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7aa6af1138
* update
* update
* update
* update
* update
* merge main
* Revert "merge main"
This reverts commit 65efbcead5.
74 lines
2.5 KiB
Python
74 lines
2.5 KiB
Python
# coding=utf-8
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# Copyright 2025 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 gc
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import unittest
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from diffusers import (
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Lumina2Transformer2DModel,
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)
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from ..testing_utils import (
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backend_empty_cache,
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enable_full_determinism,
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require_torch_accelerator,
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torch_device,
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)
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enable_full_determinism()
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@require_torch_accelerator
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class Lumina2Transformer2DModelSingleFileTests(unittest.TestCase):
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model_class = Lumina2Transformer2DModel
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ckpt_path = "https://huggingface.co/Comfy-Org/Lumina_Image_2.0_Repackaged/blob/main/split_files/diffusion_models/lumina_2_model_bf16.safetensors"
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alternate_keys_ckpt_paths = [
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"https://huggingface.co/Comfy-Org/Lumina_Image_2.0_Repackaged/blob/main/split_files/diffusion_models/lumina_2_model_bf16.safetensors"
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]
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repo_id = "Alpha-VLLM/Lumina-Image-2.0"
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def setUp(self):
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super().setUp()
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gc.collect()
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backend_empty_cache(torch_device)
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def tearDown(self):
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super().tearDown()
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gc.collect()
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backend_empty_cache(torch_device)
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def test_single_file_components(self):
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model = self.model_class.from_pretrained(self.repo_id, subfolder="transformer")
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model_single_file = self.model_class.from_single_file(self.ckpt_path)
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PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
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for param_name, param_value in model_single_file.config.items():
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if param_name in PARAMS_TO_IGNORE:
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continue
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assert model.config[param_name] == param_value, (
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f"{param_name} differs between single file loading and pretrained loading"
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)
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def test_checkpoint_loading(self):
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for ckpt_path in self.alternate_keys_ckpt_paths:
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backend_empty_cache(torch_device)
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model = self.model_class.from_single_file(ckpt_path)
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del model
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gc.collect()
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backend_empty_cache(torch_device)
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