The transformer and text encoder auto-quantize; the VAE stayed dense. VAEs are
convolutional, so torchao int8 (Linear/2D-only) does not apply, but
Float8DynamicActivationFloat8WeightConfig quantizes Conv2d/Conv3d weights with
PerTensor granularity (auto-skipping convs whose channels are not a multiple of 16,
so the 3-channel RGB head stays dense). New diffusion_vae_quant.py offers two
schemes: fp8_dynamic (torchao conv compute fp8, cc>=8.9, resident) and fp8
(diffusers layerwise storage cast, any conv, survives offload); no int8 (no Conv3d
int8 kernel). select_vae_quant_scheme walks (fp8_dynamic, fp8) with a live conv
smoke probe, an offload gate, a per-family deny list, and a force_fp32 gate; the
image + video loaders map unset vae_quant to auto, skip the vae_force_fp32 Wan
families, and record the engaged scheme. Guards _align_vae_dtype to skip the
img2img/inpaint re-cast when the VAE is quantized (its fp8 tensor subclasses reject
.to(dtype=)). Verified on a B200: %16 Conv2d/Conv3d/Linear -> Float8Tensor, conv_out
dense, forward runs.
