metadata

license: creativeml-openrail-m
base_model: lambdalabs/miniSD-diffusers
datasets:
  - kopyl/833-icons-dataset-1024-blip-large
tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - diffusers
inference: true

Text-to-image finetuning - kopyl/nano-sd-tuned-sample

This pipeline was finetuned from lambdalabs/miniSD-diffusers on the kopyl/833-icons-dataset-1024-blip-large dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['photo of a frog']:

Pipeline usage

You can use the pipeline like so:

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("kopyl/nano-sd-tuned-sample", torch_dtype=torch.float16)
prompt = "photo of a frog"
image = pipeline(prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

Epochs: 1
Learning rate: 1e-05
Batch size: 1
Gradient accumulation steps: 1
Image resolution: 256
Mixed-precision: fp16

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