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 Settings
- Draw Things
- DiffusionBee
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README.md
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output:
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url: https://replicate.delivery/yhqm/m1hiqJeAZdV1QafFjOqyKrx8xeajLqG0qWbHUw36zkX0meLNB/out-0.webp
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26 images of Queso (the dog) LoRA trained for 500 steps
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output:
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url: https://replicate.delivery/yhqm/m1hiqJeAZdV1QafFjOqyKrx8xeajLqG0qWbHUw36zkX0meLNB/out-0.webp
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# Flux Mona Lisa
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<Gallery />
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Trained on Replicate using:
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https://replicate.com/ostris/flux-dev-lora-trainer/train
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## Trigger words
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You should use `TOK` to trigger the image generation.
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## Training Details
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26 images of Queso (the dog) LoRA trained for 500 steps
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