Will Lin
51898f48e9
Frees v2/models/ to become the vendored-architecture namespace that mirrors fastvideo/models (part of making v2 self-contained / able to replace fastvideo). The v2 recipe layer (per-family card.py/loop.py/program.py + common.py + the build_*_engine re-exports) is the recipe, not the architectures, so it moves to v2/recipes/. The pure-numpy toy/parity implementations (ToyDiT etc.) move from v2/models/backend.py to v2/platform/backends/toy.py (alongside cpu.py/accel.py/torch_*). Mechanical: all imports are absolute, so v2.models.<x> -> v2.recipes.<x> and v2.models.backend -> v2.platform.backends.toy across v2/ + examples/ (89 files, 178 refs). No behavior change. CPU mini green (202 passed, 2 skipped); all v2 files compile.
80 lines
3.3 KiB
Python
80 lines
3.3 KiB
Python
"""Inference paths: Wan2.1 T2V, LTX2.3 two-stage distilled, Wan-causal streaming (design_v3 §15)."""
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from __future__ import annotations
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import numpy as np
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from v2.recipes import build_default_engine
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from v2.recipes.ltx2 import BASE_SIGMAS, REFINE_SIGMAS, build_ltx2_card
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from v2.recipes.wan21 import build_wan21_card
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from v2.recipes.wan_causal import build_wan_causal_card
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from v2.request import DiffusionParams, OutputSpec, TaskType, make_request
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def _eng():
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return build_default_engine()
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def test_wan_t2v_produces_video_and_latents():
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eng = _eng()
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req = make_request(TaskType.T2V, "wan2.1-1.3b", "a cat",
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diffusion=DiffusionParams(num_steps=4, seed=1))
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out = eng.run(req)
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assert out.artifacts["video"].frames is not None
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assert out.artifacts["latents"].latent is not None
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assert out.metrics["denoise_steps"] == 4.0
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def test_ltx2_two_stage_step_counts():
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eng = _eng()
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out = eng.run(make_request(TaskType.T2V, "ltx2-2stage-distilled", "a sunset",
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diffusion=DiffusionParams(seed=2)))
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# 8-step base + 3-step refine = the distilled schedule
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assert out.metrics["base_steps"] == float(len(BASE_SIGMAS) - 1) == 8.0
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assert out.metrics["refine_steps"] == float(len(REFINE_SIGMAS) - 1) == 3.0
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assert out.artifacts["video"].frames is not None
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def test_causal_chunk_rollout_frame_count():
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eng = _eng()
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out = eng.run(make_request(TaskType.T2V, "wan-causal-sf-1.3b", "a river",
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diffusion=DiffusionParams(seed=3)))
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assert out.metrics["chunks"] == 3.0
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# 3 chunks × 2 frames/chunk concatenated over the temporal axis
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assert out.artifacts["latents"].latent.shape[1] == 6
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def test_determinism_same_seed_bit_identical():
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eng = _eng()
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r = lambda: make_request(TaskType.T2V, "wan2.1-1.3b", "x", diffusion=DiffusionParams(num_steps=4, seed=5))
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a = eng.run(r()).artifacts["latents"].latent
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b = eng.run(r()).artifacts["latents"].latent
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assert np.array_equal(a, b)
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def test_different_seed_differs():
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eng = _eng()
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a = eng.run(make_request(TaskType.T2V, "wan2.1-1.3b", "x",
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diffusion=DiffusionParams(num_steps=4, seed=1))).artifacts["latents"].latent
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b = eng.run(make_request(TaskType.T2V, "wan2.1-1.3b", "x",
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diffusion=DiffusionParams(num_steps=4, seed=2))).artifacts["latents"].latent
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assert not np.array_equal(a, b)
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def test_streaming_emits_chunks_only_when_requested():
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eng = _eng()
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off = eng.run(make_request(TaskType.T2V, "wan2.1-1.3b", "x",
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diffusion=DiffusionParams(num_steps=4, seed=1)))
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on = eng.run(make_request(TaskType.T2V, "wan2.1-1.3b", "x",
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diffusion=DiffusionParams(num_steps=4, seed=1),
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outputs=OutputSpec(stream={"video": True})))
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assert off.metrics.get("stream_chunks", 0) == 0
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assert on.metrics.get("stream_chunks", 0) == 4 # one preview per denoise step
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def test_all_cards_recipe_runtime_bound():
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# the (recipe, runtime) pair: every card's recipe assumes a loop the card declares
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for build in (build_wan21_card, build_ltx2_card, build_wan_causal_card):
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card = build()
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assert card.recipe.assumes_loop in card.loops
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assert card.parity.interleave_required is True
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