# 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 inspect import unittest import numpy as np import torch from transformers import Gemma2Config, Gemma2ForCausalLM, GemmaTokenizer from diffusers import ( AutoencoderDC, FlowMatchEulerDiscreteScheduler, SanaPAGPipeline, SanaPipeline, SanaTransformer2DModel, ) 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 SanaPAGPipelineFastTests(PipelineTesterMixin, unittest.TestCase): pipeline_class = SanaPAGPipeline 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", ] ) test_xformers_attention = False def get_dummy_components(self): torch.manual_seed(0) transformer = SanaTransformer2DModel( patch_size=1, in_channels=4, out_channels=4, num_layers=2, num_attention_heads=2, attention_head_dim=4, num_cross_attention_heads=2, cross_attention_head_dim=4, cross_attention_dim=8, caption_channels=8, sample_size=32, ) torch.manual_seed(0) vae = 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], downsample_block_type="conv", upsample_block_type="interpolate", decoder_norm_types="rms_norm", decoder_act_fns="silu", scaling_factor=0.41407, ) torch.manual_seed(0) scheduler = FlowMatchEulerDiscreteScheduler(shift=7.0) torch.manual_seed(0) text_encoder_config = Gemma2Config( head_dim=16, hidden_size=32, initializer_range=0.02, intermediate_size=64, max_position_embeddings=8192, model_type="gemma2", num_attention_heads=2, num_hidden_layers=1, num_key_value_heads=2, vocab_size=8, attn_implementation="eager", ) text_encoder = Gemma2ForCausalLM(text_encoder_config) tokenizer = GemmaTokenizer.from_pretrained("hf-internal-testing/dummy-gemma") 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": "", "negative_prompt": "", "generator": generator, "num_inference_steps": 2, "guidance_scale": 6.0, "pag_scale": 3.0, "height": 32, "width": 32, "max_sequence_length": 16, "output_type": "pt", "complex_human_instruction": None, } 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)[0] generated_image = image[0] self.assertEqual(generated_image.shape, (3, 32, 32)) expected_image = torch.randn(3, 32, 32) max_diff = np.abs(generated_image - expected_image).max() self.assertLessEqual(max_diff, 1e10) def test_callback_inputs(self): sig = inspect.signature(self.pipeline_class.__call__) has_callback_tensor_inputs = "callback_on_step_end_tensor_inputs" in sig.parameters has_callback_step_end = "callback_on_step_end" in sig.parameters if not (has_callback_tensor_inputs and has_callback_step_end): return components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe = pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) self.assertTrue( hasattr(pipe, "_callback_tensor_inputs"), f" {self.pipeline_class} 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): # iterate over callback args for tensor_name, tensor_value in callback_kwargs.items(): # check that we're only passing in allowed tensor inputs 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 # iterate over callback args for tensor_name, tensor_value in callback_kwargs.items(): # check that we're only passing in allowed tensor inputs assert tensor_name in pipe._callback_tensor_inputs return callback_kwargs inputs = self.get_dummy_inputs(torch_device) # Test passing in a subset inputs["callback_on_step_end"] = callback_inputs_subset inputs["callback_on_step_end_tensor_inputs"] = ["latents"] output = pipe(**inputs)[0] # Test passing in a everything inputs["callback_on_step_end"] = callback_inputs_all inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs output = pipe(**inputs)[0] def callback_inputs_change_tensor(pipe, i, t, callback_kwargs): is_last = i == (pipe.num_timesteps - 1) if is_last: callback_kwargs["latents"] = torch.zeros_like(callback_kwargs["latents"]) return callback_kwargs inputs["callback_on_step_end"] = callback_inputs_change_tensor inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs output = pipe(**inputs)[0] assert output.abs().sum() < 1e10 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_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 = SanaPipeline(**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] assert np.abs(out.flatten() - out_pag_disabled.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 = ["transformer_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 # TODO(aryan): Create a dummy gemma model with smol vocab size @unittest.skip( "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." ) def test_inference_batch_consistent(self): pass @unittest.skip( "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error." ) def test_inference_batch_single_identical(self): pass def test_float16_inference(self): # Requires higher tolerance as model seems very sensitive to dtype super().test_float16_inference(expected_max_diff=0.08)