Instructions to use Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF",
	filename="Qwen2.5-VL-7B-Instruct-abliterated/Qwen2.5-VL-7B-Instruct-abliterated.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "\"Astronaut riding a horse\""
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF: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 Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF: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 Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF with Ollama:
```
ollama run hf.co/Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF:Q4_K_M
```

Unsloth Studio new

How to use Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF 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 Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF 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 Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF to start chatting

Pi new

How to use Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF: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": "Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF: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 Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF with Docker Model Runner:
```
docker model run hf.co/Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF:Q4_K_M
```

Lemonade

How to use Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen-Image-Edit-Rapid-AIO-GGUF-Q4_K_M

List all available models

lemonade list

Recommended Settings for Creating New Scenes Using a Face Reference

#29

by GRTSTUDIO - opened Jan 4

Discussion

GRTSTUDIO

Jan 4

If you are generating new scenes using an existing face, these are the best-performing
settings I’ve found after 100+ renders:

Model:

v9 Q6_K

Sampler:

LCM (beta)

Generation Settings:

CFG Scale: 1
Denoising Strength: 1
Steps: 5
Seed: Randomized

CLIP & VAE:

CLIP Loader: Q8_K_XL
VAE: Wan2.1.VAE_upscale_2x

VAE Utils / Decode Settings:

Upscale: -1
Tiling: Disabled
- Tile Size: 512
- Tile Overlap: 256
Temporal Size: 8
Temporal Overlap: 4

Image Scaling:

Image Scale Down (node): 0.50

Notes:

These settings consistently produced the highest-quality results in my tests.
Prompt quality still matters a lot. Using ChatGPT or similar tools for prompt
refinement is recommended.

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