# Copyright 2025 Alibaba Z-Image Team and The HuggingFace Team. All rights reserved. # # 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 gc import os import unittest import numpy as np import torch from transformers import Qwen2Tokenizer, Qwen3Config, Qwen3Model from diffusers import ( AutoencoderKL, FlowMatchEulerDiscreteScheduler, ZImagePipeline, ZImageTransformer2DModel, ) from ...testing_utils import torch_device from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS from ..test_pipelines_common import PipelineTesterMixin, to_np # Z-Image requires torch.use_deterministic_algorithms(False) due to complex64 RoPE operations # Cannot use enable_full_determinism() which sets it to True os.environ["CUDA_LAUNCH_BLOCKING"] = "1" os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8" torch.use_deterministic_algorithms(False) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False if hasattr(torch.backends, "cuda"): torch.backends.cuda.matmul.allow_tf32 = False # Note: Some tests (test_float16_inference, test_save_load_float16) may fail in full suite # due to RopeEmbedder cache state pollution between tests. They pass when run individually. # This is a known test isolation issue, not a functional bug. class ZImagePipelineFastTests(PipelineTesterMixin, unittest.TestCase): pipeline_class = ZImagePipeline params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"} batch_params = TEXT_TO_IMAGE_BATCH_PARAMS image_params = TEXT_TO_IMAGE_IMAGE_PARAMS image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS required_optional_params = frozenset( [ "num_inference_steps", "generator", "latents", "return_dict", "callback_on_step_end", "callback_on_step_end_tensor_inputs", ] ) supports_dduf = False test_xformers_attention = False test_layerwise_casting = True test_group_offloading = True def setUp(self): gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() torch.cuda.synchronize() torch.manual_seed(0) if torch.cuda.is_available(): torch.cuda.manual_seed_all(0) def tearDown(self): super().tearDown() gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() torch.cuda.synchronize() torch.manual_seed(0) if torch.cuda.is_available(): torch.cuda.manual_seed_all(0) def get_dummy_components(self): torch.manual_seed(0) transformer = ZImageTransformer2DModel( all_patch_size=(2,), all_f_patch_size=(1,), in_channels=16, dim=32, n_layers=2, n_refiner_layers=1, n_heads=2, n_kv_heads=2, norm_eps=1e-5, qk_norm=True, cap_feat_dim=16, rope_theta=256.0, t_scale=1000.0, axes_dims=[8, 4, 4], axes_lens=[256, 32, 32], ) torch.manual_seed(0) vae = AutoencoderKL( in_channels=3, out_channels=3, down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], block_out_channels=[32, 64], layers_per_block=1, latent_channels=16, norm_num_groups=32, sample_size=32, scaling_factor=0.3611, shift_factor=0.1159, ) torch.manual_seed(0) scheduler = FlowMatchEulerDiscreteScheduler() torch.manual_seed(0) config = Qwen3Config( hidden_size=16, intermediate_size=16, num_hidden_layers=2, num_attention_heads=2, num_key_value_heads=2, vocab_size=151936, max_position_embeddings=512, ) text_encoder = Qwen3Model(config) tokenizer = Qwen2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-Qwen2VLForConditionalGeneration") components = { "transformer": transformer, "vae": vae, "scheduler": scheduler, "text_encoder": text_encoder, "tokenizer": tokenizer, } return components def get_dummy_inputs(self, device, seed=0): if str(device).startswith("mps"): generator = torch.manual_seed(seed) else: generator = torch.Generator(device=device).manual_seed(seed) inputs = { "prompt": "dance monkey", "negative_prompt": "bad quality", "generator": generator, "num_inference_steps": 2, "guidance_scale": 3.0, "cfg_normalization": False, "cfg_truncation": 1.0, "height": 32, "width": 32, "max_sequence_length": 16, "output_type": "pt", } return inputs def test_inference(self): device = "cpu" components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(device) pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) image = pipe(**inputs).images generated_image = image[0] self.assertEqual(generated_image.shape, (3, 32, 32)) # fmt: off expected_slice = torch.tensor([0.4521, 0.4512, 0.4693, 0.5115, 0.5250, 0.5271, 0.4776, 0.4688, 0.2765, 0.2164, 0.5656, 0.6909, 0.3831, 0.5431, 0.5493, 0.4732]) # fmt: on generated_slice = generated_image.flatten() generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]]) self.assertTrue(torch.allclose(generated_slice, expected_slice, atol=5e-2)) def test_inference_batch_single_identical(self): self._test_inference_batch_single_identical(batch_size=3, expected_max_diff=1e-1) def test_num_images_per_prompt(self): import inspect sig = inspect.signature(self.pipeline_class.__call__) if "num_images_per_prompt" not in sig.parameters: return components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe = pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) batch_sizes = [1, 2] num_images_per_prompts = [1, 2] for batch_size in batch_sizes: for num_images_per_prompt in num_images_per_prompts: inputs = self.get_dummy_inputs(torch_device) for key in inputs.keys(): if key in self.batch_params: inputs[key] = batch_size * [inputs[key]] images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt)[0] assert images.shape[0] == batch_size * num_images_per_prompt del pipe gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() torch.cuda.synchronize() def test_attention_slicing_forward_pass( self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3 ): if not self.test_attention_slicing: return components = self.get_dummy_components() pipe = self.pipeline_class(**components) for component in pipe.components.values(): if hasattr(component, "set_default_attn_processor"): component.set_default_attn_processor() pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) generator_device = "cpu" inputs = self.get_dummy_inputs(generator_device) output_without_slicing = pipe(**inputs)[0] pipe.enable_attention_slicing(slice_size=1) inputs = self.get_dummy_inputs(generator_device) output_with_slicing1 = pipe(**inputs)[0] pipe.enable_attention_slicing(slice_size=2) inputs = self.get_dummy_inputs(generator_device) output_with_slicing2 = pipe(**inputs)[0] if test_max_difference: max_diff1 = np.abs(to_np(output_with_slicing1) - to_np(output_without_slicing)).max() max_diff2 = np.abs(to_np(output_with_slicing2) - to_np(output_without_slicing)).max() self.assertLess( max(max_diff1, max_diff2), expected_max_diff, "Attention slicing should not affect the inference results", ) def test_vae_tiling(self, expected_diff_max: float = 0.2): generator_device = "cpu" components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to("cpu") pipe.set_progress_bar_config(disable=None) # Without tiling inputs = self.get_dummy_inputs(generator_device) inputs["height"] = inputs["width"] = 128 output_without_tiling = pipe(**inputs)[0] # With tiling (standard AutoencoderKL doesn't accept parameters) pipe.vae.enable_tiling() inputs = self.get_dummy_inputs(generator_device) inputs["height"] = inputs["width"] = 128 output_with_tiling = pipe(**inputs)[0] self.assertLess( (to_np(output_without_tiling) - to_np(output_with_tiling)).max(), expected_diff_max, "VAE tiling should not affect the inference results", ) def test_pipeline_with_accelerator_device_map(self, expected_max_difference=5e-4): # Z-Image RoPE embeddings (complex64) have slightly higher numerical tolerance super().test_pipeline_with_accelerator_device_map(expected_max_difference=expected_max_difference) def test_group_offloading_inference(self): # Block-level offloading conflicts with RoPE cache. Pipeline-level offloading (tested separately) works fine. self.skipTest("Using test_pipeline_level_group_offloading_inference instead") def test_save_load_float16(self, expected_max_diff=1e-2): gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() torch.cuda.synchronize() torch.manual_seed(0) if torch.cuda.is_available(): torch.cuda.manual_seed_all(0) super().test_save_load_float16(expected_max_diff=expected_max_diff)