Instructions to use compaq/PoojaHegde with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use compaq/PoojaHegde 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("compaq/PoojaHegde") prompt = "UNICODE\u0000\u0000c\u0000o\u0000w\u0000b\u0000o\u0000y\u0000 \u0000s\u0000h\u0000o\u0000t\u0000 \u0000p\u0000h\u0000o\u0000t\u0000o\u0000 \u0000o\u0000f\u0000 \u0000P\u0000o\u0000o\u0000j\u0000a\u0000 \u0000H\u0000e\u0000g\u0000d\u0000e\u0000 \u0000w\u0000o\u0000m\u0000a\u0000n\u0000,\u0000c\u0000a\u0000n\u0000d\u0000i\u0000d\u0000 \u0000p\u0000h\u0000o\u0000t\u0000o\u0000 \u0000w\u0000i\u0000t\u0000h\u0000 \u0000n\u0000a\u0000t\u0000u\u0000r\u0000a\u0000l\u0000 \u0000c\u0000o\u0000l\u0000o\u0000r\u0000s\u0000,\u0000 \u0000g\u0000r\u0000i\u0000n\u0000n\u0000i\u0000n\u0000g\u0000 \u0000e\u0000x\u0000p\u0000r\u0000e\u0000s\u0000s\u0000i\u0000o\u0000n\u0000 \u0000o\u0000n\u0000 \u0000f\u0000a\u0000c\u0000e\u0000,\u0000s\u0000t\u0000u\u0000d\u0000i\u0000o\u0000 \u0000q\u0000u\u0000a\u0000l\u0000i\u0000t\u0000y\u0000,\u0000 \u0000w\u0000e\u0000a\u0000r\u0000i\u0000n\u0000g\u0000 \u0000i\u0000n\u0000t\u0000r\u0000i\u0000c\u0000a\u0000t\u0000e\u0000 \u0000e\u0000l\u0000e\u0000g\u0000a\u0000n\u0000t\u0000 \u0000s\u0000l\u0000e\u0000e\u0000v\u0000e\u0000l\u0000e\u0000s\u0000s\u0000 \u0000O\u0000l\u0000i\u0000v\u0000e\u0000 \u0000S\u0000h\u0000a\u0000r\u0000a\u0000r\u0000a\u0000 \u0000S\u0000u\u0000i\u0000t\u0000,\u0000 \u0000s\u0000t\u0000r\u0000a\u0000i\u0000g\u0000h\u0000t\u0000 \u0000h\u0000a\u0000i\u0000r\u0000,\u0000 \u0000p\u0000a\u0000s\u0000t\u0000e\u0000l\u0000 \u0000s\u0000h\u0000a\u0000d\u0000e\u0000d\u0000 \u0000m\u0000u\u0000l\u0000t\u0000i\u0000c\u0000o\u0000l\u0000o\u0000r\u0000e\u0000d\u0000 \u0000b\u0000a\u0000c\u0000k\u0000g\u0000r\u0000o\u0000u\u0000n\u0000d\u0000,\u0000 \u0000c\u0000i\u0000n\u0000e\u0000m\u0000a\u0000t\u0000i\u0000c\u0000 \u0000s\u0000o\u0000f\u0000t\u0000 \u0000l\u0000i\u0000g\u0000h\u0000t\u0000i\u0000n\u0000g\u0000<\u0000l\u0000o\u0000r\u0000a\u0000:\u0000T\u0000e\u0000s\u0000t\u0000B\u0000e\u0000d\u0000\\\u0000P\u0000o\u0000o\u0000j\u0000a\u0000_\u0000H\u0000e\u0000g\u0000d\u0000e\u0000_\u0000F\u0000l\u0000u\u0000x\u0000_\u0000K\u0000o\u0000h\u0000y\u0000a\u0000_\u0000V\u00001\u0000-\u00000\u00000\u00000\u00000\u00000\u00005\u0000.\u0000s\u0000a\u0000f\u0000e\u0000t\u0000e\u0000n\u0000s\u0000o\u0000r\u0000s\u0000:\u00001\u0000.\u00000\u0000:\u00001\u0000.\u00000\u0000>\u0000" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Pooja_Hegde_Flux_Kohya_V1-000005.safetensors
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