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7aa6af1138
* update
* update
* update
* update
* update
* merge main
* Revert "merge main"
This reverts commit 65efbcead5.
113 lines
3.9 KiB
Python
113 lines
3.9 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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import torch
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from diffusers import PixArtTransformer2DModel, Transformer2DModel
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from ...testing_utils import (
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enable_full_determinism,
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floats_tensor,
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slow,
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torch_device,
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)
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from ..test_modeling_common import ModelTesterMixin
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enable_full_determinism()
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class PixArtTransformer2DModelTests(ModelTesterMixin, unittest.TestCase):
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model_class = PixArtTransformer2DModel
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main_input_name = "hidden_states"
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# We override the items here because the transformer under consideration is small.
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model_split_percents = [0.7, 0.6, 0.6]
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@property
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def dummy_input(self):
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batch_size = 4
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in_channels = 4
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sample_size = 8
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scheduler_num_train_steps = 1000
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cross_attention_dim = 8
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seq_len = 8
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hidden_states = floats_tensor((batch_size, in_channels, sample_size, sample_size)).to(torch_device)
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timesteps = torch.randint(0, scheduler_num_train_steps, size=(batch_size,)).to(torch_device)
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encoder_hidden_states = floats_tensor((batch_size, seq_len, cross_attention_dim)).to(torch_device)
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return {
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"hidden_states": hidden_states,
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"timestep": timesteps,
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"encoder_hidden_states": encoder_hidden_states,
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"added_cond_kwargs": {"aspect_ratio": None, "resolution": None},
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}
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@property
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def input_shape(self):
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return (4, 8, 8)
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@property
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def output_shape(self):
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return (8, 8, 8)
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def prepare_init_args_and_inputs_for_common(self):
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init_dict = {
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"sample_size": 8,
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"num_layers": 1,
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"patch_size": 2,
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"attention_head_dim": 2,
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"num_attention_heads": 2,
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"in_channels": 4,
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"cross_attention_dim": 8,
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"out_channels": 8,
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"attention_bias": True,
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"activation_fn": "gelu-approximate",
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"num_embeds_ada_norm": 8,
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"norm_type": "ada_norm_single",
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"norm_elementwise_affine": False,
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"norm_eps": 1e-6,
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"use_additional_conditions": False,
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"caption_channels": None,
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}
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inputs_dict = self.dummy_input
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return init_dict, inputs_dict
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def test_output(self):
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super().test_output(
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expected_output_shape=(self.dummy_input[self.main_input_name].shape[0],) + self.output_shape
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)
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def test_gradient_checkpointing_is_applied(self):
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expected_set = {"PixArtTransformer2DModel"}
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super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
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def test_correct_class_remapping_from_dict_config(self):
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init_dict, _ = self.prepare_init_args_and_inputs_for_common()
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model = Transformer2DModel.from_config(init_dict)
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assert isinstance(model, PixArtTransformer2DModel)
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def test_correct_class_remapping_from_pretrained_config(self):
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config = PixArtTransformer2DModel.load_config("PixArt-alpha/PixArt-XL-2-1024-MS", subfolder="transformer")
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model = Transformer2DModel.from_config(config)
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assert isinstance(model, PixArtTransformer2DModel)
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@slow
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def test_correct_class_remapping(self):
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model = Transformer2DModel.from_pretrained("PixArt-alpha/PixArt-XL-2-1024-MS", subfolder="transformer")
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assert isinstance(model, PixArtTransformer2DModel)
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