Instructions to use shauray/FLUX-UNCENSORED-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use shauray/FLUX-UNCENSORED-merged with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("shauray/FLUX-UNCENSORED-merged", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
flux-uncensored
Summary
Flux base model merged with uncensored LoRA. This model is not for those looking for "safe" or watered-down outputs. It’s optimized for real-world use with fewer constraints.
Specs
- Model: Flux base
- LoRA: Uncensored version, merged directly
Usage
pretty straight forward plug-and-play model
from diffusers import FluxPipeline
import torch
pipe = FluxPipeline.from_pretrained("shauray/FLUX-UNCENSORED-merged", torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()
prompt = "A mystic cat with a sign that says hello world!"
image = pipe(prompt, guidance_scale=3.5, num_inference_steps=28, generator=torch.manual_seed(0)).images[0]
image.save("flux-dev-loaded.png")
this README has what you'd need, it's a merge from Uncensored LoRA on CivitAI
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Model tree for shauray/FLUX-UNCENSORED-merged
Base model
black-forest-labs/FLUX.1-dev