base_model: runwayml/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
inference: true
Text-to-image finetuning - omkar1799/script-sd-annalaura-model
This pipeline was finetuned from runwayml/stable-diffusion-v1-5 on the omkar1799/annalaura-diffusion-dataset dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['blue and yellow cats shopping at plant store in an annalaura watercolor drawing style', 'a turtle character dressed as teacher standing next to a chalkboard with equations on it in an annalaura watercolor drawing style', 'blue and yellow cats riding bikes together through tropical forest path in an annalaura watercolor drawing style', 'raccoon character wearing gold chain driving red sports car down highway in an annalaura watercolor drawing style']:
Pipeline usage
You can use the pipeline like so:
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("omkar1799/script-sd-annalaura-model", torch_dtype=torch.float16)
prompt = "blue and yellow cats shopping at plant store in an annalaura watercolor drawing style"
image = pipeline(prompt).images[0]
image.save("my_image.png")
Training info
These are the key hyperparameters used during training:
- Epochs: 5
- Learning rate: 1e-05
- Batch size: 1
- Gradient accumulation steps: 4
- Image resolution: 512
- Mixed-precision: fp16
Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
[TODO: describe the data used to train the model]