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297 lines
10 KiB
Python
297 lines
10 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 gc
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import inspect
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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 transformers import Qwen2Tokenizer, Qwen2VLForConditionalGeneration
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from diffusers import (
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AutoencoderKLMagvit,
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EasyAnimatePipeline,
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EasyAnimateTransformer3DModel,
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FlowMatchEulerDiscreteScheduler,
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)
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from ...testing_utils import (
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backend_empty_cache,
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enable_full_determinism,
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numpy_cosine_similarity_distance,
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require_torch_accelerator,
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slow,
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torch_device,
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)
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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, to_np
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enable_full_determinism()
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class EasyAnimatePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
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pipeline_class = EasyAnimatePipeline
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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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test_xformers_attention = False
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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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supports_dduf = False
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def get_dummy_components(self):
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torch.manual_seed(0)
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transformer = EasyAnimateTransformer3DModel(
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num_attention_heads=2,
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attention_head_dim=16,
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in_channels=4,
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out_channels=4,
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time_embed_dim=2,
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text_embed_dim=16, # Must match with tiny-random-t5
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num_layers=1,
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sample_width=16, # latent width: 2 -> final width: 16
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sample_height=16, # latent height: 2 -> final height: 16
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patch_size=2,
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)
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torch.manual_seed(0)
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vae = AutoencoderKLMagvit(
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in_channels=3,
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out_channels=3,
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down_block_types=(
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"SpatialDownBlock3D",
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"SpatialTemporalDownBlock3D",
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"SpatialTemporalDownBlock3D",
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"SpatialTemporalDownBlock3D",
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),
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up_block_types=(
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"SpatialUpBlock3D",
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"SpatialTemporalUpBlock3D",
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"SpatialTemporalUpBlock3D",
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"SpatialTemporalUpBlock3D",
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),
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block_out_channels=(8, 8, 8, 8),
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latent_channels=4,
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layers_per_block=1,
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norm_num_groups=2,
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spatial_group_norm=False,
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)
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torch.manual_seed(0)
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scheduler = FlowMatchEulerDiscreteScheduler()
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text_encoder = Qwen2VLForConditionalGeneration.from_pretrained(
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"hf-internal-testing/tiny-random-Qwen2VLForConditionalGeneration"
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)
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tokenizer = Qwen2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-Qwen2VLForConditionalGeneration")
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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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inputs = {
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"prompt": "dance monkey",
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"negative_prompt": "",
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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": 5,
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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
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generated_video = video[0]
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self.assertEqual(generated_video.shape, (5, 3, 16, 16))
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expected_video = torch.randn(5, 3, 16, 16)
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max_diff = np.abs(generated_video - expected_video).max()
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self.assertLessEqual(max_diff, 1e10)
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def test_callback_inputs(self):
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sig = inspect.signature(self.pipeline_class.__call__)
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has_callback_tensor_inputs = "callback_on_step_end_tensor_inputs" in sig.parameters
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has_callback_step_end = "callback_on_step_end" in sig.parameters
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if not (has_callback_tensor_inputs and has_callback_step_end):
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return
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components = self.get_dummy_components()
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pipe = self.pipeline_class(**components)
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pipe = pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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self.assertTrue(
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hasattr(pipe, "_callback_tensor_inputs"),
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f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs",
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)
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def callback_inputs_subset(pipe, i, t, callback_kwargs):
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# iterate over callback args
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for tensor_name, tensor_value in callback_kwargs.items():
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# check that we're only passing in allowed tensor inputs
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assert tensor_name in pipe._callback_tensor_inputs
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return callback_kwargs
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def callback_inputs_all(pipe, i, t, callback_kwargs):
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for tensor_name in pipe._callback_tensor_inputs:
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assert tensor_name in callback_kwargs
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# iterate over callback args
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for tensor_name, tensor_value in callback_kwargs.items():
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# check that we're only passing in allowed tensor inputs
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assert tensor_name in pipe._callback_tensor_inputs
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return callback_kwargs
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inputs = self.get_dummy_inputs(torch_device)
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# Test passing in a subset
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inputs["callback_on_step_end"] = callback_inputs_subset
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inputs["callback_on_step_end_tensor_inputs"] = ["latents"]
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output = pipe(**inputs)[0]
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# Test passing in a everything
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inputs["callback_on_step_end"] = callback_inputs_all
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inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
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output = pipe(**inputs)[0]
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def callback_inputs_change_tensor(pipe, i, t, callback_kwargs):
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is_last = i == (pipe.num_timesteps - 1)
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if is_last:
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callback_kwargs["latents"] = torch.zeros_like(callback_kwargs["latents"])
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return callback_kwargs
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inputs["callback_on_step_end"] = callback_inputs_change_tensor
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inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
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output = pipe(**inputs)[0]
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assert output.abs().sum() < 1e10
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def test_inference_batch_single_identical(self):
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self._test_inference_batch_single_identical(batch_size=3, expected_max_diff=1e-3)
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def test_attention_slicing_forward_pass(
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self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3
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):
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if not self.test_attention_slicing:
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return
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components = self.get_dummy_components()
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pipe = self.pipeline_class(**components)
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for component in pipe.components.values():
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if hasattr(component, "set_default_attn_processor"):
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component.set_default_attn_processor()
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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generator_device = "cpu"
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inputs = self.get_dummy_inputs(generator_device)
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output_without_slicing = pipe(**inputs)[0]
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pipe.enable_attention_slicing(slice_size=1)
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inputs = self.get_dummy_inputs(generator_device)
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output_with_slicing1 = pipe(**inputs)[0]
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pipe.enable_attention_slicing(slice_size=2)
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inputs = self.get_dummy_inputs(generator_device)
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output_with_slicing2 = pipe(**inputs)[0]
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if test_max_difference:
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max_diff1 = np.abs(to_np(output_with_slicing1) - to_np(output_without_slicing)).max()
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max_diff2 = np.abs(to_np(output_with_slicing2) - to_np(output_without_slicing)).max()
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self.assertLess(
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max(max_diff1, max_diff2),
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expected_max_diff,
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"Attention slicing should not affect the inference results",
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)
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def test_dict_tuple_outputs_equivalent(self, expected_slice=None, expected_max_difference=0.001):
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# Seems to need a higher tolerance
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return super().test_dict_tuple_outputs_equivalent(expected_slice, expected_max_difference)
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def test_encode_prompt_works_in_isolation(self):
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# Seems to need a higher tolerance
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return super().test_encode_prompt_works_in_isolation(atol=1e-3, rtol=1e-3)
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@slow
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@require_torch_accelerator
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class EasyAnimatePipelineIntegrationTests(unittest.TestCase):
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prompt = "A painting of a squirrel eating a burger."
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def setUp(self):
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super().setUp()
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gc.collect()
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backend_empty_cache(torch_device)
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def tearDown(self):
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super().tearDown()
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gc.collect()
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backend_empty_cache(torch_device)
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def test_EasyAnimate(self):
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generator = torch.Generator("cpu").manual_seed(0)
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pipe = EasyAnimatePipeline.from_pretrained("alibaba-pai/EasyAnimateV5.1-12b-zh", torch_dtype=torch.float16)
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pipe.enable_model_cpu_offload()
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prompt = self.prompt
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videos = pipe(
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prompt=prompt,
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height=480,
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width=720,
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num_frames=5,
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generator=generator,
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num_inference_steps=2,
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output_type="pt",
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).frames
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video = videos[0]
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expected_video = torch.randn(1, 5, 480, 720, 3).numpy()
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max_diff = numpy_cosine_similarity_distance(video, expected_video)
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assert max_diff < 1e-3, f"Max diff is too high. got {video}"
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