# 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 unittest import numpy as np import torch from PIL import Image from transformers import AutoTokenizer, T5EncoderModel from diffusers import AutoencoderKLWan, FlowMatchEulerDiscreteScheduler, WanVACEPipeline, WanVACETransformer3DModel from diffusers.utils.testing_utils import enable_full_determinism from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS from ..test_pipelines_common import PipelineTesterMixin enable_full_determinism() class WanVACEPipelineFastTests(PipelineTesterMixin, unittest.TestCase): pipeline_class = WanVACEPipeline 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 supports_dduf = False def get_dummy_components(self): torch.manual_seed(0) vae = AutoencoderKLWan( base_dim=3, z_dim=16, dim_mult=[1, 1, 1, 1], num_res_blocks=1, temperal_downsample=[False, True, True], ) torch.manual_seed(0) scheduler = FlowMatchEulerDiscreteScheduler(shift=7.0) text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5") tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5") torch.manual_seed(0) transformer = WanVACETransformer3DModel( patch_size=(1, 2, 2), num_attention_heads=2, attention_head_dim=12, in_channels=16, out_channels=16, text_dim=32, freq_dim=256, ffn_dim=32, num_layers=3, cross_attn_norm=True, qk_norm="rms_norm_across_heads", rope_max_seq_len=32, vace_layers=[0, 2], vace_in_channels=96, ) 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) num_frames = 17 height = 16 width = 16 video = [Image.new("RGB", (height, width))] * num_frames mask = [Image.new("L", (height, width), 0)] * num_frames inputs = { "video": video, "mask": mask, "prompt": "dance monkey", "negative_prompt": "negative", "generator": generator, "num_inference_steps": 2, "guidance_scale": 6.0, "height": 16, "width": 16, "num_frames": num_frames, "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) video = pipe(**inputs).frames[0] self.assertEqual(video.shape, (17, 3, 16, 16)) # fmt: off expected_slice = [0.4523, 0.45198, 0.44872, 0.45326, 0.45211, 0.45258, 0.45344, 0.453, 0.52431, 0.52572, 0.50701, 0.5118, 0.53717, 0.53093, 0.50557, 0.51402] # fmt: on video_slice = video.flatten() video_slice = torch.cat([video_slice[:8], video_slice[-8:]]) video_slice = [round(x, 5) for x in video_slice.tolist()] self.assertTrue(np.allclose(video_slice, expected_slice, atol=1e-3)) def test_inference_with_single_reference_image(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) inputs["reference_images"] = Image.new("RGB", (16, 16)) video = pipe(**inputs).frames[0] self.assertEqual(video.shape, (17, 3, 16, 16)) # fmt: off expected_slice = [0.45247, 0.45214, 0.44874, 0.45314, 0.45171, 0.45299, 0.45428, 0.45317, 0.51378, 0.52658, 0.53361, 0.52303, 0.46204, 0.50435, 0.52555, 0.51342] # fmt: on video_slice = video.flatten() video_slice = torch.cat([video_slice[:8], video_slice[-8:]]) video_slice = [round(x, 5) for x in video_slice.tolist()] self.assertTrue(np.allclose(video_slice, expected_slice, atol=1e-3)) def test_inference_with_multiple_reference_image(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) inputs["reference_images"] = [[Image.new("RGB", (16, 16))] * 2] video = pipe(**inputs).frames[0] self.assertEqual(video.shape, (17, 3, 16, 16)) # fmt: off expected_slice = [0.45321, 0.45221, 0.44818, 0.45375, 0.45268, 0.4519, 0.45271, 0.45253, 0.51244, 0.52223, 0.51253, 0.51321, 0.50743, 0.51177, 0.51626, 0.50983] # fmt: on video_slice = video.flatten() video_slice = torch.cat([video_slice[:8], video_slice[-8:]]) video_slice = [round(x, 5) for x in video_slice.tolist()] self.assertTrue(np.allclose(video_slice, expected_slice, atol=1e-3)) @unittest.skip("Test not supported") def test_attention_slicing_forward_pass(self): pass @unittest.skip("Errors out because passing multiple prompts at once is not yet supported by this pipeline.") def test_encode_prompt_works_in_isolation(self): pass @unittest.skip("Batching is not yet supported with this pipeline") def test_inference_batch_consistent(self): pass @unittest.skip("Batching is not yet supported with this pipeline") def test_inference_batch_single_identical(self): return super().test_inference_batch_single_identical()