# coding=utf-8 # Copyright 2026 HuggingFace Inc. # # 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 inspect import tempfile import unittest import numpy as np import torch from transformers import AutoConfig, AutoTokenizer, BertModel, T5EncoderModel from diffusers import ( AutoencoderKL, DDPMScheduler, HunyuanDiT2DModel, HunyuanDiTPAGPipeline, HunyuanDiTPipeline, ) from ...testing_utils import enable_full_determinism, 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 HunyuanDiTPAGPipelineFastTests(PipelineTesterMixin, unittest.TestCase): pipeline_class = HunyuanDiTPAGPipeline 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 = PipelineTesterMixin.required_optional_params def get_dummy_components(self): torch.manual_seed(0) transformer = HunyuanDiT2DModel( sample_size=16, num_layers=2, patch_size=2, attention_head_dim=8, num_attention_heads=3, in_channels=4, cross_attention_dim=32, cross_attention_dim_t5=32, pooled_projection_dim=16, hidden_size=24, activation_fn="gelu-approximate", ) torch.manual_seed(0) vae = AutoencoderKL() scheduler = DDPMScheduler() text_encoder = BertModel.from_pretrained("hf-internal-testing/tiny-random-BertModel") tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel") torch.manual_seed(0) config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5") text_encoder_2 = T5EncoderModel(config) tokenizer_2 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5") components = { "transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler, "text_encoder": text_encoder, "tokenizer": tokenizer, "text_encoder_2": text_encoder_2, "tokenizer_2": tokenizer_2, "safety_checker": None, "feature_extractor": None, } 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": "A painting of a squirrel eating a burger", "generator": generator, "num_inference_steps": 2, "guidance_scale": 5.0, "output_type": "np", "use_resolution_binning": False, "pag_scale": 0.0, } 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 image_slice = image[0, -3:, -3:, -1] self.assertEqual(image.shape, (1, 16, 16, 3)) expected_slice = np.array( [0.56939435, 0.34541583, 0.35915792, 0.46489206, 0.38775963, 0.45004836, 0.5957267, 0.59481275, 0.33287364] ) max_diff = np.abs(image_slice.flatten() - expected_slice).max() self.assertLessEqual(max_diff, 1e-3) @unittest.skip("Not supported.") def test_sequential_cpu_offload_forward_pass(self): # TODO(YiYi) need to fix later pass @unittest.skip("Not supported.") def test_sequential_offload_forward_pass_twice(self): # TODO(YiYi) need to fix later pass def test_inference_batch_single_identical(self): self._test_inference_batch_single_identical( expected_max_diff=1e-3, ) def test_feed_forward_chunking(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 image_slice_no_chunking = image[0, -3:, -3:, -1] pipe.transformer.enable_forward_chunking(chunk_size=1, dim=0) inputs = self.get_dummy_inputs(device) image = pipe(**inputs).images image_slice_chunking = image[0, -3:, -3:, -1] max_diff = np.abs(to_np(image_slice_no_chunking) - to_np(image_slice_chunking)).max() self.assertLess(max_diff, 1e-4) def test_fused_qkv_projections(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe = pipe.to(device) pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) inputs["return_dict"] = False image = pipe(**inputs)[0] original_image_slice = image[0, -3:, -3:, -1] pipe.transformer.fuse_qkv_projections() inputs = self.get_dummy_inputs(device) inputs["return_dict"] = False image_fused = pipe(**inputs)[0] image_slice_fused = image_fused[0, -3:, -3:, -1] pipe.transformer.unfuse_qkv_projections() inputs = self.get_dummy_inputs(device) inputs["return_dict"] = False image_disabled = pipe(**inputs)[0] image_slice_disabled = image_disabled[0, -3:, -3:, -1] assert np.allclose(original_image_slice, image_slice_fused, atol=1e-2, rtol=1e-2), ( "Fusion of QKV projections shouldn't affect the outputs." ) assert np.allclose(image_slice_fused, image_slice_disabled, atol=1e-2, rtol=1e-2), ( "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled." ) assert np.allclose(original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2), ( "Original outputs should match when fused QKV projections are disabled." ) def test_pag_disable_enable(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() # base pipeline (expect same output when pag is disabled) pipe_sd = HunyuanDiTPipeline(**components) pipe_sd = pipe_sd.to(device) pipe_sd.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) del inputs["pag_scale"] assert "pag_scale" not in inspect.signature(pipe_sd.__call__).parameters, ( f"`pag_scale` should not be a call parameter of the base pipeline {pipe_sd.__class__.__name__}." ) out = pipe_sd(**inputs).images[0, -3:, -3:, -1] components = self.get_dummy_components() # pag disabled with pag_scale=0.0 pipe_pag = self.pipeline_class(**components) pipe_pag = pipe_pag.to(device) pipe_pag.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) inputs["pag_scale"] = 0.0 out_pag_disabled = pipe_pag(**inputs).images[0, -3:, -3:, -1] # pag enabled pipe_pag = self.pipeline_class(**components) pipe_pag = pipe_pag.to(device) pipe_pag.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) inputs["pag_scale"] = 3.0 out_pag_enabled = pipe_pag(**inputs).images[0, -3:, -3:, -1] assert np.abs(out.flatten() - out_pag_disabled.flatten()).max() < 1e-3 assert np.abs(out.flatten() - out_pag_enabled.flatten()).max() > 1e-3 def test_pag_applied_layers(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() # base pipeline pipe = self.pipeline_class(**components) pipe = pipe.to(device) pipe.set_progress_bar_config(disable=None) all_self_attn_layers = [k for k in pipe.transformer.attn_processors.keys() if "attn1" in k] original_attn_procs = pipe.transformer.attn_processors pag_layers = ["blocks.0", "blocks.1"] pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) assert set(pipe.pag_attn_processors) == set(all_self_attn_layers) # blocks.0 block_0_self_attn = ["blocks.0.attn1.processor"] pipe.transformer.set_attn_processor(original_attn_procs.copy()) pag_layers = ["blocks.0"] pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) assert set(pipe.pag_attn_processors) == set(block_0_self_attn) pipe.transformer.set_attn_processor(original_attn_procs.copy()) pag_layers = ["blocks.0.attn1"] pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) assert set(pipe.pag_attn_processors) == set(block_0_self_attn) pipe.transformer.set_attn_processor(original_attn_procs.copy()) pag_layers = ["blocks.(0|1)"] pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) assert (len(pipe.pag_attn_processors)) == 2 pipe.transformer.set_attn_processor(original_attn_procs.copy()) pag_layers = ["blocks.0", r"blocks\.1"] pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) assert len(pipe.pag_attn_processors) == 2 @unittest.skip( "Test not supported as `encode_prompt` is called two times separately which deivates from about 99% of the pipelines we have." ) def test_encode_prompt_works_in_isolation(self): pass def test_save_load_optional_components(self): components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(torch_device) prompt = inputs["prompt"] generator = inputs["generator"] num_inference_steps = inputs["num_inference_steps"] output_type = inputs["output_type"] ( prompt_embeds, negative_prompt_embeds, prompt_attention_mask, negative_prompt_attention_mask, ) = pipe.encode_prompt(prompt, device=torch_device, dtype=torch.float32, text_encoder_index=0) ( prompt_embeds_2, negative_prompt_embeds_2, prompt_attention_mask_2, negative_prompt_attention_mask_2, ) = pipe.encode_prompt( prompt, device=torch_device, dtype=torch.float32, text_encoder_index=1, ) # inputs with prompt converted to embeddings inputs = { "prompt_embeds": prompt_embeds, "prompt_attention_mask": prompt_attention_mask, "negative_prompt_embeds": negative_prompt_embeds, "negative_prompt_attention_mask": negative_prompt_attention_mask, "prompt_embeds_2": prompt_embeds_2, "prompt_attention_mask_2": prompt_attention_mask_2, "negative_prompt_embeds_2": negative_prompt_embeds_2, "negative_prompt_attention_mask_2": negative_prompt_attention_mask_2, "generator": generator, "num_inference_steps": num_inference_steps, "output_type": output_type, "use_resolution_binning": False, } # set all optional components to None for optional_component in pipe._optional_components: setattr(pipe, optional_component, None) output = pipe(**inputs)[0] with tempfile.TemporaryDirectory() as tmpdir: pipe.save_pretrained(tmpdir) pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) pipe_loaded.to(torch_device) pipe_loaded.set_progress_bar_config(disable=None) for optional_component in pipe._optional_components: self.assertTrue( getattr(pipe_loaded, optional_component) is None, f"`{optional_component}` did not stay set to None after loading.", ) inputs = self.get_dummy_inputs(torch_device) generator = inputs["generator"] num_inference_steps = inputs["num_inference_steps"] output_type = inputs["output_type"] # inputs with prompt converted to embeddings inputs = { "prompt_embeds": prompt_embeds, "prompt_attention_mask": prompt_attention_mask, "negative_prompt_embeds": negative_prompt_embeds, "negative_prompt_attention_mask": negative_prompt_attention_mask, "prompt_embeds_2": prompt_embeds_2, "prompt_attention_mask_2": prompt_attention_mask_2, "negative_prompt_embeds_2": negative_prompt_embeds_2, "negative_prompt_attention_mask_2": negative_prompt_attention_mask_2, "generator": generator, "num_inference_steps": num_inference_steps, "output_type": output_type, "use_resolution_binning": False, } output_loaded = pipe_loaded(**inputs)[0] max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() self.assertLess(max_diff, 1e-4)