Instructions to use isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use isfs/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="isfs/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
- llama.cpp
How to use isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K # Run inference directly in the terminal: llama-cli -hf isfs/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-server -hf isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K # Run inference directly in the terminal: llama-cli -hf isfs/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 isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K # Run inference directly in the terminal: ./llama-cli -hf isfs/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 isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K
Use Docker
docker model run hf.co/isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K
- LM Studio
- Jan
- Ollama
How to use isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp with Ollama:
ollama run hf.co/isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K
- Unsloth Studio new
How to use isfs/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 isfs/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 isfs/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 isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp to start chatting
- Docker Model Runner
How to use isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp with Docker Model Runner:
docker model run hf.co/isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp:Q2_K
- Lemonade
How to use isfs/wan-2.2-5b-ti2v-gguf-stable-diffusion-cpp with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull isfs/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
File size: 2,168 Bytes
a1663c1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | # Install script for directory: /kaggle/working/stable-diffusion.cpp/ggml/src
# Set the install prefix
if(NOT DEFINED CMAKE_INSTALL_PREFIX)
set(CMAKE_INSTALL_PREFIX "/usr/local")
endif()
string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}")
# Set the install configuration name.
if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME)
if(BUILD_TYPE)
string(REGEX REPLACE "^[^A-Za-z0-9_]+" ""
CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}")
else()
set(CMAKE_INSTALL_CONFIG_NAME "Release")
endif()
message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"")
endif()
# Set the component getting installed.
if(NOT CMAKE_INSTALL_COMPONENT)
if(COMPONENT)
message(STATUS "Install component: \"${COMPONENT}\"")
set(CMAKE_INSTALL_COMPONENT "${COMPONENT}")
else()
set(CMAKE_INSTALL_COMPONENT)
endif()
endif()
# Install shared libraries without execute permission?
if(NOT DEFINED CMAKE_INSTALL_SO_NO_EXE)
set(CMAKE_INSTALL_SO_NO_EXE "1")
endif()
# Is this installation the result of a crosscompile?
if(NOT DEFINED CMAKE_CROSSCOMPILING)
set(CMAKE_CROSSCOMPILING "FALSE")
endif()
# Set path to fallback-tool for dependency-resolution.
if(NOT DEFINED CMAKE_OBJDUMP)
set(CMAKE_OBJDUMP "/usr/bin/objdump")
endif()
if(NOT CMAKE_INSTALL_LOCAL_ONLY)
# Include the install script for the subdirectory.
include("/kaggle/working/stable-diffusion.cpp/build/ggml/src/ggml-cpu/cmake_install.cmake")
endif()
if(CMAKE_INSTALL_COMPONENT STREQUAL "Unspecified" OR NOT CMAKE_INSTALL_COMPONENT)
file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/lib" TYPE STATIC_LIBRARY FILES "/kaggle/working/stable-diffusion.cpp/build/ggml/src/libggml-cpu.a")
endif()
if(NOT CMAKE_INSTALL_LOCAL_ONLY)
# Include the install script for the subdirectory.
include("/kaggle/working/stable-diffusion.cpp/build/ggml/src/ggml-cuda/cmake_install.cmake")
endif()
string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT
"${CMAKE_INSTALL_MANIFEST_FILES}")
if(CMAKE_INSTALL_LOCAL_ONLY)
file(WRITE "/kaggle/working/stable-diffusion.cpp/build/ggml/src/install_local_manifest.txt"
"${CMAKE_INSTALL_MANIFEST_CONTENT}")
endif()
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