118 lines
5.1 KiB
Python
118 lines
5.1 KiB
Python
"""Phase 1a: per-model sampling defaults on the card + the kwargs>SamplingParam>card>generic precedence."""
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from types import SimpleNamespace
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from v2.core.card import SamplingDefaults
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from v2.recipes.ltx2 import build_ltx2_3_card, build_ltx2_base_card, build_ltx2_card
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from v2.recipes.wan21 import (
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build_fastwan_qad_fp8_card,
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build_wan21_card,
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build_wan22_a14b_card,
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build_wan22_ti2v_card,
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build_wan_t2v_14b_card,
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)
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from v2.recipes.wan_causal import build_wan_causal_card
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from v2.registry import resolve
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from v2.video_generator import VideoGenerator, _resolve_default
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def test_wan_t2v_14b_defaults_and_registry():
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# Bucket-B port: reuses the Wan recipe/adapter, only defaults differ (720p, flow_shift 5.0).
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sd = build_wan_t2v_14b_card().sampling_defaults
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assert (sd.num_steps, sd.guidance_scale, sd.height, sd.width, sd.fps) == (50, 5.0, 720, 1280, 16)
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# the HF id resolves to the 14B card via the shared registry (not the 1.3B fallback defaults)
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build_card, _ = resolve("Wan-AI/Wan2.1-T2V-14B-Diffusers")
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assert build_card().sampling_defaults.height == 720
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def test_wan21_defaults():
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sd = build_wan21_card().sampling_defaults
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assert (sd.num_steps, sd.guidance_scale, sd.height, sd.width, sd.num_frames, sd.fps) == (50, 3.0, 480, 832, 81, 16)
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assert "overexposed" in sd.negative_prompt
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def test_wan22_ti2v_defaults():
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sd = build_wan22_ti2v_card().sampling_defaults
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assert (sd.num_steps, sd.guidance_scale, sd.height, sd.width, sd.num_frames, sd.fps) == (50, 5.0, 704, 1280, 121, 24)
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def test_wan22_a14b_defaults():
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sd = build_wan22_a14b_card().sampling_defaults
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assert (sd.num_steps, sd.guidance_scale, sd.fps) == (40, 4.0, 16)
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def test_wan_causal_defaults():
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sd = build_wan_causal_card().sampling_defaults
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assert (sd.num_steps, sd.guidance_scale, sd.height, sd.width, sd.num_frames, sd.fps) == (4, 1.0, 480, 832, 81, 16)
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def test_fastwan_qad_fp8_defaults_and_registry():
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card = build_fastwan_qad_fp8_card()
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sd = card.sampling_defaults
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assert (sd.num_steps, sd.guidance_scale, sd.height, sd.width, sd.num_frames, sd.fps) == (3, 1.0, 480, 832, 81, 16)
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assert sd.negative_prompt == ""
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assert card.components["transformer"].precision_policy == "fp8"
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assert card.precision.quantization_scheme == "fp8"
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build_card, _ = resolve("FastVideo/FastWan-QAD-FP8-1.3B")
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resolved = build_card()
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assert resolved.model_id == "fastwan-qad-fp8-1.3b"
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assert resolved.recipe.method == "qad_fp8"
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def test_ltx2_two_stage_defaults():
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sd = build_ltx2_card().sampling_defaults
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assert (sd.num_steps, sd.guidance_scale, sd.height, sd.width, sd.num_frames, sd.fps) == (8, 1.0, 1024, 1536, 121, 24)
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assert sd.negative_prompt == "" # distilled: empty negative prompt
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def test_ltx2_base_defaults():
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sd = build_ltx2_base_card().sampling_defaults
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assert (sd.num_steps, sd.guidance_scale, sd.height, sd.width) == (40, 3.0, 512, 768)
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assert "blurry" in sd.negative_prompt
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def test_ltx2_3_defaults():
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sd = build_ltx2_3_card().sampling_defaults
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assert (sd.num_steps, sd.guidance_scale, sd.height, sd.width) == (30, 3.0, 512, 768)
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assert sd.guidance_per_modality == {"video": 3.0, "audio": 7.0}
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def test_resolve_precedence():
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sd = SamplingDefaults(num_steps=8, guidance_scale=1.0, height=1024)
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# card default used when neither kwargs nor SamplingParam set it (note the num_steps alias)
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assert _resolve_default("num_inference_steps", 30, {}, None, sd, "num_steps") == 8
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assert _resolve_default("height", 480, {}, None, sd) == 1024
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# kwargs override card
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assert _resolve_default("num_inference_steps", 30, {"num_inference_steps": 4}, None, sd, "num_steps") == 4
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# SamplingParam override (no kwargs) beats card
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assert _resolve_default("height", 480, {}, SimpleNamespace(height=720), sd) == 720
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# generic fallback when card has no value and nothing else set
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assert _resolve_default("width", 832, {}, None, sd) == 832
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# empty-string negative prompt is a real value, not "unset"
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assert _resolve_default("negative_prompt", None, {}, None, SamplingDefaults(negative_prompt="")) == ""
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def test_from_pretrained_threads_compile_config(monkeypatch):
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captured = {}
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def fake_from_config(cls, config):
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captured["config"] = config
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return "ok"
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monkeypatch.setattr(VideoGenerator, "from_config", classmethod(fake_from_config))
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result = VideoGenerator.from_pretrained("model",
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enable_torch_compile=True,
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torch_compile_backend="inductor",
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torch_compile_mode="reduce-overhead",
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torch_compile_kwargs={"dynamic": False},
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enable_torch_compile_vae=True)
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assert result == "ok"
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compile_config = captured["config"].engine.compile
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assert compile_config.enabled is True
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assert compile_config.backend == "inductor"
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assert compile_config.mode == "reduce-overhead"
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assert compile_config.dynamic is False
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assert compile_config.extras == {}
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assert compile_config.vae_enabled is True
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