File size: 2,895 Bytes
ca32b0e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
# Copyright 2025 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import torch
from diffusers import WanTransformer3DModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
torch_device,
)
from ..test_modeling_common import ModelTesterMixin, TorchCompileTesterMixin
enable_full_determinism()
class WanTransformer3DTests(ModelTesterMixin, unittest.TestCase):
model_class = WanTransformer3DModel
main_input_name = "hidden_states"
uses_custom_attn_processor = True
@property
def dummy_input(self):
batch_size = 1
num_channels = 4
num_frames = 2
height = 16
width = 16
text_encoder_embedding_dim = 16
sequence_length = 12
hidden_states = torch.randn((batch_size, num_channels, num_frames, height, width)).to(torch_device)
timestep = torch.randint(0, 1000, size=(batch_size,)).to(torch_device)
encoder_hidden_states = torch.randn((batch_size, sequence_length, text_encoder_embedding_dim)).to(torch_device)
return {
"hidden_states": hidden_states,
"encoder_hidden_states": encoder_hidden_states,
"timestep": timestep,
}
@property
def input_shape(self):
return (4, 1, 16, 16)
@property
def output_shape(self):
return (4, 1, 16, 16)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"patch_size": (1, 2, 2),
"num_attention_heads": 2,
"attention_head_dim": 12,
"in_channels": 4,
"out_channels": 4,
"text_dim": 16,
"freq_dim": 256,
"ffn_dim": 32,
"num_layers": 2,
"cross_attn_norm": True,
"qk_norm": "rms_norm_across_heads",
"rope_max_seq_len": 32,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_gradient_checkpointing_is_applied(self):
expected_set = {"WanTransformer3DModel"}
super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
class WanTransformerCompileTests(TorchCompileTesterMixin, unittest.TestCase):
model_class = WanTransformer3DModel
def prepare_init_args_and_inputs_for_common(self):
return WanTransformer3DTests().prepare_init_args_and_inputs_for_common()
|