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Salomón MurielandClaude Opus 4.8
39fdd59f6c
The invisible/SynthID diffusion pass loads the whole SDXL fp16 pipeline
into VRAM via `pipeline.to("cuda")`. On an 8 GB card the weights alone
(~7 GB) leave no room for activations, so the run OOMs and there is no
in-tool way to recover short of falling back to CPU (~9 min/image).
Add an opt-in `--cpu-offload` flag (default off) that calls diffusers'
`enable_model_cpu_offload()` instead: submodules are streamed to the GPU
on demand, dropping peak VRAM to roughly the largest single submodule at
the cost of per-step transfers. CUDA-only; a no-op on cpu/mps. Threaded
through `invisible`, `all`, and `batch` to keep the knob set identical
across the three, mirroring the existing `--device`/`--pipeline` options.
Measured on a GTX 1070 Ti (8 GB): `invisible --pipeline sdxl --cpu-offload`
runs the SynthID scrub on-GPU in ~2.5 min vs ~9 min on CPU, where the
default full-VRAM path OOMs.
Test drives the placement decision with a mock pipeline (no model/GPU),
gated on torch so it runs under the `gpu` extra and skips the core CI
matrix, consistent with the model-running test policy.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
57 lines
2.1 KiB
Python
57 lines
2.1 KiB
Python
"""Unit tests for the --cpu-offload device-placement branch (mocked pipeline).
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``WatermarkRemover._move_to_device_and_optimize`` chooses between a full
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``pipeline.to("cuda")`` and ``enable_model_cpu_offload()`` (low-VRAM streaming).
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Constructing the remover is cheap -- the diffusion pipeline is lazy and the
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device string is not validated -- so the placement decision is exercised with a
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mock pipeline, no model download or GPU required. Gated on torch (the module
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imports it at top), so it runs under the ``gpu`` extra and skips the core CI
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matrix, matching the model-running test policy.
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"""
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from __future__ import annotations
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from unittest.mock import Mock
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import pytest
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pytest.importorskip("torch")
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from remove_ai_watermarks.noai.watermark_remover import WatermarkRemover
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def _remover(device: str, cpu_offload: bool) -> WatermarkRemover:
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return WatermarkRemover(device=device, pipeline="sdxl", cpu_offload=cpu_offload)
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class TestCpuOffloadPlacement:
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def test_offload_enabled_on_cuda_streams_instead_of_moving(self):
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remover = _remover("cuda", cpu_offload=True)
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pipeline = Mock()
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returned = remover._move_to_device_and_optimize(pipeline)
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pipeline.enable_model_cpu_offload.assert_called_once_with()
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pipeline.to.assert_not_called()
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# Offload leaves the pipeline object in place (accelerate hooks handle it).
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assert returned is pipeline
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def test_no_offload_moves_whole_pipeline_to_cuda(self):
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remover = _remover("cuda", cpu_offload=False)
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pipeline = Mock()
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remover._move_to_device_and_optimize(pipeline)
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pipeline.to.assert_called_once_with("cuda")
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pipeline.enable_model_cpu_offload.assert_not_called()
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def test_offload_flag_ignored_off_cuda(self):
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# The flag is CUDA-only: on cpu it must still be a plain .to("cpu").
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remover = _remover("cpu", cpu_offload=True)
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pipeline = Mock()
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remover._move_to_device_and_optimize(pipeline)
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pipeline.to.assert_called_once_with("cpu")
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pipeline.enable_model_cpu_offload.assert_not_called()
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