Instructions to use kythours/kitou with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kythours/kitou 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("kythours/kitou") prompt = "hwxjos man walks down a quiet alley, shadows stretching behind him." image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
kitou
A Flux LoRA trained on a local computer with Fluxgym

- Prompt
- hwxjos man walks down a quiet alley, shadows stretching behind him.

- Prompt
- hwxjos man ties his boots as the morning light fills the room.

- Prompt
- hwxjos man smokes alone on a balcony overlooking the city.

- Prompt
- hwxjos man lifts a backpack and steps onto the train.
Trigger words
You should use owxjos 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.
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Model tree for kythours/kitou
Base model
black-forest-labs/FLUX.1-dev