ltxv2-video

name	ltxv2-video
description	Build LTX-V2 19B video workflows — text-to-video, image-to-video, distilled model, camera control LoRAs, and two-stage upscaling
globs	["**/*.json"]

LTX-V2 19B Video Workflows

Overview

LTX-V2 (LTX-2) is a 19-billion parameter 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
• Two-stage pipeline: Generate at low res, then 2x spatial upscale in latent space
• Camera control LoRAs for cinematic movements
• Audio-video generation in a single pass (optional)

Models

Checkpoint (Installed)

Component	Node	Model	Notes
Checkpoint	CheckpointLoaderSimple	ltx-2-19b-distilled.safetensors	41GB bf16, distilled variant

Text Encoder (Installed)

Component	Node	Model	Notes
Gemma 3	CLIPLoader (type=ltxv)	gemma_3_12B_it_fp4_mixed.safetensors	9GB FP4, in text_encoders/

Loading note: The checkpoint bundles the VAE internally. The Gemma 3 text encoder loads separately. Use CLIPLoader with type: "ltxv" pointing at the text_encoders/ directory.

LoRAs (Installed)

LoRA	File	Purpose
Distilled LoRA	ltx2/ltx-2-19b-lora-camera-control-dolly-left.safetensors	Camera dolly left
Distilled LoRA (384)	ltx2/ltx-2-19b-distilled-lora-384.safetensors	Apply to base model for distilled behavior
Camera Dolly Left	ltx-2-19b-lora-camera-control-dolly-left.safetensors	Camera movement

Concept/Style LoRAs (Installed)

Located in loras/LTXV2/:

• style/PLORAV7_LTX_000010500.safetensors
• concept/head_swap_v1_13500_first_frame.safetensors
• concept/LTX-2 - Better Female Nudity.safetensors
• action/LTX2-i2v-OralSuite.safetensors
• action/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.

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
  }

LTXVLatentUpsampler (For Two-Stage Upscale)

{
  "class_type": "LTXVLatentUpsampler",
  "inputs": {
    "latent": ["<sampler_output>", 0],
    "upscale_model": ["<upscale_loader>", 0]
  }
}

Requires LatentUpscaleModelLoader with ltx-2-spatial-upscaler-x2-1.0.safetensors.

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>" 
             
       
           
  
       
           
  
       
         
          
  
          
       
      
      
      
      
      
      
      
      
  
          
       
      
      
      
     
  
              
       
          
       
       
           
  

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"           

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

1. Use VAEDecodeTiled or LTXVSpatioTemporalTiledVAEDecode instead of standard VAEDecode
2. Start at 768x512 resolution, upscale in Stage 2
3. Use FP4 Gemma text encoder (installed)
4. Consider GGUF quantized models for tighter VRAM budgets
5. Always clear_vram before switching to LTX-V2 from another model family
6. 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:

1. Stage 1: Generate at 768x512, 121 frames, 8 steps (distilled)
2. Upscale: LTXVLatentUpsampler (2x spatial) → 1536x1024
3. Stage 2: Resample the upscaled latent with 3-4 steps at CFG 1.0
4. Decode: Use tiled VAE decode for the larger resolution

This requires the spatial upscaler model: ltx-2-spatial-upscaler-x2-1.0.safetensors (place in models/latent_upscale_models/).