app.py

# built-in

from inspect import signature
import os
import subprocess
import logging
import re
import random
from string import ascii_letters, digits, punctuation
import requests
import sys
import warnings
import time
import asyncio
from functools import partial

# external

import torch
import gradio as gr
from numpy import asarray as array
from lxml.html import fromstring
from diffusers.utils import export_to_video, load_image
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file, save_file
from diffusers import StableDiffusionPipeline, CogVideoXImageToVideoPipeline
from PIL import Image, ImageDraw, ImageFont

# logging

warnings.filterwarnings("ignore")
root = logging.getLogger()
root.setLevel(logging.WARN)
handler = logging.StreamHandler(sys.stderr)
handler.setLevel(logging.WARN)
formatter = logging.Formatter('\n >>> [%(levelname)s] %(asctime)s %(name)s: %(message)s\n')
handler.setFormatter(formatter)
root.addHandler(handler)

# constant data

if torch.cuda.is_available():
    device = "cuda"
    dtype = torch.float16
else:
    device = "cpu"
    dtype = torch.float16

base = "emilianJR/epiCRealism"

# variable data



# precision data

seq=512
fps=20
width=768
height=768
image_steps=40
video_steps=20
accu=7

# ui data

css="".join(["""
input, input::placeholder {
    text-align: center !important;
}
*, *::placeholder {
    font-family: Suez One !important;
}
h1,h2,h3,h4,h5,h6 {
    width: 100%;
    text-align: center;
}
footer {
    display: none !important;
}
#col-container {
    margin: 0 auto;
}
.image-container {
    aspect-ratio: """,str(width),"/",str(height),""" !important;
}
.dropdown-arrow {
    display: none !important;
}
*:has(>.btn) {
    display: flex;
    justify-content: space-evenly;
    align-items: center;
}
.btn {
    display: flex;
}
"""])

js="""
function custom(){
    document.querySelector("div#prompt input").setAttribute("maxlength","38")
    document.querySelector("div#prompt2 input").setAttribute("maxlength","38")
}
"""

# torch pipes

image_pipe = StableDiffusionPipeline.from_pretrained(base, torch_dtype=dtype, safety_checker=None).to(device)
video_pipe = CogVideoXImageToVideoPipeline.from_pretrained(
    "THUDM/CogVideoX-5b-I2V",
    torch_dtype=torch.bfloat16
).to(device)

video_pipe.vae.enable_tiling()
video_pipe.vae.enable_slicing()
video_pipe.enable_model_cpu_offload()

# functionality

def run(cmd):
    return str(subprocess.run(cmd, shell=True, capture_output=True, env=None).stdout)

def xpath_finder(str,pattern):
    try:
        return ""+fromstring(str).xpath(pattern)[0].text_content().lower().strip()
    except:
        return ""

def translate(text,lang):
    if text == None or lang == None:
        return ""       
    text = re.sub(f'[{punctuation}]', '', re.sub('[ ]+', ' ', text)).lower().strip()
    lang = re.sub(f'[{punctuation}]', '', re.sub('[ ]+', ' ', lang)).lower().strip()    
    if text == "" or lang == "":
        return ""
    if len(text) > 38:
        raise Exception("Translation Error: Too long text!")
    user_agents = [
        'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36',
        'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36',
        'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36',
        'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.1 Safari/605.1.15',
        'Mozilla/5.0 (Macintosh; Intel Mac OS X 13_1) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.1 Safari/605.1.15'
    ]
    padded_chars = re.sub("(^-)|(-$)","",text.replace("","-").replace("- -"," ")).strip()
    query_text = f'Please translate {padded_chars}, into {lang}'
    url = f'https://www.google.com/search?q={query_text}'
    content = str(requests.get(
        url = url,
        headers = {
            'User-Agent': random.choice(user_agents)
        }
    ).content)
    translated = text
    src_lang = xpath_finder(content,'//*[@class="source-language"]')
    trgt_lang = xpath_finder(content,'//*[@class="target-language"]')
    src_text = xpath_finder(content,'//*[@id="tw-source-text"]/*')
    trgt_text = xpath_finder(content,'//*[@id="tw-target-text"]/*')
    if trgt_lang == lang:
        translated = trgt_text
    ret = re.sub(f'[{punctuation}]', '', re.sub('[ ]+', ' ', translated)).lower().strip()
    return ret
    
def generate_random_string(length):
    characters = str(ascii_letters + digits)
    return ''.join(random.choice(characters) for _ in range(length))

def pipe_generate(img,p1,p2,motion,time,title):
    global last_motion
    global pipe

    if img is None:
        img = image_pipe(
            prompt=p1,
            negative_prompt=p2,
            height=height,
            width=width,
            guidance_scale=accu,
            num_images_per_prompt=1,
            num_inference_steps=image_steps,
            max_sequence_length=seq,
            need_safetycheck=False,
            generator=torch.Generator(device).manual_seed(int(str(random.random()).split(".")[1]))
        ).images[0]

    if title != "":
        draw = ImageDraw.Draw(img)
        textheight=84
        font = ImageFont.truetype(r"OpenSans-Bold.ttf", textheight)
        textwidth = draw.textlength(title,font)
        x = (width - textwidth) // 2
        y = (height - textheight) // 2
        draw.text((x, y), title,font=font)
    
    if time == 0.0:
        return img

    return video_pipe(
        prompt=p1,
        negative_prompt=p2,
        image=img,
        num_inference_steps=video_steps,
        guidance_scale=accu,
        num_videos_per_prompt=1,
        num_frames=(fps*time),
        generator=torch.Generator(device).manual_seed(int(str(random.random()).split(".")[1]))
    ).frames[0]

def handle_generate(*_inp):

    inp = list(_inp)
    
    inp[1] = translate(inp[1],"english")
    inp[2] = translate(inp[2],"english")

    if inp[2] != "":
        inp[2] = ", related to: " + inp[2]

    inp[2] = f"faked, errored, unreal, off topic, pixelated, deformed, and semi-realistic, cgi, 3d, sketch, cartoon, drawing, anime, cropped, out of frame, low quality, textual, jpeg artifacts, ugly, duplicated, weird, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutations, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck{inp[2]}"

    if inp[1] != "":
        inp[1] = ", related to: " + inp[1]
    
    inp[1] = f'photographed, realistic, true, genuine, dynamic poze, authentic, deep field, reasonable, natural, best quality, focused, highly detailed{inp[1]}'

    print(f"""

        Positive: {inp[1]}

        Negative: {inp[2]}

    """)
    
    pipe_out = pipe_generate(*inp)
    
    name = generate_random_string(12) + ( ".png" if inp[4] == 0 else ".mp4" )
    if inp[4] == 0.0:
        pipe_out.save(name)
    else:
        export_to_video(pipe_out,name,fps=fps)
    if inp[4] == 0:
        return name, None
    else:
        return None, name

def ui():
    global result
    with gr.Blocks(theme=gr.themes.Soft(),css=css,js=js) as demo:
        gr.Markdown(f"""
            # MULTI-LANGUAGE MP4/PNG CREATOR
        """)
        with gr.Row():
            title = gr.Textbox(
                placeholder="Logo title",
                container=False,
                max_lines=1
            )
            prompt = gr.Textbox(
                elem_id="prompt",
                placeholder="Included keywords",
                container=False,
                max_lines=1
            )
        with gr.Row():
            prompt2 = gr.Textbox(
                elem_id="prompt2",
                placeholder="Excluded keywords",
                container=False,
                max_lines=1
            )
        with gr.Row():
            time = gr.Slider(
                minimum=0.0,
                maximum=600.0,
                value=0.0,
                step=5.0,
                label="Duration (0s = PNG)"
            )
         with gr.Row(elem_id="col-container"):
            with gr.Column():
                img = gr.Image(label="Upload photo",show_label=True,container=False,type="pil")
            with gr.Column():
                res_img = gr.Image(interactive=False,container=False,elem_classes="image-container", label="Result", show_label=True, type='filepath', show_share_button=False)
            with gr.Column():
                res_vid = gr.Video(interactive=False,container=False,elem_classes="image-container", label="Result", show_label=True, show_share_button=False)
        with gr.Row():
            run_button = gr.Button("Start!",elem_classes="btn",scale=0)

        gr.on(
            triggers=[
                run_button.click,
                prompt.submit,
                prompt2.submit
            ],
            fn=handle_generate,
            inputs=[img,prompt,prompt2,motion,time,title],
            outputs=[res_img,res_vid]
        )
        demo.queue().launch()

# entry

if __name__ == "__main__":
    os.chdir(os.path.abspath(os.path.dirname(__file__)))
    ui()

# end