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Text-to-Image
Diffusers
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metadata
license: creativeml-openrail-m
library_name: diffusers
tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - diffusers
  - diffusers-training
base_model: CompVis/stable-diffusion-v1-2
inference: true

Text-to-image finetuning - UtkuCicek/sd_marks

This pipeline was finetuned from CompVis/stable-diffusion-v1-2 on the UtkuCicek/new-marks-data dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['italian style mini pizza with mozerrella on the side']:

val_imgs_grid

Pipeline usage

You can use the pipeline like so:

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("UtkuCicek/sd_marks", torch_dtype=torch.float16)
prompt = "italian style mini pizza with mozerrella on the side"
image = pipeline(prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

  • Epochs: 20
  • Learning rate: 1e-06
  • Batch size: 2
  • 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.

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]