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
Add 2 files
Browse files- hybrid_inventory_model.pt +3 -0
- images/forecast.png +3 -0
hybrid_inventory_model.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:a876cbebe5c70929fa7d6fe065e97a6deeae891b8d9c528c01979a3923a7a53b
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size 2501130
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images/forecast.png
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Git LFS Details
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