Instructions to use paconaranjo/inah with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use paconaranjo/inah 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("paconaranjo/inah") prompt = "inah" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from paconaranjo/inah: direct link, hf CLI and curl.
- Browser
- Download file 807 Bytes
-
https://huggingface.co/paconaranjo/inah/resolve/main/README.md
- Command line
-
hf download hf://paconaranjo/inah/README.md
-
curl -L -o README.md https://huggingface.co/paconaranjo/inah/resolve/main/README.md
807 Bytes
metadata
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
widget:
- output:
url: sample/inah_001000_00_20241123045700.png
text: inah
- text: inah dragon under water
output:
url: images/example_f9yu0q60n.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: inah
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
inah
A Flux LoRA trained on a local computer with Fluxgym

- Prompt
- inah

- Prompt
- inah dragon under water
Trigger words
You should use inah to trigger the image generation.
Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.