Instructions to use lucataco/flux-queso with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lucataco/flux-queso 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("lucataco/flux-queso") prompt = "a portrait photo of TOK" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Flux Mona Lisa

- Prompt
- a portrait photo of TOK

- Prompt
- a portrait photo of TOK
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
Trigger words
You should use TOK to trigger the image generation.
Training Details
26 images of Queso (the dog) LoRA trained for 500 steps
prompt: "a photo of TOK"
- Downloads last month
- 6
Model tree for lucataco/flux-queso
Base model
black-forest-labs/FLUX.1-dev