Text-to-Image
Diffusers
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
dreambooth
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("msh1031/wafer-thnn-complex-prompts", dtype=torch.bfloat16, device_map="cuda")
prompt = "a pha photo of image of wafer defect captured through a Scanning Electron Microscope, a defect should be repeatedly formed in a single or multiple lines in the shape of a horse's hoop"
image = pipe(prompt).images[0]DreamBooth - msh1031/wafer-thnn-complex-prompts
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a pha photo of image of wafer defect captured through a Scanning Electron Microscope, a defect should be repeatedly formed in a single or multiple lines in the shape of a horse's hoop using DreamBooth. You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
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CompVis/stable-diffusion-v1-4