Instructions to use Simbadob/abundayoo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Simbadob/abundayoo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("undefined", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Simbadob/abundayoo") prompt = "Momo" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from Simbadob/abundayoo: direct link, hf CLI and curl.
- Browser
- Download file 569 Bytes
-
https://huggingface.co/Simbadob/abundayoo/resolve/main/README.md
- Command line
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hf download hf://Simbadob/abundayoo/README.md
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curl -L -o README.md https://huggingface.co/Simbadob/abundayoo/resolve/main/README.md
569 Bytes
metadata
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: undefined
instance_prompt: Momo
license: other
abundayoo
Model description
Abundayoo
Trigger words
You should use Momo to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
Training at fal.ai
Training was done using fal.ai/models/fal-ai/flux-lora-portrait-trainer.