Deprecate Pipelines (#6169)

* deprecate pipe

* make style

* update

* add deprecation message

* format

* remove tests for deprecated pipelines

* remove deprecation message

* make style

* fix copies

* clean up

* clean

* clean

* clean

* clean up

* clean up

* clean up toctree

* clean up

---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py → src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_cycle_diffusion.py

@@ -21,17 +21,17 @@ import torch
 from packaging import version
 from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
-from ...configuration_utils import FrozenDict
-from ...image_processor import PipelineImageInput, VaeImageProcessor
-from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...models.lora import adjust_lora_scale_text_encoder
-from ...schedulers import DDIMScheduler
-from ...utils import PIL_INTERPOLATION, USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers
-from ...utils.torch_utils import randn_tensor
-from ..pipeline_utils import DiffusionPipeline
-from .pipeline_output import StableDiffusionPipelineOutput
-from .safety_checker import StableDiffusionSafetyChecker
+from ....configuration_utils import FrozenDict
+from ....image_processor import PipelineImageInput, VaeImageProcessor
+from ....loaders import LoraLoaderMixin, TextualInversionLoaderMixin
+from ....models import AutoencoderKL, UNet2DConditionModel
+from ....models.lora import adjust_lora_scale_text_encoder
+from ....schedulers import DDIMScheduler
+from ....utils import PIL_INTERPOLATION, USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers
+from ....utils.torch_utils import randn_tensor
+from ...pipeline_utils import DiffusionPipeline
+from ...stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
+from ...stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint_legacy.py → src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_onnx_stable_diffusion_inpaint_legacy.py

@@ -6,12 +6,12 @@ import PIL.Image
 import torch
 from transformers import CLIPImageProcessor, CLIPTokenizer
 
-from ...configuration_utils import FrozenDict
-from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-from ...utils import deprecate, logging
-from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
-from ..pipeline_utils import DiffusionPipeline
-from . import StableDiffusionPipelineOutput
+from ....configuration_utils import FrozenDict
+from ....schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from ....utils import deprecate, logging
+from ...onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
+from ...pipeline_utils import DiffusionPipeline
+from ...stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py → src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_inpaint_legacy.py

@@ -21,17 +21,17 @@ import torch
 from packaging import version
 from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
-from ...configuration_utils import FrozenDict
-from ...image_processor import VaeImageProcessor
-from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...models.lora import adjust_lora_scale_text_encoder
-from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import PIL_INTERPOLATION, USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers
-from ...utils.torch_utils import randn_tensor
-from ..pipeline_utils import DiffusionPipeline
-from . import StableDiffusionPipelineOutput
-from .safety_checker import StableDiffusionSafetyChecker
+from ....configuration_utils import FrozenDict
+from ....image_processor import VaeImageProcessor
+from ....loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
+from ....models import AutoencoderKL, UNet2DConditionModel
+from ....models.lora import adjust_lora_scale_text_encoder
+from ....schedulers import KarrasDiffusionSchedulers
+from ....utils import PIL_INTERPOLATION, USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers
+from ....utils.torch_utils import randn_tensor
+from ...pipeline_utils import DiffusionPipeline
+from ...stable_diffusion import StableDiffusionPipelineOutput
+from ...stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 
 
 logger = logging.get_logger(__name__)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_model_editing.py → src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_model_editing.py

@@ -18,17 +18,17 @@ from typing import Any, Callable, Dict, List, Optional, Union
 import torch
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
-from ...image_processor import VaeImageProcessor
-from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...models.lora import adjust_lora_scale_text_encoder
-from ...schedulers import PNDMScheduler
-from ...schedulers.scheduling_utils import SchedulerMixin
-from ...utils import USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers
-from ...utils.torch_utils import randn_tensor
-from ..pipeline_utils import DiffusionPipeline
-from . import StableDiffusionPipelineOutput
-from .safety_checker import StableDiffusionSafetyChecker
+from ....image_processor import VaeImageProcessor
+from ....loaders import LoraLoaderMixin, TextualInversionLoaderMixin
+from ....models import AutoencoderKL, UNet2DConditionModel
+from ....models.lora import adjust_lora_scale_text_encoder
+from ....schedulers import PNDMScheduler
+from ....schedulers.scheduling_utils import SchedulerMixin
+from ....utils import USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers
+from ....utils.torch_utils import randn_tensor
+from ...pipeline_utils import DiffusionPipeline
+from ...stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
+from ...stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_paradigms.py → src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_paradigms.py

@@ -18,12 +18,12 @@ from typing import Any, Callable, Dict, List, Optional, Union
 import torch
 from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
-from ...image_processor import VaeImageProcessor
-from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...models.lora import adjust_lora_scale_text_encoder
-from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import (
+from ....image_processor import VaeImageProcessor
+from ....loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
+from ....models import AutoencoderKL, UNet2DConditionModel
+from ....models.lora import adjust_lora_scale_text_encoder
+from ....schedulers import KarrasDiffusionSchedulers
+from ....utils import (
     USE_PEFT_BACKEND,
     deprecate,
     logging,
@@ -31,10 +31,10 @@ from ...utils import (
     scale_lora_layers,
     unscale_lora_layers,
 )
-from ...utils.torch_utils import randn_tensor
-from ..pipeline_utils import DiffusionPipeline
-from . import StableDiffusionPipelineOutput
-from .safety_checker import StableDiffusionSafetyChecker
+from ....utils.torch_utils import randn_tensor
+from ...pipeline_utils import DiffusionPipeline
+from ...stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
+from ...stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py → src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py

@@ -28,14 +28,14 @@ from transformers import (
     CLIPTokenizer,
 )
 
-from ...image_processor import PipelineImageInput, VaeImageProcessor
-from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...models.attention_processor import Attention
-from ...models.lora import adjust_lora_scale_text_encoder
-from ...schedulers import DDIMScheduler, DDPMScheduler, EulerAncestralDiscreteScheduler, LMSDiscreteScheduler
-from ...schedulers.scheduling_ddim_inverse import DDIMInverseScheduler
-from ...utils import (
+from ....image_processor import PipelineImageInput, VaeImageProcessor
+from ....loaders import LoraLoaderMixin, TextualInversionLoaderMixin
+from ....models import AutoencoderKL, UNet2DConditionModel
+from ....models.attention_processor import Attention
+from ....models.lora import adjust_lora_scale_text_encoder
+from ....schedulers import DDIMScheduler, DDPMScheduler, EulerAncestralDiscreteScheduler, LMSDiscreteScheduler
+from ....schedulers.scheduling_ddim_inverse import DDIMInverseScheduler
+from ....utils import (
     PIL_INTERPOLATION,
     USE_PEFT_BACKEND,
     BaseOutput,
@@ -45,10 +45,10 @@ from ...utils import (
     scale_lora_layers,
     unscale_lora_layers,
 )
-from ...utils.torch_utils import randn_tensor
-from ..pipeline_utils import DiffusionPipeline
-from . import StableDiffusionPipelineOutput
-from .safety_checker import StableDiffusionSafetyChecker
+from ....utils.torch_utils import randn_tensor
+from ...pipeline_utils import DiffusionPipeline
+from ...stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
+from ...stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

src/diffusers/pipelines/stochastic_karras_ve/__init__.py → src/diffusers/pipelines/deprecated/stochastic_karras_ve/__init__.py

 from typing import TYPE_CHECKING
 
-from ...utils import DIFFUSERS_SLOW_IMPORT, _LazyModule
+from ....utils import DIFFUSERS_SLOW_IMPORT, _LazyModule
 
 
 _import_structure = {"pipeline_stochastic_karras_ve": ["KarrasVePipeline"]}

src/diffusers/pipelines/stochastic_karras_ve/pipeline_stochastic_karras_ve.py → src/diffusers/pipelines/deprecated/stochastic_karras_ve/pipeline_stochastic_karras_ve.py

@@ -16,10 +16,10 @@ from typing import List, Optional, Tuple, Union
 
 import torch
 
-from ...models import UNet2DModel
-from ...schedulers import KarrasVeScheduler
-from ...utils.torch_utils import randn_tensor
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
+from ....models import UNet2DModel
+from ....schedulers import KarrasVeScheduler
+from ....utils.torch_utils import randn_tensor
+from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
 
 
 class KarrasVePipeline(DiffusionPipeline):

src/diffusers/pipelines/versatile_diffusion/__init__.py → src/diffusers/pipelines/deprecated/versatile_diffusion/__init__.py

 from typing import TYPE_CHECKING
 
-from ...utils import (
+from ....utils import (
     DIFFUSERS_SLOW_IMPORT,
     OptionalDependencyNotAvailable,
     _LazyModule,
@@ -17,7 +17,7 @@ try:
     if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
         raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
-    from ...utils.dummy_torch_and_transformers_objects import (
+    from ....utils.dummy_torch_and_transformers_objects import (
         VersatileDiffusionDualGuidedPipeline,
         VersatileDiffusionImageVariationPipeline,
         VersatileDiffusionPipeline,
@@ -45,7 +45,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
             raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
-        from ...utils.dummy_torch_and_transformers_objects import (
+        from ....utils.dummy_torch_and_transformers_objects import (
             VersatileDiffusionDualGuidedPipeline,
             VersatileDiffusionImageVariationPipeline,
             VersatileDiffusionPipeline,

src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py → src/diffusers/pipelines/deprecated/versatile_diffusion/modeling_text_unet.py

@@ -7,10 +7,10 @@ import torch.nn.functional as F
 
 from diffusers.utils import deprecate
 
-from ...configuration_utils import ConfigMixin, register_to_config
-from ...models import ModelMixin
-from ...models.activations import get_activation
-from ...models.attention_processor import (
+from ....configuration_utils import ConfigMixin, register_to_config
+from ....models import ModelMixin
+from ....models.activations import get_activation
+from ....models.attention_processor import (
     ADDED_KV_ATTENTION_PROCESSORS,
     CROSS_ATTENTION_PROCESSORS,
     Attention,
@@ -19,8 +19,8 @@ from ...models.attention_processor import (
     AttnAddedKVProcessor2_0,
     AttnProcessor,
 )
-from ...models.dual_transformer_2d import DualTransformer2DModel
-from ...models.embeddings import (
+from ....models.dual_transformer_2d import DualTransformer2DModel
+from ....models.embeddings import (
     GaussianFourierProjection,
     ImageHintTimeEmbedding,
     ImageProjection,
@@ -31,10 +31,10 @@ from ...models.embeddings import (
     TimestepEmbedding,
     Timesteps,
 )
-from ...models.transformer_2d import Transformer2DModel
-from ...models.unet_2d_condition import UNet2DConditionOutput
-from ...utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
-from ...utils.torch_utils import apply_freeu
+from ....models.transformer_2d import Transformer2DModel
+from ....models.unet_2d_condition import UNet2DConditionOutput
+from ....utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
+from ....utils.torch_utils import apply_freeu
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion.py → src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py

@@ -5,10 +5,10 @@ import PIL.Image
 import torch
 from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModel
 
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import logging
-from ..pipeline_utils import DiffusionPipeline
+from ....models import AutoencoderKL, UNet2DConditionModel
+from ....schedulers import KarrasDiffusionSchedulers
+from ....utils import logging
+from ...pipeline_utils import DiffusionPipeline
 from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
 from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
 from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline

src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py → src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py

@@ -26,12 +26,12 @@ from transformers import (
     CLIPVisionModelWithProjection,
 )
 
-from ...image_processor import VaeImageProcessor
-from ...models import AutoencoderKL, DualTransformer2DModel, Transformer2DModel, UNet2DConditionModel
-from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import deprecate, logging
-from ...utils.torch_utils import randn_tensor
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
+from ....image_processor import VaeImageProcessor
+from ....models import AutoencoderKL, DualTransformer2DModel, Transformer2DModel, UNet2DConditionModel
+from ....schedulers import KarrasDiffusionSchedulers
+from ....utils import deprecate, logging
+from ....utils.torch_utils import randn_tensor
+from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
 from .modeling_text_unet import UNetFlatConditionModel
 
 

src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py → src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py

@@ -21,12 +21,12 @@ import torch
 import torch.utils.checkpoint
 from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
 
-from ...image_processor import VaeImageProcessor
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import deprecate, logging
-from ...utils.torch_utils import randn_tensor
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
+from ....image_processor import VaeImageProcessor
+from ....models import AutoencoderKL, UNet2DConditionModel
+from ....schedulers import KarrasDiffusionSchedulers
+from ....utils import deprecate, logging
+from ....utils.torch_utils import randn_tensor
+from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py → src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py

@@ -19,12 +19,12 @@ import torch
 import torch.utils.checkpoint
 from transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer
 
-from ...image_processor import VaeImageProcessor
-from ...models import AutoencoderKL, Transformer2DModel, UNet2DConditionModel
-from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import deprecate, logging
-from ...utils.torch_utils import randn_tensor
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
+from ....image_processor import VaeImageProcessor
+from ....models import AutoencoderKL, Transformer2DModel, UNet2DConditionModel
+from ....schedulers import KarrasDiffusionSchedulers
+from ....utils import deprecate, logging
+from ....utils.torch_utils import randn_tensor
+from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
 from .modeling_text_unet import UNetFlatConditionModel
 
 

src/diffusers/pipelines/vq_diffusion/__init__.py → src/diffusers/pipelines/deprecated/vq_diffusion/__init__.py

 from typing import TYPE_CHECKING
 
-from ...utils import (
+from ....utils import (
     DIFFUSERS_SLOW_IMPORT,
     OptionalDependencyNotAvailable,
     _LazyModule,
@@ -16,7 +16,7 @@ try:
     if not (is_transformers_available() and is_torch_available()):
         raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
-    from ...utils.dummy_torch_and_transformers_objects import (
+    from ....utils.dummy_torch_and_transformers_objects import (
         LearnedClassifierFreeSamplingEmbeddings,
         VQDiffusionPipeline,
     )
@@ -36,7 +36,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         if not (is_transformers_available() and is_torch_available()):
             raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
-        from ...utils.dummy_torch_and_transformers_objects import (
+        from ....utils.dummy_torch_and_transformers_objects import (
             LearnedClassifierFreeSamplingEmbeddings,
             VQDiffusionPipeline,
         )

src/diffusers/pipelines/vq_diffusion/pipeline_vq_diffusion.py → src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_diffusion.py

@@ -17,11 +17,11 @@ from typing import Callable, List, Optional, Tuple, Union
 import torch
 from transformers import CLIPTextModel, CLIPTokenizer
 
-from ...configuration_utils import ConfigMixin, register_to_config
-from ...models import ModelMixin, Transformer2DModel, VQModel
-from ...schedulers import VQDiffusionScheduler
-from ...utils import logging
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
+from ....configuration_utils import ConfigMixin, register_to_config
+from ....models import ModelMixin, Transformer2DModel, VQModel
+from ....schedulers import VQDiffusionScheduler
+from ....utils import logging
+from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

src/diffusers/pipelines/stable_diffusion/__init__.py

@@ -134,7 +134,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
 
     else:
         from .clip_image_project_model import CLIPImageProjection
-        from .pipeline_cycle_diffusion import CycleDiffusionPipeline
         from .pipeline_stable_diffusion import (
             StableDiffusionPipeline,
             StableDiffusionPipelineOutput,
@@ -149,9 +148,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         )
         from .pipeline_stable_diffusion_img2img import StableDiffusionImg2ImgPipeline
         from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
-        from .pipeline_stable_diffusion_inpaint_legacy import (
-            StableDiffusionInpaintPipelineLegacy,
-        )
         from .pipeline_stable_diffusion_instruct_pix2pix import (
             StableDiffusionInstructPix2PixPipeline,
         )
@@ -159,13 +155,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             StableDiffusionLatentUpscalePipeline,
         )
         from .pipeline_stable_diffusion_ldm3d import StableDiffusionLDM3DPipeline
-        from .pipeline_stable_diffusion_model_editing import (
-            StableDiffusionModelEditingPipeline,
-        )
         from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
-        from .pipeline_stable_diffusion_paradigms import (
-            StableDiffusionParadigmsPipeline,
-        )
         from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
         from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
         from .pipeline_stable_unclip import StableUnCLIPPipeline
@@ -199,9 +189,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             StableDiffusionDepth2ImgPipeline,
         )
         from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
-        from .pipeline_stable_diffusion_pix2pix_zero import (
-            StableDiffusionPix2PixZeroPipeline,
-        )
 
     try:
         if not (
@@ -234,9 +221,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .pipeline_onnx_stable_diffusion_inpaint import (
             OnnxStableDiffusionInpaintPipeline,
         )
-        from .pipeline_onnx_stable_diffusion_inpaint_legacy import (
-            OnnxStableDiffusionInpaintPipelineLegacy,
-        )
         from .pipeline_onnx_stable_diffusion_upscale import (
             OnnxStableDiffusionUpscalePipeline,
         )

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_diffedit.py

@@ -788,7 +788,6 @@ class StableDiffusionDiffEditPipeline(DiffusionPipeline, TextualInversionLoaderM
         latents = latents * self.scheduler.init_noise_sigma
         return latents
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_pix2pix_zero.StableDiffusionPix2PixZeroPipeline.prepare_image_latents
     def prepare_image_latents(self, image, batch_size, dtype, device, generator=None):
         if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
             raise ValueError(

tests/pipelines/altdiffusion/test_alt_diffusion.py

-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import gc
-import unittest
-
-import numpy as np
-import torch
-from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
-
-from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNet2DConditionModel
-from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
-    RobertaSeriesConfig,
-    RobertaSeriesModelWithTransformation,
-)
-from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, torch_device
-
-from ..pipeline_params import (
-    TEXT_TO_IMAGE_BATCH_PARAMS,
-    TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
-    TEXT_TO_IMAGE_IMAGE_PARAMS,
-    TEXT_TO_IMAGE_PARAMS,
-)
-from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
-
-
-enable_full_determinism()
-
-
-class AltDiffusionPipelineFastTests(
-    PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
-):
-    pipeline_class = AltDiffusionPipeline
-    params = TEXT_TO_IMAGE_PARAMS
-    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
-    image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
-    image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
-    callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS
-
-    def get_dummy_components(self):
-        torch.manual_seed(0)
-        unet = UNet2DConditionModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            sample_size=32,
-            in_channels=4,
-            out_channels=4,
-            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
-            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
-            cross_attention_dim=32,
-        )
-        scheduler = DDIMScheduler(
-            beta_start=0.00085,
-            beta_end=0.012,
-            beta_schedule="scaled_linear",
-            clip_sample=False,
-            set_alpha_to_one=False,
-        )
-        torch.manual_seed(0)
-        vae = AutoencoderKL(
-            block_out_channels=[32, 64],
-            in_channels=3,
-            out_channels=3,
-            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
-            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
-            latent_channels=4,
-        )
-
-        # TODO: address the non-deterministic text encoder (fails for save-load tests)
-        # torch.manual_seed(0)
-        # text_encoder_config = RobertaSeriesConfig(
-        #     hidden_size=32,
-        #     project_dim=32,
-        #     intermediate_size=37,
-        #     layer_norm_eps=1e-05,
-        #     num_attention_heads=4,
-        #     num_hidden_layers=5,
-        #     vocab_size=5002,
-        # )
-        # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)
-
-        torch.manual_seed(0)
-        text_encoder_config = CLIPTextConfig(
-            bos_token_id=0,
-            eos_token_id=2,
-            hidden_size=32,
-            projection_dim=32,
-            intermediate_size=37,
-            layer_norm_eps=1e-05,
-            num_attention_heads=4,
-            num_hidden_layers=5,
-            pad_token_id=1,
-            vocab_size=5002,
-        )
-        text_encoder = CLIPTextModel(text_encoder_config)
-
-        tokenizer = XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta")
-        tokenizer.model_max_length = 77
-
-        components = {
-            "unet": unet,
-            "scheduler": scheduler,
-            "vae": vae,
-            "text_encoder": text_encoder,
-            "tokenizer": tokenizer,
-            "safety_checker": None,
-            "feature_extractor": None,
-            "image_encoder": None,
-        }
-        return components
-
-    def get_dummy_inputs(self, device, seed=0):
-        if str(device).startswith("mps"):
-            generator = torch.manual_seed(seed)
-        else:
-            generator = torch.Generator(device=device).manual_seed(seed)
-        inputs = {
-            "prompt": "A painting of a squirrel eating a burger",
-            "generator": generator,
-            "num_inference_steps": 2,
-            "guidance_scale": 6.0,
-            "output_type": "numpy",
-        }
-        return inputs
-
-    def test_attention_slicing_forward_pass(self):
-        super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)
-
-    def test_inference_batch_single_identical(self):
-        super().test_inference_batch_single_identical(expected_max_diff=3e-3)
-
-    def test_alt_diffusion_ddim(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-
-        components = self.get_dummy_components()
-        torch.manual_seed(0)
-        text_encoder_config = RobertaSeriesConfig(
-            hidden_size=32,
-            project_dim=32,
-            intermediate_size=37,
-            layer_norm_eps=1e-05,
-            num_attention_heads=4,
-            num_hidden_layers=5,
-            vocab_size=5002,
-        )
-        # TODO: remove after fixing the non-deterministic text encoder
-        text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)
-        components["text_encoder"] = text_encoder
-
-        alt_pipe = AltDiffusionPipeline(**components)
-        alt_pipe = alt_pipe.to(device)
-        alt_pipe.set_progress_bar_config(disable=None)
-
-        inputs = self.get_dummy_inputs(device)
-        inputs["prompt"] = "A photo of an astronaut"
-        output = alt_pipe(**inputs)
-        image = output.images
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array(
-            [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093]
-        )
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-    def test_alt_diffusion_pndm(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-
-        components = self.get_dummy_components()
-        components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
-        torch.manual_seed(0)
-        text_encoder_config = RobertaSeriesConfig(
-            hidden_size=32,
-            project_dim=32,
-            intermediate_size=37,
-            layer_norm_eps=1e-05,
-            num_attention_heads=4,
-            num_hidden_layers=5,
-            vocab_size=5002,
-        )
-        # TODO: remove after fixing the non-deterministic text encoder
-        text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)
-        components["text_encoder"] = text_encoder
-        alt_pipe = AltDiffusionPipeline(**components)
-        alt_pipe = alt_pipe.to(device)
-        alt_pipe.set_progress_bar_config(disable=None)
-
-        inputs = self.get_dummy_inputs(device)
-        output = alt_pipe(**inputs)
-        image = output.images
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array(
-            [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237]
-        )
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-
-@nightly
-@require_torch_gpu
-class AltDiffusionPipelineIntegrationTests(unittest.TestCase):
-    def tearDown(self):
-        # clean up the VRAM after each test
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    def test_alt_diffusion(self):
-        # make sure here that pndm scheduler skips prk
-        alt_pipe = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion", safety_checker=None)
-        alt_pipe = alt_pipe.to(torch_device)
-        alt_pipe.set_progress_bar_config(disable=None)
-
-        prompt = "A painting of a squirrel eating a burger"
-        generator = torch.manual_seed(0)
-        output = alt_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="np")
-
-        image = output.images
-
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 512, 512, 3)
-        expected_slice = np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-    def test_alt_diffusion_fast_ddim(self):
-        scheduler = DDIMScheduler.from_pretrained("BAAI/AltDiffusion", subfolder="scheduler")
-
-        alt_pipe = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion", scheduler=scheduler, safety_checker=None)
-        alt_pipe = alt_pipe.to(torch_device)
-        alt_pipe.set_progress_bar_config(disable=None)
-
-        prompt = "A painting of a squirrel eating a burger"
-        generator = torch.manual_seed(0)
-
-        output = alt_pipe([prompt], generator=generator, num_inference_steps=2, output_type="numpy")
-        image = output.images
-
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 512, 512, 3)
-        expected_slice = np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2