mirror of
https://github.com/huggingface/diffusers.git
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85 lines
2.7 KiB
Python
85 lines
2.7 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 unittest
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from diffusers import AutoencoderDC
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from ...testing_utils import IS_GITHUB_ACTIONS, enable_full_determinism, floats_tensor, torch_device
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from ..test_modeling_common import ModelTesterMixin
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from .testing_utils import AutoencoderTesterMixin
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enable_full_determinism()
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class AutoencoderDCTests(ModelTesterMixin, AutoencoderTesterMixin, unittest.TestCase):
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model_class = AutoencoderDC
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main_input_name = "sample"
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base_precision = 1e-2
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def get_autoencoder_dc_config(self):
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return {
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"in_channels": 3,
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"latent_channels": 4,
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"attention_head_dim": 2,
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"encoder_block_types": (
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"ResBlock",
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"EfficientViTBlock",
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),
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"decoder_block_types": (
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"ResBlock",
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"EfficientViTBlock",
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),
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"encoder_block_out_channels": (8, 8),
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"decoder_block_out_channels": (8, 8),
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"encoder_qkv_multiscales": ((), (5,)),
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"decoder_qkv_multiscales": ((), (5,)),
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"encoder_layers_per_block": (1, 1),
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"decoder_layers_per_block": [1, 1],
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"downsample_block_type": "conv",
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"upsample_block_type": "interpolate",
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"decoder_norm_types": "rms_norm",
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"decoder_act_fns": "silu",
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"scaling_factor": 0.41407,
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}
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@property
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def dummy_input(self):
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batch_size = 4
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num_channels = 3
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sizes = (32, 32)
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image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
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return {"sample": image}
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@property
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def input_shape(self):
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return (3, 32, 32)
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@property
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def output_shape(self):
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return (3, 32, 32)
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def prepare_init_args_and_inputs_for_common(self):
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init_dict = self.get_autoencoder_dc_config()
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inputs_dict = self.dummy_input
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return init_dict, inputs_dict
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@unittest.skipIf(IS_GITHUB_ACTIONS, reason="Skipping test inside GitHub Actions environment")
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def test_layerwise_casting_inference(self):
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super().test_layerwise_casting_inference()
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