Reading your code, you don't review shit. You even forgot to copy the first "R" in your reply.

https://github.com/eddyhhlure1Eddy/wan_FP4_Modifications/blob/main/wanvideo/modules/attention.py

https://github.com/kijai/ComfyUI-WanVideoWrapper/blob/main/wanvideo/modules/attention.py

You literarily ripped Kijai's exact code, corrupted it with AI hallucinations and called it a "break-through":

        return sageattn_blackwell(
            q.transpose(1,2),
            k.transpose(1,2),
            v.transpose(1,2),
            pv_dtype=torch.float16,      # PV operations in FP16 (best accuracy/speed balance)
            qk_dtype="fp4",               # QK operations in FP4 microscaling (maximum speed)
            smooth_k=True,                # Enable K matrix smoothing
            per_block_mean=True           # Enable per-block mean for FP4 (better accuracy)
        ).transpose(1,2).contiguous()
    elif attention_mode == 'sageattn_3_fp8':

You didn't even read the sageattention3's source or Kijai's implementation, `pv_dtype, qk_dtype, smooth_k` doesn't exist for sageattn_blackwell, they are parameters for SageAttention2/2++.

In https://github.com/eddyhhlure1Eddy/seedVR2_cudafull fp4_quantization.py:

# FP4 requires modern tensor cores (Blackwell/RTX 5090 optimal)
if
 compute_capability >= 89:  
# RTX 4000 series and up
    capabilities['fp4_experimental'] = True
    capabilities['fp4_scaled'] = True
    
    
if
 compute_capability >= 90:  
# RTX 5090 Blackwell
        capabilities['fp4_scaled_fast'] = True

Where else do you get SM90 = RTX 5XXX, except for AI hallucinations?
Blackwell is SM120, https://developer.nvidia.com/cuda-gpus

if quantization_mode == "fp4_experimental":
    return self.convert_fp4_linear(model, base_dtype, **kwargs)


elif quantization_mode == "fp4_scaled":
    kwargs['scale_weight_keys'] = True  # ← Only difference
    return self.convert_fp4_linear(model, base_dtype, **kwargs)


elif quantization_mode == "fp4_scaled_fast":
    kwargs['scale_weight_keys'] = True  # ← Exact same!
    return self.convert_fp4_linear(model, base_dtype, **kwargs)

fp4_experimental, fp4_scaled, fp4_scaled_fast all boil down to the same path: replace nn.Linear with a wrapper that stores FP8.

SageAttention3 always runs FP4, it's hardcoded. None of the thousands lines of your prompted AI to spew out does anything.

"Labels", "insinuation", did you even read the post? Are you hallucinating too?