Upload 2649114/2974576/README.md with huggingface_hub

2649114/2974576/README.md

@@ -0,0 +1,122 @@
+---
+license: other
+tags:
+- action
+---
+
+# Forced BJ - LowN - 2974576
+
+**Model Type**: LORA
+
+**Base Model**: Wan Video 2.2 I2V-A14B
+
+**Trigger Words**: None
+
+**Tags**: action
+
+## Gallery
+
+<table>
+  <tr>
+    <td><video src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/3cd9ad70-41a2-4a83-9f8b-9db21c3209b8/original=true/131736825.mp4" width="200" controls muted autoplay loop></video></td>
+    <td><video src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/d1ea8c89-5ed9-493f-b39c-92f74b250bcf/original=true/131736922.mp4" width="200" controls muted autoplay loop></video></td>
+    <td><video src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/4a54d66d-cdf5-4c08-aef3-3d8125f239db/original=true/131736923.mp4" width="200" controls muted autoplay loop></video></td>
+  </tr>
+</table>
+
+## Description
+
+Trained on <https://yorespot.com> — where creators train faster, cheaper, and with minimal censorship (within legal limits). If you’re serious about building models, that’s where you should be.
+
+---
+
+WAN 2.2 I2V LoRA
+
+========================================================
+
+Trigger Word
+------------
+
+ysp\_forcbj24
+
+---
+
+Overview
+
+This is a dual-expert 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 — resulting in cleaner motion, stronger consistency, and better frame coherence than single-pass LoRAs.
+
+---
+
+Training Details
+
+Base Model:  
+• WAN 2.2 I2V 14B  
+• Dual-expert setup (LOW + HIGH LoRA files)
+
+Method:  
+• Musubi Tuner video LoRA workflow  
+• Two-phase training (LOW timesteps → HIGH timesteps)
+
+Dataset:  
+• 16 portrait video clips (512×512)  
+• Hand-captioned (.txt per clip)  
+• Full frame extraction (~30 FPS)  
+• Up to 33 frames per clip  
+• 4 repeats per clip
+
+Training Config:  
+• LoRA rank: 32  
+• Alpha: 32  
+• Learning rate: 2e-4  
+• Optimizer: AdamW (8-bit)  
+• Scheduler: Cosine  
+• Precision: bf16 + gradient checkpointing
+
+Training Process:  
+• 50 epochs (LOW noise model)  
+• 50 epochs (HIGH noise model)  
+• Total: 100 effective passes across both experts
+
+Training Time:  
+• ~20 hours total runtime
+
+---
+
+Recommended Usage (IMPORTANT)
+
+This is a dual-LoRA system. You MUST use both files together.
+
+HIGH noise LoRA:  
+• Strength: 0.55
+
+LOW noise LoRA:  
+• Strength: 0.90
+
+Best results come from using both simultaneously in a WAN 2.2 I2V workflow (ComfyUI dual-slot setup).
+
+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:
+
+• LOW model → structure, identity, fine detail  
+• HIGH model → motion, transitions, temporal coherence
+
+The result is smoother, more stable, and more realistic video output.
+
+---
+
+If you want to train models like this yourself — faster, cheaper, and without the usual platform limitations — train at <https://yorespot.com>.
+
+---
+
+Author: [YoReSpot](https://civitai.com/user/YoReSpot)
+
+Model: [CivitAI Model Page](https://civitai.com/models/2649114?modelVersionId=2974576)
+
+Archive: [CivArchive Page](https://civarchive.com/models/2649114?modelVersionId=2974576)
+
+<!-- Version: 20260525_upload -->