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# 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 sys
import unittest
import torch
from transformers import AutoTokenizer, UMT5EncoderModel
from diffusers import (
AuraFlowPipeline,
AuraFlowTransformer2DModel,
FlowMatchEulerDiscreteScheduler,
)
from ..testing_utils import (
floats_tensor,
is_peft_available,
require_peft_backend,
)
if is_peft_available():
pass
sys.path.append(".")
from .utils import PeftLoraLoaderMixinTests # noqa: E402
@require_peft_backend
class AuraFlowLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
pipeline_class = AuraFlowPipeline
scheduler_cls = FlowMatchEulerDiscreteScheduler
scheduler_classes = [FlowMatchEulerDiscreteScheduler]
scheduler_kwargs = {}
transformer_kwargs = {
"sample_size": 64,
"patch_size": 1,
"in_channels": 4,
"num_mmdit_layers": 1,
"num_single_dit_layers": 1,
"attention_head_dim": 16,
"num_attention_heads": 2,
"joint_attention_dim": 32,
"caption_projection_dim": 32,
"pos_embed_max_size": 64,
}
transformer_cls = AuraFlowTransformer2DModel
vae_kwargs = {
"sample_size": 32,
"in_channels": 3,
"out_channels": 3,
"block_out_channels": (4,),
"layers_per_block": 1,
"latent_channels": 4,
"norm_num_groups": 1,
"use_quant_conv": False,
"use_post_quant_conv": False,
"shift_factor": 0.0609,
"scaling_factor": 1.5035,
}
tokenizer_cls, tokenizer_id = AutoTokenizer, "hf-internal-testing/tiny-random-t5"
text_encoder_cls, text_encoder_id = UMT5EncoderModel, "hf-internal-testing/tiny-random-umt5"
text_encoder_target_modules = ["q", "k", "v", "o"]
denoiser_target_modules = ["to_q", "to_k", "to_v", "to_out.0", "linear_1"]
@property
def output_shape(self):
return (1, 8, 8, 3)
def get_dummy_inputs(self, with_generator=True):
batch_size = 1
sequence_length = 10
num_channels = 4
sizes = (32, 32)
generator = torch.manual_seed(0)
noise = floats_tensor((batch_size, num_channels) + sizes)
input_ids = torch.randint(1, sequence_length, size=(batch_size, sequence_length), generator=generator)
pipeline_inputs = {
"prompt": "A painting of a squirrel eating a burger",
"num_inference_steps": 4,
"guidance_scale": 0.0,
"height": 8,
"width": 8,
"output_type": "np",
}
if with_generator:
pipeline_inputs.update({"generator": generator})
return noise, input_ids, pipeline_inputs
@unittest.skip("Not supported in AuraFlow.")
def test_simple_inference_with_text_denoiser_block_scale(self):
pass
@unittest.skip("Not supported in AuraFlow.")
def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
pass
@unittest.skip("Not supported in AuraFlow.")
def test_modify_padding_mode(self):
pass
@unittest.skip("Text encoder LoRA is not supported in AuraFlow.")
def test_simple_inference_with_partial_text_lora(self):
pass
@unittest.skip("Text encoder LoRA is not supported in AuraFlow.")
def test_simple_inference_with_text_lora(self):
pass
@unittest.skip("Text encoder LoRA is not supported in AuraFlow.")
def test_simple_inference_with_text_lora_and_scale(self):
pass
@unittest.skip("Text encoder LoRA is not supported in AuraFlow.")
def test_simple_inference_with_text_lora_fused(self):
pass
@unittest.skip("Text encoder LoRA is not supported in AuraFlow.")
def test_simple_inference_with_text_lora_save_load(self):
pass
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