diffusers pipeline¶

Summary¶

Number of checkpoints: 0
Number of configs: 1
Number of papers: 1
- ALGORITHM: 1

Diffusers Pipeline (2023)¶

Diffusers Pipeline

Task: Diffusers Pipeline

Abstract¶

For the convenience of our community users, this inferencer supports using the pipelines from diffusers for inference to compare the results with the algorithms supported within our algorithm library.

Configs¶

Model	Dataset	Download
diffusers pipeline	-	-

Quick Start¶

sd_xl_pipeline¶

To run stable diffusion XL with mmagic inference API, follow these codes:

from mmagic.apis import MMagicInferencer

## Create a MMEdit instance and infer
editor = MMagicInferencer(model_name='diffusers_pipeline')
text_prompts = 'Japanese anime style, girl, beautiful, cute, colorful, best quality, extremely detailed'
negative_prompt = 'bad face, bad hands'
result_out_dir = 'sd_xl_japanese.png'
editor.infer(text=text_prompts,
             negative_prompt=negative_prompt,
             result_out_dir=result_out_dir)

You will get this picture:

Citation¶

@misc{von-platen-etal-2022-diffusers,
  author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Pedro Cuenca and Nathan Lambert and Kashif Rasul and Mishig Davaadorj and Thomas Wolf},
  title = {Diffusers: State-of-the-art diffusion models},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/huggingface/diffusers}}
}