Merge remote-tracking branch 'origin/main' into build/test_reports

.github/workflows/pr_tests.yml

@@ -21,7 +21,7 @@ jobs:
     runs-on: [ self-hosted, docker-gpu ]
     container:
       image: python:3.7
-      options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
 
     steps:
     - name: Checkout diffusers

.github/workflows/push_tests.yml

@@ -15,14 +15,10 @@ env:
 jobs:
   run_tests_single_gpu:
     name: Diffusers tests
-    strategy:
-      fail-fast: false
-      matrix:
-        machine_type: [ single-gpu ]
-    runs-on: [ self-hosted, docker-gpu, '${{ matrix.machine_type }}' ]
+    runs-on: [ self-hosted, docker-gpu, single-gpu ]
     container:
       image: nvcr.io/nvidia/pytorch:22.07-py3
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache
 
     steps:
     - name: Checkout diffusers
@@ -66,14 +62,10 @@ jobs:
 
   run_examples_single_gpu:
     name: Examples tests
-    strategy:
-      fail-fast: false
-      matrix:
-        machine_type: [ single-gpu ]
-    runs-on: [ self-hosted, docker-gpu, '${{ matrix.machine_type }}' ]
+    runs-on: [ self-hosted, docker-gpu, single-gpu ]
     container:
       image: nvcr.io/nvidia/pytorch:22.07-py3
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache
 
     steps:
     - name: Checkout diffusers

examples/conftest.py

@@ -32,13 +32,13 @@
 
 
 def pytest_addoption(parser):
-    from diffusers.testing_utils import pytest_addoption_shared
+    from diffusers.utils.testing_utils import pytest_addoption_shared
 
     pytest_addoption_shared(parser)
 
 
 def pytest_terminal_summary(terminalreporter):
-    from diffusers.testing_utils import pytest_terminal_summary_main
+    from diffusers.utils.testing_utils import pytest_terminal_summary_main
 
     make_reports = terminalreporter.config.getoption("--make-reports")
     if make_reports:

examples/test_examples.py

@@ -24,7 +24,7 @@
 from typing import List
 
 from accelerate.utils import write_basic_config
-from diffusers.testing_utils import slow
+from diffusers.utils import slow
 
 
 logging.basicConfig(level=logging.DEBUG)

setup.py

@@ -92,7 +92,7 @@
     "jaxlib>=0.1.65,<=0.3.6",
     "modelcards>=0.1.4",
     "numpy",
-    "onnxruntime-gpu",
+    "onnxruntime",
     "pytest",
     "pytest-timeout",
     "pytest-xdist",
@@ -178,7 +178,7 @@ def run(self):
 extras["training"] = deps_list("accelerate", "datasets", "tensorboard", "modelcards")
 extras["test"] = deps_list(
     "datasets",
-    "onnxruntime-gpu",
+    "onnxruntime",
     "pytest",
     "pytest-timeout",
     "pytest-xdist",

src/diffusers/dependency_versions_table.py

@@ -17,7 +17,7 @@
     "jaxlib": "jaxlib>=0.1.65,<=0.3.6",
     "modelcards": "modelcards>=0.1.4",
     "numpy": "numpy",
-    "onnxruntime-gpu": "onnxruntime-gpu",
+    "onnxruntime": "onnxruntime",
     "pytest": "pytest",
     "pytest-timeout": "pytest-timeout",
     "pytest-xdist": "pytest-xdist",

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -1,5 +1,4 @@
 import inspect
-import warnings
 from typing import Callable, List, Optional, Union
 
 import torch
@@ -10,7 +9,7 @@
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...pipeline_utils import DiffusionPipeline
 from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-from ...utils import logging
+from ...utils import deprecate, logging
 from . import StableDiffusionPipelineOutput
 from .safety_checker import StableDiffusionSafetyChecker
 
@@ -59,15 +58,15 @@ def __init__(
         super().__init__()
 
         if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
-            warnings.warn(
+            deprecation_message = (
                 f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                 f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
                 "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
                 " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
                 " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
-                " file",
-                DeprecationWarning,
+                " file"
             )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
             new_config = dict(scheduler.config)
             new_config["steps_offset"] = 1
             scheduler._internal_dict = FrozenDict(new_config)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -1,5 +1,4 @@
 import inspect
-import warnings
 from typing import Callable, List, Optional, Union
 
 import numpy as np
@@ -12,7 +11,7 @@
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...pipeline_utils import DiffusionPipeline
 from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-from ...utils import logging
+from ...utils import deprecate, logging
 from . import StableDiffusionPipelineOutput
 from .safety_checker import StableDiffusionSafetyChecker
 
@@ -71,15 +70,15 @@ def __init__(
         super().__init__()
 
         if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
-            warnings.warn(
+            deprecation_message = (
                 f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                 f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
                 "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
                 " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
                 " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
-                " file",
-                DeprecationWarning,
+                " file"
             )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
             new_config = dict(scheduler.config)
             new_config["steps_offset"] = 1
             scheduler._internal_dict = FrozenDict(new_config)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -1,5 +1,4 @@
 import inspect
-import warnings
 from typing import Callable, List, Optional, Union
 
 import numpy as np
@@ -13,7 +12,7 @@
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...pipeline_utils import DiffusionPipeline
 from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-from ...utils import logging
+from ...utils import deprecate, logging
 from . import StableDiffusionPipelineOutput
 from .safety_checker import StableDiffusionSafetyChecker
 
@@ -86,15 +85,15 @@ def __init__(
         logger.info("`StableDiffusionInpaintPipeline` is experimental and will very likely change in the future.")
 
         if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
-            warnings.warn(
+            deprecation_message = (
                 f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                 f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
                 "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
                 " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
                 " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
-                " file",
-                DeprecationWarning,
+                " file"
             )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
             new_config = dict(scheduler.config)
             new_config["steps_offset"] = 1
             scheduler._internal_dict = FrozenDict(new_config)

src/diffusers/pipelines/stable_diffusion/safety_checker.py

@@ -79,7 +79,7 @@ def forward(self, clip_input, images):
 
         return images, has_nsfw_concepts
 
-    @torch.inference_mode()
+    @torch.no_grad()
     def forward_onnx(self, clip_input: torch.FloatTensor, images: torch.FloatTensor):
         pooled_output = self.vision_model(clip_input)[1]  # pooled_output
         image_embeds = self.visual_projection(pooled_output)

src/diffusers/schedulers/scheduling_ddim.py

@@ -16,15 +16,14 @@
 # and https://github.com/hojonathanho/diffusion
 
 import math
-import warnings
 from dataclasses import dataclass
 from typing import Optional, Tuple, Union
 
 import numpy as np
 import torch
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput
+from ..utils import BaseOutput, deprecate
 from .scheduling_utils import SchedulerMixin
 
 
@@ -122,12 +121,12 @@ def __init__(
         steps_offset: int = 0,
         **kwargs,
     ):
-        if "tensor_format" in kwargs:
-            warnings.warn(
-                "`tensor_format` is deprecated as an argument and will be removed in version `0.5.0`."
-                "If you're running your code in PyTorch, you can safely remove this argument.",
-                DeprecationWarning,
-            )
+        deprecate(
+            "tensor_format",
+            "0.5.0",
+            "If you're running your code in PyTorch, you can safely remove this argument.",
+            take_from=kwargs,
+        )
 
         if trained_betas is not None:
             self.betas = torch.from_numpy(trained_betas)
@@ -175,17 +174,10 @@ def set_timesteps(self, num_inference_steps: int, **kwargs):
             num_inference_steps (`int`):
                 the number of diffusion steps used when generating samples with a pre-trained model.
         """
-
-        offset = self.config.steps_offset
-
-        if "offset" in kwargs:
-            warnings.warn(
-                "`offset` is deprecated as an input argument to `set_timesteps` and will be removed in v0.4.0."
-                " Please pass `steps_offset` to `__init__` instead.",
-                DeprecationWarning,
-            )
-
-            offset = kwargs["offset"]
+        deprecated_offset = deprecate(
+            "offset", "0.5.0", "Please pass `steps_offset` to `__init__` instead.", take_from=kwargs
+        )
+        offset = deprecated_offset or self.config.steps_offset
 
         self.num_inference_steps = num_inference_steps
         step_ratio = self.config.num_train_timesteps // self.num_inference_steps

src/diffusers/schedulers/scheduling_ddpm.py

@@ -15,15 +15,14 @@
 # DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
 
 import math
-import warnings
 from dataclasses import dataclass
 from typing import Optional, Tuple, Union
 
 import numpy as np
 import torch
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput
+from ..utils import BaseOutput, deprecate
 from .scheduling_utils import SchedulerMixin
 
 
@@ -115,12 +114,12 @@ def __init__(
         clip_sample: bool = True,
         **kwargs,
     ):
-        if "tensor_format" in kwargs:
-            warnings.warn(
-                "`tensor_format` is deprecated as an argument and will be removed in version `0.5.0`."
-                "If you're running your code in PyTorch, you can safely remove this argument.",
-                DeprecationWarning,
-            )
+        deprecate(
+            "tensor_format",
+            "0.5.0",
+            "If you're running your code in PyTorch, you can safely remove this argument.",
+            take_from=kwargs,
+        )
 
         if trained_betas is not None:
             self.betas = torch.from_numpy(trained_betas)

src/diffusers/schedulers/scheduling_karras_ve.py

@@ -13,15 +13,14 @@
 # limitations under the License.
 
 
-import warnings
 from dataclasses import dataclass
 from typing import Optional, Tuple, Union
 
 import numpy as np
 import torch
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput
+from ..utils import BaseOutput, deprecate
 from .scheduling_utils import SchedulerMixin
 
 
@@ -89,12 +88,12 @@ def __init__(
         s_max: float = 50,
         **kwargs,
     ):
-        if "tensor_format" in kwargs:
-            warnings.warn(
-                "`tensor_format` is deprecated as an argument and will be removed in version `0.5.0`."
-                "If you're running your code in PyTorch, you can safely remove this argument.",
-                DeprecationWarning,
-            )
+        deprecate(
+            "tensor_format",
+            "0.5.0",
+            "If you're running your code in PyTorch, you can safely remove this argument.",
+            take_from=kwargs,
+        )
 
         # setable values
         self.num_inference_steps: int = None

src/diffusers/schedulers/scheduling_lms_discrete.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import warnings
 from dataclasses import dataclass
 from typing import Optional, Tuple, Union
 
@@ -22,7 +21,7 @@
 from scipy import integrate
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput
+from ..utils import BaseOutput, deprecate
 from .scheduling_utils import SchedulerMixin
 
 
@@ -77,12 +76,12 @@ def __init__(
         trained_betas: Optional[np.ndarray] = None,
         **kwargs,
     ):
-        if "tensor_format" in kwargs:
-            warnings.warn(
-                "`tensor_format` is deprecated as an argument and will be removed in version `0.5.0`."
-                "If you're running your code in PyTorch, you can safely remove this argument.",
-                DeprecationWarning,
-            )
+        deprecate(
+            "tensor_format",
+            "0.5.0",
+            "If you're running your code in PyTorch, you can safely remove this argument.",
+            take_from=kwargs,
+        )
 
         if trained_betas is not None:
             self.betas = torch.from_numpy(trained_betas)