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dd07b19e27
* rename photon to prx * rename photon into prx * Revert .gitignore to state before commitb7fb0fe9d6* rename photon to prx * rename photon into prx * Revert .gitignore to state before commitb7fb0fe9d6* make fix-copies
84 lines
2.5 KiB
Python
84 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 unittest
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import torch
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from diffusers.models.transformers.transformer_prx import PRXTransformer2DModel
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from ...testing_utils import enable_full_determinism, torch_device
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from ..test_modeling_common import ModelTesterMixin
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enable_full_determinism()
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class PRXTransformerTests(ModelTesterMixin, unittest.TestCase):
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model_class = PRXTransformer2DModel
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main_input_name = "hidden_states"
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uses_custom_attn_processor = True
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@property
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def dummy_input(self):
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return self.prepare_dummy_input()
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@property
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def input_shape(self):
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return (16, 16, 16)
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@property
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def output_shape(self):
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return (16, 16, 16)
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def prepare_dummy_input(self, height=16, width=16):
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batch_size = 1
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num_latent_channels = 16
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sequence_length = 16
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embedding_dim = 1792
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hidden_states = torch.randn((batch_size, num_latent_channels, height, width)).to(torch_device)
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encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)
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timestep = torch.tensor([1.0]).to(torch_device).expand(batch_size)
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return {
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"hidden_states": hidden_states,
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"timestep": timestep,
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"encoder_hidden_states": encoder_hidden_states,
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}
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def prepare_init_args_and_inputs_for_common(self):
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init_dict = {
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"in_channels": 16,
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"patch_size": 2,
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"context_in_dim": 1792,
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"hidden_size": 1792,
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"mlp_ratio": 3.5,
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"num_heads": 28,
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"depth": 4, # Smaller depth for testing
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"axes_dim": [32, 32],
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"theta": 10_000,
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}
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inputs_dict = self.prepare_dummy_input()
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return init_dict, inputs_dict
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def test_gradient_checkpointing_is_applied(self):
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expected_set = {"PRXTransformer2DModel"}
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super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
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if __name__ == "__main__":
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unittest.main()
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