Text-to-Image
Diffusers
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
dreambooth
Instructions to use anic87/pcam-tumor-text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use anic87/pcam-tumor-text with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("anic87/pcam-tumor-text", dtype=torch.bfloat16, device_map="cuda") prompt = "a photo of sks tumor-pathology-tissue" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
metadata
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of sks tumor-pathology-tissue
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
DreamBooth - anic87/pcam-tumor-text
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks tumor-pathology-tissue using DreamBooth. You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.



