# TODO(cookbook-review): Design sample only. Before merge, double-check the
# selected options, schema types/ranges, and baseline values for each deployment.
# TODO: Recheck field paths and choices when the typed API in PR #1913 is finalized.
title: FastWan2.1 1.3B
summary: Distilled text-to-video serving with sparse attention.

deployments:
  cuda-rest:
    runtime: fastvideo-cuda-rest
    workload: t2v
    defaults: examples/serving/openai_fastwan21_1_3b.yaml
    env:
      FASTVIDEO_ATTENTION_BACKEND: "VIDEO_SPARSE_ATTN"
    options:
      - server.*
      - generator.engine.num_gpus
      - generator.engine.parallelism.*
      - "!generator.engine.parallelism.hsdp_*"
      - generator.engine.offload.dit_layerwise
      - generator.engine.offload.text_encoder
      - generator.engine.offload.vae
      - generator.engine.compile.enabled
      - default_request.sampling.num_frames
      - default_request.sampling.height
      - default_request.sampling.width
      - default_request.sampling.fps
      - default_request.sampling.seed
    overrides:
      # Wan uses an 8-pixel VAE stride and a 2x2 spatial DiT patch.
      default_request.sampling.height:
        minimum: 16
        multipleOf: 16
      default_request.sampling.width:
        minimum: 16
        multipleOf: 16
      # Generic stage verification accepts nonnegative seeds, including zero.
      default_request.sampling.seed:
        minimum: 0

# Optional capacity facts extend this model manifest; identity and examples come from deployments above.
capacity:
  memory_rules: wan
  components:
    transformer:
      ref: fastwan2.1-t2v-1.3b-vsa-dit
    text_encoder:
      ref: umt5-xxl-encoder
    vae:
      ref: wan2.1-vae-decoder
  # Existing examples are not measured GPU-memory evidence.
  reference_runs: []
  sources:
    - https://huggingface.co/FastVideo/FastWan2.1-T2V-1.3B-Diffusers/tree/25e7ed7f41fd8ce2fdd108688c65e8caf0ce3aef
    - https://github.com/hao-ai-lab/FastVideo/blob/81b73f6b88b18803a24510a336b0bae1b0c2c4ef/fastvideo/pipelines/basic/wan/presets.py