Instructions to use LHRuig/enzobloomsx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use LHRuig/enzobloomsx 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("LHRuig/enzobloomsx") prompt = "suit" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- 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("LHRuig/enzobloomsx")
prompt = "suit"
image = pipe(prompt).images[0]enzobloomsx

- Prompt
- suit
Model description
enzobloomsx lora
Trigger words
You should use enzobloomsx 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 LHRuig/enzobloomsx
Base model
black-forest-labs/FLUX.1-dev