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[RFD3] Build real conditioning features instead of zero tensors
Verified signatureThe pipeline ran to completion and exited zero while producing coordinates that were not a protein. Backbones came out with a mean CA-CA distance of 13.20 A against an expected 3.80, and 112 pairs closer than 3 A. ## Root cause `RFDiffusionTransformerModel.forward` had a "simplified path", taken whenever the caller passed no feature dict, that fed all-zero tensors for `Q_L_init`, `C_L`, `P_LL`, `S_I` and `Z_II`. The denoise block never passed a feature dict, so that path always ran. Those tensors are learned embeddings of real chemical and positional features, not optional inputs, so the model was being evaluated on empty conditioning at every step. Three loading bugs hid this. The scheduler could never load, because `RFDiffusionScheduler` inherited only `ConfigMixin` and so had no `from_pretrained`. Both the noise-schedule block and the denoise block silently substituted their own simplified samplers when it came back `None`. And bfloat16, which the model card recommended, crashed …40f3021
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