Instructions to use LOLML/first_flux_tune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use LOLML/first_flux_tune 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("LOLML/first_flux_tune") prompt = "I love prompts" 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("LOLML/first_flux_tune")
prompt = "I love prompts"
image = pipe(prompt).images[0]first_flux_tune

- Prompt
- I love prompts
- Negative Prompt
- I don't love prompts
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 LOLML/first_flux_tune
Base model
black-forest-labs/FLUX.1-dev