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d2styler

This is an official implimentation of D2Styler

https://github.com/onkarsus13/d2styler

Science Score: 54.0%

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    Links to: arxiv.org
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    Scientific vocabulary similarity
    Low similarity (10.9%) to scientific vocabulary
Last synced: 8 months ago · JSON representation ·

Repository

This is an official implimentation of D2Styler

Basic Info
  • Host: GitHub
  • Owner: Onkarsus13
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Size: 5.68 MB
Statistics
  • Stars: 9
  • Watchers: 1
  • Forks: 0
  • Open Issues: 3
  • Releases: 0
Created almost 2 years ago · Last pushed over 1 year ago
Metadata Files
Readme Contributing License Code of conduct Citation

README.md

D2Styler

Welcome to the official implementation of D2Styler, which has been accepted at the International Conference on Pattern Recognition (ICPR 2024).

Overview

"D2Styler: Advancing Arbitrary Style Transfer with Discrete Diffusion Methods" introduces a novel framework for style transfer called D2Styler. Leveraging VQ-GANs and discrete diffusion, this method aims to improve the quality and stability of style transfer, addressing common issues like mode-collapse and over/under-stylization. By using Adaptive Instance Normalization (AdaIN) features, D2Styler facilitates effective style transfer between images. Experimental results show that D2Styler outperforms twelve existing methods on various metrics, producing high-quality, visually appealing images. The method uses images from the WikiArt and COCO datasets. The model's architecture and its qualitative results are showcased below. The model will be available on HuggingFace 🤗, where you can download it for inference or fine-tuning.

Model Architecture

D2Styler Architecture

Results

D2Styler Results

Installation

To get started with D2Styler, follow the steps below to install the necessary dependencies:

  1. Clone the repository:

    bash git clone https://github.com/yourusername/D2Styler.git cd D2Styler

  2. Install the dependencies:

    bash pip install -e ".[torch]" pip install -e .[all,dev,notebooks]

Contributing

We welcome contributions to D2Styler! If you have any ideas for improvements or find any issues, please feel free to open an issue or submit a pull request.

For more details, please refer to our paper and our repository on HuggingFace.

Owner

  • Name: ONKAR Susladkar
  • Login: Onkarsus13
  • Kind: user

Artifitial Inteegence | Deep learning | Computer Vision | Natural language Prosessing |

Citation (CITATION.cff) 

cff-version: 1.2.0
title: 'Diffusers: State-of-the-art diffusion models'
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Patrick
    family-names: von Platen
  - given-names: Suraj
    family-names: Patil
  - given-names: Anton
    family-names: Lozhkov
  - given-names: Pedro
    family-names: Cuenca
  - given-names: Nathan
    family-names: Lambert
  - given-names: Kashif
    family-names: Rasul
  - given-names: Mishig
    family-names: Davaadorj
  - given-names: Thomas
    family-names: Wolf
repository-code: 'https://github.com/huggingface/diffusers'
abstract: >-
  Diffusers provides pretrained diffusion models across
  multiple modalities, such as vision and audio, and serves
  as a modular toolbox for inference and training of
  diffusion models.
keywords:
  - deep-learning
  - pytorch
  - image-generation
  - diffusion
  - text2image
  - image2image
  - score-based-generative-modeling
  - stable-diffusion
license: Apache-2.0
version: 0.12.1

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Dependencies

docker/diffusers-flax-cpu/Dockerfile docker
  • ubuntu 20.04 build
docker/diffusers-flax-tpu/Dockerfile docker
  • ubuntu 20.04 build
docker/diffusers-onnxruntime-cpu/Dockerfile docker
  • ubuntu 20.04 build
docker/diffusers-onnxruntime-cuda/Dockerfile docker
  • nvidia/cuda 11.6.2-cudnn8-devel-ubuntu20.04 build
docker/diffusers-pytorch-cpu/Dockerfile docker
  • ubuntu 20.04 build
docker/diffusers-pytorch-cuda/Dockerfile docker
  • nvidia/cuda 11.7.1-cudnn8-runtime-ubuntu20.04 build
examples/controlnet/requirements.txt pypi
  • accelerate >=0.16.0
  • datasets *
  • ftfy *
  • tensorboard *
  • torchvision *
  • transformers >=4.25.1
examples/controlnet/requirements_flax.txt pypi
  • Jinja2 *
  • datasets *
  • flax *
  • ftfy *
  • optax *
  • tensorboard *
  • torch *
  • torchvision *
  • transformers >=4.25.1
examples/controlnet/requirements_sdxl.txt pypi
  • Jinja2 *
  • accelerate >=0.16.0
  • datasets *
  • ftfy *
  • tensorboard *
  • torchvision *
  • transformers >=4.25.1
  • wandb *
examples/custom_diffusion/requirements.txt pypi
  • Jinja2 *
  • accelerate *
  • ftfy *
  • tensorboard *
  • torchvision *
  • transformers >=4.25.1
examples/dreambooth/requirements.txt pypi
  • Jinja2 *
  • accelerate >=0.16.0
  • ftfy *
  • tensorboard *
  • torchvision *
  • transformers >=4.25.1
examples/dreambooth/requirements_flax.txt pypi
  • Jinja2 *
  • flax *
  • ftfy *
  • optax *
  • tensorboard *
  • torch *
  • torchvision *
  • transformers >=4.25.1
examples/dreambooth/requirements_sdxl.txt pypi
  • Jinja2 *
  • accelerate >=0.16.0
  • ftfy *
  • tensorboard *
  • torchvision *
  • transformers >=4.25.1
examples/instruct_pix2pix/requirements.txt pypi
  • accelerate >=0.16.0
  • datasets *
  • ftfy *
  • tensorboard *
  • torchvision *
  • transformers >=4.25.1
examples/research_projects/colossalai/requirement.txt pypi
  • Jinja2 *
  • diffusers *
  • ftfy *
  • tensorboard *
  • torch *
  • torchvision *
  • transformers *
examples/research_projects/dreambooth_inpaint/requirements.txt pypi
  • Jinja2 *
  • accelerate >=0.16.0
  • diffusers ==0.9.0
  • ftfy *
  • tensorboard *
  • torchvision *
  • transformers >=4.21.0
examples/research_projects/intel_opts/textual_inversion/requirements.txt pypi
  • Jinja2 *
  • accelerate >=0.16.0
  • ftfy *
  • intel_extension_for_pytorch >=1.13
  • tensorboard *
  • torchvision *
  • transformers >=4.21.0
examples/research_projects/intel_opts/textual_inversion_dfq/requirements.txt pypi
  • accelerate *
  • ftfy *
  • modelcards *
  • neural-compressor *
  • tensorboard *
  • torchvision *
  • transformers >=4.25.0
examples/research_projects/lora/requirements.txt pypi
  • Jinja2 *
  • accelerate >=0.16.0
  • datasets *
  • ftfy *
  • tensorboard *
  • torchvision *
  • transformers >=4.25.1
examples/research_projects/mulit_token_textual_inversion/requirements.txt pypi
  • Jinja2 *
  • accelerate >=0.16.0
  • ftfy *
  • tensorboard *
  • torchvision *
  • transformers >=4.25.1
examples/research_projects/mulit_token_textual_inversion/requirements_flax.txt pypi
  • Jinja2 *
  • flax *
  • ftfy *
  • optax *
  • tensorboard *
  • torch *
  • torchvision *
  • transformers >=4.25.1
examples/research_projects/multi_subject_dreambooth/requirements.txt pypi
  • Jinja2 *
  • accelerate >=0.16.0
  • ftfy *
  • tensorboard *
  • torchvision *
  • transformers >=4.25.1
examples/research_projects/onnxruntime/text_to_image/requirements.txt pypi
  • accelerate >=0.16.0
  • datasets *
  • ftfy *
  • modelcards *
  • tensorboard *
  • torchvision *
  • transformers >=4.25.1
examples/research_projects/onnxruntime/textual_inversion/requirements.txt pypi
  • accelerate >=0.16.0
  • ftfy *
  • modelcards *
  • tensorboard *
  • torchvision *
  • transformers >=4.25.1
examples/research_projects/onnxruntime/unconditional_image_generation/requirements.txt pypi
  • accelerate >=0.16.0
  • datasets *
  • tensorboard *
  • torchvision *
examples/text_to_image/requirements.txt pypi
  • Jinja2 *
  • accelerate >=0.16.0
  • datasets *
  • ftfy *
  • tensorboard *
  • torchvision *
  • transformers >=4.25.1
examples/text_to_image/requirements_flax.txt pypi
  • Jinja2 *
  • datasets *
  • flax *
  • ftfy *
  • optax *
  • tensorboard *
  • torch *
  • torchvision *
  • transformers >=4.25.1
examples/textual_inversion/requirements.txt pypi
  • Jinja2 *
  • accelerate >=0.16.0
  • ftfy *
  • tensorboard *
  • torchvision *
  • transformers >=4.25.1
examples/textual_inversion/requirements_flax.txt pypi
  • Jinja2 *
  • flax *
  • ftfy *
  • optax *
  • tensorboard *
  • torch *
  • torchvision *
  • transformers >=4.25.1
examples/unconditional_image_generation/requirements.txt pypi
  • accelerate >=0.16.0
  • datasets *
  • torchvision *
pyproject.toml pypi
setup.py pypi
  • deps *
src/diffusers.egg-info/requires.txt pypi
  • Jinja2 *
  • Pillow *
  • accelerate >=0.11.0
  • black *
  • compel ==0.1.8
  • datasets *
  • filelock *
  • flax >=0.4.1
  • hf-doc-builder >=0.3.0
  • huggingface-hub >=0.13.2
  • importlib_metadata *
  • invisible-watermark >=0.2.0
  • isort >=5.5.4
  • jax *
  • jaxlib >=0.1.65
  • k-diffusion >=0.0.12
  • librosa *
  • numpy *
  • omegaconf *
  • parameterized *
  • protobuf <4,>=3.20.3
  • pytest *
  • pytest-timeout *
  • pytest-xdist *
  • regex *
  • requests *
  • requests-mock ==1.10.0
  • ruff >=0.0.241
  • safetensors >=0.3.1
  • scipy *
  • sentencepiece *
  • tensorboard *
  • torch >=1.4
  • torchvision *
  • transformers >=4.25.1
  • urllib3 <=2.0.0