From versions 1.0 to 4.0, my primary focus was eliminating the "moonwalk" effect that typically appears after 3–5 seconds of walking. After extensive testing, I’ve concluded that this effect is likely an inherent limitation of the Wan 2.2 architecture that cannot be 100% eliminated through training alone.
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Even after training the model on 20 clips (15 seconds each) covering every possible angle, the improvement was marginal and came at a cost: facial consistency drifted and "glitter" artifacts began to appear.
Seeing similar results in other creators' models reinforced this belief.
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The Pivot: SVI Integration The introduction of SVI (Spatial-Video Interpolation/Integration) changed the game. I found that by splitting the scene (starting with an 81-frame segment followed by 121-frame parts), the moonwalk issue is handled much more effectively.
With the motion stability solved via workflow, my latest model iterations have shifted focus toward improving breast physics and natural movement instead.
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Recommended Settings
While newer model versions exist, these specific settings have yielded the most stable walking results for me:
SVI Configuration: 1st Part (81 frames) | Subsequent Parts (81-121 frames)
Checkpoint (High):
wan2.2_i2v_high_noise_14B_Q8_0.ggufCheckpoint (Low):
wan2.2_i2v_low_noise_14B_Q8_0.ggufLoRA:
Wan21_I2V_14B_lightx2v_cfg_step_distill_lora_rank64.safetensorsWeight (High): 3.0
Weight (Low): 1.5
Steps: 6
LoRA Strength: 0.5 – 1.0 (I personally find 0.5 to be the sweet spot)
(Tip for Slow Motion: If you encounter "slow-mo" issues, if you using SVI, simply increase the output frame rate.)
(The breasts motion and physic work well with the M4CROM4STI4 model)
https://civitai.red/models/1852647/m4crom4sti4-huge-natural-breasts-physics-wan22-video-lora-k3nk
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Prompting Guide
Trigger Word: wlkng Prompt Strategy:
wlkng,
The subject walks casually alongside the viewer, with the camera following the subject’s movement.