Official FastVideo/LTX2-Distilled-Diffusers. New torch_ltx2.py adapters (build_torch_* dispatch on
class):
- TorchLTX2DiT: patchify-internal; per-token timestep ones(B,tok,1)*sigma (sigma direct) + per-sample
video_sigma; DiT predicts x0 so the adapter returns velocity=(x_t-x0)/sigma for the v2 flow-match step.
- TorchLTX2VAE: CausalVideoAutoencoder decode (internal per-channel un_normalize).
- TorchGemma: LTX2GemmaTextEncoderModel (Gemma + feature-extractor + connectors) -> last_hidden_state.
- ltx2 loop: real 128-ch latent geometry on cuda (32x spatial / 8x temporal; half-res base, 2x upsample).
e2e two-stage (8+3 steps) -> coherent, high-quality video (surfers at sunset), (3,9,512,768). NOTE:
still uses the v2 program's np.repeat upsampler between stages (the refine regenerates from noise so
output is faithful-quality); real LTX2LatentUpsampler swap-in is a follow-up.
SolitaryThinker
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