Instructions to use ChandrilBasu/Wagh2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ChandrilBasu/Wagh2 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("ChandrilBasu/Wagh2") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Sharvari_Wagh_2024_Flux_Kohya_LoRA_v1.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:80daf368d0d47eac777c3515298bf9583f69588f0502755b2a7da09c808e3ac0
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size 33670524
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images/11d.png
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Git LFS Details
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