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2d8a41cae8
* Add wanx pipeline, model and example * wanx_merged_v1 * change WanX into Wan * fix i2v fp32 oom error Link: https://code.alibaba-inc.com/open_wanx2/diffusers/codereview/20607813 * support t2v load fp32 ckpt * add example * final merge v1 * Update autoencoder_kl_wan.py * up * update middle, test up_block * up up * one less nn.sequential * up more * up * more * [refactor] [wip] Wan transformer/pipeline (#10926) * update * update * refactor rope * refactor pipeline * make fix-copies * add transformer test * update * update * make style * update tests * tests * conversion script * conversion script * update * docs * remove unused code * fix _toctree.yml * update dtype * fix test * fix tests: scale * up * more * Apply suggestions from code review * Apply suggestions from code review * style * Update scripts/convert_wan_to_diffusers.py * update docs * fix --------- Co-authored-by: Yitong Huang <huangyitong.hyt@alibaba-inc.com> Co-authored-by: 亚森 <wangjiayu.wjy@alibaba-inc.com> Co-authored-by: Aryan <aryan@huggingface.co>
80 lines
2.3 KiB
Python
80 lines
2.3 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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from diffusers import AutoencoderKLWan
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from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, torch_device
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from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin
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enable_full_determinism()
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class AutoencoderKLWanTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
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model_class = AutoencoderKLWan
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main_input_name = "sample"
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base_precision = 1e-2
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def get_autoencoder_kl_wan_config(self):
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return {
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"base_dim": 3,
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"z_dim": 16,
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"dim_mult": [1, 1, 1, 1],
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"num_res_blocks": 1,
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"temperal_downsample": [False, True, True],
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}
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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_frames = 9
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num_channels = 3
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sizes = (16, 16)
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image = floats_tensor((batch_size, num_channels, num_frames) + sizes).to(torch_device)
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return {"sample": image}
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@property
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def input_shape(self):
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return (3, 9, 16, 16)
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@property
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def output_shape(self):
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return (3, 9, 16, 16)
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def prepare_init_args_and_inputs_for_common(self):
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init_dict = self.get_autoencoder_kl_wan_config()
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inputs_dict = self.dummy_input
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return init_dict, inputs_dict
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@unittest.skip("Gradient checkpointing has not been implemented yet")
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def test_gradient_checkpointing_is_applied(self):
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pass
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@unittest.skip("Test not supported")
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def test_forward_with_norm_groups(self):
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pass
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@unittest.skip("RuntimeError: fill_out not implemented for 'Float8_e4m3fn'")
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def test_layerwise_casting_inference(self):
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pass
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@unittest.skip("RuntimeError: fill_out not implemented for 'Float8_e4m3fn'")
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def test_layerwise_casting_training(self):
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pass
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