File size: 1,727 Bytes
a9f6eb2 f43e010 a9f6eb2 f43e010 a9f6eb2 b293866 a9f6eb2 c15d0cd a9f6eb2 b5a6445 c4f09e7 a9f6eb2 b5a6445 |
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 50 51 52 53 54 55 56 |
---
language:
- en
thumbnail: "https://staticassetbucket.s3.us-west-1.amazonaws.com/avatar_grid.png"
tags:
- dreambooth
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
---
# Dreambooth style: Avatar
__Dreambooth finetuning of Stable Diffusion (v1.5.1) on Avatar art style by [Lambda Labs](https://lambdalabs.com/).__
## About
This text-to-image stable diffusion model was trained with dreambooth.
Put in a text prompt and generate your own Avatar style image!
## Usage
To run model locally:
```bash
pip install accelerate torchvision transformers>=4.21.0 ftfy tensorboard modelcards
```
```python
import torch
from diffusers import StableDiffusionPipeline
from torch import autocast
pipe = StableDiffusionPipeline.from_pretrained("lambdalabs/dreambooth-avatar", torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "Yoda, avatarart style person"
scale = 7.5
n_samples = 4
with autocast("cuda"):
images = pipe(n_samples*[prompt], guidance_scale=scale).images
for idx, im in enumerate(images):
im.save(f"{idx:06}.png")
```
## Model description
Base model is Stable Diffusion v1.5 and was trained using Dreambooth with 60 input images sized 512x512 displaying Avatar character images.
The model is learning to associate Avatar images with the style tokenized as 'avatarart style'.
Prior preservation was used during training using the class 'Person' to avoid training bleeding into the representations for that class.
Training ran on 2xA6000 GPUs on [Lambda GPU Cloud](https://lambdalabs.com/service/gpu-cloud) for 700 steps, batch size 4 (a couple hours, at a cost of about $4).
Author: Eole Cervenka |