Instructions to use Squiddy3/JohnMcGahon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Squiddy3/JohnMcGahon 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/JohnMcGahon") prompt = "John McGahon" 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/JohnMcGahon")
prompt = "John McGahon"
image = pipe(prompt).images[0]Flux

- Prompt
- John McGahon
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/JohnMcGahon
Base model
black-forest-labs/FLUX.1-dev