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f1a93c765f
* Improve incorrect LoRA format error message * Add flag in PeftLoraLoaderMixinTests to disable text encoder LoRA tests * Apply changes to LTX2LoraTests * Further improve incorrect LoRA format error msg following review --------- Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
268 lines
10 KiB
Python
268 lines
10 KiB
Python
# coding=utf-8
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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 sys
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import unittest
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import numpy as np
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import torch
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from transformers import Qwen2Tokenizer, Qwen3Config, Qwen3Model
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from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, ZImagePipeline, ZImageTransformer2DModel
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from ..testing_utils import floats_tensor, is_peft_available, require_peft_backend, skip_mps, torch_device
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if is_peft_available():
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from peft import LoraConfig
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sys.path.append(".")
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from .utils import PeftLoraLoaderMixinTests, check_if_lora_correctly_set # noqa: E402
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@require_peft_backend
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class ZImageLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
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pipeline_class = ZImagePipeline
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scheduler_cls = FlowMatchEulerDiscreteScheduler
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scheduler_kwargs = {}
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transformer_kwargs = {
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"all_patch_size": (2,),
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"all_f_patch_size": (1,),
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"in_channels": 16,
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"dim": 32,
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"n_layers": 2,
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"n_refiner_layers": 1,
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"n_heads": 2,
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"n_kv_heads": 2,
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"norm_eps": 1e-5,
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"qk_norm": True,
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"cap_feat_dim": 16,
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"rope_theta": 256.0,
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"t_scale": 1000.0,
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"axes_dims": [8, 4, 4],
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"axes_lens": [256, 32, 32],
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}
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transformer_cls = ZImageTransformer2DModel
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vae_kwargs = {
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"in_channels": 3,
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"out_channels": 3,
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"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
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"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
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"block_out_channels": [32, 64],
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"layers_per_block": 1,
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"latent_channels": 16,
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"norm_num_groups": 32,
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"sample_size": 32,
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"scaling_factor": 0.3611,
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"shift_factor": 0.1159,
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}
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vae_cls = AutoencoderKL
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tokenizer_cls, tokenizer_id = Qwen2Tokenizer, "hf-internal-testing/tiny-random-Qwen2VLForConditionalGeneration"
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text_encoder_cls, text_encoder_id = Qwen3Model, None # Will be created inline
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denoiser_target_modules = ["to_q", "to_k", "to_v", "to_out.0"]
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supports_text_encoder_loras = False
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@property
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def output_shape(self):
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return (1, 32, 32, 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 = 10
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num_channels = 4
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sizes = (32, 32)
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generator = torch.manual_seed(0)
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noise = floats_tensor((batch_size, 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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pipeline_inputs = {
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"prompt": "A painting of a squirrel eating a burger",
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"num_inference_steps": 4,
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"guidance_scale": 0.0,
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"height": 32,
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"width": 32,
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"max_sequence_length": 16,
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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 get_dummy_components(self, scheduler_cls=None, use_dora=False, lora_alpha=None):
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# Override to create Qwen3Model inline since it doesn't have a pretrained tiny model
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torch.manual_seed(0)
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config = Qwen3Config(
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hidden_size=16,
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intermediate_size=16,
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num_hidden_layers=2,
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num_attention_heads=2,
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num_key_value_heads=2,
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vocab_size=151936,
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max_position_embeddings=512,
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)
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text_encoder = Qwen3Model(config)
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tokenizer = Qwen2Tokenizer.from_pretrained(self.tokenizer_id)
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transformer = self.transformer_cls(**self.transformer_kwargs)
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# `x_pad_token` and `cap_pad_token` are initialized with `torch.empty`.
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# This can cause NaN data values in our testing environment. Fixating them
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# helps prevent that issue.
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with torch.no_grad():
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transformer.x_pad_token.copy_(torch.ones_like(transformer.x_pad_token.data))
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transformer.cap_pad_token.copy_(torch.ones_like(transformer.cap_pad_token.data))
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vae = self.vae_cls(**self.vae_kwargs)
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if scheduler_cls is None:
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scheduler_cls = self.scheduler_cls
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scheduler = scheduler_cls(**self.scheduler_kwargs)
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rank = 4
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lora_alpha = rank if lora_alpha is None else lora_alpha
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text_lora_config = LoraConfig(
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r=rank,
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lora_alpha=lora_alpha,
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target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
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init_lora_weights=False,
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use_dora=use_dora,
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)
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denoiser_lora_config = LoraConfig(
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r=rank,
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lora_alpha=lora_alpha,
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target_modules=self.denoiser_target_modules,
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init_lora_weights=False,
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use_dora=use_dora,
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)
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pipeline_components = {
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"transformer": transformer,
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"vae": vae,
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"scheduler": scheduler,
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"text_encoder": text_encoder,
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"tokenizer": tokenizer,
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}
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return pipeline_components, text_lora_config, denoiser_lora_config
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def test_correct_lora_configs_with_different_ranks(self):
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components, _, denoiser_lora_config = self.get_dummy_components()
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pipe = self.pipeline_class(**components)
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pipe = pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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_, _, inputs = self.get_dummy_inputs(with_generator=False)
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original_output = pipe(**inputs, generator=torch.manual_seed(0))[0]
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pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
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lora_output_same_rank = pipe(**inputs, generator=torch.manual_seed(0))[0]
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pipe.transformer.delete_adapters("adapter-1")
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denoiser = pipe.unet if self.unet_kwargs is not None else pipe.transformer
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for name, _ in denoiser.named_modules():
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if "to_k" in name and "attention" in name and "lora" not in name:
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module_name_to_rank_update = name.replace(".base_layer.", ".")
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break
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# change the rank_pattern
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updated_rank = denoiser_lora_config.r * 2
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denoiser_lora_config.rank_pattern = {module_name_to_rank_update: updated_rank}
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pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
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updated_rank_pattern = pipe.transformer.peft_config["adapter-1"].rank_pattern
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self.assertTrue(updated_rank_pattern == {module_name_to_rank_update: updated_rank})
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lora_output_diff_rank = pipe(**inputs, generator=torch.manual_seed(0))[0]
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self.assertTrue(not np.allclose(original_output, lora_output_same_rank, atol=1e-3, rtol=1e-3))
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self.assertTrue(not np.allclose(lora_output_diff_rank, lora_output_same_rank, atol=1e-3, rtol=1e-3))
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pipe.transformer.delete_adapters("adapter-1")
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# similarly change the alpha_pattern
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updated_alpha = denoiser_lora_config.lora_alpha * 2
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denoiser_lora_config.alpha_pattern = {module_name_to_rank_update: updated_alpha}
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pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
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self.assertTrue(
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pipe.transformer.peft_config["adapter-1"].alpha_pattern == {module_name_to_rank_update: updated_alpha}
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)
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lora_output_diff_alpha = pipe(**inputs, generator=torch.manual_seed(0))[0]
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self.assertTrue(not np.allclose(original_output, lora_output_diff_alpha, atol=1e-3, rtol=1e-3))
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self.assertTrue(not np.allclose(lora_output_diff_alpha, lora_output_same_rank, atol=1e-3, rtol=1e-3))
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@skip_mps
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def test_lora_fuse_nan(self):
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components, _, denoiser_lora_config = self.get_dummy_components()
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pipe = self.pipeline_class(**components)
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pipe = pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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_, _, inputs = self.get_dummy_inputs(with_generator=False)
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denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
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denoiser.add_adapter(denoiser_lora_config, "adapter-1")
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self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
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# corrupt one LoRA weight with `inf` values
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with torch.no_grad():
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possible_tower_names = ["noise_refiner"]
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filtered_tower_names = [
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tower_name for tower_name in possible_tower_names if hasattr(pipe.transformer, tower_name)
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]
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for tower_name in filtered_tower_names:
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transformer_tower = getattr(pipe.transformer, tower_name)
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transformer_tower[0].attention.to_q.lora_A["adapter-1"].weight += float("inf")
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# with `safe_fusing=True` we should see an Error
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with self.assertRaises(ValueError):
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pipe.fuse_lora(components=self.pipeline_class._lora_loadable_modules, safe_fusing=True)
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# without we should not see an error, but every image will be black
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pipe.fuse_lora(components=self.pipeline_class._lora_loadable_modules, safe_fusing=False)
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out = pipe(**inputs)[0]
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self.assertTrue(np.isnan(out).all())
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def test_lora_scale_kwargs_match_fusion(self):
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super().test_lora_scale_kwargs_match_fusion(5e-2, 5e-2)
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@unittest.skip("Needs to be debugged.")
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def test_set_adapters_match_attention_kwargs(self):
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super().test_set_adapters_match_attention_kwargs()
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@unittest.skip("Needs to be debugged.")
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def test_simple_inference_with_text_denoiser_lora_and_scale(self):
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super().test_simple_inference_with_text_denoiser_lora_and_scale()
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@unittest.skip("Not supported in ZImage.")
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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 ZImage.")
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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 ZImage.")
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def test_modify_padding_mode(self):
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pass
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