import functools
import glob
import importlib
import importlib.metadata
import inspect
import io
import logging
import multiprocessing
import os
import random
import re
import struct
import sys
import tempfile
import time
import unittest
import urllib.parse
from collections import UserDict
from contextlib import contextmanager
from io import BytesIO, StringIO
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Set
import numpy as np
import PIL.Image
import PIL.ImageOps
import requests
from numpy.linalg import norm
from packaging import version
from .constants import DIFFUSERS_REQUEST_TIMEOUT
from .import_utils import (
BACKENDS_MAPPING,
is_accelerate_available,
is_bitsandbytes_available,
is_compel_available,
is_flax_available,
is_gguf_available,
is_kernels_available,
is_note_seq_available,
is_nvidia_modelopt_available,
is_onnx_available,
is_opencv_available,
is_optimum_quanto_available,
is_peft_available,
is_timm_available,
is_torch_available,
is_torch_version,
is_torchao_available,
is_torchsde_available,
is_transformers_available,
)
from .logging import get_logger
if is_torch_available():
import torch
IS_ROCM_SYSTEM = torch.version.hip is not None
IS_CUDA_SYSTEM = torch.version.cuda is not None
IS_XPU_SYSTEM = getattr(torch.version, "xpu", None) is not None
else:
IS_ROCM_SYSTEM = False
IS_CUDA_SYSTEM = False
IS_XPU_SYSTEM = False
global_rng = random.Random()
logger = get_logger(__name__)
logger.warning(
"diffusers.utils.testing_utils' is deprecated and will be removed in a future version. "
"Determinism and device backend utilities have been moved to `diffusers.utils.torch_utils`. "
)
_required_peft_version = is_peft_available() and version.parse(
version.parse(importlib.metadata.version("peft")).base_version
) > version.parse("0.5")
_required_transformers_version = is_transformers_available() and version.parse(
version.parse(importlib.metadata.version("transformers")).base_version
) > version.parse("4.33")
USE_PEFT_BACKEND = _required_peft_version and _required_transformers_version
BIG_GPU_MEMORY = int(os.getenv("BIG_GPU_MEMORY", 40))
if is_torch_available():
import torch
# Set a backend environment variable for any extra module import required for a custom accelerator
if "DIFFUSERS_TEST_BACKEND" in os.environ:
backend = os.environ["DIFFUSERS_TEST_BACKEND"]
try:
_ = importlib.import_module(backend)
except ModuleNotFoundError as e:
raise ModuleNotFoundError(
f"Failed to import `DIFFUSERS_TEST_BACKEND` '{backend}'! This should be the name of an installed module \
to enable a specified backend.):\n{e}"
) from e
if "DIFFUSERS_TEST_DEVICE" in os.environ:
torch_device = os.environ["DIFFUSERS_TEST_DEVICE"]
try:
# try creating device to see if provided device is valid
_ = torch.device(torch_device)
except RuntimeError as e:
raise RuntimeError(
f"Unknown testing device specified by environment variable `DIFFUSERS_TEST_DEVICE`: {torch_device}"
) from e
logger.info(f"torch_device overrode to {torch_device}")
else:
if torch.cuda.is_available():
torch_device = "cuda"
elif torch.xpu.is_available():
torch_device = "xpu"
else:
torch_device = "cpu"
is_torch_higher_equal_than_1_12 = version.parse(
version.parse(torch.__version__).base_version
) >= version.parse("1.12")
if is_torch_higher_equal_than_1_12:
# Some builds of torch 1.12 don't have the mps backend registered. See #892 for more details
mps_backend_registered = hasattr(torch.backends, "mps")
torch_device = "mps" if (mps_backend_registered and torch.backends.mps.is_available()) else torch_device
from .torch_utils import get_torch_cuda_device_capability
def torch_all_close(a, b, *args, **kwargs):
if not is_torch_available():
raise ValueError("PyTorch needs to be installed to use this function.")
if not torch.allclose(a, b, *args, **kwargs):
assert False, f"Max diff is absolute {(a - b).abs().max()}. Diff tensor is {(a - b).abs()}."
return True
def numpy_cosine_similarity_distance(a, b):
similarity = np.dot(a, b) / (norm(a) * norm(b))
distance = 1.0 - similarity.mean()
return distance
def check_if_dicts_are_equal(dict1, dict2):
dict1, dict2 = dict1.copy(), dict2.copy()
for key, value in dict1.items():
if isinstance(value, set):
dict1[key] = sorted(value)
for key, value in dict2.items():
if isinstance(value, set):
dict2[key] = sorted(value)
for key in dict1:
if key not in dict2:
return False
if dict1[key] != dict2[key]:
return False
for key in dict2:
if key not in dict1:
return False
return True
def print_tensor_test(
tensor,
limit_to_slices=None,
max_torch_print=None,
filename="test_corrections.txt",
expected_tensor_name="expected_slice",
):
if max_torch_print:
torch.set_printoptions(threshold=10_000)
test_name = os.environ.get("PYTEST_CURRENT_TEST")
if not torch.is_tensor(tensor):
tensor = torch.from_numpy(tensor)
if limit_to_slices:
tensor = tensor[0, -3:, -3:, -1]
tensor_str = str(tensor.detach().cpu().flatten().to(torch.float32)).replace("\n", "")
# format is usually:
# expected_slice = np.array([-0.5713, -0.3018, -0.9814, 0.04663, -0.879, 0.76, -1.734, 0.1044, 1.161])
output_str = tensor_str.replace("tensor", f"{expected_tensor_name} = np.array")
test_file, test_class, test_fn = test_name.split("::")
test_fn = test_fn.split()[0]
with open(filename, "a") as f:
print("::".join([test_file, test_class, test_fn, output_str]), file=f)
def get_tests_dir(append_path=None):
"""
Args:
append_path: optional path to append to the tests dir path
Return:
The full path to the `tests` dir, so that the tests can be invoked from anywhere. Optionally `append_path` is
joined after the `tests` dir the former is provided.
"""
# this function caller's __file__
caller__file__ = inspect.stack()[1][1]
tests_dir = os.path.abspath(os.path.dirname(caller__file__))
while not tests_dir.endswith("tests"):
tests_dir = os.path.dirname(tests_dir)
if append_path:
return Path(tests_dir, append_path).as_posix()
else:
return tests_dir
# Taken from the following PR:
# https://github.com/huggingface/accelerate/pull/1964
def str_to_bool(value) -> int:
"""
Converts a string representation of truth to `True` (1) or `False` (0). True values are `y`, `yes`, `t`, `true`,
`on`, and `1`; False value are `n`, `no`, `f`, `false`, `off`, and `0`;
"""
value = value.lower()
if value in ("y", "yes", "t", "true", "on", "1"):
return 1
elif value in ("n", "no", "f", "false", "off", "0"):
return 0
else:
raise ValueError(f"invalid truth value {value}")
def parse_flag_from_env(key, default=False):
try:
value = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
_value = default
else:
# KEY is set, convert it to True or False.
try:
_value = str_to_bool(value)
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(f"If set, {key} must be yes or no.")
return _value
_run_slow_tests = parse_flag_from_env("RUN_SLOW", default=False)
_run_nightly_tests = parse_flag_from_env("RUN_NIGHTLY", default=False)
_run_compile_tests = parse_flag_from_env("RUN_COMPILE", default=False)
def floats_tensor(shape, scale=1.0, rng=None, name=None):
"""Creates a random float32 tensor"""
if rng is None:
rng = global_rng
total_dims = 1
for dim in shape:
total_dims *= dim
values = []
for _ in range(total_dims):
values.append(rng.random() * scale)
return torch.tensor(data=values, dtype=torch.float).view(shape).contiguous()
def slow(test_case):
"""
Decorator marking a test as slow.
Slow tests are skipped by default. Set the RUN_SLOW environment variable to a truthy value to run them.
"""
return unittest.skipUnless(_run_slow_tests, "test is slow")(test_case)
def nightly(test_case):
"""
Decorator marking a test that runs nightly in the diffusers CI.
Slow tests are skipped by default. Set the RUN_NIGHTLY environment variable to a truthy value to run them.
"""
return unittest.skipUnless(_run_nightly_tests, "test is nightly")(test_case)
def is_torch_compile(test_case):
"""
Decorator marking a test that runs compile tests in the diffusers CI.
Compile tests are skipped by default. Set the RUN_COMPILE environment variable to a truthy value to run them.
"""
return unittest.skipUnless(_run_compile_tests, "test is torch compile")(test_case)
def require_torch(test_case):
"""
Decorator marking a test that requires PyTorch. These tests are skipped when PyTorch isn't installed.
"""
return unittest.skipUnless(is_torch_available(), "test requires PyTorch")(test_case)
def require_torch_2(test_case):
"""
Decorator marking a test that requires PyTorch 2. These tests are skipped when it isn't installed.
"""
return unittest.skipUnless(is_torch_available() and is_torch_version(">=", "2.0.0"), "test requires PyTorch 2")(
test_case
)
def require_torch_version_greater_equal(torch_version):
"""Decorator marking a test that requires torch with a specific version or greater."""
def decorator(test_case):
correct_torch_version = is_torch_available() and is_torch_version(">=", torch_version)
return unittest.skipUnless(
correct_torch_version, f"test requires torch with the version greater than or equal to {torch_version}"
)(test_case)
return decorator
def require_torch_version_greater(torch_version):
"""Decorator marking a test that requires torch with a specific version greater."""
def decorator(test_case):
correct_torch_version = is_torch_available() and is_torch_version(">", torch_version)
return unittest.skipUnless(
correct_torch_version, f"test requires torch with the version greater than {torch_version}"
)(test_case)
return decorator
def require_torch_gpu(test_case):
"""Decorator marking a test that requires CUDA and PyTorch."""
return unittest.skipUnless(is_torch_available() and torch_device == "cuda", "test requires PyTorch+CUDA")(
test_case
)
def require_torch_cuda_compatibility(expected_compute_capability):
def decorator(test_case):
if torch.cuda.is_available():
current_compute_capability = get_torch_cuda_device_capability()
return unittest.skipUnless(
float(current_compute_capability) == float(expected_compute_capability),
"Test not supported for this compute capability.",
)
return decorator
# These decorators are for accelerator-specific behaviours that are not GPU-specific
def require_torch_accelerator(test_case):
"""Decorator marking a test that requires an accelerator backend and PyTorch."""
return unittest.skipUnless(is_torch_available() and torch_device != "cpu", "test requires accelerator+PyTorch")(
test_case
)
def require_torch_multi_gpu(test_case):
"""
Decorator marking a test that requires a multi-GPU setup (in PyTorch). These tests are skipped on a machine without
multiple GPUs. To run *only* the multi_gpu tests, assuming all test names contain multi_gpu: $ pytest -sv ./tests
-k "multi_gpu"
"""
if not is_torch_available():
return unittest.skip("test requires PyTorch")(test_case)
import torch
return unittest.skipUnless(torch.cuda.device_count() > 1, "test requires multiple GPUs")(test_case)
def require_torch_multi_accelerator(test_case):
"""
Decorator marking a test that requires a multi-accelerator setup (in PyTorch). These tests are skipped on a machine
without multiple hardware accelerators.
"""
if not is_torch_available():
return unittest.skip("test requires PyTorch")(test_case)
import torch
return unittest.skipUnless(
torch.cuda.device_count() > 1 or torch.xpu.device_count() > 1, "test requires multiple hardware accelerators"
)(test_case)
def require_torch_accelerator_with_fp16(test_case):
"""Decorator marking a test that requires an accelerator with support for the FP16 data type."""
return unittest.skipUnless(_is_torch_fp16_available(torch_device), "test requires accelerator with fp16 support")(
test_case
)
def require_torch_accelerator_with_fp64(test_case):
"""Decorator marking a test that requires an accelerator with support for the FP64 data type."""
return unittest.skipUnless(_is_torch_fp64_available(torch_device), "test requires accelerator with fp64 support")(
test_case
)
def require_big_gpu_with_torch_cuda(test_case):
"""
Decorator marking a test that requires a bigger GPU (24GB) for execution. Some example pipelines: Flux, SD3, Cog,
etc.
"""
if not is_torch_available():
return unittest.skip("test requires PyTorch")(test_case)
import torch
if not torch.cuda.is_available():
return unittest.skip("test requires PyTorch CUDA")(test_case)
device_properties = torch.cuda.get_device_properties(0)
total_memory = device_properties.total_memory / (1024**3)
return unittest.skipUnless(
total_memory >= BIG_GPU_MEMORY, f"test requires a GPU with at least {BIG_GPU_MEMORY} GB memory"
)(test_case)
def require_big_accelerator(test_case):
"""
Decorator marking a test that requires a bigger hardware accelerator (24GB) for execution. Some example pipelines:
Flux, SD3, Cog, etc.
"""
import pytest
test_case = pytest.mark.big_accelerator(test_case)
if not is_torch_available():
return unittest.skip("test requires PyTorch")(test_case)
import torch
if not (torch.cuda.is_available() or torch.xpu.is_available()):
return unittest.skip("test requires PyTorch CUDA")(test_case)
if torch.xpu.is_available():
device_properties = torch.xpu.get_device_properties(0)
else:
device_properties = torch.cuda.get_device_properties(0)
total_memory = device_properties.total_memory / (1024**3)
return unittest.skipUnless(
total_memory >= BIG_GPU_MEMORY,
f"test requires a hardware accelerator with at least {BIG_GPU_MEMORY} GB memory",
)(test_case)
def require_torch_accelerator_with_training(test_case):
"""Decorator marking a test that requires an accelerator with support for training."""
return unittest.skipUnless(
is_torch_available() and backend_supports_training(torch_device),
"test requires accelerator with training support",
)(test_case)
def skip_mps(test_case):
"""Decorator marking a test to skip if torch_device is 'mps'"""
return unittest.skipUnless(torch_device != "mps", "test requires non 'mps' device")(test_case)
def require_flax(test_case):
"""
Decorator marking a test that requires JAX & Flax. These tests are skipped when one / both are not installed
"""
return unittest.skipUnless(is_flax_available(), "test requires JAX & Flax")(test_case)
def require_compel(test_case):
"""
Decorator marking a test that requires compel: https://github.com/damian0815/compel. These tests are skipped when
the library is not installed.
"""
return unittest.skipUnless(is_compel_available(), "test requires compel")(test_case)
def require_onnxruntime(test_case):
"""
Decorator marking a test that requires onnxruntime. These tests are skipped when onnxruntime isn't installed.
"""
return unittest.skipUnless(is_onnx_available(), "test requires onnxruntime")(test_case)
def require_note_seq(test_case):
"""
Decorator marking a test that requires note_seq. These tests are skipped when note_seq isn't installed.
"""
return unittest.skipUnless(is_note_seq_available(), "test requires note_seq")(test_case)
def require_accelerator(test_case):
"""
Decorator marking a test that requires a hardware accelerator backend. These tests are skipped when there are no
hardware accelerator available.
"""
return unittest.skipUnless(torch_device != "cpu", "test requires a hardware accelerator")(test_case)
def require_torchsde(test_case):
"""
Decorator marking a test that requires torchsde. These tests are skipped when torchsde isn't installed.
"""
return unittest.skipUnless(is_torchsde_available(), "test requires torchsde")(test_case)
def require_peft_backend(test_case):
"""
Decorator marking a test that requires PEFT backend, this would require some specific versions of PEFT and
transformers.
"""
return unittest.skipUnless(USE_PEFT_BACKEND, "test requires PEFT backend")(test_case)
def require_timm(test_case):
"""
Decorator marking a test that requires timm. These tests are skipped when timm isn't installed.
"""
return unittest.skipUnless(is_timm_available(), "test requires timm")(test_case)
def require_bitsandbytes(test_case):
"""
Decorator marking a test that requires bitsandbytes. These tests are skipped when bitsandbytes isn't installed.
"""
return unittest.skipUnless(is_bitsandbytes_available(), "test requires bitsandbytes")(test_case)
def require_quanto(test_case):
"""
Decorator marking a test that requires quanto. These tests are skipped when quanto isn't installed.
"""
return unittest.skipUnless(is_optimum_quanto_available(), "test requires quanto")(test_case)
def require_accelerate(test_case):
"""
Decorator marking a test that requires accelerate. These tests are skipped when accelerate isn't installed.
"""
return unittest.skipUnless(is_accelerate_available(), "test requires accelerate")(test_case)
def require_peft_version_greater(peft_version):
"""
Decorator marking a test that requires PEFT backend with a specific version, this would require some specific
versions of PEFT and transformers.
"""
def decorator(test_case):
correct_peft_version = is_peft_available() and version.parse(
version.parse(importlib.metadata.version("peft")).base_version
) > version.parse(peft_version)
return unittest.skipUnless(
correct_peft_version, f"test requires PEFT backend with the version greater than {peft_version}"
)(test_case)
return decorator
def require_transformers_version_greater(transformers_version):
"""
Decorator marking a test that requires transformers with a specific version, this would require some specific
versions of PEFT and transformers.
"""
def decorator(test_case):
correct_transformers_version = is_transformers_available() and version.parse(
version.parse(importlib.metadata.version("transformers")).base_version
) > version.parse(transformers_version)
return unittest.skipUnless(
correct_transformers_version,
f"test requires transformers with the version greater than {transformers_version}",
)(test_case)
return decorator
def require_accelerate_version_greater(accelerate_version):
def decorator(test_case):
correct_accelerate_version = is_accelerate_available() and version.parse(
version.parse(importlib.metadata.version("accelerate")).base_version
) > version.parse(accelerate_version)
return unittest.skipUnless(
correct_accelerate_version, f"Test requires accelerate with the version greater than {accelerate_version}."
)(test_case)
return decorator
def require_bitsandbytes_version_greater(bnb_version):
def decorator(test_case):
correct_bnb_version = is_bitsandbytes_available() and version.parse(
version.parse(importlib.metadata.version("bitsandbytes")).base_version
) > version.parse(bnb_version)
return unittest.skipUnless(
correct_bnb_version, f"Test requires bitsandbytes with the version greater than {bnb_version}."
)(test_case)
return decorator
def require_hf_hub_version_greater(hf_hub_version):
def decorator(test_case):
correct_hf_hub_version = version.parse(
version.parse(importlib.metadata.version("huggingface_hub")).base_version
) > version.parse(hf_hub_version)
return unittest.skipUnless(
correct_hf_hub_version, f"Test requires huggingface_hub with the version greater than {hf_hub_version}."
)(test_case)
return decorator
def require_gguf_version_greater_or_equal(gguf_version):
def decorator(test_case):
correct_gguf_version = is_gguf_available() and version.parse(
version.parse(importlib.metadata.version("gguf")).base_version
) >= version.parse(gguf_version)
return unittest.skipUnless(
correct_gguf_version, f"Test requires gguf with the version greater than {gguf_version}."
)(test_case)
return decorator
def require_torchao_version_greater_or_equal(torchao_version):
def decorator(test_case):
correct_torchao_version = is_torchao_available() and version.parse(
version.parse(importlib.metadata.version("torchao")).base_version
) >= version.parse(torchao_version)
return unittest.skipUnless(
correct_torchao_version, f"Test requires torchao with version greater than {torchao_version}."
)(test_case)
return decorator
def require_modelopt_version_greater_or_equal(modelopt_version):
def decorator(test_case):
correct_nvidia_modelopt_version = is_nvidia_modelopt_available() and version.parse(
version.parse(importlib.metadata.version("modelopt")).base_version
) >= version.parse(modelopt_version)
return unittest.skipUnless(
correct_nvidia_modelopt_version, f"Test requires modelopt with version greater than {modelopt_version}."
)(test_case)
return decorator
def require_kernels_version_greater_or_equal(kernels_version):
def decorator(test_case):
correct_kernels_version = is_kernels_available() and version.parse(
version.parse(importlib.metadata.version("kernels")).base_version
) >= version.parse(kernels_version)
return unittest.skipUnless(
correct_kernels_version, f"Test requires kernels with version greater than {kernels_version}."
)(test_case)
return decorator
def deprecate_after_peft_backend(test_case):
"""
Decorator marking a test that will be skipped after PEFT backend
"""
return unittest.skipUnless(not USE_PEFT_BACKEND, "test skipped in favor of PEFT backend")(test_case)
def get_python_version():
sys_info = sys.version_info
major, minor = sys_info.major, sys_info.minor
return major, minor
def load_numpy(arry: str | np.ndarray, local_path: str | None = None) -> np.ndarray:
if isinstance(arry, str):
if local_path is not None:
# local_path can be passed to correct images of tests
return Path(local_path, arry.split("/")[-5], arry.split("/")[-2], arry.split("/")[-1]).as_posix()
elif arry.startswith("http://") or arry.startswith("https://"):
response = requests.get(arry, timeout=DIFFUSERS_REQUEST_TIMEOUT)
response.raise_for_status()
arry = np.load(BytesIO(response.content))
elif os.path.isfile(arry):
arry = np.load(arry)
else:
raise ValueError(
f"Incorrect path or url, URLs must start with `http://` or `https://`, and {arry} is not a valid path"
)
elif isinstance(arry, np.ndarray):
pass
else:
raise ValueError(
"Incorrect format used for numpy ndarray. Should be an url linking to an image, a local path, or a"
" ndarray."
)
return arry
def load_pt(url: str, map_location: str | None = None, weights_only: bool | None = True):
response = requests.get(url, timeout=DIFFUSERS_REQUEST_TIMEOUT)
response.raise_for_status()
arry = torch.load(BytesIO(response.content), map_location=map_location, weights_only=weights_only)
return arry
def load_image(image: str | PIL.Image.Image) -> PIL.Image.Image:
"""
Loads `image` to a PIL Image.
Args:
image (`str` or `PIL.Image.Image`):
The image to convert to the PIL Image format.
Returns:
`PIL.Image.Image`:
A PIL Image.
"""
if isinstance(image, str):
if image.startswith("http://") or image.startswith("https://"):
image = PIL.Image.open(requests.get(image, stream=True, timeout=DIFFUSERS_REQUEST_TIMEOUT).raw)
elif os.path.isfile(image):
image = PIL.Image.open(image)
else:
raise ValueError(
f"Incorrect path or url, URLs must start with `http://` or `https://`, and {image} is not a valid path"
)
elif isinstance(image, PIL.Image.Image):
image = image
else:
raise ValueError(
"Incorrect format used for image. Should be an url linking to an image, a local path, or a PIL image."
)
image = PIL.ImageOps.exif_transpose(image)
image = image.convert("RGB")
return image
def preprocess_image(image: PIL.Image, batch_size: int):
w, h = image.size
w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
image = image.resize((w, h), resample=PIL.Image.LANCZOS)
image = np.array(image).astype(np.float32) / 255.0
image = np.vstack([image[None].transpose(0, 3, 1, 2)] * batch_size)
image = torch.from_numpy(image)
return 2.0 * image - 1.0
def export_to_gif(image: list[PIL.Image.Image], output_gif_path: str = None) -> str:
if output_gif_path is None:
output_gif_path = tempfile.NamedTemporaryFile(suffix=".gif").name
image[0].save(
output_gif_path,
save_all=True,
append_images=image[1:],
optimize=False,
duration=100,
loop=0,
)
return output_gif_path
@contextmanager
def buffered_writer(raw_f):
f = io.BufferedWriter(raw_f)
yield f
f.flush()
def export_to_ply(mesh, output_ply_path: str = None):
"""
Write a PLY file for a mesh.
"""
if output_ply_path is None:
output_ply_path = tempfile.NamedTemporaryFile(suffix=".ply").name
coords = mesh.verts.detach().cpu().numpy()
faces = mesh.faces.cpu().numpy()
rgb = np.stack([mesh.vertex_channels[x].detach().cpu().numpy() for x in "RGB"], axis=1)
with buffered_writer(open(output_ply_path, "wb")) as f:
f.write(b"ply\n")
f.write(b"format binary_little_endian 1.0\n")
f.write(bytes(f"element vertex {len(coords)}\n", "ascii"))
f.write(b"property float x\n")
f.write(b"property float y\n")
f.write(b"property float z\n")
if rgb is not None:
f.write(b"property uchar red\n")
f.write(b"property uchar green\n")
f.write(b"property uchar blue\n")
if faces is not None:
f.write(bytes(f"element face {len(faces)}\n", "ascii"))
f.write(b"property list uchar int vertex_index\n")
f.write(b"end_header\n")
if rgb is not None:
rgb = (rgb * 255.499).round().astype(int)
vertices = [
(*coord, *rgb)
for coord, rgb in zip(
coords.tolist(),
rgb.tolist(),
)
]
format = struct.Struct("