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 import imageio import numpy as np # sys.path.append('./code') sys.path.append('/home/user/app/code') # set up diffvg # os.system('git clone https://github.com/BachiLi/diffvg.git') os.system('git submodule update --init') os.chdir('diffvg') os.system('git submodule update --init --recursive') os.system('python setup.py install --user') sys.path.append("/home/user/.local/lib/python3.10/site-packages/diffvg-0.0.1-py3.10-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 = None model = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).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="""

Font-To-Sketch: Morphing Any Font to a Visual Representation

""" DESCRIPTION="""This demo builds on the [Word-As-Image for Semantic Typography](https://wordasimage.github.io/Word-As-Image-Page/) work to support **any** font and morphing whole words and phrases to a visual representation of a given semantic concept. This project started as part of an ongoing effort with the [ARBML](https://arbml.github.io/website/) community to build open-source Arabic tools using machine learning.""" DESCRIPTION+="""The demo currently supports the following scripts: **Arabic**, **Simplified Chinese**, **Cyrillic**, **Greek**, **Latin**, **Tamil**. Therefore you can write the text in any language using those scripts. To add support for more fonts please check the [GitHub ReadMe](https://raw.githubusercontent.com/BKHMSI/Font-To-Sketch).""" # DESCRIPTION += '\n

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

' DESCRIPTION += '\n

Note: it takes about 5 minutes for 250 iterations to generate the final GIF. For faster inference without waiting in queue, you can Open In Colab

' if (SPACE_ID := os.getenv('SPACE_ID')) is not None: DESCRIPTION = DESCRIPTION.replace("

", " ") DESCRIPTION += f'or Duplicate the Space and upgrade to GPU in settings.

' else: DESCRIPTION = DESCRIPTION.replace("either", "") DESCRIPTION += "Example of Outputs" warnings.filterwarnings("ignore") pydiffvg.set_print_timing(False) gamma = 1.0 def set_config(semantic_concept, word, script, prompt_suffix, font_name, num_steps, seed, is_seed_rand, dist_loss_weight, pixel_dist_kernel_blur, pixel_dist_sigma, angeles_w): cfg_d = edict() cfg_d.config = "code/config/base.yaml" cfg_d.experiment = "default" 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.prompt_suffix = prompt_suffix cfg.word = word cfg.optimized_letter = word cfg.script = script.lower() if cfg.script == "simplified chinese": cfg.script = "chinese" script_path = f"code/data/fonts/{cfg.script}" cfg.font = [x for x in os.listdir(script_path) if "ttf" in x][0][:-4] if is_seed_rand == "Random Seed": cfg.seed = np.random.randint(10000) else: cfg.seed = int(seed) cfg.num_iter = num_steps cfg.batch_size = 1 cfg.loss.tone.dist_loss_weight = int(dist_loss_weight) cfg.loss.tone.pixel_dist_kernel_blur = int(pixel_dist_kernel_blur) cfg.loss.tone.pixel_dist_sigma = int(pixel_dist_sigma) cfg.loss.conformal.angeles_w = angeles_w cfg.caption = f"a {cfg.semantic_concept}. {cfg.prompt_suffix}" cfg.log_dir = f"{cfg.script}" if cfg.optimized_letter in cfg.word: cfg.optimized_letter = cfg.optimized_letter else: raise gr.Error(f'letter should be in word') # if ' ' in cfg.word: # cfg.optimized_letter = cfg.optimized_letter.replace(' ', '_') cfg.letter = f"{cfg.font}_{cfg.optimized_letter}_scaled" cfg.target = f"code/data/init/{cfg.letter}" if ' ' in cfg.target: cfg.target = cfg.target.replace(' ', '_') # set experiment dir signature = f"{cfg.word}_{cfg.semantic_concept}_{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(word, semantic_concept, script, num_steps, seed): prompt_suffix = "minimal flat 2d vector. lineal color. trending on artstation" is_seed_rand = "Use Set Value" return list(next(run_main_app(semantic_concept, word, script, prompt_suffix, font_name, num_steps, seed, is_seed_rand, 100, 201, 30, 0.5, 1))) def run_main_app(semantic_concept, word, script, prompt_suffix, font_name, num_steps, seed, is_seed_rand, dist_loss_weight, pixel_dist_kernel_blur, pixel_dist_sigma, angeles_w, example=0): cfg = set_config(semantic_concept, word, script, prompt_suffix, font_name, num_steps, seed, is_seed_rand, dist_loss_weight, pixel_dist_kernel_blur, pixel_dist_sigma, angeles_w) pydiffvg.set_use_gpu(torch.cuda.is_available()) print("preprocessing") preprocess(cfg.font, cfg.word, cfg.optimized_letter, cfg.script, 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=True, label='Initializing'),gr.update(visible=False),gr.update(value=cfg.caption,visible=True),gr.update(value=cfg.seed,visible=True) 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)) gif_frames = [] skip = 5 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),gr.update(value=cfg.caption,visible=True),gr.update(value=cfg.seed,visible=True) x = img.unsqueeze(0).permute(0, 3, 1, 2) # HWC -> NCHW if step % skip == 0: img_tensor = x.detach().cpu() img_tensor = torch.nn.functional.interpolate(img_tensor, size=(200, 200), mode='bicubic', align_corners=False) img_tensor = img_tensor.permute(0, 3, 2, 1).squeeze(0) gif_frames += [img_tensor.numpy()] 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) filename = os.path.join(cfg.experiment_dir, "final.gif") # writer = imageio.get_writer(filename, fps=20) # for frame in gif_frames: writer.append_data(frame) # writer.close() gif_frames = np.array(gif_frames) * 255 print(gif_frames[0]) imageio.mimsave(filename, gif_frames.astype(np.uint8)) # imageio.mimsave(filename, np.array(gif_frames)) yield gr.update(value=filename_init,visible=True),gr.update(visible=False),gr.update(value=filename,visible=True),gr.update(value=cfg.caption,visible=True),gr.update(value=cfg.seed,visible=True) with gr.Blocks() as demo: gr.HTML(TITLE) gr.Markdown(DESCRIPTION) with gr.Row(): with gr.Column(): word = gr.Text( label='Text', max_lines=1, placeholder= 'Enter text. For example: قطة|猫|γάτα|кошка|பூனை|Cat' ) semantic_concept = gr.Text( label='Concept', max_lines=1, placeholder= 'Enter a semantic concept that you want your text to morph into (in English). For example: cat' ) script = gr.Dropdown( ["Arabic", "Simplified Chinese", "Cyrillic", "Greek", "Latin", "Tamil"], value="Arabic", label="Font Script" ) prompt_suffix = gr.Text( label='Prompt Suffix', max_lines=1, value="minimal flat 2d vector. lineal color. trending on artstation" ) with gr.Row(): with gr.Accordion("Advanced Parameters", open=False, visible=True): with gr.Row(): is_seed_rand = gr.Radio(["Random Seed", "Use Set Value"], label="Use Random Seed", value="Random Seed") seed = gr.Number( label='Seed (Set Value)', value=42 ) angeles_w = gr.Number( label='ACAP Deformation Loss Weight', value=0.5 ) dist_loss_weight = gr.Number( label='Tone Loss: dist_loss_weight', value=100 ) pixel_dist_kernel_blur = gr.Number( label='Tone Loss: pixel_dist_kernel_blur', value=201 ) pixel_dist_sigma = gr.Number( label='Tone Loss: pixel_dist_sigma', value=30 ) num_steps = gr.Slider(label='Optimization Iterations', minimum=0, maximum=500, step=10, value=250) font_name = gr.Text(value=None,visible=False,label="Font Name") def on_select(evt: gr.SelectData): return evt.value font_name.value = "ArefRuqaa" run = gr.Button('Generate') with gr.Column(): with gr.Row(): prompt = gr.Text( label='Prompt', visible=False, max_lines=1, interactive=False, ) seed_value = gr.Text( label='Seed Used', visible=False, max_lines=1, interactive=False, ) result0 = gr.Image(type="filepath", label="Initial Word").style(height=250) result1 = gr.Image(type="filepath", label="Optimization Process").style(height=300) result2 = gr.Image(type="filepath", label="Final GIF",visible=False).style(height=300) with gr.Row(): # examples examples = [ ["قطة", "Cat", "Arabic", 50, 42], # ["猫", "Cat", "Simplified Chinese", 250, 42], # ["γάτα", "Cat", "Greek", 250, 42], # ["кошка", "Cat", "Cyrillic", 250, 42], # ["பூனை", "Cat", "Tamil", 250, 42], ] demo.queue(max_size=10, concurrency_count=2) gr.Examples(examples=examples, inputs=[ word, semantic_concept, script, num_steps, seed ], outputs=[ result0, result1, result2, prompt, seed_value ], fn=run_main_ex, cache_examples=True) # inputs inputs = [ semantic_concept, word, script, prompt_suffix, font_name, num_steps, seed, is_seed_rand, dist_loss_weight, pixel_dist_kernel_blur, pixel_dist_sigma, angeles_w ] outputs = [ result0, result1, result2, prompt, seed_value ] run.click(fn=run_main_app, inputs=inputs, outputs=outputs, queue=True) demo.launch(share=False)