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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 numpy as np
import pytest
import torch
from transformers import AutoTokenizer, GemmaForCausalLM
from diffusers import (
AutoencoderKL,
FlowMatchEulerDiscreteScheduler,
Lumina2Pipeline,
Lumina2Transformer2DModel,
)
from ..testing_utils import floats_tensor, is_torch_version, require_peft_backend, skip_mps, torch_device
sys.path.append(".")
from .utils import PeftLoraLoaderMixinTests, check_if_lora_correctly_set # noqa: E402
@require_peft_backend
class Lumina2LoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
pipeline_class = Lumina2Pipeline
scheduler_cls = FlowMatchEulerDiscreteScheduler
scheduler_classes = [FlowMatchEulerDiscreteScheduler]
scheduler_kwargs = {}
transformer_kwargs = {
"sample_size": 4,
"patch_size": 2,
"in_channels": 4,
"hidden_size": 8,
"num_layers": 2,
"num_attention_heads": 1,
"num_kv_heads": 1,
"multiple_of": 16,
"ffn_dim_multiplier": None,
"norm_eps": 1e-5,
"scaling_factor": 1.0,
"axes_dim_rope": [4, 2, 2],
"cap_feat_dim": 8,
}
transformer_cls = Lumina2Transformer2DModel
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,
}
vae_cls = AutoencoderKL
tokenizer_cls, tokenizer_id = AutoTokenizer, "hf-internal-testing/dummy-gemma"
text_encoder_cls, text_encoder_id = GemmaForCausalLM, "hf-internal-testing/dummy-gemma-diffusers"
@property
def output_shape(self):
return (1, 4, 4, 3)
def get_dummy_inputs(self, with_generator=True):
batch_size = 1
sequence_length = 16
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": 2,
"guidance_scale": 5.0,
"height": 32,
"width": 32,
"output_type": "np",
}
if with_generator:
pipeline_inputs.update({"generator": generator})
return noise, input_ids, pipeline_inputs
@unittest.skip("Not supported in Lumina2.")
def test_simple_inference_with_text_denoiser_block_scale(self):
pass
@unittest.skip("Not supported in Lumina2.")
def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
pass
@unittest.skip("Not supported in Lumina2.")
def test_modify_padding_mode(self):
pass
@unittest.skip("Text encoder LoRA is not supported in Lumina2.")
def test_simple_inference_with_partial_text_lora(self):
pass
@unittest.skip("Text encoder LoRA is not supported in Lumina2.")
def test_simple_inference_with_text_lora(self):
pass
@unittest.skip("Text encoder LoRA is not supported in Lumina2.")
def test_simple_inference_with_text_lora_and_scale(self):
pass
@unittest.skip("Text encoder LoRA is not supported in Lumina2.")
def test_simple_inference_with_text_lora_fused(self):
pass
@unittest.skip("Text encoder LoRA is not supported in Lumina2.")
def test_simple_inference_with_text_lora_save_load(self):
pass
@skip_mps
@pytest.mark.xfail(
condition=torch.device(torch_device).type == "cpu" and is_torch_version(">=", "2.5"),
reason="Test currently fails on CPU and PyTorch 2.5.1 but not on PyTorch 2.4.1.",
strict=False,
)
def test_lora_fuse_nan(self):
for scheduler_cls in self.scheduler_classes:
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
_, _, inputs = self.get_dummy_inputs(with_generator=False)
if "text_encoder" in self.pipeline_class._lora_loadable_modules:
pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
self.assertTrue(
check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
)
denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
denoiser.add_adapter(denoiser_lora_config, "adapter-1")
self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
# corrupt one LoRA weight with `inf` values
with torch.no_grad():
pipe.transformer.layers[0].attn.to_q.lora_A["adapter-1"].weight += float("inf")
# with `safe_fusing=True` we should see an Error
with self.assertRaises(ValueError):
pipe.fuse_lora(components=self.pipeline_class._lora_loadable_modules, safe_fusing=True)
# without we should not see an error, but every image will be black
pipe.fuse_lora(components=self.pipeline_class._lora_loadable_modules, safe_fusing=False)
out = pipe(**inputs)[0]
self.assertTrue(np.isnan(out).all())
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