mirror of
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214 lines
7.4 KiB
Python
214 lines
7.4 KiB
Python
# Copyright 2025 The HuggingFace Team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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import torch
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from PIL import Image
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from transformers import AutoTokenizer, T5EncoderModel
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from diffusers import AutoencoderKLWan, FlowMatchEulerDiscreteScheduler, WanVACEPipeline, WanVACETransformer3DModel
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from diffusers.utils.testing_utils import enable_full_determinism
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from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
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from ..test_pipelines_common import PipelineTesterMixin
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enable_full_determinism()
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class WanVACEPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
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pipeline_class = WanVACEPipeline
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params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"}
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batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
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image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
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image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
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required_optional_params = frozenset(
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[
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"num_inference_steps",
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"generator",
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"latents",
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"return_dict",
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"callback_on_step_end",
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"callback_on_step_end_tensor_inputs",
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]
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)
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test_xformers_attention = False
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supports_dduf = False
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def get_dummy_components(self):
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torch.manual_seed(0)
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vae = AutoencoderKLWan(
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base_dim=3,
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z_dim=16,
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dim_mult=[1, 1, 1, 1],
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num_res_blocks=1,
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temperal_downsample=[False, True, True],
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)
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torch.manual_seed(0)
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scheduler = FlowMatchEulerDiscreteScheduler(shift=7.0)
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text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
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tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
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torch.manual_seed(0)
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transformer = WanVACETransformer3DModel(
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patch_size=(1, 2, 2),
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num_attention_heads=2,
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attention_head_dim=12,
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in_channels=16,
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out_channels=16,
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text_dim=32,
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freq_dim=256,
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ffn_dim=32,
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num_layers=3,
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cross_attn_norm=True,
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qk_norm="rms_norm_across_heads",
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rope_max_seq_len=32,
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vace_layers=[0, 2],
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vace_in_channels=96,
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)
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components = {
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"transformer": transformer,
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"vae": vae,
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"scheduler": scheduler,
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"text_encoder": text_encoder,
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"tokenizer": tokenizer,
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}
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return components
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def get_dummy_inputs(self, device, seed=0):
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if str(device).startswith("mps"):
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generator = torch.manual_seed(seed)
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else:
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generator = torch.Generator(device=device).manual_seed(seed)
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num_frames = 17
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height = 16
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width = 16
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video = [Image.new("RGB", (height, width))] * num_frames
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mask = [Image.new("L", (height, width), 0)] * num_frames
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inputs = {
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"video": video,
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"mask": mask,
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"prompt": "dance monkey",
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"negative_prompt": "negative",
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"generator": generator,
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"num_inference_steps": 2,
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"guidance_scale": 6.0,
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"height": 16,
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"width": 16,
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"num_frames": num_frames,
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"max_sequence_length": 16,
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"output_type": "pt",
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}
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return inputs
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def test_inference(self):
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device = "cpu"
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components = self.get_dummy_components()
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pipe = self.pipeline_class(**components)
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pipe.to(device)
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pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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video = pipe(**inputs).frames[0]
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self.assertEqual(video.shape, (17, 3, 16, 16))
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# fmt: off
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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]
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# fmt: on
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video_slice = video.flatten()
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video_slice = torch.cat([video_slice[:8], video_slice[-8:]])
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video_slice = [round(x, 5) for x in video_slice.tolist()]
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self.assertTrue(np.allclose(video_slice, expected_slice, atol=1e-3))
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def test_inference_with_single_reference_image(self):
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device = "cpu"
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components = self.get_dummy_components()
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pipe = self.pipeline_class(**components)
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pipe.to(device)
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pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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inputs["reference_images"] = Image.new("RGB", (16, 16))
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video = pipe(**inputs).frames[0]
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self.assertEqual(video.shape, (17, 3, 16, 16))
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# fmt: off
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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]
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# fmt: on
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video_slice = video.flatten()
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video_slice = torch.cat([video_slice[:8], video_slice[-8:]])
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video_slice = [round(x, 5) for x in video_slice.tolist()]
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self.assertTrue(np.allclose(video_slice, expected_slice, atol=1e-3))
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def test_inference_with_multiple_reference_image(self):
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device = "cpu"
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components = self.get_dummy_components()
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pipe = self.pipeline_class(**components)
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pipe.to(device)
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pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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inputs["reference_images"] = [[Image.new("RGB", (16, 16))] * 2]
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video = pipe(**inputs).frames[0]
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self.assertEqual(video.shape, (17, 3, 16, 16))
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# fmt: off
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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]
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# fmt: on
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video_slice = video.flatten()
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video_slice = torch.cat([video_slice[:8], video_slice[-8:]])
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video_slice = [round(x, 5) for x in video_slice.tolist()]
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self.assertTrue(np.allclose(video_slice, expected_slice, atol=1e-3))
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@unittest.skip("Test not supported")
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def test_attention_slicing_forward_pass(self):
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pass
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@unittest.skip("Errors out because passing multiple prompts at once is not yet supported by this pipeline.")
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def test_encode_prompt_works_in_isolation(self):
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pass
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@unittest.skip("Batching is not yet supported with this pipeline")
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def test_inference_batch_consistent(self):
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pass
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@unittest.skip("Batching is not yet supported with this pipeline")
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def test_inference_batch_single_identical(self):
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return super().test_inference_batch_single_identical()
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@unittest.skip(
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"AutoencoderKLWan encoded latents are always in FP32. This test is not designed to handle mixed dtype inputs"
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)
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def test_float16_inference(self):
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pass
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@unittest.skip(
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"AutoencoderKLWan encoded latents are always in FP32. This test is not designed to handle mixed dtype inputs"
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)
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def test_save_load_float16(self):
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pass
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