Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
dreambooth
Instructions to use oliverN/path-to-save-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use oliverN/path-to-save-model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("oliverN/path-to-save-model", dtype=torch.bfloat16, device_map="cuda") prompt = "a photo of sks woman" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("oliverN/path-to-save-model", dtype=torch.bfloat16, device_map="cuda")
prompt = "a photo of sks woman"
image = pipe(prompt).images[0]DreamBooth - oliverN/path-to-save-model
This is a dreambooth model derived from stabilityai/stable-diffusion-2. The weights were trained on a photo of sks woman using DreamBooth. You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
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Model tree for oliverN/path-to-save-model
Base model
stabilityai/stable-diffusion-2