Instructions to use kythours/hwxo1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kythours/hwxo1 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/hwxo1") prompt = "hwxo1 is reading under a tree" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
hwxo
A Flux LoRA trained on a local computer with Fluxgym

- Prompt
- hwxo1 is reading under a tree

- Prompt
- hwxo1 is running on the beach

- Prompt
- hwxo1 is cooking in the kitchen

- Prompt
- hwxo1 is painting on a canvas

- Prompt
- hwxo1 is biking in the forest

- Prompt
- hwxo1 is playing guitar on a rooftop

- Prompt
- hwxo1 is dancing in the rain

- Prompt
- hwxo1 is feeding pigeons in a square

- Prompt
- hwxo1 is fishing by the lake

- Prompt
- hwxo1 is writing at a café
Trigger words
You should use hwxo1 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/hwxo1
Base model
black-forest-labs/FLUX.1-dev