app.py

import gradio as gr
import requests
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration
import torch
from diffusers import AnimateDiffPipeline, LCMScheduler, MotionAdapter
from moviepy.editor import concatenate_videoclips, AudioFileClip
from moviepy.video.io.ImageSequenceClip import ImageSequenceClip
from transformers import AutoProcessor, MusicgenForConditionalGeneration
import scipy.io.wavfile
import re
import numpy as np
import os
import io
import tempfile

# 定义图像到文本函数
def img2text(image):
    processor = BlipProcessor.from_pretrained("blip-image-captioning-large")
    model = BlipForConditionalGeneration.from_pretrained("blip-image-captioning-large")
    inputs = processor(image, return_tensors="pt")
    out = model.generate(**inputs)
    caption = processor.decode(out[0], skip_special_tokens=True)
    print(caption)
    return caption

# 定义文本生成函数
def text2text(user_input):
    api_key = "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"
    base_url = "https://openrouter.ai/api/v1"
    
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }
    
    data = {
        "model": "openai/gpt-3.5-turbo",
        "messages": [
            {
                "role": "system", 
                "content": (
                    "You are an expert who is very good at writing stories. Please expand it into a continuous story based on the input, and logically cut the story into sentences. Each sentence is a scene (as many sentences and scenes as possible, and at least 10 sentences). Each sentence is required The content of the sentence description should be detailed and do not use rhetorical techniques, and no ambiguous words such as pronouns should appear in the sentence. Be as detailed as possible to accurately describe who is doing what, and the scene descriptions before and after should have a certain correlation. In addition, I require your answer to follow a certain format. Let me give you an example. For example, I enter: a dolphin jumping out of the water at sunset. "
                    "Your answer format: "
                    """
                    [1] The sun nears the horizon, illuminating the calm sea surface with a warm glow. 
                    [2] A dolphin swims swiftly below the calm sea surface, moving closer to the top. 
                    [3] The dolphin uses its powerful tail fin to prepare for a leap out of the water. 
                    [4] The dolphin's body starts to emerge from the water, exposing itself above the surface. 
                    [5] The dolphin leaps completely out of the water, creating an arch with its body in the air. 
                    [6] The dolphin rotates its body in the air before it begins its descent back to the water. 
                    [7] The dolphin's head and back make the first contact with the water, creating a splash. 
                    [8] The dolphin fully submerges under the water, causing the splashes around it to slowly disperse. 
                    [9] The dolphin moves forward underwater, gradually disappearing into the dimming light of the sunset. 
                    """
                    "My input is as follows, please answer me in the format without adding any other words."
                )
            },
            { "role": "user", "content": user_input }
        ]
    }

    response = requests.post(f"{base_url}/chat/completions", headers=headers, json=data)
    response.raise_for_status()
    completion = response.json()
    print(completion['choices'][0]['message']['content'])
    return completion['choices'][0]['message']['content']




import torch
from diffusers import AnimateDiffPipeline, LCMScheduler, MotionAdapter
from diffusers.utils import export_to_gif
import re
def text2vid(input_text):
    # 使用正则表达式分割输入文本并提取句子
    sentences = re.findall(r'\[\d+\] (.+?)(?:\n|\Z)', input_text)

    # 加载动作适配器和动画扩散管道
    adapter = MotionAdapter.from_pretrained("/home/u2022211776/jupyterlab/AnimateLCM", config_file="/home/u2022211776/jupyterlab/AnimateLCM/config.json", torch_dtype=torch.float16)
    pipe = AnimateDiffPipeline.from_pretrained("/home/u2022211776/jupyterlab/epiCRealism", motion_adapter=adapter, torch_dtype=torch.float16)
    pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, beta_schedule="linear")

    # 加载LoRA权重
    pipe.load_lora_weights("/home/u2022211776/jupyterlab/AnimateLCM", weight_name="AnimateLCM_sd15_t2v_lora.safetensors", adapter_name="lcm-lora")

    # 设置适配器并启用功能
    try:
        pipe.set_adapters(["lcm-lora"], [0.8])
    except ValueError as e:
        print("Ignoring the error:", str(e))
    pipe.enable_vae_slicing()
    pipe.enable_model_cpu_offload()
    
    all_frames = []  # 存储所有句子的所有帧

    # 循环遍历每个句子，生成动画并导出为GIF
    for index, sentence in enumerate(sentences):
        output = pipe(
            #prompt=sentence + ", 4k, high resolution",
            prompt=sentence + ", cartoon",
            negative_prompt="bad quality, worse quality, low resolution",
            num_frames=24,
            guidance_scale=2.0,
            num_inference_steps=6,
            generator=torch.Generator("cpu").manual_seed(0)
        )
        frames = output.frames[0]
        all_frames.extend(frames)  # 添加每个句子的帧到all_frames

    return all_frames




# 定义文本到视频函数
def text2vid(input_text):
    sentences = re.findall(r'\[\d+\] (.+?)(?:\n|\Z)', input_text)
    adapter = MotionAdapter.from_pretrained("AnimateLCM", config_file="AnimateLCM/config.json", torch_dtype=torch.float16)
    pipe = AnimateDiffPipeline.from_pretrained("epiCRealism", motion_adapter=adapter, torch_dtype=torch.float16)
    pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, beta_schedule="linear")
    pipe.load_lora_weights("AnimateLCM", weight_name="AnimateLCM_sd15_t2v_lora.safetensors", adapter_name="lcm-lora")
    try:
        pipe.set_adapters(["lcm-lora"], [0.8])
    except ValueError as e:
        print("Ignoring the error:", str(e))
    pipe.enable_vae_slicing()
    pipe.enable_model_cpu_offload()
    
    video_frames = []
    for sentence in sentences:
        output = pipe(
            prompt=sentence + ", 4k, high resolution",
            negative_prompt="bad quality, worse quality, low resolution",
            num_frames=24,
            guidance_scale=2.0,
            num_inference_steps=6,
            generator=torch.Generator("cpu").manual_seed(0)
        )
        video_frames.extend(output.frames[0])
    
    return video_frames

def text2text_A(user_input):
    # 设置API密钥和基础URL
    api_key = "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"
    base_url = "https://openrouter.ai/api/v1"
    
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }
    
    data = {
        "model": "openai/gpt-3.5-turbo",
        "messages": [
            {
                "role": "system", 
                "content": (
                    "You are an expert in music criticism, please match this story with a suitable musical style based on my input and describe it, please make sure you follow my format output and do not add any other statements e.g. Input: in a small tavern everyone danced, the bartender poured drinks for everyone, everyone had a good time and was very happy and sang and danced. Output: 80s pop track with bassy drums and synth."
                    "Again, please make sure you follow the format of the output, here is my input:"

                )
            },
            { "role": "user", "content": user_input }
        ]
    }

    response = requests.post(f"{base_url}/chat/completions", headers=headers, json=data)
    response.raise_for_status()  # 确保请求成功

    completion = response.json()
    print(completion['choices'][0]['message']['content'])
    return completion['choices'][0]['message']['content']
    
# 定义文本到音频函数
def text2audio(text_input, duration_seconds):
    processor = AutoProcessor.from_pretrained("musicgen-small")
    model = MusicgenForConditionalGeneration.from_pretrained("musicgen-small")
    inputs = processor(text=[text_input], padding=True, return_tensors="pt")
    max_new_tokens = int((duration_seconds / 5) * 256)
    audio_values = model.generate(**inputs, max_new_tokens=max_new_tokens)
    print(duration_seconds)
    return audio_values[0, 0].numpy(), model.config.audio_encoder.sampling_rate

# 定义生成结果视频的函数
def result_generate(video_clip, audio_clip):
    video = video_clip.set_audio(audio_clip)
    video_buffer = BytesIO()
    video.write_videofile(video_buffer, codec="libx264", audio_codec="aac")
    video_buffer.seek(0)
    return video_buffer

def generate_video(image):
    # 获取图像描述
    text = img2text(image)
    # 生成详细的文本场景描述
    sentences = text2text(text)
    # 生成视频帧
    video_frames = text2vid(sentences)
    
    # 转换视频帧为numpy数组
    video_frames = [np.array(frame) for frame in video_frames]

    # 创建视频片段
    video_clip = ImageSequenceClip(video_frames, fps=24)
    video_duration = video_clip.duration

    # 生成音频数据
    audio_text = text2text_A(text)
    audio_data, audio_rate = text2audio(audio_text, video_duration)

    # 将音频数据写入临时文件
    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmpfile:
        scipy.io.wavfile.write(tmpfile, audio_rate, audio_data)
        tmpfile_path = tmpfile.name

    # 创建AudioFileClip对象
    audio_clip = AudioFileClip(tmpfile_path)

    # 将音频添加到视频中
    video_clip = video_clip.set_audio(audio_clip)
    print("audio_done")

    # 将视频写入临时文件
    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as tmpfile:
        video_clip.write_videofile(tmpfile.name, codec="libx264", audio_codec="aac")
        video_file_path = tmpfile.name

    # 读取临时文件数据并删除
    with open(video_file_path, 'rb') as f:
        video_data = f.read()
    os.remove(video_file_path)
    os.remove(tmpfile_path)
    print("video_done")
    return video_data
    
# 定义 Gradio 接口
interface = gr.Interface(
    fn=lambda img: generate_video(img),
    inputs=gr.Image(type="pil"),
    outputs=gr.Video(),
    title="InspiroV Video Generation",
    description="Upload an image to generate a video",
    theme="soft"
)

# 启动 Gradio 应用
interface.launch()