DreamBooth model of Kratos from God of War
This is a Stable Diffusion model fine-tuned on the person concept with DreamBooth. It can be used by adding the string krts person
to any prompt.
Check out the exampls below β to see a few practical examples on how to use it.
If you are curious to learn more about the training script, then I suggest you to visit the reportπ I created with Weights & Biases π.
This model was created as part of the DreamBooth Hackathon π₯. Visit the organisation page for instructions on how to take part!
Description
This is a Stable Diffusion model fine-tuned on matteopilotto/kratos
dataset containing 10 images of Kratos πͺ from God of War for the wildcard theme using CompVis/stable-diffusion-v1-4
pre-trained model.
Example Output
Prompt: "An illustration of krts person punk playing electric guitar, tristan eaton, victo ngai, artgerm, rhads, ross draws"
Negative prompt: "low contrast, blurry, low resolution, warped"
Resolution: 512 x 512
Guidance Scale: 7
Inference steps: 50
Seeds: [556850, 459286, 768745, 594109]
Prompt: "a drawing of krts person wearing a Spider-man costume in the style of Marvel comics"
Negative prompt: "low contrast, blurry, low resolution, warped"
Resolution: 512 x 512
Guidance Scale: 7
Inference steps: 50
Seeds: [553766, 537908, 147395, 343240]
Prompt: "an illustration of krts person sitting in a movie theater eating popcorn watching a movie, unreal engine, cozy indoor lighting, artstation, detailed, digital painting, cinematic, character design by mark ryden and pixar and hayao miyazaki, unreal 5, daz, hyperrealistic, octane render"
Negative prompt: "low contrast, blurry, low resolution, warped"
Resolution: 512 x 512
Guidance Scale: 7
Inference steps: 50
Seeds: [737986, 488711, 799063, 121111]
Usage
import torch
from diffusers import StableDiffusionPipeline
# set device-agnostic code
device = (
'mps' if torch.backends.mps.is_available()
else 'cuda' if torch.cuda.is_available()
else 'cpu'
)
# load pre-trained model
pretrained_ckpt = 'matteopilotto/kratos-sd-v1-4-dreambooth'
pipeline = StableDiffusionPipeline.from_pretrained(pretrained_ckpt).to(device)
# stable diffusion hyperparameters
unique_token = 'krts'
class_type = 'person'
prompt = f'An illustration of {unique_token} {class_type} punk playing electric guitar, tristan eaton, victo ngai, artgerm, rhads, ross draws'
negative_prompt = 'low contrast, blurry, low resolution, warped'
guidance_scale = 7
h = 512
w = 512
inference_steps = 50
seed = 594109
# set generator for reproducibility
generator = torch.Generator(device=device).manual_seed(seed)
# generate image
image = pipeline(
prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
height=h,
width=w,
num_inference_steps=inference_steps,
generator=generator
).images[0]
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