Instructions to use glif-loradex-trainer/kklors_flux_dev_data_moshing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use glif-loradex-trainer/kklors_flux_dev_data_moshing 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("glif-loradex-trainer/kklors_flux_dev_data_moshing") prompt = "cars on a street in a city MOSH" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
flux_dev_data_moshing
Model trained with AI Toolkit by Ostris under the Glif Loradex program by Glif user kklors.

- Prompt
- cars on a street in a city MOSH

- Prompt
- red mercedesMOSH

- Prompt
- colorful patterns MOSH

- Prompt
- close up of a nike sneaker, shoes MOSH

- Prompt
- young girl in a red sweater MOSH

- Prompt
- crowded restaurant MOSH
Trigger words
You should use MOSH to trigger the image generation.
Download model
Weights for this model are available in Safetensors format. Download them in the Files & versions tab.
License
This model is licensed under the flux-1-dev-non-commercial-license.
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Model tree for glif-loradex-trainer/kklors_flux_dev_data_moshing
Base model
black-forest-labs/FLUX.1-dev