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Merge branch 'main' into compile_utils

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This commit is contained in:
Sayak Paul
2025-06-26 06:55:57 +05:30
committed by GitHub
parent 45ff4a8827 8846635873
commit e2c7d17510
11 changed files with 92 additions and 107 deletions
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6
src/diffusers/hooks/group_offloading.py
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@@ -96,9 +96,6 @@ class ModuleGroup:
else:
self.cpu_param_dict = self._init_cpu_param_dict()
if self.stream is None and self.record_stream:
raise ValueError("`record_stream` cannot be True when `stream` is None.")
def _init_cpu_param_dict(self):
cpu_param_dict = {}
if self.stream is None:
@@ -513,6 +510,9 @@ def apply_group_offloading(
else:
raise ValueError("Using streams for data transfer requires a CUDA device, or an Intel XPU device.")
if not use_stream and record_stream:
raise ValueError("`record_stream` cannot be True when `use_stream=False`.")
_raise_error_if_accelerate_model_or_sequential_hook_present(module)
if offload_type == "block_level":

12
src/diffusers/loaders/lora_base.py
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@@ -1022,15 +1022,3 @@ class LoraBaseMixin:
@classmethod
def _optionally_disable_offloading(cls, _pipeline):
return _func_optionally_disable_offloading(_pipeline=_pipeline)
@classmethod
def _fetch_state_dict(cls, *args, **kwargs):
deprecation_message = f"Using the `_fetch_state_dict()` method from {cls} has been deprecated and will be removed in a future version. Please use `from diffusers.loaders.lora_base import _fetch_state_dict`."
deprecate("_fetch_state_dict", "0.35.0", deprecation_message)
return _fetch_state_dict(*args, **kwargs)
@classmethod
def _best_guess_weight_name(cls, *args, **kwargs):
deprecation_message = f"Using the `_best_guess_weight_name()` method from {cls} has been deprecated and will be removed in a future version. Please use `from diffusers.loaders.lora_base import _best_guess_weight_name`."
deprecate("_best_guess_weight_name", "0.35.0", deprecation_message)
return _best_guess_weight_name(*args, **kwargs)

18
src/diffusers/models/autoencoders/autoencoder_kl_cosmos.py
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@@ -110,8 +110,11 @@ class CosmosPatchEmbed3d(nn.Module):
self.patch_size = patch_size
self.patch_method = patch_method
self.register_buffer("wavelets", _WAVELETS[patch_method], persistent=False)
self.register_buffer("_arange", torch.arange(_WAVELETS[patch_method].shape[0]), persistent=False)
wavelets = _WAVELETS.get(patch_method).clone()
arange = torch.arange(wavelets.shape[0])
self.register_buffer("wavelets", wavelets, persistent=False)
self.register_buffer("_arange", arange, persistent=False)
def _dwt(self, hidden_states: torch.Tensor, mode: str = "reflect", rescale=False) -> torch.Tensor:
dtype = hidden_states.dtype
@@ -185,12 +188,11 @@ class CosmosUnpatcher3d(nn.Module):
self.patch_size = patch_size
self.patch_method = patch_method
self.register_buffer("wavelets", _WAVELETS[patch_method], persistent=False)
self.register_buffer(
"_arange",
torch.arange(_WAVELETS[patch_method].shape[0]),
persistent=False,
)
wavelets = _WAVELETS.get(patch_method).clone()
arange = torch.arange(wavelets.shape[0])
self.register_buffer("wavelets", wavelets, persistent=False)
self.register_buffer("_arange", arange, persistent=False)
def _idwt(self, hidden_states: torch.Tensor, rescale: bool = False) -> torch.Tensor:
device = hidden_states.device

6
src/diffusers/pipelines/omnigen/pipeline_omnigen.py
View File

@@ -23,12 +23,14 @@ from ...image_processor import PipelineImageInput, VaeImageProcessor
from ...models.autoencoders import AutoencoderKL
from ...models.transformers import OmniGenTransformer2DModel
from ...schedulers import FlowMatchEulerDiscreteScheduler
from ...utils import is_torch_xla_available, logging, replace_example_docstring
from ...utils import is_torch_xla_available, is_torchvision_available, logging, replace_example_docstring
from ...utils.torch_utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from .processor_omnigen import OmniGenMultiModalProcessor
if is_torchvision_available():
from .processor_omnigen import OmniGenMultiModalProcessor
if is_torch_xla_available():
XLA_AVAILABLE = True
else:

7
src/diffusers/pipelines/omnigen/processor_omnigen.py
View File

@@ -18,7 +18,12 @@ from typing import Dict, List
import numpy as np
import torch
from PIL import Image
from torchvision import transforms
from ...utils import is_torchvision_available
if is_torchvision_available():
from torchvision import transforms
def crop_image(pil_image, max_image_size):

39
tests/lora/test_deprecated_utilities.py
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@@ -1,39 +0,0 @@
import os
import tempfile
import unittest
import torch
from diffusers.loaders.lora_base import LoraBaseMixin
class UtilityMethodDeprecationTests(unittest.TestCase):
def test_fetch_state_dict_cls_method_raises_warning(self):
state_dict = torch.nn.Linear(3, 3).state_dict()
with self.assertWarns(FutureWarning) as warning:
_ = LoraBaseMixin._fetch_state_dict(
state_dict,
weight_name=None,
use_safetensors=False,
local_files_only=True,
cache_dir=None,
force_download=False,
proxies=None,
token=None,
revision=None,
subfolder=None,
user_agent=None,
allow_pickle=None,
)
warning_message = str(warning.warnings[0].message)
assert "Using the `_fetch_state_dict()` method from" in warning_message
def test_best_guess_weight_name_cls_method_raises_warning(self):
with tempfile.TemporaryDirectory() as tmpdir:
state_dict = torch.nn.Linear(3, 3).state_dict()
torch.save(state_dict, os.path.join(tmpdir, "pytorch_lora_weights.bin"))
with self.assertWarns(FutureWarning) as warning:
_ = LoraBaseMixin._best_guess_weight_name(pretrained_model_name_or_path_or_dict=tmpdir)
warning_message = str(warning.warnings[0].message)
assert "Using the `_best_guess_weight_name()` method from" in warning_message

19
tests/models/test_modeling_common.py
View File

@@ -1528,14 +1528,16 @@ class ModelTesterMixin:
test_fn(torch.float8_e5m2, torch.float32)
test_fn(torch.float8_e4m3fn, torch.bfloat16)
@torch.no_grad()
def test_layerwise_casting_inference(self):
from diffusers.hooks.layerwise_casting import DEFAULT_SKIP_MODULES_PATTERN, SUPPORTED_PYTORCH_LAYERS
torch.manual_seed(0)
config, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**config).eval()
model = model.to(torch_device)
base_slice = model(**inputs_dict)[0].flatten().detach().cpu().numpy()
model = self.model_class(**config)
model.eval()
model.to(torch_device)
base_slice = model(**inputs_dict)[0].detach().flatten().cpu().numpy()
def check_linear_dtype(module, storage_dtype, compute_dtype):
patterns_to_check = DEFAULT_SKIP_MODULES_PATTERN
@@ -1573,6 +1575,7 @@ class ModelTesterMixin:
test_layerwise_casting(torch.float8_e4m3fn, torch.bfloat16)
@require_torch_accelerator
@torch.no_grad()
def test_layerwise_casting_memory(self):
MB_TOLERANCE = 0.2
LEAST_COMPUTE_CAPABILITY = 8.0
@@ -1706,10 +1709,6 @@ class ModelTesterMixin:
if not self.model_class._supports_group_offloading:
pytest.skip("Model does not support group offloading.")
torch.manual_seed(0)
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
torch.manual_seed(0)
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
@@ -1725,7 +1724,7 @@ class ModelTesterMixin:
**additional_kwargs,
)
has_safetensors = glob.glob(f"{tmpdir}/*.safetensors")
assert has_safetensors, "No safetensors found in the directory."
self.assertTrue(len(has_safetensors) > 0, "No safetensors found in the offload directory.")
_ = model(**inputs_dict)[0]
def test_auto_model(self, expected_max_diff=5e-5):
@@ -2126,7 +2125,7 @@ class LoraHotSwappingForModelTesterMixin:
@parameterized.expand([(11, 11), (7, 13), (13, 7)]) # important to test small to large and vice versa
def test_hotswapping_compiled_model_conv2d(self, rank0, rank1):
if "unet" not in self.model_class.__name__.lower():
return
pytest.skip("Test only applies to UNet.")
# It's important to add this context to raise an error on recompilation
target_modules = ["conv", "conv1", "conv2"]
@@ -2136,7 +2135,7 @@ class LoraHotSwappingForModelTesterMixin:
@parameterized.expand([(11, 11), (7, 13), (13, 7)]) # important to test small to large and vice versa
def test_hotswapping_compiled_model_both_linear_and_conv2d(self, rank0, rank1):
if "unet" not in self.model_class.__name__.lower():
return
pytest.skip("Test only applies to UNet.")
# It's important to add this context to raise an error on recompilation
target_modules = ["to_q", "conv"]

3
tests/pipelines/kandinsky2_2/test_kandinsky_controlnet.py
View File

@@ -289,6 +289,5 @@ class KandinskyV22ControlnetPipelineIntegrationTests(unittest.TestCase):
image = output.images[0]
assert image.shape == (512, 512, 3)
max_diff = numpy_cosine_similarity_distance(expected_image.flatten(), image.flatten())
assert max_diff < 1e-4
assert max_diff < 2e-4

33
tests/pipelines/ledits_pp/test_ledits_pp_stable_diffusion.py
View File

@@ -29,6 +29,7 @@ from diffusers import (
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import (
Expectations,
backend_empty_cache,
enable_full_determinism,
floats_tensor,
@@ -244,7 +245,35 @@ class LEditsPPPipelineStableDiffusionSlowTests(unittest.TestCase):
output_slice = reconstruction[150:153, 140:143, -1]
output_slice = output_slice.flatten()
expected_slice = np.array(
[0.9453125, 0.93310547, 0.84521484, 0.94628906, 0.9111328, 0.80859375, 0.93847656, 0.9042969, 0.8144531]
expected_slices = Expectations(
{
("xpu", 3): np.array(
[
0.9511719,
0.94140625,
0.87597656,
0.9472656,
0.9296875,
0.8378906,
0.94433594,
0.91503906,
0.8491211,
]
),
("cuda", 7): np.array(
[
0.9453125,
0.93310547,
0.84521484,
0.94628906,
0.9111328,
0.80859375,
0.93847656,
0.9042969,
0.8144531,
]
),
}
)
expected_slice = expected_slices.get_expectation()
assert np.abs(output_slice - expected_slice).max() < 1e-2

6
tests/pipelines/test_pipelines_common.py
View File

@@ -49,6 +49,7 @@ from diffusers.utils.source_code_parsing_utils import ReturnNameVisitor
from diffusers.utils.testing_utils import (
CaptureLogger,
backend_empty_cache,
numpy_cosine_similarity_distance,
require_accelerate_version_greater,
require_accelerator,
require_hf_hub_version_greater,
@@ -1394,9 +1395,8 @@ class PipelineTesterMixin:
fp16_inputs["generator"] = self.get_generator(0)
output_fp16 = pipe_fp16(**fp16_inputs)[0]
max_diff = np.abs(to_np(output) - to_np(output_fp16)).max()
self.assertLess(max_diff, expected_max_diff, "The outputs of the fp16 and fp32 pipelines are too different.")
max_diff = numpy_cosine_similarity_distance(output.flatten(), output_fp16.flatten())
assert max_diff < 2e-4
@unittest.skipIf(torch_device not in ["cuda", "xpu"], reason="float16 requires CUDA or XPU")
@require_accelerator

50
tests/quantization/gguf/test_gguf.py
View File

@@ -286,33 +286,33 @@ class SD35LargeGGUFSingleFileTests(GGUFSingleFileTesterMixin, unittest.TestCase)
{
("xpu", 3): np.array(
[
0.19335938,
0.3125,
0.3203125,
0.1328125,
0.3046875,
0.296875,
0.11914062,
0.2890625,
0.2890625,
0.16796875,
0.30273438,
0.33203125,
0.14648438,
0.31640625,
0.33007812,
0.16210938,
0.2734375,
0.27734375,
0.109375,
0.27148438,
0.2578125,
0.1015625,
0.2578125,
0.2578125,
0.14453125,
0.26953125,
0.29492188,
0.12890625,
0.3046875,
0.30859375,
0.17773438,
0.33789062,
0.33203125,
0.16796875,
0.34570312,
0.32421875,
0.28710938,
0.30078125,
0.11132812,
0.27734375,
0.27929688,
0.15625,
0.33203125,
0.31445312,
0.31054688,
0.296875,
0.15234375,
0.3203125,
0.29492188,
0.140625,
0.3046875,
0.28515625,
]
),
("cuda", 7): np.array(