# Wan Fun Control Debugging Summary - Session End

## Current State ✅❌

**✅ What's Working:**
- BoyoWanFunImageSampler successfully generates clean images (no garbage)
- Model patching approach is correct (video model → single image)
- VAE encoding path works without errors
- Control strength 0.0 = perfect normal images

**❌ What's NOT Working:**
- Zero control influence at any strength (0.1 → 2.0)
- Control signal not affecting generation at all

## Root Cause Analysis 🔍

**Discovery:** Wan 2.1 Fun Control config shows `"in_dim": 48` 
- Expected: 32 control channels + 16 latent channels = 48 total
- **Problem:** Our control might still be wrong format/channels

## Action Plan for Next Session

### A) WanVideoWrapper Forensic Logging 🕵️

**Add logging to official WanVideoWrapper at these key points:**

```python
# 1. Control latents input
log.info(f"🔍 control_latents input shape: {control_latents.shape}")
log.info(f"🔍 control_latents dtype: {control_latents.dtype}")

# 2. Image conditioning 
log.info(f"🔍 image_cond shape: {image_cond.shape}")

# 3. The critical concatenation
if (control_start_percent <= current_step_percentage <= control_end_percent):
    image_cond_input = torch.cat([control_latents.to(z), image_cond.to(z)])
    log.info(f"🔍 CONTROL ON - image_cond_input shape: {image_cond_input.shape}")
else:
    image_cond_input = torch.cat([torch.zeros_like(control_latents, dtype=dtype), image_cond.to(z)])
    log.info(f"🔍 CONTROL OFF - image_cond_input shape: {image_cond_input.shape}")

# 4. Y parameter to transformer
log.info(f"🔍 y parameter: {type(kwargs.get('y', 'None'))}")
if 'y' in kwargs:
    log.info(f"🔍 y[0] shape: {kwargs['y'][0].shape}")

# 5. Main latent being processed
log.info(f"🔍 z (main latent) shape: {z.shape}")
```

### B) BoyoWanFunImageSampler Current Issues 🛠️

**Files:** `BoyoControl.py` (current version with 32-channel control)

**Known Issues to Fix:**
1. **Control tensor format** - Need exact match to working implementation
2. **Y parameter structure** - Verify list format `[tensor]` vs direct tensor
3. **Step timing logic** - Current percentage calculation might be wrong
4. **Channel ordering** - Maybe control/latent order is swapped

**Next Session Goals:**
1. **Compare logs** - Official vs our implementation
2. **Fix tensor shapes** - Match exactly what works
3. **Test control influence** - Should work at 0.3-0.5 strength
4. **Verify pose and depth** - Both control types working

## Quick Reference 📋

**Current BoyoWanFunImageSampler logic:**
```python
# Our current approach:
image_cond_input = torch.cat([control_latents, x], dim=1)  # 32 + 16 = 48 channels
kwargs['y'] = [image_cond_input]
```

**Expected working result:** Control influence visible at 0.3-0.8 strength

---
*Resume point: Add logging to WanVideoWrapper, run comparison test, fix tensor format discrepancies* 🚀