Instructions to use ponpoke/flux2-klein-9b-uncensored-text-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ponpoke/flux2-klein-9b-uncensored-text-encoder with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf ponpoke/flux2-klein-9b-uncensored-text-encoder:Q4_K_M # Run inference directly in the terminal: llama cli -hf ponpoke/flux2-klein-9b-uncensored-text-encoder:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ponpoke/flux2-klein-9b-uncensored-text-encoder:Q4_K_M # Run inference directly in the terminal: llama cli -hf ponpoke/flux2-klein-9b-uncensored-text-encoder:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf ponpoke/flux2-klein-9b-uncensored-text-encoder:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf ponpoke/flux2-klein-9b-uncensored-text-encoder:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf ponpoke/flux2-klein-9b-uncensored-text-encoder:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf ponpoke/flux2-klein-9b-uncensored-text-encoder:Q4_K_M
Use Docker
docker model run hf.co/ponpoke/flux2-klein-9b-uncensored-text-encoder:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use ponpoke/flux2-klein-9b-uncensored-text-encoder with Ollama:
ollama run hf.co/ponpoke/flux2-klein-9b-uncensored-text-encoder:Q4_K_M
- Unsloth Studio
How to use ponpoke/flux2-klein-9b-uncensored-text-encoder with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ponpoke/flux2-klein-9b-uncensored-text-encoder to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ponpoke/flux2-klein-9b-uncensored-text-encoder to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ponpoke/flux2-klein-9b-uncensored-text-encoder to start chatting
- Pi
How to use ponpoke/flux2-klein-9b-uncensored-text-encoder with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ponpoke/flux2-klein-9b-uncensored-text-encoder:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "ponpoke/flux2-klein-9b-uncensored-text-encoder:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use ponpoke/flux2-klein-9b-uncensored-text-encoder with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ponpoke/flux2-klein-9b-uncensored-text-encoder:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "ponpoke/flux2-klein-9b-uncensored-text-encoder:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use ponpoke/flux2-klein-9b-uncensored-text-encoder with Docker Model Runner:
docker model run hf.co/ponpoke/flux2-klein-9b-uncensored-text-encoder:Q4_K_M
- Lemonade
How to use ponpoke/flux2-klein-9b-uncensored-text-encoder with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ponpoke/flux2-klein-9b-uncensored-text-encoder:Q4_K_M
Run and chat with the model
lemonade run user.flux2-klein-9b-uncensored-text-encoder-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use ponpoke/flux2-klein-9b-uncensored-text-encoder with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ponpoke/flux2-klein-9b-uncensored-text-encoder:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default ponpoke/flux2-klein-9b-uncensored-text-encoder:Q4_K_M
Run Hermes
hermes
- Atomic Chat
Does this work?
Can anyone share if this works, and if does, how to set it up in ConfyUI along with flux2-klein-9b. I could not make it work with standard CLIP loader, makes no difference what so ever.
Thx
The standard Flux2 Klein-9b isn't trained on much besides the intended safe content, you still need to find one that is trained on it or use a LORA
The standard Flux2 Klein-9b isn't trained on much besides the intended safe content, you still need to find one that is trained on it or use a LORA
So whats the point of this? If you have to use a lora then just use the lora and dont need this right?
The text encoder and the LoRA solve two different problems.
Uncensored text encoder = removes the blocked communication path
LoRA = adds the missing visual knowledge to the DiT
Without the uncensored text encoder, the prompt conditioning may still be distorted or blocked before reaching the DiT. Without the LoRA, the DiT cannot draw concepts it was never trained on. They are complementary, not redundant.
Makes sense! Thank you.
What you said is exactly my thinking before but since I've never used an uncensored text encoder but all the nsfw loras still work, never thought an uncensored text encoder would do anything.
any idea of setting up workflow on ComfyUI to specifically generate the realistic and NSFW content.
Mine outputs look like AI-ish style and incomplete ( like not really following promts )of picture with both Normal and NSFW prompts.
Here is the current workflow btw. for both image generation and editing
Thank you in advance.
you just use the comfyui templates for the model. They do the kinda complicated conditioning path for you. And if you need to add a lora, which you will need) you put it a load lora node or more between diffusion model loader and whatever comes next.
But as said above that text encoder will enable very little if anything by itself.
Talking of actual uncensored. try Chroma1hd, it's a classic. Which is Flux1 based. But a very promising successor is the fairly new Kroma0.1 lora for Krea 2. (same basic thing from the same guy who made Chroma). It completely uncensors Krea2 (without needing a uncensored clip) - except for prompt enhancing.

