Spaces:
Running
Running
| # 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 FluxPipeline, 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.bfloat16 | |
| else: | |
| device = "cpu" | |
| dtype = torch.bfloat16 | |
| base = "black-forest-labs/FLUX.1-schnell" | |
| # variable data | |
| additional_image = None | |
| # precision data | |
| seq=512 | |
| fps=20 | |
| width=1280 | |
| height=720 | |
| image_steps=8 | |
| video_steps=20 | |
| accu=4.5 | |
| # 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 = FluxPipeline.from_pretrained(base, torch_dtype=dtype, safety_checker=None).to(device) | |
| video_pipe = CogVideoXImageToVideoPipeline.from_pretrained( | |
| "THUDM/CogVideoX-5b-I2V", | |
| torch_dtype=dtype | |
| ).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,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, | |
| generator=torch.Generator(device).manual_seed(int(str(random.random()).split(".")[1])) | |
| ).images[0] | |
| additional_image = True | |
| 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) | |
| additional_image = img if additional_image else None | |
| if time == 0.0: | |
| return img | |
| return video_pipe( | |
| prompt=p1, | |
| negative_prompt=p2.replace("textual content, ",""), | |
| 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): | |
| additional_image = None | |
| inp = list(_inp) | |
| inp[1] = translate(inp[1],"english") | |
| inp[2] = translate(inp[2],"english") | |
| if inp[2] != "": | |
| inp[2] = ", " + inp[2] | |
| inp[2] = f"textual content, pixelated photo, unrealistic situation, cgi quality, sketch quality, cartoon quality, drawing quality, anime quality, cropping, out of frame, worst quality, low quality, rendering artifacts, duplicated elements, weird body look, mutation, blurry photo, bad body anatomy, unproportional body parts, cloned face, disfigured body, malformed photo, missing body parts, extra body parts, fused body parts{inp[2]}" | |
| if inp[1] != "": | |
| inp[1] = ", " + inp[1] | |
| inp[1] = f'true reality, natural colors, realistic, beautiful pretty look, genuine and authentic reality, logical reasonable photo content, natural look, masterpiece photo, highly detailed photo{inp[1]}' | |
| print(f""" | |
| Positive: {inp[1]} | |
| Negative: {inp[2]} | |
| """) | |
| pipe_out = pipe_generate(*inp) | |
| name = generate_random_string(12) + ( ".png" if inp[3] == 0.0 else ".mp4" ) | |
| if inp[3] == 0.0: | |
| pipe_out.save(name) | |
| else: | |
| export_to_video(pipe_out,name,fps=fps) | |
| if inp[3] == 0.0: | |
| return name, None | |
| else: | |
| return additional_image, name | |
| def ui(): | |
| global result | |
| with gr.Blocks(theme=gr.themes.Citrus(),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,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 |