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update

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This commit is contained in:
DN6
2025-07-17 13:05:43 +05:30
parent 625cc8ede8
commit 80702d222d
3 changed files with 61 additions and 100 deletions
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0
tests/modular/__init__.py → tests/modular_pipelines/__init__.py
View File

30
tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_modular.py → tests/modular_pipelines/stable_diffusion_xl/test_modular_pipeline_stable_diffusion_xl.py
View File

@@ -40,12 +40,6 @@ from diffusers.utils.testing_utils import (
from ...models.unets.test_models_unet_2d_condition import (
create_ip_adapter_state_dict,
)
from ..pipeline_params import (
IMAGE_INPAINTING_BATCH_PARAMS,
IMAGE_INPAINTING_PARAMS,
TEXT_TO_IMAGE_BATCH_PARAMS,
TEXT_TO_IMAGE_PARAMS,
)
from ..test_modular_pipelines_common import (
ModularPipelineTesterMixin,
)
@@ -62,12 +56,18 @@ class SDXLModularTests:
pipeline_class = StableDiffusionXLModularPipeline
pipeline_blocks_class = StableDiffusionXLAutoBlocks
repo = "hf-internal-testing/tiny-sdxl-modular"
params = (TEXT_TO_IMAGE_PARAMS | IMAGE_INPAINTING_PARAMS) - {
"guidance_scale",
"prompt_embeds",
"negative_prompt_embeds",
}
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS | IMAGE_INPAINTING_BATCH_PARAMS
params = frozenset(
[
"prompt",
"height",
"width",
"negative_prompt",
"cross_attention_kwargs",
"image",
"mask_image",
]
)
batch_params = frozenset(["prompt", "negative_prompt", "image", "mask_image"])
def get_pipeline(self, components_manager=None, torch_dtype=torch.float32):
pipeline = self.pipeline_blocks_class().init_pipeline(self.repo, components_manager=components_manager)
@@ -99,9 +99,9 @@ class SDXLModularTests:
assert image.shape == expected_image_shape
assert np.abs(image_slice.flatten() - expected_slice).max() < expected_max_diff, (
f"image_slice: {image_slice.flatten()}, expected_slice: {expected_slice}"
)
assert (
np.abs(image_slice.flatten() - expected_slice).max() < expected_max_diff
), f"Image Slice does not match expected slice"
class SDXLModularIPAdapterTests:

131
tests/modular/test_modular_pipelines_common.py → tests/modular_pipelines/test_modular_pipelines_common.py
View File

@@ -2,13 +2,11 @@ import gc
import unittest
from typing import Callable, Union
from diffusers.utils.dummy_pt_objects import ModularPipeline, ModularPipelineBlocks
import numpy as np
import torch
import diffusers
from diffusers import (
DiffusionPipeline,
)
from diffusers.utils import logging
from diffusers.utils.testing_utils import (
backend_empty_cache,
@@ -42,7 +40,7 @@ class ModularPipelineTesterMixin:
# Canonical parameters that are passed to `__call__` regardless
# of the type of pipeline. They are always optional and have common
# sense default values.
required_optional_params = frozenset(
optional_params = frozenset(
[
"num_inference_steps",
"num_images_per_prompt",
@@ -51,7 +49,7 @@ class ModularPipelineTesterMixin:
]
)
# this is modular specific: generator needs to be a intermediate input because it's mutable
required_intermediate_params = frozenset(
intermediate_params = frozenset(
[
"generator",
]
@@ -63,7 +61,7 @@ class ModularPipelineTesterMixin:
return generator
@property
def pipeline_class(self) -> Union[Callable, DiffusionPipeline]:
def pipeline_class(self) -> Union[Callable, ModularPipeline]:
raise NotImplementedError(
"You need to set the attribute `pipeline_class = ClassNameOfPipeline` in the child test class. "
"See existing pipeline tests for reference."
@@ -76,7 +74,7 @@ class ModularPipelineTesterMixin:
)
@property
def pipeline_blocks_class(self) -> Union[Callable, DiffusionPipeline]:
def pipeline_blocks_class(self) -> Union[Callable, ModularPipelineBlocks]:
raise NotImplementedError(
"You need to set the attribute `pipeline_blocks_class = ClassNameOfPipelineBlocks` in the child test class. "
"See existing pipeline tests for reference."
@@ -139,49 +137,21 @@ class ModularPipelineTesterMixin:
def test_pipeline_call_signature(self):
pipe = self.get_pipeline()
parameters = pipe.blocks.input_names
optional_parameters = pipe.default_call_parameters
input_parameters = pipe.blocks.input_names
intermediate_parameters = pipe.blocks.intermediate_input_names
optional_parameters = pipe.default_call_parameters
remaining_required_parameters = set()
def _check_for_parameters(parameters, expected_parameters, param_type):
remaining_parameters = set(param for param in parameters if param not in expected_parameters)
assert (
len(remaining_parameters) == 0
), f"Required {param_type} parameters not present: {remaining_parameters}"
for param in self.params:
if param not in parameters:
remaining_required_parameters.add(param)
_check_for_parameters(self.params, input_parameters, "input")
_check_for_parameters(self.intermediate_params, intermediate_parameters, "intermediate")
_check_for_parameters(self.optional_params, optional_parameters, "optional")
self.assertTrue(
len(remaining_required_parameters) == 0,
f"Required parameters not present: {remaining_required_parameters}",
)
remaining_required_intermediate_parameters = set()
for param in self.required_intermediate_params:
if param not in intermediate_parameters:
remaining_required_intermediate_parameters.add(param)
self.assertTrue(
len(remaining_required_intermediate_parameters) == 0,
f"Required intermediate parameters not present: {remaining_required_intermediate_parameters}",
)
remaining_required_optional_parameters = set()
for param in self.required_optional_params:
if param not in optional_parameters:
remaining_required_optional_parameters.add(param)
self.assertTrue(
len(remaining_required_optional_parameters) == 0,
f"Required optional parameters not present: {remaining_required_optional_parameters}",
)
def test_inference_batch_consistent(self, batch_sizes=[2]):
self._test_inference_batch_consistent(batch_sizes=batch_sizes)
def _test_inference_batch_consistent(
self, batch_sizes=[2], additional_params_copy_to_batched_inputs=["num_inference_steps"], batch_generator=True
):
def test_inference_batch_consistent(self, batch_sizes=[2], batch_generator=True):
pipe = self.get_pipeline()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
@@ -203,16 +173,7 @@ class ModularPipelineTesterMixin:
continue
value = inputs[name]
if name == "prompt":
len_prompt = len(value)
# make unequal batch sizes
batched_input[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]
# make last batch super long
batched_input[name][-1] = 100 * "very long"
else:
batched_input[name] = batch_size * [value]
batched_input[name] = batch_size * [value]
if batch_generator and "generator" in inputs:
batched_input["generator"] = [self.get_generator(i) for i in range(batch_size)]
@@ -225,21 +186,18 @@ class ModularPipelineTesterMixin:
logger.setLevel(level=diffusers.logging.WARNING)
for batch_size, batched_input in zip(batch_sizes, batched_inputs):
output = pipe(**batched_input, output="images")
assert len(output) == batch_size
assert len(output) == batch_size, "Output is different from expected batch size"
def test_inference_batch_single_identical(self, batch_size=3, expected_max_diff=1e-4):
self._test_inference_batch_single_identical(batch_size=batch_size, expected_max_diff=expected_max_diff)
def _test_inference_batch_single_identical(
def test_batch_inference_identical_to_single(
self,
batch_size=2,
expected_max_diff=1e-4,
additional_params_copy_to_batched_inputs=["num_inference_steps"],
):
pipe = self.get_pipeline()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
# Reset generator in case it is has been used in self.get_dummy_inputs
inputs["generator"] = self.get_generator(0)
@@ -255,13 +213,7 @@ class ModularPipelineTesterMixin:
continue
value = inputs[name]
if name == "prompt":
len_prompt = len(value)
batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]
batched_inputs[name][-1] = 100 * "very long"
else:
batched_inputs[name] = batch_size * [value]
batched_inputs[name] = batch_size * [value]
if "generator" in inputs:
batched_inputs["generator"] = [self.get_generator(i) for i in range(batch_size)]
@@ -269,26 +221,22 @@ class ModularPipelineTesterMixin:
if "batch_size" in inputs:
batched_inputs["batch_size"] = batch_size
for arg in additional_params_copy_to_batched_inputs:
batched_inputs[arg] = inputs[arg]
output = pipe(**inputs, output="images")
output_batch = pipe(**batched_inputs, output="images")
assert output_batch.shape[0] == batch_size
max_diff = np.abs(to_np(output_batch[0]) - to_np(output[0])).max()
assert max_diff < expected_max_diff
assert max_diff < expected_max_diff, "Batch inference results different from single inference results"
@unittest.skipIf(torch_device not in ["cuda", "xpu"], reason="float16 requires CUDA or XPU")
@require_accelerator
def test_float16_inference(self, expected_max_diff=5e-2):
pipe = self.get_pipeline(torch_dtype=torch.float32)
pipe.to(torch_device)
pipe = self.get_pipeline()
pipe.to(torch_device, torch.float32)
pipe.set_progress_bar_config(disable=None)
pipe_fp16 = self.get_pipeline(torch_dtype=torch.float16)
pipe_fp16 = self.get_pipeline()
pipe_fp16.to(torch_device, torch.float16)
pipe_fp16.set_progress_bar_config(disable=None)
@@ -309,7 +257,7 @@ class ModularPipelineTesterMixin:
output_fp16 = output_fp16.cpu()
max_diff = numpy_cosine_similarity_distance(output.flatten(), output_fp16.flatten())
assert max_diff < expected_max_diff
assert max_diff < expected_max_diff, "FP16 inference is different from FP32 inference"
@require_accelerator
def test_to_device(self):
@@ -320,19 +268,32 @@ class ModularPipelineTesterMixin:
model_devices = [
component.device.type for component in pipe.components.values() if hasattr(component, "device")
]
self.assertTrue(all(device == "cpu" for device in model_devices))
output_cpu = pipe(**self.get_dummy_inputs("cpu"), output="images")
self.assertTrue(np.isnan(output_cpu).sum() == 0)
assert all(device == "cpu" for device in model_devices), "All pipeline components are not on CPU"
pipe.to(torch_device)
model_devices = [
component.device.type for component in pipe.components.values() if hasattr(component, "device")
]
self.assertTrue(all(device == torch_device for device in model_devices))
assert all(
device == torch_device for device in model_devices
), "All pipeline components are not on accelerator device"
output_device = pipe(**self.get_dummy_inputs(torch_device), output="images")
self.assertTrue(np.isnan(to_np(output_device)).sum() == 0)
def test_inference_is_not_nan_cpu(self):
pipe = self.get_pipeline()
pipe.set_progress_bar_config(disable=None)
pipe.to("cpu")
output = pipe(**self.get_dummy_inputs("cpu"), output="np")
assert np.isnan(to_np(output)).sum() == 0, "CPU Inference returns NaN"
@require_accelerator
def test_inferece_is_not_nan(self):
pipe = self.get_pipeline()
pipe.set_progress_bar_config(disable=None)
pipe.to(torch_device)
output = pipe(**self.get_dummy_inputs(torch_device), output="np")
assert np.isnan(to_np(output)).sum() == 0, "Accelerator Inference returns NaN"
def test_num_images_per_prompt(self):
pipe = self.get_pipeline()