from PIL import Image
import numpy as np
import gradio as gr
#import paddlehub as hub
import urllib
import cv2
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
from diffusers import EulerAncestralDiscreteScheduler
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
from mutagen.wave import WAVE
import time
import base64
import gradio as gr
from sentence_transformers import SentenceTransformer
import httpx
import json
from utils import get_tags_for_prompts, get_mubert_tags_embeddings, get_pat
minilm = SentenceTransformer('all-MiniLM-L6-v2')
mubert_tags_embeddings = get_mubert_tags_embeddings(minilm)
def get_track_by_tags(tags, pat, duration, maxit=20, loop=False):
if loop:
mode = "loop"
else:
mode = "track"
r = httpx.post('https://api-b2b.mubert.com/v2/RecordTrackTTM',
json={
"method": "RecordTrackTTM",
"params": {
"pat": pat,
"duration": duration,
"tags": tags,
"mode": mode
}
})
rdata = json.loads(r.text)
assert rdata['status'] == 1, rdata['error']['text']
trackurl = rdata['data']['tasks'][0]['download_link']
print('Generating track ', end='')
for i in range(maxit):
r = httpx.get(trackurl)
if r.status_code == 200:
return trackurl
time.sleep(1)
def generate_track_by_prompt(prompt):
try:
pat = get_pat("mail@mail.com")
_, tags = get_tags_for_prompts(minilm, mubert_tags_embeddings, [prompt, ])[0]
result = get_track_by_tags(tags, pat, int(30), loop=False)
print(result)
return result
except Exception as e:
return str(e)
#img_to_text = gr.Blocks.load(name="spaces/fffiloni/CLIP-Interrogator-2")
#text_to_music = gr.Interface.load("spaces/fffiloni/text-2-music")
#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', '默认']
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', 'Detailed']
tips = {"en": "Tips: The input text will be translated into English 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_id = "runwayml/stable-diffusion-v1-5"
eulera = EulerAncestralDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, scheduler=eulera)
pipe = pipe.to("cuda")
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)
language_code = 'en'
#if language_code != 'en':
#text_prompts = language_translation_model.translate(text_prompts, language_code, 'en')
except Exception as e:
error_text = str(e)
return {status_text:error_text, language_tips_text:gr.update(visible=False), translated_language:text_prompts, trigger_component: gr.update(value=count, visible=False)}
if language_code in tips:
tips_text = tips[language_code]
else:
tips_text = tips['en']
if language_code == 'en':
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)}
def get_result(text_prompts, style_indx, musicAI_indx, duration):
style = style_list_EN[style_indx]
prompt = style + "," + text_prompts
sdresult = pipe(prompt, negative_prompt = "out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", num_inference_steps=50, guidance_scale=7, width=576, height=576)
image_output = sdresult.images[0] if not sdresult.nsfw_content_detected[0] else Image.open("nsfw_placeholder.jpg")
print("Generated image with prompt " + prompt)
# 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.save(imagefile, image_output.format)
interrogate_prompt = prompt
#interrogate_prompt = img_to_text(imagefile, 'fast', 4, fn_index=1)[0]
print(interrogate_prompt)
spec_image, music_output = get_music(interrogate_prompt + ", " + style_list_EN[style_indx], musicAI_indx, duration)
video_merged = merge_video(music_output, image_output)
return {spec_result:spec_image, imgfile_result:image_output, musicfile_result:"audio.wav", video_result:video_merged, status_text:'Success', share_button:gr.update(visible=True), community_icon:gr.update(visible=True), loading_icon:gr.update(visible=True)}
def get_music(prompt, musicAI_indx, duration):
mp3file_name = "audio.mp3"
wavfile_name = "audio.wav"
if musicAI_indx == 0:
if duration == 5:
width_duration=512
else :
width_duration = 512 + ((int(duration)-5) * 128)
spec = pipe2(prompt, height=512, width=width_duration).images[0]
print(spec)
wav = wav_bytes_from_spectrogram_image(spec)
with open(wavfile_name, "wb") as f:
f.write(wav[0].getbuffer())
#Convert to mp3, for video merging function
wavfile = AudioSegment.from_wav(wavfile_name)
wavfile.export(mp3file_name, format="mp3")
return spec, mp3file_name
else:
#result = text_to_music(prompt, fn_index=0)
result = generate_track_by_prompt(prompt)
print(f"""—————
NEW RESULTS
prompt : {prompt}
music : {result}
———————
""")
url = result
data = urllib.request.urlopen(url)
f = open(mp3file_name,'wb')
f.write(data.read())
f.close()
#Convert to wav, for sharing function only supports wav file
mp3file = AudioSegment.from_mp3(mp3file_name)
mp3file.export(wavfile_name, format="wav")
return None, mp3file_name
def merge_video(mp3file_name, image):
print('wav audio converted to mp3 audio' )
print('now getting duration of this mp3 audio' )
#getting audio clip's duration
audio_length = int(MP3(mp3file_name).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')
#W, H should be the same as input image
out = cv2.VideoWriter(file_name, fourcc, fps, (576, 576))
# 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)
cv_img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
for _ in range(slide_time * fps):
#cv_img = cv2.resize(np.array(cv_img),
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(mp3file_name) #.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'
def change_music_generator(current_choice):
if current_choice == 0:
return gr.update(visible=True)
return gr.update(visible=False)
title="文生图生音乐视频 Text to Image to Music to Video with Riffusion"
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%;
}
#share-btn-container {
display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
}
#share-btn {
all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0;
}
#share-btn * {
all: unset;
}
#share-btn-container div:nth-child(-n+2){
width: auto !important;
min-height: 0px !important;
}
#share-btn-container .wrap {
display: none !important;
}
"""
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)'
],
[
'少女在时代广场，舞蹈',
'写实风格(Realistic style)'
],
[
'Peking Opera at New York',
'默认(Default)'
],
[
'古风少女',
'水彩(Watercolor)'
],
[
'辐射游戏角色',
'默认(Default)'
],
[
'Fallout game character',
'默认(Default)'
],
[
'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',
'默认(Default)'
],
[
'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',
'默认(Default)'
],
[
'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',
'默认(Default)'
],
[
'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',
'默认(Default)'
],
[
'一只猫坐在椅子上，戴着一副墨镜,海盗风格',
'默认(Default)'
],
[
'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',
'默认(Default)'
],
[
'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.",
elem_id="input-prompt",
).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)', '默认(Default)'], value='默认(Default)', type="index")
musicAI = gr.Dropdown(label="音乐生成技术(AI Music Generator)", choices=['Riffusion', 'Mubert AI'], value='Riffusion', type="index")
duration_input = gr.Slider(label="Duration in seconds", minimum=5, maximum=10, step=1, value=5, elem_id="duration-slider", visible=True)
status_text = gr.Textbox(
label="处理状态(Process status)",
show_label=True,
max_lines=1,
interactive=False
)
with gr.Column(elem_id="col-container"):
with gr.Group(elem_id="share-btn-container"):
community_icon = gr.HTML(community_icon_html, visible=False)
loading_icon = gr.HTML(loading_icon_html, visible=False)
share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
share_button.click(None, [], [], _js=share_js)
video_result = gr.Video(type=None, label='Final Merged video', elem_id="output-video")
imgfile_result = gr.Image(label="Art Cover", elem_id="output-img")
musicfile_result = gr.Audio(type='filepath', label="Generated Music Track", elem_id="output-music")
spec_result = gr.Image(label="Spectrogram Image")
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 = [""]
musicAI.change(fn=change_music_generator, inputs=[musicAI], outputs=[duration_input])
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, musicAI, duration_input], outputs=[spec_result, imgfile_result, musicfile_result, video_result, status_text, share_button, community_icon, loading_icon])
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().launch()