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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.
107 lines
3.8 KiB
Python
107 lines
3.8 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 pytest
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import torch
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from diffusers import QwenImageTransformer2DModel
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from ...testing_utils import enable_full_determinism, torch_device
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from ..test_modeling_common import ModelTesterMixin, TorchCompileTesterMixin
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enable_full_determinism()
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class QwenImageTransformerTests(ModelTesterMixin, unittest.TestCase):
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model_class = QwenImageTransformer2DModel
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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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# Skip setting testing with default: AttnProcessor
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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)
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@property
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def output_shape(self):
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return (16, 16)
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def prepare_dummy_input(self, height=4, width=4):
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batch_size = 1
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num_latent_channels = embedding_dim = 16
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sequence_length = 7
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vae_scale_factor = 4
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hidden_states = torch.randn((batch_size, height * width, num_latent_channels)).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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encoder_hidden_states_mask = torch.ones((batch_size, sequence_length)).to(torch_device, torch.long)
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timestep = torch.tensor([1.0]).to(torch_device).expand(batch_size)
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orig_height = height * 2 * vae_scale_factor
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orig_width = width * 2 * vae_scale_factor
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img_shapes = [(1, orig_height // vae_scale_factor // 2, orig_width // vae_scale_factor // 2)] * batch_size
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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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"encoder_hidden_states_mask": encoder_hidden_states_mask,
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"timestep": timestep,
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"img_shapes": img_shapes,
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"txt_seq_lens": encoder_hidden_states_mask.sum(dim=1).tolist(),
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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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"patch_size": 2,
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"in_channels": 16,
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"out_channels": 4,
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"num_layers": 2,
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"attention_head_dim": 16,
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"num_attention_heads": 3,
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"joint_attention_dim": 16,
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"guidance_embeds": False,
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"axes_dims_rope": (8, 4, 4),
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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 = {"QwenImageTransformer2DModel"}
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super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
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class QwenImageTransformerCompileTests(TorchCompileTesterMixin, unittest.TestCase):
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model_class = QwenImageTransformer2DModel
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def prepare_init_args_and_inputs_for_common(self):
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return QwenImageTransformerTests().prepare_init_args_and_inputs_for_common()
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def prepare_dummy_input(self, height, width):
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return QwenImageTransformerTests().prepare_dummy_input(height=height, width=width)
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@pytest.mark.xfail(condition=True, reason="RoPE needs to be revisited.", strict=True)
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def test_torch_compile_recompilation_and_graph_break(self):
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super().test_torch_compile_recompilation_and_graph_break()
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