WAN 2.2 Text-to-Video (T2V) Workflows

Overview

WAN 2.2 T2V generates videos from text prompts using a 14B parameter MoE (Mixture of Experts) architecture split across two specialized models:

• HighNoise model: Handles early denoising — establishes structure, motion, composition
• LowNoise model: Handles late denoising — refines details, sharpens output

This dual-model technique is the same as FLF/I2V (see wan-flf-video skill) but without image conditioning nodes.

Key difference from I2V/FLF: T2V does NOT use

CLIPVisionEncode

WanFirstLastFrameToVideo

EmptyHunyuanLatentVideo

Models

UNET (Installed)

Wan2_2-T2V-A14B_HIGH_fp8_e4m3fn_scaled_KJ.safetensors

UNETLoader

Wan2_2-T2V-A14B-LOW_fp8_e4m3fn_scaled_KJ.safetensors

UNETLoader

Text Encoder

CLIPLoader

wan

umt5_xxl_fp8_e4m3fn_scaled.safetensors

VAE

VAELoader

wan_2.1_vae.safetensors

VACE Modules (Installed — For Advanced Control)

Wan2_2_Fun_VACE_module_A14B_HIGH_bf16.safetensors

Wan2_2_Fun_VACE_module_A14B_LOW_bf16.safetensors

VACE modules add reference image / pose / depth conditioning to T2V. See WanVideoWrapper section below.

Lightning LoRAs (Installed)

T2V Lightning v1.1 (Paired Hi/Lo)

wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise

Unknown/no tags/

wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise

Unknown/no tags/

T2V Lightning Seko V2.0 (Alternative Paired)

Wan2.2_HN_T2V_Lightning_4steps-lora-rank64-Seko_V2.0_HIGH

Wan2.2_HN_T2V_Lightning_4steps-lora-rank64-Seko_V2.0_LOW

T2V CFG-Step Distill (Higher Quality)

lightx2v_T2V_14B_cfg_step_distill_v2_lora_rank128_bf16

Sampler Settings

Lightning (4-Step, Recommended for Speed)

Standard (20-Step, Full Quality)

ModelSamplingSD3

Required for WAN 2.2 flow matching. Apply to BOTH models:

{
  "class_type": "ModelSamplingSD3",
  "inputs": { "model": ["<unet>", 0], "shift": 8 }
}

T2V shift values:

• Standard: shift=8 (good balance of motion and detail)
• Lightning: shift=5 (lower shift for distilled models)
• Range 6–9: Higher shift = more detail, lower shift = stronger motion

EmptyHunyuanLatentVideo

Creates the initial video latent for T2V (no image input):

{
  "class_type": "EmptyHunyuanLatentVideo",
  "inputs": {
    "width": 832,
    "height": 480,
    "length": 81,
    "batch_size": 1
  }
}

This replaces

WanFirstLastFrameToVideo

Negative Prompt

The tones are vibrant, overexposed, static, details are unclear, subtitles, style, work, painting, image, still, overall grayish, worst quality, low quality, JPEG compression artifacts, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, distorted limbs, merged fingers, motionless image, cluttered background, three legs, many people in the background, walking backwards

Pipeline Flow

UNETLoader (HIGH T2V) → ModelSamplingSD3 (shift) → LoraLoaderModelOnly (Hi Lightning) → MODEL_HI
UNETLoader (LOW T2V) → ModelSamplingSD3 (shift) → LoraLoaderModelOnly (Lo Lightning) → MODEL_LO
CLIPLoader (wan) → CLIP
  ├─ CLIPTextEncode (positive) → CONDITIONING
  └─ CLIPTextEncode (negative) → CONDITIONING
VAELoader → VAE

EmptyHunyuanLatentVideo (832x480, 81 frames) → LATENT

KSamplerAdvanced (Hi: MODEL_HI, steps 0-2, add_noise=enable, return_leftover=enable)
  → noisy LATENT
KSamplerAdvanced (Lo: MODEL_LO, steps 2-4, add_noise=disable, return_leftover=disable)
  → final LATENT

VAEDecode → IMAGE → VHS_VideoCombine → MP4

Complete Workflow: T2V Lightning (4-Step)

{
  "1": { "class_type": "UNETLoader", "inputs": { "unet_name": "Wan2_2-T2V-A14B_HIGH_fp8_e4m3fn_scaled_KJ.safetensors", "weight_dtype": "default" }, "_meta": { "title": "UNET HighNoise T2V" }},
  "2": { "class_type": "UNETLoader", "inputs": { "unet_name": "Wan2_2-T2V-A14B-LOW_fp8_e4m3fn_scaled_KJ.safetensors", "weight_dtype": "default" }, "_meta": { "title": "UNET LowNoise T2V" }},
  "3": { "class_type": "ModelSamplingSD3", "inputs": { "model": ["1", 0], "shift": 5 }, "_meta": { "title": "Hi Shift" }},
  "4": { "class_type": "ModelSamplingSD3", "inputs": { "model": ["2", 0], "shift": 5 }, "_meta": { "title": "Lo Shift" }},
  "5": { "class_type": "LoraLoaderModelOnly", "inputs": {
    "model": ["3", 0],
    "lora_name": "Unknown\\no tags\\wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors",
    "strength_model": 1.0
  }, "_meta": { "title": "Hi Lightning" }},
  "6": { "class_type": "LoraLoaderModelOnly", "inputs": {
    "model": ["4", 0],
    "lora_name": "Unknown\\no tags\\wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors",
    "strength_model": 1.0
  }, "_meta": { "title": "Lo Lightning" }},
  "7": { "class_type": "CLIPLoader", "inputs": { "clip_name": "umt5_xxl_fp8_e4m3fn_scaled.safetensors", "type": "wan" }},
  "8": { "class_type": "VAELoader", "inputs": { "vae_name": "wan_2.1_vae.safetensors" }},
  "9": { "class_type": "CLIPTextEncode", "inputs": { "clip": ["7", 0], "text": "<positive prompt describing the video scene and motion>" }, "_meta": { "title": "Positive" }},
  "10": { "class_type": "CLIPTextEncode", "inputs": { "clip": ["7", 0], "text": "The tones are vibrant, overexposed, static, details are unclear, subtitles, worst quality, low quality, motionless image" }, "_meta": { "title": "Negative" }},
  "11": { "class_type": "EmptyHunyuanLatentVideo", "inputs": {
    "width": 832, "height": 480, "length": 81, "batch_size": 1
  }},
  "12": { "class_type": "KSamplerAdvanced", "inputs": {
    "model": ["5", 0],
    "positive": ["9", 0],
    "negative": ["10", 0],
    "latent_image": ["11", 0],
    "add_noise": "enable", "noise_seed": 0, "steps": 4, "cfg": 1,
    "sampler_name": "euler", "scheduler": "simple",
    "start_at_step": 0, "end_at_step": 2, "return_with_leftover_noise": "enable"
  }, "_meta": { "title": "Hi Pass" }},
  "13": { "class_type": "KSamplerAdvanced", "inputs": {
    "model": ["6", 0],
    "positive": ["9", 0],
    "negative": ["10", 0],
    "latent_image": ["12", 0],
    "add_noise": "disable", "noise_seed": 0, "steps": 4, "cfg": 1,
    "sampler_name": "euler", "scheduler": "simple",
    "start_at_step": 2, "end_at_step": 4, "return_with_leftover_noise": "disable"
  }, "_meta": { "title": "Lo Pass" }},
  "14": { "class_type": "VAEDecode", "inputs": { "samples": ["13", 0], "vae": ["8", 0] }},
  "15": { "class_type": "VHS_VideoCombine", "inputs": {
    "images": ["14", 0], "frame_rate": 16, "loop_count": 0,
    "filename_prefix": "wan_t2v", "format": "video/h264-mp4",
    "pingpong": false, "save_output": true,
    "pix_fmt": "yuv420p", "crf": 19, "save_metadata": true, "trim_to_audio": false
  }}
}

Complete Workflow: T2V Standard (20-Step)

Same structure as above but replace the LoRA and sampler settings:

• Remove LoRA nodes (5 and 6) — connect ModelSamplingSD3 outputs directly to KSamplerAdvanced
• Change

  shift

  to 8 in ModelSamplingSD3 nodes
• Change KSamplerAdvanced settings:

  • steps

    : 20,

    cfg

    : 3.5
  • Pass 1:

    start_at_step

    : 0,

    end_at_step

    : 10
  • Pass 2:

    start_at_step

    : 10,

    end_at_step

    : 20

WanVideoWrapper Approach (Advanced)

For more control, use the WanVideoWrapper custom node pack. Key differences from native:

• Uses

  WanVideoModelLoader

  →

  WANVIDEOMODEL

  type
• Uses

  WanVideoSampler

  with built-in shift parameter
• Supports TeaCache, context windows, block swap for VRAM management

WanVideoWrapper T2V Pipeline

WanVideoModelLoader (T2V model) → WANVIDEOMODEL
WanVideoVAELoader → WANVAE
WanVideoTextEncode (positive + negative prompts) → WANVIDEOTEXTEMBEDS
WanVideoImageToVideoEncode (no images — creates empty embeds for T2V)
  → WANVIDIMAGE_EMBEDS

WanVideoSampler (model, image_embeds, text_embeds, steps, cfg, shift, scheduler)
  → LATENT

WanVideoDecode → IMAGE → VHS_VideoCombine → MP4

WanVideoSampler T2V Settings

Concept LoRAs (Installed)

Located in

loras/Wan Video 2.2 T2V-A14B/

• concept/PussyLoRA_HighNoise_Wan2.2_HearmemanAI.safetensors

  + LowNoise pair

Apply concept LoRAs the same way as lightning LoRAs — match hi/lo to the correct model pass. Use

LoraLoaderModelOnly

Resolution & Frame Count

Resolutions

Width and height must be divisible by 16.

Frame Count (

4n + 1

)

• 81 frames at 16fps = ~5 seconds (default, recommended)
• 49 frames at 16fps = ~3 seconds (faster)
• 121 frames at 16fps = ~7.5 seconds (longer, more VRAM)

Frame Rate

Standard: 16 fps for WAN 2.2 output.

VRAM Considerations

• Always

  clear_vram

  before switching to WAN T2V from another model family
• Lightning (4 steps) dramatically reduces generation time: ~70s vs ~5-10 min for 20 steps
• Only one UNET is active during each pass — they swap in/out

Prompt Tips

Describe motion and temporal progression, not just a scene:

Good: "A beautiful young woman slowly walks through a blooming cherry blossom garden, petals drifting in the breeze, soft sunlight filtering through branches, cinematic slow motion, 4K quality"
Bad: "woman in garden"

Include motion cues: "slowly walks", "camera pans", "wind blowing", "gradually reveals"

T2V vs I2V/FLF Comparison