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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 sys
import unittest
import torch
from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2Tokenizer
from diffusers import (
AutoencoderKLQwenImage,
FlowMatchEulerDiscreteScheduler,
QwenImagePipeline,
QwenImageTransformer2DModel,
)
from ..testing_utils import floats_tensor, require_peft_backend
sys.path.append(".")
from .utils import PeftLoraLoaderMixinTests # noqa: E402
@require_peft_backend
class QwenImageLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
pipeline_class = QwenImagePipeline
scheduler_cls = FlowMatchEulerDiscreteScheduler
scheduler_kwargs = {}
transformer_kwargs = {
"patch_size": 2,
"in_channels": 16,
"out_channels": 4,
"num_layers": 2,
"attention_head_dim": 16,
"num_attention_heads": 3,
"joint_attention_dim": 16,
"guidance_embeds": False,
"axes_dims_rope": (8, 4, 4),
}
transformer_cls = QwenImageTransformer2DModel
z_dim = 4
vae_kwargs = {
"base_dim": z_dim * 6,
"z_dim": z_dim,
"dim_mult": [1, 2, 4],
"num_res_blocks": 1,
"temperal_downsample": [False, True],
"latents_mean": [0.0] * 4,
"latents_std": [1.0] * 4,
}
vae_cls = AutoencoderKLQwenImage
tokenizer_cls, tokenizer_id = Qwen2Tokenizer, "hf-internal-testing/tiny-random-Qwen25VLForCondGen"
text_encoder_cls, text_encoder_id = (
Qwen2_5_VLForConditionalGeneration,
"hf-internal-testing/tiny-random-Qwen25VLForCondGen",
)
denoiser_target_modules = ["to_q", "to_k", "to_v", "to_out.0"]
supports_text_encoder_loras = False
@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 Qwen Image.")
def test_simple_inference_with_text_denoiser_block_scale(self):
pass
@unittest.skip("Not supported in Qwen Image.")
def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
pass
@unittest.skip("Not supported in Qwen Image.")
def test_modify_padding_mode(self):
pass
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