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
metadata
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 🧨
This model is a diffusion model for unconditional image generation of cute 🦋.
Usage
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('shellypeng/animever10-god-model')
image = pipeline().images[0]
image