MAM-E weights
Collection
This collection contains the weights of several experiments of the MAM-E project • 6 items • Updated
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("Likalto4/Breast_unconditional_64", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]This model is a diffusion model for unconditional image generation of mammograms of size 64x64 pixels. The model was trained with 1000 images using the DDPM architecture. The model was trained for 50 epochs with a batch size of 64, using around 11 GB of GPU memory.
Usage
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained({hub_model_id})
image = pipeline().images[0]
image
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