Instructions to use jahanking/forecaseter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jahanking/forecaseter 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("jahanking/forecaseter") prompt = "-" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Jk Hydrid inventory model

- Prompt
- -
Trigger words
You should use arima to trigger the image generation.
You should use inventory to trigger the image generation.
You should use text classification to trigger the image generation.
Download model
Weights for this model are available in PyTorch format.
Download them in the Files & versions tab.
- Downloads last month
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Model tree for jahanking/forecaseter
Base model
black-forest-labs/FLUX.1-dev