AI Influencer Video Generation with WAN 2.2 in ComfyUI

The first video I generated of my AI influencer was horrifying. Started with a perfect still image. Beautiful face, great lighting, exactly the character I'd spent weeks developing. Three seconds later, she looked like a completely different person who'd been stung by bees.

That was my introduction to AI video generation. The gap between "this looks amazing in images" and "this works in video" is massive. I've since figured out how to bridge it, but let me tell you: video is where most AI influencer projects go to die.

This guide covers everything I learned getting WAN 2.2 to produce videos that actually look good. Including the many failures along the way.

Quick Answer: AI influencer videos with WAN 2.2 work by using image-to-video generation with your character's consistent face as the starting frame. The challenge is keeping that face recognizable throughout motion. Combine careful motion prompting, face consistency techniques, and conservative movement settings to produce short-form video content that doesn't immediately scream "AI."

Why Video Matters (And Why It's Hard)

Let me share some numbers that convinced me to figure this out.

My AI influencer's Instagram engagement on static images: 3-5%. Same character's Reels engagement: 12-18%. Video gets 3-4x the reach of images on every major platform. The algorithms push video hard.

But here's the problem: video is exponentially harder to get right. With images, if 30% of your generations are good, you just use those 30%. With video, one bad frame ruins the entire clip. You need every single frame to work.

Why WAN 2.2 Specifically

I've tested most video models at this point. WAN 2.2 hits a sweet spot for influencer content specifically:

Image-to-video that actually preserves faces. Most video models butcher faces. WAN 2.2 is notably better at keeping facial features stable, though far from perfect.

Natural human motion. Some models produce technically smooth video where people move like robots. WAN 2.2 produces movement that looks like actual humans, with the subtle shifts and imperfections that read as authentic.

Local generation. No API costs, no content restrictions, rapid iteration. When you're testing dozens of settings to find what works, paying per generation would bankrupt you.

ComfyUI integration. Connects to your existing character workflow. Your LoRAs, IPAdapter setups, and prompting strategies carry over. The full pipeline lives in one place.

Hot take: video generation is where local infrastructure pays off most. Cloud APIs charge premium prices for video, and you'll generate hundreds of test clips figuring out settings. Local generation makes that experimentation practical.

Hardware Reality Check

Before you spend hours on setup, let's talk hardware. WAN 2.2 video generation is hungry.

Minimum (It'll Work, Barely)

• GPU: RTX 3080 10GB or equivalent
• RAM: 32GB system memory
• Storage: NVMe SSD (spinning drives will make you cry)

At minimum specs, you're looking at 512x896 resolution and generation times of 5-10 minutes per 3-second clip. Usable but slow.

What I Actually Use

• GPU: RTX 4090 24GB
• RAM: 64GB system memory
• Storage: Fast NVMe with plenty of headroom

This handles 768x1344 in about 3-4 minutes per 3-second clip. Still not fast, but practical for production.

The Time Investment

Even on good hardware:

• 512x896: 2-4 minutes per 3-second clip
• 768x1344: 4-8 minutes per 3-second clip
• 1080x1920 native: Don't bother, upscale instead

I typically generate at 768x1344 and upscale to 1080x1920. Better quality-to-time ratio than native high-res generation.

Setting Up WAN 2.2 in ComfyUI

The installation process isn't hard but isn't obvious either.

Get The Models

Download WAN 2.2 from official sources (Hugging Face has them). You need:

• Main model weights (several GB)
• VAE files
• Associated CLIP models

Folder structure:

/ComfyUI/models/
├── wan/
│   ├── wan2.2_base.safetensors
│   └── wan2.2_vae.safetensors
└── clip/
    └── required_clip_models

Install Required Nodes

Through ComfyUI Manager, search and install:

• ComfyUI-WAN-Video-Nodes
• ComfyUI-VideoHelperSuite
• Any motion control nodes you want to experiment with

Restart ComfyUI after installing. The nodes should appear in your node search.

Verify Everything Works

Build a minimal test workflow:

1. Load any image
2. Connect to WAN 2.2 sampler
3. Generate a 2-second test clip
4. Verify it plays without errors

If this works, your installation is good. If not, check the console for specific error messages. Usually it's missing models or node versions.

The Starting Image Problem

Here's what took me too long to understand: video quality is mostly determined before you even touch video generation. The input image is everything.

What Makes a Good Video Input

Neutral expression. Slight smile works. Dramatic expressions limit where the video can go and often break during animation.

Natural pose. Relaxed, symmetrical-ish. Extreme poses look weird when animated and cause more artifacts.

Good lighting. Even, soft lighting. Harsh shadows create problems when the face moves.

Proper framing. Head and shoulders for face content. Leave space around the face for movement.

High resolution. Match or exceed your target output. 768x1344 minimum for portrait video.

My Image Generation Workflow

Before video generation, I run my standard character pipeline but optimized for video:

[Load Checkpoint + Character LoRA]
    ↓
[IPAdapter with character reference]
    ↓
[FaceID for face locking]
    ↓
[CLIP encode: prompt optimized for neutral pose]
    ↓
[KSampler]
    ↓
[Face Detailer] - Extra important for video
    ↓
[Output for video input]

The prompt I use for video input images is deliberately boring:

chrname woman, neutral expression, slight head tilt,
soft natural lighting, looking slightly toward camera,
head and shoulders portrait, professional photography

No dramatic scenes. No complex poses. Boring is good for video inputs.

If you don't have a character LoRA set up, check out my guide on training LoRAs for AI influencer characters. For non-LoRA approaches, my IPAdapter workflow guide covers alternatives.

Basic Video Generation Workflow

With a good input image, here's the core video workflow.

Node Structure

[Load Image (your character)]
    ↓
[WAN 2.2 Image Encoder]
    ↓
[Motion Prompt Encoder]
    ↓
[WAN 2.2 Sampler]
    ↓
[WAN 2.2 VAE Decode]
    ↓
[Video Output/Preview]

Motion Prompts That Actually Work

This is where I failed repeatedly before figuring it out. Motion prompts are not like image prompts. Less is genuinely more.

For talking head content (what most people need):

subtle head movement, natural breathing, soft blinking,
gentle expression shifts

That's it. Resist the urge to add more. More detail = more things to go wrong.

For lifestyle/ambient content:

slight swaying, gentle movement, looking around slowly,
relaxed idle motion

What NOT to do:

turning head dramatically, rapid expression changes,
energetic movement, looking in multiple directions

I made this mistake constantly. Wanted dynamic video, wrote dynamic prompts, got face-melting nightmares.

My Settings (After Lots of Testing)

• Steps: 40 (sweet spot for quality vs time)
• CFG: 7.5 (higher = more prompt adherence but stiffer motion)
• Frame count: 48-72 frames (2-3 seconds at 24fps)
• Denoise: 0.75 (lower = more faithful to input, higher = more motion)

Start with these. Adjust based on results for your specific character.

The Face Consistency Battle

This is the hard part. This is where 90% of AI influencer video projects fail.

Why Faces Break

During video generation, the model is essentially hallucinating each frame based on motion guidance. It's trying to maintain consistency, but small errors compound. By frame 72, those errors have accumulated into a different person.

The model doesn't "know" your character. It's just trying to animate an image plausibly.

Prevention Strategies

Conservative motion first. I cannot stress this enough. Subtle motion preserves faces better than dynamic motion. Always.

High drift risk prompts:

turning head sharply, rapid blinking, animated talking,
dramatic expression changes

Low drift risk prompts:

slight head movement, soft breathing, gentle blink,
relaxed micro-expressions

Shorter clips. 2-3 seconds is more reliable than 5-6 seconds. More frames = more drift opportunity. Generate multiple short clips and edit together rather than one long clip.

Perfect input images. Any inconsistency in your input amplifies through generation. Use your best, cleanest character image. Run Face Detailer on it first.

When Faces Still Break

Sometimes they will despite your best efforts. Options:

Regenerate with different seed. Sometimes random chance produces better results. I typically generate 3-5 versions of any clip I actually want to use.

Extract and fix frames. Export the video as frames, identify problematic ones, regenerate just those faces through your image pipeline, recomposite. Time intensive but works for critical content.

Use the good parts. A 3-second clip might have 1.5 good seconds. Trim to the good portion.

Optimizing for Social Platforms

Different platforms, different requirements.

Instagram Reels

• Resolution: 1080x1920 (9:16)
• Duration: 15-90 seconds typical
• Frame rate: 30fps

I generate at 768x1344 and upscale to 1080x1920. Results look great and generate way faster than native 1080.

TikTok

• Resolution: 1080x1920 (9:16)
• Duration: 15-180 seconds
• Frame rate: 30fps

TikTok especially rewards the first 3 seconds. Hook immediately. Your AI character looking into camera with slight expression change is often enough.

YouTube Shorts

• Resolution: 1080x1920 (9:16)
• Duration: Up to 60 seconds
• Frame rate: 30fps

YouTube is more forgiving on quality but rewards watch time. Multiple cuts of AI content often work better than one long clip.

Post-Processing Pipeline

After generation:

1. Upscale if needed (ESRGAN works for video frames)
2. Frame rate conversion from 24fps to 30fps (interpolation)
3. Color grade to match your character's established look
4. Add audio (voiceover, music, ambient sound)
5. Export in H.264 or HEVC for upload

Building Production Workflows

For consistent output, build reusable workflows.

My Template

INPUT:
├── [Load Character Image] - Swap per video
├── [Motion Prompt Text] - Adjust per video
└── [Settings Preset] - Usually keep consistent

PROCESSING:
├── [WAN 2.2 Pipeline]
└── [Face Region Attention Boost (if supported)]

OUTPUT:
├── [Frame Interpolation (24→30fps)]
├── [Upscale to 1080p]
├── [Video Encode H.264]
└── [Save with timestamp naming]

Batch Generation Strategy

When I need multiple videos (content week, for example):

1. Generate 5-10 input images in one session
2. Queue video generations overnight
3. Morning review: sort into keep/maybe/reject
4. Keep ~30-40% for actual use
5. Polish keepers with audio and color

This produces maybe 3-5 usable videos from an overnight batch. Not a huge hit rate, but consistent enough for regular posting.

Troubleshooting The Usual Problems

Face Morphing Mid-Video

Cause: Usually too aggressive motion prompts or too many frames.

Fix: Simplify motion prompts. Generate shorter clips. Ensure input image is clean.

Jerky/Robotic Movement

Cause: CFG too high, steps too low, or poor motion prompting.

Fix: Lower CFG to 6-7, increase steps to 50, use more natural motion language.

Blurry Output

Cause: Low base resolution, poor VAE settings, or compression issues.

Fix: Generate at higher resolution, verify VAE is correct for model, check export settings.

Character Completely Wrong

Cause: Random failure, bad seed, corrupted generation.

Fix: Regenerate with different seed. Check that models loaded correctly. Sometimes ComfyUI just has a bad run.

Generation Takes Forever

Cause: Resolution too high, steps too high, or insufficient VRAM causing offloading.

Fix: Reduce resolution, lower steps to 30-40, or generate smaller clips and upscale.

The Realistic Expectations Talk

Let me be direct about what to expect.

AI influencer video is not going to look as good as your images. The technology isn't there yet. What you can achieve is "good enough that most people won't notice" for short clips with limited motion.

I've been doing this for months. My video success rate is maybe 30-40%. The rest is either obviously AI or my character drifts too much. That's after optimizing everything. Early on, success rate was maybe 10%.

If you need guaranteed consistency in every frame, AI video isn't there yet. If you need "usually good enough for social media where people scroll past in 3 seconds anyway," it's achievable.

For those who want video generation capabilities without the workflow complexity, platforms like Apatero.com handle the infrastructure and optimization. Worth considering if you'd rather focus on content than technical troubleshooting.

Frequently Asked Questions

How long can videos be?

Practical single-generation limit is 3-6 seconds. Longer content requires generating multiple clips and editing together. My typical workflow is 2-3 second clips combined in editing.

Can I add voice to the videos?

Yes. Generate video first (silent), then add audio. Voice synthesis works well. Some tools can adjust lip sync in post, though results vary.

How do I make it look less AI?

Subtle motion prompts. Proper frame rate. Good lighting in source image. Natural, limited movement. The "AI look" usually comes from unnatural motion or face artifacts.

Full body movement possible?

Yes, but face consistency becomes much harder with more complex motion. For body-focused content, consider slightly distant or partially obscured face angles.

What's the cost vs cloud services?

After hardware investment, local generation is basically free (electricity). Cloud video APIs charge $0.10-0.50+ per second. I generate hundreds of test clips monthly. Cloud pricing would be prohibitive.

How often should I post video?

Quality over quantity. 2-4 well-produced videos per week outperforms daily garbage. One good video with clean face and natural motion beats five with obvious artifacts.

Can I combine AI video with real footage?

Absolutely. Many AI influencer accounts use b-roll, location shots, and real elements mixed with AI character footage. The AI character doesn't need to carry every frame.

Do platforms require AI disclosure?

Increasingly yes. Check current policies for your platforms. Beyond requirements, some audiences appreciate transparency while others prefer the illusion.

Copyright on AI-generated video?

Generated content using your own character is typically yours. Using copyrighted music, brands, or other protected elements remains restricted as with any content.

What about live video/streaming?

AI influencers cannot do live video authentically with current tech. Some use real humans for live streams and AI for other content. The mismatch is risky but some make it work.

What's Next

WAN 2.2 is genuinely usable for AI influencer video, but it takes work to get right. The learning curve is steep, the failure rate is high, and the results (when they work) are good enough for social media but not cinematic.

If you're serious about this, start building your video workflow alongside your image workflow. The skills compound, and video is increasingly important for platform reach.

For the complete picture on AI influencer creation, check out my main ComfyUI workflow guide covering character creation from scratch, and my guide on IPAdapter/FaceID workflows for consistency techniques that carry over to video.

Video is hard. But video is also where the algorithm rewards you. Figure it out, and your AI influencer has a significant advantage over image-only competitors.