Text-to-image finetuning - nbadrinath/ikea_10
This pipeline was finetuned from runwayml/stable-diffusion-v1-5 on the nbadrinath/ikea_all_images dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['red chair']:
Pipeline usage
You can use the pipeline like so:
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("nbadrinath/ikea_10", torch_dtype=torch.float16)
prompt = "red chair"
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: 4
- Image resolution: 512
- Mixed-precision: fp16
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]
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Model tree for nbadrinath/ikea_10
Base model
runwayml/stable-diffusion-v1-5