# coding=utf-8 # Copyright 2025 Latte Team and 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 gc import inspect import tempfile import unittest import numpy as np import torch from transformers import AutoConfig, AutoTokenizer, T5EncoderModel from diffusers import ( AutoencoderKL, DDIMScheduler, FasterCacheConfig, LattePipeline, LatteTransformer3DModel, PyramidAttentionBroadcastConfig, ) from diffusers.utils.import_utils import is_xformers_available from ...testing_utils import ( backend_empty_cache, enable_full_determinism, numpy_cosine_similarity_distance, require_torch_accelerator, slow, 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 ( FasterCacheTesterMixin, PipelineTesterMixin, PyramidAttentionBroadcastTesterMixin, to_np, ) enable_full_determinism() class LattePipelineFastTests( PipelineTesterMixin, PyramidAttentionBroadcastTesterMixin, FasterCacheTesterMixin, unittest.TestCase ): pipeline_class = LattePipeline 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 test_layerwise_casting = True test_group_offloading = True pab_config = PyramidAttentionBroadcastConfig( spatial_attention_block_skip_range=2, temporal_attention_block_skip_range=2, cross_attention_block_skip_range=2, spatial_attention_timestep_skip_range=(100, 700), temporal_attention_timestep_skip_range=(100, 800), cross_attention_timestep_skip_range=(100, 800), spatial_attention_block_identifiers=["transformer_blocks"], temporal_attention_block_identifiers=["temporal_transformer_blocks"], cross_attention_block_identifiers=["transformer_blocks"], ) faster_cache_config = FasterCacheConfig( spatial_attention_block_skip_range=2, temporal_attention_block_skip_range=2, spatial_attention_timestep_skip_range=(-1, 901), temporal_attention_timestep_skip_range=(-1, 901), unconditional_batch_skip_range=2, attention_weight_callback=lambda _: 0.5, ) def get_dummy_components(self, num_layers: int = 1): torch.manual_seed(0) transformer = LatteTransformer3DModel( sample_size=8, num_layers=num_layers, patch_size=2, attention_head_dim=8, num_attention_heads=3, caption_channels=32, in_channels=4, cross_attention_dim=24, out_channels=8, attention_bias=True, activation_fn="gelu-approximate", num_embeds_ada_norm=1000, norm_type="ada_norm_single", norm_elementwise_affine=False, norm_eps=1e-6, ) torch.manual_seed(0) vae = AutoencoderKL() scheduler = DDIMScheduler() config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5") text_encoder = T5EncoderModel(config) tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5") components = { "transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler, "text_encoder": text_encoder.eval(), "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": "A painting of a squirrel eating a burger", "negative_prompt": "low quality", "generator": generator, "num_inference_steps": 2, "guidance_scale": 5.0, "height": 8, "width": 8, "video_length": 1, "output_type": "pt", "clean_caption": False, } 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) video = pipe(**inputs).frames generated_video = video[0] self.assertEqual(generated_video.shape, (1, 3, 8, 8)) expected_video = torch.randn(1, 3, 8, 8) max_diff = np.abs(generated_video - expected_video).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_inference_batch_single_identical(self): self._test_inference_batch_single_identical(batch_size=3, expected_max_diff=1e-3) @unittest.skip("Not supported.") def test_attention_slicing_forward_pass(self): pass @unittest.skipIf( torch_device != "cuda" or not is_xformers_available(), reason="XFormers attention is only available with CUDA and `xformers` installed", ) def test_xformers_attention_forwardGenerator_pass(self): super()._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=False) @unittest.skip("Test not supported because `encode_prompt()` has multiple returns.") def test_encode_prompt_works_in_isolation(self): pass def test_save_load_optional_components(self): if not hasattr(self.pipeline_class, "_optional_components"): 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) inputs = self.get_dummy_inputs(torch_device) prompt = inputs["prompt"] generator = inputs["generator"] ( prompt_embeds, negative_prompt_embeds, ) = pipe.encode_prompt(prompt) # inputs with prompt converted to embeddings inputs = { "prompt_embeds": prompt_embeds, "negative_prompt": None, "negative_prompt_embeds": negative_prompt_embeds, "generator": generator, "num_inference_steps": 2, "guidance_scale": 5.0, "height": 8, "width": 8, "video_length": 1, "mask_feature": False, "output_type": "pt", "clean_caption": 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, safe_serialization=False) pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) pipe_loaded.to(torch_device) for component in pipe_loaded.components.values(): if hasattr(component, "set_default_attn_processor"): component.set_default_attn_processor() 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.", ) output_loaded = pipe_loaded(**inputs)[0] max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() self.assertLess(max_diff, 1.0) @slow @require_torch_accelerator class LattePipelineIntegrationTests(unittest.TestCase): prompt = "A painting of a squirrel eating a burger." def setUp(self): super().setUp() gc.collect() backend_empty_cache(torch_device) def tearDown(self): super().tearDown() gc.collect() backend_empty_cache(torch_device) def test_latte(self): generator = torch.Generator("cpu").manual_seed(0) pipe = LattePipeline.from_pretrained("maxin-cn/Latte-1", torch_dtype=torch.float16) pipe.enable_model_cpu_offload(device=torch_device) prompt = self.prompt videos = pipe( prompt=prompt, height=512, width=512, generator=generator, num_inference_steps=2, clean_caption=False, ).frames video = videos[0] expected_video = torch.randn(1, 512, 512, 3).numpy() max_diff = numpy_cosine_similarity_distance(video.flatten(), expected_video) assert max_diff < 1e-3, f"Max diff is too high. got {video.flatten()}"