Instructions to use W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows 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 W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows: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 W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows: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 W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows:Q4_K_M

Use Docker

docker model run hf.co/W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows:Q4_K_M

LM Studio
Jan
Ollama
How to use W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows with Ollama:
```
ollama run hf.co/W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows:Q4_K_M
```

Unsloth Studio

How to use W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows 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 W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows 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 W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows to start chatting

How to use W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows: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": "W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows with Docker Model Runner:
```
docker model run hf.co/W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows:Q4_K_M
```

Lemonade

How to use W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows:Q4_K_M

Run and chat with the model

lemonade run user.Krea2_Turbo_LowVram_AllFiles_WorkFlows-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows: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 W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows: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 "W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows: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"

Krea2 Turbo LowVram — All Files & Workflows

Personal collection of models and supporting files to run Krea2 Turbo (Int8) on ComfyUI on GPUs with limited VRAM (tested on 15GB).

Files

File	Type	ComfyUI folder
`krea2_turbo_int8_convrot.safetensors`	Main diffusion model (Int8)	`models/diffusion_models`
`qwen3vl-4b-q4_k_m.gguf`	Text encoder (Qwen3-VL, GGUF format)	`models/text_encoders`
`qwen3vl_4b_fp8_scaled.safetensors`	Text encoder (Qwen3-VL, fp8 format)	`models/text_encoders`
`qwen_image_vae.safetensors`	VAE	`models/vae`
`krea2_darkbrush.safetensors`	LoRA	`models/loras`

Note: the two text encoders (gguf and fp8) are alternatives — you typically only need to download one of them, depending on the loader node used (GGUF loader vs standard loader).

Workflows

Workflow	Description
image_krea2_turbo_t2i WORK_Tested.json	Standard text-to-image generation with Krea2 Turbo.
image_krea2_turbo_t2i_int8.json	Text-to-image generation using the Int8-quantized model — lower VRAM usage
image_krea2_turbo_int8_image_style_reference.json	Int8 pipeline with style reference conditioning — upload one or more reference images to guide the aesthetic/style of the output.

To use: download the JSON file and drag it directly onto the ComfyUI canvas, or load it via Workflow → Open.

Requirements

ComfyUI
Custom nodes:

from huggingface_hub import hf_hub_download

hf_hub_download(
    repo_id="W-Zil/Krea2_Turbo_LowVram_AllFiles_WorkFlows",
    filename="FILE_NAME",
    local_dir="/path/to/destination",
    token="YOUR_TOKEN",
)

Status

✅ Tested and working — ComfyUI + custom nodes + models

Downloads last month: 5

GGUF

Model size

4B params

Architecture

qwen3vl

Hardware compatibility

4-bit

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