license: creativeml-openrail-m
library_name: diffusers
tags:
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
base_model: runwayml/stable-diffusion-v1-5
inference: true
instance_prompt: disney style
Cartoonify
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5 with additional fine-tuning of the text encoder. The weights were trained from a popular animation studio using DreamBooth. Use the tokens disney style in your prompts for the effect.
You can find some example images below:
Intended uses & limitations
How to use
import torch
from diffusers import StableDiffusionPipeline
# basic usage
repo_id = "lavaman131/cartoonify"
device = torch.device("cuda")
torch_dtype = torch.float16 if device.type in ["mps", "cuda"] else torch.float32
pipeline = StableDiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch_dtype).to(device)
image = pipeline("PROMPT GOES HERE").images[0]
image.save("output.png")
Limitations and bias
As with any diffusion model, playing around with the prompt and classifier-free guidance parameter is required until you get the results you want. Zoomed-out subjects seem to loose clairity in the face. For additional safety in image generation, we use the Stable Diffusion safety checker.
Training details
The model was fine-tuned for 3500 steps on around 200 images of modern Disney characters, backgrounds, and animals. The ratios for each were 70%, 20%, and 10% respectively on an RTX A5000 GPU (24GB VRAM).
The training code used can be found here. The regularization images used for training can be found here.