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https://github.com/huggingface/diffusers.git
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7aa6af1138
* update
* update
* update
* update
* update
* merge main
* Revert "merge main"
This reverts commit 65efbcead5.
90 lines
3.1 KiB
Python
90 lines
3.1 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 OmniGenTransformer2DModel
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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 OmniGenTransformerTests(ModelTesterMixin, unittest.TestCase):
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model_class = OmniGenTransformer2DModel
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main_input_name = "hidden_states"
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uses_custom_attn_processor = True
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model_split_percents = [0.1, 0.1, 0.1]
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@property
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def dummy_input(self):
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batch_size = 2
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num_channels = 4
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height = 8
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width = 8
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sequence_length = 24
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hidden_states = torch.randn((batch_size, num_channels, height, width)).to(torch_device)
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timestep = torch.rand(size=(batch_size,), dtype=hidden_states.dtype).to(torch_device)
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input_ids = torch.randint(0, 10, (batch_size, sequence_length)).to(torch_device)
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input_img_latents = [torch.randn((1, num_channels, height, width)).to(torch_device)]
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input_image_sizes = {0: [[0, 0 + height * width // 2 // 2]]}
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attn_seq_length = sequence_length + 1 + height * width // 2 // 2
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attention_mask = torch.ones((batch_size, attn_seq_length, attn_seq_length)).to(torch_device)
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position_ids = torch.LongTensor([list(range(attn_seq_length))] * batch_size).to(torch_device)
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return {
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"hidden_states": hidden_states,
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"timestep": timestep,
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"input_ids": input_ids,
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"input_img_latents": input_img_latents,
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"input_image_sizes": input_image_sizes,
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"attention_mask": attention_mask,
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"position_ids": position_ids,
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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 (4, 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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"hidden_size": 16,
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"num_attention_heads": 4,
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"num_key_value_heads": 4,
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"intermediate_size": 32,
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"num_layers": 20,
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"pad_token_id": 0,
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"vocab_size": 1000,
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"in_channels": 4,
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"time_step_dim": 4,
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"rope_scaling": {"long_factor": list(range(1, 3)), "short_factor": list(range(1, 3))},
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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_gradient_checkpointing_is_applied(self):
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expected_set = {"OmniGenTransformer2DModel"}
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super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
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