fix(python): README.md

README copy.md

@@ -1,304 +0,0 @@
----
-title: SVGRender
-emoji: 💻
-colorFrom: gray
-colorTo: yellow
-sdk: gradio
-sdk_version: 4.20.1
-python_version: 3.10.12
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-<h1 id="ptsvg" align="center">Pytorch-SVGRender</h1>
-
-<p align="center">
-    <a href="https://www.python.org/"><img src="https://img.shields.io/badge/python-3.10-or?logo=python" alt="pyhton"></a>
-    <a href="http://mozilla.org/MPL/2.0/"><img src="https://img.shields.io/badge/license-MPL2.0-orange" alt="license"></a>
-    <a href="https://ximinng.github.io/PyTorch-SVGRender-project/"><img src="https://img.shields.io/badge/website-Gitpage-yellow" alt="website"></a>
-    <a href="https://pytorch-svgrender.readthedocs.io/en/latest/index.html"><img src="https://img.shields.io/badge/docs-readthedocs-purple" alt="docs"></a>
-</p>
-
-<div align="center">
-<img src="./assets/logo.png" style="width: 350px; height: 300px;" alt="Pytorch-SVGRender">
-<p><strong>Pytorch-SVGRender: </strong>The go-to library for differentiable rendering methods for SVG generation.</p>
-</div>
-<p align="center">
-    <a href="#recent-updates">Updates</a> •
-    <a href="#table-of-contents">Table of Contents</a> •
-    <a href="#installation">Installation</a> •
-    <a href="#quickstart">Quickstart</a> •
-    <a href="#faq">FAQ</a> •
-    <a href="#todo">TODO</a> •
-    <a href="#acknowledgement">Acknowledgment</a> •
-    <a href="#citation">Citation</a> •
-    <a href="#licence">Licence</a>
-</p>
-
-<h2 align="center">Recent Updates</h2>
-
-- [12/2023] 🔥 We open-sourced Pytorch-SVGRender V1.0.
-
-<h2 align="center">Table of Contents</h2>
-<p align="right"><a href="#ptsvg"><sup>▴ Back to top</sup></a></p>
-
-### 1. Image Vectorization
-
-- DiffVG: Differentiable Vector Graphics Rasterization for Editing and Learning (`SIGGRAPH 2020`)
-
-  [[Project]](https://people.csail.mit.edu/tzumao/diffvg/) [[Paper]](https://cseweb.ucsd.edu/~tzli/diffvg/diffvg.pdf) [[Code]](https://github.com/BachiLi/diffvg)
-
-  DiffVG is a differentiable rasterizer for 2D vector graphics. **This repository is heavily based on DiffVG.**
-
-- LIVE: Towards Layer-wise Image Vectorization (`CVPR 2022`)
-
-  [[Project]](https://ma-xu.github.io/LIVE/) [[Paper]](https://ma-xu.github.io/LIVE/index_files/CVPR22_LIVE_main.pdf) [[Code]](https://github.com/Picsart-AI-Research/LIVE-Layerwise-Image-Vectorization)
-
-- CLIPasso: Semantically-Aware Object Sketching (`SIGGRAPH 2022`)
-
-  [[Project]](https://clipasso.github.io/clipasso/) [[Paper]](https://arxiv.org/abs/2202.05822) [[Code]](https://github.com/yael-vinker/CLIPasso)
-
-- CLIPascene: Scene Sketching with Different Types and Levels of Abstraction (`ICCV 2023`)
-
-  [[Project]](https://clipascene.github.io/CLIPascene/) [[Paper]](https://arxiv.org/abs/2211.17256) [[Code]](https://github.com/yael-vinker/SceneSketch)
-
-### 2. Text-to-SVG Synthesis
-
-- CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders (`NIPS 2022`)
-
-  [[Paper]](https://arxiv.org/abs/2106.14843) [[Code]](https://github.com/kvfrans/clipdraw)
-
-- StyleCLIPDraw: Coupling Content and Style in Text-to-Drawing Synthesis
-
-  [[Live]](https://slideslive.com/38970834/styleclipdraw-coupling-content-and-style-in-texttodrawing-synthesis?ref=account-folder-92044-folders) [[Paper]](https://arxiv.org/abs/2202.12362) [[Code]](https://github.com/pschaldenbrand/StyleCLIPDraw)
-
-- CLIPFont: Texture Guided Vector WordArt Generation (`BMVC 2022`)
-
-  [[Paper]](https://bmvc2022.mpi-inf.mpg.de/0543.pdf) [[Code]](https://github.com/songyiren98/CLIPFont)
-
-- VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models (`CVPR 2023`)
-
-  [[Project]](https://vectorfusion.github.io/) [[Paper]](https://openaccess.thecvf.com/content/CVPR2023/papers/Jain_VectorFusion_Text-to-SVG_by_Abstracting_Pixel-Based_Diffusion_Models_CVPR_2023_paper.pdf)
-
-- DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models (`NIPS 2023`)
-
-  [[Project]](https://ximinng.github.io/DiffSketcher-project/) [[Live]](https://neurips.cc/virtual/2023/poster/72425) [[Paper]](https://arxiv.org/abs/2306.14685) [[Code]](https://github.com/ximinng/DiffSketcher)
-
-- Word-As-Image for Semantic Typography (`SIGGRAPH 2023`)
-
-  [[Project]](https://wordasimage.github.io/Word-As-Image-Page/) [[Paper]](https://arxiv.org/abs/2303.01818) [[Code]](https://github.com/Shiriluz/Word-As-Image)
-
-- SVGDreamer: Text Guided SVG Generation with Diffusion Model (`CVPR 2024`)
-
-  [[Project]](https://ximinng.github.io/SVGDreamer-project/) [[Paper]](https://arxiv.org/abs/2312.16476) [[code]](https://github.com/ximinng/SVGDreamer)
-
-<h2 align="center">Installation</h2>
-
-You can follow the steps below to quickly get up and running with PyTorch-SVGRender.
-These steps will let you run quick inference locally.
-
-In the top level directory run,
-
-```bash
-sh script/install.sh
-```
-
-Note: Make sure that the script file has execution **permissions** (you can give them using `chmod +x script.sh`), and
-then run the script.
-
-For more information, please refer to
-the [Install.md](https://github.com/ximinng/PyTorch-SVGRender/blob/main/Install.md).
-
-<h2 align="center">Quickstart</h2>
-<p align="right"><a href="#ptsvg"><sup>▴ Back to top</sup></a></p>
-
-**For more information, [read the docs](https://pytorch-svgrender.readthedocs.io/en/latest/index.html).**
-
-### 1. Basic Usage
-
-**DiffVG** vectorizes any raster images:
-
-```shell
-python svg_render.py x=diffvg target='./data/fallingwater.png'
-# change 'num_paths' and 'num_iter' for better results
-python svg_render.py x=diffvg target='./data/fallingwater.png' x.num_paths=512 x.num_iter=2000
-```
-
-**LIVE** vectorizes the raster emojis images (in original PNG format):
-
-```shell
-python svg_render.py x=live target='./data/simile.png'
-# change 'num_paths' and 'schedule_each' for better results
-python svg_render.py x=live target='./data/simile.png' x.num_paths=5 x.schedule_each=1
-```
-
-**CLIPasso** synthesizes vectorized sketches from images:
-
-**note:** first download the U2Net model `sh script/download_u2net.sh`.
-
-```shell
-python svg_render.py x=clipasso target='./data/horse.png'
-```
-
-**CLIPascene** synthesizes vectorized sketches from images:
-
-**note:** first download the U2Net model `sh script/download_u2net.sh`, and make sure the `./data/background` folder and
-the `./data/scene` folder exist with target images.
-
-```shell
-python svg_render.py x=clipascene target='ballerina.png'
-```
-
-**CLIPDraw** synthesizes SVGs based on text prompts:
-
-```shell
-python svg_render.py x=clipdraw "prompt='a photo of a cat'"
-```
-
-**StyleCLIPDraw** synthesizes SVG based on a text prompt and a reference image:
-
-```shell
-python svg_render.py x=styleclipdraw "prompt='a photo of a cat'" target='./data/starry.png'
-```
-
-**CLIPFont** styles vector fonts according to text prompts:
-
-```shell
-python svg_render.py x=clipfont "prompt='Starry Night by Vincent van gogh'" target='./data/alphabet1.svg'
-```
-
----
-
-> Because the following methods rely on stable diffusion, add `diffuser.download=True` to the command the **first time** you
-run the script.
-
-**SVGDreamer** generates various styles of SVG based on text prompts. It supports the use of six vector primitives,
-including Iconography, Sketch, Pixel Art, Low-Poly, Painting, and Ink and Wash.
-
-```shell
-# primitive: iconography
-## 1. German shepherd
-python svg_render.py x=svgdreamer "prompt='A colorful German shepherd in vector art. tending on artstation.'" save_step=30 x.guidance.n_particle=6 x.guidance.vsd_n_particle=4 x.guidance.phi_n_particle=2 result_path='./svgdreamer/GermanShepherd'
-## 2. sydney opera house
-python svg_render.py x=svgdreamer "prompt='Sydney opera house. oil painting. by Van Gogh'" save_step=30 x.guidance.n_particle=6 x.guidance.vsd_n_particle=4 x.guidance.phi_n_particle=2 x.num_paths=512 result_path='./svgdreamer/Sydney'
-# primitive: low-ploy
-python svg_render.py x=svgdreamer "prompt='A picture of a bald eagle. low-ploy. polygon'" x.style='low-poly' save_step=30 x.guidance.n_particle=6 x.guidance.vsd_n_particle=4 x.guidance.phi_n_particle=2 x.guidance.num_iter=1000 result_path='./svgdreamer/eagle'
-# primitive: pixel-art
-python svg_render.py x=svgdreamer "prompt='Darth vader with lightsaber. ultrarealistic. pixelart. trending on artstation.'" x.style='pixelart' save_step=30 x.guidance.n_particle=6 x.guidance.vsd_n_particle=4 x.guidance.phi_n_particle=2 x.guidance.num_iter=1000 result_path='./svgdreamer/DarthVader'
-# primitive: painting
-python svg_render.py x=svgdreamer "prompt='self portrait of Van Gogh. oil painting. cmyk portrait. multi colored. defiant and beautiful. cmyk. expressive eyes.'" x.style='painting' save_step=50 x.guidance.n_particle=6 x.guidance.vsd_n_particle=4 x.guidance.phi_n_particle=2 x.guidance.t_schedule='randint' x.num_paths=1500 result_path='./svgdreamer/VanGogh_portrait'
-# primitive: sketch
-python svg_render.py x=svgdreamer "prompt='A free-hand drawing of A speeding Lamborghini. black and white drawing.'" x.style='sketch' save_step=30 x.guidance.n_particle=6 x.guidance.vsd_n_particle=4 x.guidance.phi_n_particle=2 x.guidance.t_schedule='randint' x.num_paths=128 result_path='./svgdreamer/Lamborghini'
-# primitive: ink and wash
-python svg_render.py x=svgdreamer "prompt='Big Wild Goose Pagoda. ink style. Minimalist abstract art grayscale watercolor.'" x.style='ink' save_step=30 x.guidance.n_particle=6 x.guidance.vsd_n_particle=4 x.guidance.phi_n_particle=2 x.guidance.t_schedule='randint' x.num_paths=128 x.width=6 result_path='./svgdreamer/BigWildGoosePagoda'
-```
-
-**VectorFusion** synthesizes SVGs in various styles based on text prompts:
-
-```shell
-# Iconography style
-python svg_render.py x=vectorfusion x.style='iconography' "prompt='a panda rowing a boat in a pond. minimal flat 2d vector icon. lineal color. trending on artstation.'"
-# PixelArt style
-python svg_render.py x=vectorfusion x.style='pixelart' "prompt='a panda rowing a boat in a pond. pixel art. trending on artstation.'"
-# Sketch style
-python svg_render.py x=vectorfusion x.style='sketch' "prompt='a panda rowing a boat in a pond. minimal 2d line drawing. trending on artstation.'"
-```
-
-Following SVGDreamer, we've added three additional styles (`Paining`, `Ink and Wash` and `low-ploy`) to VectorFusion.
-
-**DiffSketcher** synthesizes vector sketches based on text prompts:
-
-```shell
-# DiffSketcher
-python svg_render.py x=diffsketcher "prompt='a photo of Sydney opera house'" x.token_ind=5 seed=8019
-# DiffSketcher, variable stroke width
-python svg_render.py x=diffsketcher "prompt='a photo of Sydney opera house'" x.token_ind=5 x.optim_width=True seed=8019
-# DiffSketcher RGBA version
-python svg_render.py x=diffsketcher "prompt='a photo of Sydney opera house'" x.token_ind=5 x.optim_width=True x.optim_rgba=True x.optim_opacity=False seed=8019
-# DiffSketcher + style transfer
-python svg_render.py x=stylediffsketcher "prompt='The French Revolution. highly detailed. 8k. ornate. intricate. cinematic. dehazed. atmospheric. oil painting. by Van Gogh'" x.token_ind=4 x.num_paths=2000 target='./data/starry.png' seed=876809
-```
-
-**Word-As-Image** follow a text prompt to style a letter in a word:
-
-```shell
-# Inject the meaning of the word bunny into the 'Y' in the word 'BUNNY'
-python svg_render.py x=wordasimage x.word='BUNNY' prompt='BUNNY' x.optim_letter='Y'
-```
-
-### 2. SDS Loss based Approach
-
-This is achieved by utilizing a pretrained text-to-image diffusion model as a strong image prior to supervise the
-training of the PyDiffVG, enabling rendering SVG alignment with the text. This remarkable capability is fundamentally
-grounded in the use of Score Distillation Sampling (SDS). SDS acts as the core mechanism that lifts raster images from
-diffusion models to the SVG domain, enabling the training of SVG parameters without images.
-This includes the methods VectorFusion, DiffSketcher and SVGDreamer.
-
-We only compare the performance of SDS, which means that no other loss is used:
-
-```shell
-# SDS loss
-python svg_render.py x=vectorfusion "prompt='a panda rowing a boat in a pond. minimal flat 2d vector icon. lineal color. trending on artstation.'"
-# Input Augmentation SDS loss (LSDS loss)
-python svg_render.py x=vectorfusion x.style='sketch' "prompt='an elephant. minimal 2d line drawing. trending on artstation.'"
-# Input Augmentation SDS loss (ASDS loss)
-python svg_render.py x=diffsketcher "prompt='an elephant. minimal 2d line drawing. trending on artstation.'" x.token_ind=2 x.sds.grad_scale=1 x.sds.num_aug=4 x.clip.vis_loss=0 x.perceptual.coeff=0 x.opacity_delta=0.3 
-# Vectorized Particle-based Score Distillation (VPSD loss)
-python svg_render.py x=svgdreamer "prompt='a panda rowing a boat in a pond. minimal flat 2d vector icon. lineal color. trending on artstation.'" save_step=60 x.guidance.n_particle=6 x.guidance.vsd_n_particle=4 x.guidance.phi_n_particle=2 
-```
-
-<h2 align="center">FAQ</h2>
-<p align="right"><a href="#ptsvg"><sup>▴ Back to top</sup></a></p>
-
-- Q: Where can I get more scripts and visualizations?
-- A: check the [pytorch-svgrender.readthedocs.io](https://pytorch-svgrender.readthedocs.io/en/latest/index.html).
-
-- Q: An error says HuggingFace cannot find the model in the disk cache.
-- A: Add *`diffuser.download=True`* to the command for downloading model checkpoints the **first time** you run the script.
-
-<h2 align="center">TODO</h2>
-<p align="right"><a href="#ptsvg"><sup>▴ Back to top</sup></a></p>
-
-- [x] integrated SVGDreamer.
-
-<h2 align="center">Acknowledgement</h2>
-<p align="right"><a href="#ptsvg"><sup>▴ Back to top</sup></a></p>
-
-The project is built based on the following repository:
-
-[BachiLi/diffvg](https://github.com/BachiLi/diffvg),
-[huggingface/diffusers](https://github.com/huggingface/diffusers),
-[threestudio-project/threestudio](https://github.com/threestudio-project/threestudio),
-[yael-vinker/CLIPasso](https://github.com/yael-vinker/CLIPasso),
-[ximinng/DiffSketcher](https://github.com/ximinng/DiffSketcher),
-[THUDM/ImageReward](https://github.com/THUDM/ImageReward),
-[advimman/lama](https://github.com/advimman/lama)
-
-We gratefully thank the authors for their wonderful works.
-
-<h2 align="center">Citation</h2>
-<p align="right"><a href="#ptsvg"><sup>▴ Back to top</sup></a></p>
-
-If you use this code for your research, please cite the following work:
-
-```
-@article{xing2023svgdreamer,
-    title={SVGDreamer: Text Guided SVG Generation with Diffusion Model},
-    author={Xing, Ximing and Zhou, Haitao and Wang, Chuang and Zhang, Jing and Xu, Dong and Yu, Qian},
-    journal={arXiv preprint arXiv:2312.16476},
-    year={2023}
-}
-@inproceedings{xing2023diffsketcher,
-    title={DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models},
-    author={XiMing Xing and Chuang Wang and Haitao Zhou and Jing Zhang and Qian Yu and Dong Xu},
-    booktitle={Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS)},
-    year={2023},
-    url={https://openreview.net/forum?id=CY1xatvEQj}
-}
-```
-
-<h2 align="center">Licence</h2>
-<p align="right"><a href="#ptsvg"><sup>▴ Back to top</sup></a></p>
-
-This work is licensed under a **Mozilla Public License Version 2.0**.

README.md

@@ -5,6 +5,7 @@ colorFrom: indigo
 colorTo: blue
 sdk: gradio
 sdk_version: 4.21.0
+python_version: 3.10.12
 app_file: app.py
 pinned: false
 license: apache-2.0