| name | ltxv2-video |
| description | Build Lightricks LTX-2 / LTX-2.3 video workflows — text-to-video, image-to-video, GGUF and bundled checkpoints, distilled model, camera control LoRAs, synchronized audio, two-stage upscaling, and swapping alternate/GGUF base models |
| globs | ["**/*.json"] |
LTX-2 / LTX-2.3 Video Workflows
Version naming (read this first)
There is no "LTX 3.2" or "LTX2.3" as separate products — the user's shorthand refers to Lightricks LTX-2.3, a point release of the LTX-2 family. The lineage is:
- LTX-Video (2024) — first text-to-video model from Lightricks.
- LTX-2 / LTX-V2 (Oct 2025) — 19B-class DiT audio-video foundation model. Bundled checkpoint
ltx-2-19b-distilled.safetensors, Gemma 3 12B text encoder.
- LTX-2.3 (released ~March 2026) — 22B-parameter DiT update. Rebuilt VAE (sharper textures/faces/hair/text), ~4x larger text connector (text projection) for prompt adherence, native 9:16 portrait, LoRA support, HiFi-GAN vocoder for cleaner synchronized audio, up to 4K@50fps / ~20s clips. Apache 2.0. Distributed primarily as GGUF UNets (community quants) plus separate VAE / text-encoder / text-projection files — NOT a single bundled checkpoint like LTX-2.
When the user says "LTX3.2" / "LTX2.3", treat it as LTX-2.3. This skill covers both LTX-2 (bundled checkpoint path) and LTX-2.3 (GGUF UNet path).
⭐ Render-verified correct setup (read this FIRST — 2026-06-19)
The GGUF-UNet + DualCLIPLoader + gemma_3_12B_it_fp4_mixed path documented later
in this skill (the Aitrepreneur installer path) produces soft/mushy video with
inaccurate faces and eyes. It runs, but it is NOT the quality path. The setup
below is the official Comfy-Org template, render-proven sharp (1280×704, accurate
faces, synchronized 48 kHz stereo audio).
Models (exact, render-verified)
| Component | File | Source repo | Folder | Notes |
|---|
| Checkpoint | ltx-2.3-22b-dev.safetensors (46 GB, max quality) or ltx-2.3-22b-dev-fp8.safetensors (~23 GB, official VRAM-friendly) | Lightricks/LTX-2.3 / Lightricks/LTX-2.3-fp8 | checkpoints/ (NOT unet/) | The checkpoint carries the transformer and the audio VAE. Loaded by CheckpointLoaderSimple + reused by LTXVAudioVAELoader + LTXAVTextEncoderLoader. |
| Gemma text encoder | gemma_3_12B_it_fp8_scaled.safetensors (13 GB) | Comfy-Org/ltx-2 → split_files/text_encoders/ | text_encoders/ | Use fp8_scaled (unpacked). The Aitrepreneur fp4_mixed mirror file is truncated (5.3 GB vs 9.4 GB) AND a packed-fp4 layout core can't reshape → shape [15360,1920] invalid for input 27582328. |
| Distilled speed LoRA | ltx_2.3_22b_distilled_1.1_lora_dynamic_fro09_avg_rank_111_bf16.safetensors @ 0.5 | Comfy-Org/ltx-2.3 → split_files/loras/ | loras/ | The newer dynamic rank-111 distilled LoRA — NOT the older ...384-1.1. |
| Gemma abliterated LoRA ⭐ | gemma-3-12b-it-abliterated_lora_rank64_bf16.safetensors @ 1.0 | Comfy-Org/ltx-2 → split_files/loras/ | loras/ | Applied to the text-encoder CLIP via a LoraLoader. This is the prompt-accuracy / correct-eyes fix. Missing this = subtly-wrong faces. |
| Spatial upscaler | ltx-2.3-spatial-upscaler-x2-1.1.safetensors | Lightricks/LTX-2.3 | latent_upscale_models/ | Used by the stage-2 LTXVLatentUpsampler. Use x2-1.1, not x2-1.0. |
Node stack (the right one)
LTXAVTextEncoderLoader (CORE, comfy_extras/nodes_lt_audio.py) — loads gemma + the full checkpoint together via comfy.sd.load_clip([gemma, ckpt], type=LTXV). This is the audio-video encoder driving both video and audio/voice. Do NOT use DualCLIPLoader(type=ltxv) + a separate ltx-2.3_text_projection file — that is the legacy video-only path and yields mush.- Gemma abliterated LoRA via a
LoraLoader (CLIP LoRA) on the encoder output → CLIPTextEncode.
- Two-stage: base sample (~768×512) →
LTXVLatentUpsampler (×2 spatial, uses the upscaler model + the checkpoint VAE) → refine sample → 1280×704 output. The upscale is the sharpness. A single-stage graph is visibly softer.
- Guider: the Comfy-Org template uses plain
CFGGuider cfg=1 (distilled); the LTXVideo repo example uses MultimodalGuider + GuiderParameters (separate AUDIO/VIDEO) + ClownSampler_Beta (RES4LYF). Both produce sharp output — the LoRAs + two-stage matter more than the guider.
- ffmpeg is required for the final mux:
<comfy-venv>/python -m pip install imageio-ffmpeg, then reboot. CreateVideo/SaveVideo/VHS_VideoCombine fail with ffmpeg ... could not be found otherwise.
Custom nodes
ComfyUI-LTXVideo (LTXV* nodes, MultimodalGuider, GuiderParameters, LTXVPreprocess, LTXVTiledVAEDecode, GemmaAPITextEncode, LTXFloatToInt) + RES4LYF (ClownSampler_Beta, only for the repo-example sampler). LTXAVTextEncoderLoader, ResizeImageMaskNode, CreateVideo, SaveVideo, ManualSigmas, LTXVScheduler, the Primitive* nodes are all CORE ComfyUI.
Quality troubleshooting (symptom → cause → fix)
- Mushy/garbage, no clear subject → empty positive prompt, or
DualCLIPLoader+projection text encoder. Fix: set a prompt; use LTXAVTextEncoderLoader.
- Coherent but soft/blurry, faces & eyes slightly wrong → no two-stage upscale and/or missing the gemma abliterated LoRA and/or the old distilled LoRA. Fix: full two-stage template + both LoRAs above.
status: success but no video file / outputs only has a math or text node → the output node (SaveVideo/VHS) failed validation and was silently dropped; the graph short-circuited. Check the ComfyUI log for Failed to validate prompt for output N and fix that node (missing ffmpeg, a broken connection, a model-not-in-list).DualCLIPLoader reshape [15360,1920] invalid for input 27582328 → wrong/truncated gemma → use gemma_3_12B_it_fp8_scaled.LatentUpscaleModelLoader: ...x2-1.0 not in list → reference ...x2-1.1.- SaveVideo writes to a subfolder (
video/<prefix>_NNNNN.mp4) — its history outputs entry isn't under images/videos/gifs, so a naive "find the video" check misses it. Look on disk under output/video/.
MCP UI→API converter gotchas (src/services/workflow-converter.ts)
The official template exercised several convertUiToApi gaps (all now fixed — keep in mind if a template still mis-converts):
- V3 dynamic combos (
COMFY_DYNAMICCOMBO_V3, e.g. ResizeImageMaskNode.resize_type): each selected option's nested input must be keyed <combo>.<nested> (e.g. resize_type.longer_size, resize_type.width), NOT flat — ComfyUI rebuilds the nested dict via dynamic_paths/finalize_prefix. A flat key is rejected required_input_missing.
Reroute is virtual — its connections must be passed through (consumer resolves to the Reroute's input), else everything downstream dangles and the graph short-circuits.VHS_VideoCombine stores widgets_values as a name→value object, not a positional array.- Typed
Primitive* nodes (PrimitiveInt/Float/Boolean/StringMultiline) are real executable nodes — keep them as link sources, don't bake their values into a consumer's widgets_values by index (mis-positions V3 nested inputs).
Pack
packs/ltx-2.3-txt2vid (and the i2v/flf/extender variants) should be built on this official two-stage template. For a no-input-file T2V pack, set the template's bypass_i2v / "Switch to Text to Video?" boolean true and feed the I2V image input a blank EmptyImage (discarded at runtime but still validates).
Source note: the install scripts below pull LTX-2.3 files from a third-party mirror repo huggingface.co/Aitrepreneur/FLX, not the official Lightricks/LTX-2.3 repo. The official weights live at huggingface.co/Lightricks/LTX-2.3. Filenames/quants match what those scripts download.
Overview
LTX-2 is a DiT-based video foundation model from Lightricks. It uses a Gemma 3 12B text encoder and supports both text-to-video (T2V) and image-to-video (I2V). Key features:
- Distilled model for fast 8-step generation; dev model for higher quality (~20+ steps)
- Two-stage pipeline: Generate at low res, then 2x spatial upscale in latent space
- Camera control LoRAs for cinematic movements
- Synchronized audio-video generation in a single pass (LTX-2.3 audio VAE + HiFi-GAN vocoder)
- GGUF quantization (LTX-2.3) for low-VRAM local inference via ComfyUI-GGUF
Models
LTX-2 (bundled checkpoint path)
| Component | Node | Model | Notes |
|---|
| Checkpoint | CheckpointLoaderSimple | ltx-2-19b-distilled.safetensors | 41GB bf16, distilled variant; bundles VAE internally |
| Gemma 3 | CLIPLoader (type=ltxv) | gemma_3_12B_it_fp4_mixed.safetensors | 9GB FP4, in text_encoders/ |
Loading note (LTX-2): The bundled checkpoint contains the VAE internally. The Gemma 3 text encoder loads separately via CLIPLoader with type: "ltxv" pointing at text_encoders/.
LTX-2.3 (GGUF UNet path — current install)
LTX-2.3 ships as a separate GGUF UNet + standalone VAE + text encoder + text projection, not a single bundled checkpoint. The install scripts (see below) place files like this:
| Component | Node | Model file | Folder | Notes |
|---|
| UNet (GGUF) | UnetLoaderGGUF ("Unet Loader (GGUF)", bootleg category, from ComfyUI-GGUF) | ltx-2.3-22b-dev-Q4_K_S.gguf / -Q5_K_S.gguf / -Q8_0.gguf | models/unet/ | 22B dev model. Q4_K_S <12GB VRAM, Q5_K_S 12–16GB, Q8_0 24GB+ |
| Video VAE | VAELoader | LTX23_video_vae_bf16.safetensors | models/vae/ | rebuilt LTX-2.3 VAE |
| Audio VAE | VAELoader | LTX23_audio_vae_bf16.safetensors | models/vae/ | only for audio-sync output |
| Gemma 3 | CLIPLoader (type=ltxv) | gemma_3_12B_it_fp4_mixed.safetensors | models/text_encoders/ | same FP4 encoder as LTX-2 |
| Text projection | loaded with the text encoder | ltx-2.3_text_projection_bf16.safetensors | models/text_encoders/ | the enlarged text connector new in 2.3 |
| Spatial upscaler | LatentUpscaleModelLoader | ltx-2.3-spatial-upscaler-x2-1.1.safetensors | models/latent_upscale_models/ | replaces LTX-2's ...x2-1.0 |
Loading note (LTX-2.3): Because the UNet is a bare GGUF, the VAE no longer comes "for free" with a checkpoint — load LTX23_video_vae_bf16.safetensors explicitly with VAELoader. Place GGUF UNets in models/unet/ and use the GGUF Unet loader. Some community 2.3 workflows pair gemma_3_12B_it.safetensors (full) instead of the FP4 mixed file; the installer uses the FP4 mixed one.
Install scripts (Step-by-step source of truth)
Three installers (by "Aitrepreneur") were used; they all download from HF = https://huggingface.co/Aitrepreneur/FLX/resolve/main:
LTX-2-3-MODELS-NODES_INSTALL-V2.bat — run from ...\ComfyUI_windows_portable\ComfyUI\. Locks the current pip env into a constraints file, sanitizes each node's requirements.txt (strips torch/file-wheels/extra-index lines), clones nodes, downloads models. Flags: /update, /force, /dryrun, /restore.LTX-2-3-ULTRA-COMFYUI-MANAGER_AUTO_INSTALL-V2.bat — full one-click: downloads ComfyUI portable v0.22.0, installs 7-Zip/Git if missing, clones the same nodes, downloads the same models, then launches ComfyUI.LTX-2-3-AUTO_INSTALL-RUNPOD-V2.sh — Linux/RunPod. Recreates a clean venv, pins torch 2.4.0 / torchvision 0.19.0 / torchaudio 2.4.0 / xformers 0.0.27.post2 on cu121, transformers 4.51.3, tokenizers >=0.21,<0.22, timm 1.0.15. Pins ComfyUI-LTXVideo to commit cd5d371518afb07d6b3641be8012f644f25269fc for workflow compatibility, and verifies the LTXVideo import at the end.
Exact model download URLs (all ?download=true from the FLX mirror), grouped by target folder:
models/text_encoders/ltx-2.3_text_projection_bf16.safetensors
models/text_encoders/gemma_3_12B_it_fp4_mixed.safetensors
models/vae/LTX23_video_vae_bf16.safetensors
models/vae/LTX23_audio_vae_bf16.safetensors
models/unet/ltx-2.3-22b-dev-<Q4_K_S|Q5_K_S|Q8_0>.gguf
models/latent_upscale_models/ltx-2.3-spatial-upscaler-x2-1.1.safetensors
models/loras/ltx-2.3-22b-distilled-lora-384-1.1.safetensors
models/loras/ltx-2-19b-ic-lora-detailer.safetensors
Custom nodes cloned by all three scripts:
| Node | Repo |
|---|
| ComfyUI-Manager | github.com/ltdrdata/ComfyUI-Manager |
| ComfyUI-GGUF (GGUF UNet loader) | github.com/city96/ComfyUI-GGUF |
ComfyUI-LTXVideo (pin cd5d371… on RunPod) | github.com/Lightricks/ComfyUI-LTXVideo |
| rgthree-comfy | github.com/rgthree/rgthree-comfy |
| ComfyUI-Easy-Use | github.com/yolain/ComfyUI-Easy-Use |
| ComfyUI-KJNodes | github.com/kijai/ComfyUI-KJNodes |
| RES4LYF (advanced samplers e.g. res_2s) | github.com/ClownsharkBatwing/RES4LYF |
| ComfyUI-Custom-Scripts | github.com/pythongosssss/ComfyUI-Custom-Scripts |
| ComfyUI-VideoHelperSuite | github.com/Kosinkadink/ComfyUI-VideoHelperSuite |
| ComfyUI-WanVideoWrapper | github.com/kijai/ComfyUI-WanVideoWrapper |
| ComfyUI-Impact-Pack | github.com/ltdrdata/ComfyUI-Impact-Pack |
| Comfyui_TTP_Toolset | github.com/TTPlanetPig/Comfyui_TTP_Toolset |
| ComfyMath | github.com/evanspearman/ComfyMath |
| WhatDreamsCost-ComfyUI | github.com/WhatDreamsCost/WhatDreamsCost-ComfyUI |
LoRAs (Installed)
| LoRA | File | Purpose |
|---|
| Distilled LoRA (384, 2.3) | loras/ltx-2.3-22b-distilled-lora-384-1.1.safetensors | Apply to the 2.3 dev UNet for fast distilled behavior |
| IC-LoRA detailer | loras/ltx-2-19b-ic-lora-detailer.safetensors | Detail/refinement IC-LoRA |
| Distilled LoRA (384, LTX-2) | ltx2/ltx-2-19b-distilled-lora-384.safetensors | Apply to LTX-2 base for distilled behavior |
| Camera Dolly Left | ltx-2-19b-lora-camera-control-dolly-left.safetensors | Camera movement (see Camera Control section) |
Concept/Style LoRAs (Installed)
Located in loras/LTXV2/:
style/PLORAV7_LTX_000010500.safetensorsconcept/head_swap_v1_13500_first_frame.safetensorsconcept/LTX-2 - Better Female Nudity.safetensorsaction/LTX2-i2v-OralSuite.safetensorsaction/LTX2-i2v-SexThrust.safetensors- And more in
concept/ and action/ subfolders
Key Nodes
LTXVConditioning
Binds text conditioning with frame rate information:
{
"class_type": "LTXVConditioning",
"inputs": {
"positive": ["<clip_text_encode>", 0],
"negative": ["<clip_text_encode_neg>", 0],
"frame_rate": 25
}
}
EmptyLTXVLatentVideo
Creates the initial video latent (for T2V):
{
"class_type": "EmptyLTXVLatentVideo",
"inputs": {
"width": 768,
"height": 512,
"length": 97,
"batch_size": 1
}
}
Frame count constraint: Must be 8n + 1 (9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, 97, 105, 113, 121).
LTXVScheduler
Dedicated sigma schedule for LTX-V2 latent space:
{
"class_type": "LTXVScheduler",
"inputs": {
"steps": 8,
"max_shift": 2.05,
"base_shift": 0.95,
"stretch": true,
"terminal": 0.1
}
}
Connect the optional latent input for latent-aware shift scaling.
Feeding a prior stage's output into I2V (e.g. Krea2 image → LTX video). The
LoadImage that feeds LTXVImgToVideo.image needs the source frame registered
as a ComfyUI INPUT. When that frame is an OUTPUT from an earlier stage, call
stage_output_as_input with its { filename, subfolder?, type? } and drop
the returned input filename into LoadImage. (For a file already on local
disk, upload_image.) NEVER copy the output file into, or guess, a
filesystem input/ path — ComfyUI's input/output dirs may be CUSTOM
(--input-directory / --output-directory), so a guessed path makes
LoadImage reject the file (Invalid image file) and wastes the render.
stage_output_as_input goes through the server API (/view → /upload/image)
and resolves the real dirs correctly.
VERIFY A VIDEO RENDER VIA THE FILESYSTEM, NOT /history. VHS_VideoCombine
(and similar video nodes) write the .mp4 but frequently do NOT register the
output in ComfyUI's /history — the prompt shows done with an empty outputs map
and no error. Do NOT conclude the render "silently dropped" from
get_history / get_job_status alone. Confirm the file with
list_output_images (it now lists videos too, with kind: "video") — match
the filename_prefix (e.g. ltxv2_….mp4) and check the mtime is fresh — then
chain it into the next stage with stage_output_as_input.
LTXVImgToVideo (For I2V)
All-in-one node that encodes image, creates latent, and wraps conditioning:
{
"class_type": "LTXVImgToVideo",
"inputs": {
"positive": ["<conditioning>", 0],
"negative": ["<conditioning>", 0],
"vae": ["<checkpoint>", 2],
"image": ["<load_image>", 0],
"width": 768,
"height": 512,
"length": 97,
"batch_size": 1,
"strength": 0.6
}
}
Gotcha — strength controls motion; DON'T set it to 1.0. LTXVImgToVideo.strength
is how strongly the output adheres to the start image: higher = more adherence = LESS
motion. Setting it to 1.0 pins every frame to the start image → a FROZEN i2v with
ZERO motion (the storyboard frames come out basically identical). Keep the verified
value ~0.6 (as in the example above) for proper motion. If a generated i2v clip
shows little/no motion, the FIRST thing to check is that strength wasn't bumped toward
1.0.
LTXVLatentUpsampler (For Two-Stage Upscale)
{
"class_type": "LTXVLatentUpsampler",
"inputs": {
"latent": ["<sampler_output>", 0],
"upscale_model": ["<upscale_loader>", 0]
}
}
Requires LatentUpscaleModelLoader. Use ltx-2.3-spatial-upscaler-x2-1.1.safetensors for LTX-2.3 (or ltx-2-spatial-upscaler-x2-1.0.safetensors for LTX-2).
Sampler Settings
Distilled Model (Installed)
Uses SamplerCustomAdvanced with manual sigmas, NOT standard KSampler:
| Parameter | Stage 1 (Generate) | Stage 2 (Upscale) |
|---|
| sampler | euler | euler |
| steps | 8 | 4 |
| cfg | 1.0 | 1.0 |
| scheduler | LTXVScheduler | Manual sigmas |
Stage 1 sigmas (via LTXVScheduler): max_shift=2.05, base_shift=0.95, stretch=true, terminal=0.1
Stage 2 sigmas (manual, for upscale refinement): 0.909375, 0.725, 0.421875, 0.0
Base Model (If Using Distilled LoRA on Base)
| Parameter | Value |
|---|
| sampler | res_2s |
| steps | 20 |
| cfg | 4.0 |
| scheduler | LTXVScheduler |
| distilled_lora_strength | 0.6 |
Resolution and Frame Count
Resolutions (Must be multiples of 32)
| Aspect | Stage 1 | After 2x Upscale | Notes |
|---|
| 3:2 landscape | 768x512 | 1536x1024 | Default |
| 16:9 landscape | 960x544 | 1920x1088 | Official example |
| 1:1 square | 640x640 | 1280x1280 | |
| 4:3 landscape | 704x512 | 1408x1024 | |
Start at lower resolution for Stage 1 to manage VRAM, then upscale.
Frame Count (8n + 1)
| Frames | Duration @25fps | Duration @24fps | Notes |
|---|
| 49 | 1.96s | 2.04s | Quick test |
| 81 | 3.24s | 3.38s | Short clip |
| 97 | 3.88s | 4.04s | Default |
| 121 | 4.84s | 5.04s | Official example, recommended |
| 161 | 6.44s | 6.71s | Longer clip |
| 257 | 10.28s | 10.71s | Maximum |
Frame Rate
Standard: 25 fps (conditioned via LTXVConditioning). 24 and 30 fps also supported.
Pipeline Flow: T2V Distilled
CheckpointLoaderSimple → MODEL + VAE
CLIPLoader (ltxv, gemma_3_12B_it_fp4_mixed) → CLIP
├─ CLIPTextEncode (positive) → CONDITIONING
└─ CLIPTextEncode (negative) → CONDITIONING
LTXVConditioning (positive, negative, frame_rate=25) → pos/neg CONDITIONING
EmptyLTXVLatentVideo (768x512, 121 frames) → LATENT
LTXVScheduler (steps=8, max_shift=2.05, base_shift=0.95) → SIGMAS
SamplerCustomAdvanced (model, sigmas, positive, negative, latent)
→ Stage 1 LATENT
[Optional: LTXVLatentUpsampler → 2x LATENT → SamplerCustomAdvanced Stage 2]
VAEDecode (or LTXVSpatioTemporalTiledVAEDecode for VRAM savings) → IMAGE
VHS_VideoCombine (or CreateVideo + SaveVideo) → MP4
Complete Workflow: T2V Distilled (8-Step)
{
"1": { "class_type": "CheckpointLoaderSimple", "inputs": { "ckpt_name": "ltx-2-19b-distilled.safetensors" }},
"2": { "class_type": "CLIPLoader", "inputs": { "clip_name": "gemma_3_12B_it_fp4_mixed.safetensors", "type": "ltxv" }},
"3": { "class_type": "CLIPTextEncode", "inputs": { "clip": ["2", 0], "text": "<positive prompt>" }},
"4": { "class_type": "CLIPTextEncode", "inputs": { "clip": ["2", 0], "text": "" }},
"5": { "class_type": "LTXVConditioning", "inputs": {
"positive": ["3", 0], "negative": ["4", 0], "frame_rate": 25
}},
"6": { "class_type": "EmptyLTXVLatentVideo", "inputs": {
"width": 768, "height": 512, "length": 121, "batch_size": 1
}},
"7": { "class_type": "LTXVScheduler", "inputs": {
"steps": 8, "max_shift": 2.05, "base_shift": 0.95,
"stretch": true, "terminal": 0.1, "latent": ["6", 0]
}},
"8": { "class_type": "KSamplerSelect", "inputs": { "sampler_name": "euler" }},
"9": { "class_type": "SamplerCustomAdvanced", "inputs": {
"model": ["1", 0],
"positive": ["5", 0],
"negative": ["5", 1],
"sigmas": ["7", 0],
"latent_image": ["6", 0],
"noise": ["10", 0],
"sampler": ["8", 0],
"guider": ["11", 0]
}},
"10": { "class_type": "RandomNoise", "inputs": { "noise_seed": 42 }},
"11": { "class_type": "CFGGuider", "inputs": {
"model": ["1", 0],
"positive": ["5", 0],
"negative": ["5", 1],
"cfg": 1.0
}},
"12": { "class_type": "VAEDecode", "inputs": { "samples": ["9", 0], "vae": ["1", 2] }},
"13": { "class_type": "VHS_VideoCombine", "inputs": {
"images": ["12", 0], "frame_rate": 25, "loop_count": 0,
"filename_prefix": "ltxv2", "format": "video/h264-mp4",
"pingpong": false, "save_output": true,
"pix_fmt": "yuv420p", "crf": 19, "save_metadata": true, "trim_to_audio": false
}}
}
Alternative simple output (built-in nodes instead of VHS):
{
"12": { "class_type": "VAEDecode", "inputs": { "samples": ["9", 0], "vae": ["1", 2] }},
"13": { "class_type": "CreateVideo", "inputs": { "images": ["12", 0], "fps": 25 }},
"14": { "class_type": "SaveVideo", "inputs": { "video": ["13", 0], "filename_prefix": "video/ltxv2", "format": "auto", "codec": "auto" }}
}
Complete Workflow: LTX-2.3 GGUF (dev, T2V)
The LTX-2.3 path differs from LTX-2 in three places: the model is a GGUF UNet loaded with UnetLoaderGGUF (no CheckpointLoaderSimple), the VAE is loaded separately with VAELoader, and the dev model wants more steps (~20+) at low CFG. Everything downstream (LTXVConditioning, EmptyLTXVLatentVideo, LTXVScheduler, SamplerCustomAdvanced) is the same.
{
"1": { "class_type": "UnetLoaderGGUF", "inputs": { "unet_name": "ltx-2.3-22b-dev-Q8_0.gguf" }},
"2": { "class_type": "VAELoader", "inputs": { "vae_name": "LTX23_video_vae_bf16.safetensors" }},
"3": { "class_type": "CLIPLoader", "inputs": { "clip_name": "gemma_3_12B_it_fp4_mixed.safetensors", "type": "ltxv" }},
"4": { "class_type": "CLIPTextEncode", "inputs": { "clip": ["3", 0], "text": "<positive prompt>" }},
"5": { "class_type": "CLIPTextEncode", "inputs": { "clip": ["3", 0], "text": "" }},
"6": { "class_type": "LTXVConditioning", "inputs": {
"positive": ["4", 0], "negative": ["5", 0], "frame_rate": 25
}},
"7": { "class_type": "EmptyLTXVLatentVideo", "inputs": {
"width": 768, "height": 512, "length": 121, "batch_size": 1
}},
"8": { "class_type": "LTXVScheduler", "inputs": {
"steps": 20, "max_shift": 2.05, "base_shift": 0.95,
"stretch": true, "terminal": 0.1, "latent": ["7", 0]
}},
"9": { "class_type": "KSamplerSelect", "inputs": { "sampler_name": "euler" }},
"10": { "class_type": "RandomNoise", "inputs": { "noise_seed": 42 }},
"11": { "class_type": "CFGGuider", "inputs": {
"model": ["1", 0], "positive": ["6", 0], "negative": ["6", 1], "cfg": 3.0
}},
"12": { "class_type": "SamplerCustomAdvanced", "inputs": {
"model": ["1", 0], "positive": ["6", 0], "negative": ["6", 1],
"sigmas": ["8", 0], "latent_image": ["7", 0],
"noise": ["10", 0], "sampler": ["9", 0], "guider": ["11", 0]
}},
"13": { "class_type": "VAEDecode", "inputs": { "samples": ["12", 0], "vae": ["2", 0] }},
"14": { "class_type": "CreateVideo", "inputs": { "images": ["13", 0], "fps": 25 }},
"15": { "class_type": "SaveVideo", "inputs": { "video": ["14", 0], "filename_prefix": "video/ltxv23", "format": "auto", "codec": "auto" }}
}
For the distilled 2.3 path, apply ltx-2.3-22b-distilled-lora-384-1.1.safetensors to the GGUF UNet with LoraLoaderModelOnly and drop steps to 8, cfg 1.0 (same distilled settings as LTX-2). Note the VAE comes from node ["2", 0] (the separate VAELoader), not from the model loader.
Camera Control LoRAs
Seven official camera control LoRAs from Lightricks:
| Movement | LoRA File |
|---|
| Dolly Left | ltx-2-19b-lora-camera-control-dolly-left.safetensors |
| Dolly Right | ltx-2-19b-lora-camera-control-dolly-right.safetensors |
| Dolly In | ltx-2-19b-lora-camera-control-dolly-in.safetensors |
| Dolly Out | ltx-2-19b-lora-camera-control-dolly-out.safetensors |
| Jib Up | ltx-2-19b-lora-camera-control-jib-up.safetensors |
| Jib Down | ltx-2-19b-lora-camera-control-jib-down.safetensors |
| Static | ltx-2-19b-lora-camera-control-static.safetensors |
Usage: Apply with LoraLoaderModelOnly at strength 1.0. Do NOT describe camera movement in your prompt — the LoRA handles it.
{
"class_type": "LoraLoaderModelOnly",
"inputs": {
"model": ["<checkpoint>", 0],
"lora_name": "ltx-2-19b-lora-camera-control-dolly-left.safetensors",
"strength_model": 1.0
}
}
Cannot combine camera control LoRA with IC-LoRA (canny/depth/pose) in the same generation.
Concept/Style LoRAs
Apply with LoraLoaderModelOnly. Typical strength: 0.5–1.0.
{
"class_type": "LoraLoaderModelOnly",
"inputs": {
"model": ["<checkpoint_or_camera_lora>", 0],
"lora_name": "LTXV2\\concept\\LTX-2 - Better Female Nudity.safetensors",
"strength_model": 0.8
}
}
Concept/style LoRAs CAN be stacked with camera control LoRAs.
VRAM Considerations
| Config | VRAM | Notes |
|---|
| bf16 checkpoint + FP4 Gemma | ~24GB+ | Tight on RTX 4090, may OOM |
| FP8 checkpoint + FP4 Gemma | ~16-20GB | Recommended for 24GB GPUs |
| bf16 + tiled VAE decode | ~22GB | Use LTXVSpatioTemporalTiledVAEDecode |
VRAM warnings from MEMORY.md: "LTXV2 can OOM on 24GB — suggest FP8 quantized models or --lowvram"
Tips for 24GB GPUs
- Use
VAEDecodeTiled or LTXVSpatioTemporalTiledVAEDecode instead of standard VAEDecode
- Start at 768x512 resolution, upscale in Stage 2
- Use FP4 Gemma text encoder (installed)
- For LTX-2.3, pick the GGUF quant to match VRAM: Q4_K_S (<12GB), Q5_K_S (12–16GB), Q8_0 (24GB+). The dev GGUF needs ~20+ steps; the distilled LoRA path runs ~8 steps
- Always
clear_vram before switching to LTX-V2 from another model family
- Reduce frame count to 81 or 49 if OOM persists
Prompt Style
Natural language descriptions. Be specific about motion, camera angles, and temporal progression:
Good: "A woman with flowing auburn hair walks through a sun-dappled forest, leaves falling gently around her, soft golden hour lighting, cinematic depth of field"
Bad: "woman, forest, walking"
Describe the entire scene progression, not just a single moment. Include lighting, mood, and motion cues.
Two-Stage Upscale Pattern
For production quality, generate at low resolution then upscale:
- Stage 1: Generate at 768x512, 121 frames, 8 steps (distilled)
- Upscale:
LTXVLatentUpsampler (2x spatial) → 1536x1024
- Stage 2: Resample the upscaled latent with 3-4 steps at CFG 1.0
- Decode: Use tiled VAE decode for the larger resolution
This requires the spatial upscaler model in models/latent_upscale_models/: ltx-2.3-spatial-upscaler-x2-1.1.safetensors (LTX-2.3) or ltx-2-spatial-upscaler-x2-1.0.safetensors (LTX-2).
Using alternate / GGUF base models (incl. the "sulphur" model)
You can swap the LTX UNet for any LTX-2.3-compatible base model. The most-asked-about one is Sulphur 2 (the user's "sulphur2Base_dev.safetensors" — see name note below).
What Sulphur 2 actually is (verified June 2026)
- It exists and is real. Sulphur 2 is an uncensored, realism-leaning finetune/derivative of LTX-2.3 (22B DiT), marketed as a drop-in replacement inside existing LTX-2.3 ComfyUI graphs (T2V + I2V + the other 2.3 formats). It is NOT its own architecture and is not LTX-2 (19B) compatible — it targets the LTX-2.3 stack (2.3 VAE + Gemma 3 text encoder + 2.3 text projection).
- Filename caveat: there is no file literally named
sulphur2Base_dev.safetensors. The real base checkpoints are sulphur_dev_bf16.safetensors (~46 GB) and sulphur_dev_fp8mixed.safetensors (~29 GB). There is also a distilled variant (sulphur_distil_bf16.safetensors) and a LoRA (sulphur_lora_rank_768.safetensors). Treat "sulphur2Base_dev" as the user's shorthand for the Sulphur 2 base dev checkpoint.
- GGUF version: confirmed.
vantagewithai/Sulphur-2-Base-GGUF hosts sulphur_dev-<quant>.gguf for Q3_K_S/M, Q4_0/1/K_S/K_M, Q5_0/1/K_S/K_M, Q6_K, Q8_0 (~10–23 GB). There is also a Civitai/Sulphur-2-distilled-fp8 and Civitai listings ("Sulphur 2 Base", "Rebels Sulphur 2 GGUF").
- Hosting: HF
SulphurAI/Sulphur-2-base (safetensors + a bundled Qwen-based prompt-enhancer GGUF), HF vantagewithai/Sulphur-2-Base-GGUF (the GGUF quants), and Civitai mirrors. Uncensored open weights — in scope to document; nothing here is fabricated, but verify the exact repo/license yourself before downloading.
How to load it (it slots straight into the LTX-2.3 GGUF workflow above)
The GGUF quant is just a different UNet — load it with the same UnetLoaderGGUF node, keep the rest of the 2.3 graph identical:
- Put
sulphur_dev-Q8_0.gguf (or your chosen quant) in models/unet/.
- In the LTX-2.3 GGUF workflow above, change node
"1":
"1": { "class_type": "UnetLoaderGGUF", "inputs": { "unet_name": "sulphur_dev-Q8_0.gguf" }}
- Keep the same LTX-2.3 companions:
VAELoader → LTX23_video_vae_bf16.safetensors, CLIPLoader (type=ltxv) → gemma_3_12B_it_fp4_mixed.safetensors, plus ltx-2.3_text_projection_bf16.safetensors. These must match the LTX-2.3 architecture — do not pair it with LTX-2 (19B) VAE/encoder.
- For the bf16/fp8 safetensors (non-GGUF) variants, load with the LTX checkpoint/diffusion-model loader the workflow uses for the safetensors path (Lightricks recommends the native LTX Video nodes documented at docs.ltx.video, not the auto-generated Diffusers snippet) rather than
UnetLoaderGGUF.
- Obey the same constraints as any LTX-2.3 gen: frame count
8n+1, resolution multiples of 32, LTXVConditioning frame_rate, dev model ~20+ steps / distilled ~8 steps.
General rule for ANY alternate LTX base model
To verify a third-party model is usable before wiring it up:
- Confirm the architecture/version it was trained on (LTX-2 19B vs LTX-2.3 22B). Mixing a 2.3 UNet with a 2.0 VAE/encoder will fail or produce garbage.
- For GGUF: requires the ComfyUI-GGUF custom node (installed by the scripts), file in
models/unet/, loaded via UnetLoaderGGUF. Match the correct VAE + text encoder + text projection for that LTX version.
- For safetensors finetunes: load like the matching official checkpoint, keep the official VAE/encoder of the same version.
- If you only have a LoRA (e.g.
sulphur_lora_rank_768.safetensors), apply it to the matching base UNet with LoraLoaderModelOnly instead of swapping the whole model.
Troubleshooting
LTXVideo "kornia" import error (pad ImportError)
Symptom: ComfyUI-LTXVideo fails to load with an ImportError from kornia.geometry.transform.pyramid — pad can no longer be imported. This happens with kornia 0.8.3+, which stopped exporting pad from that module.
What the fix does (FIX-LTXVIDEO-KORNIA.bat, run from the ComfyUI_windows_portable folder): it patches ComfyUI/custom_nodes/ComfyUI-LTXVideo/pyramid_blending.py:
- Backs the file up to
pyramid_blending.py.bak_kornia_fix.
- Removes the broken
pad, line from the from kornia.geometry.transform.pyramid import ( ... ) block.
- Inserts a compatibility shim right after
import torch.nn.functional as F:
pad = F.pad
- Verifies
pad = F.pad is present and the broken import is gone.
Manual equivalent if you don't run the .bat — edit pyramid_blending.py: delete pad, from the kornia import list and add pad = F.pad after the import torch.nn.functional as F line, then restart ComfyUI. (Alternatively, pin kornia to a pre-0.8.3 release, but the patch is the lighter-touch fix and is what the install set ships.)
LTXVideo version / workflow mismatch
The RunPod installer pins ComfyUI-LTXVideo to commit cd5d371518afb07d6b3641be8012f644f25269fc for workflow compatibility. If 2.3 workflows error on the latest LTXVideo, check out that commit. Torch is pinned to 2.4.0 + cu121; do not let a node's requirements.txt upgrade torch (the installers sanitize requirements to prevent this).
