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https://github.com/huggingface/diffusers.git
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5e3b7d2d8a
* Update EasyAnimate V5.1 * Add docs && add tests && Fix comments problems in transformer3d and vae * delete comments and remove useless import * delete process * Update EXAMPLE_DOC_STRING * rename transformer file * make fix-copies * make style * refactor pt. 1 * update toctree.yml * add model tests * Update layer_norm for norm_added_q and norm_added_k in Attention * Fix processor problem * refactor vae * Fix problem in comments * refactor tiling; remove einops dependency * fix docs path * make fix-copies * Update src/diffusers/pipelines/easyanimate/pipeline_easyanimate_control.py * update _toctree.yml * fix test * update * update * update * make fix-copies * fix tests --------- Co-authored-by: Aryan <aryan@huggingface.co> Co-authored-by: Aryan <contact.aryanvs@gmail.com> Co-authored-by: YiYi Xu <yixu310@gmail.com> Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
88 lines
2.8 KiB
Python
88 lines
2.8 KiB
Python
# coding=utf-8
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# Copyright 2024 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 EasyAnimateTransformer3DModel
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from diffusers.utils.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 EasyAnimateTransformerTests(ModelTesterMixin, unittest.TestCase):
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model_class = EasyAnimateTransformer3DModel
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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 = 2
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height = 16
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width = 16
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embedding_dim = 16
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sequence_length = 16
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hidden_states = torch.randn((batch_size, num_channels, num_frames, 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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return {
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"hidden_states": hidden_states,
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"timestep": timestep,
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"timestep_cond": None,
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"encoder_hidden_states": encoder_hidden_states,
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"encoder_hidden_states_t5": None,
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"inpaint_latents": None,
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"control_latents": None,
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}
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@property
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def input_shape(self):
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return (4, 2, 16, 16)
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@property
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def output_shape(self):
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return (4, 2, 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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"attention_head_dim": 16,
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"num_attention_heads": 2,
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"in_channels": 4,
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"mmdit_layers": 2,
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"num_layers": 2,
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"out_channels": 4,
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"patch_size": 2,
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"sample_height": 60,
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"sample_width": 90,
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"text_embed_dim": 16,
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"time_embed_dim": 8,
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"time_position_encoding_type": "3d_rope",
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"timestep_activation_fn": "silu",
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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 = {"EasyAnimateTransformer3DModel"}
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super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
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