Instructions to use pimentooliver/fungi-sd-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use pimentooliver/fungi-sd-diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("pimentooliver/fungi-sd-diffusion", 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
metadata
datasets:
- pimentooliver/fungi
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
This is a fine-tune of CompVis/stable-diffusion-v1-4. It has been fine tuned on a dataset of fungi imagery which has been clustered to represent 'species'. Each 'species' has been assigned a generated name in an attempt to fine-tune the model on nonexistent fungal species. Unfortunately, this model has been impacted by catastrophic forgetting. It will be retrained soon, upload only for academic use.