Instructions to use rorito/AmateurPhotography with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rorito/AmateurPhotography 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("rorito/AmateurPhotography") prompt = "UNICODE\u0000\u0000I\u0000n\u0000 \u0000a\u0000 \u0000w\u0000i\u0000d\u0000e\u0000 \u0000s\u0000h\u0000o\u0000t\u0000 \u0000o\u0000f\u0000 \u0000a\u0000 \u0000n\u0000i\u0000g\u0000h\u0000t\u0000 \u0000c\u0000l\u0000u\u0000b\u0000,\u0000 \u0000a\u0000 \u0000m\u0000a\u0000n\u0000 \u0000d\u0000r\u0000e\u0000s\u0000s\u0000e\u0000d\u0000 \u0000a\u0000s\u0000 \u0000t\u0000h\u0000e\u0000 \u0000g\u0000r\u0000u\u0000m\u0000p\u0000y\u0000 \u0000c\u0000h\u0000a\u0000r\u0000a\u0000c\u0000t\u0000e\u0000r\u0000 \u0000r\u0000e\u0000s\u0000e\u0000m\u0000b\u0000l\u0000i\u0000n\u0000g\u0000 \u0000S\u0000p\u0000o\u0000n\u0000g\u0000e\u0000B\u0000o\u0000b\u0000 \u0000i\u0000s\u0000 \u0000h\u0000o\u0000l\u0000d\u0000i\u0000n\u0000g\u0000 \u0000a\u0000 \u0000b\u0000a\u0000l\u0000l\u0000o\u0000o\u0000n\u0000.\u0000 \u0000A\u0000b\u0000o\u0000v\u0000e\u0000 \u0000h\u0000i\u0000m\u0000,\u0000 \u0000a\u0000 \u0000b\u0000a\u0000n\u0000n\u0000e\u0000r\u0000 \u0000r\u0000e\u0000a\u0000d\u0000s\u0000:\u0000 \u001c\u0000T\u0000H\u0000I\u0000S\u0000 \u0000I\u0000S\u0000 \u0000F\u0000I\u0000N\u0000E\u0000.\u0000 \u0000E\u0000V\u0000E\u0000R\u0000Y\u0000T\u0000H\u0000I\u0000N\u0000G\u0000 \u0000I\u0000S\u0000 \u0000F\u0000I\u0000N\u0000E\u0000. \u001d\u0000 \u0000P\u0000e\u0000o\u0000p\u0000l\u0000e\u0000 \u0000d\u0000r\u0000i\u0000n\u0000k\u0000i\u0000n\u0000g\u0000 \u0000h\u0000a\u0000p\u0000p\u0000i\u0000l\u0000y\u0000 \u0000w\u0000h\u0000i\u0000l\u0000e\u0000 \u0000h\u0000e\u0000 \u0000s\u0000t\u0000a\u0000n\u0000d\u0000s\u0000 \u0000t\u0000h\u0000e\u0000r\u0000e\u0000,\u0000 \u0000c\u0000l\u0000e\u0000a\u0000r\u0000l\u0000y\u0000 \u0000n\u0000o\u0000t\u0000 \u0000h\u0000a\u0000v\u0000i\u0000n\u0000g\u0000 \u0000i\u0000t\u0000 \u0000<\u0000l\u0000o\u0000r\u0000a\u0000:\u0000a\u0000m\u0000a\u0000t\u0000e\u0000u\u0000r\u0000p\u0000h\u0000o\u0000t\u0000o\u0000-\u0000v\u00006\u0000-\u0000f\u0000o\u0000r\u0000c\u0000u\u0000:\u00000\u0000.\u00008\u0000>\u0000" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
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("rorito/AmateurPhotography")
prompt = "UNICODE\u0000\u0000I\u0000n\u0000 \u0000a\u0000 \u0000w\u0000i\u0000d\u0000e\u0000 \u0000s\u0000h\u0000o\u0000t\u0000 \u0000o\u0000f\u0000 \u0000a\u0000 \u0000n\u0000i\u0000g\u0000h\u0000t\u0000 \u0000c\u0000l\u0000u\u0000b\u0000,\u0000 \u0000a\u0000 \u0000m\u0000a\u0000n\u0000 \u0000d\u0000r\u0000e\u0000s\u0000s\u0000e\u0000d\u0000 \u0000a\u0000s\u0000 \u0000t\u0000h\u0000e\u0000 \u0000g\u0000r\u0000u\u0000m\u0000p\u0000y\u0000 \u0000c\u0000h\u0000a\u0000r\u0000a\u0000c\u0000t\u0000e\u0000r\u0000 \u0000r\u0000e\u0000s\u0000e\u0000m\u0000b\u0000l\u0000i\u0000n\u0000g\u0000 \u0000S\u0000p\u0000o\u0000n\u0000g\u0000e\u0000B\u0000o\u0000b\u0000 \u0000i\u0000s\u0000 \u0000h\u0000o\u0000l\u0000d\u0000i\u0000n\u0000g\u0000 \u0000a\u0000 \u0000b\u0000a\u0000l\u0000l\u0000o\u0000o\u0000n\u0000.\u0000 \u0000A\u0000b\u0000o\u0000v\u0000e\u0000 \u0000h\u0000i\u0000m\u0000,\u0000 \u0000a\u0000 \u0000b\u0000a\u0000n\u0000n\u0000e\u0000r\u0000 \u0000r\u0000e\u0000a\u0000d\u0000s\u0000:\u0000 \u001c\u0000T\u0000H\u0000I\u0000S\u0000 \u0000I\u0000S\u0000 \u0000F\u0000I\u0000N\u0000E\u0000.\u0000 \u0000E\u0000V\u0000E\u0000R\u0000Y\u0000T\u0000H\u0000I\u0000N\u0000G\u0000 \u0000I\u0000S\u0000 \u0000F\u0000I\u0000N\u0000E\u0000. \u001d\u0000 \u0000P\u0000e\u0000o\u0000p\u0000l\u0000e\u0000 \u0000d\u0000r\u0000i\u0000n\u0000k\u0000i\u0000n\u0000g\u0000 \u0000h\u0000a\u0000p\u0000p\u0000i\u0000l\u0000y\u0000 \u0000w\u0000h\u0000i\u0000l\u0000e\u0000 \u0000h\u0000e\u0000 \u0000s\u0000t\u0000a\u0000n\u0000d\u0000s\u0000 \u0000t\u0000h\u0000e\u0000r\u0000e\u0000,\u0000 \u0000c\u0000l\u0000e\u0000a\u0000r\u0000l\u0000y\u0000 \u0000n\u0000o\u0000t\u0000 \u0000h\u0000a\u0000v\u0000i\u0000n\u0000g\u0000 \u0000i\u0000t\u0000 \u0000<\u0000l\u0000o\u0000r\u0000a\u0000:\u0000a\u0000m\u0000a\u0000t\u0000e\u0000u\u0000r\u0000p\u0000h\u0000o\u0000t\u0000o\u0000-\u0000v\u00006\u0000-\u0000f\u0000o\u0000r\u0000c\u0000u\u0000:\u00000\u0000.\u00008\u0000>\u0000"
image = pipe(prompt).images[0]AmateurPhotography

- Prompt
- UNICODEIn a wide shot of a night club, a man dressed as the grumpy character resembling SpongeBob is holding a balloon. Above him, a banner reads: THIS IS FINE. EVERYTHING IS FINE. People drinking happily while he stands there, clearly not having it <lora:amateurphoto-v6-forcu:0.8>
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
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