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| # 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 spaces | |
| import torch | |
| import gradio as gr | |
| from pathos.multiprocessing import ProcessPool as Pool | |
| from numpy import asarray as array | |
| from lxml.html import fromstring | |
| from diffusers.utils import export_to_gif, load_image | |
| from huggingface_hub import hf_hub_download | |
| from safetensors.torch import load_file, save_file | |
| from diffusers import FluxPipeline, DiffusionPipeline, AnimateDiffPipeline, MotionAdapter, EulerAncestralDiscreteScheduler, DDIMScheduler, StableDiffusionXLPipeline, UNet2DConditionModel, AutoencoderKL, UNet3DConditionModel | |
| # logging | |
| warnings.filterwarnings("ignore") | |
| root = logging.getLogger() | |
| root.setLevel(logging.DEBUG) | |
| handler = logging.StreamHandler(sys.stdout) | |
| handler.setLevel(logging.DEBUG) | |
| formatter = logging.Formatter('\n >>> [%(levelname)s] %(asctime)s %(name)s: %(message)s\n') | |
| handler.setFormatter(formatter) | |
| root.addHandler(handler) | |
| handler2 = logging.StreamHandler(sys.stderr) | |
| handler2.setLevel(logging.DEBUG) | |
| formatter = logging.Formatter('\n >>> [%(levelname)s] %(asctime)s %(name)s: %(message)s\n') | |
| handler2.setFormatter(formatter) | |
| root.addHandler(handler2) | |
| # constant data | |
| dtype = torch.float16 | |
| device = "cuda" | |
| #repo = "ByteDance/AnimateDiff-Lightning" | |
| #ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors" | |
| #base = "emilianJR/epiCRealism" | |
| base = "SG161222/Realistic_Vision_V6.0_B1_noVAE" | |
| #vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse").to(device, dtype=dtype) | |
| #unet = UNet2DConditionModel.from_config("emilianJR/epiCRealism",subfolder="unet").to(device, dtype).load_state_dict(load_file(hf_hub_download("emilianJR/epiCRealism", "unet/diffusion_pytorch_model.safetensors"), device=device), strict=False) | |
| adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-3", torch_dtype=dtype, device=device) | |
| # variable data | |
| last_motion="" | |
| result = [] | |
| # precision data | |
| seq=512 | |
| fast=False | |
| fps=10 | |
| time=1 | |
| width=896 | |
| height=896 | |
| step=50 | |
| accu=7.5 | |
| # 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; | |
| max-width: 15cm; | |
| } | |
| .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 | |
| pipe = AnimateDiffPipeline.from_pretrained(base, motion_adapter=adapter).to(device) | |
| pipe.scheduler = DDIMScheduler( | |
| clip_sample=False, | |
| beta_start=0.00085, | |
| beta_end=0.012, | |
| beta_schedule="linear", | |
| timestep_spacing="trailing", | |
| steps_offset=1 | |
| ) | |
| #pipe.unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device), strict=False) | |
| pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter-plus_sd15.bin") | |
| pipe.enable_free_init(method="butterworth", use_fast_sampling=fast) | |
| pipe_flux = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, token=os.getenv("hf_token")).to(device,dtype=dtype) | |
| # Parallelism | |
| def parallel(func,*args): | |
| with Pool(nodes=len(args)) as pool: | |
| res = pool.imap(func, *args) | |
| return list(res) | |
| # 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('[\s+]', ' ', text)).lower().strip() | |
| lang = re.sub(f'[{punctuation}]', '', re.sub('[\s+]', ' ', 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('[\s+]', ' ', translated)).lower().strip() | |
| print(ret) | |
| 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): | |
| global last_motion | |
| global pipe | |
| if last_motion != motion: | |
| if last_motion != "": | |
| pipe.unload_lora_weights() | |
| if motion != "": | |
| pipe.load_lora_weights(motion, adapter_name="motion") | |
| pipe.fuse_lora() | |
| pipe.set_adapters("motion", [0.7]) | |
| last_motion = motion | |
| pipe.to(device,dtype=dtype) | |
| if img == None: | |
| img = pipe( | |
| prompt=p1, | |
| height=height, | |
| width=width, | |
| guidance_scale=accu, | |
| num_inference_steps=step, | |
| max_sequence_length=seq, | |
| generator=torch.Generator("cuda").manual_seed(0) | |
| ).images[0] | |
| return pipe( | |
| prompt=p1, | |
| negative_prompt=p2, | |
| height=height, | |
| width=width, | |
| ip_adapter_image=img.convert("RGB"), | |
| num_inference_steps=step, | |
| guidance_scale=accu, | |
| num_frames=(fps*time) | |
| ) | |
| def handle_generate(*inp): | |
| inp = list(inp) | |
| inp[1] = translate(inp[1],"english") | |
| inp[2] = translate(inp[2],"english") | |
| if inp[2] != "": | |
| inp[2] = f", {inp[2]}" | |
| inp[2] = f"(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime), text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck{inp[2]}" | |
| _do = ['photographed', 'realistic', 'dynamic poze', 'deep field', 'reasonable', "natural", 'rough', 'best quality', 'focused', "highly detailed"] | |
| if inp[1] != "": | |
| _do.append(f"a new {inp[1]} content in the image") | |
| inp[1] = ", ".join(_do) | |
| ln = len(result) | |
| parallel_args = [inp for i in range(ln)] | |
| pipe_out = parallel( pipe_generate, *parallel_args ) | |
| names = [] | |
| for i in pipe_out: | |
| name = generate_random_string(12)+".png" | |
| export_to_gif(i.frames[0],name,fps=fps) | |
| names.append( name ) | |
| return names | |
| def ui(): | |
| global result | |
| with gr.Blocks(theme=gr.themes.Soft(),css=css,js=js) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.Markdown(f""" | |
| # MULTI-LANGUAGE GIF CREATOR | |
| """) | |
| with gr.Row(): | |
| img = gr.Image(label="STATIC PHOTO",show_label=True,container=True,type="pil") | |
| with gr.Row(): | |
| prompt = gr.Textbox( | |
| elem_id="prompt", | |
| placeholder="INCLUDE", | |
| container=False, | |
| max_lines=1 | |
| ) | |
| with gr.Row(): | |
| prompt2 = gr.Textbox( | |
| elem_id="prompt2", | |
| placeholder="EXCLUDE", | |
| container=False, | |
| max_lines=1 | |
| ) | |
| with gr.Row(): | |
| motion = gr.Dropdown( | |
| label='CAMERA', | |
| show_label=True, | |
| container=True, | |
| choices=[ | |
| ("(No Effect)", ""), | |
| ("Zoom in", "guoyww/animatediff-motion-lora-zoom-in"), | |
| ("Zoom out", "guoyww/animatediff-motion-lora-zoom-out"), | |
| ("Tilt up", "guoyww/animatediff-motion-lora-tilt-up"), | |
| ("Tilt down", "guoyww/animatediff-motion-lora-tilt-down"), | |
| ("Pan left", "guoyww/animatediff-motion-lora-pan-left"), | |
| ("Pan right", "guoyww/animatediff-motion-lora-pan-right"), | |
| ("Roll left", "guoyww/animatediff-motion-lora-rolling-anticlockwise"), | |
| ("Roll right", "guoyww/animatediff-motion-lora-rolling-clockwise"), | |
| ], | |
| value="", | |
| interactive=True | |
| ) | |
| with gr.Row(): | |
| run_button = gr.Button("START",elem_classes="btn",scale=0) | |
| with gr.Row(): | |
| result.append(gr.Image(interactive=False,elem_classes="image-container", label="Result", show_label=False, type='filepath', show_share_button=False)) | |
| gr.on( | |
| triggers=[ | |
| run_button.click, | |
| prompt.submit, | |
| prompt2.submit | |
| ], | |
| fn=handle_generate, | |
| inputs=[img,prompt,prompt2,motion], | |
| outputs=result | |
| ) | |
| demo.queue().launch() | |
| # entry | |
| if __name__ == "__main__": | |
| os.chdir(os.path.abspath(os.path.dirname(__file__))) | |
| ui() | |
| # end | |