metadata
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
datasets:
- MaxReynolds/MyPatternDataset
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
Text-to-image finetuning - MaxReynolds/MyPatternModel
This pipeline was finetuned from CompVis/stable-diffusion-v1-4 on the MaxReynolds/MyPatternDataset dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['']:
Pipeline usage
You can use the pipeline like so:
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("MaxReynolds/MyPatternModel", torch_dtype=torch.float16)
prompt = "<r4nd0m-l4b3l>"
image = pipeline(prompt).images[0]
image.save("my_image.png")
Training info
These are the key hyperparameters used during training:
- Epochs: 25
- Learning rate: 1e-05
- Batch size: 1
- Gradient accumulation steps: 4
- Image resolution: 512
- Mixed-precision: fp16
More information on all the CLI arguments and the environment are available on your wandb run page.