Test · Speed vs fidelity

Same Wan 2.2, Different Results: Fast Mode vs Full Workflow, Tested Side by Side

Two local apps, Loomance and WanGP, the same Wan 2.2 image-to-video model, the same PC, the same images and prompts. One is built for speed and low VRAM, the other for staying faithful to your image. We rendered both five times per scene and compared every clip to the source image, frame by frame.

Published 21 October 2026 · Updated 27 October 2026 · By Vismantas Šleinota · Measured on RTX 5070 12GB, 64GB DDR5-6000

Quick answer: the model is the same, so the settings decide the result. 4-step "lightning" modes are about 10× faster (≈1.5 min vs ≈13–16 min at 832×480) but add the most errors: floating steam clouds, a vanished curtain, a moved "static" camera, smoothed skin. With full 30-step sampling the gap gets much smaller. In a simple one-person scene at 1536×864, both tools kept the face close to the source. In a busy scene with six dogs, WanGP melted or merged characters in 3 of 5 runs; Loomance had no melted or merged characters in 5 of 5, but did redraw some faces and once stacked two heads awkwardly.

Honest scope: one PC, two scenes, five runs per tool per scene, random seeds. The other tool is WanGP v18.19, a popular free all-in-one Wan 2.2 app, installed through Pinokio and used with its default settings. Loomance was the October 2026 build, before the 0.2 speed update. I build Loomance, so I've tried hard to show where it lost too. Judge the clips yourself.

What we compared

	Loomance (Oct 2026, before 0.2)	WanGP v18.19
Model	Wan 2.2 I2V A14B fp8 (high + low noise)	Wan 2.2 I2V A14B (default and 4-step "lightning")
Engine	ComfyUI workflow with extra stability steps, 12 + 12 steps	Own engine, 30 steps default, or 4 steps in fast mode
Memory approach	Model in VRAM, ~8–11 GB	Model in system RAM, streamed: ~4–5 GB VRAM, ~50 GB RAM
Interpolation	RIFE, 24 FPS	RIFE, 32 FPS (we re-timed all clips to 24 FPS so faster playback doesn't hide errors)

Test 1: fast mode vs full sampling (one person, 832×480)

Prompt: "The woman slowly lifts the coffee cup and takes a small sip, then lowers it and smiles, still looking out the window. Steam rises from the cup. A light breeze moves her hair and the sheer curtain. Static camera."

Source image on top. Bottom: Loomance, WanGP at 30 steps, WanGP in 4-step fast mode. All re-timed to 24 FPS.

Frame 121 compared to the source: Loomance and the 30-step tool keep the curtain and the window; the 4-step fast mode loses the curtain, moves the camera and puts a cloud of steam over the face — Frame 121. In 4-step fast mode the curtain is gone, the window frame has moved in from the right, and a cloud of steam floats over her face.

4-step fast mode (~1.5 min): floating steam "blobs" detached from the cup, freckles fading, smoothed skin, the curtain replaced, and the camera drifting even though the prompt said "static camera".
30 steps (~13 min): much cleaner. The curtain stays and the steam behaves. The camera still slowly pushes in, and steam passed over the face mid-clip.
Loomance (~13–16 min): camera stays put, curtain and background unchanged, no steam artifacts.

Test 2: six characters (2D style, 832×480, 5 runs each)

Busy scenes are where video models struggle most: every character has to keep its face and body while others move around it. Prompt: "The puppy in the front pushes the red ball with its nose and the ball rolls forward. The dark brown puppy wags its tail and barks happily. The two adult dogs lean their heads together and smile. The puppy on the left tilts its head curiously. The wooden swing sways gently in the breeze. Static camera, hand-painted 2D animation style."

The first run of each tool (Loomance left, WanGP right), source image on top, both at 24 FPS. Watch the left puppy's head in the right clip.

Zoomed crops from the six-dog test: source image, Loomance and WanGP. WanGP melts the left puppy's head, merges two right puppies and turns a tail into horn-like shapes; Loomance stacks the adult dogs' heads awkwardly — Zoomed crops, same frame from each tool next to the source. The last row is a Loomance miss.

Six dogs, 5 runs each	Loomance	WanGP, 30 steps
Runs with melted or merged characters	0 of 5	3 of 5
All six dogs still in frame at the end	3 of 5	2 of 5
Faces redrawn (clean, but no longer the source character)	Several, mostly puppies	Most runs, often all dogs
Other issues	Heads stacked awkwardly (1 run), small dark specks in the sky	Tail turned into "horns", eyes winking or uneven
Ball pushed forward as prompted	0 of 5	0 of 5

Neither tool is clean here. At 832×480 each puppy's head is only about 90 pixels wide, and both redraw faces. The difference is the kind of error: Loomance's mistakes stayed clean drawings (a turned head, a new expression), while WanGP more often produced shapes that aren't dogs anymore. When you play the clips, those show up as faces that "boil" from frame to frame.

Test 3: one character at high resolution (1536×864, 53 frames, 5 runs each)

Shorter prompt so the action fits in ~2 seconds: "The woman lifts the coffee cup to her lips and takes a small sip, then lowers it and smiles. Static camera." Here the result was close, and this is where WanGP did best.

High-resolution test: clearly visible faces from Loomance and WanGP look close to the source; below, WanGP's camera reframe, hair spike and colour fade, and a Loomance run where the face turned and the eyes went wide — Top: faces picked where no cup covers them. Bottom: the worst moments from both tools.

One person, 1536×864, 5 runs each	Loomance	WanGP, 30 steps
Face close to the source (visible frames)	Yes, more expression changes	Yes, very close
Full action (sip, lower, smile)	3 of 5	1 of 5
Clear errors	1 run: face turned, eyes wide, looks like a different person	1 run: camera reframed + hair spike; 1 run: colours washed out
Time per clip	~56 min	~40 min
Peak VRAM	~10.9 GB	~4–5 GB (≈50 GB RAM)

Part of WanGP's high face fidelity comes from doing less: in four of five runs she was still holding the cup to her lips at the end, so most of the clip shows the face in the source pose. Loomance more often finished the whole action, which means more expression and more chances to drift. Both behaviours are useful, depending on what you need.

So which should you use?

You want many quick variations or you're short on VRAM: a fast all-in-one tool like WanGP is the right pick. Expect more errors and changed characters, especially in fast mode and busy scenes.
You want the characters from your image to stay the same and fewer artifacts: use full sampling and accept longer renders. Queue them up and let them run overnight; you'll pick the best clips in the morning.
Many characters or a lot of motion: this is where the extra stability steps mattered most in our tests.
Whatever you use: check results at 24 FPS and zoomed in. Faster playback and small previews hide most errors.

The real cost isn't the minutes per render, it's the minutes per usable clip. A 1.5-minute render you have to redo ten times isn't faster than a 15-minute render you keep.

How Loomance fits in

Loomance is the free desktop app I build. It installs ComfyUI and Wan 2.2 and runs a workflow tuned for fidelity: the stability steps are already set, so you don't need to build node graphs. It isn't the fastest option, and at 1536×864 a 12GB card hits its VRAM limit around 53 frames (details). Tools that stream the model from RAM avoid that limit.

Tools and sources

Wan 2.2, the open-source video model by Alibaba's Wan team
ComfyUI, the engine behind Loomance
GPU at 100% but your render is stuck?, the VRAM limit test on the same PC
Wan 2.2 on an 8GB GPU? VRAM and RAM tests

VŠ

Vismantas Šleinota builds Loomance, a free local AI animation app, as a solo project in Lithuania, and discusses local AI video as u/noise_to_motion on Reddit. Every clip on this page was rendered on his own PC. Got different results with your setup? Send them to [email protected].

Frequently asked questions

Do 4-step "lightning" Wan 2.2 models lower quality?

In our tests, yes. A 4-step render took about 1.5 minutes instead of 13–16, but added floating steam artifacts, smoothed skin, lost freckles, replaced the curtain and moved a camera that the prompt asked to keep static. With full 30-step sampling most of these disappeared.

Why do characters change or melt in Wan 2.2 videos?

The model redraws every frame, and small characters at low resolution have few pixels to hold their identity. In a six-dog scene at 832×480, both tools redrew faces. Melted heads and merged bodies appeared in 3 of 5 runs with WanGP at 30 steps and in 0 of 5 with Loomance's stability workflow. Higher resolution, fewer characters and full sampling all help.

Is a faster Wan 2.2 tool better if I have little VRAM?

Tools that keep the model in system RAM can render high resolutions and long clips with only about 4–5 GB of VRAM, but they need a lot of RAM (about 50 GB in our test). They are a good choice for low-VRAM cards. Check the results zoomed in at normal playback speed, since fast modes trade fidelity for speed.

How should I compare AI video tools fairly?

Use the same image, prompt and resolution, render several times with random seeds, play everything at the same frame rate (we used 24 FPS), and compare frames where the face is clearly visible against the source image. Count usable clips, not just render time.

Try the fidelity-first workflow

Loomance is free to use (ad-supported) and renders locally on your NVIDIA GPU. It sets up ComfyUI and Wan 2.2 for you. No node graphs, no cloud uploads.

Download free