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@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+NATURE_texture.png filter=lfs diff=lfs merge=lfs -text
+diffvg/build/lib.linux-x86_64-cpython-38/diffvg.so filter=lfs diff=lfs merge=lfs -text
+diffvg/build/temp.linux-x86_64-cpython-38/CMakeFiles/diffvg.dir/diffvg_generated_diffvg.cpp.o filter=lfs diff=lfs merge=lfs -text
+diffvg/dist/diffvg-0.0.1-py3.8-linux-x86_64.egg filter=lfs diff=lfs merge=lfs -text

.gradio/certificate.pem

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+-----END CERTIFICATE-----

LICENSE

@@ -0,0 +1,437 @@
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README.md

@@ -1,12 +1,166 @@
 ---
-title: Textured Word Illustration
-emoji: 🏆
-colorFrom: gray
-colorTo: red
+title: Textured_Word_Illustration
+app_file: app.py
 sdk: gradio
 sdk_version: 5.9.0
-app_file: app.py
-pinned: false
 ---
+# Word-As-Image for Semantic Typography (SIGGRAPH 2023 - Honorable Mention Award)
+
+
+
+[![arXiv](https://img.shields.io/badge/📃-arXiv%20-red.svg)](https://arxiv.org/abs/2303.01818)
+[![webpage](https://img.shields.io/badge/🌐-Website%20-blue.svg)](https://wordasimage.github.io/Word-As-Image-Page/) 
+[![Huggingface space](https://img.shields.io/badge/🤗-Demo%20-yellow.svg)](https://huggingface.co/spaces/SemanticTypography/Word-As-Image)
+[![Youtube](https://img.shields.io/badge/📽️-Video%20-orchid.svg)](https://www.youtube.com/watch?v=9D12a6RCQaw)
+
+<br>
+<div align="center">
+    <img src="images/teaser.png" width="100%">
+</div>
+<br><br>
+A few examples of our <b>W</b>ord-<b>A</b>s-<b>I</b>mage illustrations in various fonts and for different textual concept. The semantically adjusted letters are created
+completely automatically using our method, and can then be used for further creative design as we illustrate here.<br><br>
+
+> Shir Iluz*, Yael Vinker*, Amir Hertz, Daniel Berio, Daniel Cohen-Or, Ariel Shamir    
+> \* Denotes equal contribution  
+>
+>A word-as-image is a semantic typography technique where a word illustration
+presents a visualization of the meaning of the word, while also
+preserving its readability. We present a method to create word-as-image
+illustrations automatically. This task is highly challenging as it requires
+semantic understanding of the word and a creative idea of where and how to
+depict these semantics in a visually pleasing and legible manner. We rely on
+the remarkable ability of recent large pretrained language-vision models to
+distill textual concepts visually. We target simple, concise, black-and-white
+designs that convey the semantics clearly.We deliberately do not change the
+color or texture of the letters and do not use embellishments. Our method
+optimizes the outline of each letter to convey the desired concept, guided by
+a pretrained Stable Diffusion model. We incorporate additional loss terms
+to ensure the legibility of the text and the preservation of the style of the
+font. We show high quality and engaging results on numerous examples
+and compare to alternative techniques.
+
+
+## Description
+Official implementation of Word-As-Image for Semantic Typography paper.
+<br>
+
+## Setup
+
+1. Clone the repo:
+```bash
+git clone https://github.com/WordAsImage/Word-As-Image.git
+cd Word-As-Image
+```
+2. Create a new conda environment and install the libraries:
+```bash
+conda create --name word python=3.8.15
+conda activate word
+pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113
+conda install -y numpy scikit-image
+conda install -y -c anaconda cmake
+conda install -y -c conda-forge ffmpeg
+pip install svgwrite svgpathtools cssutils numba torch-tools scikit-fmm easydict visdom freetype-py shapely
+pip install opencv-python==4.5.4.60  
+pip install kornia==0.6.8
+pip install wandb
+pip install shapely
+```
+
+3. Install diffusers:
+```bash
+pip install diffusers==0.8
+pip install transformers scipy ftfy accelerate
+```
+4. Install diffvg:
+```bash
+git clone https://github.com/BachiLi/diffvg.git
+cd diffvg
+git submodule update --init --recursive
+python setup.py install
+```
+
+5. Paste your HuggingFace [access token](https://huggingface.co/settings/tokens) for StableDiffusion in the TOKEN file.
+## Run Experiments 
+```bash
+conda activate word
+cd Word-As-Image
+
+# Please modify the parameters accordingly in the file and run:
+bash run_word_as_image.sh
+
+# Or run :
+python code/main.py --experiment <experiment> --semantic_concept <concept> --optimized_letter <letter> --seed <seed> --font <font_name> --use_wandb <0/1> --wandb_user <user name> 
+```
+* ```--semantic_concept``` : the semantic concept to insert
+* ```--optimized_letter``` : one letter in the word to optimize
+* ```--font``` : font name, the <font name>.ttf file should be located in code/data/fonts/
+
+Optional arguments:
+* ```--word``` : The text to work on, default: the semantic concept
+* ```--config``` : Path to config file, default: code/config/base.yaml
+* ```--experiment``` : You can specify any experiment in the config file, default: conformal_0.5_dist_pixel_100_kernel201
+* ```--log_dir``` : Default: output folder
+* ```--prompt_suffix``` : Default: "minimal flat 2d vector. lineal color. trending on artstation"
+
+### Examples
+```bash
+python code/main.py  --semantic_concept "BUNNY" --optimized_letter "Y" --font "KaushanScript-Regular" --seed 0
+```
+<br>
+<div align="center">
+    <img src="images/KaushanScript-Regular_BUNNY_Y.svg" width="22%">
+</div>
+
+
+```bash
+python code/main.py  --semantic_concept "LEAVES" --word "NATURE" --optimized_letter "T" --font "HobeauxRococeaux-Sherman" --seed 0
+```
+<br>
+<div align="center">
+    <img src="images/HobeauxRococeaux-Sherman_NATURE_T.svg" width="25%">
+</div>
+
+* Pay attention, as the arguments are case-sensitive, but it can handle both upper and lowercase letters depending on the input letters.
+
+
+## Tips
+If the outcome does not meet your quality expectations, you could try the following options:
+
+1. Adjusting the weight 𝛼 of the L𝑎𝑐𝑎𝑝 loss, which preserves the letter's structure after deformation.
+2. Modifying the 𝜎 parameter of the low-pass filter used in the L𝑡𝑜𝑛𝑒 loss, which limits the degree of deviation from the original letter.
+3. Changing the number of control points, as this can influence the outputs.
+4. Experimenting with different seeds, as each may produce slightly different results.
+5. Changing the font type, as this can also result in various outputs.
+
+
+
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## Acknowledgement
+Our implementation is based ob Stable Diffusion text-to-image model from Hugging Face's [Diffusers](https://github.com/huggingface/diffusers) library, combined with [Diffvg](https://github.com/BachiLi/diffvg). The framework is built on [Live](https://github.com/Picsart-AI-Research/LIVE-Layerwise-Image-Vectorization).
+    
+## Citation
+If you use this code for your research, please cite the following work: 
+```
+@article{IluzVinker2023,
+    author = {Iluz, Shir and Vinker, Yael and Hertz, Amir and Berio, Daniel and Cohen-Or, Daniel and Shamir, Ariel},
+    title = {Word-As-Image for Semantic Typography},
+    year = {2023},
+    issue_date = {August 2023},
+    publisher = {Association for Computing Machinery},
+    address = {New York, NY, USA},
+    volume = {42},
+    number = {4},
+    issn = {0730-0301},
+    url = {https://doi.org/10.1145/3592123},
+    doi = {10.1145/3592123},
+    journal = {ACM Trans. Graph.},
+    month = {jul},
+    articleno = {151},
+    numpages = {11},
+    keywords = {semantic typography, SVG, stable diffusion, fonts}
+}
+```
+    
+## Licence
+This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/).

TOKEN

@@ -0,0 +1 @@
+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

app.py

@@ -0,0 +1,29 @@
+
+import gradio as gr
+
+# Define the function to run the command
+def run_command(concept, word, letter):
+    # Add your logic here for generating the image and saving the filename
+    # Example placeholders:
+    generated_image_path = "generated_image.png"  # Path to the generated image
+    saved_filename = f"{concept}_{word}_{letter}.png"  # Example saved filename
+    return generated_image_path, saved_filename
+
+# Create the Gradio interface
+ui = gr.Interface(
+    fn=run_command,
+    inputs=[
+        gr.Textbox(label="Prompt Semantic Concept", placeholder="Enter a semantic concept, e.g., FLOWER"),
+        gr.Textbox(label="Prompt Word", placeholder="Enter a word, e.g., FLOWER"),
+        gr.Textbox(label="Prompt Letter", placeholder="Enter a letter, e.g., O")
+    ],
+    outputs=[
+        gr.Image(label="Generated Image"),
+        gr.Textbox(label="Saved Filename")
+    ],
+    title="Stable Diffusion Texture Generator",
+    description="Enter a semantic concept, word, and letter to generate a texture image."
+)
+
+# Launch the UI
+ui.launch(debug=True)

code/bezier.py

@@ -0,0 +1,122 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.special import binom
+from numpy.linalg import norm
+
+def num_bezier(n_ctrl, degree=3):
+    if type(n_ctrl) == np.ndarray:
+        n_ctrl = len(n_ctrl)
+    return int((n_ctrl - 1) / degree)
+
+def bernstein(n, i):
+    bi = binom(n, i)
+    return lambda t, bi=bi, n=n, i=i: bi * t**i * (1 - t)**(n - i)
+
+def bezier(P, t, d=0):
+    '''Bezier curve of degree len(P)-1. d is the derivative order (0 gives positions)'''
+    n = P.shape[0] - 1
+    if d > 0:
+        Q = np.diff(P, axis=0)*n
+        return bezier(Q, t, d-1)
+    B = np.vstack([bernstein(n, i)(t) for i, p in enumerate(P)])
+    return (P.T @ B).T
+
+def cubic_bezier(P, t):
+    return (1.0-t)**3*P[0] + 3*(1.0-t)**2*t*P[1] + 3*(1.0-t)*t**2*P[2] + t**3*P[3]
+
+def bezier_piecewise(Cp, subd=100, degree=3, d=0):
+    ''' sample a piecewise Bezier curve given a sequence of control points'''
+    num = num_bezier(Cp.shape[0], degree)
+    X = []
+    for i in range(num):
+        P = Cp[i*degree:i*degree+degree+1, :]
+        t = np.linspace(0, 1., subd)[:-1]
+        Y = bezier(P, t, d)
+        X += [Y]
+    X.append(Cp[-1])
+    X = np.vstack(X)
+    return X
+
+def compute_beziers(beziers, subd=100, degree=3):
+    chain = beziers_to_chain(beziers)
+    return bezier_piecewise(chain, subd, degree)
+
+def plot_control_polygon(Cp, degree=3, lw=0.5, linecolor=np.ones(3)*0.1):
+    n_bezier = num_bezier(len(Cp), degree)
+    for i in range(n_bezier):
+        cp = Cp[i*degree:i*degree+degree+1, :]
+        if degree==3:
+            plt.plot(cp[0:2,0], cp[0:2, 1], ':', color=linecolor, linewidth=lw)
+            plt.plot(cp[2:,0], cp[2:,1], ':', color=linecolor, linewidth=lw)
+            plt.plot(cp[:,0], cp[:,1], 'o', color=[0, 0.5, 1.], markersize=4)
+        else:
+            plt.plot(cp[:,0], cp[:,1], ':', color=linecolor, linewidth=lw)
+            plt.plot(cp[:,0], cp[:,1], 'o', color=[0, 0.5, 1.])
+
+
+def chain_to_beziers(chain, degree=3):
+    ''' Convert Bezier chain to list of curve segments (4 control points each)'''
+    num = num_bezier(chain.shape[0], degree)
+    beziers = []
+    for i in range(num):
+        beziers.append(chain[i*degree:i*degree+degree+1,:])
+    return beziers
+
+
+def beziers_to_chain(beziers):
+    ''' Convert list of Bezier curve segments to a piecewise bezier chain (shares vertices)'''
+    n = len(beziers)
+    chain = []
+    for i in range(n):
+        chain.append(list(beziers[i][:-1]))
+    chain.append([beziers[-1][-1]])
+    return np.array(sum(chain, []))
+
+
+def split_cubic(bez, t):
+    p1, p2, p3, p4 = bez
+
+    p12 = (p2 - p1) * t + p1
+    p23 = (p3 - p2) * t + p2
+    p34 = (p4 - p3) * t + p3
+
+    p123 = (p23 - p12) * t + p12
+    p234 = (p34 - p23) * t + p23
+
+    p1234 = (p234 - p123) * t + p123
+
+    return np.array([p1, p12, p123, p1234]), np.array([p1234, p234, p34, p4])
+
+
+def approx_arc_length(bez):
+    c0, c1, c2, c3 = bez
+    v0 = norm(c1-c0)*0.15
+    v1 = norm(-0.558983582205757*c0 + 0.325650248872424*c1 + 0.208983582205757*c2 + 0.024349751127576*c3)
+    v2 = norm(c3-c0+c2-c1)*0.26666666666666666
+    v3 = norm(-0.024349751127576*c0 - 0.208983582205757*c1 - 0.325650248872424*c2 + 0.558983582205757*c3)
+    v4 = norm(c3-c2)*.15
+    return v0 + v1 + v2 + v3 + v4
+
+
+def subdivide_bezier(bez, thresh):
+    stack = [bez]
+    res = []
+    while stack:
+        bez = stack.pop()
+        l = approx_arc_length(bez)
+        if l < thresh:
+            res.append(bez)
+        else:
+            b1, b2 = split_cubic(bez, 0.5)
+            stack += [b2, b1]
+    return res
+
+def subdivide_bezier_chain(C, thresh):
+    beziers = chain_to_beziers(C)
+    res = []
+    for bez in beziers:
+        res += subdivide_bezier(bez, thresh)
+    return beziers_to_chain(res)
+
+
+

code/config.py

@@ -0,0 +1,104 @@
+import argparse
+import os.path as osp
+import yaml
+import random
+from easydict import EasyDict as edict
+import numpy.random as npr
+import torch
+from utils import (
+    edict_2_dict,
+    check_and_create_dir,
+    update)
+import wandb
+import warnings
+warnings.filterwarnings("ignore")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, default="code/config/base.yaml")
+    parser.add_argument("--experiment", type=str, default="conformal_0.5_dist_pixel_100_kernel201")
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument('--log_dir', metavar='DIR', default="output")
+    parser.add_argument('--font', type=str, default="none", help="font name")
+    parser.add_argument('--semantic_concept', type=str, help="the semantic concept to insert")
+    parser.add_argument('--word', type=str, default="none", help="the text to work on")
+    parser.add_argument('--prompt_suffix', type=str, default="minimal flat 2d vector. lineal color."
+                                                             " trending on artstation")
+    parser.add_argument('--optimized_letter', type=str, default="none", help="the letter in the word to optimize")
+    parser.add_argument('--batch_size', type=int, default=1)
+    parser.add_argument('--use_wandb', type=int, default=0)
+    parser.add_argument('--wandb_user', type=str, default="none")
+
+    cfg = edict()
+    args = parser.parse_args()
+    with open('TOKEN', 'r') as f:
+        setattr(args, 'token', f.read().replace('\n', ''))
+    cfg.config = args.config
+    cfg.experiment = args.experiment
+    cfg.seed = args.seed
+    cfg.font = args.font
+    cfg.semantic_concept = args.semantic_concept
+    cfg.word = cfg.semantic_concept if args.word == "none" else args.word
+    if " " in cfg.word:
+      raise ValueError(f'no spaces are allowed')
+    cfg.caption = f"a {args.semantic_concept}. {args.prompt_suffix}"
+    cfg.log_dir = f"{args.log_dir}/{args.experiment}_{cfg.word}"
+    if args.optimized_letter in cfg.word:
+        cfg.optimized_letter = args.optimized_letter
+    else:
+      raise ValueError(f'letter should be in word')
+    cfg.batch_size = args.batch_size
+    cfg.token = args.token
+    cfg.use_wandb = args.use_wandb
+    cfg.wandb_user = args.wandb_user
+    cfg.letter = f"{args.font}_{args.optimized_letter}_scaled"
+    cfg.target = f"code/data/init/{cfg.letter}"
+
+    return cfg
+
+
+def set_config():
+
+    cfg_arg = parse_args()
+    with open(cfg_arg.config, 'r') as f:
+        cfg_full = yaml.load(f, Loader=yaml.FullLoader)
+
+    # recursively traverse parent_config pointers in the config dicts
+    cfg_key = cfg_arg.experiment
+    cfgs = [cfg_arg]
+    while cfg_key:
+        cfgs.append(cfg_full[cfg_key])
+        cfg_key = cfgs[-1].get('parent_config', 'baseline')
+
+    # allowing children configs to override their parents
+    cfg = edict()
+    for options in reversed(cfgs):
+        update(cfg, options)
+    del cfgs
+
+    # 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')
+    print('Config:', cfg)
+
+    # 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.use_wandb:
+        wandb.init(project="Word-As-Image", entity=cfg.wandb_user,
+                   config=cfg, name=f"{signature}", id=wandb.util.generate_id())
+
+    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

code/config/base.yaml

@@ -0,0 +1,46 @@
+baseline:
+  parent_config: ''
+  save:
+    init: true
+    image: true
+    video: true
+    video_frame_freq: 1
+  trainable:
+    point: true
+  lr_base:
+    point: 1
+  lr:
+    lr_init: 0.002
+    lr_final: 0.0008
+    lr_delay_mult: 0.1
+    lr_delay_steps: 100
+  num_iter: 2
+  render_size: 600
+  cut_size: 512
+  level_of_cc: 0 # 0 - original number of cc / 1 - recommended / 2 - more control points
+  seed: 0
+  diffusion:
+    model: "runwayml/stable-diffusion-v1-5"
+    timesteps: 1000
+    guidance_scale: 100
+  loss:
+    use_sds_loss: true
+    tone:
+      use_tone_loss: false
+    conformal:
+      use_conformal_loss: false
+
+conformal_0.5_dist_pixel_100_kernel201:
+  parent_config: baseline
+  level_of_cc: 1
+  loss:
+    tone:
+      use_tone_loss: true
+      dist_loss_weight: 100
+      pixel_dist_kernel_blur: 201
+      pixel_dist_sigma: 30
+    conformal:
+      use_conformal_loss: true
+      angeles_w: 0.5
+
+