This pipeline was finetuned from runwayml/stable-diffusion-v1-5 on the umesh16071973/New_Floorplan_demo_dataset dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['/home/tfe/tfm/giovan/testes/LoRas/Model']:

Pipeline usage

You can use the pipeline like so:

python

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("gigio-br/model_Test", torch_dtype=torch.float16)
prompt = "/home/tfe/tfm/giovan/testes/LoRas/Model"
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: 8
Image resolution: 256
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]