36df317293
Comment-only pass over the Python this PR touches: drop what the code already says, collapse multi-line explanations that still read on one line, and keep the reasoning that is not recoverable from the code. No code, docstring semantics or behaviour changes; verified with an AST comparison against the previous revision, and the backend suite is unchanged (same 37 environment failures as before: the API integration tests that need a live keyed server, the flash-attn install hooks, and the GPU memory fields).
1911 lines
76 KiB
Python
1911 lines
76 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
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"""VideoBackend lifecycle on a faked torch/diffusers runtime (CPU-only, offline).
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Mirrors test_diffusion_backend's fake_runtime pattern: explicit fake signatures so
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the signature-gated kwargs actually exercise, sys.modules stubs so no real ML
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stack loads."""
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import contextlib
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import sys
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import time
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import types
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import pytest
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from core.inference.video import (
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VideoBackend,
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_detect_load_family,
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get_video_backend,
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resolve_video_model_kind,
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)
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from core.inference.video_families import VIDEO_CANCELLED_MSG, VIDEO_NOT_LOADED_MSG
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class _FakeDtype:
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def __init__(self, name: str) -> None:
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self._name = name
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def __repr__(self) -> str:
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return f"torch.{self._name}"
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__str__ = __repr__
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class _FakeGenerator:
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def __init__(self, device = None) -> None:
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self.device = device
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self.manual = None
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def seed(self) -> int:
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return 4242
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def manual_seed(self, value: int):
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self.manual = value
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return self
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class _FakeVae:
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def __init__(self) -> None:
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self.tiled = False
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def enable_tiling(self) -> None:
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self.tiled = True
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class _FakePipe:
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def __init__(self) -> None:
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self.moved_to = None
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self.vae = _FakeVae()
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self.last_kwargs = None
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self._interrupt = False
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def to(self, device):
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self.moved_to = device
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return self
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def enable_vae_tiling(self) -> None:
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self.vae.tiled = True
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# Explicit signature so generate()'s signature-gated kwargs actually engage; **kwargs would defeat
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# the gates.
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def __call__(
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self,
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*,
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prompt = None,
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negative_prompt = None,
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num_inference_steps = None,
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guidance_scale = None,
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width = None,
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height = None,
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num_frames = None,
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frame_rate = None,
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generator = None,
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sigmas = None,
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callback_on_step_end = None,
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**kwargs,
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):
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self.last_kwargs = {
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"prompt": prompt,
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"negative_prompt": negative_prompt,
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"num_inference_steps": num_inference_steps,
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"guidance_scale": guidance_scale,
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"width": width,
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"height": height,
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"num_frames": num_frames,
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"frame_rate": frame_rate,
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"sigmas": sigmas,
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**kwargs,
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}
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if callback_on_step_end is not None:
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for step in range(int(num_inference_steps or 1)):
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callback_on_step_end(self, step, 0, {})
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if self._interrupt:
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break
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frames = [[object() for _ in range(int(num_frames or 1))]]
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return types.SimpleNamespace(frames = frames, audio = None)
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class _FakePipeline:
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last: dict = {}
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@classmethod
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def from_pretrained(cls, base, **kwargs):
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_FakePipeline.last = {"base": base, **kwargs}
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return _FakePipe()
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class _FakeTransformer:
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last: dict = {}
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@classmethod
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def from_single_file(cls, path, **kwargs):
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_FakeTransformer.last = {"path": path, **kwargs}
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return object()
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# ── Wan2.2 fakes: a per-DiT trackable transformer so the dual-DiT optimisation tests can assert
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# speed / cache / attention engaged on BOTH experts, plus single-DiT (TI2V-5B) and dual-DiT MoE
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# (A14B) pipeline fakes. The MoE __call__ carries guidance_scale_2 so the cfg2 signature-gate
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# exercises; the single-DiT __call__ omits it so the gate proves it is NOT threaded there.
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class _FakeWanDiT:
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"""One Wan denoiser. Records which optimisation helpers touched it (the loader
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applies each once per expert on an MoE load), so a test can prove BOTH experts
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were covered. compile_repeated_blocks / enable_cache / set_attention_backend are
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exactly the attribute names the imported helpers look for."""
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def __init__(self) -> None:
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self.compiled = False
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self.cache_config = None
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self.attention = None
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def compile_repeated_blocks(self, **kwargs) -> None:
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self.compiled = True
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def enable_cache(self, config) -> None:
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self.cache_config = config
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def disable_cache(self) -> None:
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self.cache_config = None
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def set_attention_backend(self, backend) -> None:
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self.attention = backend
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@contextlib.contextmanager
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def cache_context(self, name):
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# Real Wan / HV15 / LTX pipelines open a cache_context around the denoise loop; the
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# First-Block-Cache hook needs it, so the fake transformer provides it too.
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yield
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class _FakeWanVae:
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def __init__(self) -> None:
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self.tiled = False
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def enable_tiling(self) -> None:
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self.tiled = True
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def to(self, *args, **kwargs):
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return self
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class _FakeWanPipeBase:
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"""Shared Wan pipeline state. Subclasses provide the __call__ with the right
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explicit signature (with/without guidance_scale_2) so the generate() cfg2 and
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frame_rate signature-gates actually exercise -- ``**kwargs`` alone would hide the
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parameter names inspect.signature reads."""
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moe: bool = False
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def __init__(self) -> None:
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self.vae = _FakeWanVae()
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self.transformer = _FakeWanDiT()
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self.transformer_2 = _FakeWanDiT() if self.moe else None
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self.components = {"transformer": self.transformer, "vae": self.vae}
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if self.moe:
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self.components["transformer_2"] = self.transformer_2
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self.moved_to = None
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self.last_kwargs = None
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self._interrupt = False
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def to(self, device):
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self.moved_to = device
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return self
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def enable_vae_tiling(self) -> None:
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self.vae.tiled = True
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def _finish(self, num_inference_steps, num_frames, callback_on_step_end):
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if callback_on_step_end is not None:
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for step in range(int(num_inference_steps or 1)):
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callback_on_step_end(self, step, 0, {})
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if self._interrupt:
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break
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frames = [[object() for _ in range(int(num_frames or 1))]]
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return types.SimpleNamespace(frames = frames, audio = None)
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class _FakeWanPipeSingle(_FakeWanPipeBase):
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"""Single-DiT Wan pipeline (TI2V-5B): NO guidance_scale_2 in the signature, so the
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cfg2 gate must not thread it."""
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moe = False
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def __call__(
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self,
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*,
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prompt = None,
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negative_prompt = None,
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num_inference_steps = None,
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guidance_scale = None,
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width = None,
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height = None,
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num_frames = None,
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generator = None,
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callback_on_step_end = None,
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**kwargs,
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):
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self.last_kwargs = {
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"prompt": prompt,
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"negative_prompt": negative_prompt,
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"num_inference_steps": num_inference_steps,
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"guidance_scale": guidance_scale,
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"width": width,
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"height": height,
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"num_frames": num_frames,
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**kwargs,
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}
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with self.transformer.cache_context("cond"): # real Wan pipeline wraps the denoise loop
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pass
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return self._finish(num_inference_steps, num_frames, callback_on_step_end)
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class _FakeWanPipeMoE(_FakeWanPipeBase):
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"""Dual-DiT MoE Wan pipeline (A14B): guidance_scale_2 IS in the signature, matching
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WanPipeline.__call__ in diffusers 0.39, so the cfg2 gate threads it."""
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moe = True
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def __call__(
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self,
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*,
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prompt = None,
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negative_prompt = None,
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num_inference_steps = None,
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guidance_scale = None,
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guidance_scale_2 = None,
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width = None,
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height = None,
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num_frames = None,
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generator = None,
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callback_on_step_end = None,
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**kwargs,
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):
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self.last_kwargs = {
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"prompt": prompt,
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"negative_prompt": negative_prompt,
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"num_inference_steps": num_inference_steps,
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"guidance_scale": guidance_scale,
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"guidance_scale_2": guidance_scale_2,
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"width": width,
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"height": height,
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"num_frames": num_frames,
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**kwargs,
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}
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with self.transformer.cache_context("cond"): # real Wan pipeline wraps the denoise loop
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pass
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return self._finish(num_inference_steps, num_frames, callback_on_step_end)
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class _FakeWanPipelineSingle:
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"""WanPipeline fake (from_pretrained). One class serves both families and picks the
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single-DiT / dual-DiT pipe by the repo id, exactly as diffusers dispatches on the
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repo's model_index.json (A14B lists transformer_2, TI2V-5B does not)."""
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last: dict = {}
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@classmethod
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def from_pretrained(cls, repo, **kwargs):
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_FakeWanPipelineSingle.last = {"repo": repo, **kwargs}
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moe = "a14b" in str(repo).lower()
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return _FakeWanPipeMoE() if moe else _FakeWanPipeSingle()
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# ── HunyuanVideo-1.5 fakes: __call__ has NO guidance kwarg and NO callback_on_step_end (matching
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# diffusers 0.39), a guider object carries the CFG scale, and the denoise loop drives
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# scheduler.step, so the guider write and the scheduler-wrap progress/cancel paths exercise.
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class _FakeHV15Scheduler:
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def __init__(self) -> None:
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self.calls = 0
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# Test hook fired from the ORIGINAL step (inside the wrapped call), letting a test cancel
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# mid-denoise exactly as a user request would land.
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self.on_step = None
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def step(self, *args, **kwargs):
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self.calls += 1
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if self.on_step is not None:
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self.on_step(self.calls)
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return object()
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class _FakeHV15Pipe:
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def __init__(self) -> None:
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self.vae = _FakeWanVae()
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self.transformer = _FakeWanDiT()
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self.scheduler = _FakeHV15Scheduler()
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self.guider = types.SimpleNamespace(guidance_scale = 6.0)
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self.components = {"transformer": self.transformer, "vae": self.vae}
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self.moved_to = None
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self.last_kwargs = None
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self.hooks_freed = 0
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def maybe_free_model_hooks(self):
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self.hooks_freed += 1
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def to(self, device):
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self.moved_to = device
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return self
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def enable_vae_tiling(self) -> None:
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self.vae.tiled = True
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def __call__(
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self,
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*,
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prompt = None,
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negative_prompt = None,
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height = None,
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width = None,
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num_frames = None,
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num_inference_steps = None,
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generator = None,
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**kwargs,
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):
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self.last_kwargs = {
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"prompt": prompt,
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"negative_prompt": negative_prompt,
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"num_inference_steps": num_inference_steps,
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"width": width,
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"height": height,
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"num_frames": num_frames,
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**kwargs,
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}
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with self.transformer.cache_context("cond"): # real HV15 pipeline wraps the denoise loop
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pass
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for _ in range(int(num_inference_steps or 1)):
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self.scheduler.step()
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frames = [[object() for _ in range(int(num_frames or 1))]]
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return types.SimpleNamespace(frames = frames, audio = None)
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class _FakeHV15Pipeline:
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last: dict = {}
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instance = None
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@classmethod
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def from_pretrained(cls, repo, **kwargs):
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_FakeHV15Pipeline.last = {"repo": repo, **kwargs}
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_FakeHV15Pipeline.instance = _FakeHV15Pipe()
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return _FakeHV15Pipeline.instance
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@pytest.fixture
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def fake_runtime(monkeypatch):
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torch = types.ModuleType("torch")
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torch.bfloat16 = _FakeDtype("bfloat16")
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torch.float16 = _FakeDtype("float16")
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torch.float32 = _FakeDtype("float32")
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torch.Generator = _FakeGenerator
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torch.cuda = types.SimpleNamespace(is_available = lambda: False)
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torch.backends = types.SimpleNamespace(mps = None)
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torch.inference_mode = lambda: contextlib.nullcontext()
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diffusers = types.ModuleType("diffusers")
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diffusers.GGUFQuantizationConfig = lambda compute_dtype = None: ("quant", compute_dtype)
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diffusers.LTX2Pipeline = _FakePipeline
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diffusers.LTX2VideoTransformer3DModel = _FakeTransformer
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# Wan2.2: one pipeline class serves both families (it dispatches on the repo id).
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diffusers.WanPipeline = _FakeWanPipelineSingle
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diffusers.WanTransformer3DModel = _FakeTransformer
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diffusers.HunyuanVideo15Pipeline = _FakeHV15Pipeline
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diffusers.HunyuanVideo15Transformer3DModel = _FakeTransformer
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diffusers.FirstBlockCacheConfig = lambda threshold = None: ("fbcache", threshold)
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monkeypatch.setitem(sys.modules, "torch", torch)
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monkeypatch.setitem(sys.modules, "diffusers", diffusers)
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monkeypatch.setattr("core.inference.video.clear_gpu_cache", lambda: None)
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# MP4 encode needs real frames + PyAV; the contract under test is the byte handoff, so stub the
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# encoder.
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monkeypatch.setattr(
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VideoBackend, "_encode_mp4", staticmethod(lambda frames, fps, audio, pipe: b"MP4")
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)
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_FakePipeline.last = {}
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_FakeTransformer.last = {}
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yield
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def _load_gguf(backend, tmp_path):
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(tmp_path / "model.gguf").write_bytes(b"weights")
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return backend.load_pipeline(
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str(tmp_path),
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gguf_filename = "model.gguf",
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base_repo = "Lightricks/LTX-2",
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family_override = "ltx-2",
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)
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def test_resolve_kind():
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assert resolve_video_model_kind("x.gguf", None) == "gguf"
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assert resolve_video_model_kind("x.safetensors", None) == "single_file"
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assert resolve_video_model_kind(None, None) == "pipeline"
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with pytest.raises(ValueError):
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resolve_video_model_kind(None, "bogus")
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def test_validate_rejects_unknown_and_untrusted():
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backend = VideoBackend()
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with pytest.raises(ValueError, match = "not a supported"):
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backend.validate_load_request("someorg/some-image-model")
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# A known family but an untrusted repo id must not open from_pretrained.
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with pytest.raises(ValueError, match = "limited to"):
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backend.validate_load_request("evil/ltx-2-repack")
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# GGUF loads stay open to any repo (single-file read, no pickle).
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fam = backend.validate_load_request(
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"anyorg/ltx-2-GGUF", gguf_filename = "x.gguf", model_kind = "gguf"
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)
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assert fam.name == "ltx-2"
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with pytest.raises(ValueError, match = "filename"):
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backend.validate_load_request("unsloth/LTX-2.3-GGUF", model_kind = "gguf")
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def test_validate_gates_base_repo_and_local_paths(tmp_path):
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backend = VideoBackend()
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# An arbitrary remote base_repo must not reach from_pretrained via a GGUF pick.
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with pytest.raises(ValueError, match = "base_repo"):
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backend.validate_load_request(
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"unsloth/LTX-2.3-GGUF",
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gguf_filename = "x.gguf",
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model_kind = "gguf",
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base_repo = "evil/companions",
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)
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# The family base and local dirs stay allowed.
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fam = backend.validate_load_request(
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"unsloth/LTX-2.3-GGUF",
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gguf_filename = "x.gguf",
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model_kind = "gguf",
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base_repo = "Lightricks/LTX-2",
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)
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assert fam.name == "ltx-2"
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# A local dir without the picked checkpoint fails BEFORE the GPU handoff.
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with pytest.raises(ValueError):
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backend.validate_load_request(
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str(tmp_path), gguf_filename = "missing.gguf", family_override = "ltx-2"
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)
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# A path-shaped repo id that does not exist fails validation too.
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with pytest.raises(ValueError, match = "does not exist"):
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backend.validate_load_request(
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str(tmp_path / "nope" / "model.gguf"),
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gguf_filename = "model.gguf",
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family_override = "ltx-2",
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)
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def test_validate_rejects_kind_extension_mismatch(tmp_path):
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backend = VideoBackend()
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# model_kind single_file with a .gguf file, or gguf with a non-.gguf file, must be rejected BEFORE
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# the GPU handoff (mirrors the image loader), not fail in the wrong single-file loader after the
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# route evicted the resident model.
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with pytest.raises(ValueError, match = "needs model_kind 'gguf'"):
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backend.validate_load_request(
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"unsloth/LTX-2.3-GGUF",
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gguf_filename = "x.gguf",
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model_kind = "single_file",
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family_override = "ltx-2",
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)
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with pytest.raises(ValueError, match = "requires a .gguf"):
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backend.validate_load_request(
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"unsloth/LTX-2.3",
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gguf_filename = "x.safetensors",
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model_kind = "gguf",
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family_override = "ltx-2",
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)
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with pytest.raises(ValueError, match = "not a loadable single-file checkpoint"):
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backend.validate_load_request(
|
|
"unsloth/LTX-2.3",
|
|
gguf_filename = "readme.md",
|
|
model_kind = "single_file",
|
|
family_override = "ltx-2",
|
|
)
|
|
|
|
|
|
def test_validate_rejects_local_file_suffix_kind_mismatch(tmp_path):
|
|
backend = VideoBackend()
|
|
# A local FILE is handed straight to the gguf/single_file loader: _resolve_checkpoint_path returns
|
|
# the file itself, IGNORING gguf_filename, so the file's OWN suffix must match the kind. Such a
|
|
# mismatch slips past the gguf_filename suffix checks, so reject it HERE, before the route evicts
|
|
# the resident GPU owner.
|
|
gguf_file = tmp_path / "ltx.gguf"
|
|
gguf_file.write_bytes(b"weights")
|
|
safetensors_file = tmp_path / "ltx.safetensors"
|
|
safetensors_file.write_bytes(b"weights")
|
|
with pytest.raises(ValueError, match = "not a .safetensors file"):
|
|
backend.validate_load_request(
|
|
str(gguf_file),
|
|
gguf_filename = "ltx.safetensors",
|
|
model_kind = "single_file",
|
|
family_override = "ltx-2",
|
|
)
|
|
with pytest.raises(ValueError, match = "not a .gguf file"):
|
|
backend.validate_load_request(
|
|
str(safetensors_file),
|
|
gguf_filename = "ltx.gguf",
|
|
model_kind = "gguf",
|
|
family_override = "ltx-2",
|
|
)
|
|
# Matching pairs still validate: the local file's suffix agrees with the resolved kind.
|
|
assert (
|
|
backend.validate_load_request(
|
|
str(gguf_file),
|
|
gguf_filename = "ltx.gguf",
|
|
model_kind = "gguf",
|
|
family_override = "ltx-2",
|
|
).name
|
|
== "ltx-2"
|
|
)
|
|
assert (
|
|
backend.validate_load_request(
|
|
str(safetensors_file),
|
|
gguf_filename = "ltx.safetensors",
|
|
model_kind = "single_file",
|
|
family_override = "ltx-2",
|
|
).name
|
|
== "ltx-2"
|
|
)
|
|
|
|
|
|
def test_validate_rejects_windows_shaped_missing_checkpoint(tmp_path):
|
|
backend = VideoBackend()
|
|
# A missing Windows-shaped local pick (backslash path, or a C:/ drive path) must fail HERE, not be
|
|
# treated as a Hub repo and fail after the route evicts the resident GPU owner.
|
|
with pytest.raises(ValueError, match = "does not exist"):
|
|
backend.validate_load_request(
|
|
"C:\\models\\ltx.gguf",
|
|
gguf_filename = "ltx.gguf",
|
|
family_override = "ltx-2",
|
|
)
|
|
# A bare "org/name" Hub id (no path shape) is still left for the background load to resolve.
|
|
fam = backend.validate_load_request(
|
|
"unsloth/LTX-2.3-GGUF",
|
|
gguf_filename = "ltx.gguf",
|
|
family_override = "ltx-2",
|
|
)
|
|
assert fam.name == "ltx-2"
|
|
|
|
|
|
def test_validate_rejects_local_pipeline_without_model_index(tmp_path):
|
|
backend = VideoBackend()
|
|
d = tmp_path / "ltx-local"
|
|
(d / "transformer").mkdir(parents = True)
|
|
(d / "transformer" / "diffusion_pytorch_model.safetensors").write_bytes(b"x")
|
|
# A local dir resolved to a video family but missing model_index.json is not a loadable diffusers
|
|
# pipeline; it must fail preflight BEFORE the route evicts the resident model.
|
|
with pytest.raises(ValueError, match = "model_index.json"):
|
|
backend.validate_load_request(str(d), family_override = "ltx-2")
|
|
# With a model_index.json it is a valid local pipeline pick and passes preflight.
|
|
(d / "model_index.json").write_text("{}")
|
|
fam = backend.validate_load_request(str(d), family_override = "ltx-2")
|
|
assert fam.name == "ltx-2"
|
|
|
|
|
|
def test_validate_rejects_local_file_picked_as_pipeline(tmp_path):
|
|
backend = VideoBackend()
|
|
# A local FILE (a bare .safetensors) sent as a pipeline is not a diffusers directory, so
|
|
# from_pretrained fails deep in the background load AFTER eviction. The preflight rejects it HERE,
|
|
# gating on .exists() (not .is_dir()) so it catches files as well as directories.
|
|
f = tmp_path / "ltx-2.safetensors"
|
|
f.write_bytes(b"x")
|
|
with pytest.raises(ValueError, match = "model_index.json"):
|
|
backend.validate_load_request(str(f), model_kind = "pipeline", family_override = "ltx-2")
|
|
|
|
|
|
def test_validate_rejects_local_base_repo_without_model_index(tmp_path):
|
|
backend = VideoBackend()
|
|
# A local base_repo dir that is NOT a diffusers pipeline passes the any-existing-path trust check,
|
|
# but the base loads via from_pretrained, so reject it HERE before the route hands the GPU to
|
|
# VIDEO: the pipeline-kind shape check covers only repo_id.
|
|
bad_base = tmp_path / "bare-base"
|
|
bad_base.mkdir()
|
|
with pytest.raises(ValueError, match = "model_index.json"):
|
|
backend.validate_load_request(
|
|
"unsloth/LTX-2.3-GGUF",
|
|
gguf_filename = "x.gguf",
|
|
model_kind = "gguf",
|
|
base_repo = str(bad_base),
|
|
)
|
|
# A local base_repo that IS a real pipeline dir passes the gate.
|
|
(bad_base / "model_index.json").write_text("{}")
|
|
fam = backend.validate_load_request(
|
|
"unsloth/LTX-2.3-GGUF",
|
|
gguf_filename = "x.gguf",
|
|
model_kind = "gguf",
|
|
base_repo = str(bad_base),
|
|
)
|
|
assert fam.name == "ltx-2"
|
|
|
|
|
|
def test_validate_rejects_gguf_repo_as_pipeline():
|
|
backend = VideoBackend()
|
|
# A -GGUF repo with no quant filename resolves to the pipeline kind and would only fail minutes
|
|
# later in from_pretrained, AFTER evicting the GPU owner.
|
|
with pytest.raises(ValueError, match = "pick one of its .gguf files"):
|
|
backend.validate_load_request("unsloth/LTX-2.3-GGUF")
|
|
with pytest.raises(ValueError, match = "pick one of its .gguf files"):
|
|
backend.validate_load_request("unsloth/Wan2.2-TI2V-5B-GGUF/")
|
|
|
|
|
|
def test_detect_load_family_filename_fallback():
|
|
# Repo id alone carries the family.
|
|
fam = _detect_load_family("Lightricks/LTX-2", None, None)
|
|
assert fam is not None and fam.name == "ltx-2"
|
|
# Repo id is opaque but the picked filename carries the family: fall back to the combined path so
|
|
# validate and _run_load agree.
|
|
fam = _detect_load_family("someorg/quants", "ltx-2-19b-Q4_K_M.gguf", None)
|
|
assert fam is not None and fam.name == "ltx-2"
|
|
# No filename and no recognisable repo id: no family.
|
|
assert _detect_load_family("someorg/quants", None, None) is None
|
|
# An explicit override resolves by name/alias and skips the filename fallback: a bogus override
|
|
# stays None even when the filename would have matched.
|
|
fam = _detect_load_family("someorg/quants", "ltx-2-19b-Q4_K_M.gguf", "ltxv")
|
|
assert fam is not None and fam.name == "ltx-2"
|
|
assert _detect_load_family("someorg/quants", "ltx-2-19b-Q4_K_M.gguf", "bogus") is None
|
|
|
|
|
|
def test_detect_load_family_cached_hub_arch_fallback(monkeypatch):
|
|
# A CACHED HUB GGUF is admitted to the picker by its general.architecture, but an opaque repo id +
|
|
# renamed file carry no family token, so name detection misses. The local-file arch read misses
|
|
# too, so without a cache fallback the loader 400s a SUPPORTED checkpoint the picker offered.
|
|
import huggingface_hub
|
|
|
|
import utils.models.gguf_metadata as gguf_meta
|
|
|
|
# No local file at Path(repo_id)/filename; resolve the arch from the cached blob instead.
|
|
monkeypatch.setattr(
|
|
huggingface_hub,
|
|
"try_to_load_from_cache",
|
|
lambda repo_id, filename, **kw: "/fake/cache/blobs/model.gguf",
|
|
)
|
|
monkeypatch.setattr(
|
|
gguf_meta, "read_gguf_general_metadata", lambda path: {"general.architecture": "ltxv"}
|
|
)
|
|
fam = _detect_load_family("someorg/opaque-quants", "model.gguf", None)
|
|
assert fam is not None and fam.name == "ltx-2"
|
|
|
|
# A cache MISS (blob not present -> None) still yields None (400 exactly as before).
|
|
monkeypatch.setattr(huggingface_hub, "try_to_load_from_cache", lambda *a, **k: None)
|
|
assert _detect_load_family("someorg/opaque-quants", "model.gguf", None) is None
|
|
|
|
# A recognised-but-unsupported video arch (wan has no backend family in this build) stays None, so
|
|
# an unsupported cached pick 400s just like the local-dir case.
|
|
monkeypatch.setattr(
|
|
huggingface_hub, "try_to_load_from_cache", lambda *a, **k: "/fake/cache/blobs/model.gguf"
|
|
)
|
|
monkeypatch.setattr(
|
|
gguf_meta, "read_gguf_general_metadata", lambda path: {"general.architecture": "wan"}
|
|
)
|
|
assert _detect_load_family("someorg/opaque-quants", "model.gguf", None) is None
|
|
|
|
# The blob lives in a NON-active cache root (legacy / default): the active probe misses, but the
|
|
# per-root probe finds it, so a GGUF the picker offered from any root resolves.
|
|
import hub.utils.paths as hub_paths
|
|
|
|
monkeypatch.setattr(hub_paths, "legacy_hf_cache_dir", lambda: "/fake/legacy")
|
|
monkeypatch.setattr(hub_paths, "hf_default_cache_dir", lambda: "/fake/default")
|
|
monkeypatch.setattr(
|
|
gguf_meta, "read_gguf_general_metadata", lambda path: {"general.architecture": "ltxv"}
|
|
)
|
|
monkeypatch.setattr(
|
|
huggingface_hub,
|
|
"try_to_load_from_cache",
|
|
# Active root (cache_dir absent) misses; only the legacy/default roots have the blob.
|
|
lambda repo_id, filename, cache_dir = None: (
|
|
"/fake/legacy/blobs/model.gguf" if cache_dir else None
|
|
),
|
|
)
|
|
fam = _detect_load_family("someorg/opaque-quants", "model.gguf", None)
|
|
assert fam is not None and fam.name == "ltx-2"
|
|
|
|
|
|
def test_loading_repo_ids_guards_in_flight_delete():
|
|
# During a background load status()["loaded"] is still False, but the target repo (+ companion
|
|
# base) is downloading, so the delete-cached guard needs loading_repo_ids to avoid yanking blobs
|
|
# from under the in-flight download.
|
|
from core.inference.video import _VideoLoadingState
|
|
|
|
backend = VideoBackend()
|
|
assert backend.loading_repo_ids() == () # idle: nothing to guard
|
|
backend._loading = _VideoLoadingState(repo_id = "org/ckpt", base_repo = "Lightricks/LTX-2")
|
|
assert set(backend.loading_repo_ids()) == {"org/ckpt", "Lightricks/LTX-2"}
|
|
# An errored load is no longer in flight, so the files are safe to delete.
|
|
backend._loading = _VideoLoadingState(
|
|
repo_id = "org/ckpt", base_repo = "Lightricks/LTX-2", error = "boom"
|
|
)
|
|
assert backend.loading_repo_ids() == ()
|
|
# A load whose base equals the repo (or is empty) yields just the one id.
|
|
backend._loading = _VideoLoadingState(repo_id = "org/ckpt", base_repo = "")
|
|
assert backend.loading_repo_ids() == ("org/ckpt",)
|
|
|
|
|
|
def test_load_generate_unload_gguf(fake_runtime, tmp_path):
|
|
backend = VideoBackend()
|
|
status = _load_gguf(backend, tmp_path)
|
|
assert status["loaded"] is True and status["family"] == "ltx-2"
|
|
assert status["model_kind"] == "gguf"
|
|
assert status["has_audio"] is True
|
|
# The GGUF transformer is dequant-configured and assembled onto the base repo.
|
|
assert _FakeTransformer.last["path"].endswith("model.gguf")
|
|
assert _FakeTransformer.last["quantization_config"][0] == "quant"
|
|
assert _FakePipeline.last["base"] == "Lightricks/LTX-2"
|
|
assert "transformer" in _FakePipeline.last
|
|
# Video decode is the memory peak: tiling is always on.
|
|
assert status["vae_tiling"] is True
|
|
assert status["defaults"]["frame_step"] == 8
|
|
|
|
result = backend.generate(
|
|
prompt = "a sloth surfing", width = 1000, height = 700, num_frames = 120, fps = 24
|
|
)
|
|
call = backend._state.pipe.last_kwargs
|
|
# Shape snapping happened BEFORE the pipe call: /32 sizes, 8k+1 frames.
|
|
assert (call["width"], call["height"]) == (992, 672)
|
|
assert call["num_frames"] == 113
|
|
assert call["frame_rate"] == 24.0
|
|
assert result["mp4_bytes"] == b"MP4"
|
|
assert result["num_frames"] == 113 and result["fps"] == 24
|
|
assert result["has_audio"] is False # fake pipe returned no audio track
|
|
assert 0 <= result["seed"] < 2**53
|
|
|
|
status = backend.unload()
|
|
assert status["loaded"] is False
|
|
|
|
|
|
def test_load_holds_generate_lock_across_placement(fake_runtime, tmp_path, monkeypatch):
|
|
# The video load must hold _generate_lock across GPU placement (apply_memory_plan) so an unload /
|
|
# arbiter eviction -- which barriers on _generate_lock before freeing -- can't hand the GPU to
|
|
# another backend while a multi-GB pipeline is still being moved onto it. Verify unload() blocks
|
|
# until placement releases the lock, and the superseded load then aborts without committing.
|
|
import threading
|
|
|
|
from core.inference import video as video_mod
|
|
|
|
backend = VideoBackend()
|
|
placement_started = threading.Event()
|
|
release_placement = threading.Event()
|
|
real_apply = video_mod.apply_memory_plan
|
|
|
|
def blocking_apply(pipe, plan, **kw):
|
|
placement_started.set()
|
|
assert release_placement.wait(timeout = 5), "test placement barrier never released"
|
|
return real_apply(pipe, plan, **kw)
|
|
|
|
monkeypatch.setattr(video_mod, "apply_memory_plan", blocking_apply)
|
|
|
|
load_exc = []
|
|
|
|
def do_load():
|
|
try:
|
|
_load_gguf(backend, tmp_path)
|
|
except Exception as e: # noqa: BLE001 -- the concurrent unload supersedes this load
|
|
load_exc.append(e)
|
|
|
|
load_thread = threading.Thread(target = do_load)
|
|
load_thread.start()
|
|
assert placement_started.wait(timeout = 5), "load never reached placement"
|
|
|
|
# Placement is in flight, holding _generate_lock, so unload() must block on its barrier.
|
|
unload_done = []
|
|
|
|
def do_unload():
|
|
backend.unload()
|
|
unload_done.append(True)
|
|
|
|
unload_thread = threading.Thread(target = do_unload)
|
|
unload_thread.start()
|
|
unload_thread.join(timeout = 0.5)
|
|
assert not unload_done, "unload() returned while placement still held _generate_lock (the race)"
|
|
|
|
# Release placement; unload()'s barrier then passes and its teardown runs strictly AFTER the
|
|
# load's placement+commit, so no two pipelines are ever resident.
|
|
release_placement.set()
|
|
unload_thread.join(timeout = 5)
|
|
load_thread.join(timeout = 5)
|
|
assert unload_done, "unload() did not complete after placement released _generate_lock"
|
|
assert not load_thread.is_alive() and not load_exc
|
|
assert backend._state is None # unload's teardown ran after the load, leaving nothing resident
|
|
|
|
|
|
def test_load_records_engaged_speed_optims(fake_runtime, tmp_path, monkeypatch):
|
|
# Regression: the load tail once re-ran the already-filtered speed_optims tuple through ``.items()``
|
|
# as if it were still the raw applied dict, so every real-GPU load crashed with 'tuple' object has
|
|
# no attribute 'items'. The fake runtime forces every optim False, so this only reproduces when
|
|
# one is made to engage.
|
|
from core.inference import video as video_mod
|
|
|
|
monkeypatch.setattr(
|
|
video_mod,
|
|
"apply_speed_optims",
|
|
lambda *a, **k: {"channels_last": True, "cudnn_benchmark": False},
|
|
)
|
|
backend = VideoBackend()
|
|
status = _load_gguf(backend, tmp_path)
|
|
assert status["loaded"] is True
|
|
assert status["speed_optims"] == ["channels_last"]
|
|
|
|
|
|
def test_generate_defaults_from_variant(fake_runtime, tmp_path):
|
|
# A distilled GGUF pick defaults to the few-step no-CFG schedule.
|
|
(tmp_path / "ltx-2.3-22b-distilled-1.1-Q4_K_M.gguf").write_bytes(b"w")
|
|
backend = VideoBackend()
|
|
backend.load_pipeline(
|
|
str(tmp_path),
|
|
gguf_filename = "ltx-2.3-22b-distilled-1.1-Q4_K_M.gguf",
|
|
base_repo = "Lightricks/LTX-2",
|
|
family_override = "ltx-2",
|
|
)
|
|
backend.generate(prompt = "a sloth")
|
|
call = backend._state.pipe.last_kwargs
|
|
assert call["num_inference_steps"] == 8
|
|
assert call["guidance_scale"] == 1.0
|
|
# At the distilled default step count the calibrated ltx_core curve is passed verbatim (the DiT was
|
|
# trained against it; the scheduler's own 8-step spacing lands far off).
|
|
from core.inference.video_ltx2 import LTX23_DISTILLED_SIGMAS
|
|
|
|
assert call["sigmas"] == list(LTX23_DISTILLED_SIGMAS)
|
|
|
|
|
|
def test_generate_distilled_custom_steps_keep_scheduler_spacing(fake_runtime, tmp_path):
|
|
# A non-default step count on the distilled DiT has no calibrated list, so the scheduler's spacing
|
|
# applies and no sigmas kwarg is injected.
|
|
(tmp_path / "ltx-2.3-22b-distilled-1.1-Q4_K_M.gguf").write_bytes(b"w")
|
|
backend = VideoBackend()
|
|
backend.load_pipeline(
|
|
str(tmp_path),
|
|
gguf_filename = "ltx-2.3-22b-distilled-1.1-Q4_K_M.gguf",
|
|
base_repo = "Lightricks/LTX-2",
|
|
family_override = "ltx-2",
|
|
)
|
|
backend.generate(prompt = "a sloth", steps = 12)
|
|
call = backend._state.pipe.last_kwargs
|
|
assert call["num_inference_steps"] == 12
|
|
assert call["sigmas"] is None
|
|
|
|
|
|
def test_generate_dev_base_never_gets_distilled_sigmas(fake_runtime, tmp_path):
|
|
# The dev/base DiT uses the resolution-shifted scheduler spacing even at 8 steps: the calibrated
|
|
# list is distilled-only.
|
|
(tmp_path / "ltx-2.3-22b-dev-Q4_K_M.gguf").write_bytes(b"w")
|
|
backend = VideoBackend()
|
|
backend.load_pipeline(
|
|
str(tmp_path),
|
|
gguf_filename = "ltx-2.3-22b-dev-Q4_K_M.gguf",
|
|
base_repo = "Lightricks/LTX-2",
|
|
family_override = "ltx-2",
|
|
)
|
|
backend.generate(prompt = "a sloth", steps = 8)
|
|
call = backend._state.pipe.last_kwargs
|
|
assert call["num_inference_steps"] == 8
|
|
assert call["sigmas"] is None
|
|
|
|
|
|
def test_ltx23_verbatim_sigmas_restores_scheduler_config():
|
|
# The context manager must neutralise exactly the transforms that distort explicit sigmas and put
|
|
# the original values back afterwards, even on error.
|
|
from core.inference.video_ltx2 import ltx23_verbatim_sigmas
|
|
|
|
class _Cfg(dict):
|
|
pass
|
|
|
|
class _Sched:
|
|
def __init__(self):
|
|
self.config = _Cfg(use_dynamic_shifting = True, shift = 1.0, shift_terminal = 0.1)
|
|
|
|
def register_to_config(self, **kw):
|
|
self.config.update(kw)
|
|
|
|
pipe = types.SimpleNamespace(scheduler = _Sched())
|
|
with ltx23_verbatim_sigmas(pipe):
|
|
assert pipe.scheduler.config["use_dynamic_shifting"] is False
|
|
assert pipe.scheduler.config["shift_terminal"] is None
|
|
assert pipe.scheduler.config["use_dynamic_shifting"] is True
|
|
assert pipe.scheduler.config["shift_terminal"] == 0.1
|
|
with pytest.raises(RuntimeError):
|
|
with ltx23_verbatim_sigmas(pipe):
|
|
raise RuntimeError("boom")
|
|
assert pipe.scheduler.config["use_dynamic_shifting"] is True
|
|
# A pipe without a scheduler is a no-op, not a crash.
|
|
with ltx23_verbatim_sigmas(types.SimpleNamespace()):
|
|
pass
|
|
|
|
|
|
def test_generate_resets_step_cache_only_when_engaged(fake_runtime, tmp_path):
|
|
# FBCache residuals live on the long-lived DiT(s) and survive a generation, so the next clip at a
|
|
# new resolution would crash on stale state. generate must reset them when a cache is engaged
|
|
# (diffusers 0.39 exposes _reset_stateful_cache on the transformer) and must not touch an uncached
|
|
# load. transformer_2 (the Wan dual expert) resets too when present.
|
|
import dataclasses
|
|
|
|
(tmp_path / "ltx-2.3-22b-distilled-1.1-Q4_K_M.gguf").write_bytes(b"w")
|
|
backend = VideoBackend()
|
|
backend.load_pipeline(
|
|
str(tmp_path),
|
|
gguf_filename = "ltx-2.3-22b-distilled-1.1-Q4_K_M.gguf",
|
|
base_repo = "Lightricks/LTX-2",
|
|
family_override = "ltx-2",
|
|
)
|
|
resets = []
|
|
backend._state.pipe.transformer = types.SimpleNamespace(
|
|
_reset_stateful_cache = lambda: resets.append("transformer")
|
|
)
|
|
backend._state.pipe.transformer_2 = types.SimpleNamespace(
|
|
_reset_stateful_cache = lambda: resets.append("transformer_2")
|
|
)
|
|
# No cache engaged -> no reset.
|
|
backend.generate(prompt = "a sloth")
|
|
assert resets == []
|
|
# Cache engaged: both resident DiTs reset before the pipe call.
|
|
backend._state = dataclasses.replace(backend._state, transformer_cache = "fbcache")
|
|
backend.generate(prompt = "a sloth")
|
|
assert resets == ["transformer", "transformer_2"]
|
|
|
|
|
|
def test_is_ltx23_checkpoint_gguf(monkeypatch, tmp_path):
|
|
# diffusers maps every LTX-2 single file to the 2.0 config, so a 2.3 checkpoint (9-row modulation
|
|
# tables in the header) must be detected and routed to the full 2.3 assembly. A 2.0 header must
|
|
# not, and an unreadable header falls back to the stock path (False), never raises.
|
|
from core.inference.video_ltx2 import is_ltx23_checkpoint
|
|
|
|
def _reader_for(shapes):
|
|
tensors = [types.SimpleNamespace(name = n, shape = s) for n, s in shapes.items()]
|
|
return lambda path: types.SimpleNamespace(tensors = tensors)
|
|
|
|
gguf = types.ModuleType("gguf")
|
|
# GGUF headers store dims in GGML (reversed) order.
|
|
gguf.GGUFReader = _reader_for(
|
|
{
|
|
"model.diffusion_model.transformer_blocks.0.scale_shift_table": (4096, 9),
|
|
}
|
|
)
|
|
monkeypatch.setitem(sys.modules, "gguf", gguf)
|
|
path = tmp_path / "ltx23.gguf"
|
|
path.write_bytes(b"x")
|
|
assert is_ltx23_checkpoint(path) is True
|
|
|
|
gguf.GGUFReader = _reader_for(
|
|
{
|
|
"model.diffusion_model.transformer_blocks.0.scale_shift_table": (4096, 6),
|
|
}
|
|
)
|
|
assert is_ltx23_checkpoint(path) is False
|
|
|
|
def _boom(path):
|
|
raise RuntimeError("bad magic")
|
|
|
|
gguf.GGUFReader = _boom
|
|
assert is_ltx23_checkpoint(path) is False
|
|
|
|
|
|
def test_is_ltx23_checkpoint_safetensors(monkeypatch, tmp_path):
|
|
from core.inference.video_ltx2 import is_ltx23_checkpoint
|
|
|
|
class _FakeSlice:
|
|
def __init__(self, shape):
|
|
self._shape = shape
|
|
|
|
def get_shape(self):
|
|
return self._shape
|
|
|
|
class _FakeSafe:
|
|
def __init__(self, shapes):
|
|
self._shapes = shapes
|
|
|
|
def __enter__(self):
|
|
return self
|
|
|
|
def __exit__(self, *args):
|
|
return False
|
|
|
|
def keys(self):
|
|
return list(self._shapes)
|
|
|
|
def get_slice(self, name):
|
|
return _FakeSlice(self._shapes[name])
|
|
|
|
shapes = {
|
|
"model.diffusion_model.transformer_blocks.0.scale_shift_table": (9, 4096),
|
|
}
|
|
safetensors = types.ModuleType("safetensors")
|
|
safetensors.safe_open = lambda path, framework = None: _FakeSafe(shapes)
|
|
monkeypatch.setitem(sys.modules, "safetensors", safetensors)
|
|
path = tmp_path / "ltx23.safetensors"
|
|
path.write_bytes(b"x")
|
|
assert is_ltx23_checkpoint(path) is True
|
|
|
|
|
|
def test_ltx23_split_and_variant(tmp_path):
|
|
# Pure functions: combined-checkpoint partitioning and companion-set choice.
|
|
from core.inference.video_ltx2 import _split_checkpoint, checkpoint_variant
|
|
|
|
state = {
|
|
"model.diffusion_model.transformer_blocks.0.attn1.to_q.weight": 1,
|
|
"model.diffusion_model.video_embeddings_connector.learnable_registers": 2,
|
|
"model.diffusion_model.prompt_adaln_single.linear.weight": 3,
|
|
"text_embedding_projection.video_aggregate_embed.weight": 4,
|
|
"vae.decoder.conv_in.weight": 5,
|
|
"audio_vae.encoder.conv_in.weight": 6,
|
|
"vocoder.bwe_generator.conv_pre.weight": 7,
|
|
}
|
|
groups = _split_checkpoint(state)
|
|
assert set(groups["dit"]) == {
|
|
"transformer_blocks.0.attn1.to_q.weight",
|
|
"prompt_adaln_single.linear.weight",
|
|
}
|
|
assert set(groups["connectors"]) == {
|
|
"video_embeddings_connector.learnable_registers",
|
|
"text_embedding_projection.video_aggregate_embed.weight",
|
|
}
|
|
assert groups["vae"] == {"decoder.conv_in.weight": 5}
|
|
assert groups["audio_vae"] == {"encoder.conv_in.weight": 6}
|
|
assert groups["vocoder"] == {"bwe_generator.conv_pre.weight": 7}
|
|
|
|
assert checkpoint_variant("x/ltx-2.3-22b-distilled-1.1-Q4_K_M.gguf") == "distilled"
|
|
assert checkpoint_variant("x/ltx-2.3-22b-dev-Q8_0.gguf") == "dev"
|
|
|
|
|
|
def test_ltx23_scaled_fp8_refused(monkeypatch, tmp_path):
|
|
# The Lightricks fp8 files carry .weight_scale/.input_scale companions, so a plain dtype cast would
|
|
# corrupt them and the loader must refuse with a pointer to the supported GGUF path.
|
|
from core.inference import video_ltx2
|
|
|
|
# Stub the module tree so this also runs under the CI sim, which blocks the real diffusers import.
|
|
diffusers = types.ModuleType("diffusers")
|
|
diffusers.LTX2Pipeline = object
|
|
loaders = types.ModuleType("diffusers.loaders")
|
|
sfu = types.ModuleType("diffusers.loaders.single_file_utils")
|
|
sfu.load_single_file_checkpoint = lambda path: {
|
|
"model.diffusion_model.transformer_blocks.0.attn1.to_q.weight": object(),
|
|
"model.diffusion_model.transformer_blocks.0.attn1.to_q.weight_scale": object(),
|
|
}
|
|
diffusers.loaders = loaders
|
|
loaders.single_file_utils = sfu
|
|
monkeypatch.setitem(sys.modules, "diffusers", diffusers)
|
|
monkeypatch.setitem(sys.modules, "diffusers.loaders", loaders)
|
|
monkeypatch.setitem(sys.modules, "diffusers.loaders.single_file_utils", sfu)
|
|
monkeypatch.setitem(sys.modules, "transformers", types.ModuleType("transformers"))
|
|
|
|
path = tmp_path / "ltx-2.3-22b-distilled-fp8.safetensors"
|
|
path.write_bytes(b"x")
|
|
with pytest.raises(ValueError, match = "scaled fp8"):
|
|
video_ltx2.load_ltx23_pipeline(
|
|
path, base_repo = "Lightricks/LTX-2", torch_dtype = None, is_gguf = False
|
|
)
|
|
|
|
|
|
def test_generate_without_load_raises(fake_runtime):
|
|
backend = VideoBackend()
|
|
with pytest.raises(RuntimeError, match = VIDEO_NOT_LOADED_MSG):
|
|
backend.generate(prompt = "x")
|
|
|
|
|
|
def test_generate_progress_and_cancel_idle(fake_runtime):
|
|
backend = VideoBackend()
|
|
# The idle shape carries the image-endpoint-compatible aliases (total_steps / fraction) so one
|
|
# poller works against both generate-progress APIs.
|
|
assert backend.generate_progress() == {"active": False, "total_steps": 0, "fraction": 0.0}
|
|
assert backend.cancel_generate() is False
|
|
|
|
|
|
def test_generate_progress_derives_total_steps_and_fraction(fake_runtime):
|
|
# A mid-denoise poll must report fraction = step / total under BOTH field names: a client polling
|
|
# the image API's shape against video used to read total_steps=null / fraction=0.
|
|
backend = VideoBackend()
|
|
backend._gen = {"active": True, "phase": "denoise", "step": 5, "total": 20}
|
|
gen = backend.generate_progress()
|
|
assert gen["total"] == 20 and gen["total_steps"] == 20
|
|
assert gen["step"] == 5 and gen["fraction"] == 0.25
|
|
|
|
|
|
def test_failed_background_generate_retains_terminal_error(fake_runtime, tmp_path, monkeypatch):
|
|
# A page mounted AFTER a background job failed (a browser reload during a minutes-long generation)
|
|
# reads the outcome from this retained terminal record -- its only diagnosis. So a failure must
|
|
# stay pollable as active=False + phase="failed" + error, repeatedly, until the next job.
|
|
backend = VideoBackend()
|
|
_load_gguf(backend, tmp_path)
|
|
|
|
def _boom(
|
|
self,
|
|
*,
|
|
prompt = None,
|
|
negative_prompt = None,
|
|
num_inference_steps = None,
|
|
guidance_scale = None,
|
|
width = None,
|
|
height = None,
|
|
num_frames = None,
|
|
frame_rate = None,
|
|
generator = None,
|
|
callback_on_step_end = None,
|
|
**kwargs,
|
|
):
|
|
raise ValueError("frames exceed the device memory")
|
|
|
|
monkeypatch.setattr(type(backend._state.pipe), "__call__", _boom)
|
|
backend.begin_generate(prompt = "a clip")
|
|
deadline = time.monotonic() + 10
|
|
while backend.generate_progress()["active"] and time.monotonic() < deadline:
|
|
time.sleep(0.01)
|
|
gen = backend.generate_progress()
|
|
assert gen["active"] is False
|
|
assert gen["phase"] == "failed"
|
|
assert gen["error"] == "frames exceed the device memory"
|
|
# Re-poll: a mount-time probe is a second read of the same record, not a one-shot drain.
|
|
assert backend.generate_progress()["phase"] == "failed"
|
|
|
|
|
|
def test_cache_bytes_counts_incomplete_blobs(fake_runtime, tmp_path, monkeypatch):
|
|
# scan_cache_dir skips in-flight *.incomplete blobs, so the old counter froze at the last completed
|
|
# blob for the whole multi-GB shard pull. The walk must count both, without double-counting
|
|
# snapshot symlinks.
|
|
import core.inference.video as video_mod
|
|
|
|
repo_dir = tmp_path / "models--Wan-AI--Wan2.2-TI2V-5B-Diffusers"
|
|
blobs = repo_dir / "blobs"
|
|
blobs.mkdir(parents = True)
|
|
(blobs / "aa11").write_bytes(b"x" * 1000) # completed blob
|
|
(blobs / "bb22.incomplete").write_bytes(b"y" * 500) # in-flight shard
|
|
snap = repo_dir / "snapshots" / "deadbeef"
|
|
snap.mkdir(parents = True)
|
|
(snap / "model_index.json").symlink_to(blobs / "aa11") # must not double-count
|
|
# The live cache root, not huggingface_hub's import-time constant: the counter follows a
|
|
# mid-session cache-folder change, which the constant does not.
|
|
monkeypatch.setattr(video_mod, "hub_cache_dir", lambda: str(tmp_path))
|
|
|
|
backend = VideoBackend()
|
|
assert backend._cache_bytes("Wan-AI/Wan2.2-TI2V-5B-Diffusers") == 1500
|
|
assert backend._cache_bytes("Wan-AI/absent-repo") == 0
|
|
assert backend._cache_bytes(None) == 0
|
|
|
|
|
|
def test_hv15_guider_and_scheduler_progress(fake_runtime):
|
|
# HunyuanVideo-1.5: no guidance kwarg (CFG set on the guider), no step callback (progress via the
|
|
# scheduler.step wrapper, restored afterwards).
|
|
backend = VideoBackend()
|
|
status = backend.load_pipeline(
|
|
"hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v",
|
|
model_kind = "pipeline",
|
|
)
|
|
assert status["family"] == "hunyuanvideo-1.5"
|
|
assert status["has_audio"] is False
|
|
assert status["defaults"]["frame_step"] == 4
|
|
|
|
pipe = _FakeHV15Pipeline.instance
|
|
result = backend.generate(
|
|
prompt = "a fox in the snow", steps = 4, guidance = 3.5, num_frames = 9, fps = 24
|
|
)
|
|
assert "guidance_scale" not in pipe.last_kwargs
|
|
assert "callback_on_step_end" not in pipe.last_kwargs
|
|
assert pipe.guider.guidance_scale == 3.5
|
|
# One wrapped tick per denoise step, then the original method back in place.
|
|
assert pipe.scheduler.calls == 4
|
|
assert pipe.scheduler.step.__func__ is _FakeHV15Scheduler.step
|
|
assert result["num_frames"] == 9 and result["has_audio"] is False
|
|
|
|
|
|
def test_hv15_cancel_unwinds_scheduler_loop(fake_runtime):
|
|
backend = VideoBackend()
|
|
backend.load_pipeline(
|
|
"hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v",
|
|
model_kind = "pipeline",
|
|
)
|
|
pipe = _FakeHV15Pipeline.instance
|
|
# The cancel lands during the FIRST real step; the next wrapped call must raise out of the denoise
|
|
# loop and generate() must surface the cancelled sentinel.
|
|
pipe.scheduler.on_step = lambda n: backend.cancel_generate() if n == 1 else None
|
|
with pytest.raises(RuntimeError, match = VIDEO_CANCELLED_MSG):
|
|
backend.generate(prompt = "a fox", steps = 4)
|
|
assert pipe.scheduler.calls == 1
|
|
# The wrapper must restore scheduler.step even on the exception path.
|
|
assert pipe.scheduler.step.__func__ is _FakeHV15Scheduler.step
|
|
# The exception unwound pipe.__call__ before its own end-of-call cleanup, so generate() must have
|
|
# freed the offload hooks itself (VRAM would otherwise stay onloaded until the next request).
|
|
assert pipe.hooks_freed == 1
|
|
|
|
|
|
def test_cancel_during_export_discards_clip(fake_runtime, monkeypatch):
|
|
# A cancel landing during the (blocking, uncancellable) export/mux must still discard the clip:
|
|
# cancel_generate() already reported success, so generate() must raise the cancelled sentinel
|
|
# rather than return the clip to be persisted.
|
|
backend = VideoBackend()
|
|
backend.load_pipeline(
|
|
"hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v",
|
|
model_kind = "pipeline",
|
|
)
|
|
|
|
def _encode_and_cancel(frames, fps, audio, pipe):
|
|
backend.cancel_generate() # cancel arrives mid-mux, after the last denoise-step check
|
|
return b"MP4"
|
|
|
|
monkeypatch.setattr(VideoBackend, "_encode_mp4", staticmethod(_encode_and_cancel))
|
|
with pytest.raises(RuntimeError, match = VIDEO_CANCELLED_MSG):
|
|
backend.generate(prompt = "a fox", steps = 4)
|
|
|
|
|
|
def test_singleton():
|
|
assert get_video_backend() is get_video_backend()
|
|
|
|
|
|
# ── Wan2.2 ─────────────────────────────────────────────────────────────────────
|
|
|
|
|
|
def test_load_wan_ti2v_5b_pipeline(fake_runtime):
|
|
# A full-pipeline load of the single-DiT TI2V-5B repo: WanPipeline.from_pretrained, no audio,
|
|
# tiling forced on, and the 4k+1 frame lattice surfaced.
|
|
backend = VideoBackend()
|
|
status = backend.load_pipeline("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")
|
|
assert status["loaded"] is True
|
|
assert status["family"] == "wan2.2-ti2v-5b"
|
|
assert status["model_kind"] == "pipeline"
|
|
assert status["has_audio"] is False
|
|
assert status["vae_tiling"] is True
|
|
assert status["defaults"]["frame_step"] == 4
|
|
assert status["transformer_quant"] is None
|
|
assert _FakeWanPipelineSingle.last["repo"] == "Wan-AI/Wan2.2-TI2V-5B-Diffusers"
|
|
|
|
|
|
def test_video_dense_speed_defaults_to_compile_profile(fake_runtime):
|
|
# A clip denoise amortises the one-time compile within a single run, so an UNSET speed on a dense
|
|
# (pipeline) load resolves to `default` -- never `max`, never `off`. Explicit "off" is honored.
|
|
backend = VideoBackend()
|
|
status = backend.load_pipeline("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")
|
|
assert status["speed_mode"] == "default"
|
|
assert status["resolved"]["speed_mode"]["source"] == "auto"
|
|
backend.unload()
|
|
status_off = backend.load_pipeline(
|
|
"Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline", speed_mode = "off"
|
|
)
|
|
assert status_off["speed_mode"] == "off"
|
|
assert status_off["resolved"]["speed_mode"]["source"] == "explicit"
|
|
|
|
|
|
def test_video_speed_off_suppresses_auto_dtype_quant(fake_runtime, monkeypatch):
|
|
# An explicit Speed="off" pipeline load with Precision left at auto must NOT promote the unset
|
|
# precision to auto-quant: that would engage torchao quantization (and force speed back to default),
|
|
# breaking the bit-exact request. Mirrors the image backend.
|
|
import core.inference.video as video_mod
|
|
|
|
monkeypatch.setattr(video_mod, "dense_transformer_supported", lambda target: True)
|
|
calls: list = []
|
|
monkeypatch.setattr(
|
|
video_mod, "quantize_transformer", lambda view, target, **kw: calls.append(True) or "int8"
|
|
)
|
|
|
|
backend = VideoBackend()
|
|
# speed=off + precision auto (unset): no auto-quant, speed stays off (bit-exact).
|
|
status = backend.load_pipeline(
|
|
"Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline", speed_mode = "off"
|
|
)
|
|
assert calls == [] # quantize_transformer never ran
|
|
assert status["transformer_quant"] is None
|
|
assert status["speed_mode"] == "off"
|
|
backend.unload()
|
|
|
|
# Control: with speed NOT off, the auto precision promotion still engages the dense quant, so the
|
|
# suppression above is specific to speed=off.
|
|
backend.load_pipeline("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")
|
|
assert calls == [True]
|
|
|
|
|
|
def test_video_step_cache_auto_from_default_schedule(fake_runtime, tmp_path):
|
|
# Unset step cache is AUTO, decided from the model's default schedule: Wan's 50-step default
|
|
# engages FBCache at load; the LTX distilled 8-step default keeps it off. Both are re-checked per
|
|
# generation (toggle test below).
|
|
backend = VideoBackend()
|
|
status = backend.load_pipeline("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")
|
|
assert status["transformer_cache"] == "fbcache"
|
|
assert status["resolved"]["transformer_cache"]["source"] == "auto"
|
|
backend.unload()
|
|
|
|
(tmp_path / "ltx-2.3-22b-distilled-1.1-Q4_K_M.gguf").write_bytes(b"w")
|
|
status2 = backend.load_pipeline(
|
|
str(tmp_path),
|
|
gguf_filename = "ltx-2.3-22b-distilled-1.1-Q4_K_M.gguf",
|
|
base_repo = "Lightricks/LTX-2",
|
|
family_override = "ltx-2",
|
|
)
|
|
assert status2["transformer_cache"] is None
|
|
assert status2["resolved"]["transformer_cache"]["source"] == "auto"
|
|
backend.unload()
|
|
|
|
|
|
def test_video_step_cache_auto_toggles_on_actual_steps(fake_runtime):
|
|
# The AUTO decision follows the ACTUAL step count of each generation: a few-step request drops the
|
|
# load-time cache, a many-step request restores it. An explicit "off" never toggles.
|
|
backend = VideoBackend()
|
|
backend.load_pipeline("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")
|
|
assert backend.status()["transformer_cache"] == "fbcache"
|
|
backend.generate(prompt = "a sloth", steps = 8)
|
|
assert backend.status()["transformer_cache"] is None
|
|
backend.generate(prompt = "a sloth", steps = 30)
|
|
assert backend.status()["transformer_cache"] == "fbcache"
|
|
backend.unload()
|
|
|
|
backend.load_pipeline(
|
|
"Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline", transformer_cache = "off"
|
|
)
|
|
assert backend.status()["transformer_cache"] is None
|
|
backend.generate(prompt = "a sloth", steps = 30)
|
|
assert backend.status()["transformer_cache"] is None
|
|
backend.unload()
|
|
|
|
|
|
def test_wan_frame_snapping_4k_plus_1(fake_runtime):
|
|
# Wan snaps num_frames to 4k+1 (temporal factor 4), unlike LTX-2's 8k+1.
|
|
backend = VideoBackend()
|
|
backend.load_pipeline("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")
|
|
backend.generate(prompt = "a sloth", width = 1000, height = 700, num_frames = 120)
|
|
call = backend._state.pipe.last_kwargs
|
|
assert call["num_frames"] == 117 # 4*29 + 1
|
|
# /32 spatial snap for TI2V-5B: its VAE is 16x spatial * patch 2 = 32, so 1000x700 gives 992x672
|
|
# (not the /16 992x688).
|
|
assert (call["width"], call["height"]) == (992, 672)
|
|
|
|
|
|
def test_wan_ti2v_defaults_applied(fake_runtime):
|
|
# No steps/guidance passed -> the Wan pipeline defaults (50 / 5.0).
|
|
backend = VideoBackend()
|
|
backend.load_pipeline("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")
|
|
backend.generate(prompt = "a sloth")
|
|
call = backend._state.pipe.last_kwargs
|
|
assert call["num_inference_steps"] == 50
|
|
assert call["guidance_scale"] == 5.0
|
|
|
|
|
|
def test_wan_ti2v_does_not_thread_cfg2(fake_runtime):
|
|
# The single-DiT TI2V pipeline has no guidance_scale_2 in its signature, so a request value must
|
|
# NOT be threaded (WanPipeline raises on it when boundary_ratio is None).
|
|
backend = VideoBackend()
|
|
backend.load_pipeline("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")
|
|
backend.generate(prompt = "a sloth", guidance_2 = 3.5)
|
|
call = backend._state.pipe.last_kwargs
|
|
assert "guidance_scale_2" not in call
|
|
|
|
|
|
def test_wan_a14b_dual_dit_pipeline_loads(fake_runtime):
|
|
# The A14B repo builds a dual-DiT MoE pipeline (transformer + transformer_2).
|
|
backend = VideoBackend()
|
|
status = backend.load_pipeline("Wan-AI/Wan2.2-T2V-A14B-Diffusers", model_kind = "pipeline")
|
|
assert status["loaded"] is True and status["family"] == "wan2.2-t2v-a14b"
|
|
pipe = backend._state.pipe
|
|
assert pipe.transformer is not None and pipe.transformer_2 is not None
|
|
|
|
|
|
def test_wan_a14b_cfg2_threaded_when_signature_has_it(fake_runtime):
|
|
# The MoE pipeline's __call__ carries guidance_scale_2, so an explicit guidance_2 is threaded
|
|
# through as that kwarg (the cfg2_kwarg the family declares).
|
|
backend = VideoBackend()
|
|
backend.load_pipeline("Wan-AI/Wan2.2-T2V-A14B-Diffusers", model_kind = "pipeline")
|
|
backend.generate(prompt = "a sloth", guidance = 5.0, guidance_2 = 3.0)
|
|
call = backend._state.pipe.last_kwargs
|
|
assert call["guidance_scale"] == 5.0
|
|
assert call["guidance_scale_2"] == 3.0
|
|
|
|
# A None guidance_2 must NOT be threaded, so the pipeline defaults it itself.
|
|
backend.generate(prompt = "a sloth", guidance = 5.0)
|
|
call2 = backend._state.pipe.last_kwargs
|
|
assert call2["guidance_scale_2"] is None
|
|
|
|
|
|
def test_wan_a14b_step_cache_applies_to_both_dits(fake_runtime):
|
|
# A dual-DiT MoE load must engage the step cache on BOTH experts, not just the first: transformer_2
|
|
# handles the low-noise steps and would otherwise run uncached.
|
|
backend = VideoBackend()
|
|
status = backend.load_pipeline(
|
|
"Wan-AI/Wan2.2-T2V-A14B-Diffusers",
|
|
model_kind = "pipeline",
|
|
transformer_cache = "fbcache",
|
|
)
|
|
pipe = backend._state.pipe
|
|
assert pipe.transformer.cache_config is not None
|
|
assert pipe.transformer_2.cache_config is not None
|
|
assert status["transformer_cache"] == "fbcache"
|
|
|
|
|
|
def test_wan_a14b_attention_applies_to_both_dits(fake_runtime, monkeypatch):
|
|
# An explicit attention backend must be set on both experts. The fake runtime is a CPU target,
|
|
# where the NVIDIA gate drops explicit kernels, so pin the gate open.
|
|
from core.inference import diffusion_attention as attn_mod
|
|
|
|
monkeypatch.setattr(attn_mod, "_is_cuda_nvidia", lambda target: True)
|
|
backend = VideoBackend()
|
|
backend.load_pipeline(
|
|
"Wan-AI/Wan2.2-T2V-A14B-Diffusers",
|
|
model_kind = "pipeline",
|
|
attention_backend = "cudnn",
|
|
)
|
|
pipe = backend._state.pipe
|
|
assert pipe.transformer.attention is not None
|
|
assert pipe.transformer_2.attention is not None
|
|
# Both experts got the SAME kernel.
|
|
assert pipe.transformer.attention == pipe.transformer_2.attention
|
|
|
|
|
|
def test_wan_ti2v_single_dit_only_touches_one(fake_runtime):
|
|
# A single-DiT load must not fabricate a second expert or try to optimise one.
|
|
backend = VideoBackend()
|
|
backend.load_pipeline(
|
|
"Wan-AI/Wan2.2-TI2V-5B-Diffusers",
|
|
model_kind = "pipeline",
|
|
transformer_cache = "fbcache",
|
|
)
|
|
pipe = backend._state.pipe
|
|
assert pipe.transformer_2 is None
|
|
assert pipe.transformer.cache_config is not None
|
|
|
|
|
|
def test_wan_a14b_dense_quant_applies_to_both_dits(fake_runtime, monkeypatch):
|
|
# transformer_quant on a pipeline load quantises the dense DiT(s). On CPU the real dense path is
|
|
# unsupported, so stub the two quant seams to record which pipe view each helper saw: BOTH experts
|
|
# must be quantised (via the _SecondDiTView proxy), and status must report the scheme.
|
|
import core.inference.video as video_mod
|
|
|
|
monkeypatch.setattr(video_mod, "dense_transformer_supported", lambda target: True)
|
|
quantised = []
|
|
|
|
def _fake_quant(
|
|
view,
|
|
target,
|
|
*,
|
|
mode,
|
|
family,
|
|
logger = None,
|
|
):
|
|
# The helper reads view.transformer; record the object it would quantise so the test proves the
|
|
# second expert was reached through the proxy.
|
|
quantised.append(view.transformer)
|
|
return "int8"
|
|
|
|
monkeypatch.setattr(video_mod, "quantize_transformer", _fake_quant)
|
|
|
|
backend = VideoBackend()
|
|
status = backend.load_pipeline(
|
|
"Wan-AI/Wan2.2-T2V-A14B-Diffusers",
|
|
model_kind = "pipeline",
|
|
transformer_quant = "int8",
|
|
)
|
|
pipe = backend._state.pipe
|
|
# Both experts were passed to quantize_transformer, in that order.
|
|
assert quantised == [pipe.transformer, pipe.transformer_2]
|
|
assert status["transformer_quant"] == "int8"
|
|
|
|
|
|
def test_dense_quant_skipped_under_offload(fake_runtime, monkeypatch):
|
|
# Offload hooks move modules with Module.to(), which torchao quantized tensors reject (a hard crash
|
|
# on the A14B gate run). When the memory plan resolves to any offload policy, quant must be
|
|
# SKIPPED, not attempted: the load succeeds dense and the record explains why.
|
|
import core.inference.video as video_mod
|
|
|
|
monkeypatch.setattr(video_mod, "dense_transformer_supported", lambda target: True)
|
|
quantised = []
|
|
|
|
def _fake_quant(
|
|
view,
|
|
target,
|
|
*,
|
|
mode,
|
|
family,
|
|
logger = None,
|
|
):
|
|
quantised.append(view.transformer)
|
|
return "int8"
|
|
|
|
monkeypatch.setattr(video_mod, "quantize_transformer", _fake_quant)
|
|
# The CPU fake target never plans an offload, so force one at the plan seam and stub the apply
|
|
# step, which would else call offload hooks the fake pipe lacks.
|
|
import dataclasses
|
|
|
|
real_plan = video_mod.plan_diffusion_memory
|
|
monkeypatch.setattr(
|
|
video_mod,
|
|
"plan_diffusion_memory",
|
|
lambda **kwargs: dataclasses.replace(real_plan(**kwargs), offload_policy = "model"),
|
|
)
|
|
monkeypatch.setattr(
|
|
video_mod,
|
|
"apply_memory_plan",
|
|
lambda pipe, plan, device = None, logger = None: ("model", True),
|
|
)
|
|
|
|
backend = VideoBackend()
|
|
status = backend.load_pipeline(
|
|
"Wan-AI/Wan2.2-T2V-A14B-Diffusers",
|
|
model_kind = "pipeline",
|
|
transformer_quant = "int8",
|
|
)
|
|
assert status["offload_policy"] == "model"
|
|
assert quantised == []
|
|
assert status["transformer_quant"] is None
|
|
assert "offload moves the DiT" in status["resolved"]["transformer_quant"]["reason"]
|
|
|
|
|
|
def test_wan_a14b_partial_quant_fails_the_load(fake_runtime, monkeypatch):
|
|
# If the first expert quantises but the second doesn't, the pipe is left at mismatched precision
|
|
# with no way back (in-place mutation), so the load must fail cleanly rather than run mixed with
|
|
# quant reported off.
|
|
import core.inference.video as video_mod
|
|
|
|
monkeypatch.setattr(video_mod, "dense_transformer_supported", lambda target: True)
|
|
outcomes = iter(["int8", None])
|
|
monkeypatch.setattr(
|
|
video_mod,
|
|
"quantize_transformer",
|
|
lambda view, target, *, mode, family, logger = None: next(outcomes),
|
|
)
|
|
|
|
backend = VideoBackend()
|
|
with pytest.raises(RuntimeError, match = "1/2 experts"):
|
|
backend.load_pipeline(
|
|
"Wan-AI/Wan2.2-T2V-A14B-Diffusers",
|
|
model_kind = "pipeline",
|
|
transformer_quant = "int8",
|
|
)
|
|
assert backend.status()["loaded"] is False
|
|
|
|
|
|
def test_wan_ti2v_dense_quant_applies_to_single_dit(fake_runtime, monkeypatch):
|
|
# A single-DiT pipeline load quantises exactly one transformer.
|
|
import core.inference.video as video_mod
|
|
|
|
monkeypatch.setattr(video_mod, "dense_transformer_supported", lambda target: True)
|
|
quantised = []
|
|
|
|
def _fake_quant(
|
|
view,
|
|
target,
|
|
*,
|
|
mode,
|
|
family,
|
|
logger = None,
|
|
):
|
|
quantised.append(view.transformer)
|
|
return "fp8"
|
|
|
|
monkeypatch.setattr(video_mod, "quantize_transformer", _fake_quant)
|
|
|
|
backend = VideoBackend()
|
|
status = backend.load_pipeline(
|
|
"Wan-AI/Wan2.2-TI2V-5B-Diffusers",
|
|
model_kind = "pipeline",
|
|
transformer_quant = "fp8",
|
|
)
|
|
assert quantised == [backend._state.pipe.transformer]
|
|
assert status["transformer_quant"] == "fp8"
|
|
|
|
|
|
def test_wan_validate_trusted_repos(fake_runtime):
|
|
# The two Wan base repos are trusted for non-GGUF (pipeline) loads; an unrelated repo carrying the
|
|
# family name is not.
|
|
backend = VideoBackend()
|
|
fam = backend.validate_load_request("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")
|
|
assert fam.name == "wan2.2-ti2v-5b"
|
|
fam2 = backend.validate_load_request("Wan-AI/Wan2.2-T2V-A14B-Diffusers", model_kind = "pipeline")
|
|
assert fam2.name == "wan2.2-t2v-a14b"
|
|
with pytest.raises(ValueError, match = "limited to"):
|
|
backend.validate_load_request("evil/wan2.2-ti2v-5b-repack", model_kind = "pipeline")
|
|
# A bad transformer_quant scheme is rejected cheaply at validate time.
|
|
with pytest.raises(ValueError, match = "transformer_quant"):
|
|
backend.validate_load_request(
|
|
"Wan-AI/Wan2.2-TI2V-5B-Diffusers",
|
|
model_kind = "pipeline",
|
|
transformer_quant = "bogus",
|
|
)
|
|
|
|
|
|
def test_wan_a14b_refuses_single_file_loads(fake_runtime):
|
|
# A single gguf/safetensors checkpoint carries only one of the A14B's two experts, and the pipeline
|
|
# would pull the other dense bf16 from the base repo outside the memory plan, so validate refuses
|
|
# it up front.
|
|
backend = VideoBackend()
|
|
with pytest.raises(ValueError, match = "dual-expert"):
|
|
backend.validate_load_request(
|
|
"QuantStack/Wan2.2-T2V-A14B-GGUF",
|
|
gguf_filename = "HighNoise/Wan2.2-T2V-A14B-HighNoise-Q4_K_M.gguf",
|
|
)
|
|
# The single-DiT 5B family still accepts GGUF.
|
|
fam = backend.validate_load_request(
|
|
"QuantStack/Wan2.2-TI2V-5B-GGUF",
|
|
gguf_filename = "Wan2.2-TI2V-5B-Q4_K_M.gguf",
|
|
)
|
|
assert fam.name == "wan2.2-ti2v-5b"
|
|
|
|
|
|
def test_second_dit_view_write_through():
|
|
# Attribute writes on the proxy must land on the real pipe (a helper's side effect would else
|
|
# vanish with the temporary view); a ``transformer`` write mirrors onto the second expert.
|
|
from core.inference.video import _SecondDiTView
|
|
|
|
pipe = types.SimpleNamespace(transformer = "t1", transformer_2 = "t2", flag = None)
|
|
view = _SecondDiTView(pipe)
|
|
assert view.transformer == "t2"
|
|
view.transformer = "t2-compiled"
|
|
assert pipe.transformer_2 == "t2-compiled" and pipe.transformer == "t1"
|
|
view.flag = "set"
|
|
assert pipe.flag == "set"
|
|
|
|
|
|
# ── scoped base-repo download ─────────────────────────────────────────────────
|
|
|
|
|
|
def _sibling(name, size):
|
|
return types.SimpleNamespace(rfilename = name, size = size)
|
|
|
|
|
|
_LTX2_SIBLINGS = [
|
|
_sibling("model_index.json", 10),
|
|
_sibling("ltx-2-19b-packaged-fp8.safetensors", 170),
|
|
_sibling("transformer/config.json", 1),
|
|
_sibling("transformer/diffusion_pytorch_model-00001-of-00002.safetensors", 20),
|
|
_sibling("transformer/diffusion_pytorch_model-00002-of-00002.safetensors", 18),
|
|
_sibling("text_encoder/model-00001-of-00002.safetensors", 25),
|
|
_sibling("text_encoder/model-00002-of-00002.safetensors", 25),
|
|
_sibling("text_encoder/diffusion_pytorch_model-00001-of-00002.safetensors", 25),
|
|
_sibling("text_encoder/diffusion_pytorch_model-00002-of-00002.safetensors", 25),
|
|
_sibling("vae/diffusion_pytorch_model.safetensors", 3),
|
|
_sibling("tokenizer/tokenizer.model", 1),
|
|
_sibling("tokenizer/chat_template.jinja", 1),
|
|
_sibling("assets/example.mp4", 500),
|
|
]
|
|
|
|
|
|
def test_base_download_files_scopes_pipeline_pull():
|
|
# A pipeline load skips the packaged root checkpoint, the duplicate text-encoder shard naming and
|
|
# non-weight assets, and keeps everything else.
|
|
info = types.SimpleNamespace(siblings = _LTX2_SIBLINGS)
|
|
files = dict(VideoBackend._base_download_files(info, "pipeline"))
|
|
assert "ltx-2-19b-packaged-fp8.safetensors" not in files
|
|
assert "text_encoder/diffusion_pytorch_model-00001-of-00002.safetensors" not in files
|
|
assert "assets/example.mp4" not in files
|
|
assert files["text_encoder/model-00001-of-00002.safetensors"] == 25
|
|
assert files["transformer/diffusion_pytorch_model-00001-of-00002.safetensors"] == 20
|
|
# The standalone chat template must survive the whitelist: apply_chat_template reads it at
|
|
# generation time and it is not embedded in tokenizer_config.json.
|
|
assert "tokenizer/chat_template.jinja" in files
|
|
assert sum(files.values()) == 10 + 1 + 20 + 18 + 25 + 25 + 3 + 1 + 1
|
|
|
|
|
|
def test_base_download_files_gguf_drops_transformer():
|
|
# A GGUF/single-file checkpoint replaces the DiT: the base transformer never pulls.
|
|
info = types.SimpleNamespace(siblings = _LTX2_SIBLINGS)
|
|
names = [n for n, _ in VideoBackend._base_download_files(info, "gguf")]
|
|
assert not any(n.startswith("transformer/") for n in names)
|
|
assert "text_encoder/model-00001-of-00002.safetensors" in names
|
|
|
|
|
|
def test_load_progress_clamps_overshoot(fake_runtime, monkeypatch):
|
|
# The cache scan counts blobs a broader previous pull left behind, so the reported counter must
|
|
# never exceed the scoped estimate (no "282 GB of 263 GB").
|
|
backend = VideoBackend()
|
|
backend._loading = types.SimpleNamespace(
|
|
repo_id = "Lightricks/LTX-2", base_repo = None, expected_bytes = 100, error = None
|
|
)
|
|
monkeypatch.setattr(VideoBackend, "_cache_bytes", lambda self, repo: 150)
|
|
progress = backend.load_progress()
|
|
assert progress["phase"] == "finalizing"
|
|
assert progress["downloaded_bytes"] == 100
|
|
assert progress["expected_bytes"] == 100
|
|
|
|
|
|
def test_pipeline_load_uses_predownloaded_dir(fake_runtime, tmp_path):
|
|
# When the scoped pre-download produced a local snapshot, from_pretrained must receive that dir,
|
|
# keeping diffusers' own broader snapshot sweep off the hub.
|
|
backend = VideoBackend()
|
|
backend.load_pipeline(
|
|
"Lightricks/LTX-2",
|
|
model_kind = "pipeline",
|
|
_base_local_dir = str(tmp_path),
|
|
)
|
|
assert _FakePipeline.last["base"] == str(tmp_path)
|
|
backend.unload()
|
|
|
|
|
|
def test_base_download_files_ltx23_keeps_only_shared_components():
|
|
# A 2.3 checkpoint supplies the DiT, connectors, both VAEs and the vocoder, so the base pull
|
|
# shrinks to scheduler + text encoder + tokenizer (+ root manifest).
|
|
siblings = _LTX2_SIBLINGS + [
|
|
_sibling("scheduler/scheduler_config.json", 1),
|
|
_sibling("connectors/diffusion_pytorch_model.safetensors", 3),
|
|
_sibling("latent_upsampler/diffusion_pytorch_model.safetensors", 1),
|
|
]
|
|
info = types.SimpleNamespace(siblings = siblings)
|
|
names = [n for n, _ in VideoBackend._base_download_files(info, "gguf", ltx23 = True)]
|
|
assert "model_index.json" in names
|
|
assert "scheduler/scheduler_config.json" in names
|
|
assert "text_encoder/model-00001-of-00002.safetensors" in names
|
|
assert "tokenizer/tokenizer.model" in names
|
|
assert not any(
|
|
n.startswith(("vae/", "connectors/", "latent_upsampler/", "transformer/")) for n in names
|
|
)
|
|
|
|
|
|
def test_hv15_720p_repo_gets_720p_family_defaults():
|
|
# The 720p repack is trusted, but it must resolve its OWN family entry: the generic
|
|
# hunyuanvideo-1.5 entry would default generation to 832x480.
|
|
from core.inference.video_families import detect_video_family
|
|
|
|
fam = detect_video_family("hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v")
|
|
assert fam is not None and fam.name == "hunyuanvideo-1.5-720p"
|
|
assert fam.resolution_presets[0] == (1280, 720)
|
|
assert fam.base_repo.endswith("720p_t2v")
|
|
# The 480p repo keeps the original entry.
|
|
fam480 = detect_video_family("hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v")
|
|
assert fam480 is not None and fam480.name == "hunyuanvideo-1.5"
|
|
assert fam480.resolution_presets[0] == (832, 480)
|
|
|
|
|
|
def test_predownload_base_honors_cancel_between_files(monkeypatch):
|
|
# A warm-cache sweep returns each file instantly without consulting the event, so the loop must
|
|
# check it explicitly or an unload mid-predownload is ignored.
|
|
backend = VideoBackend()
|
|
backend._cancel_event.set()
|
|
calls: list = []
|
|
monkeypatch.setattr(
|
|
"utils.hf_xet_fallback.hf_hub_download_with_xet_fallback",
|
|
lambda repo, fn, tok, **kw: (calls.append(fn), f"/cache/{fn}")[1],
|
|
)
|
|
|
|
class _Api:
|
|
def __init__(self, token = None):
|
|
pass
|
|
|
|
def model_info(
|
|
self,
|
|
repo,
|
|
files_metadata = True,
|
|
):
|
|
return types.SimpleNamespace(
|
|
siblings = [
|
|
_sibling("model_index.json", 1),
|
|
_sibling("vae/diffusion_pytorch_model.safetensors", 2),
|
|
]
|
|
)
|
|
|
|
import huggingface_hub
|
|
|
|
monkeypatch.setattr(huggingface_hub, "HfApi", _Api)
|
|
with pytest.raises(RuntimeError, match = "cancelled"):
|
|
backend._predownload_base("base/repo", None, "pipeline")
|
|
assert calls == []
|
|
|
|
|
|
def test_detect_load_family_arch_fallback_for_local_gguf(tmp_path, monkeypatch):
|
|
# A local GGUF is admitted to the Video picker by its general.architecture, but its path name may
|
|
# carry no whole-segment family token (a renamed "model.gguf"). The loader must resolve the same
|
|
# family the picker offered by reading the arch, not only the name.
|
|
from core.inference import video as vid
|
|
from core.inference.video_families import detect_video_family
|
|
|
|
d = tmp_path / "my-videos"
|
|
d.mkdir()
|
|
(d / "model.gguf").write_bytes(b"GGUF") # exists; content irrelevant (reader is patched)
|
|
|
|
# Name-only detection misses it (no "ltx" token in the path or filename).
|
|
assert detect_video_family(str(d)) is None
|
|
assert detect_video_family(f"{d}/model.gguf") is None
|
|
|
|
# ltxv arch resolves to the ltx-2 family via the arch fallback.
|
|
monkeypatch.setattr(
|
|
"utils.models.gguf_metadata.read_gguf_general_metadata",
|
|
lambda p: {"general.architecture": "ltxv"},
|
|
)
|
|
fam = vid._detect_load_family(str(d), "model.gguf", None)
|
|
assert fam is not None and fam.name == "ltx-2"
|
|
|
|
# A video arch with no backend family (wan) stays None, so the loader 400s as before.
|
|
monkeypatch.setattr(
|
|
"utils.models.gguf_metadata.read_gguf_general_metadata",
|
|
lambda p: {"general.architecture": "wan"},
|
|
)
|
|
assert vid._detect_load_family(str(d), "model.gguf", None) is None
|
|
|
|
# An explicit family_override skips the arch read entirely (worker parity).
|
|
monkeypatch.setattr(
|
|
"utils.models.gguf_metadata.read_gguf_general_metadata",
|
|
lambda p: {"general.architecture": "ltxv"},
|
|
)
|
|
assert vid._detect_load_family(str(d), "model.gguf", "ltx-2").name == "ltx-2"
|
|
|
|
|
|
class _PlanSibling:
|
|
def __init__(self, rfilename: str, size: int) -> None:
|
|
self.rfilename = rfilename
|
|
self.size = size
|
|
|
|
|
|
class _PlanInfo:
|
|
def __init__(self, siblings) -> None:
|
|
self.siblings = siblings
|
|
|
|
|
|
_LTX_BASE_SIBLINGS = [
|
|
_PlanSibling("model_index.json", 1000),
|
|
_PlanSibling("scheduler/scheduler_config.json", 1000),
|
|
_PlanSibling("tokenizer/tokenizer.json", 5_000_000),
|
|
_PlanSibling("text_encoder/model-00001-of-00005.safetensors", 10_000_000_000),
|
|
_PlanSibling("vae/diffusion_pytorch_model.safetensors", 2_400_000_000),
|
|
_PlanSibling("vocoder/diffusion_pytorch_model.safetensors", 200_000_000),
|
|
_PlanSibling("connectors/diffusion_pytorch_model.safetensors", 2_900_000_000),
|
|
_PlanSibling("transformer/diffusion_pytorch_model.safetensors", 37_800_000_000),
|
|
]
|
|
|
|
_LTX23_REPO_SIBLINGS = [
|
|
_PlanSibling("ltx-2.3-22b-distilled.gguf", 12_000_000_000),
|
|
_PlanSibling("vae/ltx-2.3-22b-distilled_video_vae.safetensors", 2_400_000_000),
|
|
_PlanSibling("vae/ltx-2.3-22b-distilled_audio_vae.safetensors", 200_000_000),
|
|
_PlanSibling(
|
|
"text_encoders/ltx-2.3-22b-distilled_embeddings_connectors.safetensors", 900_000_000
|
|
),
|
|
_PlanSibling("vae/ltx-2.3-22b-dev_video_vae.safetensors", 2_400_000_000),
|
|
]
|
|
|
|
|
|
def _plan_api(monkeypatch, repos):
|
|
class _Api:
|
|
def model_info(
|
|
self,
|
|
repo_id,
|
|
files_metadata = False,
|
|
token = None,
|
|
):
|
|
return _PlanInfo(repos[repo_id])
|
|
|
|
monkeypatch.setattr("huggingface_hub.HfApi", lambda *a, **k: _Api())
|
|
|
|
|
|
def test_download_plan_narrows_an_ltx23_pick_and_stages_its_extras(monkeypatch):
|
|
# A 2.3 checkpoint brings its own VAEs, vocoder and connectors, so staging the 2.0 base's copies
|
|
# downloads gigabytes the pipeline never opens -- and the companion files it DOES read were left
|
|
# out of the plan entirely, so they were pulled inline at load time, outside the panel's progress,
|
|
# cancel and disk preflight.
|
|
_plan_api(
|
|
monkeypatch,
|
|
{
|
|
"unsloth/LTX-2.3-GGUF": _LTX23_REPO_SIBLINGS,
|
|
"Lightricks/LTX-2": _LTX_BASE_SIBLINGS,
|
|
},
|
|
)
|
|
|
|
plan = VideoBackend().download_plan(
|
|
"unsloth/LTX-2.3-GGUF",
|
|
gguf_filename = "ltx-2.3-22b-distilled.gguf",
|
|
family_override = "ltx-2",
|
|
)
|
|
|
|
by_repo = {e["repo_id"]: e for e in plan["entries"]}
|
|
# Checkpoint and extras share one repo, so they must be ONE entry: two jobs for the same repo would
|
|
# collide on the scoped job key.
|
|
assert set(by_repo) == {"unsloth/LTX-2.3-GGUF", "Lightricks/LTX-2"}
|
|
ckpt = by_repo["unsloth/LTX-2.3-GGUF"]
|
|
assert ckpt["gguf_filename"] == "ltx-2.3-22b-distilled.gguf"
|
|
assert "vae/ltx-2.3-22b-distilled_video_vae.safetensors" in ckpt["files"]
|
|
assert "vae/ltx-2.3-22b-distilled_audio_vae.safetensors" in ckpt["files"]
|
|
assert "text_encoders/ltx-2.3-22b-distilled_embeddings_connectors.safetensors" in ckpt["files"]
|
|
# The other variant's companions are not this checkpoint's.
|
|
assert "vae/ltx-2.3-22b-dev_video_vae.safetensors" not in ckpt["files"]
|
|
|
|
base = by_repo["Lightricks/LTX-2"]
|
|
assert "scheduler/scheduler_config.json" in base["files"]
|
|
assert any(f.startswith("text_encoder/") for f in base["files"])
|
|
for dropped in ("vae/", "vocoder/", "connectors/", "transformer/"):
|
|
assert not any(f.startswith(dropped) for f in base["files"]), dropped
|
|
assert plan["total_bytes"] == ckpt["bytes"] + base["bytes"]
|
|
|
|
|
|
def test_download_plan_keeps_the_wide_base_for_a_plain_ltx2_pick(monkeypatch):
|
|
# Only 2.3 checkpoints supply their own companions; a 2.0 pick still needs the base's.
|
|
_plan_api(
|
|
monkeypatch,
|
|
{
|
|
"someone/LTX-2-GGUF": [_PlanSibling("ltx-2-dev.gguf", 12_000_000_000)],
|
|
"Lightricks/LTX-2": _LTX_BASE_SIBLINGS,
|
|
},
|
|
)
|
|
|
|
plan = VideoBackend().download_plan(
|
|
"someone/LTX-2-GGUF", gguf_filename = "ltx-2-dev.gguf", family_override = "ltx-2"
|
|
)
|
|
|
|
base = next(e for e in plan["entries"] if e["repo_id"] == "Lightricks/LTX-2")
|
|
assert any(f.startswith("vae/") for f in base["files"])
|
|
assert any(f.startswith("connectors/") for f in base["files"])
|
|
# The GGUF still replaces the DiT, so the transformer shards stay out.
|
|
assert not any(f.startswith("transformer/") for f in base["files"])
|