import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("ridzy619/pothole-unconditional-generation", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Unconditional Pothole Images Generation
Generating New Images
from diffusers import DDPMPipeline
import torch
from PIL import Image
pipe = DDPMPipeline.from_pretrained("ridzy619/pothole-unconditional-generation")
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe.to(device)
images = pipe(batch_size=16).images
def make_grid(images, rows, cols):
w, h = images[0].size
grid = Image.new('RGB', size=(cols*w, rows*h))
for i, image in enumerate(images):
grid.paste(image, box=(i%cols*w, i//cols*h))
return grid
images_grid = make_grid(images, 4, 4)
images_grid
- Downloads last month
- -
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support