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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 CosmosTransformer3DModel
from diffusers.utils.testing_utils import enable_full_determinism, torch_device
from ..test_modeling_common import ModelTesterMixin
enable_full_determinism()
class CosmosTransformer3DModelTests(ModelTesterMixin, unittest.TestCase):
model_class = CosmosTransformer3DModel
main_input_name = "hidden_states"
uses_custom_attn_processor = True
@property
def dummy_input(self):
batch_size = 1
num_channels = 4
num_frames = 1
height = 16
width = 16
text_embed_dim = 16
sequence_length = 12
fps = 30
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_embed_dim)).to(torch_device)
attention_mask = torch.ones((batch_size, sequence_length)).to(torch_device)
padding_mask = torch.zeros(batch_size, 1, height, width).to(torch_device)
return {
"hidden_states": hidden_states,
"timestep": timestep,
"encoder_hidden_states": encoder_hidden_states,
"attention_mask": attention_mask,
"fps": fps,
"padding_mask": padding_mask,
}
@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 = {
"in_channels": 4,
"out_channels": 4,
"num_attention_heads": 2,
"attention_head_dim": 12,
"num_layers": 2,
"mlp_ratio": 2,
"text_embed_dim": 16,
"adaln_lora_dim": 4,
"max_size": (4, 32, 32),
"patch_size": (1, 2, 2),
"rope_scale": (2.0, 1.0, 1.0),
"concat_padding_mask": True,
"extra_pos_embed_type": "learnable",
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_gradient_checkpointing_is_applied(self):
expected_set = {"CosmosTransformer3DModel"}
super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
class CosmosTransformer3DModelVideoToWorldTests(ModelTesterMixin, unittest.TestCase):
model_class = CosmosTransformer3DModel
main_input_name = "hidden_states"
uses_custom_attn_processor = True
@property
def dummy_input(self):
batch_size = 1
num_channels = 4
num_frames = 1
height = 16
width = 16
text_embed_dim = 16
sequence_length = 12
fps = 30
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_embed_dim)).to(torch_device)
attention_mask = torch.ones((batch_size, sequence_length)).to(torch_device)
condition_mask = torch.ones(batch_size, 1, num_frames, height, width).to(torch_device)
padding_mask = torch.zeros(batch_size, 1, height, width).to(torch_device)
return {
"hidden_states": hidden_states,
"timestep": timestep,
"encoder_hidden_states": encoder_hidden_states,
"attention_mask": attention_mask,
"fps": fps,
"condition_mask": condition_mask,
"padding_mask": padding_mask,
}
@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 = {
"in_channels": 4 + 1,
"out_channels": 4,
"num_attention_heads": 2,
"attention_head_dim": 12,
"num_layers": 2,
"mlp_ratio": 2,
"text_embed_dim": 16,
"adaln_lora_dim": 4,
"max_size": (4, 32, 32),
"patch_size": (1, 2, 2),
"rope_scale": (2.0, 1.0, 1.0),
"concat_padding_mask": True,
"extra_pos_embed_type": "learnable",
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_gradient_checkpointing_is_applied(self):
expected_set = {"CosmosTransformer3DModel"}
super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
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