Instructions to use Squiddy3/PearseDoherty with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Squiddy3/PearseDoherty with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Squiddy3/PearseDoherty") prompt = "Pearse Doherty" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("Squiddy3/PearseDoherty")
prompt = "Pearse Doherty"
image = pipe(prompt).images[0]Flux

- Prompt
- Pearse Doherty
Model description
Pearse Doherty
Trigger words
You should use Pearse Doherty to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
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Model tree for Squiddy3/PearseDoherty
Base model
black-forest-labs/FLUX.1-dev