app.py

import gradio as gr
import torch
import imageio
from diffusers import DiffusionPipeline

MODEL_ID = "Linum-AI/linum-v2-720p"

device = "cuda" if torch.cuda.is_available() else "cpu"

pipe = DiffusionPipeline.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.float16,
    variant="fp16"
).to(device)

def generate_video(prompt, steps, cfg, seed):
    generator = torch.Generator(device=device).manual_seed(seed)

    video = pipe(
        prompt=prompt,
        num_inference_steps=steps,
        guidance_scale=cfg,
        generator=generator
    ).frames

    output_path = "output.mp4"
    imageio.mimsave(output_path, video, fps=8)

    return output_path

with gr.Blocks(title="Linum v2 – Text to Video") as demo:
    gr.Markdown("# 🎬 Linum v2 – Text to Video (720p)")
    gr.Markdown("Masukkan prompt → generate video AI")

    prompt = gr.Textbox(
        label="Prompt",
        placeholder="A cinematic POV walking through misty mountains at sunrise"
    )

    steps = gr.Slider(10, 50, value=25, step=1, label="Inference Steps")
    cfg = gr.Slider(1, 15, value=7, step=0.5, label="CFG Scale")
    seed = gr.Number(value=42, label="Seed")

    run = gr.Button("Generate Video 🚀")
    output = gr.Video(label="Result")

    run.click(
        fn=generate_video,
        inputs=[prompt, steps, cfg, seed],
        outputs=output
    )

demo.launch()