Make repo structure consistent (#1862)

* move files a bit

* more refactors

* fix more

* more fixes

* fix more onnx

* make style

* upload

* fix

* up

* fix more

* up again

* up

* small fix

* Update src/diffusers/__init__.py

Co-authored-by: Pedro Cuenca <[email protected]>

* correct

Co-authored-by: Pedro Cuenca <[email protected]>

Author: patrickvonplaten

docs/README.md

@@ -155,9 +155,9 @@ adds a link to its documentation with this syntax: \[\`XXXClass\`\] or \[\`funct
 function to be in the main package.
 
 If you want to create a link to some internal class or function, you need to
-provide its path. For instance: \[\`pipeline_utils.ImagePipelineOutput\`\]. This will be converted into a link with
-`pipeline_utils.ImagePipelineOutput` in the description. To get rid of the path and only keep the name of the object you are
-linking to in the description, add a ~: \[\`~pipeline_utils.ImagePipelineOutput\`\] will generate a link with `ImagePipelineOutput` in the description.
+provide its path. For instance: \[\`pipelines.ImagePipelineOutput\`\]. This will be converted into a link with
+`pipelines.ImagePipelineOutput` in the description. To get rid of the path and only keep the name of the object you are
+linking to in the description, add a ~: \[\`~pipelines.ImagePipelineOutput\`\] will generate a link with `ImagePipelineOutput` in the description.
 
 The same works for methods so you can either use \[\`XXXClass.method\`\] or \[~\`XXXClass.method\`\].
 

docs/source/api/diffusion_pipeline.mdx

@@ -39,4 +39,4 @@ Any pipeline object can be saved locally with [`~DiffusionPipeline.save_pretrain
 ## ImagePipelineOutput
 By default diffusion pipelines return an object of class
 
-[[autodoc]] pipeline_utils.ImagePipelineOutput
+[[autodoc]] pipelines.ImagePipelineOutput

docs/source/api/models.mdx

@@ -41,13 +41,13 @@ The models are built on the base class ['ModelMixin'] that is a `torch.nn.module
 [[autodoc]] models.vae.DecoderOutput
 
 ## VQEncoderOutput
-[[autodoc]] models.vae.VQEncoderOutput
+[[autodoc]] models.vq_model.VQEncoderOutput
 
 ## VQModel
 [[autodoc]] VQModel
 
 ## AutoencoderKLOutput
-[[autodoc]] models.vae.AutoencoderKLOutput
+[[autodoc]] models.autoencoder_kl.AutoencoderKLOutput
 
 ## AutoencoderKL
 [[autodoc]] AutoencoderKL

docs/source/api/outputs.mdx

@@ -25,7 +25,7 @@ pipeline = DDIMPipeline.from_pretrained("google/ddpm-cifar10-32")
 outputs = pipeline()
 ```
 
-The `outputs` object is a [`~pipeline_utils.ImagePipelineOutput`], as we can see in the
+The `outputs` object is a [`~pipelines.ImagePipelineOutput`], as we can see in the
 documentation of that class below, it means it has an image attribute.
 
 You can access each attribute as you would usually do, and if that attribute has not been returned by the model, you will get `None`:

examples/community/bit_diffusion.py

@@ -2,8 +2,7 @@
 
 import torch
 
-from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
-from diffusers.pipeline_utils import ImagePipelineOutput
+from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNet2DConditionModel
 from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
 from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput
 from einops import rearrange, reduce

examples/community/checkpoint_merger.py

@@ -5,13 +5,7 @@
 import torch
 
 from diffusers import DiffusionPipeline, __version__
-from diffusers.pipeline_utils import (
-    CONFIG_NAME,
-    DIFFUSERS_CACHE,
-    ONNX_WEIGHTS_NAME,
-    SCHEDULER_CONFIG_NAME,
-    WEIGHTS_NAME,
-)
+from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, SCHEDULER_CONFIG_NAME, WEIGHTS_NAME
 from huggingface_hub import snapshot_download
 
 

examples/community/composable_stable_diffusion.py

@@ -17,21 +17,21 @@
 
 import torch
 
-from diffusers.utils import is_accelerate_available
-from packaging import version
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
-
-from ...configuration_utils import FrozenDict
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...pipeline_utils import DiffusionPipeline
-from ...schedulers import (
+from diffusers import DiffusionPipeline
+from diffusers.configuration_utils import FrozenDict
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.schedulers import (
     DDIMScheduler,
     DPMSolverMultistepScheduler,
     EulerAncestralDiscreteScheduler,
     EulerDiscreteScheduler,
     LMSDiscreteScheduler,
     PNDMScheduler,
 )
+from diffusers.utils import is_accelerate_available
+from packaging import version
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+
 from ...utils import deprecate, logging
 from . import StableDiffusionPipelineOutput
 from .safety_checker import StableDiffusionSafetyChecker

examples/community/imagic_stable_diffusion.py

@@ -12,8 +12,8 @@
 
 import PIL
 from accelerate import Accelerator
+from diffusers import DiffusionPipeline
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
-from diffusers.pipeline_utils import DiffusionPipeline
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler

examples/community/img2img_inpainting.py

@@ -5,9 +5,9 @@
 import torch
 
 import PIL
+from diffusers import DiffusionPipeline
 from diffusers.configuration_utils import FrozenDict
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
-from diffusers.pipeline_utils import DiffusionPipeline
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler

examples/community/interpolate_stable_diffusion.py

@@ -6,9 +6,9 @@
 import numpy as np
 import torch
 
+from diffusers import DiffusionPipeline
 from diffusers.configuration_utils import FrozenDict
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
-from diffusers.pipeline_utils import DiffusionPipeline
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler