File size: 2,911 Bytes
a9d1952 5294eb3 9555829 5294eb3 a9d1952 5294eb3 26ba271 5294eb3 9555829 5294eb3 |
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 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
---
license: creativeml-openrail-m
language:
- en
thumbnail: "https://huggingface.co/Norod78/sd2-simpsons-blip/raw/main/example/sd2-simpsons-blip-sample_tile_resized.jpg"
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
datasets:
- Norod78/simpsons-blip-captions
inference: true
---
# Simpsons diffusion v2.0
*Stable Diffusion v2.0 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](https://colab.research.google.com/gist/Norod/ee6ee3c4bf11c2d2be531d728ec30824/buildimagedatasetwithblipcaptionsanduploadtohf.ipynb)
Training was done using a slightly modified version of Hugging-Face's text to image training [example script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py)
## About
Put in a text prompt and generate cartoony/simpsony images
## AUTOMATIC1111 webui checkpoint
The [main](https://huggingface.co/Norod78/sd2-simpsons-blip/tree/main) folder contains a .ckpt and a .yaml file to be put in [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) "stable-diffusion-webui/models/Stable-diffusion" folder and used to generate images
## Sample code
```py
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=8.5
seed=777
steps=50
cartoon_model_path = "Norod78/sd2-simpsons-blip"
cartoon_pipe = StableDiffusionPipeline.from_pretrained(cartoon_model_path, scheduler=lms, torch_dtype=torch.float16)
cartoon_pipe.to("cuda")
def generate(prompt, file_prefix ,samples):
torch.manual_seed(seed)
prompt += ", Very detailed, clean, high quality, sharp image"
cartoon_images = cartoon_pipe([prompt] * samples, num_inference_steps=steps, guidance_scale=guidance_scale)["images"]
for idx, image in enumerate(cartoon_images):
image.save(f"{file_prefix}-{idx}-{seed}-sd2-simpsons-blip.jpg")
generate("An oil painting of Snoop Dogg as a simpsons character", "01_SnoopDog", 4)
generate("Gal Gadot, cartoon", "02_GalGadot", 4)
generate("A cartoony Simpsons town", "03_SimpsonsTown", 4)
generate("Pikachu with the Simpsons, Eric Wallis", "04_PikachuSimpsons", 4)
```
## Dataset and Training
Finetuned for 10,000 iterations upon [stabilityai/stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base) on [BLIP captioned Simpsons images](https://huggingface.co/datasets/Norod78/simpsons-blip-captions) using 1xA5000 GPU on my home desktop computer
Trained by [@Norod78](https://twitter.com/Norod78)
|