mirror of
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106 lines
3.3 KiB
Python
106 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 ConsisIDTransformer3DModel
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from diffusers.utils.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 ConsisIDTransformerTests(ModelTesterMixin, unittest.TestCase):
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model_class = ConsisIDTransformer3DModel
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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
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num_channels = 4
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num_frames = 1
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height = 8
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width = 8
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embedding_dim = 8
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sequence_length = 8
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hidden_states = torch.randn((batch_size, num_frames, 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.randint(0, 1000, size=(batch_size,)).to(torch_device)
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id_vit_hidden = [torch.ones([batch_size, 2, 2]).to(torch_device)] * 1
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id_cond = torch.ones(batch_size, 2).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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"id_vit_hidden": id_vit_hidden,
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"id_cond": id_cond,
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}
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@property
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def input_shape(self):
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return (1, 4, 8, 8)
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@property
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def output_shape(self):
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return (1, 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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"num_attention_heads": 2,
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"attention_head_dim": 8,
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"in_channels": 4,
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"out_channels": 4,
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"time_embed_dim": 2,
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"text_embed_dim": 8,
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"num_layers": 1,
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"sample_width": 8,
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"sample_height": 8,
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"sample_frames": 8,
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"patch_size": 2,
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"temporal_compression_ratio": 4,
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"max_text_seq_length": 8,
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"cross_attn_interval": 1,
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"is_kps": False,
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"is_train_face": True,
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"cross_attn_dim_head": 1,
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"cross_attn_num_heads": 1,
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"LFE_id_dim": 2,
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"LFE_vit_dim": 2,
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"LFE_depth": 5,
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"LFE_dim_head": 8,
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"LFE_num_heads": 2,
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"LFE_num_id_token": 1,
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"LFE_num_querie": 1,
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"LFE_output_dim": 10,
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"LFE_ff_mult": 1,
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"LFE_num_scale": 1,
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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 = {"ConsisIDTransformer3DModel"}
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super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
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