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Optimize nvfp4 lora applying. (#11854)

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comfyanonymous 2026-01-13 16:23:58 -08:00 committed by GitHub
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commit 15b312de7a
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1 changed files with 2 additions and 10 deletions
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comfy/float.py
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@ -165,20 +165,12 @@ def stochastic_round_quantize_nvfp4(x, per_tensor_scale, pad_16x, seed=0):
block_scale = max_abs / F4_E2M1_MAX
scaled_block_scales = block_scale / per_tensor_scale.to(block_scale.dtype)
scaled_block_scales_fp8 = torch.clamp(scaled_block_scales, max=F8_E4M3_MAX).to(torch.float8_e4m3fn)
total_scale = per_tensor_scale.to(x.dtype) * scaled_block_scales_fp8.to(x.dtype)
# Handle zero blocks (from padding): avoid 0/0 NaN
zero_scale_mask = (total_scale == 0)
total_scale_safe = torch.where(zero_scale_mask, torch.ones_like(total_scale), total_scale)
x = x / total_scale_safe.unsqueeze(-1)
x /= (per_tensor_scale.to(x.dtype) * scaled_block_scales_fp8.to(x.dtype)).unsqueeze(-1)
generator = torch.Generator(device=x.device)
generator.manual_seed(seed)
x = torch.where(zero_scale_mask.unsqueeze(-1), torch.zeros_like(x), x)
x = x.view(orig_shape)
x = x.view(orig_shape).nan_to_num()
data_lp = stochastic_float_to_fp4_e2m1(x, generator=generator)
blocked_scales = to_blocked(scaled_block_scales_fp8, flatten=False)