Updated README, README_zh, and gradio_demo

README.md

@@ -82,6 +82,12 @@ of the **CogVideoX** open-source model.
 + [cli_demo](inference/cli_demo.py): A more detailed explanation of the inference code, mentioning the significance of common parameters.
 + [cli_vae_demo](inference/cli_vae_demo.py): Executing the VAE inference code alone currently requires 71GB of memory, but it will be optimized in the future.
 + [convert_demo](inference/convert_demo.py): How to convert user input into a format suitable for CogVideoX. Because CogVideoX is trained on long caption, we need to convert the input text to be consistent with the training distribution using a LLM. By default, the script uses GLM4, but it can also be replaced with any other LLM such as GPT, Gemini, etc.
++ [gradio_demo](gradio_demo.py): A simple gradio web UI demonstrating how to use the CogVideoX-2B model to generate videos.
+
+<div style="text-align: center;">
+    <img src="resources/gradio_demo.png" style="width: 100%; height: auto;" />
+</div>
+
 + [web_demo](inference/web_demo.py): A simple streamlit web application demonstrating how to use the CogVideoX-2B model to generate videos.
 
 <div style="text-align: center;">

README_zh.md

@@ -77,6 +77,12 @@ CogVideoX是 [清影](https://chatglm.cn/video?fr=osm_cogvideox) 同源的开源
 + [cli_demo](inference/cli_demo.py): 更详细的推理代码讲解，常见参数的意义，在这里都会提及。
 + [cli_vae_demo](inference/cli_vae_demo.py): 单独执行VAE的推理代码，目前需要71GB显存，将来会优化。
 + [convert_demo](inference/convert_demo.py): 如何将用户的输入转换成适合 CogVideoX的长输入。因为CogVideoX是在长文本上训练的，所以我们需要把输入文本的分布通过LLM转换为和训练一致的长文本。脚本中默认使用GLM4，也可以替换为GPT、Gemini等任意大语言模型。
++ [gradio_demo](gradio_demo.py): 一个简单的gradio网页应用，展示如何使用 CogVideoX-2B 模型生成视频。
+
+<div style="text-align: center;">
+    <img src="resources/gradio_demo.png" style="width: 100%; height: auto;" />
+</div>
+
 + [web_demo](inference/web_demo.py): 一个简单的streamlit网页应用，展示如何使用 CogVideoX-2B 模型生成视频。
 
 <div style="text-align: center;">

gradio_demo.py

@@ -9,7 +9,6 @@ import torch
 from diffusers import CogVideoXPipeline
 from datetime import datetime, timedelta
 from openai import OpenAI
-import spaces
 import imageio
 import moviepy.editor as mp
 from typing import List, Union
@@ -88,7 +87,6 @@ def convert_prompt(prompt: str, retry_times: int = 3) -> str:
     return prompt
 
 
-@spaces.GPU(duration=240)
 def infer(
         prompt: str,
         num_inference_steps: int,