license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2
datasets:
- rgres/AerialDreams
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
Text-to-image finetuning - rgres/Seg2Map-finetuned
This pipeline was finetuned from stabilityai/stable-diffusion-2 on the rgres/AerialDreams dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ["Chemin de Saint-Antoine, Saint-Cyr-sur-Mer, Toulon, Var, Provence-Alpes-Cote d'Azur, Frane", 'Aerial view of Rond-Point de la 1e Armee Francaise - Lieutenant Paul Meyer, Mulhouse, Haut-Rhin, Grand Est, France metropolitaine, 68100, France', '31, Rue Molière, SS ace Coeur, Pyramides, La Roche-sur-Yon, Vendee, Pays de la Loire, France metropolitaine, 85000, France', 'Aerial view of Mourenx, Pau, Pyrenees-Atlantiques, Nouvelle-Aquitaine, France metropolitaine, 64150, France', '17 rue du moutier, Angousrine-Vileneuve-Les-Escaldes, Pyrenees Orientales, Occitanie, France metropolitaine, 66760, France']:
Pipeline usage
You can use the pipeline like so:
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("rgres/Seg2Map-finetuned", torch_dtype=torch.float16)
prompt = "Chemin de Saint-Antoine, Saint-Cyr-sur-Mer, Toulon, Var, Provence-Alpes-Cote d'Azur, Frane"
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
More information on all the CLI arguments and the environment are available on your wandb run page.