zfbok / DiffSynth-Studio

Enjoy the magic of Diffusion models!

DiffSynth Studio

Introduction

DiffSynth Studio is a Diffusion engine. We have restructured architectures including Text Encoder, UNet, VAE, among others, maintaining compatibility with models from the open-source community while enhancing computational performance. We provide many interesting features. Enjoy the magic of Diffusion models!

Roadmap

• Aug 29, 2023. I propose DiffSynth, a video synthesis framework.
  • Project Page.
  • The source codes are released in EasyNLP.
  • The technical report (ECML PKDD 2024) is released on arXiv.
• Oct 1, 2023. I release an early version of this project, namely FastSDXL. A try for building a diffusion engine.
  • The source codes are released on GitHub.
  • FastSDXL includes a trainable OLSS scheduler for efficiency improvement.
    • The original repo of OLSS is here.
    • The technical report (CIKM 2023) is released on arXiv.
    • A demo video is shown on Bilibili.
    • Since OLSS requires additional training, we don't implement it in this project.
• Nov 15, 2023. I propose FastBlend, a powerful video deflickering algorithm.
  • The sd-webui extension is released on GitHub.
  • Demo videos are shown on Bilibili, including three tasks.
    • Video deflickering
    • Video interpolation
    • Image-driven video rendering
  • The technical report is released on arXiv.
  • An unofficial ComfyUI extension developed by other users is released on GitHub.
• Dec 8, 2023. I decide to develop a new Project, aiming to release the potential of diffusion models, especially in video synthesis.
• Jan 29, 2024. I propose Diffutoon, a fantastic solution for toon shading.
  • Project Page.
  • The source codes are released in this project.
  • The technical report (IJCAI 2024) is released on arXiv.
• Until now, DiffSynth Studio has supported the following models:
  • Stable Diffusion
  • Stable Diffusion XL
  • ControlNet
  • AnimateDiff
  • Ip-Adapter
  • ESRGAN
  • RIFE
  • Hunyuan-DiT

Installation

Create Python environment:

conda env create -f environment.yml

We find that sometimes conda cannot install cupy correctly, please install it manually. See this document for more details.

Enter the Python environment:

conda activate DiffSynthStudio

Usage (in WebUI)

python -m streamlit run DiffSynth_Studio.py

sdxl_turbo_ui.mp4

Usage (in Python code)

The Python examples are in examples. We provide an overview here.

Image Synthesis

Generate high-resolution images, by breaking the limitation of diffusion models! examples/image_synthesis

512*512	1024*1024	2048*2048	4096*4096

1024*1024	2048*2048

Toon Shading

Render realistic videos in a flatten style and enable video editing features. examples/Diffutoon

Diffutoon.mp4

Diffutoon_edit.mp4

Video Stylization

Video stylization without video models. examples/diffsynth

winter_stone.mp4

Chinese Models

Use Hunyuan-DiT to generate images with Chinese prompts. We also support LoRA fine-tuning of this model. examples/hunyuan_dit

Prompt: 少女手捧鲜花，坐在公园的长椅上，夕阳的余晖洒在少女的脸庞，整个画面充满诗意的美感

1024x1024	2048x2048 (highres-fix)

Prompt: 一只小狗蹦蹦跳跳，周围是姹紫嫣红的鲜花，远处是山脉

Without LoRA	With LoRA