metadata
base_model: runwayml/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
- diffusers-training
inference: true
controlnet-Jieya/model_out_canny
These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. You can find some example images below.
prompt: gothic fractals prompt: gothic fractals prompt: gothic fractals
Intended uses & limitations
How to use
from datasets import Dataset
from pathlib import Path
from PIL import Image
face_image = Path("images/original")
canny_image = Path("images/canny_images")
def entry_for_id(entry_id):
if type(entry_id) == int:
entry_id = f"{entry_id:05}"
image = Image.open(face_image/f"fractal_{entry_id}.jpg")
image_cond = Image.open(canny_image/f"canny_fractal_{entry_id}.jpg")
caption = "gothic fractals"
return {
"image": image,
"canny_images": image_cond,
"caption": caption,
}
def generate_entries():
for x in range(1, 6463): # Start from 1 and go up to 6462
yield entry_for_id(x)
ds = Dataset.from_generator(generate_entries)
ds.push_to_hub('Jieya/fractal_image_6462')
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
[TODO: describe the data used to train the model]