Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
dreambooth
Instructions to use imann63/trained_models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use imann63/trained_models with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("imann63/trained_models", dtype=torch.bfloat16, device_map="cuda") prompt = "photo of a special powerline station" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| license: creativeml-openrail-m | |
| base_model: models/diffuser | |
| instance_prompt: photo of a special powerline station | |
| tags: | |
| - stable-diffusion | |
| - stable-diffusion-diffusers | |
| - text-to-image | |
| - diffusers | |
| - dreambooth | |
| inference: true | |
| # DreamBooth - imann63/trained_models | |
| This is a dreambooth model derived from models/diffuser. The weights were trained on photo of a special powerline station using [DreamBooth](https://dreambooth.github.io/). | |
| You can find some example images in the following. | |
| DreamBooth for the text encoder was enabled: False. | |