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# 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 LTX2VideoTransformer3DModel
from ...testing_utils import enable_full_determinism, torch_device
from ..test_modeling_common import ModelTesterMixin, TorchCompileTesterMixin
enable_full_determinism()
class LTX2TransformerTests(ModelTesterMixin, unittest.TestCase):
model_class = LTX2VideoTransformer3DModel
main_input_name = "hidden_states"
uses_custom_attn_processor = True
@property
def dummy_input(self):
# Common
batch_size = 2
# Video
num_frames = 2
num_channels = 4
height = 16
width = 16
# Audio
audio_num_frames = 9
audio_num_channels = 2
num_mel_bins = 2
# Text
embedding_dim = 16
sequence_length = 16
hidden_states = torch.randn((batch_size, num_frames * height * width, num_channels)).to(torch_device)
audio_hidden_states = torch.randn((batch_size, audio_num_frames, audio_num_channels * num_mel_bins)).to(
torch_device
)
encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)
audio_encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)
encoder_attention_mask = torch.ones((batch_size, sequence_length)).bool().to(torch_device)
timestep = torch.rand((batch_size,)).to(torch_device) * 1000
return {
"hidden_states": hidden_states,
"audio_hidden_states": audio_hidden_states,
"encoder_hidden_states": encoder_hidden_states,
"audio_encoder_hidden_states": audio_encoder_hidden_states,
"timestep": timestep,
"encoder_attention_mask": encoder_attention_mask,
"num_frames": num_frames,
"height": height,
"width": width,
"audio_num_frames": audio_num_frames,
"fps": 25.0,
}
@property
def input_shape(self):
return (512, 4)
@property
def output_shape(self):
return (512, 4)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"in_channels": 4,
"out_channels": 4,
"patch_size": 1,
"patch_size_t": 1,
"num_attention_heads": 2,
"attention_head_dim": 8,
"cross_attention_dim": 16,
"audio_in_channels": 4,
"audio_out_channels": 4,
"audio_num_attention_heads": 2,
"audio_attention_head_dim": 4,
"audio_cross_attention_dim": 8,
"num_layers": 2,
"qk_norm": "rms_norm_across_heads",
"caption_channels": 16,
"rope_double_precision": False,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_gradient_checkpointing_is_applied(self):
expected_set = {"LTX2VideoTransformer3DModel"}
super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
# def test_ltx2_consistency(self, seed=0, dtype=torch.float32):
# torch.manual_seed(seed)
# init_dict, _ = self.prepare_init_args_and_inputs_for_common()
# # Calculate dummy inputs in a custom manner to ensure compatibility with original code
# batch_size = 2
# num_frames = 9
# latent_frames = 2
# text_embedding_dim = 16
# text_seq_len = 16
# fps = 25.0
# sampling_rate = 16000.0
# hop_length = 160.0
# sigma = torch.rand((1,), generator=torch.manual_seed(seed), dtype=dtype, device="cpu") * 1000
# timestep = (sigma * torch.ones((batch_size,), dtype=dtype, device="cpu")).to(device=torch_device)
# num_channels = 4
# latent_height = 4
# latent_width = 4
# hidden_states = torch.randn(
# (batch_size, num_channels, latent_frames, latent_height, latent_width),
# generator=torch.manual_seed(seed),
# dtype=dtype,
# device="cpu",
# )
# # Patchify video latents (with patch_size (1, 1, 1))
# hidden_states = hidden_states.reshape(batch_size, -1, latent_frames, 1, latent_height, 1, latent_width, 1)
# hidden_states = hidden_states.permute(0, 2, 4, 6, 1, 3, 5, 7).flatten(4, 7).flatten(1, 3)
# encoder_hidden_states = torch.randn(
# (batch_size, text_seq_len, text_embedding_dim),
# generator=torch.manual_seed(seed),
# dtype=dtype,
# device="cpu",
# )
# audio_num_channels = 2
# num_mel_bins = 2
# latent_length = int((sampling_rate / hop_length / 4) * (num_frames / fps))
# audio_hidden_states = torch.randn(
# (batch_size, audio_num_channels, latent_length, num_mel_bins),
# generator=torch.manual_seed(seed),
# dtype=dtype,
# device="cpu",
# )
# # Patchify audio latents
# audio_hidden_states = audio_hidden_states.transpose(1, 2).flatten(2, 3)
# audio_encoder_hidden_states = torch.randn(
# (batch_size, text_seq_len, text_embedding_dim),
# generator=torch.manual_seed(seed),
# dtype=dtype,
# device="cpu",
# )
# inputs_dict = {
# "hidden_states": hidden_states.to(device=torch_device),
# "audio_hidden_states": audio_hidden_states.to(device=torch_device),
# "encoder_hidden_states": encoder_hidden_states.to(device=torch_device),
# "audio_encoder_hidden_states": audio_encoder_hidden_states.to(device=torch_device),
# "timestep": timestep,
# "num_frames": latent_frames,
# "height": latent_height,
# "width": latent_width,
# "audio_num_frames": num_frames,
# "fps": 25.0,
# }
# model = self.model_class.from_pretrained(
# "diffusers-internal-dev/dummy-ltx2",
# subfolder="transformer",
# device_map="cpu",
# )
# # torch.manual_seed(seed)
# # model = self.model_class(**init_dict)
# model.to(torch_device)
# model.eval()
# with attention_backend("native"):
# with torch.no_grad():
# output = model(**inputs_dict)
# video_output, audio_output = output.to_tuple()
# self.assertIsNotNone(video_output)
# self.assertIsNotNone(audio_output)
# # input & output have to have the same shape
# video_expected_shape = (batch_size, latent_frames * latent_height * latent_width, num_channels)
# self.assertEqual(video_output.shape, video_expected_shape, "Video input and output shapes do not match")
# audio_expected_shape = (batch_size, latent_length, audio_num_channels * num_mel_bins)
# self.assertEqual(audio_output.shape, audio_expected_shape, "Audio input and output shapes do not match")
# # Check against expected slice
# # fmt: off
# video_expected_slice = torch.tensor([0.4783, 1.6954, -1.2092, 0.1762, 0.7801, 1.2025, -1.4525, -0.2721, 0.3354, 1.9144, -1.5546, 0.0831, 0.4391, 1.7012, -1.7373, -0.2676])
# audio_expected_slice = torch.tensor([-0.4236, 0.4750, 0.3901, -0.4339, -0.2782, 0.4357, 0.4526, -0.3927, -0.0980, 0.4870, 0.3964, -0.3169, -0.3974, 0.4408, 0.3809, -0.4692])
# # fmt: on
# video_output_flat = video_output.cpu().flatten().float()
# video_generated_slice = torch.cat([video_output_flat[:8], video_output_flat[-8:]])
# self.assertTrue(torch.allclose(video_generated_slice, video_expected_slice, atol=1e-4))
# audio_output_flat = audio_output.cpu().flatten().float()
# audio_generated_slice = torch.cat([audio_output_flat[:8], audio_output_flat[-8:]])
# self.assertTrue(torch.allclose(audio_generated_slice, audio_expected_slice, atol=1e-4))
class LTX2TransformerCompileTests(TorchCompileTesterMixin, unittest.TestCase):
model_class = LTX2VideoTransformer3DModel
def prepare_init_args_and_inputs_for_common(self):
return LTX2TransformerTests().prepare_init_args_and_inputs_for_common()
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