[misc]: add CPU regression tests for the on-device uint8 frame path

Two tests certifying #1362's frame semantics without a GPU:

- frames_match_legacy_cpu_loop: the quantize-then-grid path reproduces
  the legacy make_grid -> *255 -> uint8 per-frame loop bit-exactly for
  in-range fp32 pixels (batch>1 nrow=6 grid layout, odd frame count,
  uint8 HWC contract). Verified to pass against main's legacy loop too,
  so it pins both sides of the equivalence.
- frames_clamp_out_of_range_pixels: out-of-[0,1] VAE output saturates
  at 0/255 instead of wrapping mod 256; fails on the pre-#1362 loop.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

fastvideo/tests/entrypoints/test_video_generator.py

@@ -2,8 +2,10 @@ import os
 from types import SimpleNamespace
 import warnings
 
+import numpy as np
 import pytest
 import torch
+from einops import rearrange
 
 import fastvideo.entrypoints.video_generator as video_generator_module
 from fastvideo.api import (
@@ -271,6 +273,70 @@ def test_generate_single_video_return_frames_still_materializes_output(tmp_path)
     assert result["video_path"] is None
 
 
+def test_generate_single_video_frames_match_legacy_cpu_loop(tmp_path):
+    """The on-device quantize path (#1362) must reproduce the legacy
+    per-frame CPU loop (make_grid -> permute -> *255 -> uint8) bit-exactly
+    for in-range fp32 pixels: same uint8 dtype, same HWC grid layout with
+    nrow=6 (batch>1), odd frame count. CPU-only: on CUDA the float->uint8
+    cast may differ by <=1 LSB, but on CPU both orderings run identical
+    fp32 ops, so exact equality is required."""
+    torch.manual_seed(0)
+    output = torch.rand((2, 3, 3, 16, 16), dtype=torch.float32)
+    output_batch = _single_video_output_batch(output)
+    fastvideo_args = _single_video_args()
+    generator = _single_video_generator(output_batch, fastvideo_args)
+    sampling_param = _small_sampling_param(save_video=False, return_frames=True)
+    sampling_param.num_frames = 3
+    sampling_param.num_videos_per_prompt = 2
+
+    result = generator._generate_single_video(
+        prompt="grid parity",
+        sampling_param=sampling_param,
+        fastvideo_args=fastvideo_args,
+        output_path=str(tmp_path / "unused.mp4"),
+    )
+
+    legacy_frames = []
+    for x in rearrange(output, "b c t h w -> t b c h w"):
+        grid = video_generator_module.torchvision.utils.make_grid(x, nrow=6)
+        grid = grid.permute(1, 2, 0).squeeze(-1)
+        legacy_frames.append((grid * 255).to(torch.uint8).contiguous().cpu().numpy())
+
+    torch.testing.assert_close(result["samples"], output)
+    assert len(result["frames"]) == 3
+    for got, want in zip(result["frames"], legacy_frames, strict=True):
+        assert got.dtype == np.uint8
+        assert got.shape == want.shape
+        np.testing.assert_array_equal(got, want)
+
+
+def test_generate_single_video_frames_clamp_out_of_range_pixels(tmp_path):
+    """VAE output slightly outside [0, 1] must saturate at 0/255 in the
+    uint8 frames. The pre-#1362 unclamped cast wrapped mod 256 (e.g.
+    1.5 -> 126). CPU-only."""
+    output = torch.full((1, 3, 2, 16, 16), 1.5, dtype=torch.float32)
+    output[:, :, 1] = -0.5
+    output_batch = _single_video_output_batch(output)
+    fastvideo_args = _single_video_args()
+    generator = _single_video_generator(output_batch, fastvideo_args)
+
+    result = generator._generate_single_video(
+        prompt="clamp",
+        sampling_param=_small_sampling_param(save_video=False, return_frames=True),
+        fastvideo_args=fastvideo_args,
+        output_path=str(tmp_path / "unused.mp4"),
+    )
+
+    frames = result["frames"]
+    assert len(frames) == 2
+    # make_grid passes a single image through without grid padding, so
+    # every pixel comes from the (clamped) output tensor.
+    assert frames[0].dtype == np.uint8
+    assert frames[0].shape == (16, 16, 3)
+    assert (frames[0] == 255).all()
+    assert (frames[1] == 0).all()
+
+
 def test_generate_single_video_save_video_still_builds_frames(monkeypatch, tmp_path):
     output = torch.ones((1, 3, 2, 16, 16), dtype=torch.float32) * 0.5
     output_batch = _single_video_output_batch(output)