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refactor cogvideox video to video pipeline tests to the new mixin structure #14289

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refactor cogvideox video to video pipeline tests to the new mixin structure #14289

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197 changes: 40 additions & 157 deletions tests/pipelines/cogvideo/test_cogvideox_video2video.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,8 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.

import inspect
import unittest

import numpy as np
import torch
Expand All @@ -22,36 +20,25 @@

from diffusers import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel, CogVideoXVideoToVideoPipeline, DDIMScheduler

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 (
from ..testing_utils import (
BasePipelineTesterConfig,
MemoryTesterMixin,
PipelineTesterMixin,
check_qkv_fusion_matches_attn_procs_length,
check_qkv_fusion_processors_exist,
to_np,
)


enable_full_determinism()


class CogVideoXVideoToVideoPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
class CogVideoXVideoToVideoPipelineTesterConfig(BasePipelineTesterConfig):
pipeline_class = CogVideoXVideoToVideoPipeline
params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"}
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS.union({"video"})
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",
]
required_input_params_in_call_signature = frozenset(
["prompt", "negative_prompt", "height", "width", "guidance_scale", "prompt_embeds", "negative_prompt_embeds"]
)
batch_input_params = frozenset(["prompt", "video"])
# CogVideoX is a video pipeline: it exposes `num_videos_per_prompt`, not the base default `num_images_per_prompt`.
optional_input_params = frozenset(
["num_inference_steps", "num_videos_per_prompt", "generator", "latents", "output_type", "return_dict"]
)
test_xformers_attention = False

def get_dummy_components(self):
torch.manual_seed(0)
Expand Down Expand Up @@ -112,21 +99,16 @@ def get_dummy_components(self):
}
return components

def get_dummy_inputs(self, device, seed: int = 0, num_frames: int = 8):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)

def get_dummy_inputs(self):
video_height = 16
video_width = 16
video = [Image.new("RGB", (video_width, video_height))] * num_frames
video = [Image.new("RGB", (video_width, video_height))] * 8

inputs = {
"video": video,
"prompt": "dance monkey",
"negative_prompt": "",
"generator": generator,
"generator": self.get_generator(0),
"num_inference_steps": 2,
"strength": 0.5,
"guidance_scale": 6.0,
Expand All @@ -138,135 +120,38 @@ def get_dummy_inputs(self, device, seed: int = 0, num_frames: int = 8):
}
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)
class TestCogVideoXVideoToVideoPipeline(CogVideoXVideoToVideoPipelineTesterConfig, PipelineTesterMixin):
def test_inference(self):
# Run on CPU: the expected slice below is CPU-specific.
pipe = self.get_pipeline()

inputs = self.get_dummy_inputs(device)
inputs = self.get_dummy_inputs()
video = pipe(**inputs).frames
generated_video = video[0]
assert generated_video.shape == (8, 3, 16, 16)

self.assertEqual(generated_video.shape, (8, 3, 16, 16))
expected_video = torch.randn(8, 3, 16, 16)
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
# fmt: off
expected_slice = torch.tensor([0.5644, 0.6029, 0.6017, 0.5937, 0.5991, 0.5907, 0.6141, 0.5340, 0.3184, 0.4219, 0.4406, 0.4330, 0.4692, 0.4547, 0.4562, 0.5092])
# fmt: on

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
generated_slice = generated_video.flatten()
generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]])
assert torch.allclose(generated_slice, expected_slice, atol=1e-3)

def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(batch_size=3, expected_max_diff=1e-3)

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",
)
super().test_inference_batch_single_identical(batch_size=3, expected_max_diff=1e-3)

def test_vae_tiling(self, expected_diff_max: float = 0.2):
# Since VideoToVideo uses both encoder and decoder tiling, there seems to be much more numerical
# difference. We seem to need a higher tolerance here...
# TODO(aryan): Look into this more deeply
expected_diff_max = 0.4

generator_device = "cpu"
components = self.get_dummy_components()

pipe = self.pipeline_class(**components)
pipe.to("cpu")
pipe.set_progress_bar_config(disable=None)
pipe = self.get_pipeline()

# Without tiling
inputs = self.get_dummy_inputs(generator_device)
inputs = self.get_dummy_inputs()
inputs["height"] = inputs["width"] = 128
output_without_tiling = pipe(**inputs)[0]

Expand All @@ -277,24 +162,18 @@ def test_vae_tiling(self, expected_diff_max: float = 0.2):
tile_overlap_factor_height=1 / 12,
tile_overlap_factor_width=1 / 12,
)
inputs = self.get_dummy_inputs(generator_device)
inputs = self.get_dummy_inputs()
inputs["height"] = inputs["width"] = 128
output_with_tiling = pipe(**inputs)[0]

self.assertLess(
(to_np(output_without_tiling) - to_np(output_with_tiling)).max(),
expected_diff_max,
"VAE tiling should not affect the inference results",
assert (output_without_tiling - output_with_tiling).max() < expected_diff_max, (
"VAE tiling should not affect the inference results"
)

def test_fused_qkv_projections(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
pipe = self.get_pipeline()

inputs = self.get_dummy_inputs(device)
inputs = self.get_dummy_inputs()
frames = pipe(**inputs).frames # [B, F, C, H, W]
original_image_slice = frames[0, -2:, -1, -3:, -3:]

Expand All @@ -306,12 +185,12 @@ def test_fused_qkv_projections(self):
pipe.transformer, pipe.transformer.original_attn_processors
), "Something wrong with the attention processors concerning the fused QKV projections."

inputs = self.get_dummy_inputs(device)
inputs = self.get_dummy_inputs()
frames = pipe(**inputs).frames
image_slice_fused = frames[0, -2:, -1, -3:, -3:]

pipe.transformer.unfuse_qkv_projections()
inputs = self.get_dummy_inputs(device)
inputs = self.get_dummy_inputs()
frames = pipe(**inputs).frames
image_slice_disabled = frames[0, -2:, -1, -3:, -3:]

Expand All @@ -324,3 +203,7 @@ def test_fused_qkv_projections(self):
assert np.allclose(original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2), (
"Original outputs should match when fused QKV projections are disabled."
)


class TestCogVideoXVideoToVideoPipelineMemory(CogVideoXVideoToVideoPipelineTesterConfig, MemoryTesterMixin):
pass
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