch
+from diffusers import WanPipeline, AutoencoderKLWan
+from diffusers.utils import export_to_video
+
+# Model setup
+dtype = torch.bfloat16
+device = "cuda"
+model_id = "FastDM/Wan2.2-T2V-A14B-Merge-Lightning-V1.0-Diffusers"
+
+print("Loading model... this may take a while.")
+vae = AutoencoderKLWan.from_pretrained(
+    model_id, subfolder="vae", torch_dtype=torch.float32
+)
+pipe = WanPipeline.from_pretrained(
+    model_id, vae=vae, torch_dtype=dtype
+).to(device)
+
+# Default values
+DEFAULT_PROMPT = "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
+DEFAULT_NEGATIVE = "bad quality, blurry, distorted, extra limbs, watermark, text"
+
+@spaces.GPU(duration=60)
+def generate_video(prompt, negative_prompt, height, width, num_frames, steps, guidance):
+    video = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        height=height,
+        width=width,
+        num_frames=num_frames,
+        guidance_scale=guidance,
+        num_inference_steps=steps,
+    ).frames[0]
+
+    output_path = "t2v_out.mp4"
+    export_to_video(video, output_path, fps=16)
+    return output_path
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🎬 Wan2.2 Text-to-Video Demo")
+    gr.Markdown("Generate short AI videos from text prompts.")
+
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt", value=DEFAULT_PROMPT, lines=3)
+            negative_prompt = gr.Textbox(label="Negative Prompt", value=DEFAULT_NEGATIVE, lines=2)
+            height = gr.Slider(256, 1280, value=720, step=64, label="Height")
+            width = gr.Slider(256, 1280, value=1280, step=64, label="Width")
+            num_frames = gr.Slider(16, 128, value=81, step=1, label="Number of Frames")
+            steps = gr.Slider(1, 20, value=4, step=1, label="Inference Steps")
+            guidance = gr.Slider(0.1, 10.0, value=1.0, step=0.1, label="Guidance Scale")
+            generate_btn = gr.Button("🚀 Generate Video")
+
+        with gr.Column():
+            video_output = gr.Video(label="Generated Video")
+
+    generate_btn.click(
+        fn=generate_video,
+        inputs=[prompt, negative_prompt, height, width, num_frames, steps, guidance],
+        outputs=[video_output],
+    )
+
+demo.launch()