import gradio as gr import os import argparse from easydict import EasyDict as edict import yaml import os.path as osp import random import numpy.random as npr import sys sys.path.append('/home/user/app/code') # set up diffvg os.system('git clone https://github.com/BachiLi/diffvg.git') os.chdir('diffvg') os.system('git submodule update --init --recursive') os.system('python setup.py install --user') sys.path.append("/home/user/.local/lib/python3.8/site-packages/diffvg-0.0.1-py3.8-linux-x86_64.egg") os.chdir('/home/user/app') import torch from diffusers import StableDiffusionPipeline device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_auth_token=os.environ['HF_TOKEN']).to(device) from typing import Mapping from tqdm import tqdm import torch from torch.optim.lr_scheduler import LambdaLR import pydiffvg import save_svg from losses import SDSLoss, ToneLoss, ConformalLoss from utils import ( edict_2_dict, update, check_and_create_dir, get_data_augs, save_image, preprocess, learning_rate_decay, combine_word) import warnings TITLE="""

Word-As-Image for Semantic Typography

""" DESCRIPTION="""A demo for [Word-As-Image for Semantic Typography](https://wordasimage.github.io/Word-As-Image-Page/). By using Word-as-Image, a visual representation of the meaning of the word is created while maintaining legibility of the text and font style. Please select a semantic concept word and a letter you wish to generate, it will take ~5 minutes to perform 500 iterations.""" DESCRIPTION += '\n

This demo is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

' if (SPACE_ID := os.getenv('SPACE_ID')) is not None: DESCRIPTION += f'\n

For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. Duplicate Space

' warnings.filterwarnings("ignore") pydiffvg.set_print_timing(False) gamma = 1.0 def set_config(semantic_concept, word, letter, font_name, num_steps): cfg_d = edict() cfg_d.config = "code/config/base.yaml" cfg_d.experiment = "demo" with open(cfg_d.config, 'r') as f: cfg_full = yaml.load(f, Loader=yaml.FullLoader) cfg_key = cfg_d.experiment cfgs = [cfg_d] while cfg_key: cfgs.append(cfg_full[cfg_key]) cfg_key = cfgs[-1].get('parent_config', 'baseline') cfg = edict() for options in reversed(cfgs): update(cfg, options) del cfgs cfg.semantic_concept = semantic_concept cfg.word = word cfg.optimized_letter = letter cfg.font = font_name cfg.seed = 0 cfg.num_iter = num_steps if ' ' in cfg.word: raise gr.Error(f'should be only one word') cfg.caption = f"a {cfg.semantic_concept}. {cfg.prompt_suffix}" cfg.log_dir = f"output/{cfg.experiment}_{cfg.word}" if cfg.optimized_letter in cfg.word: cfg.optimized_letter = cfg.optimized_letter else: raise gr.Error(f'letter should be in word') cfg.letter = f"{cfg.font}_{cfg.optimized_letter}_scaled" cfg.target = f"code/data/init/{cfg.letter}" # set experiment dir signature = f"{cfg.letter}_concept_{cfg.semantic_concept}_seed_{cfg.seed}" cfg.experiment_dir = \ osp.join(cfg.log_dir, cfg.font, signature) configfile = osp.join(cfg.experiment_dir, 'config.yaml') # create experiment dir and save config check_and_create_dir(configfile) with open(osp.join(configfile), 'w') as f: yaml.dump(edict_2_dict(cfg), f) if cfg.seed is not None: random.seed(cfg.seed) npr.seed(cfg.seed) torch.manual_seed(cfg.seed) torch.backends.cudnn.benchmark = False else: assert False return cfg def init_shapes(svg_path, trainable: Mapping[str, bool]): svg = f'{svg_path}.svg' canvas_width, canvas_height, shapes_init, shape_groups_init = pydiffvg.svg_to_scene(svg) parameters = edict() # path points if trainable.point: parameters.point = [] for path in shapes_init: path.points.requires_grad = True parameters.point.append(path.points) return shapes_init, shape_groups_init, parameters def run_main_ex(semantic_concept, word, letter, font_name, num_steps): return list(next(run_main_app(semantic_concept, word, letter, font_name, num_steps, 1))) def run_main_app(semantic_concept, word, letter, font_name, num_steps, example=0): cfg = set_config(semantic_concept, word, letter, font_name, num_steps) pydiffvg.set_use_gpu(torch.cuda.is_available()) print("preprocessing") preprocess(cfg.font, cfg.word, cfg.optimized_letter, cfg.level_of_cc) filename_init = os.path.join("code/data/init/", f"{cfg.font}_{cfg.word}_scaled.svg").replace(" ", "_") if not example: yield gr.update(value=filename_init,visible=True),gr.update(visible=False),gr.update(visible=False) sds_loss = SDSLoss(cfg, device, model) h, w = cfg.render_size, cfg.render_size data_augs = get_data_augs(cfg.cut_size) render = pydiffvg.RenderFunction.apply # initialize shape print('initializing shape') shapes, shape_groups, parameters = init_shapes(svg_path=cfg.target, trainable=cfg.trainable) scene_args = pydiffvg.RenderFunction.serialize_scene(w, h, shapes, shape_groups) img_init = render(w, h, 2, 2, 0, None, *scene_args) img_init = img_init[:, :, 3:4] * img_init[:, :, :3] + \ torch.ones(img_init.shape[0], img_init.shape[1], 3, device=device) * (1 - img_init[:, :, 3:4]) img_init = img_init[:, :, :3] tone_loss = ToneLoss(cfg) tone_loss.set_image_init(img_init) num_iter = cfg.num_iter pg = [{'params': parameters["point"], 'lr': cfg.lr_base["point"]}] optim = torch.optim.Adam(pg, betas=(0.9, 0.9), eps=1e-6) conformal_loss = ConformalLoss(parameters, device, cfg.optimized_letter, shape_groups) lr_lambda = lambda step: learning_rate_decay(step, cfg.lr.lr_init, cfg.lr.lr_final, num_iter, lr_delay_steps=cfg.lr.lr_delay_steps, lr_delay_mult=cfg.lr.lr_delay_mult) / cfg.lr.lr_init scheduler = LambdaLR(optim, lr_lambda=lr_lambda, last_epoch=-1) # lr.base * lrlambda_f print("start training") # training loop t_range = tqdm(range(num_iter)) for step in t_range: optim.zero_grad() # render image scene_args = pydiffvg.RenderFunction.serialize_scene(w, h, shapes, shape_groups) img = render(w, h, 2, 2, step, None, *scene_args) # compose image with white background img = img[:, :, 3:4] * img[:, :, :3] + torch.ones(img.shape[0], img.shape[1], 3, device=device) * ( 1 - img[:, :, 3:4]) img = img[:, :, :3] filename = os.path.join( cfg.experiment_dir, "video-svg", f"iter{step:04d}.svg") check_and_create_dir(filename) save_svg.save_svg(filename, w, h, shapes, shape_groups) if not example: yield gr.update(visible=True),gr.update(value=filename, label=f'iters: {step} / {num_iter}', visible=True),gr.update(visible=False) x = img.unsqueeze(0).permute(0, 3, 1, 2) # HWC -> NCHW x = x.repeat(cfg.batch_size, 1, 1, 1) x_aug = data_augs.forward(x) # compute diffusion loss per pixel loss = sds_loss(x_aug) tone_loss_res = tone_loss(x, step) loss = loss + tone_loss_res loss_angles = conformal_loss() loss_angles = cfg.loss.conformal.angeles_w * loss_angles loss = loss + loss_angles loss.backward() optim.step() scheduler.step() filename = os.path.join( cfg.experiment_dir, "output-svg", "output.svg") check_and_create_dir(filename) save_svg.save_svg( filename, w, h, shapes, shape_groups) combine_word(cfg.word, cfg.optimized_letter, cfg.font, cfg.experiment_dir) image = os.path.join(cfg.experiment_dir,f"{cfg.font}_{cfg.word}_{cfg.optimized_letter}.svg") yield gr.update(value=filename_init,visible=True),gr.update(visible=False),gr.update(value=image,visible=True) with gr.Blocks() as demo: gr.HTML(TITLE) gr.Markdown(DESCRIPTION) with gr.Row(): with gr.Column(): semantic_concept = gr.Text( label='Semantic Concept', max_lines=1, placeholder= 'Enter a semantic concept. For example: BUNNY' ) word = gr.Text( label='Word', max_lines=1, placeholder= 'Enter a word. For example: BUNNY' ) letter = gr.Text( label='Letter', max_lines=1, placeholder= 'Choose a letter in the word to optimize. For example: Y' ) num_steps = gr.Slider(label='Optimization Iterations', minimum=0, maximum=500, step=10, value=500) font_name = gr.Text(value=None,visible=False,label="Font Name") gallery = gr.Gallery(value=[(os.path.join("images","KaushanScript-Regular.png"),"KaushanScript-Regular"), (os.path.join("images","IndieFlower-Regular.png"),"IndieFlower-Regular"),(os.path.join("images","Quicksand.png"),"Quicksand"), (os.path.join("images","Saira-Regular.png"),"Saira-Regular"), (os.path.join("images","LuckiestGuy-Regular.png"),"LuckiestGuy-Regular"),(os.path.join("images","DeliusUnicase-Regular.png"),"DeliusUnicase-Regular"), (os.path.join("images","Noteworthy-Bold.png"),"Noteworthy-Bold"), (os.path.join("images","HobeauxRococeaux-Sherman.png"),"HobeauxRococeaux-Sherman")],label="Font Name").style(grid=4) def on_select(evt: gr.SelectData): return evt.value gallery.select(fn=on_select, inputs=None, outputs=font_name) run = gr.Button('Generate') with gr.Column(): result0 = gr.Image(type="filepath", label="Initial Word").style(height=333) result1 = gr.Image(type="filepath", label="Optimization Process").style(height=110) result2 = gr.Image(type="filepath", label="Final Result",visible=False).style(height=333) with gr.Row(): # examples examples = [ [ "BUNNY", "BUNNY", "Y", "KaushanScript-Regular", 500 ], [ "LION", "LION", "O", "Quicksand", 500 ], [ "FROG", "FROG", "G", "IndieFlower-Regular", 500 ], [ "CAT", "CAT", "C", "LuckiestGuy-Regular", 500 ], ] demo.queue(max_size=10, concurrency_count=2) gr.Examples(examples=examples, inputs=[ semantic_concept, word, letter, font_name, num_steps ], outputs=[ result0, result1, result2 ], fn=run_main_ex, cache_examples=True) # inputs inputs = [ semantic_concept, word, letter, font_name, num_steps ] outputs = [ result0, result1, result2 ] run.click(fn=run_main_app, inputs=inputs, outputs=outputs, queue=True) demo.launch(share=False)