# 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, ZImageInpaintPipeline, ZImageTransformer2DModel, ) from diffusers.utils.testing_utils import floats_tensor from ...testing_utils import torch_device from ..pipeline_params import ( IMAGE_TO_IMAGE_IMAGE_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_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 # Note: Z-Image does not support FP16 inference due to complex64 RoPE embeddings 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 class ZImageInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase): pipeline_class = ZImageInpaintPipeline params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"cross_attention_kwargs"} batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS image_params = frozenset(["image", "mask_image"]) image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS required_optional_params = frozenset( [ "num_inference_steps", "strength", "generator", "latents", "return_dict", "callback_on_step_end", "callback_on_step_end_tensor_inputs", ] ) 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], ) # `x_pad_token` and `cap_pad_token` are initialized with `torch.empty` which contains # uninitialized memory. Set them to known values for deterministic test behavior. with torch.no_grad(): transformer.x_pad_token.copy_(torch.ones_like(transformer.x_pad_token.data)) transformer.cap_pad_token.copy_(torch.ones_like(transformer.cap_pad_token.data)) 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): import random if str(device).startswith("mps"): generator = torch.manual_seed(seed) else: generator = torch.Generator(device=device).manual_seed(seed) image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device) # Create mask: 1 = inpaint region, 0 = preserve region mask_image = torch.zeros((1, 1, 32, 32), device=device) mask_image[:, :, 8:24, 8:24] = 1.0 # Inpaint center region inputs = { "prompt": "dance monkey", "negative_prompt": "bad quality", "image": image, "mask_image": mask_image, "strength": 1.0, "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": "np", } 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, (32, 32, 3)) def test_inference_batch_single_identical(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) 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 = 0.7): import random 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 # Generate a larger image for the input inputs["image"] = floats_tensor((1, 3, 128, 128), rng=random.Random(0)).to("cpu") # Generate a larger mask for the input mask = torch.zeros((1, 1, 128, 128), device="cpu") mask[:, :, 32:96, 32:96] = 1.0 inputs["mask_image"] = mask 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 inputs["image"] = floats_tensor((1, 3, 128, 128), rng=random.Random(0)).to("cpu") inputs["mask_image"] = mask 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=1e-3): # Z-Image RoPE embeddings (complex64) have slightly higher numerical tolerance # Inpainting mask blending adds additional numerical variance 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): # Z-Image does not support FP16 due to complex64 RoPE embeddings self.skipTest("Z-Image does not support FP16 inference") def test_float16_inference(self, expected_max_diff=5e-2): # Z-Image does not support FP16 due to complex64 RoPE embeddings self.skipTest("Z-Image does not support FP16 inference") def test_strength_parameter(self): """Test that strength parameter affects the output correctly.""" device = "cpu" components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(device) pipe.set_progress_bar_config(disable=None) # Test with different strength values inputs_low_strength = self.get_dummy_inputs(device) inputs_low_strength["strength"] = 0.2 inputs_high_strength = self.get_dummy_inputs(device) inputs_high_strength["strength"] = 0.8 # Both should complete without errors output_low = pipe(**inputs_low_strength).images[0] output_high = pipe(**inputs_high_strength).images[0] # Outputs should be different (different amount of transformation) self.assertFalse(np.allclose(output_low, output_high, atol=1e-3)) def test_invalid_strength(self): """Test that invalid strength values raise appropriate errors.""" device = "cpu" components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(device) inputs = self.get_dummy_inputs(device) # Test strength < 0 inputs["strength"] = -0.1 with self.assertRaises(ValueError): pipe(**inputs) # Test strength > 1 inputs["strength"] = 1.5 with self.assertRaises(ValueError): pipe(**inputs) def test_mask_inpainting(self): """Test that the mask properly controls which regions are inpainted.""" device = "cpu" components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(device) pipe.set_progress_bar_config(disable=None) # Generate with full mask (inpaint everything) inputs_full = self.get_dummy_inputs(device) inputs_full["mask_image"] = torch.ones((1, 1, 32, 32), device=device) # Generate with no mask (preserve everything) inputs_none = self.get_dummy_inputs(device) inputs_none["mask_image"] = torch.zeros((1, 1, 32, 32), device=device) # Both should complete without errors output_full = pipe(**inputs_full).images[0] output_none = pipe(**inputs_none).images[0] # Outputs should be different (full inpaint vs preserve) self.assertFalse(np.allclose(output_full, output_none, atol=1e-3))