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 new
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: 3,963 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | import { app } from "../../scripts/app.js";
import { api } from "../../scripts/api.js";
function addMenuHandler(nodeType, cb) {
const getOpts = nodeType.prototype.getExtraMenuOptions;
nodeType.prototype.getExtraMenuOptions = function () {
const r = getOpts.apply(this, arguments);
cb.apply(this, arguments);
return r;
};
}
function distance(node1, node2) {
let dx = (node1.pos[0] + node1.size[0]/2) - (node2.pos[0] + node2.size[0]/2);
let dy = (node1.pos[1] + node1.size[1]/2) - (node2.pos[1] + node2.size[1]/2);
return Math.sqrt(dx * dx + dy * dy);
}
function lookup_nearest_nodes(node) {
let nearest_distance = Infinity;
let nearest_node = null;
for(let other of app.graph._nodes) {
if(other === node)
continue;
let dist = distance(node, other);
if (dist < nearest_distance && dist < 1000) {
nearest_distance = dist;
nearest_node = other;
}
}
return nearest_node;
}
function lookup_nearest_inputs(node) {
let input_map = {};
for(let i in node.inputs) {
let input = node.inputs[i];
if(input.link || input_map[input.type])
continue;
input_map[input.type] = {distance: Infinity, input_name: input.name, node: null, slot: null};
}
let x = node.pos[0];
let y = node.pos[1] + node.size[1]/2;
for(let other of app.graph._nodes) {
if(other === node || !other.outputs)
continue;
let dx = x - (other.pos[0] + other.size[0]);
let dy = y - (other.pos[1] + other.size[1]/2);
if(dx < 0)
continue;
let dist = Math.sqrt(dx * dx + dy * dy);
for(let input_type in input_map) {
for(let j in other.outputs) {
let output = other.outputs[j];
if(output.type == input_type) {
if(input_map[input_type].distance > dist) {
input_map[input_type].distance = dist;
input_map[input_type].node = other;
input_map[input_type].slot = parseInt(j);
}
}
}
}
}
let res = {};
for (let i in input_map) {
if (input_map[i].node) {
res[i] = input_map[i];
}
}
return res;
}
function connect_inputs(nearest_inputs, node) {
for(let i in nearest_inputs) {
let info = nearest_inputs[i];
info.node.connect(info.slot, node.id, info.input_name);
}
}
function node_info_copy(src, dest, connect_both, copy_shape) {
// copy input connections
for(let i in src.inputs) {
let input = src.inputs[i];
if (input.widget !== undefined) {
const destWidget = dest.widgets.find(x => x.name === input.widget.name);
dest.convertWidgetToInput(destWidget);
}
if(input.link) {
let link = app.graph.links[input.link];
let src_node = app.graph.getNodeById(link.origin_id);
src_node.connect(link.origin_slot, dest.id, input.name);
}
}
// copy output connections
if(connect_both) {
let output_links = {};
for(let i in src.outputs) {
let output = src.outputs[i];
if(output.links) {
let links = [];
for(let j in output.links) {
links.push(app.graph.links[output.links[j]]);
}
output_links[output.name] = links;
}
}
for(let i in dest.outputs) {
let links = output_links[dest.outputs[i].name];
if(links) {
for(let j in links) {
let link = links[j];
let target_node = app.graph.getNodeById(link.target_id);
dest.connect(parseInt(i), target_node, link.target_slot);
}
}
}
}
if(copy_shape) {
dest.color = src.color;
dest.bgcolor = src.bgcolor;
dest.size = max(src.size, dest.size);
}
app.graph.afterChange();
}
app.registerExtension({
name: "Comfy.Manager.NodeFixer",
beforeRegisterNodeDef(nodeType, nodeData, app) {
addMenuHandler(nodeType, function (_, options) {
options.push({
content: "Fix node (recreate)",
callback: () => {
let new_node = LiteGraph.createNode(nodeType.comfyClass);
new_node.pos = [this.pos[0], this.pos[1]];
app.canvas.graph.add(new_node, false);
node_info_copy(this, new_node, true);
app.canvas.graph.remove(this);
requestAnimationFrame(() => app.canvas.setDirty(true, true))
},
});
});
}
});
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