metadata

license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
datasets:
  - iamkaikai/amazing_logos_v2
tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - diffusers
inference: true

Text-to-image finetuning - iamkaikai/amazing-logos

This pipeline was finetuned from runwayml/stable-diffusion-v1-5 on the iamkaikai/amazing_logos_v2 dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['Simple elegant logo for Digital Art, D A triangle Symmetrical, successful vibe, minimalist, thought-provoking, abstract, recognizable, black and white']:

Pipeline usage

You can use the pipeline like so:

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("iamkaikai/amazing-logos", torch_dtype=torch.float16)
prompt = "Simple elegant logo for Digital Art, D A triangle Symmetrical, successful vibe, minimalist, thought-provoking, abstract, recognizable, black and white"
image = pipeline(prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

Epochs: 17
Learning rate: 1e-07
Batch size: 1
Gradient accumulation steps: 1
Image resolution: 512
Mixed-precision: fp16

More information on all the CLI arguments and the environment are available on your wandb run page.