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* add modular pipeline for qwen-image 2.1 * split out a turbo blockset and drop the guider from the text encoders * Update tests/modular_pipelines/qwenimage21/test_modular_pipeline_qwenimage21_turbo.py * Update tests/modular_pipelines/qwenimage21/test_modular_pipeline_qwenimage21.py --------- Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
78 lines
2.9 KiB
Python
78 lines
2.9 KiB
Python
# coding=utf-8
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# Copyright 2026 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import os
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from unittest import mock
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import pytest
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import torch
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from huggingface_hub import hf_hub_download
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from diffusers.utils.torch_utils import TorchDeviceBackend
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def backend_memory_allocated(device: str) -> int:
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"""
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Bytes currently allocated on `device`. `tests/testing_utils.py` only exposes the *peak* allocation, which cannot
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show memory being released. Skips on backends that do not implement `memory_allocated()` (e.g. mps).
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"""
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device_module = getattr(torch, torch.device(device).type)
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if not hasattr(device_module, "memory_allocated"):
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pytest.skip(f"`memory_allocated()` is not implemented for {device}.")
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return device_module.memory_allocated()
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def patch_free_memory(free_bytes: int, total_bytes: int = 80 * 1024):
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"""
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Simulate `free_bytes` of free device memory on whichever backend backs `torch_device`.
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`TorchDeviceBackend.mem_get_info` returns `(free, total)` and is the single point where `AutoOffloadStrategy` learns how
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much memory is available, so patching it makes offloading decisions deterministic instead of dependent on the real
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free memory of the test hardware (an 80GB GPU never runs low on a handful of KB-sized models).
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"""
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return mock.patch.object(TorchDeviceBackend, "mem_get_info", return_value=(free_bytes, total_bytes))
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def get_specified_components(path_or_repo_id, cache_dir=None):
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"""
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The component names a `modular_model_index.json` actually points at a checkpoint for. Returns `None` when the
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index cannot be fetched, which callers treat as "skip the comparison".
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"""
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if os.path.isdir(path_or_repo_id):
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config_path = os.path.join(path_or_repo_id, "modular_model_index.json")
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else:
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try:
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config_path = hf_hub_download(
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repo_id=path_or_repo_id,
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filename="modular_model_index.json",
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local_dir=cache_dir,
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)
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except Exception:
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return None
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with open(config_path) as f:
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config = json.load(f)
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components = set()
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for k, v in config.items():
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if v is None or isinstance(v, (str, int, float, bool)):
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continue
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for entry in v:
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if isinstance(entry, dict) and (entry.get("repo") or entry.get("pretrained_model_name_or_path")):
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components.add(k)
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break
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return components
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