Text-to-Image
Diffusers
TensorBoard
PyTorch
StableDiffusionPipeline
stable-diffusion-v2-1-base
diffusion-models-class
Instructions to use CSAle/DilbertDiffusion2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CSAle/DilbertDiffusion2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CSAle/DilbertDiffusion2", dtype=torch.bfloat16, device_map="cuda") prompt = "dilbert walking his dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
File size: 643 Bytes
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license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion-v2-1-base
- text-to-image
- diffusion-models-class
widget:
- text: dilbert walking his dog
---
# DreamBooth model for the Dilbert concept trained by CSAle on the CSAle/DilbertDiffusionDataset dataset.
This is a Stable Diffusion model fine-tuned on the Dilbert concept. It can be used by modifying the `instance_prompt`: **dilbert**
## Description
A DilbertDiffusion model
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('CSAle/DilbertDiffusion2')
image = pipeline().images[0]
image
```
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