fix: convert diffusers from submodule to normal folder

diffusers/docs/source/en/api/attnprocessor.md

+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+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.
+-->
+
+# Attention Processor
+
+An attention processor is a class for applying different types of attention mechanisms.
+
+## AttnProcessor
+
+[[autodoc]] models.attention_processor.AttnProcessor
+
+[[autodoc]] models.attention_processor.AttnProcessor2_0
+
+[[autodoc]] models.attention_processor.AttnAddedKVProcessor
+
+[[autodoc]] models.attention_processor.AttnAddedKVProcessor2_0
+
+[[autodoc]] models.attention_processor.AttnProcessorNPU
+
+[[autodoc]] models.attention_processor.FusedAttnProcessor2_0
+
+## Allegro
+
+[[autodoc]] models.attention_processor.AllegroAttnProcessor2_0
+
+## AuraFlow
+
+[[autodoc]] models.attention_processor.AuraFlowAttnProcessor2_0
+
+[[autodoc]] models.attention_processor.FusedAuraFlowAttnProcessor2_0
+
+## CogVideoX
+
+[[autodoc]] models.attention_processor.CogVideoXAttnProcessor2_0
+
+[[autodoc]] models.attention_processor.FusedCogVideoXAttnProcessor2_0
+
+## CrossFrameAttnProcessor
+
+[[autodoc]] pipelines.deprecated.text_to_video_synthesis.pipeline_text_to_video_zero.CrossFrameAttnProcessor
+
+## Custom Diffusion
+
+[[autodoc]] models.attention_processor.CustomDiffusionAttnProcessor
+
+[[autodoc]] models.attention_processor.CustomDiffusionAttnProcessor2_0
+
+[[autodoc]] models.attention_processor.CustomDiffusionXFormersAttnProcessor
+
+## Flux
+
+[[autodoc]] models.attention_processor.FluxAttnProcessor2_0
+
+[[autodoc]] models.attention_processor.FusedFluxAttnProcessor2_0
+
+[[autodoc]] models.attention_processor.FluxSingleAttnProcessor2_0
+
+## Hunyuan
+
+[[autodoc]] models.attention_processor.HunyuanAttnProcessor2_0
+
+[[autodoc]] models.attention_processor.FusedHunyuanAttnProcessor2_0
+
+[[autodoc]] models.attention_processor.PAGHunyuanAttnProcessor2_0
+
+[[autodoc]] models.attention_processor.PAGCFGHunyuanAttnProcessor2_0
+
+## IdentitySelfAttnProcessor2_0
+
+[[autodoc]] models.attention_processor.PAGIdentitySelfAttnProcessor2_0
+
+[[autodoc]] models.attention_processor.PAGCFGIdentitySelfAttnProcessor2_0
+
+## IP-Adapter
+
+[[autodoc]] models.attention_processor.IPAdapterAttnProcessor
+
+[[autodoc]] models.attention_processor.IPAdapterAttnProcessor2_0
+
+[[autodoc]] models.attention_processor.SD3IPAdapterJointAttnProcessor2_0
+
+## JointAttnProcessor2_0
+
+[[autodoc]] models.attention_processor.JointAttnProcessor2_0
+
+[[autodoc]] models.attention_processor.PAGJointAttnProcessor2_0
+
+[[autodoc]] models.attention_processor.PAGCFGJointAttnProcessor2_0
+
+[[autodoc]] models.attention_processor.FusedJointAttnProcessor2_0
+
+## LoRA
+
+[[autodoc]] models.attention_processor.LoRAAttnProcessor
+
+[[autodoc]] models.attention_processor.LoRAAttnProcessor2_0
+
+[[autodoc]] models.attention_processor.LoRAAttnAddedKVProcessor
+
+[[autodoc]] models.attention_processor.LoRAXFormersAttnProcessor
+
+## Lumina-T2X
+
+[[autodoc]] models.attention_processor.LuminaAttnProcessor2_0
+
+## Mochi
+
+[[autodoc]] models.attention_processor.MochiAttnProcessor2_0
+
+[[autodoc]] models.attention_processor.MochiVaeAttnProcessor2_0
+
+## Sana
+
+[[autodoc]] models.attention_processor.SanaLinearAttnProcessor2_0
+
+[[autodoc]] models.attention_processor.SanaMultiscaleAttnProcessor2_0
+
+[[autodoc]] models.attention_processor.PAGCFGSanaLinearAttnProcessor2_0
+
+[[autodoc]] models.attention_processor.PAGIdentitySanaLinearAttnProcessor2_0
+
+## Stable Audio
+
+[[autodoc]] models.attention_processor.StableAudioAttnProcessor2_0
+
+## SlicedAttnProcessor
+
+[[autodoc]] models.attention_processor.SlicedAttnProcessor
+
+[[autodoc]] models.attention_processor.SlicedAttnAddedKVProcessor
+
+## XFormersAttnProcessor
+
+[[autodoc]] models.attention_processor.XFormersAttnProcessor
+
+[[autodoc]] models.attention_processor.XFormersAttnAddedKVProcessor
+
+## XLAFlashAttnProcessor2_0
+
+[[autodoc]] models.attention_processor.XLAFlashAttnProcessor2_0
+
+## XFormersJointAttnProcessor
+
+[[autodoc]] models.attention_processor.XFormersJointAttnProcessor
+
+## IPAdapterXFormersAttnProcessor
+
+[[autodoc]] models.attention_processor.IPAdapterXFormersAttnProcessor
+
+## FluxIPAdapterJointAttnProcessor2_0
+
+[[autodoc]] models.attention_processor.FluxIPAdapterJointAttnProcessor2_0
+
+
+## XLAFluxFlashAttnProcessor2_0
+
+[[autodoc]] models.attention_processor.XLAFluxFlashAttnProcessor2_0
\ No newline at end of file

diffusers/docs/source/en/api/cache.md

+<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
+
+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. -->
+
+# Caching methods
+
+Cache methods speedup diffusion transformers by storing and reusing intermediate outputs of specific layers, such as attention and feedforward layers, instead of recalculating them at each inference step.
+
+## CacheMixin
+
+[[autodoc]] CacheMixin
+
+## PyramidAttentionBroadcastConfig
+
+[[autodoc]] PyramidAttentionBroadcastConfig
+
+[[autodoc]] apply_pyramid_attention_broadcast
+
+## FasterCacheConfig
+
+[[autodoc]] FasterCacheConfig
+
+[[autodoc]] apply_faster_cache
+
+## FirstBlockCacheConfig
+
+[[autodoc]] FirstBlockCacheConfig
+
+[[autodoc]] apply_first_block_cache
+
+### TaylorSeerCacheConfig
+
+[[autodoc]] TaylorSeerCacheConfig
+
+[[autodoc]] apply_taylorseer_cache

diffusers/docs/source/en/api/configuration.md

+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+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.
+-->
+
+# Configuration
+
+Schedulers from [`~schedulers.scheduling_utils.SchedulerMixin`] and models from [`ModelMixin`] inherit from [`ConfigMixin`] which stores all the parameters that are passed to their respective `__init__` methods in a JSON-configuration file.
+
+> [!TIP]
+> To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `hf auth login`.
+
+## ConfigMixin
+
+[[autodoc]] ConfigMixin
+	- load_config
+	- from_config
+	- save_config
+	- to_json_file
+	- to_json_string

diffusers/docs/source/en/api/image_processor.md

+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+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.
+-->
+
+# VAE Image Processor
+
+The [`VaeImageProcessor`] provides a unified API for [`StableDiffusionPipeline`]s to prepare image inputs for VAE encoding and post-processing outputs once they're decoded. This includes transformations such as resizing, normalization, and conversion between PIL Image, PyTorch, and NumPy arrays.
+
+All pipelines with [`VaeImageProcessor`] accept PIL Image, PyTorch tensor, or NumPy arrays as image inputs and return outputs based on the `output_type` argument by the user. You can pass encoded image latents directly to the pipeline and return latents from the pipeline as a specific output with the `output_type` argument (for example `output_type="latent"`). This allows you to take the generated latents from one pipeline and pass it to another pipeline as input without leaving the latent space. It also makes it much easier to use multiple pipelines together by passing PyTorch tensors directly between different pipelines.
+
+## VaeImageProcessor
+
+[[autodoc]] image_processor.VaeImageProcessor
+
+## InpaintProcessor
+
+The [`InpaintProcessor`] accepts `mask` and `image` inputs and process them together. Optionally, it can accept padding_mask_crop and apply mask overlay.
+
+[[autodoc]] image_processor.InpaintProcessor
+
+## VaeImageProcessorLDM3D
+
+The [`VaeImageProcessorLDM3D`] accepts RGB and depth inputs and returns RGB and depth outputs.
+
+[[autodoc]] image_processor.VaeImageProcessorLDM3D
+
+## PixArtImageProcessor
+
+[[autodoc]] image_processor.PixArtImageProcessor
+
+## IPAdapterMaskProcessor
+
+[[autodoc]] image_processor.IPAdapterMaskProcessor

diffusers/docs/source/en/api/internal_classes_overview.md

+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+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.
+-->
+
+# Overview
+
+The APIs in this section are more experimental and prone to breaking changes. Most of them are used internally for development, but they may also be useful to you if you're interested in building a diffusion model with some custom parts or if you're interested in some of our helper utilities for working with 🤗 Diffusers.

diffusers/docs/source/en/api/loaders/ip_adapter.md

+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+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.
+-->
+
+# IP-Adapter
+
+[IP-Adapter](https://hf.co/papers/2308.06721) is a lightweight adapter that enables prompting a diffusion model with an image. This method decouples the cross-attention layers of the image and text features. The image features are generated from an image encoder.
+
+> [!TIP]
+> Learn how to load and use an IP-Adapter checkpoint and image in the [IP-Adapter](../../using-diffusers/ip_adapter) guide,.
+
+## IPAdapterMixin
+
+[[autodoc]] loaders.ip_adapter.IPAdapterMixin
+
+## SD3IPAdapterMixin
+
+[[autodoc]] loaders.ip_adapter.SD3IPAdapterMixin
+    - all
+    - is_ip_adapter_active
+
+## IPAdapterMaskProcessor
+
+[[autodoc]] image_processor.IPAdapterMaskProcessor
\ No newline at end of file

diffusers/docs/source/en/api/loaders/lora.md

+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+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.
+-->
+
+# LoRA
+
+LoRA is a fast and lightweight training method that inserts and trains a significantly smaller number of parameters instead of all the model parameters. This produces a smaller file (~100 MBs) and makes it easier to quickly train a model to learn a new concept. LoRA weights are typically loaded into the denoiser, text encoder or both. The denoiser usually corresponds to a UNet ([`UNet2DConditionModel`], for example) or a Transformer ([`SD3Transformer2DModel`], for example). There are several classes for loading LoRA weights:
+
+- [`StableDiffusionLoraLoaderMixin`] provides functions for loading and unloading, fusing and unfusing, enabling and disabling, and more functions for managing LoRA weights. This class can be used with any model.
+- [`StableDiffusionXLLoraLoaderMixin`] is a [Stable Diffusion (SDXL)](../../api/pipelines/stable_diffusion/stable_diffusion_xl) version of the [`StableDiffusionLoraLoaderMixin`] class for loading and saving LoRA weights. It can only be used with the SDXL model.
+- [`SD3LoraLoaderMixin`] provides similar functions for [Stable Diffusion 3](https://huggingface.co/blog/sd3).
+- [`FluxLoraLoaderMixin`] provides similar functions for [Flux](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux).
+- [`CogVideoXLoraLoaderMixin`] provides similar functions for [CogVideoX](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogvideox).
+- [`Mochi1LoraLoaderMixin`] provides similar functions for [Mochi](https://huggingface.co/docs/diffusers/main/en/api/pipelines/mochi).
+- [`AuraFlowLoraLoaderMixin`] provides similar functions for [AuraFlow](https://huggingface.co/fal/AuraFlow).
+- [`LTXVideoLoraLoaderMixin`] provides similar functions for [LTX-Video](https://huggingface.co/docs/diffusers/main/en/api/pipelines/ltx_video).
+- [`SanaLoraLoaderMixin`] provides similar functions for [Sana](https://huggingface.co/docs/diffusers/main/en/api/pipelines/sana).
+- [`HeliosLoraLoaderMixin`] provides similar functions for [HunyuanVideo](https://huggingface.co/docs/diffusers/main/en/api/pipelines/helios).
+- [`HunyuanVideoLoraLoaderMixin`] provides similar functions for [HunyuanVideo](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hunyuan_video).
+- [`Lumina2LoraLoaderMixin`] provides similar functions for [Lumina2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/lumina2).
+- [`WanLoraLoaderMixin`] provides similar functions for [Wan](https://huggingface.co/docs/diffusers/main/en/api/pipelines/wan).
+- [`SkyReelsV2LoraLoaderMixin`] provides similar functions for [SkyReels-V2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/skyreels_v2).
+- [`CogView4LoraLoaderMixin`] provides similar functions for [CogView4](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogview4).
+- [`AmusedLoraLoaderMixin`] is for the [`AmusedPipeline`].
+- [`HiDreamImageLoraLoaderMixin`] provides similar functions for [HiDream Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hidream)
+- [`QwenImageLoraLoaderMixin`] provides similar functions for [Qwen Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/qwen).
+- [`ZImageLoraLoaderMixin`] provides similar functions for [Z-Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/zimage).
+- [`Flux2LoraLoaderMixin`] provides similar functions for [Flux2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux2).
+- [`LTX2LoraLoaderMixin`] provides similar functions for [Flux2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/ltx2).
+- [`LoraBaseMixin`] provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.
+
+> [!TIP]
+> To learn more about how to load LoRA weights, see the [LoRA](../../tutorials/using_peft_for_inference) loading guide.
+
+## LoraBaseMixin
+
+[[autodoc]] loaders.lora_base.LoraBaseMixin
+
+## StableDiffusionLoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.StableDiffusionLoraLoaderMixin
+
+## StableDiffusionXLLoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.StableDiffusionXLLoraLoaderMixin
+
+## SD3LoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.SD3LoraLoaderMixin
+
+## FluxLoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.FluxLoraLoaderMixin
+
+## Flux2LoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.Flux2LoraLoaderMixin
+
+## LTX2LoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.LTX2LoraLoaderMixin
+
+## CogVideoXLoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.CogVideoXLoraLoaderMixin
+
+## Mochi1LoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.Mochi1LoraLoaderMixin
+## AuraFlowLoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.AuraFlowLoraLoaderMixin
+
+## LTXVideoLoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.LTXVideoLoraLoaderMixin
+
+## SanaLoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.SanaLoraLoaderMixin
+
+## HeliosLoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.HeliosLoraLoaderMixin
+
+## HunyuanVideoLoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.HunyuanVideoLoraLoaderMixin
+
+## Lumina2LoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.Lumina2LoraLoaderMixin
+
+## CogView4LoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.CogView4LoraLoaderMixin
+
+## WanLoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.WanLoraLoaderMixin
+
+## SkyReelsV2LoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.SkyReelsV2LoraLoaderMixin
+
+## AmusedLoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.AmusedLoraLoaderMixin
+
+## HiDreamImageLoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.HiDreamImageLoraLoaderMixin
+
+## QwenImageLoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.QwenImageLoraLoaderMixin
+
+## ZImageLoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.ZImageLoraLoaderMixin
+
+## KandinskyLoraLoaderMixin
+[[autodoc]] loaders.lora_pipeline.KandinskyLoraLoaderMixin
+
+## LoraBaseMixin
+
+[[autodoc]] loaders.lora_base.LoraBaseMixin
\ No newline at end of file

diffusers/docs/source/en/api/loaders/peft.md

+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+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.
+-->
+
+# PEFT
+
+Diffusers supports loading adapters such as [LoRA](../../tutorials/using_peft_for_inference) with the [PEFT](https://huggingface.co/docs/peft/index) library with the [`~loaders.peft.PeftAdapterMixin`] class. This allows modeling classes in Diffusers like [`UNet2DConditionModel`], [`SD3Transformer2DModel`] to operate with an adapter.
+
+> [!TIP]
+> Refer to the [Inference with PEFT](../../tutorials/using_peft_for_inference.md) tutorial for an overview of how to use PEFT in Diffusers for inference.
+
+## PeftAdapterMixin
+
+[[autodoc]] loaders.peft.PeftAdapterMixin

diffusers/docs/source/en/api/loaders/single_file.md

+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+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.
+-->
+
+# Single files
+
+The [`~loaders.FromSingleFileMixin.from_single_file`] method allows you to load:
+
+* a model stored in a single file, which is useful if you're working with models from the diffusion ecosystem, like Automatic1111, and commonly rely on a single-file layout to store and share models
+* a model stored in their originally distributed layout, which is useful if you're working with models finetuned with other services, and want to load it directly into Diffusers model objects and pipelines
+
+> [!TIP]
+> Read the [Model files and layouts](../../using-diffusers/other-formats) guide to learn more about the Diffusers-multifolder layout versus the single-file layout, and how to load models stored in these different layouts.
+
+## Supported pipelines
+
+- [`StableDiffusionPipeline`]
+- [`StableDiffusionImg2ImgPipeline`]
+- [`StableDiffusionInpaintPipeline`]
+- [`StableDiffusionControlNetPipeline`]
+- [`StableDiffusionControlNetImg2ImgPipeline`]
+- [`StableDiffusionControlNetInpaintPipeline`]
+- [`StableDiffusionUpscalePipeline`]
+- [`StableDiffusionXLPipeline`]
+- [`StableDiffusionXLImg2ImgPipeline`]
+- [`StableDiffusionXLInpaintPipeline`]
+- [`StableDiffusionXLInstructPix2PixPipeline`]
+- [`StableDiffusionXLControlNetPipeline`]
+- [`StableDiffusionXLKDiffusionPipeline`]
+- [`StableDiffusion3Pipeline`]
+- [`LatentConsistencyModelPipeline`]
+- [`LatentConsistencyModelImg2ImgPipeline`]
+- [`StableDiffusionControlNetXSPipeline`]
+- [`StableDiffusionXLControlNetXSPipeline`]
+- [`LEditsPPPipelineStableDiffusion`]
+- [`LEditsPPPipelineStableDiffusionXL`]
+- [`PIAPipeline`]
+
+## Supported models
+
+- [`UNet2DConditionModel`]
+- [`StableCascadeUNet`]
+- [`AutoencoderKL`]
+- [`ControlNetModel`]
+- [`SD3Transformer2DModel`]
+- [`FluxTransformer2DModel`]
+
+## FromSingleFileMixin
+
+[[autodoc]] loaders.single_file.FromSingleFileMixin
+
+## FromOriginalModelMixin
+
+[[autodoc]] loaders.single_file_model.FromOriginalModelMixin

diffusers/docs/source/en/api/loaders/textual_inversion.md

+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+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.
+-->
+
+# Textual Inversion
+
+Textual Inversion is a training method for personalizing models by learning new text embeddings from a few example images. The file produced from training is extremely small (a few KBs) and the new embeddings can be loaded into the text encoder.
+
+[`TextualInversionLoaderMixin`] provides a function for loading Textual Inversion embeddings from Diffusers and Automatic1111 into the text encoder and loading a special token to activate the embeddings.
+
+> [!TIP]
+> To learn more about how to load Textual Inversion embeddings, see the [Textual Inversion](../../using-diffusers/textual_inversion_inference) loading guide.
+
+## TextualInversionLoaderMixin
+
+[[autodoc]] loaders.textual_inversion.TextualInversionLoaderMixin
\ No newline at end of file

diffusers/docs/source/en/api/loaders/transformer_sd3.md

+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+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.
+-->
+
+# SD3Transformer2D
+
+This class is useful when *only* loading weights into a [`SD3Transformer2DModel`]. If you need to load weights into the text encoder or a text encoder and SD3Transformer2DModel, check [`SD3LoraLoaderMixin`](lora#diffusers.loaders.SD3LoraLoaderMixin) class instead.
+
+The [`SD3Transformer2DLoadersMixin`] class currently only loads IP-Adapter weights, but will be used in the future to save weights and load LoRAs.
+
+> [!TIP]
+> To learn more about how to load LoRA weights, see the [LoRA](../../tutorials/using_peft_for_inference) loading guide.
+
+## SD3Transformer2DLoadersMixin
+
+[[autodoc]] loaders.transformer_sd3.SD3Transformer2DLoadersMixin
+    - all
+    - _load_ip_adapter_weights
\ No newline at end of file

diffusers/docs/source/en/api/loaders/unet.md

+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+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.
+-->
+
+# UNet
+
+Some training methods - like LoRA and Custom Diffusion - typically target the UNet's attention layers, but these training methods can also target other non-attention layers. Instead of training all of a model's parameters, only a subset of the parameters are trained, which is faster and more efficient. This class is useful if you're *only* loading weights into a UNet. If you need to load weights into the text encoder or a text encoder and UNet, try using the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] function instead.
+
+The [`UNet2DConditionLoadersMixin`] class provides functions for loading and saving weights, fusing and unfusing LoRAs, disabling and enabling LoRAs, and setting and deleting adapters.
+
+> [!TIP]
+> To learn more about how to load LoRA weights, see the [LoRA](../../tutorials/using_peft_for_inference) guide.
+
+## UNet2DConditionLoadersMixin
+
+[[autodoc]] loaders.unet.UNet2DConditionLoadersMixin
\ No newline at end of file

diffusers/docs/source/en/api/logging.md

+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+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.
+-->
+
+# Logging
+
+🤗 Diffusers has a centralized logging system to easily manage the verbosity of the library. The default verbosity is set to `WARNING`.
+
+To change the verbosity level, use one of the direct setters. For instance, to change the verbosity to the `INFO` level.
+
+```python
+import diffusers
+
+diffusers.logging.set_verbosity_info()
+```
+
+You can also use the environment variable `DIFFUSERS_VERBOSITY` to override the default verbosity. You can set it
+to one of the following: `debug`, `info`, `warning`, `error`, `critical`. For example:
+
+```bash
+DIFFUSERS_VERBOSITY=error ./myprogram.py
+```
+
+Additionally, some `warnings` can be disabled by setting the environment variable
+`DIFFUSERS_NO_ADVISORY_WARNINGS` to a true value, like `1`. This disables any warning logged by
+[`logger.warning_advice`]. For example:
+
+```bash
+DIFFUSERS_NO_ADVISORY_WARNINGS=1 ./myprogram.py
+```
+
+Here is an example of how to use the same logger as the library in your own module or script:
+
+```python
+from diffusers.utils import logging
+
+logging.set_verbosity_info()
+logger = logging.get_logger("diffusers")
+logger.info("INFO")
+logger.warning("WARN")
+```
+
+
+All methods of the logging module are documented below. The main methods are
+[`logging.get_verbosity`] to get the current level of verbosity in the logger and
+[`logging.set_verbosity`] to set the verbosity to the level of your choice.
+
+In order from the least verbose to the most verbose:
+
+|                                                    Method | Integer value |                                         Description |
+|----------------------------------------------------------:|--------------:|----------------------------------------------------:|
+| `diffusers.logging.CRITICAL` or `diffusers.logging.FATAL` |            50 |                only report the most critical errors |
+|                                 `diffusers.logging.ERROR` |            40 |                                  only report errors |
+|   `diffusers.logging.WARNING` or `diffusers.logging.WARN` |            30 |           only report errors and warnings (default) |
+|                                  `diffusers.logging.INFO` |            20 | only report errors, warnings, and basic information |
+|                                 `diffusers.logging.DEBUG` |            10 |                              report all information |
+
+By default, `tqdm` progress bars are displayed during model download. [`logging.disable_progress_bar`] and [`logging.enable_progress_bar`] are used to enable or disable this behavior.
+
+## Base setters
+
+[[autodoc]] utils.logging.set_verbosity_error
+
+[[autodoc]] utils.logging.set_verbosity_warning
+
+[[autodoc]] utils.logging.set_verbosity_info
+
+[[autodoc]] utils.logging.set_verbosity_debug
+
+## Other functions
+
+[[autodoc]] utils.logging.get_verbosity
+
+[[autodoc]] utils.logging.set_verbosity
+
+[[autodoc]] utils.logging.get_logger
+
+[[autodoc]] utils.logging.enable_default_handler
+
+[[autodoc]] utils.logging.disable_default_handler
+
+[[autodoc]] utils.logging.enable_explicit_format
+
+[[autodoc]] utils.logging.reset_format
+
+[[autodoc]] utils.logging.enable_progress_bar
+
+[[autodoc]] utils.logging.disable_progress_bar

diffusers/docs/source/en/api/models/allegro_transformer3d.md

+<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
+
+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. -->
+
+# AllegroTransformer3DModel
+
+A Diffusion Transformer model for 3D data from [Allegro](https://github.com/rhymes-ai/Allegro) was introduced in [Allegro: Open the Black Box of Commercial-Level Video Generation Model](https://huggingface.co/papers/2410.15458) by RhymesAI.
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import AllegroTransformer3DModel
+
+transformer = AllegroTransformer3DModel.from_pretrained("rhymes-ai/Allegro", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
+```
+
+## AllegroTransformer3DModel
+
+[[autodoc]] AllegroTransformer3DModel
+
+## Transformer2DModelOutput
+
+[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

diffusers/docs/source/en/api/models/asymmetricautoencoderkl.md

+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+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.
+-->
+
+# AsymmetricAutoencoderKL
+
+Improved larger variational autoencoder (VAE) model with KL loss for inpainting task: [Designing a Better Asymmetric VQGAN for StableDiffusion](https://huggingface.co/papers/2306.04632) by Zixin Zhu, Xuelu Feng, Dongdong Chen, Jianmin Bao, Le Wang, Yinpeng Chen, Lu Yuan, Gang Hua.
+
+The abstract from the paper is:
+
+*StableDiffusion is a revolutionary text-to-image generator that is causing a stir in the world of image generation and editing. Unlike traditional methods that learn a diffusion model in pixel space, StableDiffusion learns a diffusion model in the latent space via a VQGAN, ensuring both efficiency and quality. It not only supports image generation tasks, but also enables image editing for real images, such as image inpainting and local editing. However, we have observed that the vanilla VQGAN used in StableDiffusion leads to significant information loss, causing distortion artifacts even in non-edited image regions. To this end, we propose a new asymmetric VQGAN with two simple designs. Firstly, in addition to the input from the encoder, the decoder contains a conditional branch that incorporates information from task-specific priors, such as the unmasked image region in inpainting. Secondly, the decoder is much heavier than the encoder, allowing for more detailed recovery while only slightly increasing the total inference cost. The training cost of our asymmetric VQGAN is cheap, and we only need to retrain a new asymmetric decoder while keeping the vanilla VQGAN encoder and StableDiffusion unchanged. Our asymmetric VQGAN can be widely used in StableDiffusion-based inpainting and local editing methods. Extensive experiments demonstrate that it can significantly improve the inpainting and editing performance, while maintaining the original text-to-image capability. The code is available at https://github.com/buxiangzhiren/Asymmetric_VQGAN*
+
+Evaluation results can be found in section 4.1 of the original paper.
+
+## Available checkpoints
+
+* [https://huggingface.co/cross-attention/asymmetric-autoencoder-kl-x-1-5](https://huggingface.co/cross-attention/asymmetric-autoencoder-kl-x-1-5)
+* [https://huggingface.co/cross-attention/asymmetric-autoencoder-kl-x-2](https://huggingface.co/cross-attention/asymmetric-autoencoder-kl-x-2)
+
+## Example Usage
+
+```python
+from diffusers import AsymmetricAutoencoderKL, StableDiffusionInpaintPipeline
+from diffusers.utils import load_image, make_image_grid
+
+
+prompt = "a photo of a person with beard"
+img_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/celeba_hq_256.png"
+mask_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"
+
+original_image = load_image(img_url).resize((512, 512))
+mask_image = load_image(mask_url).resize((512, 512))
+
+pipe = StableDiffusionInpaintPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-inpainting")
+pipe.vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5")
+pipe.to("cuda")
+
+image = pipe(prompt=prompt, image=original_image, mask_image=mask_image).images[0]
+make_image_grid([original_image, mask_image, image], rows=1, cols=3)
+```
+
+## AsymmetricAutoencoderKL
+
+[[autodoc]] models.autoencoders.autoencoder_asym_kl.AsymmetricAutoencoderKL
+
+## AutoencoderKLOutput
+
+[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
+
+## DecoderOutput
+
+[[autodoc]] models.autoencoders.vae.DecoderOutput