wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp

Instructions to use desirel/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use desirel/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="desirel/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp",
	filename="Wan2.2-TI2V-5B-Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use desirel/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp 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 desirel/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K
# Run inference directly in the terminal:
llama cli -hf desirel/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf desirel/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K
# Run inference directly in the terminal:
llama cli -hf desirel/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K

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 desirel/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf desirel/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K

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 desirel/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf desirel/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K

Use Docker

docker model run hf.co/desirel/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K

LM Studio
Jan
Ollama
How to use desirel/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp with Ollama:
```
ollama run hf.co/desirel/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K
```

Unsloth Studio

How to use desirel/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp 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 desirel/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp 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 desirel/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for desirel/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp to start chatting

Atomic Chat new
Docker Model Runner
How to use desirel/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp with Docker Model Runner:
```
docker model run hf.co/desirel/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K
```

Lemonade

How to use desirel/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull desirel/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K

Run and chat with the model

lemonade run user.wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp-Q2_K

List all available models

lemonade list

wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp / stable-diffusion-cpp-nvidia /thirdparty /CMakeFiles /zip.dir /cmake_clean.cmake

desirel

Duplicate from isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp

892cf79 10 days ago

Raw

History Blame Contribute Delete

234 Bytes

	file(REMOVE_RECURSE
	"CMakeFiles/zip.dir/zip.c.o"
	"CMakeFiles/zip.dir/zip.c.o.d"
	)

	# Per-language clean rules from dependency scanning.
	foreach(lang C)
	include(CMakeFiles/zip.dir/cmake_clean_${lang}.cmake OPTIONAL)
	endforeach()