Delete app.py

app.py

@@ -1,163 +0,0 @@
-import os
-import uuid
-import GPUtil
-import gradio as gr
-import psutil
-import spaces
-from videosys import CogVideoXConfig, CogVideoXPABConfig, VideoSysEngine
-from transformers import pipeline
-
-os.environ["GRADIO_TEMP_DIR"] = os.path.join(os.getcwd(), ".tmp_outputs")
-os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
-
-# 번역기 설정
-translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en")
-
-def translate_to_english(text):
-    if any('\uAC00' <= char <= '\uD7A3' for char in text):
-        return translator(text, max_length=512)[0]['translation_text']
-    return text
-
-def load_model(model_name, enable_video_sys=False, pab_threshold=[100, 850], pab_range=2):
-    pab_config = CogVideoXPABConfig(spatial_threshold=pab_threshold, spatial_range=pab_range)
-    config = CogVideoXConfig(model_name, enable_pab=enable_video_sys, pab_config=pab_config)
-    engine = VideoSysEngine(config)
-    return engine
-
-def generate(engine, prompt, num_inference_steps=50, guidance_scale=6.0):
-    translated_prompt = translate_to_english(prompt)
-    video = engine.generate(translated_prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale).video[0]
-
-    unique_filename = f"{uuid.uuid4().hex}.mp4"
-    output_path = os.path.join("./.tmp_outputs", unique_filename)
-
-    engine.save_video(video, output_path)
-    return output_path
-
-@spaces.GPU()
-def generate_vanilla(model_name, prompt, num_inference_steps, guidance_scale, progress=gr.Progress(track_tqdm=True)):
-    engine = load_model(model_name)
-    video_path = generate(engine, prompt, num_inference_steps, guidance_scale)
-    return video_path
-
-@spaces.GPU()
-def generate_vs(
-    model_name,
-    prompt,
-    num_inference_steps,
-    guidance_scale,
-    threshold_start,
-    threshold_end,
-    gap,
-    progress=gr.Progress(track_tqdm=True),
-):
-    threshold = [int(threshold_end), int(threshold_start)]
-    gap = int(gap)
-    engine = load_model(model_name, enable_video_sys=True, pab_threshold=threshold, pab_range=gap)
-    video_path = generate(engine, prompt, num_inference_steps, guidance_scale)
-    return video_path
-
-def get_server_status():
-    cpu_percent = psutil.cpu_percent()
-    memory = psutil.virtual_memory()
-    disk = psutil.disk_usage("/")
-    try:
-        gpus = GPUtil.getGPUs()
-        if gpus:
-            gpu = gpus[0]
-            gpu_memory = f"{gpu.memoryUsed}/{gpu.memoryTotal}MB ({gpu.memoryUtil*100:.1f}%)"
-        else:
-            gpu_memory = "GPU를 찾을 수 없음"
-    except:
-        gpu_memory = "GPU 정보를 사용할 수 없음"
-
-    return {
-        "cpu": f"{cpu_percent}%",
-        "memory": f"{memory.percent}%",
-        "disk": f"{disk.percent}%",
-        "gpu_memory": gpu_memory,
-    }
-
-def update_server_status():
-    status = get_server_status()
-    return (status["cpu"], status["memory"], status["disk"], status["gpu_memory"])
-
-css = """
-footer {
-    visibility: hidden;
-}
-"""
-
-with gr.Blocks(theme="Yntec/HaleyCH_Theme_Orange", css=css) as demo:
-    with gr.Row():
-        with gr.Column():
-            prompt = gr.Textbox(label="프롬프트 (200단어 이내)", value="바다 위의 일몰.", lines=3)
-
-            with gr.Column():
-                gr.Markdown("**생성 매개변수**<br>")
-                with gr.Row():
-                    model_name = gr.Radio(
-                        ["THUDM/CogVideoX-2b", "THUDM/CogVideoX-5b"], label="모델 유형", value="THUDM/CogVideoX-2b"
-                    )
-                with gr.Row():
-                    num_inference_steps = gr.Number(label="추론 단계", value=50)
-                    guidance_scale = gr.Number(label="가이던스 스케일", value=6.0)
-                with gr.Row():
-                    pab_range = gr.Number(
-                        label="PAB 브로드캐스트 범위", value=2, precision=0, info="브로드캐스트 타임스텝 범위."
-                    )
-                    pab_threshold_start = gr.Number(label="PAB 시작 타임스텝", value=850, info="1000 단계에서 시작.")
-                    pab_threshold_end = gr.Number(label="PAB 종료 타임스텝", value=100, info="0 단계에서 종료.")
-                with gr.Row():
-                    generate_button_vs = gr.Button("⚡️ VideoSys로 비디오 생성 (더 빠름)")
-                    generate_button = gr.Button("🎬 비디오 생성 (원본)")
-                with gr.Column(elem_classes="server-status"):
-                    gr.Markdown("#### 서버 상태")
-
-                    with gr.Row():
-                        cpu_status = gr.Textbox(label="CPU", scale=1)
-                        memory_status = gr.Textbox(label="메모리", scale=1)
-
-                    with gr.Row():
-                        disk_status = gr.Textbox(label="디스크", scale=1)
-                        gpu_status = gr.Textbox(label="GPU 메모리", scale=1)
-
-                    with gr.Row():
-                        refresh_button = gr.Button("새로고침")
-
-        with gr.Column():
-            with gr.Row():
-                video_output_vs = gr.Video(label="VideoSys를 사용한 CogVideoX", width=720, height=480)
-            with gr.Row():
-                video_output = gr.Video(label="CogVideoX", width=720, height=480)
-
-    generate_button.click(
-        generate_vanilla,
-        inputs=[model_name, prompt, num_inference_steps, guidance_scale],
-        outputs=[video_output],
-        concurrency_id="gen",
-        concurrency_limit=1,
-    )
-
-    generate_button_vs.click(
-        generate_vs,
-        inputs=[
-            model_name,
-            prompt,
-            num_inference_steps,
-            guidance_scale,
-            pab_threshold_start,
-            pab_threshold_end,
-            pab_range,
-        ],
-        outputs=[video_output_vs],
-        concurrency_id="gen",
-        concurrency_limit=1,
-    )
-
-    refresh_button.click(update_server_status, outputs=[cpu_status, memory_status, disk_status, gpu_status])
-    demo.load(update_server_status, outputs=[cpu_status, memory_status, disk_status, gpu_status], every=1)
-
-if __name__ == "__main__":
-    demo.queue(max_size=10, default_concurrency_limit=1)
-    demo.launch()