import unittest import numpy as np import torch from transformers import AutoTokenizer from transformers.models.t5gemma.configuration_t5gemma import T5GemmaConfig, T5GemmaModuleConfig from transformers.models.t5gemma.modeling_t5gemma import T5GemmaEncoder from diffusers.models import AutoencoderDC, AutoencoderKL from diffusers.models.transformers.transformer_prx import PRXTransformer2DModel from diffusers.pipelines.prx.pipeline_prx import PRXPipeline from diffusers.schedulers import FlowMatchEulerDiscreteScheduler from ..pipeline_params import TEXT_TO_IMAGE_PARAMS from ..test_pipelines_common import PipelineTesterMixin class PRXPipelineFastTests(PipelineTesterMixin, unittest.TestCase): pipeline_class = PRXPipeline params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"} batch_params = frozenset(["prompt", "negative_prompt", "num_images_per_prompt"]) test_xformers_attention = False test_layerwise_casting = True test_group_offloading = True @classmethod def setUpClass(cls): # Ensure PRXPipeline has an _execution_device property expected by __call__ if not isinstance(getattr(PRXPipeline, "_execution_device", None), property): try: setattr(PRXPipeline, "_execution_device", property(lambda self: torch.device("cpu"))) except Exception: pass def get_dummy_components(self): torch.manual_seed(0) transformer = PRXTransformer2DModel( patch_size=1, in_channels=4, context_in_dim=8, hidden_size=8, mlp_ratio=2.0, num_heads=2, depth=1, axes_dim=[2, 2], ) torch.manual_seed(0) vae = AutoencoderKL( sample_size=32, in_channels=3, out_channels=3, block_out_channels=(4,), layers_per_block=1, latent_channels=4, norm_num_groups=1, use_quant_conv=False, use_post_quant_conv=False, shift_factor=0.0, scaling_factor=1.0, ).eval() torch.manual_seed(0) scheduler = FlowMatchEulerDiscreteScheduler() torch.manual_seed(0) tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/dummy-gemma") tokenizer.model_max_length = 64 torch.manual_seed(0) encoder_params = { "vocab_size": tokenizer.vocab_size, "hidden_size": 8, "intermediate_size": 16, "num_hidden_layers": 1, "num_attention_heads": 2, "num_key_value_heads": 1, "head_dim": 4, "max_position_embeddings": 64, "layer_types": ["full_attention"], "attention_bias": False, "attention_dropout": 0.0, "dropout_rate": 0.0, "hidden_activation": "gelu_pytorch_tanh", "rms_norm_eps": 1e-06, "attn_logit_softcapping": 50.0, "final_logit_softcapping": 30.0, "query_pre_attn_scalar": 4, "rope_theta": 10000.0, "sliding_window": 4096, } encoder_config = T5GemmaModuleConfig(**encoder_params) text_encoder_config = T5GemmaConfig(encoder=encoder_config, is_encoder_decoder=False, **encoder_params) text_encoder = T5GemmaEncoder(text_encoder_config.encoder) return { "transformer": transformer, "vae": vae, "scheduler": scheduler, "text_encoder": text_encoder, "tokenizer": tokenizer, } 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) return { "prompt": "", "negative_prompt": "", "generator": generator, "num_inference_steps": 2, "guidance_scale": 1.0, "height": 32, "width": 32, "output_type": "pt", "use_resolution_binning": False, } def test_inference(self): device = "cpu" components = self.get_dummy_components() pipe = PRXPipeline(**components) pipe.to(device) pipe.set_progress_bar_config(disable=None) try: pipe.register_to_config(_execution_device="cpu") except Exception: pass inputs = self.get_dummy_inputs(device) image = pipe(**inputs)[0] generated_image = image[0] self.assertEqual(generated_image.shape, (3, 32, 32)) expected_image = torch.zeros(3, 32, 32) max_diff = np.abs(generated_image - expected_image).max() self.assertLessEqual(max_diff, 1e10) def test_callback_inputs(self): components = self.get_dummy_components() pipe = PRXPipeline(**components) pipe = pipe.to("cpu") pipe.set_progress_bar_config(disable=None) try: pipe.register_to_config(_execution_device="cpu") except Exception: pass self.assertTrue( hasattr(pipe, "_callback_tensor_inputs"), f" {PRXPipeline} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs", ) def callback_inputs_subset(pipe, i, t, callback_kwargs): for tensor_name in callback_kwargs.keys(): assert tensor_name in pipe._callback_tensor_inputs return callback_kwargs def callback_inputs_all(pipe, i, t, callback_kwargs): for tensor_name in pipe._callback_tensor_inputs: assert tensor_name in callback_kwargs for tensor_name in callback_kwargs.keys(): assert tensor_name in pipe._callback_tensor_inputs return callback_kwargs inputs = self.get_dummy_inputs("cpu") inputs["callback_on_step_end"] = callback_inputs_subset inputs["callback_on_step_end_tensor_inputs"] = ["latents"] _ = pipe(**inputs)[0] inputs["callback_on_step_end"] = callback_inputs_all inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs _ = pipe(**inputs)[0] def test_attention_slicing_forward_pass(self, 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("cpu") pipe.set_progress_bar_config(disable=None) def to_np_local(tensor): if isinstance(tensor, torch.Tensor): return tensor.detach().cpu().numpy() return tensor 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] max_diff1 = np.abs(to_np_local(output_with_slicing1) - to_np_local(output_without_slicing)).max() max_diff2 = np.abs(to_np_local(output_with_slicing2) - to_np_local(output_without_slicing)).max() self.assertLess(max(max_diff1, max_diff2), expected_max_diff) def test_inference_with_autoencoder_dc(self): """Test PRXPipeline with AutoencoderDC (DCAE) instead of AutoencoderKL.""" device = "cpu" components = self.get_dummy_components() torch.manual_seed(0) vae_dc = AutoencoderDC( in_channels=3, latent_channels=4, attention_head_dim=2, encoder_block_types=( "ResBlock", "EfficientViTBlock", ), decoder_block_types=( "ResBlock", "EfficientViTBlock", ), encoder_block_out_channels=(8, 8), decoder_block_out_channels=(8, 8), encoder_qkv_multiscales=((), (5,)), decoder_qkv_multiscales=((), (5,)), encoder_layers_per_block=(1, 1), decoder_layers_per_block=(1, 1), upsample_block_type="interpolate", downsample_block_type="stride_conv", decoder_norm_types="rms_norm", decoder_act_fns="silu", ).eval() components["vae"] = vae_dc pipe = PRXPipeline(**components) pipe.to(device) pipe.set_progress_bar_config(disable=None) expected_scale_factor = vae_dc.spatial_compression_ratio self.assertEqual(pipe.vae_scale_factor, expected_scale_factor) inputs = self.get_dummy_inputs(device) image = pipe(**inputs)[0] generated_image = image[0] self.assertEqual(generated_image.shape, (3, 32, 32)) expected_image = torch.zeros(3, 32, 32) max_diff = np.abs(generated_image - expected_image).max() self.assertLessEqual(max_diff, 1e10) @unittest.skip("Custom T5GemmaEncoder not compatible with transformers v5.") def test_loading_with_variants(self): pass @unittest.skip("Custom T5GemmaEncoder not compatible with transformers v5.") def test_pipeline_with_accelerator_device_map(self): pass @unittest.skip("Custom T5GemmaEncoder not compatible with transformers v5.") def test_save_load_local(self): pass @unittest.skip("Custom T5GemmaEncoder not compatible with transformers v5.") def test_save_load_optional_components(self): pass @unittest.skip("Custom T5GemmaEncoder not compatible with transformers v5.") def test_dtype_dict(self): pass @unittest.skip("Custom T5GemmaEncoder not compatible with transformers v5.") def test_dtype_alias(self): pass