import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("heyyai/mikao00", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]mikao00 on Stable Diffusion via Dreambooth trained on the fast-DreamBooth.ipynb by TheLastBen notebook
model by cormacncheese
This your the Stable Diffusion model fine-tuned the mikao00 concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the instance_prompt(s): mikao000(1).JPG, mikao000(2).JPG, mikao000(3).JPG, mikao000(4).JPG, mikao000(5).png, mikao000(6).png, mikao000(7).JPG, mikao000(8).JPG, mikao000(9).JPG, mikao000(10).JPG, mikao000(12).JPG, mikao000(13).JPG, mikao000(14).jpeg
You can also train your own concepts and upload them to the library by using the fast-DremaBooth.ipynb by TheLastBen.
And you can run your new concept via diffusers: Colab Notebook for Inference, Spaces with the Public Concepts loaded
Here are the images used for training this concept:
mikao000(14).jpeg
mikao000(13).JPG
mikao000(12).JPG
mikao000(10).JPG
mikao000(9).JPG
mikao000(8).JPG
mikao000(7).JPG
mikao000(6).png
mikao000(5).png
mikao000(4).JPG
mikao000(3).JPG
mikao000(2).JPG
mikao000(1).JPG
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