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yitongl/
sparse_quant_exp

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sfp4_v4_sparse09_hpo_on_ours_p_init2050 checkpoint-700

This upload contains the consolidated WanTransformer3DModel transformer weights from:

checkpoints/sfp4_v4_sparse09_hpo_on_ours_p_init2050_1n_interactive/checkpoint-700

Contents:

transformer/config.json
transformer/diffusion_pytorch_model.safetensors
backend_snapshot/
standalone_inference/

Training run:

run name: sfp4_v4_sparse09_hpo_on_ours_p_init2050_1n_interactive
source init: sfp4_v4_sparse06_hpo_on_ours_p_1n_interactive_v2 checkpoint-2050
attention backend: SPARSE_FP4_OURS_P_ATTN
high precision output for backward: enabled
VSA sparsity: 0.9

This package does not include the distributed optimizer/training-state checkpoint. Use the original distributed_checkpoint/ directory if exact training resume state is required.

backend_snapshot/ contains the local FastVideo backend code used by this checkpoint, including SPARSE_FP4_OURS_P_ATTN, its Triton forward/backward kernel, FP4 quant helpers, VSA metadata helper, backend wiring, and the exact SFT launch scripts.

It also includes the inference entrypoint snapshot and an example script:

backend_snapshot/scripts/inference/run_sfp4_ours_p_checkpoint_700.sh
backend_snapshot/training_attention_settings.json
standalone_inference/

Attention setup for this checkpoint:

self-attention: SPARSE_FP4_OURS_P_ATTN, FP4 Q/K/V, sparse 64-token VSA tiles, group-local P quant, dropped-tile mean compensation
cross-attention: dense SDPA fallback, not FP4/sparse
force-dense paths: dense SDPA

standalone_inference/ is a portable helper for normal inference. It contains an overlay installer, a runner that downloads/loads the checkpoint-700 transformer weights, and the sparse FP4 backend files required by this checkpoint.

yitongl/sparse_quant_exp

sfp4_v4_sparse09_hpo_on_ours_p_init2050 checkpoint-700

yitongl/
sparse_quant_exp