File size: 1,741 Bytes
66b1a91 b158f1d b223a3b b158f1d b223a3b b158f1d b223a3b b158f1d 66b1a91 |
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 |
---
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
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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
```python
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] |