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[lora] tests for exclude_modules with Wan VACE (#11843)
* wan vace. * update * update * import problem
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Sayak Paul
GitHub
217
tests/lora/test_lora_layers_wanvace.py
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217
tests/lora/test_lora_layers_wanvace.py
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# Copyright 2025 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import sys
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import tempfile
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import unittest
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import numpy as np
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import pytest
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import safetensors.torch
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import torch
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from PIL import Image
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from transformers import AutoTokenizer, T5EncoderModel
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from diffusers import AutoencoderKLWan, FlowMatchEulerDiscreteScheduler, WanVACEPipeline, WanVACETransformer3DModel
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from diffusers.utils.import_utils import is_peft_available
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from diffusers.utils.testing_utils import (
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floats_tensor,
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require_peft_backend,
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require_peft_version_greater,
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skip_mps,
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torch_device,
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)
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if is_peft_available():
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from peft.utils import get_peft_model_state_dict
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sys.path.append(".")
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from utils import PeftLoraLoaderMixinTests # noqa: E402
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@require_peft_backend
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@skip_mps
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class WanVACELoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
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pipeline_class = WanVACEPipeline
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scheduler_cls = FlowMatchEulerDiscreteScheduler
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scheduler_classes = [FlowMatchEulerDiscreteScheduler]
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scheduler_kwargs = {}
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transformer_kwargs = {
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"patch_size": (1, 2, 2),
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"num_attention_heads": 2,
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"attention_head_dim": 8,
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"in_channels": 4,
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"out_channels": 4,
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"text_dim": 32,
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"freq_dim": 16,
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"ffn_dim": 16,
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"num_layers": 2,
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"cross_attn_norm": True,
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"qk_norm": "rms_norm_across_heads",
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"rope_max_seq_len": 16,
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"vace_layers": [0],
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"vace_in_channels": 72,
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}
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transformer_cls = WanVACETransformer3DModel
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vae_kwargs = {
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"base_dim": 3,
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"z_dim": 4,
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"dim_mult": [1, 1, 1, 1],
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"latents_mean": torch.randn(4).numpy().tolist(),
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"latents_std": torch.randn(4).numpy().tolist(),
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"num_res_blocks": 1,
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"temperal_downsample": [False, True, True],
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}
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vae_cls = AutoencoderKLWan
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has_two_text_encoders = True
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tokenizer_cls, tokenizer_id = AutoTokenizer, "hf-internal-testing/tiny-random-t5"
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text_encoder_cls, text_encoder_id = T5EncoderModel, "hf-internal-testing/tiny-random-t5"
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text_encoder_target_modules = ["q", "k", "v", "o"]
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@property
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def output_shape(self):
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return (1, 9, 16, 16, 3)
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def get_dummy_inputs(self, with_generator=True):
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batch_size = 1
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sequence_length = 16
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num_channels = 4
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num_frames = 9
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num_latent_frames = 3 # (num_frames - 1) // temporal_compression_ratio + 1
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sizes = (4, 4)
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height, width = 16, 16
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generator = torch.manual_seed(0)
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noise = floats_tensor((batch_size, num_latent_frames, num_channels) + sizes)
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input_ids = torch.randint(1, sequence_length, size=(batch_size, sequence_length), generator=generator)
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video = [Image.new("RGB", (height, width))] * num_frames
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mask = [Image.new("L", (height, width), 0)] * num_frames
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pipeline_inputs = {
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"video": video,
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"mask": mask,
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"prompt": "",
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"num_frames": num_frames,
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"num_inference_steps": 1,
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"guidance_scale": 6.0,
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"height": height,
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"width": height,
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"max_sequence_length": sequence_length,
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"output_type": "np",
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}
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if with_generator:
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pipeline_inputs.update({"generator": generator})
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return noise, input_ids, pipeline_inputs
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def test_simple_inference_with_text_lora_denoiser_fused_multi(self):
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super().test_simple_inference_with_text_lora_denoiser_fused_multi(expected_atol=9e-3)
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def test_simple_inference_with_text_denoiser_lora_unfused(self):
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super().test_simple_inference_with_text_denoiser_lora_unfused(expected_atol=9e-3)
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@unittest.skip("Not supported in Wan VACE.")
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def test_simple_inference_with_text_denoiser_block_scale(self):
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pass
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@unittest.skip("Not supported in Wan VACE.")
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def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
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pass
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@unittest.skip("Not supported in Wan VACE.")
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def test_modify_padding_mode(self):
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pass
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@unittest.skip("Text encoder LoRA is not supported in Wan VACE.")
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def test_simple_inference_with_partial_text_lora(self):
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pass
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@unittest.skip("Text encoder LoRA is not supported in Wan VACE.")
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def test_simple_inference_with_text_lora(self):
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pass
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@unittest.skip("Text encoder LoRA is not supported in Wan VACE.")
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def test_simple_inference_with_text_lora_and_scale(self):
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pass
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@unittest.skip("Text encoder LoRA is not supported in Wan VACE.")
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def test_simple_inference_with_text_lora_fused(self):
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pass
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@unittest.skip("Text encoder LoRA is not supported in Wan VACE.")
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def test_simple_inference_with_text_lora_save_load(self):
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pass
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@pytest.mark.xfail(
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condition=True,
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reason="RuntimeError: Input type (float) and bias type (c10::BFloat16) should be the same",
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strict=True,
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)
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def test_layerwise_casting_inference_denoiser(self):
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super().test_layerwise_casting_inference_denoiser()
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@require_peft_version_greater("0.13.2")
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def test_lora_exclude_modules_wanvace(self):
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scheduler_cls = self.scheduler_classes[0]
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exclude_module_name = "vace_blocks.0.proj_out"
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components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
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pipe = self.pipeline_class(**components).to(torch_device)
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_, _, inputs = self.get_dummy_inputs(with_generator=False)
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output_no_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
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self.assertTrue(output_no_lora.shape == self.output_shape)
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# only supported for `denoiser` now
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denoiser_lora_config.target_modules = ["proj_out"]
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denoiser_lora_config.exclude_modules = [exclude_module_name]
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pipe, _ = self.add_adapters_to_pipeline(
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pipe, text_lora_config=text_lora_config, denoiser_lora_config=denoiser_lora_config
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)
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# The state dict shouldn't contain the modules to be excluded from LoRA.
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state_dict_from_model = get_peft_model_state_dict(pipe.transformer, adapter_name="default")
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self.assertTrue(not any(exclude_module_name in k for k in state_dict_from_model))
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self.assertTrue(any("proj_out" in k for k in state_dict_from_model))
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output_lora_exclude_modules = pipe(**inputs, generator=torch.manual_seed(0))[0]
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with tempfile.TemporaryDirectory() as tmpdir:
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modules_to_save = self._get_modules_to_save(pipe, has_denoiser=True)
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lora_state_dicts = self._get_lora_state_dicts(modules_to_save)
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self.pipeline_class.save_lora_weights(save_directory=tmpdir, **lora_state_dicts)
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pipe.unload_lora_weights()
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# Check in the loaded state dict.
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loaded_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
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self.assertTrue(not any(exclude_module_name in k for k in loaded_state_dict))
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self.assertTrue(any("proj_out" in k for k in loaded_state_dict))
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# Check in the state dict obtained after loading LoRA.
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pipe.load_lora_weights(tmpdir)
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state_dict_from_model = get_peft_model_state_dict(pipe.transformer, adapter_name="default_0")
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self.assertTrue(not any(exclude_module_name in k for k in state_dict_from_model))
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self.assertTrue(any("proj_out" in k for k in state_dict_from_model))
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output_lora_pretrained = pipe(**inputs, generator=torch.manual_seed(0))[0]
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self.assertTrue(
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not np.allclose(output_no_lora, output_lora_exclude_modules, atol=1e-3, rtol=1e-3),
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"LoRA should change outputs.",
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)
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self.assertTrue(
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np.allclose(output_lora_exclude_modules, output_lora_pretrained, atol=1e-3, rtol=1e-3),
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"Lora outputs should match.",
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)
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