Instructions to use kellempxt/checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kellempxt/checkpoints with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kellempxt/checkpoints", 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 Settings
- Draw Things
- DiffusionBee
File size: 583 Bytes
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license: mit
tags:
- stable-diffusion
- text-to-image
- checkpoint
library_name: diffusers
pipeline_tag: text-to-image
---
# Checkpoints Collection
A collection of Stable Diffusion checkpoints organized by base model type.
## Structure
- `pony/` - Pony Diffusion models
- `sdxl/` - SDXL models
- `flux/` - Flux models
- `sd15/` - SD 1.5 models
## Usage
```python
from diffusers import StableDiffusionXLPipeline
pipe = StableDiffusionXLPipeline.from_single_file(
"https://huggingface.co/kellempxt/checkpoints/resolve/main/sdxl/model_name/model.safetensors"
)
```
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