Ltxv2 Video

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)

Text Encoder (Installed)

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)

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:

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)

Resolution and Frame Count

Resolutions (Must be multiples of 32)

Start at lower resolution for Stage 1 to manage VRAM, then upscale.

Frame Count (8n + 1)

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" }}
}

Camera Control LoRAs

Seven official camera control LoRAs from Lightricks:

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

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/).