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name: setup-and-weights description: "Installs and diagnoses the official FlashVSR CUDA/LCSA runtime, validates v1 or v1.1 model bundles, and wires ModelManager, projection, decoder, and context assets." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

FlashVSR Setup and Weights

Use this sub-skill before any FlashVSR model allocation. It owns the Python, PyTorch, CUDA, Block-Sparse-Attention, model-bundle, ModelManager, projection, decoder, and positive-context gates. Route prepared video geometry, temporal framing, full/tiny/tiny-long selection, and pipeline calls to inference.

Route by symptom

• Fresh install, pkg_resources, compiler, CUDA, or extension build: read install.md.
• Acquire or validate v1/v1.1 files; wire ModelManager and support modules: read weights-and-model-manager.md.
• Import, LFS pointer, detector, state-dict, context, or memory failure: read troubleshooting.md.

Required target profile

The verified backend-smoke profile is Python 3.11, PyTorch 2.6.0+cu124, CUDA 12.4, and NVIDIA A100 SM80. FlashVSR's official Wan DiT imports and calls block_sparse_attn; a CUDA-enabled torch import alone is not sufficient. Build the extension separately for the target toolkit/ABI/SM profile and require:

python -c "from block_sparse_attn import block_sparse_attn_func; print('LCSA import OK')"

The import gate and a small bf16 CUDA streaming-attention call passed on the profile above. Real FlashVSR checkpoint loading remains a separate native gate. Run the bundled read-only diagnostic from any directory:

python <skill-root>/sub-skills/setup-and-weights/scripts/check_environment.py

It reports target-version and backend readiness without downloading, building, or printing module/cache locations.

Version-atomic weight gate

Use one application-owned model directory containing the official v1 or v1.1 files:

diffusion_pytorch_model_streaming_dmd.safetensors
Wan2.1_VAE.pth
LQ_proj_in.ckpt
TCDecoder.ckpt
README.md

Before ModelManager, reject missing, empty, and Git LFS pointer files:

python <skill-root>/sub-skills/setup-and-weights/scripts/check_weights.py \
  <MODEL_DIR> --version v1.1

Do not mix versions. Full uses the DiT and Wan VAE; tiny and tiny-long use the DiT and TCDecoder. All routes use the matching LQ projection and positive cross-attention context.

Wiring invariants

• Construct ModelManager(torch_dtype=torch.bfloat16, device="cpu"); load explicit local files and stop unless wan_video_dit is detected. Full must also detect wan_video_vae.
• v1 uses Buffer_LQ4x_Proj; v1.1 uses Causal_LQ4x_Proj. Both use (in_dim=3, out_dim=1536, layer_num=1) and load LQ_proj_in.ckpt strictly.
• Tiny routes build TCDecoder with channels [512,256,128,128] and latent channels 16+768; load non-strictly but review all missing/unexpected keys.
• Package projection/decoder support in the application. They are not stable public diffsynth exports; do not depend on a source-checkout utility path.
• Load an application-owned context tensor, require shape [1,512,4096], and call pipe.init_cross_kv(context_tensor=context) once before inference.

Verification boundary

Completed: package and FlashVSR-class imports, CUDA hardware probe, block_sparse_attn import, bf16 kernel smoke, diagnostic helper checks, and synthetic incomplete-bundle checks. Not completed: real v1/v1.1 checkpoint loading, strict projection/decoder compatibility against published weights, or full/tiny/tiny-long model inference. Do not promote setup checks into an end-to-end inference claim.