Instructions to use C0deC/flux2-binpacking-lora_10K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use C0deC/flux2-binpacking-lora_10K 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.2-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("C0deC/flux2-binpacking-lora_10K") prompt = "bin packing diagram" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 0930fb682e99873d40d1b3336adf0ec1d4e6a0822ac2dd9fcfd8226a5e6ad099
- Size of remote file:
- 1.4 kB
- SHA256:
- 0ea39d5520f32c49588c091b92e3f737396699c2fa98cf95f99ff6f3f1f88983
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