from PIL import Image
import numpy as np
import gradio as gr
import paddlehub as hub
import urllib
import cv2
from datasets import load_dataset
import re
import os
import requests
from share_btn import community_icon_html, loading_icon_html, share_js
import torch
from spectro import wav_bytes_from_spectrogram_image
from diffusers import StableDiffusionPipeline
import io
from os import path
from pydub import AudioSegment
import moviepy.video.io.ImageSequenceClip
from moviepy.editor import *
import mutagen
from mutagen.mp3 import MP3
img_to_text = gr.Blocks.load(name="spaces/pharma/CLIP-Interrogator")
language_translation_model = hub.Module(name='baidu_translate')
language_recognition_model = hub.Module(name='baidu_language_recognition')
style_list = ['古风', '油画', '水彩', '卡通', '二次元', '浮世绘', '蒸汽波艺术', 'low poly', '像素风格', '概念艺术', '未来主义', '赛博朋克', '写实风格', '洛丽塔风格', '巴洛克风格', '超现实主义', '探索无限']
style_list_EN = ['Chinese Ancient Style', 'Oil painting', 'Watercolor', 'Cartoon', 'Anime', 'Ukiyoe', 'Vaporwave', 'low poly', 'Pixel Style', 'Conceptual Art', 'Futurism', 'Cyberpunk', 'Realistic style', 'Lolita style', 'Baroque style', 'Surrealism', '']
tips = {"en": "Tips: The input text will be translated into Chinese for generation",
"jp": "ヒント: 入力テキストは生成のために中国語に翻訳されます",
"kor": "힌트: 입력 텍스트는 생성을 위해 중국어로 번역됩니다"}
count = 0
model_id2 = "riffusion/riffusion-model-v1"
pipe2 = StableDiffusionPipeline.from_pretrained(model_id2, torch_dtype=torch.float16)
pipe2 = pipe2.to("cuda")
def translate_language_example(text_prompts, style_indx):
return translate_language(text_prompts)
def translate_language(text_prompts):
global count
try:
count += 1
tips_text = None
language_code = language_recognition_model.recognize(text_prompts)
if language_code != 'zh':
text_prompts = language_translation_model.translate(text_prompts, language_code, 'zh')
except Exception as e:
error_text = str(e)
return {status_text:error_text, language_tips_text:gr.update(visible=False)}
if language_code in tips:
tips_text = tips[language_code]
else:
tips_text = tips['en']
if language_code == 'zh':
return {language_tips_text:gr.update(visible=False), translated_language:text_prompts, trigger_component: gr.update(value=count, visible=False)}
else:
return {language_tips_text:gr.update(visible=True, value=tips_text), translated_language:text_prompts, trigger_component: gr.update(value=count, visible=False)}
word_list_dataset = load_dataset("stabilityai/word-list", data_files="list.txt", use_auth_token=True)
word_list = word_list_dataset["train"]['text']
def get_result(text_prompts, style_indx):
style = style_list[style_indx]
prompt = "," + style
for filter in word_list:
if re.search(rf"\b{filter}\b", text_prompts):
raise gr.Error("Unsafe content found. Please try again with different prompts.")
model_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = text_prompts
image_output = pipe(prompt).images[0]
print("file name: " + image_output.filename)
# Encode your PIL Image as a JPEG without writing to disk
imagefile = "imageoutput.png"
#img_np = np.array(image_output[0])
#img_nparray= cv2.cvtColor(img_np, cv2.COLOR_BGR2RGBA)
#img_blue_correction = Image.fromarray(img_nparray)
#img_blue_correction.save(imagefile, img_blue_correction.format)
image_output[0].save(imagefile, image_output[0].format)
interrogate_prompt = img_to_text(imagefile, fn_index=1)[0]
print(interrogate_prompt)
spec_image, music_output = get_music(interrogate_prompt + ", " + style_list_EN[style_indx])
video_merged = merge_video(music_output, image_output)
return {spec_result:spec_image, video_result:video_merged, status_text:'Success'}
def get_music(prompt):
spec = pipe(prompt).images[0]
print(spec)
wav = wav_bytes_from_spectrogram_image(spec)
with open("output.wav", "wb") as f:
f.write(wav[0].getbuffer())
return spec, 'output.wav'
def merge_video(music, img_list):
#Convert to mp3
#music.export("audio.mp3", format="mp3")
print('wav audio converted to mp3 audio' )
print('now getting duration of this mp3 audio' )
#getting audio clip's duration
audio_length = int(MP3(music).info.length)
print('Audio length is :',audio_length)
file_name = 'video_no_audio.mp4'
fps = 12
slide_time = audio_length
fourcc = cv2.VideoWriter.fourcc(*'MJPG')
out = cv2.VideoWriter(file_name, fourcc, fps, (1024, 1024))
for image in img_list:
cv_img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
for _ in range(slide_time * fps):
#cv_img = cv2.resize(np.array(cv_img), (1024, 1024))
out.write(cv_img)
out.release()
#String a list of images into a video and write to memory
print('video clip created successfully from images')
# loading video file
print('Starting video and audio merge')
videoclip = VideoFileClip(file_name) #("/content/gdrive/My Drive/AI/my_video1.mp4")
print('loading video-clip')
# loading audio file
audioclip = AudioFileClip(music) #.subclip(0, 15)
print('loading mp3-format audio')
# adding audio to the video clip
mergedclip = videoclip.set_audio(audioclip)
print('video and audio merged successfully')
#Getting size and frame count of merged video file
print('Getting size and frame count of merged video file')
duration = mergedclip.duration
frame_count = mergedclip.fps
print('duration is:',duration)
print('frame count :', frame_count)
mergedclip.to_videofile('mergedvideo.mp4')
return 'mergedvideo.mp4'
title="文生图生音乐视频 Text to Image to Music to Video"
description="An AI art generation pipeline, which supports text-to-image-to-music task."
css = """
.gradio-container {
font-family: 'IBM Plex Sans', sans-serif;
}
.gr-button {
color: white;
border-color: black;
background: black;
}
input[type='range'] {
accent-color: black;
}
.dark input[type='range'] {
accent-color: #dfdfdf;
}
.container {
max-width: 730px;
margin: auto;
padding-top: 1.5rem;
}
#gallery {
min-height: 22rem;
margin-bottom: 15px;
margin-left: auto;
margin-right: auto;
border-bottom-right-radius: .5rem !important;
border-bottom-left-radius: .5rem !important;
}
#gallery>div>.h-full {
min-height: 20rem;
}
.details:hover {
text-decoration: underline;
}
.gr-button {
white-space: nowrap;
}
.gr-button:focus {
border-color: rgb(147 197 253 / var(--tw-border-opacity));
outline: none;
box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
--tw-border-opacity: 1;
--tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
--tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
--tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
--tw-ring-opacity: .5;
}
.footer {
margin-bottom: 45px;
margin-top: 35px;
text-align: center;
border-bottom: 1px solid #e5e5e5;
}
.footer>p {
font-size: .8rem;
display: inline-block;
padding: 0 10px;
transform: translateY(10px);
background: white;
}
.dark .footer {
border-color: #303030;
}
.dark .footer>p {
background: #0b0f19;
}
.prompt h4{
margin: 1.25em 0 .25em 0;
font-weight: bold;
font-size: 115%;
}
"""
block = gr.Blocks(css=css)
examples = [
[
'蒙娜丽莎，赛博朋克，宝丽来，33毫米',
'蒸汽波艺术(Vaporwave)'
],
[
'一条由闪电制成的令人敬畏的龙',
'概念艺术(Conceptual Art)'
],
[
'An awesome dragon made of lightning',
'概念艺术(Conceptual Art)'
],
[
'嫦娥在时代广场，戏曲',
'写实风格(Realistic style)'
],
[
'Peking Opera at New York',
'探索无限(Explore infinity)'
],
[
'古风少女',
'水彩(Watercolor)'
],
[
'辐射游戏角色',
'探索无限(Explore infinity)'
],
[
'Fallout game character',
'探索无限(Explore infinity)'
],
[
'Traditional Chinese Painting',
'古风(Ancient Style)'
],
[
'原神游戏截图，pixiv, 二次元绘画作品',
'二次元(Anime)'
],
[
'Genshin Impact Game Screenshot, pixiv, Anime Painting Artworks',
'二次元(Anime)'
],
[
'原神角色设定, 哪吒, pixiv, 二次元绘画',
'二次元(Anime)'
],
[
'Genshin Impact Character Design, Harry Potter, pixiv, Anime Painting',
'二次元(Anime)'
],
[
'巨狼，飘雪，蓝色大片烟雾，毛发细致，烟雾缭绕，高清，3d，cg感，侧面照',
'探索无限(Explore infinity)'
],
[
'汉服少女，中国山水画，青山绿水，溪水长流，古风，科技都市，丹青水墨，中国风',
'赛博朋克(Cyberpunk)'
],
[
'戴着墨镜的赛博朋克女孩肖像，在夕阳下的城市中, 油画风格',
'赛博朋克(Cyberpunk)'
],
[
'Portrait of a cyberpunk girl with sunglasses, in the city sunset, oil painting',
'赛博朋克(Cyberpunk)'
],
[
'暗黑破坏神',
'探索无限(Explore infinity)'
],
[
'火焰，凤凰，少女，未来感，高清，3d，精致面容，cg感，古风，唯美，毛发细致，上半身立绘',
'探索无限(Explore infinity)'
],
[
'浮世绘日本科幻哑光绘画，概念艺术，动漫风格神道寺禅园英雄动作序列，包豪斯',
'探索无限(Explore infinity)'
],
[
'一只猫坐在椅子上，戴着一副墨镜,海盗风格',
'探索无限(Explore infinity)'
],
[
'稲妻で作られた畏敬の念を抱かせる竜、コンセプトアート',
'油画(Oil painting)'
],
[
'번개로 만든 경외스러운 용, 개념 예술',
'油画(Oil painting)'
],
[
'梵高猫头鹰',
'蒸汽波艺术(Vaporwave)'
],
[
'萨尔瓦多·达利描绘古代文明的超现实主义梦幻油画',
'写实风格(Realistic style)'
],
[
'夕阳日落时，阳光落在云层上，海面波涛汹涌，风景，胶片感',
'探索无限(Explore infinity)'
],
[
'Sunset, the sun falls on the clouds, the sea is rough, the scenery is filmy',
'油画(Oil painting)'
],
[
'夕日が沈むと、雲の上に太陽の光が落ち、海面は波が荒く、風景、フィルム感',
'油画(Oil painting)'
],
[
'석양이 질 때 햇빛이 구름 위에 떨어지고, 해수면의 파도가 용솟음치며, 풍경, 필름감',
'油画(Oil painting)'
],
]
with block:
gr.HTML(
"""
"""
)
with gr.Group():
with gr.Box():
with gr.Row().style(mobile_collapse=False, equal_height=True):
text = gr.Textbox(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt, multiple languages are supported now.",
).style(
border=(True, False, True, True),
rounded=(True, False, False, True),
container=False,
)
btn = gr.Button("Generate image").style(
margin=False,
rounded=(False, True, True, False),
)
language_tips_text = gr.Textbox(label="language tips", show_label=False, visible=False, max_lines=1)
styles = gr.Dropdown(label="风格(style)", choices=['古风(Ancient Style)', '油画(Oil painting)', '水彩(Watercolor)',
'卡通(Cartoon)', '二次元(Anime)', '浮世绘(Ukiyoe)', '蒸汽波艺术(Vaporwave)', 'low poly',
'像素风格(Pixel Style)', '概念艺术(Conceptual Art)', '未来主义(Futurism)', '赛博朋克(Cyberpunk)', '写实风格(Realistic style)',
'洛丽塔风格(Lolita style)', '巴洛克风格(Baroque style)', '超现实主义(Surrealism)', '探索无限(Explore infinity)'], value='探索无限(Explore infinity)', type="index")
status_text = gr.Textbox(
label="处理状态(Process status)",
show_label=True,
max_lines=1,
interactive=False
)
spec_result = gr.Image()
video_result = gr.Video(type=None, label='Final Merged video')
trigger_component = gr.Textbox(vaule="", visible=False) # This component is used for triggering inference funtion.
translated_language = gr.Textbox(vaule="", visible=False)
ex = gr.Examples(examples=examples, fn=translate_language_example, inputs=[text, styles], outputs=[language_tips_text, status_text, trigger_component, translated_language], cache_examples=False)
ex.dataset.headers = [""]
text.submit(translate_language, inputs=[text], outputs=[language_tips_text, status_text, trigger_component, translated_language])
btn.click(translate_language, inputs=[text], outputs=[language_tips_text, status_text, trigger_component, translated_language])
trigger_component.change(fn=get_result, inputs=[translated_language, styles], outputs=[spec_result, video_result, status_text])
gr.Markdown(
"""
Space by [@DGSpitzer](https://www.youtube.com/channel/UCzzsYBF4qwtMwJaPJZ5SuPg)❤️ [@大谷的游戏创作小屋](https://space.bilibili.com/176003)
[](https://twitter.com/DGSpitzer)
"""
)
gr.HTML('''
''')
block.queue(concurrency_count=128).launch()