mirror of
https://github.com/huggingface/diffusers.git
synced 2026-01-29 07:22:12 +03:00
7aa6af1138
* update
* update
* update
* update
* update
* merge main
* Revert "merge main"
This reverts commit 65efbcead5.
113 lines
3.1 KiB
Python
113 lines
3.1 KiB
Python
# coding=utf-8
|
|
# 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 LuminaNextDiT2DModel
|
|
|
|
from ...testing_utils import (
|
|
enable_full_determinism,
|
|
torch_device,
|
|
)
|
|
from ..test_modeling_common import ModelTesterMixin
|
|
|
|
|
|
enable_full_determinism()
|
|
|
|
|
|
class LuminaNextDiT2DModelTransformerTests(ModelTesterMixin, unittest.TestCase):
|
|
model_class = LuminaNextDiT2DModel
|
|
main_input_name = "hidden_states"
|
|
uses_custom_attn_processor = True
|
|
|
|
@property
|
|
def dummy_input(self):
|
|
"""
|
|
Args:
|
|
None
|
|
Returns:
|
|
Dict: Dictionary of dummy input tensors
|
|
"""
|
|
batch_size = 2 # N
|
|
num_channels = 4 # C
|
|
height = width = 16 # H, W
|
|
embedding_dim = 32 # D
|
|
sequence_length = 16 # L
|
|
|
|
hidden_states = torch.randn((batch_size, num_channels, height, width)).to(torch_device)
|
|
encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)
|
|
timestep = torch.rand(size=(batch_size,)).to(torch_device)
|
|
encoder_mask = torch.randn(size=(batch_size, sequence_length)).to(torch_device)
|
|
image_rotary_emb = torch.randn((384, 384, 4)).to(torch_device)
|
|
|
|
return {
|
|
"hidden_states": hidden_states,
|
|
"encoder_hidden_states": encoder_hidden_states,
|
|
"timestep": timestep,
|
|
"encoder_mask": encoder_mask,
|
|
"image_rotary_emb": image_rotary_emb,
|
|
"cross_attention_kwargs": {},
|
|
}
|
|
|
|
@property
|
|
def input_shape(self):
|
|
"""
|
|
Args:
|
|
None
|
|
Returns:
|
|
Tuple: (int, int, int)
|
|
"""
|
|
return (4, 16, 16)
|
|
|
|
@property
|
|
def output_shape(self):
|
|
"""
|
|
Args:
|
|
None
|
|
Returns:
|
|
Tuple: (int, int, int)
|
|
"""
|
|
return (4, 16, 16)
|
|
|
|
def prepare_init_args_and_inputs_for_common(self):
|
|
"""
|
|
Args:
|
|
None
|
|
|
|
Returns:
|
|
Tuple: (Dict, Dict)
|
|
"""
|
|
init_dict = {
|
|
"sample_size": 16,
|
|
"patch_size": 2,
|
|
"in_channels": 4,
|
|
"hidden_size": 24,
|
|
"num_layers": 2,
|
|
"num_attention_heads": 3,
|
|
"num_kv_heads": 1,
|
|
"multiple_of": 16,
|
|
"ffn_dim_multiplier": None,
|
|
"norm_eps": 1e-5,
|
|
"learn_sigma": False,
|
|
"qk_norm": True,
|
|
"cross_attention_dim": 32,
|
|
"scaling_factor": 1.0,
|
|
}
|
|
|
|
inputs_dict = self.dummy_input
|
|
return init_dict, inputs_dict
|