Fix all missing optional import statements from pipeline folders (#4272)

* fix circular import

* fix imports when watermark not specified

* fix all pipelines

src/diffusers/loaders.py

@@ -25,7 +25,6 @@ import torch.nn.functional as F
 from huggingface_hub import hf_hub_download
 from torch import nn
 
-from .models.lora import LoRACompatibleConv, LoRACompatibleLinear, LoRAConv2dLayer, LoRALinearLayer
 from .utils import (
     DIFFUSERS_CACHE,
     HF_HUB_OFFLINE,
@@ -69,7 +68,7 @@ CUSTOM_DIFFUSION_WEIGHT_NAME_SAFE = "pytorch_custom_diffusion_weights.safetensor
 class PatchedLoraProjection(nn.Module):
     def __init__(self, regular_linear_layer, lora_scale=1, network_alpha=None, rank=4, dtype=None):
         super().__init__()
-        from .models.attention_processor import LoRALinearLayer
+        from .models.lora import LoRALinearLayer
 
         self.regular_linear_layer = regular_linear_layer
 
@@ -244,6 +243,7 @@ class UNet2DConditionLoadersMixin:
             SlicedAttnAddedKVProcessor,
             XFormersAttnProcessor,
         )
+        from .models.lora import LoRACompatibleConv, LoRACompatibleLinear, LoRAConv2dLayer, LoRALinearLayer
 
         cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
         force_download = kwargs.pop("force_download", False)

src/diffusers/pipelines/alt_diffusion/__init__.py

@@ -5,7 +5,7 @@ import numpy as np
 import PIL
 from PIL import Image
 
-from ...utils import BaseOutput, is_torch_available, is_transformers_available
+from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
 
 
 @dataclass
@@ -27,7 +27,12 @@ class AltDiffusionPipelineOutput(BaseOutput):
     nsfw_content_detected: Optional[List[bool]]
 
 
-if is_transformers_available() and is_torch_available():
+try:
+    if not (is_transformers_available() and is_torch_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
+else:
     from .modeling_roberta_series import RobertaSeriesModelWithTransformation
     from .pipeline_alt_diffusion import AltDiffusionPipeline
     from .pipeline_alt_diffusion_img2img import AltDiffusionImg2ImgPipeline

src/diffusers/pipelines/controlnet/__init__.py

@@ -7,7 +7,12 @@ from ...utils import (
 )
 
 
-if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
+try:
+    if not (is_transformers_available() and is_torch_available() and is_invisible_watermark_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
+else:
     from .pipeline_controlnet_sd_xl import StableDiffusionXLControlNetPipeline
 
 

src/diffusers/pipelines/kandinsky/__init__.py

@@ -2,7 +2,6 @@ from ...utils import (
     OptionalDependencyNotAvailable,
     is_torch_available,
     is_transformers_available,
-    is_transformers_version,
 )
 
 
@@ -10,7 +9,7 @@ try:
     if not (is_transformers_available() and is_torch_available()):
         raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
-    from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
+    from ...utils.dummy_torch_and_transformers_objects import *
 else:
     from .pipeline_kandinsky import KandinskyPipeline
     from .pipeline_kandinsky_img2img import KandinskyImg2ImgPipeline

src/diffusers/pipelines/kandinsky2_2/__init__.py

@@ -1,7 +1,20 @@
-from .pipeline_kandinsky2_2 import KandinskyV22Pipeline
-from .pipeline_kandinsky2_2_controlnet import KandinskyV22ControlnetPipeline
-from .pipeline_kandinsky2_2_controlnet_img2img import KandinskyV22ControlnetImg2ImgPipeline
-from .pipeline_kandinsky2_2_img2img import KandinskyV22Img2ImgPipeline
-from .pipeline_kandinsky2_2_inpainting import KandinskyV22InpaintPipeline
-from .pipeline_kandinsky2_2_prior import KandinskyV22PriorPipeline
-from .pipeline_kandinsky2_2_prior_emb2emb import KandinskyV22PriorEmb2EmbPipeline
+from ...utils import (
+    OptionalDependencyNotAvailable,
+    is_torch_available,
+    is_transformers_available,
+)
+
+
+try:
+    if not (is_transformers_available() and is_torch_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils.dummy_torch_and_transformers_objects import *
+else:
+    from .pipeline_kandinsky2_2 import KandinskyV22Pipeline
+    from .pipeline_kandinsky2_2_controlnet import KandinskyV22ControlnetPipeline
+    from .pipeline_kandinsky2_2_controlnet_img2img import KandinskyV22ControlnetImg2ImgPipeline
+    from .pipeline_kandinsky2_2_img2img import KandinskyV22Img2ImgPipeline
+    from .pipeline_kandinsky2_2_inpainting import KandinskyV22InpaintPipeline
+    from .pipeline_kandinsky2_2_prior import KandinskyV22PriorPipeline
+    from .pipeline_kandinsky2_2_prior_emb2emb import KandinskyV22PriorEmb2EmbPipeline

src/diffusers/pipelines/latent_diffusion/__init__.py

@@ -1,6 +1,11 @@
-from ...utils import is_transformers_available
+from ...utils import OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
 from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline
 
 
-if is_transformers_available():
+try:
+    if not (is_transformers_available() and is_torch_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
+else:
     from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline

src/diffusers/pipelines/paint_by_example/__init__.py

@@ -5,9 +5,14 @@ import numpy as np
 import PIL
 from PIL import Image
 
-from ...utils import is_torch_available, is_transformers_available
+from ...utils import OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
 
 
-if is_transformers_available() and is_torch_available():
+try:
+    if not (is_transformers_available() and is_torch_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
+else:
     from .image_encoder import PaintByExampleImageEncoder
     from .pipeline_paint_by_example import PaintByExamplePipeline

src/diffusers/pipelines/semantic_stable_diffusion/__init__.py

@@ -6,7 +6,7 @@ import numpy as np
 import PIL
 from PIL import Image
 
-from ...utils import BaseOutput, is_torch_available, is_transformers_available
+from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
 
 
 @dataclass
@@ -27,5 +27,10 @@ class SemanticStableDiffusionPipelineOutput(BaseOutput):
     nsfw_content_detected: Optional[List[bool]]
 
 
-if is_transformers_available() and is_torch_available():
+try:
+    if not (is_transformers_available() and is_torch_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
+else:
     from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline

src/diffusers/pipelines/stable_diffusion_safe/__init__.py

@@ -6,7 +6,7 @@ import numpy as np
 import PIL
 from PIL import Image
 
-from ...utils import BaseOutput, is_torch_available, is_transformers_available
+from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
 
 
 @dataclass
@@ -66,6 +66,11 @@ class StableDiffusionSafePipelineOutput(BaseOutput):
     applied_safety_concept: Optional[str]
 
 
-if is_transformers_available() and is_torch_available():
+try:
+    if not (is_transformers_available() and is_torch_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils.dummy_torch_and_transformers_objects import *
+else:
     from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
     from .safety_checker import SafeStableDiffusionSafetyChecker

src/diffusers/pipelines/stable_diffusion_xl/__init__.py

@@ -4,7 +4,13 @@ from typing import List, Optional, Union
 import numpy as np
 import PIL
 
-from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available
+from ...utils import (
+    BaseOutput,
+    OptionalDependencyNotAvailable,
+    is_invisible_watermark_available,
+    is_torch_available,
+    is_transformers_available,
+)
 
 
 @dataclass
@@ -21,7 +27,12 @@ class StableDiffusionXLPipelineOutput(BaseOutput):
     images: Union[List[PIL.Image.Image], np.ndarray]
 
 
-if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
+try:
+    if not (is_transformers_available() and is_torch_available() and is_invisible_watermark_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
+else:
     from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
     from .pipeline_stable_diffusion_xl_img2img import StableDiffusionXLImg2ImgPipeline
     from .pipeline_stable_diffusion_xl_inpaint import StableDiffusionXLInpaintPipeline

src/diffusers/pipelines/vq_diffusion/__init__.py

@@ -1,5 +1,10 @@
-from ...utils import is_torch_available, is_transformers_available
+from ...utils import OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
 
 
-if is_transformers_available() and is_torch_available():
+try:
+    if not (is_transformers_available() and is_torch_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils.dummy_torch_and_transformers_objects import *
+else:
     from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline