onamissiononamission/Forced_BJ_HighN_lora

Forced BJ

Model description

Trained on https://yorespot.com \u2014 where creators train faster, cheaper, and with minimal censorship (within legal limits). If you\u2019re serious about building models, that\u2019s where you should be.

For the hottest working Loras for wan2.2 that CivitAI has removed visit the site above we got them before they were deleted.

WAN 2.2 I2V LoRA

Trigger Word ysp_forcbj24

Overview This is a WAN 2.2 I2V LoRA trained for high-quality portrait video generation. It uses a two-phase training approach to properly separate low-noise detail learning and high-noise motion structure \u2014 resulting in cleaner motion, stronger consistency, and better frame coherence than single-pass LoRAs.

Training Details Base Model:\u2022 WAN 2.2 I2V 14B\u2022 (LOW + HIGH LoRA files) Method:\u2022 Musubi Tuner video LoRA workflow\u2022 Two-phase training (LOW timesteps \u2192 HIGH timesteps) Dataset:\u2022 16 portrait video clips (512\u00d7512)\u2022 Hand-captioned (.txt per clip)\u2022 Full frame extraction (~30 FPS)\u2022 Up to 33 frames per clip\u2022 4 repeats per clip Training Config:\u2022 LoRA rank: 32\u2022 Alpha: 32\u2022 Learning rate: 2e-4\u2022 Optimizer: AdamW (8-bit)\u2022 Scheduler: Cosine\u2022 Precision: bf16 + gradient checkpointing Training Process:\u2022 50 epochs (LOW noise model)\u2022 50 epochs (HIGH noise model)\u2022 Total: 100 effective passes across both experts Training Time:\u2022 ~20 hours total runtime

Recommended Usage (IMPORTANT) This is a dual-LoRA system. You MUST use both files together. HIGH noise LoRA:\u2022 Strength: 0.55 LOW noise LoRA:\u2022 Strength: 0.90 Best results come from using both simultaneously in a WAN 2.2 I2V workflow. Using only one will significantly reduce quality.

Why This Matters Most LoRAs fail at motion consistency or overfit to static detail. This setup avoids that by splitting the learning problem: \u2022 LOW model \u2192 structure, identity, fine detail\u2022 HIGH model \u2192 motion, transitions, temporal coherence The result is smoother, more stable, and more realistic video output.

If you want to train models li...

Model Details

• Type: lora
• Base Model: Wan Video 2.2 I2V-A14B
• Version: HighN
• Upvotes: 85.0
• Downloads: 2927
• Size: 585.1 MB

Credits

Original model by: YoReSpot Civitai: Link