Instructions to use vidfom/Ltx-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use vidfom/Ltx-3 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="vidfom/Ltx-3", filename="ComfyUI/models/text_encoders/gemma-3-12b-it-qat-UD-Q4_K_XL.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use vidfom/Ltx-3 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf vidfom/Ltx-3:UD-Q4_K_XL # Run inference directly in the terminal: llama-cli -hf vidfom/Ltx-3:UD-Q4_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf vidfom/Ltx-3:UD-Q4_K_XL # Run inference directly in the terminal: llama-cli -hf vidfom/Ltx-3:UD-Q4_K_XL
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 vidfom/Ltx-3:UD-Q4_K_XL # Run inference directly in the terminal: ./llama-cli -hf vidfom/Ltx-3:UD-Q4_K_XL
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 vidfom/Ltx-3:UD-Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf vidfom/Ltx-3:UD-Q4_K_XL
Use Docker
docker model run hf.co/vidfom/Ltx-3:UD-Q4_K_XL
- LM Studio
- Jan
- Ollama
How to use vidfom/Ltx-3 with Ollama:
ollama run hf.co/vidfom/Ltx-3:UD-Q4_K_XL
- Unsloth Studio
How to use vidfom/Ltx-3 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 vidfom/Ltx-3 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 vidfom/Ltx-3 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for vidfom/Ltx-3 to start chatting
- Docker Model Runner
How to use vidfom/Ltx-3 with Docker Model Runner:
docker model run hf.co/vidfom/Ltx-3:UD-Q4_K_XL
- Lemonade
How to use vidfom/Ltx-3 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull vidfom/Ltx-3:UD-Q4_K_XL
Run and chat with the model
lemonade run user.Ltx-3-UD-Q4_K_XL
List all available models
lemonade list
File size: 2,862 Bytes
e00eceb | 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 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 | .easyuse-prompt-styles{
overflow: auto;
}
.easyuse-prompt-styles .tools{
display:flex;
justify-content:space-between;
height:30px;
padding-bottom:10px;
border-bottom:2px solid var(--border-color);
}
.easyuse-prompt-styles .tools button.delete{
height:30px;
border-radius: 8px;
border: 2px solid var(--border-color);
font-size:11px;
background:var(--comfy-input-bg);
color:var(--error-text);
box-shadow:none;
cursor:pointer;
}
.easyuse-prompt-styles .tools button.delete:hover{
filter: brightness(1.2);
}
.easyuse-prompt-styles .tools textarea.search{
flex:1;
margin-left:10px;
height:20px;
line-height:20px;
border-radius: 8px;
border: 2px solid var(--border-color);
font-size:11px;
background:var(--comfy-input-bg);
color:var(--input-text);
box-shadow:none;
padding:4px 10px;
outline: none;
resize: none;
appearance:none;
}
.easyuse-prompt-styles-list{
list-style: none;
padding: 0;
margin: 0;
min-height: 150px;
height: calc(100% - 40px);
overflow: auto;
/*display: flex;*/
/*flex-wrap: wrap;*/
}
.easyuse-prompt-styles-list.no-top{
height: auto;
}
.easyuse-prompt-styles-tag{
display: inline-block;
vertical-align: middle;
margin-top: 8px;
margin-right: 8px;
padding:4px;
color: var(--input-text);
background-color: var(--comfy-input-bg);
border-radius: 8px;
border: 2px solid var(--border-color);
font-size:11px;
cursor:pointer;
}
.easyuse-prompt-styles-tag.hide{
display:none;
}
.easyuse-prompt-styles-tag:hover{
filter: brightness(1.2);
}
.easyuse-prompt-styles-tag input{
--ring-color: transparent;
position: relative;
box-shadow: none;
border: 2px solid var(--border-color);
border-radius: 2px;
background: linear-gradient(135deg, var(--comfy-menu-bg) 0%, var(--comfy-input-bg) 60%);
}
.easyuse-prompt-styles-tag input[type=checkbox]:checked{
border: 1px solid var(--theme-color-light);
background-color: var(--theme-color-light);
background-image: url("data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3cpath d='M12.207 4.793a1 1 0 010 1.414l-5 5a1 1 0 01-1.414 0l-2-2a1 1 0 011.414-1.414L6.5 9.086l4.293-4.293a1 1 0 011.414 0z'/%3e%3c/svg%3e");
}
.easyuse-prompt-styles-tag input[type=checkbox]{
color-adjust: exact;
display: inline-block;
flex-shrink: 0;
vertical-align: middle;
appearance: none;
border: 2px solid var(--border-color);
background-origin: border-box;
padding: 0;
width: 1rem;
height: 1rem;
border-radius:4px;
color:var(--theme-color-light);
user-select: none;
}
.easyuse-prompt-styles-tag span{
margin:0 4px;
vertical-align: middle;
}
#show_image_id{
width:128px;
height:128px;
} |