# Copyright 2025 The HuggingFace Team. # # 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 random import unittest import numpy as np import torch from transformers import Qwen2_5_VLConfig, Qwen2_5_VLForConditionalGeneration, Qwen2Tokenizer from diffusers import ( AutoencoderKLQwenImage, FlowMatchEulerDiscreteScheduler, QwenImageInpaintPipeline, QwenImageTransformer2DModel, ) from ...testing_utils import enable_full_determinism, floats_tensor, 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 enable_full_determinism() class QwenImageInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase): pipeline_class = QwenImageInpaintPipeline 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 get_dummy_components(self): torch.manual_seed(0) transformer = QwenImageTransformer2DModel( patch_size=2, in_channels=16, out_channels=4, num_layers=2, attention_head_dim=16, num_attention_heads=3, joint_attention_dim=16, guidance_embeds=False, axes_dims_rope=(8, 4, 4), ) torch.manual_seed(0) z_dim = 4 vae = AutoencoderKLQwenImage( base_dim=z_dim * 6, z_dim=z_dim, dim_mult=[1, 2, 4], num_res_blocks=1, temperal_downsample=[False, True], # fmt: off latents_mean=[0.0] * 4, latents_std=[1.0] * 4, # fmt: on ) torch.manual_seed(0) scheduler = FlowMatchEulerDiscreteScheduler() torch.manual_seed(0) config = Qwen2_5_VLConfig( text_config={ "hidden_size": 16, "intermediate_size": 16, "num_hidden_layers": 2, "num_attention_heads": 2, "num_key_value_heads": 2, "rope_scaling": { "mrope_section": [1, 1, 2], "rope_type": "default", "type": "default", }, "rope_theta": 1000000.0, }, vision_config={ "depth": 2, "hidden_size": 16, "intermediate_size": 16, "num_heads": 2, "out_hidden_size": 16, }, hidden_size=16, vocab_size=152064, vision_end_token_id=151653, vision_start_token_id=151652, vision_token_id=151654, ) text_encoder = Qwen2_5_VLForConditionalGeneration(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): image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device) mask_image = torch.ones((1, 1, 32, 32)).to(device) 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", "image": image, "mask_image": mask_image, "generator": generator, "num_inference_steps": 2, "guidance_scale": 3.0, "true_cfg_scale": 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)) def test_inference_batch_single_identical(self): self._test_inference_batch_single_identical(batch_size=3, expected_max_diff=1e-1) 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 pipe.vae.enable_tiling( tile_sample_min_height=96, tile_sample_min_width=96, tile_sample_stride_height=64, tile_sample_stride_width=64, ) 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", )