Instructions to use teohyc/my_first_diffusion_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use teohyc/my_first_diffusion_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("teohyc/my_first_diffusion_model", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| tags: | |
| - pytorch | |
| - diffusers | |
| - unconditional-image-generation | |
| - diffusion-models-class | |
| # Model Card for Unit 1 of the [Diffusion Models Class](https://github.com/huggingface/diffusion-models-class) | |
| This model is a diffusion model for unconditional image generation of cute . | |
| ## Usage | |
| ```python | |
| from diffusers import DDPMPipeline | |
| pipeline = DDPMPipeline.from_pretrained('teohyc/my_first_diffusion_model') | |
| image = pipeline().images[0] | |
| image |