Potential bug in sd3_clip : encode_token_weights (depending how Stability trained it)

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In the method encode_token_weights in sd3_clip.py, the empty conditioning is generated as:

out = torch.zeros((1, 77, 4096), device=comfy.model_management.intermediate_device())
pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device())

This seems correct for pooled, but 'out' would presumably need to be 77 * 2, as per the SD3 model diagram. Similarly, not including padded zeroes for CLIPs/T5 when missing seems wrong.

It would depend on how Stability originally trained the model. Either the unconditional generated this way (presuming blank prompts) wouldn't match the tensor size of a conditional with all 3 text encoders (154 long), or the unconditional would need to be created as zeroes based on the length of the conditional.

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Seems perhaps you don't really need to pad the CLIP or T5 sections if not using them. They use different positional embeddings within their sub-range, so the model wouldn't be looking for them at specific indices.