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: 3,135 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 | # 1 "CMakeCUDACompilerId.cu"
# 476 "CMakeCUDACompilerId.cu"
extern const char *info_compiler;
extern const char *info_simulate;
# 789 "CMakeCUDACompilerId.cu"
static const char info_version[50];
# 818 "CMakeCUDACompilerId.cu"
static const char info_simulate_version[41];
# 838 "CMakeCUDACompilerId.cu"
extern const char *info_platform;
extern const char *info_arch;
extern const char *info_host_compiler;
static const char info_host_compiler_version[55];
# 881 "CMakeCUDACompilerId.cu"
extern const char *info_language_standard_default;
# 899 "CMakeCUDACompilerId.cu"
extern const char *info_language_extensions_default;
# 476 "CMakeCUDACompilerId.cu"
const char *info_compiler = ((const char *)"INFO:compiler[NVIDIA]");
const char *info_simulate = ((const char *)"INFO:simulate[GNU]");
# 789 "CMakeCUDACompilerId.cu"
static const char info_version[50] = {((char)73),((char)78),((char)70),((char)79),((char)58),((char)99),((char)111),((char)109),((char)112),((char)105),((char)108),((char)101),((char)114),((char)95),((char)118),((char)101),((char)114),((char)115),((char)105),((char)111),((char)110),((char)91),((char)48),((char)48),((char)48),((char)48),((char)48),((char)48),((char)49),((char)50),((char)46),((char)48),((char)48),((char)48),((char)48),((char)48),((char)48),((char)48),((char)53),((char)46),((char)48),((char)48),((char)48),((char)48),((char)48),((char)48),((char)56),((char)50),((char)93),((char)0)};
# 818 "CMakeCUDACompilerId.cu"
static const char info_simulate_version[41] = {((char)73),((char)78),((char)70),((char)79),((char)58),((char)115),((char)105),((char)109),((char)117),((char)108),((char)97),((char)116),((char)101),((char)95),((char)118),((char)101),((char)114),((char)115),((char)105),((char)111),((char)110),((char)91),((char)48),((char)48),((char)48),((char)48),((char)48),((char)48),((char)49),((char)49),((char)46),((char)48),((char)48),((char)48),((char)48),((char)48),((char)48),((char)48),((char)52),((char)93),((char)0)};
# 838 "CMakeCUDACompilerId.cu"
const char *info_platform = ((const char *)"INFO:platform[Linux]");
const char *info_arch = ((const char *)"INFO:arch[]");
const char *info_host_compiler = ((const char *)"INFO:host_compiler[GNU]");
static const char info_host_compiler_version[55] = {((char)73),((char)78),((char)70),((char)79),((char)58),((char)104),((char)111),((char)115),((char)116),((char)95),((char)99),((char)111),((char)109),((char)112),((char)105),((char)108),((char)101),((char)114),((char)95),((char)118),((char)101),((char)114),((char)115),((char)105),((char)111),((char)110),((char)91),((char)48),((char)48),((char)48),((char)48),((char)48),((char)48),((char)49),((char)49),((char)46),((char)48),((char)48),((char)48),((char)48),((char)48),((char)48),((char)48),((char)52),((char)46),((char)48),((char)48),((char)48),((char)48),((char)48),((char)48),((char)48),((char)48),((char)93),((char)0)};
# 881 "CMakeCUDACompilerId.cu"
const char *info_language_standard_default = ((const char *)"INFO:standard_default[17]");
# 899 "CMakeCUDACompilerId.cu"
const char *info_language_extensions_default = ((const char *)"INFO:extensions_default[ON]");
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