metadata

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
inference: true

Text-to-image finetuning - nbadrinath/ikea_room_designs_sd1.5_full_finetuning_030720240944

This pipeline was finetuned from runwayml/stable-diffusion-v1-5 on the nbadrinath/ikea_dataset_5.0 dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ["Organize your jewelry, makeup, and small items effortlessly with this light pink, three-tier storage box featuring a lid. Measuring 22 cm, it's perfect for sorting and finding what you need easily in your Ikea collection."]:

Pipeline usage

You can use the pipeline like so:

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("nbadrinath/ikea_room_designs_sd1.5_full_finetuning_030720240944", torch_dtype=torch.float16)
prompt = "Organize your jewelry, makeup, and small items effortlessly with this light pink, three-tier storage box featuring a lid. Measuring 22 cm, it's perfect for sorting and finding what you need easily in your Ikea collection."
image = pipeline(prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

Epochs: 55
Learning rate: 1e-05
Batch size: 2
Gradient accumulation steps: 4
Image resolution: 512
Mixed-precision: bf16

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]