File size: 1,554 Bytes
f296796 016df98 f296796 016df98 f296796 016df98 81171dc 12cba2c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 |
---
language: zh
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- multilingual
- English(En)
- Chinese(Zh)
- Spanish(Es)
- French(Fr)
- Russian(Ru)
- Japanese(Ja)
- Korean(Ko)
- Arabic(Ar)
- Italian(It)
- diffusers
widget:
- text: "一张<鸣人>男孩的照片"
example_title: 一张<鸣人>男孩的照片
---
# This is a DreamBooth model finetuned from the multilingual text-to-image model AltDiffusion.
Dreambooth is one of the method of finetune the pretrained text-to-image model.Given as input just a few images of a subject, it learns to bind a unique identifier with that specific subject.
AltDiffusion which is a multilingual text-to-image model.It currently supports 9 languages and will support more in future.
We can use the dreambooth to finetune the AltDiffusion,so that we can get a multilingual Dreambooth model which could supports 9 languages.
Here we give a example model finetuned use a dozen pictures of Uzumaki Naruto downloaded from web.The example code of inference is shown bellow.You can have a try and maybe train your own dreambooth.Hopes have fun!
## Example code of inference
```
from diffusers import AltDiffusionPipeline, DPMSolverMultistepScheduler
import torch
pipe = AltDiffusionPipeline.from_pretrained("BAAI/DreamBooth-AltDiffusion")
pipe = pipe.to("cuda")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
prompt = "一张<鸣人>男孩的照片"
image = pipe(prompt, num_inference_steps=25).images[0]
image.show()
```
|