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. It establishes structure, motion, composition
• LowNoise model handles late denoising. It 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, or any image input. It uses EmptyHunyuanLatentVideo for latent initialization and text-only conditioning.

Models

UNET (Installed)

Model	Loader	Notes
Wan2_2-T2V-A14B_HIGH_fp8_e4m3fn_scaled_KJ.safetensors	UNETLoader	HighNoise expert, 14.3GB FP8
Wan2_2-T2V-A14B-LOW_fp8_e4m3fn_scaled_KJ.safetensors	UNETLoader	LowNoise expert, 14.3GB FP8

Text Encoder

Component	Node	Model	Notes
CLIP (T5)	CLIPLoader (type=wan)	umt5_xxl_fp8_e4m3fn_scaled.safetensors	UMT5-XXL fp8, in clip/

VAE

Component	Node	Model
VAE	VAELoader	wan_2.1_vae.safetensors

VACE Modules (Installed — For Advanced Control)

Model	Size	Notes
Wan2_2_Fun_VACE_module_A14B_HIGH_bf16.safetensors	5.8GB	HighNoise VACE module
Wan2_2_Fun_VACE_module_A14B_LOW_bf16.safetensors	5.8GB	LowNoise VACE module

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

Lightning LoRAs (Installed)

T2V Lightning v1.1 (Paired Hi/Lo)

LoRA	Applies To	Path
wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise	HighNoise UNET	Unknown/no tags/
wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise	LowNoise UNET	Unknown/no tags/

T2V Lightning Seko V2.0 (Alternative Paired)

LoRA	Path
Wan2.2_HN_T2V_Lightning_4steps-lora-rank64-Seko_V2.0_HIGH	Root loras/
Wan2.2_HN_T2V_Lightning_4steps-lora-rank64-Seko_V2.0_LOW	Root loras/

T2V CFG-Step Distill (Higher Quality)

LoRA	Path	Notes
lightx2v_T2V_14B_cfg_step_distill_v2_lora_rank128_bf16	Root loras/	CFG+step distilled, use with more steps

Sampler Settings

Lightning (4-Step, Recommended for Speed)

Parameter	Pass 1 (Hi)	Pass 2 (Lo)
model	Hi + Hi Lightning LoRA	Lo + Lo Lightning LoRA
add_noise	enable	disable
steps	4	4
cfg	1.0	1.0
sampler_name	euler	euler
scheduler	simple	simple
start_at_step	0	2
end_at_step	2	4
return_with_leftover_noise	enable	disable

Standard (20-Step, Full Quality)

Parameter	Pass 1 (Hi)	Pass 2 (Lo)
model	Hi + ModelSamplingSD3 (shift=8)	Lo + ModelSamplingSD3 (shift=8)
add_noise	enable	disable
steps	20	20
cfg	3.5	3.5
sampler_name	euler	euler
scheduler	simple	simple
start_at_step	0	10
end_at_step	10	20
return_with_leftover_noise	enable	disable

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 to 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 (which is for FLF/I2V only). The latent goes directly to KSamplerAdvanced Pass 1.

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
• LoRAs load via WanVideoLoraSelect → WanVideoModelLoader lora input

⚠️ CRITICAL: merge_loras=false with fp8-scaled models

When a LoRA (e.g. the lightx2v 4-step lightning hi/lo LoRAs) is loaded onto an fp8-quantized model (quantization=fp8_e4m3fn_scaled) via WanVideoLoraSelect, set the node's merge_loras widget to false. The default merge_loras=true tries to bake the LoRA into the already-quantized fp8 weights and hard-crashes ComfyUI during LoRA loading with no Python traceback (the process dies, which looks like an unexplained restart/OOM). merge_loras=false applies the LoRA as a runtime patch instead, which is fp8-safe. Use merge_loras=true only on non-quantized bf16/fp16 models. Pairs cleanly with WanVideoBlockSwap for fp8 14B on 24GB cards.

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

Parameter	Standard	Lightning	Notes
steps	30	4
cfg	6.0	1.0
shift	5.0	5.0	Flow matching shift
scheduler	unipc	euler
force_offload	true	true

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 with strength 0.5 to 1.0.

Resolution & Frame Count

Resolutions

Aspect	Resolution	Notes
Landscape 16:9	832x480	Default, recommended
Portrait 9:16	480x832
720p landscape	1280x720	Higher quality, more VRAM
720p portrait	720x1280

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

Config	VRAM	Notes
Dual FP8 models + UMT5 fp8	~22-24GB	Tight on RTX 4090
Single FP8 model (no dual)	~14-16GB	Lower quality but safer
With VACE modules	+5.8GB per module	Very tight, may need block swap

• Always clear_vram before switching to WAN T2V from another model family
• Lightning (4 steps) cuts generation time to ~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 in addition to the 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

Feature	T2V	I2V/FLF
Input	Text only	Text + start/end images
Latent init	EmptyHunyuanLatentVideo	WanFirstLastFrameToVideo
CLIPVision	Not used	Required
Models	T2V-specific (HIGH/LOW)	I2V-specific (HIGH/LOW)
Lightning LoRAs	T2V-specific	I2V-specific
Creativity	Full creative freedom	Constrained by input frames
Use case	Original content	Transitions, animations

Sources

• Official: none found.
• Empirical: sampler values, wiring, and prompt notes from working graphs in packs/ and observed renders; not a vendor prompting guide.