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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
2.8 KiB
Python
90 lines
2.8 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 Lumina2Transformer2DModel
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from ...testing_utils import (
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enable_full_determinism,
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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 Lumina2Transformer2DModelTransformerTests(ModelTesterMixin, unittest.TestCase):
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model_class = Lumina2Transformer2DModel
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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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batch_size = 2 # N
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num_channels = 4 # C
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height = width = 16 # H, W
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embedding_dim = 32 # D
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sequence_length = 16 # L
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hidden_states = torch.randn((batch_size, num_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.rand(size=(batch_size,)).to(torch_device)
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attention_mask = torch.ones(size=(batch_size, sequence_length), dtype=torch.bool).to(torch_device)
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return {
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"hidden_states": hidden_states,
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"encoder_hidden_states": encoder_hidden_states,
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"timestep": timestep,
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"encoder_attention_mask": attention_mask,
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}
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@property
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def input_shape(self):
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return (4, 16, 16)
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@property
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def output_shape(self):
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return (4, 16, 16)
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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": 16,
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"patch_size": 2,
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"in_channels": 4,
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"hidden_size": 24,
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"num_layers": 2,
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"num_refiner_layers": 1,
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"num_attention_heads": 3,
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"num_kv_heads": 1,
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"multiple_of": 2,
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"ffn_dim_multiplier": None,
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"norm_eps": 1e-5,
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"scaling_factor": 1.0,
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"axes_dim_rope": (4, 2, 2),
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"axes_lens": (128, 128, 128),
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"cap_feat_dim": 32,
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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 = {"Lumina2Transformer2DModel"}
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super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
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