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
| 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 | |
| ``` | |