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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.
97 lines
3.3 KiB
Python
97 lines
3.3 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 HiDreamImageTransformer2DModel
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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 HiDreamTransformerTests(ModelTesterMixin, unittest.TestCase):
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model_class = HiDreamImageTransformer2DModel
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main_input_name = "hidden_states"
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model_split_percents = [0.8, 0.8, 0.9]
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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 = width = 32
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embedding_dim_t5, embedding_dim_llama, embedding_dim_pooled = 8, 4, 8
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sequence_length = 8
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hidden_states = torch.randn((batch_size, num_channels, height, width)).to(torch_device)
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encoder_hidden_states_t5 = torch.randn((batch_size, sequence_length, embedding_dim_t5)).to(torch_device)
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encoder_hidden_states_llama3 = torch.randn((batch_size, batch_size, sequence_length, embedding_dim_llama)).to(
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torch_device
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)
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pooled_embeds = torch.randn((batch_size, embedding_dim_pooled)).to(torch_device)
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timesteps = torch.randint(0, 1000, size=(batch_size,)).to(torch_device)
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return {
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"hidden_states": hidden_states,
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"encoder_hidden_states_t5": encoder_hidden_states_t5,
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"encoder_hidden_states_llama3": encoder_hidden_states_llama3,
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"pooled_embeds": pooled_embeds,
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"timesteps": timesteps,
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}
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@property
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def input_shape(self):
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return (4, 32, 32)
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@property
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def output_shape(self):
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return (4, 32, 32)
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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": 4,
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"out_channels": 4,
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"num_layers": 1,
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"num_single_layers": 1,
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"attention_head_dim": 8,
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"num_attention_heads": 4,
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"caption_channels": [8, 4],
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"text_emb_dim": 8,
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"num_routed_experts": 2,
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"num_activated_experts": 2,
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"axes_dims_rope": (4, 2, 2),
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"max_resolution": (32, 32),
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"llama_layers": (0, 1),
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"force_inference_output": True, # TODO: as we don't implement MoE loss in training tests.
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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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@unittest.skip("HiDreamImageTransformer2DModel uses a dedicated attention processor. This test doesn't apply")
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def test_set_attn_processor_for_determinism(self):
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pass
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def test_gradient_checkpointing_is_applied(self):
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expected_set = {"HiDreamImageTransformer2DModel"}
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super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
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