2.5 KiB
DiffSynth Studio
Introduction
DiffSynth is a new 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. This version is currently in its initial stage, supporting SD and SDXL architectures. In the future, we plan to develop more interesting features based on this new codebase.
Installation
Create Python environment:
conda env create -f environment.yml
Enter the Python environment:
conda activate DiffSynthStudio
Usage (in WebUI)
python -m streamlit run Diffsynth_Studio.py
https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/93085557-73f3-4eee-a205-9829591ef954
Usage (in Python code)
Example 1: Stable Diffusion
We can generate images with very high resolution. Please see examples/sd_text_to_image.py for more details.
| 512*512 | 1024*1024 | 2048*2048 | 4096*4096 |
|---|---|---|---|
Example 2: Stable Diffusion XL
Generate images with Stable Diffusion XL. Please see examples/sdxl_text_to_image.py for more details.
| 1024*1024 | 2048*2048 |
|---|---|
Example 3: Stable Diffusion XL Turbo
Generate images with Stable Diffusion XL Turbo. You can see examples/sdxl_turbo.py for more details, but we highly recommend you to use it in the WebUI.
| "black car" | "red car" |
|---|---|
Example 4: Toon Shading
A very interesting example. Please see examples/sd_toon_shading.py for more details.
https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/53532f0e-39b1-4791-b920-c975d52ec24a