Instructions to use hgjc/ltx-ugc-bundle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LTX.io
How to use hgjc/ltx-ugc-bundle with LTX.io:
# Install the LTX-2 pipelines git clone https://github.com/Lightricks/LTX-2.git cd LTX-2 uv sync --frozen
# Download the weights from this repo, plus the Gemma text encoder hf download hgjc/ltx-ugc-bundle --local-dir models/ltx-ugc-bundle hf download google/gemma-3-12b-it-qat-q4_0-unquantized --local-dir models/gemma-3-12b
# Fast pipeline (distilled model, no distilled LoRA needed) uv run python -m ltx_pipelines.distilled \ --distilled-checkpoint-path models/ltx-ugc-bundle/<distilled-checkpoint>.safetensors \ --spatial-upsampler-path models/ltx-ugc-bundle/<spatial-upsampler>.safetensors \ --gemma-root models/gemma-3-12b \ --prompt "A beautiful sunset over the ocean" \ --output-path output.mp4 # For image-to-video, add: --image path/to/image.jpg 0 0.8# HQ pipeline (two-stage, higher quality) uv run python -m ltx_pipelines.ti2vid_two_stages_hq \ --checkpoint-path models/ltx-ugc-bundle/<checkpoint>.safetensors \ --distilled-lora models/ltx-ugc-bundle/<distilled-lora>.safetensors 0.8 \ --spatial-upsampler-path models/ltx-ugc-bundle/<spatial-upsampler>.safetensors \ --gemma-root models/gemma-3-12b \ --prompt "A beautiful sunset over the ocean" \ --output-path output.mp4 # For image-to-video, add: --image path/to/image.jpg 0 0.8 - Notebooks
- Google Colab
- Kaggle
File size: 3,132 Bytes
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import {app} from "../../../scripts/app.js";
import {api} from "../../../scripts/api.js";
let watched_nodes = {}
let resolve = undefined
let testURL = api.apiURL("/VHS_test")
let errors = []
api.addEventListener("executed", async function ({detail}) {
if (watched_nodes && watched_nodes[detail?.node]) {
if (detail?.output?.unfinished_batch) {
return
}
let requestBody = {tests: watched_nodes[detail.node], output: detail.output}
try {
let req = await fetch(api.apiURL("/VHS_test"),
{method: "POST", body: JSON.stringify(requestBody)});
let testResult = await req.json()
if (testResult.length != 0) {
errors.push(testResult)
}
} catch(e) {
errors.push(e)
}
if (!(watched_nodes.length -= 1)) {
resolve()
}
}
});
const workflowService = app.extensionManager.workflow
async function runTest(file) {
if (!file?.name?.endsWith(".json")) {
return false
}
let workflow = JSON.parse(await file.text())
await app.loadGraphData(workflow)
//NOTE: API is not used so workflow data is actually processed
watched_nodes = workflow.tests
errors = []
let p = new Promise((r) => resolve = r)
await app.queuePrompt()
//block until execution completes
await p
watched_nodes = {}
if (errors.length > 0) {
app.ui.dialog.show("Failed " + errors.length + " tests:\n" + errors)
return true
}
await workflowService.closeWorkflow(workflowService.activeWorkflow, {warnIfUnsaved: false})
return false
}
let iconOverride = document.createElement("style")
iconOverride.innerHTML = `.VHSTestIcon:before {content: '🧪';}`
document.body.append(iconOverride)
let testSidebar = {id: 'VHStest', title: 'VHS Test', icon: 'VHSTestIcon', type: 'custom',
render: (e) => {
e.innerHTML = `Select a folder containing tests
<input>
Or select a single test
<input>
`
const folderInput = e.children[0]
const fileInput = e.children[1]
Object.assign(folderInput, {
type: "file",
webkitdirectory: true,
onchange: async function() {
const startTime = Date.now()
let failedTests = false
for(const file of this.files) {
failedTests ||= await runTest(file)
}
this.value=""
if (!failedTests) {
console.log("All tests passed in " + ((Date.now() - startTime)/1000) + "s")
}
},
});
Object.assign(fileInput, {
type: "file",
accept: ".json",
onchange: async function() {
if (this.files.length) {
if(!(await runTest(this.files[0]))) {
console.log("Test complete")
}
this.value=""
}
},
});
}}
app.extensionManager.registerSidebarTab(testSidebar)
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