license: creativeml-openrail-m
language:
- en
thumbnail: >-
https://huggingface.co/Norod78/sd-simpsons-model/raw/main/examples/00496-2202810362-A%20beautiful%20hungry%20demon%20girl,%20John%20Philip%20Falter,%20Very%20detailed%20painting,%20Mark%20Ryden.jpg
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
datasets:
- Norod78/simpsons-blip-captions
inference: true
Simpsons diffusion
*Stable Diffusion fine tuned on images related to "The Simpsons"
If you want more details on how to generate your own blip cpationed dataset see this colab
Training was done using a slightly modified version of Hugging-Face's text to image training example script
About
Put in a text prompt and generate cartoony/simpsony images
A beautiful hungry demon girl, John Philip Falter, Very detailed painting, Mark Ryden
Gal Gadot, cartoon
More examples
The examples folder contains a few images generated by this model's ckpt file using stable-diffusion-webui which means their EXIF info contain the parameter used to generate them
Sample code
from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
import torch
# this will substitute the default PNDM scheduler for K-LMS
lms = LMSDiscreteScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear"
)
guidance_scale=9
seed=7777
steps=100
model_id = "Norod78/sd-simpsons-model"
pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=lms, torch_dtype=torch.float16)
pipe.to("cuda")
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]
image.save("astronaut_rides_horse.png")
Dataset and Training
Finetuned for 10,000 iterations upon Runway ML's Stable-Diffusion v1.5 on BLIP captioned Simpsons images using 1xA5000 GPU on my home desktop computer
Trained by @Norod78