Text-to-Image
Diffusers
Safetensors
PyTorch
StableDiffusionPipeline
unconditional-image-generation
diffusion-models-class
Instructions to use shellypeng/model_am with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use shellypeng/model_am with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("shellypeng/model_am", 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
- Local Apps
- Draw Things
- DiffusionBee
| license: mit | |
| tags: | |
| - pytorch | |
| - diffusers | |
| - unconditional-image-generation | |
| - diffusion-models-class | |
| pipeline_tag: text-to-image | |
| inference: true | |
| # 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('shellypeng/animever10-god-model') | |
| image = pipeline().images[0] | |
| image | |
| ``` | |