update

test sequential cpu offload

.github/workflows/pr_tests.yml

@@ -266,6 +266,7 @@ jobs:
         # TODO (sayakpaul, DN6): revisit `--no-deps`
         python -m pip install -U peft@git+https://github.com/huggingface/peft.git --no-deps
         python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
+        python -m uv pip install -U tokenizers
         pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
 
     - name: Environment

.github/workflows/push_tests.yml

@@ -83,7 +83,7 @@ jobs:
           python utils/print_env.py
       - name: PyTorch CUDA checkpoint tests on Ubuntu
         env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
           # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
           CUBLAS_WORKSPACE_CONFIG: :16:8
         run: |

docs/source/en/api/pipelines/aura_flow.md

@@ -62,6 +62,33 @@ image = pipeline(prompt).images[0]
 image.save("auraflow.png")
 ```
 
+Loading [GGUF checkpoints](https://huggingface.co/docs/diffusers/quantization/gguf) are also supported:
+
+```py
+import torch
+from diffusers import (
+    AuraFlowPipeline,
+    GGUFQuantizationConfig,
+    AuraFlowTransformer2DModel,
+)
+
+transformer = AuraFlowTransformer2DModel.from_single_file(
+    "https://huggingface.co/city96/AuraFlow-v0.3-gguf/blob/main/aura_flow_0.3-Q2_K.gguf",
+    quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
+    torch_dtype=torch.bfloat16,
+)
+
+pipeline = AuraFlowPipeline.from_pretrained(
+    "fal/AuraFlow-v0.3",
+    transformer=transformer,
+    torch_dtype=torch.bfloat16,
+)
+
+prompt = "a cute pony in a field of flowers"
+image = pipeline(prompt).images[0]
+image.save("auraflow.png")
+```
+
 ## AuraFlowPipeline
 
 [[autodoc]] AuraFlowPipeline

docs/source/en/api/pipelines/flux.md

@@ -367,7 +367,7 @@ transformer_8bit = FluxTransformer2DModel.from_pretrained(
 
 pipeline = FluxPipeline.from_pretrained(
     "black-forest-labs/FLUX.1-dev",
-    text_encoder=text_encoder_8bit,
+    text_encoder_2=text_encoder_8bit,
     transformer=transformer_8bit,
     torch_dtype=torch.float16,
     device_map="balanced",

docs/source/en/api/pipelines/hunyuan_video.md

@@ -16,7 +16,7 @@
 
 [HunyuanVideo](https://www.arxiv.org/abs/2412.03603) by Tencent.
 
-*Recent advancements in video generation have significantly impacted daily life for both individuals and industries. However, the leading video generation models remain closed-source, resulting in a notable performance gap between industry capabilities and those available to the public. In this report, we introduce HunyuanVideo, an innovative open-source video foundation model that demonstrates performance in video generation comparable to, or even surpassing, that of leading closed-source models. HunyuanVideo encompasses a comprehensive framework that integrates several key elements, including data curation, advanced architectural design, progressive model scaling and training, and an efficient infrastructure tailored for large-scale model training and inference. As a result, we successfully trained a video generative model with over 13 billion parameters, making it the largest among all open-source models. We conducted extensive experiments and implemented a series of targeted designs to ensure high visual quality, motion dynamics, text-video alignment, and advanced filming techniques. According to evaluations by professionals, HunyuanVideo outperforms previous state-of-the-art models, including Runway Gen-3, Luma 1.6, and three top-performing Chinese video generative models. By releasing the code for the foundation model and its applications, we aim to bridge the gap between closed-source and open-source communities. This initiative will empower individuals within the community to experiment with their ideas, fostering a more dynamic and vibrant video generation ecosystem. The code is publicly available at [this https URL](https://github.com/Tencent/HunyuanVideo).*
+*Recent advancements in video generation have significantly impacted daily life for both individuals and industries. However, the leading video generation models remain closed-source, resulting in a notable performance gap between industry capabilities and those available to the public. In this report, we introduce HunyuanVideo, an innovative open-source video foundation model that demonstrates performance in video generation comparable to, or even surpassing, that of leading closed-source models. HunyuanVideo encompasses a comprehensive framework that integrates several key elements, including data curation, advanced architectural design, progressive model scaling and training, and an efficient infrastructure tailored for large-scale model training and inference. As a result, we successfully trained a video generative model with over 13 billion parameters, making it the largest among all open-source models. We conducted extensive experiments and implemented a series of targeted designs to ensure high visual quality, motion dynamics, text-video alignment, and advanced filming techniques. According to evaluations by professionals, HunyuanVideo outperforms previous state-of-the-art models, including Runway Gen-3, Luma 1.6, and three top-performing Chinese video generative models. By releasing the code for the foundation model and its applications, we aim to bridge the gap between closed-source and open-source communities. This initiative will empower individuals within the community to experiment with their ideas, fostering a more dynamic and vibrant video generation ecosystem. The code is publicly available at [this https URL](https://github.com/tencent/HunyuanVideo).*
 
 <Tip>
 
@@ -45,14 +45,14 @@ from diffusers.utils import export_to_video
 
 quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
 transformer_8bit = HunyuanVideoTransformer3DModel.from_pretrained(
-    "tencent/HunyuanVideo",
+    "hunyuanvideo-community/HunyuanVideo",
     subfolder="transformer",
     quantization_config=quant_config,
-    torch_dtype=torch.float16,
+    torch_dtype=torch.bfloat16,
 )
 
 pipeline = HunyuanVideoPipeline.from_pretrained(
-    "tencent/HunyuanVideo",
+    "hunyuanvideo-community/HunyuanVideo",
     transformer=transformer_8bit,
     torch_dtype=torch.float16,
     device_map="balanced",

docs/source/en/api/pipelines/sana.md

@@ -59,10 +59,10 @@ Refer to the [Quantization](../../quantization/overview) overview to learn more
 ```py
 import torch
 from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, SanaTransformer2DModel, SanaPipeline
-from transformers import BitsAndBytesConfig as BitsAndBytesConfig, AutoModelForCausalLM
+from transformers import BitsAndBytesConfig as BitsAndBytesConfig, AutoModel
 
 quant_config = BitsAndBytesConfig(load_in_8bit=True)
-text_encoder_8bit = AutoModelForCausalLM.from_pretrained(
+text_encoder_8bit = AutoModel.from_pretrained(
     "Efficient-Large-Model/Sana_1600M_1024px_diffusers",
     subfolder="text_encoder",
     quantization_config=quant_config,

docs/source/en/using-diffusers/text-img2vid.md

@@ -78,10 +78,10 @@ from diffusers import HunyuanVideoPipeline, HunyuanVideoTransformer3DModel
 from diffusers.utils import export_to_video
 
 transformer = HunyuanVideoTransformer3DModel.from_pretrained(
-    "tencent/HunyuanVideo", subfolder="transformer", torch_dtype=torch.bfloat16
+    "hunyuanvideo-community/HunyuanVideo", subfolder="transformer", torch_dtype=torch.bfloat16
 )
 pipe = HunyuanVideoPipeline.from_pretrained(
-  "tencent/HunyuanVideo", transformer=transformer, torch_dtype=torch.float16
+  "hunyuanvideo-community/HunyuanVideo", transformer=transformer, torch_dtype=torch.float16
 )
 
 # reduce memory requirements

examples/advanced_diffusion_training/README.md

@@ -67,6 +67,17 @@ write_basic_config()
 When running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups.
 Note also that we use PEFT library as backend for LoRA training, make sure to have `peft>=0.6.0` installed in your environment.
 
+Lastly, we recommend logging into your HF account so that your trained LoRA is automatically uploaded to the hub:
+```bash
+huggingface-cli login
+```
+This command will prompt you for a token. Copy-paste yours from your [settings/tokens](https://huggingface.co/settings/tokens),and press Enter.
+
+> [!NOTE]
+> In the examples below we use `wandb` to document the training runs. To do the same, make sure to install `wandb`:
+> `pip install wandb`
+> Alternatively, you can use other tools / train without reporting by modifying the flag  `--report_to="wandb"`.
+
 ### Pivotal Tuning
 **Training with text encoder(s)**
 

examples/advanced_diffusion_training/README_flux.md

@@ -65,6 +65,17 @@ write_basic_config()
 When running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups.
 Note also that we use PEFT library as backend for LoRA training, make sure to have `peft>=0.6.0` installed in your environment.
 
+Lastly, we recommend logging into your HF account so that your trained LoRA is automatically uploaded to the hub:
+```bash
+huggingface-cli login
+```
+This command will prompt you for a token. Copy-paste yours from your [settings/tokens](https://huggingface.co/settings/tokens),and press Enter.
+
+> [!NOTE]
+> In the examples below we use `wandb` to document the training runs. To do the same, make sure to install `wandb`:
+> `pip install wandb`
+> Alternatively, you can use other tools / train without reporting by modifying the flag  `--report_to="wandb"`.
+
 ### Target Modules
 When LoRA was first adapted from language models to diffusion models, it was applied to the cross-attention layers in the Unet that relate the image representations with the prompts that describe them. 
 More recently, SOTA text-to-image diffusion models replaced the Unet with a diffusion Transformer(DiT). With this change, we may also want to explore 

examples/community/README.md

@@ -33,12 +33,12 @@ Please also check out our [Community Scripts](https://github.com/huggingface/dif
 | Bit Diffusion                                                                                                                         | Diffusion on discrete data                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | [Bit Diffusion](#bit-diffusion)                                                           | -  |                       [Stuti R.](https://github.com/kingstut) |
 | K-Diffusion Stable Diffusion                                                                                                          | Run Stable Diffusion with any of [K-Diffusion's samplers](https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/sampling.py)                                                                                                                                                                                                                                                                                                                                                                  | [Stable Diffusion with K Diffusion](#stable-diffusion-with-k-diffusion)                   | -  |    [Patrick von Platen](https://github.com/patrickvonplaten/) |
 | Checkpoint Merger Pipeline                                                                                                            | Diffusion Pipeline that enables merging of saved model checkpoints                                                                                                                                                                                                                                                                                                                                                                                                                                       | [Checkpoint Merger Pipeline](#checkpoint-merger-pipeline)                                 | -                                                                                                                                                                                                                  | [Naga Sai Abhinay Devarinti](https://github.com/Abhinay1997/) |
-| Stable Diffusion v1.1-1.4 Comparison                                                                                                  | Run all 4 model checkpoints for Stable Diffusion and compare their results together                                                                                                                                                                                                                                                                                                                                                                                                                      | [Stable Diffusion Comparison](#stable-diffusion-comparisons)                              | - |        [Suvaditya Mukherjee](https://github.com/suvadityamuk) |
+| Stable Diffusion v1.1-1.4 Comparison                                                                                                  | Run all 4 model checkpoints for Stable Diffusion and compare their results together                                                                                                                                                                                                                                                                                                                                                                                                                      | [Stable Diffusion Comparison](#stable-diffusion-comparisons)                              | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/stable_diffusion_comparison.ipynb) |        [Suvaditya Mukherjee](https://github.com/suvadityamuk) |
 | MagicMix                                                                                                                              | Diffusion Pipeline for semantic mixing of an image and a text prompt                                                                                                                                                                                                                                                                                                                                                                                                                                     | [MagicMix](#magic-mix)                                                                    | - |                    [Partho Das](https://github.com/daspartho) |
-| Stable UnCLIP                                                                                                                         | Diffusion Pipeline for combining prior model (generate clip image embedding from text, UnCLIPPipeline `"kakaobrain/karlo-v1-alpha"`) and decoder pipeline (decode clip image embedding to image, StableDiffusionImageVariationPipeline `"lambdalabs/sd-image-variations-diffusers"` ).                                                                                                                                                                                                                   | [Stable UnCLIP](#stable-unclip)                                                           | -  |                                [Ray Wang](https://wrong.wang) |
-| UnCLIP Text Interpolation Pipeline                                                                                                    | Diffusion Pipeline that allows passing two prompts and produces images while interpolating between the text-embeddings of the two prompts                                                                                                                                                                                                                                                                                                                                                                | [UnCLIP Text Interpolation Pipeline](#unclip-text-interpolation-pipeline)                 | -                                                                                                                                                                                                                  | [Naga Sai Abhinay Devarinti](https://github.com/Abhinay1997/) |
+| Stable UnCLIP                                                                                                                         | Diffusion Pipeline for combining prior model (generate clip image embedding from text, UnCLIPPipeline `"kakaobrain/karlo-v1-alpha"`) and decoder pipeline (decode clip image embedding to image, StableDiffusionImageVariationPipeline `"lambdalabs/sd-image-variations-diffusers"` ).                                                                                                                                                                                                                   | [Stable UnCLIP](#stable-unclip)                                                           | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/stable_unclip.ipynb)  |                                [Ray Wang](https://wrong.wang) |
+| UnCLIP Text Interpolation Pipeline                                                                                                    | Diffusion Pipeline that allows passing two prompts and produces images while interpolating between the text-embeddings of the two prompts                                                                                                                                                                                                                                                                                                                                                                | [UnCLIP Text Interpolation Pipeline](#unclip-text-interpolation-pipeline)                 | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/unclip_text_interpolation.ipynb)| [Naga Sai Abhinay Devarinti](https://github.com/Abhinay1997/) |
 | UnCLIP Image Interpolation Pipeline                                                                                                   | Diffusion Pipeline that allows passing two images/image_embeddings and produces images while interpolating between their image-embeddings                                                                                                                                                                                                                                                                                                                                                                | [UnCLIP Image Interpolation Pipeline](#unclip-image-interpolation-pipeline)               | -                                                                                                                                                                                                                  | [Naga Sai Abhinay Devarinti](https://github.com/Abhinay1997/) |
-| DDIM Noise Comparative Analysis Pipeline                                                                                              | Investigating how the diffusion models learn visual concepts from each noise level (which is a contribution of [P2 weighting (CVPR 2022)](https://arxiv.org/abs/2204.00227))                                                                                                                                                                                                                                                                                                                             | [DDIM Noise Comparative Analysis Pipeline](#ddim-noise-comparative-analysis-pipeline)     | - |              [Aengus (Duc-Anh)](https://github.com/aengusng8) |
+| DDIM Noise Comparative Analysis Pipeline                                                                                              | Investigating how the diffusion models learn visual concepts from each noise level (which is a contribution of [P2 weighting (CVPR 2022)](https://arxiv.org/abs/2204.00227))                                                                                                                                                                                                                                                                                                                             | [DDIM Noise Comparative Analysis Pipeline](#ddim-noise-comparative-analysis-pipeline)     | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/ddim_noise_comparative_analysis.ipynb)|              [Aengus (Duc-Anh)](https://github.com/aengusng8) |
 | CLIP Guided Img2Img Stable Diffusion Pipeline                                                                                         | Doing CLIP guidance for image to image generation with Stable Diffusion                                                                                                                                                                                                                                                                                                                                                                                                                                  | [CLIP Guided Img2Img Stable Diffusion](#clip-guided-img2img-stable-diffusion)             | - |               [Nipun Jindal](https://github.com/nipunjindal/) |
 | TensorRT Stable Diffusion Text to Image Pipeline                                                                                                    | Accelerates the Stable Diffusion Text2Image Pipeline using TensorRT                                                                                                                                                                                                                                                                                                                                                                                                                                      | [TensorRT Stable Diffusion Text to Image Pipeline](#tensorrt-text2image-stable-diffusion-pipeline)      | - |              [Asfiya Baig](https://github.com/asfiyab-nvidia) |
 | EDICT Image Editing Pipeline                                                                                                          | Diffusion pipeline for text-guided image editing                                                                                                                                                                                                                                                                                                                                                                                                                                                         | [EDICT Image Editing Pipeline](#edict-image-editing-pipeline)                             | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/edict_image_pipeline.ipynb) |                    [Joqsan Azocar](https://github.com/Joqsan) |
@@ -50,7 +50,7 @@ Please also check out our [Community Scripts](https://github.com/huggingface/dif
 |   IADB Pipeline                                                                                                    | Implementation of [Iterative α-(de)Blending: a Minimalist Deterministic Diffusion Model](https://arxiv.org/abs/2305.03486)                                                                                                                                                                                                                                                                                                                                                                                                                                      | [IADB Pipeline](#iadb-pipeline)      | - |              [Thomas Chambon](https://github.com/tchambon)
 |   Zero1to3 Pipeline                                                                                                    | Implementation of [Zero-1-to-3: Zero-shot One Image to 3D Object](https://arxiv.org/abs/2303.11328)                                                                                                                                                                                                                                                                                                                                                                                                                                      | [Zero1to3 Pipeline](#zero1to3-pipeline)      | - |              [Xin Kong](https://github.com/kxhit) |
 | Stable Diffusion XL Long Weighted Prompt Pipeline | A pipeline support unlimited length of prompt and negative prompt, use A1111 style of prompt weighting | [Stable Diffusion XL Long Weighted Prompt Pipeline](#stable-diffusion-xl-long-weighted-prompt-pipeline) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1LsqilswLR40XLLcp6XFOl5nKb_wOe26W?usp=sharing) | [Andrew Zhu](https://xhinker.medium.com/) |
-| FABRIC - Stable Diffusion with feedback Pipeline | pipeline supports feedback from liked and disliked images | [Stable Diffusion Fabric Pipeline](#stable-diffusion-fabric-pipeline) | - | [Shauray Singh](https://shauray8.github.io/about_shauray/) |
+| FABRIC - Stable Diffusion with feedback Pipeline | pipeline supports feedback from liked and disliked images | [Stable Diffusion Fabric Pipeline](#stable-diffusion-fabric-pipeline) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/stable_diffusion_fabric.ipynb)| [Shauray Singh](https://shauray8.github.io/about_shauray/) |
 | sketch inpaint - Inpainting with non-inpaint Stable Diffusion | sketch inpaint much like in automatic1111 | [Masked Im2Im Stable Diffusion Pipeline](#stable-diffusion-masked-im2im) | - | [Anatoly Belikov](https://github.com/noskill) |
 | sketch inpaint xl - Inpainting with non-inpaint Stable Diffusion | sketch inpaint much like in automatic1111 | [Masked Im2Im Stable Diffusion XL Pipeline](#stable-diffusion-xl-masked-im2im) | - | [Anatoly Belikov](https://github.com/noskill) |
 | prompt-to-prompt | change parts of a prompt and retain image structure (see [paper page](https://prompt-to-prompt.github.io/)) | [Prompt2Prompt Pipeline](#prompt2prompt-pipeline) | - | [Umer H. Adil](https://twitter.com/UmerHAdil) |

examples/community/adaptive_mask_inpainting.py

@@ -416,10 +416,14 @@ class AdaptiveMaskInpaintPipeline(
                 " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
             )
 
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        is_unet_version_less_0_9_0 = (
+            unet is not None
+            and hasattr(unet.config, "_diffusers_version")
+            and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
+        )
+        is_unet_sample_size_less_64 = (
+            unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        )
         if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
             deprecation_message = (
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
@@ -438,7 +442,7 @@ class AdaptiveMaskInpaintPipeline(
             unet._internal_dict = FrozenDict(new_config)
 
         # Check shapes, assume num_channels_latents == 4, num_channels_mask == 1, num_channels_masked == 4
-        if unet.config.in_channels != 9:
+        if unet is not None and unet.config.in_channels != 9:
             logger.info(f"You have loaded a UNet with {unet.config.in_channels} input channels which.")
 
         self.register_modules(

examples/community/composable_stable_diffusion.py

@@ -132,10 +132,14 @@ class ComposableStableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin)
                 " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
             )
 
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        is_unet_version_less_0_9_0 = (
+            unet is not None
+            and hasattr(unet.config, "_diffusers_version")
+            and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
+        )
+        is_unet_sample_size_less_64 = (
+            unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        )
         if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
             deprecation_message = (
                 "The configuration file of the unet has set the default `sample_size` to smaller than"

examples/community/instaflow_one_step.py

@@ -152,10 +152,14 @@ class InstaFlowPipeline(
                 " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
             )
 
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        is_unet_version_less_0_9_0 = (
+            unet is not None
+            and hasattr(unet.config, "_diffusers_version")
+            and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
+        )
+        is_unet_sample_size_less_64 = (
+            unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        )
         if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
             deprecation_message = (
                 "The configuration file of the unet has set the default `sample_size` to smaller than"

examples/community/ip_adapter_face_id.py

@@ -234,10 +234,14 @@ class IPAdapterFaceIDStableDiffusionPipeline(
                 " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
             )
 
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        is_unet_version_less_0_9_0 = (
+            unet is not None
+            and hasattr(unet.config, "_diffusers_version")
+            and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
+        )
+        is_unet_sample_size_less_64 = (
+            unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        )
         if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
             deprecation_message = (
                 "The configuration file of the unet has set the default `sample_size` to smaller than"

examples/community/llm_grounded_diffusion.py

@@ -379,10 +379,14 @@ class LLMGroundedDiffusionPipeline(
                 " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
             )
 
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        is_unet_version_less_0_9_0 = (
+            unet is not None
+            and hasattr(unet.config, "_diffusers_version")
+            and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
+        )
+        is_unet_sample_size_less_64 = (
+            unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        )
         if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
             deprecation_message = (
                 "The configuration file of the unet has set the default `sample_size` to smaller than"

examples/community/lpw_stable_diffusion.py

@@ -539,10 +539,14 @@ class StableDiffusionLongPromptWeightingPipeline(
                 " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
             )
 
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        is_unet_version_less_0_9_0 = (
+            unet is not None
+            and hasattr(unet.config, "_diffusers_version")
+            and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
+        )
+        is_unet_sample_size_less_64 = (
+            unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        )
         if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
             deprecation_message = (
                 "The configuration file of the unet has set the default `sample_size` to smaller than"

examples/community/lpw_stable_diffusion_xl.py

@@ -678,7 +678,11 @@ class SDXLLongPromptWeightingPipeline(
         self.mask_processor = VaeImageProcessor(
             vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True
         )
-        self.default_sample_size = self.unet.config.sample_size
+        self.default_sample_size = (
+            self.unet.config.sample_size
+            if hasattr(self, "unet") and self.unet is not None and hasattr(self.unet.config, "sample_size")
+            else 128
+        )
 
         add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()
 
@@ -827,7 +831,9 @@ class SDXLLongPromptWeightingPipeline(
                 )
 
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                pooled_prompt_embeds = prompt_embeds[0]
+                if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
+                    pooled_prompt_embeds = prompt_embeds[0]
+
                 prompt_embeds = prompt_embeds.hidden_states[-2]
 
                 prompt_embeds_list.append(prompt_embeds)
@@ -879,7 +885,8 @@ class SDXLLongPromptWeightingPipeline(
                     output_hidden_states=True,
                 )
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
+                    negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                 negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
 
                 negative_prompt_embeds_list.append(negative_prompt_embeds)

examples/community/matryoshka.py

@@ -3793,10 +3793,14 @@ class MatryoshkaPipeline(
         #     new_config["clip_sample"] = False
         #     scheduler._internal_dict = FrozenDict(new_config)
 
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        is_unet_version_less_0_9_0 = (
+            unet is not None
+            and hasattr(unet.config, "_diffusers_version")
+            and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
+        )
+        is_unet_sample_size_less_64 = (
+            unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        )
         if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
             deprecation_message = (
                 "The configuration file of the unet has set the default `sample_size` to smaller than"

examples/community/pipeline_demofusion_sdxl.py

@@ -168,7 +168,11 @@ class DemoFusionSDXLPipeline(
         self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
         self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
         self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-        self.default_sample_size = self.unet.config.sample_size
+        self.default_sample_size = (
+            self.unet.config.sample_size
+            if hasattr(self, "unet") and self.unet is not None and hasattr(self.unet.config, "sample_size")
+            else 128
+        )
 
         add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()
 
@@ -290,7 +294,9 @@ class DemoFusionSDXLPipeline(
                 )
 
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                pooled_prompt_embeds = prompt_embeds[0]
+                if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
+                    pooled_prompt_embeds = prompt_embeds[0]
+
                 prompt_embeds = prompt_embeds.hidden_states[-2]
 
                 prompt_embeds_list.append(prompt_embeds)
@@ -342,7 +348,8 @@ class DemoFusionSDXLPipeline(
                     output_hidden_states=True,
                 )
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
+                    negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                 negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
 
                 negative_prompt_embeds_list.append(negative_prompt_embeds)

examples/community/pipeline_fabric.py

@@ -150,10 +150,14 @@ class FabricPipeline(DiffusionPipeline):
     ):
         super().__init__()
 
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        is_unet_version_less_0_9_0 = (
+            unet is not None
+            and hasattr(unet.config, "_diffusers_version")
+            and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
+        )
+        is_unet_sample_size_less_64 = (
+            unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        )
         if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
             deprecation_message = (
                 "The configuration file of the unet has set the default `sample_size` to smaller than"

examples/community/pipeline_flux_differential_img2img.py

@@ -875,10 +875,10 @@ class FluxDifferentialImg2ImgPipeline(DiffusionPipeline, FluxLoraLoaderMixin):
         image_seq_len = (int(height) // self.vae_scale_factor) * (int(width) // self.vae_scale_factor)
         mu = calculate_shift(
             image_seq_len,
-            self.scheduler.config.base_image_seq_len,
-            self.scheduler.config.max_image_seq_len,
-            self.scheduler.config.base_shift,
-            self.scheduler.config.max_shift,
+            self.scheduler.config.get("base_image_seq_len", 256),
+            self.scheduler.config.get("max_image_seq_len", 4096),
+            self.scheduler.config.get("base_shift", 0.5),
+            self.scheduler.config.get("max_shift", 1.16),
         )
         timesteps, num_inference_steps = retrieve_timesteps(
             self.scheduler,

examples/community/pipeline_flux_rf_inversion.py

@@ -820,10 +820,10 @@ class RFInversionFluxPipeline(
         image_seq_len = (int(height) // self.vae_scale_factor // 2) * (int(width) // self.vae_scale_factor // 2)
         mu = calculate_shift(
             image_seq_len,
-            self.scheduler.config.base_image_seq_len,
-            self.scheduler.config.max_image_seq_len,
-            self.scheduler.config.base_shift,
-            self.scheduler.config.max_shift,
+            self.scheduler.config.get("base_image_seq_len", 256),
+            self.scheduler.config.get("max_image_seq_len", 4096),
+            self.scheduler.config.get("base_shift", 0.5),
+            self.scheduler.config.get("max_shift", 1.16),
         )
         timesteps, num_inference_steps = retrieve_timesteps(
             self.scheduler,
@@ -990,10 +990,10 @@ class RFInversionFluxPipeline(
         image_seq_len = (int(height) // self.vae_scale_factor // 2) * (int(width) // self.vae_scale_factor // 2)
         mu = calculate_shift(
             image_seq_len,
-            self.scheduler.config.base_image_seq_len,
-            self.scheduler.config.max_image_seq_len,
-            self.scheduler.config.base_shift,
-            self.scheduler.config.max_shift,
+            self.scheduler.config.get("base_image_seq_len", 256),
+            self.scheduler.config.get("max_image_seq_len", 4096),
+            self.scheduler.config.get("base_shift", 0.5),
+            self.scheduler.config.get("max_shift", 1.16),
         )
         timesteps, num_inversion_steps = retrieve_timesteps(
             self.scheduler,

examples/community/pipeline_flux_with_cfg.py

@@ -64,6 +64,7 @@ EXAMPLE_DOC_STRING = """
 """
 
 
+# Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift
 def calculate_shift(
     image_seq_len,
     base_seq_len: int = 256,
@@ -755,10 +756,10 @@ class FluxCFGPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFileMixi
         image_seq_len = latents.shape[1]
         mu = calculate_shift(
             image_seq_len,
-            self.scheduler.config.base_image_seq_len,
-            self.scheduler.config.max_image_seq_len,
-            self.scheduler.config.base_shift,
-            self.scheduler.config.max_shift,
+            self.scheduler.config.get("base_image_seq_len", 256),
+            self.scheduler.config.get("max_image_seq_len", 4096),
+            self.scheduler.config.get("base_shift", 0.5),
+            self.scheduler.config.get("max_shift", 1.16),
         )
         timesteps, num_inference_steps = retrieve_timesteps(
             self.scheduler,

examples/community/pipeline_kolors_differential_img2img.py

@@ -216,7 +216,11 @@ class KolorsDifferentialImg2ImgPipeline(
             vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_convert_grayscale=True
         )
 
-        self.default_sample_size = self.unet.config.sample_size
+        self.default_sample_size = (
+            self.unet.config.sample_size
+            if hasattr(self, "unet") and self.unet is not None and hasattr(self.unet.config, "sample_size")
+            else 128
+        )
 
     # Copied from diffusers.pipelines.kolors.pipeline_kolors.KolorsPipeline.encode_prompt
     def encode_prompt(

examples/community/pipeline_prompt2prompt.py

@@ -174,10 +174,14 @@ class Prompt2PromptPipeline(
                 " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
             )
 
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        is_unet_version_less_0_9_0 = (
+            unet is not None
+            and hasattr(unet.config, "_diffusers_version")
+            and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
+        )
+        is_unet_sample_size_less_64 = (
+            unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        )
         if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
             deprecation_message = (
                 "The configuration file of the unet has set the default `sample_size` to smaller than"

examples/community/pipeline_sdxl_style_aligned.py

@@ -494,7 +494,11 @@ class StyleAlignedSDXLPipeline(
             vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True
         )
 
-        self.default_sample_size = self.unet.config.sample_size
+        self.default_sample_size = (
+            self.unet.config.sample_size
+            if hasattr(self, "unet") and self.unet is not None and hasattr(self.unet.config, "sample_size")
+            else 128
+        )
 
         add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()
 
@@ -628,7 +632,9 @@ class StyleAlignedSDXLPipeline(
                 prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
 
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                pooled_prompt_embeds = prompt_embeds[0]
+                if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
+                    pooled_prompt_embeds = prompt_embeds[0]
+
                 if clip_skip is None:
                     prompt_embeds = prompt_embeds.hidden_states[-2]
                 else:
@@ -688,7 +694,8 @@ class StyleAlignedSDXLPipeline(
                     output_hidden_states=True,
                 )
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
+                    negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                 negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
 
                 negative_prompt_embeds_list.append(negative_prompt_embeds)

examples/community/pipeline_stable_diffusion_boxdiff.py

@@ -460,10 +460,14 @@ class StableDiffusionBoxDiffPipeline(
                 " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
             )
 
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        is_unet_version_less_0_9_0 = (
+            unet is not None
+            and hasattr(unet.config, "_diffusers_version")
+            and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
+        )
+        is_unet_sample_size_less_64 = (
+            unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        )
         if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
             deprecation_message = (
                 "The configuration file of the unet has set the default `sample_size` to smaller than"

examples/community/pipeline_stable_diffusion_pag.py

@@ -427,10 +427,14 @@ class StableDiffusionPAGPipeline(
                 " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
             )
 
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        is_unet_version_less_0_9_0 = (
+            unet is not None
+            and hasattr(unet.config, "_diffusers_version")
+            and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
+        )
+        is_unet_sample_size_less_64 = (
+            unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        )
         if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
             deprecation_message = (
                 "The configuration file of the unet has set the default `sample_size` to smaller than"

examples/community/pipeline_stable_diffusion_xl_controlnet_adapter.py

@@ -231,7 +231,11 @@ class StableDiffusionXLControlNetAdapterPipeline(
         self.control_image_processor = VaeImageProcessor(
             vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
         )
-        self.default_sample_size = self.unet.config.sample_size
+        self.default_sample_size = (
+            self.unet.config.sample_size
+            if hasattr(self, "unet") and self.unet is not None and hasattr(self.unet.config, "sample_size")
+            else 128
+        )
 
     # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt
     def encode_prompt(
@@ -359,7 +363,9 @@ class StableDiffusionXLControlNetAdapterPipeline(
                 prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
 
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                pooled_prompt_embeds = prompt_embeds[0]
+                if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
+                    pooled_prompt_embeds = prompt_embeds[0]
+
                 if clip_skip is None:
                     prompt_embeds = prompt_embeds.hidden_states[-2]
                 else:
@@ -419,7 +425,8 @@ class StableDiffusionXLControlNetAdapterPipeline(
                     output_hidden_states=True,
                 )
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
+                    negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                 negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
 
                 negative_prompt_embeds_list.append(negative_prompt_embeds)

examples/community/pipeline_stable_diffusion_xl_controlnet_adapter_inpaint.py

@@ -379,7 +379,11 @@ class StableDiffusionXLControlNetAdapterInpaintPipeline(
         self.control_image_processor = VaeImageProcessor(
             vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
         )
-        self.default_sample_size = self.unet.config.sample_size
+        self.default_sample_size = (
+            self.unet.config.sample_size
+            if hasattr(self, "unet") and self.unet is not None and hasattr(self.unet.config, "sample_size")
+            else 128
+        )
 
     # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt
     def encode_prompt(
@@ -507,7 +511,9 @@ class StableDiffusionXLControlNetAdapterInpaintPipeline(
                 prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
 
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                pooled_prompt_embeds = prompt_embeds[0]
+                if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
+                    pooled_prompt_embeds = prompt_embeds[0]
+
                 if clip_skip is None:
                     prompt_embeds = prompt_embeds.hidden_states[-2]
                 else:
@@ -567,7 +573,8 @@ class StableDiffusionXLControlNetAdapterInpaintPipeline(
                     output_hidden_states=True,
                 )
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
+                    negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                 negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
 
                 negative_prompt_embeds_list.append(negative_prompt_embeds)

examples/community/pipeline_stable_diffusion_xl_differential_img2img.py

@@ -394,7 +394,9 @@ class StableDiffusionXLDifferentialImg2ImgPipeline(
                 prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
 
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                pooled_prompt_embeds = prompt_embeds[0]
+                if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
+                    pooled_prompt_embeds = prompt_embeds[0]
+
                 if clip_skip is None:
                     prompt_embeds = prompt_embeds.hidden_states[-2]
                 else:
@@ -454,7 +456,8 @@ class StableDiffusionXLDifferentialImg2ImgPipeline(
                     output_hidden_states=True,
                 )
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
+                    negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                 negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
 
                 negative_prompt_embeds_list.append(negative_prompt_embeds)

examples/community/pipeline_stable_diffusion_xl_ipex.py

@@ -256,7 +256,11 @@ class StableDiffusionXLPipelineIpex(
         self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
         self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
 
-        self.default_sample_size = self.unet.config.sample_size
+        self.default_sample_size = (
+            self.unet.config.sample_size
+            if hasattr(self, "unet") and self.unet is not None and hasattr(self.unet.config, "sample_size")
+            else 128
+        )
 
         add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()
 
@@ -390,7 +394,9 @@ class StableDiffusionXLPipelineIpex(
                 prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
 
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                pooled_prompt_embeds = prompt_embeds[0]
+                if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
+                    pooled_prompt_embeds = prompt_embeds[0]
+
                 if clip_skip is None:
                     prompt_embeds = prompt_embeds.hidden_states[-2]
                 else:
@@ -450,7 +456,8 @@ class StableDiffusionXLPipelineIpex(
                     output_hidden_states=True,
                 )
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
+                    negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                 negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
 
                 negative_prompt_embeds_list.append(negative_prompt_embeds)

examples/community/pipeline_zero1to3.py

@@ -151,10 +151,14 @@ class Zero1to3StableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin):
                 " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
             )
 
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        is_unet_version_less_0_9_0 = (
+            unet is not None
+            and hasattr(unet.config, "_diffusers_version")
+            and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
+        )
+        is_unet_sample_size_less_64 = (
+            unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        )
         if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
             deprecation_message = (
                 "The configuration file of the unet has set the default `sample_size` to smaller than"

examples/community/rerender_a_video.py

@@ -632,7 +632,7 @@ class RerenderAVideoPipeline(StableDiffusionControlNetImg2ImgPipeline):
                 The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                 instead.
             frames (`List[np.ndarray]` or `torch.Tensor`): The input images to be used as the starting point for the image generation process.
-            control_frames (`List[np.ndarray]` or `torch.Tensor`): The ControlNet input images condition to provide guidance to the `unet` for generation.
+            control_frames (`List[np.ndarray]` or `torch.Tensor` or `Callable`): The ControlNet input images condition to provide guidance to the `unet` for generation or any callable object to convert frame to control_frame.
             strength ('float'): SDEdit strength.
             num_inference_steps (`int`, *optional*, defaults to 50):
                 The number of denoising steps. More denoising steps usually lead to a higher quality image at the
@@ -789,7 +789,7 @@ class RerenderAVideoPipeline(StableDiffusionControlNetImg2ImgPipeline):
         # Currently we only support single control
         if isinstance(controlnet, ControlNetModel):
             control_image = self.prepare_control_image(
-                image=control_frames[0],
+                image=control_frames(frames[0]) if callable(control_frames) else control_frames[0],
                 width=width,
                 height=height,
                 batch_size=batch_size,
@@ -908,6 +908,9 @@ class RerenderAVideoPipeline(StableDiffusionControlNetImg2ImgPipeline):
                     if callback is not None and i % callback_steps == 0:
                         callback(i, t, latents)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         if not output_type == "latent":
             image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
         else:
@@ -924,7 +927,7 @@ class RerenderAVideoPipeline(StableDiffusionControlNetImg2ImgPipeline):
         for idx in range(1, len(frames)):
             image = frames[idx]
             prev_image = frames[idx - 1]
-            control_image = control_frames[idx]
+            control_image = control_frames(image) if callable(control_frames) else control_frames[idx]
             # 5.1 prepare frames
             image = self.image_processor.preprocess(image).to(dtype=self.dtype)
             prev_image = self.image_processor.preprocess(prev_image).to(dtype=self.dtype)

examples/community/stable_diffusion_ipex.py

@@ -148,10 +148,14 @@ class StableDiffusionIPEXPipeline(
                 " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
             )
 
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        is_unet_version_less_0_9_0 = (
+            unet is not None
+            and hasattr(unet.config, "_diffusers_version")
+            and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
+        )
+        is_unet_sample_size_less_64 = (
+            unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        )
         if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
             deprecation_message = (
                 "The configuration file of the unet has set the default `sample_size` to smaller than"

examples/community/stable_diffusion_reference.py

@@ -181,10 +181,14 @@ class StableDiffusionReferencePipeline(
                 " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
             )
 
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        is_unet_version_less_0_9_0 = (
+            unet is not None
+            and hasattr(unet.config, "_diffusers_version")
+            and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
+        )
+        is_unet_sample_size_less_64 = (
+            unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        )
         if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
             deprecation_message = (
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
@@ -202,7 +206,7 @@ class StableDiffusionReferencePipeline(
             new_config["sample_size"] = 64
             unet._internal_dict = FrozenDict(new_config)
         # Check shapes, assume num_channels_latents == 4, num_channels_mask == 1, num_channels_masked == 4
-        if unet.config.in_channels != 4:
+        if unet is not None and unet.config.in_channels != 4:
             logger.warning(
                 f"You have loaded a UNet with {unet.config.in_channels} input channels, whereas by default,"
                 f" {self.__class__} assumes that `pipeline.unet` has 4 input channels: 4 for `num_channels_latents`,"

examples/community/stable_diffusion_repaint.py

@@ -236,10 +236,14 @@ class StableDiffusionRepaintPipeline(
                 " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
             )
 
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        is_unet_version_less_0_9_0 = (
+            unet is not None
+            and hasattr(unet.config, "_diffusers_version")
+            and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
+        )
+        is_unet_sample_size_less_64 = (
+            unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        )
         if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
             deprecation_message = (
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
@@ -257,7 +261,7 @@ class StableDiffusionRepaintPipeline(
             new_config["sample_size"] = 64
             unet._internal_dict = FrozenDict(new_config)
         # Check shapes, assume num_channels_latents == 4, num_channels_mask == 1, num_channels_masked == 4
-        if unet.config.in_channels != 4:
+        if unet is not None and unet.config.in_channels != 4:
             logger.warning(
                 f"You have loaded a UNet with {unet.config.in_channels} input channels, whereas by default,"
                 f" {self.__class__} assumes that `pipeline.unet` has 4 input channels: 4 for `num_channels_latents`,"

examples/community/stable_diffusion_tensorrt_img2img.py

@@ -753,10 +753,14 @@ class TensorRTStableDiffusionImg2ImgPipeline(DiffusionPipeline):
                 " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
             )
 
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        is_unet_version_less_0_9_0 = (
+            unet is not None
+            and hasattr(unet.config, "_diffusers_version")
+            and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
+        )
+        is_unet_sample_size_less_64 = (
+            unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        )
         if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
             deprecation_message = (
                 "The configuration file of the unet has set the default `sample_size` to smaller than"

examples/community/stable_diffusion_tensorrt_inpaint.py

@@ -757,10 +757,14 @@ class TensorRTStableDiffusionInpaintPipeline(DiffusionPipeline):
                 " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
             )
 
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        is_unet_version_less_0_9_0 = (
+            unet is not None
+            and hasattr(unet.config, "_diffusers_version")
+            and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
+        )
+        is_unet_sample_size_less_64 = (
+            unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        )
         if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
             deprecation_message = (
                 "The configuration file of the unet has set the default `sample_size` to smaller than"

examples/community/stable_diffusion_tensorrt_txt2img.py

@@ -669,10 +669,14 @@ class TensorRTStableDiffusionPipeline(DiffusionPipeline):
                 " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
             )
 
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        is_unet_version_less_0_9_0 = (
+            unet is not None
+            and hasattr(unet.config, "_diffusers_version")
+            and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
+        )
+        is_unet_sample_size_less_64 = (
+            unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        )
         if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
             deprecation_message = (
                 "The configuration file of the unet has set the default `sample_size` to smaller than"

examples/research_projects/sd3_lora_colab/train_dreambooth_lora_sd3_miniature.py

@@ -765,7 +765,7 @@ def main(args):
         lora_state_dict = StableDiffusion3Pipeline.lora_state_dict(input_dir)
 
         transformer_state_dict = {
-            f'{k.replace("transformer.", "")}': v for k, v in lora_state_dict.items() if k.startswith("unet.")
+            f'{k.replace("transformer.", "")}': v for k, v in lora_state_dict.items() if k.startswith("transformer.")
         }
         transformer_state_dict = convert_unet_state_dict_to_peft(transformer_state_dict)
         incompatible_keys = set_peft_model_state_dict(transformer_, transformer_state_dict, adapter_name="default")

scripts/convert_sana_to_diffusers.py

@@ -25,6 +25,7 @@ from diffusers.utils.import_utils import is_accelerate_available
 CTX = init_empty_weights if is_accelerate_available else nullcontext
 
 ckpt_ids = [
+    "Efficient-Large-Model/Sana_1600M_4Kpx_BF16/checkpoints/Sana_1600M_4Kpx_BF16.pth",
     "Efficient-Large-Model/Sana_1600M_2Kpx_BF16/checkpoints/Sana_1600M_2Kpx_BF16.pth",
     "Efficient-Large-Model/Sana_1600M_1024px_MultiLing/checkpoints/Sana_1600M_1024px_MultiLing.pth",
     "Efficient-Large-Model/Sana_1600M_1024px_BF16/checkpoints/Sana_1600M_1024px_BF16.pth",
@@ -89,7 +90,10 @@ def main(args):
     converted_state_dict["caption_norm.weight"] = state_dict.pop("attention_y_norm.weight")
 
     # scheduler
-    flow_shift = 3.0
+    if args.image_size == 4096:
+        flow_shift = 6.0
+    else:
+        flow_shift = 3.0
 
     # model config
     if args.model_type == "SanaMS_1600M_P1_D20":
@@ -99,7 +103,7 @@ def main(args):
     else:
         raise ValueError(f"{args.model_type} is not supported.")
     # Positional embedding interpolation scale.
-    interpolation_scale = {512: None, 1024: None, 2048: 1.0}
+    interpolation_scale = {512: None, 1024: None, 2048: 1.0, 4096: 2.0}
 
     for depth in range(layer_num):
         # Transformer blocks.
@@ -272,9 +276,9 @@ if __name__ == "__main__":
         "--image_size",
         default=1024,
         type=int,
-        choices=[512, 1024, 2048],
+        choices=[512, 1024, 2048, 4096],
         required=False,
-        help="Image size of pretrained model, 512, 1024 or 2048.",
+        help="Image size of pretrained model, 512, 1024, 2048 or 4096.",
     )
     parser.add_argument(
         "--model_type", default="SanaMS_1600M_P1_D20", type=str, choices=["SanaMS_1600M_P1_D20", "SanaMS_600M_P1_D28"]

setup.py

@@ -135,6 +135,7 @@ _deps = [
     "transformers>=4.41.2",
     "urllib3<=2.0.0",
     "black",
+    "phonemizer",
 ]
 
 # this is a lookup table with items like:
@@ -227,6 +228,7 @@ extras["test"] = deps_list(
     "scipy",
     "torchvision",
     "transformers",
+    "phonemizer",
 )
 extras["torch"] = deps_list("torch", "accelerate")
 

src/diffusers/dependency_versions_table.py

@@ -43,4 +43,5 @@ deps = {
     "transformers": "transformers>=4.41.2",
     "urllib3": "urllib3<=2.0.0",
     "black": "black",
+    "phonemizer": "phonemizer",
 }

src/diffusers/loaders/lora_base.py

@@ -28,13 +28,20 @@ from ..models.modeling_utils import ModelMixin, load_state_dict
 from ..utils import (
     USE_PEFT_BACKEND,
     _get_model_file,
+    convert_state_dict_to_diffusers,
+    convert_state_dict_to_peft,
     delete_adapter_layers,
     deprecate,
+    get_adapter_name,
+    get_peft_kwargs,
     is_accelerate_available,
     is_peft_available,
+    is_peft_version,
     is_transformers_available,
+    is_transformers_version,
     logging,
     recurse_remove_peft_layers,
+    scale_lora_layers,
     set_adapter_layers,
     set_weights_and_activate_adapters,
 )
@@ -43,6 +50,8 @@ from ..utils import (
 if is_transformers_available():
     from transformers import PreTrainedModel
 
+    from ..models.lora import text_encoder_attn_modules, text_encoder_mlp_modules
+
 if is_peft_available():
     from peft.tuners.tuners_utils import BaseTunerLayer
 
@@ -297,6 +306,152 @@ def _best_guess_weight_name(
     return weight_name
 
 
+def _load_lora_into_text_encoder(
+    state_dict,
+    network_alphas,
+    text_encoder,
+    prefix=None,
+    lora_scale=1.0,
+    text_encoder_name="text_encoder",
+    adapter_name=None,
+    _pipeline=None,
+    low_cpu_mem_usage=False,
+):
+    if not USE_PEFT_BACKEND:
+        raise ValueError("PEFT backend is required for this method.")
+
+    peft_kwargs = {}
+    if low_cpu_mem_usage:
+        if not is_peft_version(">=", "0.13.1"):
+            raise ValueError(
+                "`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
+            )
+        if not is_transformers_version(">", "4.45.2"):
+            # Note from sayakpaul: It's not in `transformers` stable yet.
+            # https://github.com/huggingface/transformers/pull/33725/
+            raise ValueError(
+                "`low_cpu_mem_usage=True` is not compatible with this `transformers` version. Please update it with `pip install -U transformers`."
+            )
+        peft_kwargs["low_cpu_mem_usage"] = low_cpu_mem_usage
+
+    from peft import LoraConfig
+
+    # If the serialization format is new (introduced in https://github.com/huggingface/diffusers/pull/2918),
+    # then the `state_dict` keys should have `unet_name` and/or `text_encoder_name` as
+    # their prefixes.
+    keys = list(state_dict.keys())
+    prefix = text_encoder_name if prefix is None else prefix
+
+    # Safe prefix to check with.
+    if any(text_encoder_name in key for key in keys):
+        # Load the layers corresponding to text encoder and make necessary adjustments.
+        text_encoder_keys = [k for k in keys if k.startswith(prefix) and k.split(".")[0] == prefix]
+        text_encoder_lora_state_dict = {
+            k.replace(f"{prefix}.", ""): v for k, v in state_dict.items() if k in text_encoder_keys
+        }
+
+        if len(text_encoder_lora_state_dict) > 0:
+            logger.info(f"Loading {prefix}.")
+            rank = {}
+            text_encoder_lora_state_dict = convert_state_dict_to_diffusers(text_encoder_lora_state_dict)
+
+            # convert state dict
+            text_encoder_lora_state_dict = convert_state_dict_to_peft(text_encoder_lora_state_dict)
+
+            for name, _ in text_encoder_attn_modules(text_encoder):
+                for module in ("out_proj", "q_proj", "k_proj", "v_proj"):
+                    rank_key = f"{name}.{module}.lora_B.weight"
+                    if rank_key not in text_encoder_lora_state_dict:
+                        continue
+                    rank[rank_key] = text_encoder_lora_state_dict[rank_key].shape[1]
+
+            for name, _ in text_encoder_mlp_modules(text_encoder):
+                for module in ("fc1", "fc2"):
+                    rank_key = f"{name}.{module}.lora_B.weight"
+                    if rank_key not in text_encoder_lora_state_dict:
+                        continue
+                    rank[rank_key] = text_encoder_lora_state_dict[rank_key].shape[1]
+
+            if network_alphas is not None:
+                alpha_keys = [k for k in network_alphas.keys() if k.startswith(prefix) and k.split(".")[0] == prefix]
+                network_alphas = {k.replace(f"{prefix}.", ""): v for k, v in network_alphas.items() if k in alpha_keys}
+
+            lora_config_kwargs = get_peft_kwargs(rank, network_alphas, text_encoder_lora_state_dict, is_unet=False)
+
+            if "use_dora" in lora_config_kwargs:
+                if lora_config_kwargs["use_dora"]:
+                    if is_peft_version("<", "0.9.0"):
+                        raise ValueError(
+                            "You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`."
+                        )
+                else:
+                    if is_peft_version("<", "0.9.0"):
+                        lora_config_kwargs.pop("use_dora")
+
+            if "lora_bias" in lora_config_kwargs:
+                if lora_config_kwargs["lora_bias"]:
+                    if is_peft_version("<=", "0.13.2"):
+                        raise ValueError(
+                            "You need `peft` 0.14.0 at least to use `bias` in LoRAs. Please upgrade your installation of `peft`."
+                        )
+                else:
+                    if is_peft_version("<=", "0.13.2"):
+                        lora_config_kwargs.pop("lora_bias")
+
+            lora_config = LoraConfig(**lora_config_kwargs)
+
+            # adapter_name
+            if adapter_name is None:
+                adapter_name = get_adapter_name(text_encoder)
+
+            is_model_cpu_offload, is_sequential_cpu_offload = _func_optionally_disable_offloading(_pipeline)
+
+            # inject LoRA layers and load the state dict
+            # in transformers we automatically check whether the adapter name is already in use or not
+            text_encoder.load_adapter(
+                adapter_name=adapter_name,
+                adapter_state_dict=text_encoder_lora_state_dict,
+                peft_config=lora_config,
+                **peft_kwargs,
+            )
+
+            # scale LoRA layers with `lora_scale`
+            scale_lora_layers(text_encoder, weight=lora_scale)
+
+            text_encoder.to(device=text_encoder.device, dtype=text_encoder.dtype)
+
+            # Offload back.
+            if is_model_cpu_offload:
+                _pipeline.enable_model_cpu_offload()
+            elif is_sequential_cpu_offload:
+                _pipeline.enable_sequential_cpu_offload()
+            # Unsafe code />
+
+
+def _func_optionally_disable_offloading(_pipeline):
+    is_model_cpu_offload = False
+    is_sequential_cpu_offload = False
+
+    if _pipeline is not None and _pipeline.hf_device_map is None:
+        for _, component in _pipeline.components.items():
+            if isinstance(component, nn.Module) and hasattr(component, "_hf_hook"):
+                if not is_model_cpu_offload:
+                    is_model_cpu_offload = isinstance(component._hf_hook, CpuOffload)
+                if not is_sequential_cpu_offload:
+                    is_sequential_cpu_offload = (
+                        isinstance(component._hf_hook, AlignDevicesHook)
+                        or hasattr(component._hf_hook, "hooks")
+                        and isinstance(component._hf_hook.hooks[0], AlignDevicesHook)
+                    )
+
+                logger.info(
+                    "Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again."
+                )
+                remove_hook_from_module(component, recurse=is_sequential_cpu_offload)
+
+    return (is_model_cpu_offload, is_sequential_cpu_offload)
+
+
 class LoraBaseMixin:
     """Utility class for handling LoRAs."""
 
@@ -327,27 +482,7 @@ class LoraBaseMixin:
             tuple:
                 A tuple indicating if `is_model_cpu_offload` or `is_sequential_cpu_offload` is True.
         """
-        is_model_cpu_offload = False
-        is_sequential_cpu_offload = False
-
-        if _pipeline is not None and _pipeline.hf_device_map is None:
-            for _, component in _pipeline.components.items():
-                if isinstance(component, nn.Module) and hasattr(component, "_hf_hook"):
-                    if not is_model_cpu_offload:
-                        is_model_cpu_offload = isinstance(component._hf_hook, CpuOffload)
-                    if not is_sequential_cpu_offload:
-                        is_sequential_cpu_offload = (
-                            isinstance(component._hf_hook, AlignDevicesHook)
-                            or hasattr(component._hf_hook, "hooks")
-                            and isinstance(component._hf_hook.hooks[0], AlignDevicesHook)
-                        )
-
-                    logger.info(
-                        "Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again."
-                    )
-                    remove_hook_from_module(component, recurse=is_sequential_cpu_offload)
-
-        return (is_model_cpu_offload, is_sequential_cpu_offload)
+        return _func_optionally_disable_offloading(_pipeline=_pipeline)
 
     @classmethod
     def _fetch_state_dict(cls, *args, **kwargs):

src/diffusers/loaders/lora_pipeline.py

@@ -20,20 +20,21 @@ from huggingface_hub.utils import validate_hf_hub_args
 
 from ..utils import (
     USE_PEFT_BACKEND,
-    convert_state_dict_to_diffusers,
-    convert_state_dict_to_peft,
     deprecate,
-    get_adapter_name,
-    get_peft_kwargs,
     is_peft_available,
     is_peft_version,
     is_torch_version,
     is_transformers_available,
     is_transformers_version,
     logging,
-    scale_lora_layers,
 )
-from .lora_base import LORA_WEIGHT_NAME, LORA_WEIGHT_NAME_SAFE, LoraBaseMixin, _fetch_state_dict  # noqa
+from .lora_base import (  # noqa
+    LORA_WEIGHT_NAME,
+    LORA_WEIGHT_NAME_SAFE,
+    LoraBaseMixin,
+    _fetch_state_dict,
+    _load_lora_into_text_encoder,
+)
 from .lora_conversion_utils import (
     _convert_bfl_flux_control_lora_to_diffusers,
     _convert_hunyuan_video_lora_to_diffusers,
@@ -55,9 +56,6 @@ if is_torch_version(">=", "1.9.0"):
         _LOW_CPU_MEM_USAGE_DEFAULT_LORA = True
 
 
-if is_transformers_available():
-    from ..models.lora import text_encoder_attn_modules, text_encoder_mlp_modules
-
 logger = logging.get_logger(__name__)
 
 TEXT_ENCODER_NAME = "text_encoder"
@@ -349,119 +347,17 @@ class StableDiffusionLoraLoaderMixin(LoraBaseMixin):
                 Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
                 weights.
         """
-        if not USE_PEFT_BACKEND:
-            raise ValueError("PEFT backend is required for this method.")
-
-        peft_kwargs = {}
-        if low_cpu_mem_usage:
-            if not is_peft_version(">=", "0.13.1"):
-                raise ValueError(
-                    "`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
-                )
-            if not is_transformers_version(">", "4.45.2"):
-                # Note from sayakpaul: It's not in `transformers` stable yet.
-                # https://github.com/huggingface/transformers/pull/33725/
-                raise ValueError(
-                    "`low_cpu_mem_usage=True` is not compatible with this `transformers` version. Please update it with `pip install -U transformers`."
-                )
-            peft_kwargs["low_cpu_mem_usage"] = low_cpu_mem_usage
-
-        from peft import LoraConfig
-
-        # If the serialization format is new (introduced in https://github.com/huggingface/diffusers/pull/2918),
-        # then the `state_dict` keys should have `self.unet_name` and/or `self.text_encoder_name` as
-        # their prefixes.
-        keys = list(state_dict.keys())
-        prefix = cls.text_encoder_name if prefix is None else prefix
-
-        # Safe prefix to check with.
-        if any(cls.text_encoder_name in key for key in keys):
-            # Load the layers corresponding to text encoder and make necessary adjustments.
-            text_encoder_keys = [k for k in keys if k.startswith(prefix) and k.split(".")[0] == prefix]
-            text_encoder_lora_state_dict = {
-                k.replace(f"{prefix}.", ""): v for k, v in state_dict.items() if k in text_encoder_keys
-            }
-
-            if len(text_encoder_lora_state_dict) > 0:
-                logger.info(f"Loading {prefix}.")
-                rank = {}
-                text_encoder_lora_state_dict = convert_state_dict_to_diffusers(text_encoder_lora_state_dict)
-
-                # convert state dict
-                text_encoder_lora_state_dict = convert_state_dict_to_peft(text_encoder_lora_state_dict)
-
-                for name, _ in text_encoder_attn_modules(text_encoder):
-                    for module in ("out_proj", "q_proj", "k_proj", "v_proj"):
-                        rank_key = f"{name}.{module}.lora_B.weight"
-                        if rank_key not in text_encoder_lora_state_dict:
-                            continue
-                        rank[rank_key] = text_encoder_lora_state_dict[rank_key].shape[1]
-
-                for name, _ in text_encoder_mlp_modules(text_encoder):
-                    for module in ("fc1", "fc2"):
-                        rank_key = f"{name}.{module}.lora_B.weight"
-                        if rank_key not in text_encoder_lora_state_dict:
-                            continue
-                        rank[rank_key] = text_encoder_lora_state_dict[rank_key].shape[1]
-
-                if network_alphas is not None:
-                    alpha_keys = [
-                        k for k in network_alphas.keys() if k.startswith(prefix) and k.split(".")[0] == prefix
-                    ]
-                    network_alphas = {
-                        k.replace(f"{prefix}.", ""): v for k, v in network_alphas.items() if k in alpha_keys
-                    }
-
-                lora_config_kwargs = get_peft_kwargs(rank, network_alphas, text_encoder_lora_state_dict, is_unet=False)
-
-                if "use_dora" in lora_config_kwargs:
-                    if lora_config_kwargs["use_dora"]:
-                        if is_peft_version("<", "0.9.0"):
-                            raise ValueError(
-                                "You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`."
-                            )
-                    else:
-                        if is_peft_version("<", "0.9.0"):
-                            lora_config_kwargs.pop("use_dora")
-
-                if "lora_bias" in lora_config_kwargs:
-                    if lora_config_kwargs["lora_bias"]:
-                        if is_peft_version("<=", "0.13.2"):
-                            raise ValueError(
-                                "You need `peft` 0.14.0 at least to use `bias` in LoRAs. Please upgrade your installation of `peft`."
-                            )
-                    else:
-                        if is_peft_version("<=", "0.13.2"):
-                            lora_config_kwargs.pop("lora_bias")
-
-                lora_config = LoraConfig(**lora_config_kwargs)
-
-                # adapter_name
-                if adapter_name is None:
-                    adapter_name = get_adapter_name(text_encoder)
-
-                is_model_cpu_offload, is_sequential_cpu_offload = cls._optionally_disable_offloading(_pipeline)
-
-                # inject LoRA layers and load the state dict
-                # in transformers we automatically check whether the adapter name is already in use or not
-                text_encoder.load_adapter(
-                    adapter_name=adapter_name,
-                    adapter_state_dict=text_encoder_lora_state_dict,
-                    peft_config=lora_config,
-                    **peft_kwargs,
-                )
-
-                # scale LoRA layers with `lora_scale`
-                scale_lora_layers(text_encoder, weight=lora_scale)
-
-                text_encoder.to(device=text_encoder.device, dtype=text_encoder.dtype)
-
-                # Offload back.
-                if is_model_cpu_offload:
-                    _pipeline.enable_model_cpu_offload()
-                elif is_sequential_cpu_offload:
-                    _pipeline.enable_sequential_cpu_offload()
-                # Unsafe code />
+        _load_lora_into_text_encoder(
+            state_dict=state_dict,
+            network_alphas=network_alphas,
+            lora_scale=lora_scale,
+            text_encoder=text_encoder,
+            prefix=prefix,
+            text_encoder_name=cls.text_encoder_name,
+            adapter_name=adapter_name,
+            _pipeline=_pipeline,
+            low_cpu_mem_usage=low_cpu_mem_usage,
+        )
 
     @classmethod
     def save_lora_weights(
@@ -892,119 +788,17 @@ class StableDiffusionXLLoraLoaderMixin(LoraBaseMixin):
                 Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
                 weights.
         """
-        if not USE_PEFT_BACKEND:
-            raise ValueError("PEFT backend is required for this method.")
-
-        peft_kwargs = {}
-        if low_cpu_mem_usage:
-            if not is_peft_version(">=", "0.13.1"):
-                raise ValueError(
-                    "`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
-                )
-            if not is_transformers_version(">", "4.45.2"):
-                # Note from sayakpaul: It's not in `transformers` stable yet.
-                # https://github.com/huggingface/transformers/pull/33725/
-                raise ValueError(
-                    "`low_cpu_mem_usage=True` is not compatible with this `transformers` version. Please update it with `pip install -U transformers`."
-                )
-            peft_kwargs["low_cpu_mem_usage"] = low_cpu_mem_usage
-
-        from peft import LoraConfig
-
-        # If the serialization format is new (introduced in https://github.com/huggingface/diffusers/pull/2918),
-        # then the `state_dict` keys should have `self.unet_name` and/or `self.text_encoder_name` as
-        # their prefixes.
-        keys = list(state_dict.keys())
-        prefix = cls.text_encoder_name if prefix is None else prefix
-
-        # Safe prefix to check with.
-        if any(cls.text_encoder_name in key for key in keys):
-            # Load the layers corresponding to text encoder and make necessary adjustments.
-            text_encoder_keys = [k for k in keys if k.startswith(prefix) and k.split(".")[0] == prefix]
-            text_encoder_lora_state_dict = {
-                k.replace(f"{prefix}.", ""): v for k, v in state_dict.items() if k in text_encoder_keys
-            }
-
-            if len(text_encoder_lora_state_dict) > 0:
-                logger.info(f"Loading {prefix}.")
-                rank = {}
-                text_encoder_lora_state_dict = convert_state_dict_to_diffusers(text_encoder_lora_state_dict)
-
-                # convert state dict
-                text_encoder_lora_state_dict = convert_state_dict_to_peft(text_encoder_lora_state_dict)
-
-                for name, _ in text_encoder_attn_modules(text_encoder):
-                    for module in ("out_proj", "q_proj", "k_proj", "v_proj"):
-                        rank_key = f"{name}.{module}.lora_B.weight"
-                        if rank_key not in text_encoder_lora_state_dict:
-                            continue
-                        rank[rank_key] = text_encoder_lora_state_dict[rank_key].shape[1]
-
-                for name, _ in text_encoder_mlp_modules(text_encoder):
-                    for module in ("fc1", "fc2"):
-                        rank_key = f"{name}.{module}.lora_B.weight"
-                        if rank_key not in text_encoder_lora_state_dict:
-                            continue
-                        rank[rank_key] = text_encoder_lora_state_dict[rank_key].shape[1]
-
-                if network_alphas is not None:
-                    alpha_keys = [
-                        k for k in network_alphas.keys() if k.startswith(prefix) and k.split(".")[0] == prefix
-                    ]
-                    network_alphas = {
-                        k.replace(f"{prefix}.", ""): v for k, v in network_alphas.items() if k in alpha_keys
-                    }
-
-                lora_config_kwargs = get_peft_kwargs(rank, network_alphas, text_encoder_lora_state_dict, is_unet=False)
-
-                if "use_dora" in lora_config_kwargs:
-                    if lora_config_kwargs["use_dora"]:
-                        if is_peft_version("<", "0.9.0"):
-                            raise ValueError(
-                                "You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`."
-                            )
-                    else:
-                        if is_peft_version("<", "0.9.0"):
-                            lora_config_kwargs.pop("use_dora")
-
-                if "lora_bias" in lora_config_kwargs:
-                    if lora_config_kwargs["lora_bias"]:
-                        if is_peft_version("<=", "0.13.2"):
-                            raise ValueError(
-                                "You need `peft` 0.14.0 at least to use `bias` in LoRAs. Please upgrade your installation of `peft`."
-                            )
-                    else:
-                        if is_peft_version("<=", "0.13.2"):
-                            lora_config_kwargs.pop("lora_bias")
-
-                lora_config = LoraConfig(**lora_config_kwargs)
-
-                # adapter_name
-                if adapter_name is None:
-                    adapter_name = get_adapter_name(text_encoder)
-
-                is_model_cpu_offload, is_sequential_cpu_offload = cls._optionally_disable_offloading(_pipeline)
-
-                # inject LoRA layers and load the state dict
-                # in transformers we automatically check whether the adapter name is already in use or not
-                text_encoder.load_adapter(
-                    adapter_name=adapter_name,
-                    adapter_state_dict=text_encoder_lora_state_dict,
-                    peft_config=lora_config,
-                    **peft_kwargs,
-                )
-
-                # scale LoRA layers with `lora_scale`
-                scale_lora_layers(text_encoder, weight=lora_scale)
-
-                text_encoder.to(device=text_encoder.device, dtype=text_encoder.dtype)
-
-                # Offload back.
-                if is_model_cpu_offload:
-                    _pipeline.enable_model_cpu_offload()
-                elif is_sequential_cpu_offload:
-                    _pipeline.enable_sequential_cpu_offload()
-                # Unsafe code />
+        _load_lora_into_text_encoder(
+            state_dict=state_dict,
+            network_alphas=network_alphas,
+            lora_scale=lora_scale,
+            text_encoder=text_encoder,
+            prefix=prefix,
+            text_encoder_name=cls.text_encoder_name,
+            adapter_name=adapter_name,
+            _pipeline=_pipeline,
+            low_cpu_mem_usage=low_cpu_mem_usage,
+        )
 
     @classmethod
     def save_lora_weights(
@@ -1401,119 +1195,17 @@ class SD3LoraLoaderMixin(LoraBaseMixin):
                 Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
                 weights.
         """
-        if not USE_PEFT_BACKEND:
-            raise ValueError("PEFT backend is required for this method.")
-
-        peft_kwargs = {}
-        if low_cpu_mem_usage:
-            if not is_peft_version(">=", "0.13.1"):
-                raise ValueError(
-                    "`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
-                )
-            if not is_transformers_version(">", "4.45.2"):
-                # Note from sayakpaul: It's not in `transformers` stable yet.
-                # https://github.com/huggingface/transformers/pull/33725/
-                raise ValueError(
-                    "`low_cpu_mem_usage=True` is not compatible with this `transformers` version. Please update it with `pip install -U transformers`."
-                )
-            peft_kwargs["low_cpu_mem_usage"] = low_cpu_mem_usage
-
-        from peft import LoraConfig
-
-        # If the serialization format is new (introduced in https://github.com/huggingface/diffusers/pull/2918),
-        # then the `state_dict` keys should have `self.unet_name` and/or `self.text_encoder_name` as
-        # their prefixes.
-        keys = list(state_dict.keys())
-        prefix = cls.text_encoder_name if prefix is None else prefix
-
-        # Safe prefix to check with.
-        if any(cls.text_encoder_name in key for key in keys):
-            # Load the layers corresponding to text encoder and make necessary adjustments.
-            text_encoder_keys = [k for k in keys if k.startswith(prefix) and k.split(".")[0] == prefix]
-            text_encoder_lora_state_dict = {
-                k.replace(f"{prefix}.", ""): v for k, v in state_dict.items() if k in text_encoder_keys
-            }
-
-            if len(text_encoder_lora_state_dict) > 0:
-                logger.info(f"Loading {prefix}.")
-                rank = {}
-                text_encoder_lora_state_dict = convert_state_dict_to_diffusers(text_encoder_lora_state_dict)
-
-                # convert state dict
-                text_encoder_lora_state_dict = convert_state_dict_to_peft(text_encoder_lora_state_dict)
-
-                for name, _ in text_encoder_attn_modules(text_encoder):
-                    for module in ("out_proj", "q_proj", "k_proj", "v_proj"):
-                        rank_key = f"{name}.{module}.lora_B.weight"
-                        if rank_key not in text_encoder_lora_state_dict:
-                            continue
-                        rank[rank_key] = text_encoder_lora_state_dict[rank_key].shape[1]
-
-                for name, _ in text_encoder_mlp_modules(text_encoder):
-                    for module in ("fc1", "fc2"):
-                        rank_key = f"{name}.{module}.lora_B.weight"
-                        if rank_key not in text_encoder_lora_state_dict:
-                            continue
-                        rank[rank_key] = text_encoder_lora_state_dict[rank_key].shape[1]
-
-                if network_alphas is not None:
-                    alpha_keys = [
-                        k for k in network_alphas.keys() if k.startswith(prefix) and k.split(".")[0] == prefix
-                    ]
-                    network_alphas = {
-                        k.replace(f"{prefix}.", ""): v for k, v in network_alphas.items() if k in alpha_keys
-                    }
-
-                lora_config_kwargs = get_peft_kwargs(rank, network_alphas, text_encoder_lora_state_dict, is_unet=False)
-
-                if "use_dora" in lora_config_kwargs:
-                    if lora_config_kwargs["use_dora"]:
-                        if is_peft_version("<", "0.9.0"):
-                            raise ValueError(
-                                "You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`."
-                            )
-                    else:
-                        if is_peft_version("<", "0.9.0"):
-                            lora_config_kwargs.pop("use_dora")
-
-                if "lora_bias" in lora_config_kwargs:
-                    if lora_config_kwargs["lora_bias"]:
-                        if is_peft_version("<=", "0.13.2"):
-                            raise ValueError(
-                                "You need `peft` 0.14.0 at least to use `bias` in LoRAs. Please upgrade your installation of `peft`."
-                            )
-                    else:
-                        if is_peft_version("<=", "0.13.2"):
-                            lora_config_kwargs.pop("lora_bias")
-
-                lora_config = LoraConfig(**lora_config_kwargs)
-
-                # adapter_name
-                if adapter_name is None:
-                    adapter_name = get_adapter_name(text_encoder)
-
-                is_model_cpu_offload, is_sequential_cpu_offload = cls._optionally_disable_offloading(_pipeline)
-
-                # inject LoRA layers and load the state dict
-                # in transformers we automatically check whether the adapter name is already in use or not
-                text_encoder.load_adapter(
-                    adapter_name=adapter_name,
-                    adapter_state_dict=text_encoder_lora_state_dict,
-                    peft_config=lora_config,
-                    **peft_kwargs,
-                )
-
-                # scale LoRA layers with `lora_scale`
-                scale_lora_layers(text_encoder, weight=lora_scale)
-
-                text_encoder.to(device=text_encoder.device, dtype=text_encoder.dtype)
-
-                # Offload back.
-                if is_model_cpu_offload:
-                    _pipeline.enable_model_cpu_offload()
-                elif is_sequential_cpu_offload:
-                    _pipeline.enable_sequential_cpu_offload()
-                # Unsafe code />
+        _load_lora_into_text_encoder(
+            state_dict=state_dict,
+            network_alphas=network_alphas,
+            lora_scale=lora_scale,
+            text_encoder=text_encoder,
+            prefix=prefix,
+            text_encoder_name=cls.text_encoder_name,
+            adapter_name=adapter_name,
+            _pipeline=_pipeline,
+            low_cpu_mem_usage=low_cpu_mem_usage,
+        )
 
     @classmethod
     def save_lora_weights(
@@ -2033,119 +1725,17 @@ class FluxLoraLoaderMixin(LoraBaseMixin):
                 Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
                 weights.
         """
-        if not USE_PEFT_BACKEND:
-            raise ValueError("PEFT backend is required for this method.")
-
-        peft_kwargs = {}
-        if low_cpu_mem_usage:
-            if not is_peft_version(">=", "0.13.1"):
-                raise ValueError(
-                    "`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
-                )
-            if not is_transformers_version(">", "4.45.2"):
-                # Note from sayakpaul: It's not in `transformers` stable yet.
-                # https://github.com/huggingface/transformers/pull/33725/
-                raise ValueError(
-                    "`low_cpu_mem_usage=True` is not compatible with this `transformers` version. Please update it with `pip install -U transformers`."
-                )
-            peft_kwargs["low_cpu_mem_usage"] = low_cpu_mem_usage
-
-        from peft import LoraConfig
-
-        # If the serialization format is new (introduced in https://github.com/huggingface/diffusers/pull/2918),
-        # then the `state_dict` keys should have `self.unet_name` and/or `self.text_encoder_name` as
-        # their prefixes.
-        keys = list(state_dict.keys())
-        prefix = cls.text_encoder_name if prefix is None else prefix
-
-        # Safe prefix to check with.
-        if any(cls.text_encoder_name in key for key in keys):
-            # Load the layers corresponding to text encoder and make necessary adjustments.
-            text_encoder_keys = [k for k in keys if k.startswith(prefix) and k.split(".")[0] == prefix]
-            text_encoder_lora_state_dict = {
-                k.replace(f"{prefix}.", ""): v for k, v in state_dict.items() if k in text_encoder_keys
-            }
-
-            if len(text_encoder_lora_state_dict) > 0:
-                logger.info(f"Loading {prefix}.")
-                rank = {}
-                text_encoder_lora_state_dict = convert_state_dict_to_diffusers(text_encoder_lora_state_dict)
-
-                # convert state dict
-                text_encoder_lora_state_dict = convert_state_dict_to_peft(text_encoder_lora_state_dict)
-
-                for name, _ in text_encoder_attn_modules(text_encoder):
-                    for module in ("out_proj", "q_proj", "k_proj", "v_proj"):
-                        rank_key = f"{name}.{module}.lora_B.weight"
-                        if rank_key not in text_encoder_lora_state_dict:
-                            continue
-                        rank[rank_key] = text_encoder_lora_state_dict[rank_key].shape[1]
-
-                for name, _ in text_encoder_mlp_modules(text_encoder):
-                    for module in ("fc1", "fc2"):
-                        rank_key = f"{name}.{module}.lora_B.weight"
-                        if rank_key not in text_encoder_lora_state_dict:
-                            continue
-                        rank[rank_key] = text_encoder_lora_state_dict[rank_key].shape[1]
-
-                if network_alphas is not None:
-                    alpha_keys = [
-                        k for k in network_alphas.keys() if k.startswith(prefix) and k.split(".")[0] == prefix
-                    ]
-                    network_alphas = {
-                        k.replace(f"{prefix}.", ""): v for k, v in network_alphas.items() if k in alpha_keys
-                    }
-
-                lora_config_kwargs = get_peft_kwargs(rank, network_alphas, text_encoder_lora_state_dict, is_unet=False)
-
-                if "use_dora" in lora_config_kwargs:
-                    if lora_config_kwargs["use_dora"]:
-                        if is_peft_version("<", "0.9.0"):
-                            raise ValueError(
-                                "You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`."
-                            )
-                    else:
-                        if is_peft_version("<", "0.9.0"):
-                            lora_config_kwargs.pop("use_dora")
-
-                if "lora_bias" in lora_config_kwargs:
-                    if lora_config_kwargs["lora_bias"]:
-                        if is_peft_version("<=", "0.13.2"):
-                            raise ValueError(
-                                "You need `peft` 0.14.0 at least to use `bias` in LoRAs. Please upgrade your installation of `peft`."
-                            )
-                    else:
-                        if is_peft_version("<=", "0.13.2"):
-                            lora_config_kwargs.pop("lora_bias")
-
-                lora_config = LoraConfig(**lora_config_kwargs)
-
-                # adapter_name
-                if adapter_name is None:
-                    adapter_name = get_adapter_name(text_encoder)
-
-                is_model_cpu_offload, is_sequential_cpu_offload = cls._optionally_disable_offloading(_pipeline)
-
-                # inject LoRA layers and load the state dict
-                # in transformers we automatically check whether the adapter name is already in use or not
-                text_encoder.load_adapter(
-                    adapter_name=adapter_name,
-                    adapter_state_dict=text_encoder_lora_state_dict,
-                    peft_config=lora_config,
-                    **peft_kwargs,
-                )
-
-                # scale LoRA layers with `lora_scale`
-                scale_lora_layers(text_encoder, weight=lora_scale)
-
-                text_encoder.to(device=text_encoder.device, dtype=text_encoder.dtype)
-
-                # Offload back.
-                if is_model_cpu_offload:
-                    _pipeline.enable_model_cpu_offload()
-                elif is_sequential_cpu_offload:
-                    _pipeline.enable_sequential_cpu_offload()
-                # Unsafe code />
+        _load_lora_into_text_encoder(
+            state_dict=state_dict,
+            network_alphas=network_alphas,
+            lora_scale=lora_scale,
+            text_encoder=text_encoder,
+            prefix=prefix,
+            text_encoder_name=cls.text_encoder_name,
+            adapter_name=adapter_name,
+            _pipeline=_pipeline,
+            low_cpu_mem_usage=low_cpu_mem_usage,
+        )
 
     @classmethod
     # Copied from diffusers.loaders.lora_pipeline.StableDiffusionLoraLoaderMixin.save_lora_weights with unet->transformer
@@ -2204,7 +1794,7 @@ class FluxLoraLoaderMixin(LoraBaseMixin):
 
     def fuse_lora(
         self,
-        components: List[str] = ["transformer", "text_encoder"],
+        components: List[str] = ["transformer"],
         lora_scale: float = 1.0,
         safe_fusing: bool = False,
         adapter_names: Optional[List[str]] = None,
@@ -2598,119 +2188,17 @@ class AmusedLoraLoaderMixin(StableDiffusionLoraLoaderMixin):
                 Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
                 weights.
         """
-        if not USE_PEFT_BACKEND:
-            raise ValueError("PEFT backend is required for this method.")
-
-        peft_kwargs = {}
-        if low_cpu_mem_usage:
-            if not is_peft_version(">=", "0.13.1"):
-                raise ValueError(
-                    "`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
-                )
-            if not is_transformers_version(">", "4.45.2"):
-                # Note from sayakpaul: It's not in `transformers` stable yet.
-                # https://github.com/huggingface/transformers/pull/33725/
-                raise ValueError(
-                    "`low_cpu_mem_usage=True` is not compatible with this `transformers` version. Please update it with `pip install -U transformers`."
-                )
-            peft_kwargs["low_cpu_mem_usage"] = low_cpu_mem_usage
-
-        from peft import LoraConfig
-
-        # If the serialization format is new (introduced in https://github.com/huggingface/diffusers/pull/2918),
-        # then the `state_dict` keys should have `self.unet_name` and/or `self.text_encoder_name` as
-        # their prefixes.
-        keys = list(state_dict.keys())
-        prefix = cls.text_encoder_name if prefix is None else prefix
-
-        # Safe prefix to check with.
-        if any(cls.text_encoder_name in key for key in keys):
-            # Load the layers corresponding to text encoder and make necessary adjustments.
-            text_encoder_keys = [k for k in keys if k.startswith(prefix) and k.split(".")[0] == prefix]
-            text_encoder_lora_state_dict = {
-                k.replace(f"{prefix}.", ""): v for k, v in state_dict.items() if k in text_encoder_keys
-            }
-
-            if len(text_encoder_lora_state_dict) > 0:
-                logger.info(f"Loading {prefix}.")
-                rank = {}
-                text_encoder_lora_state_dict = convert_state_dict_to_diffusers(text_encoder_lora_state_dict)
-
-                # convert state dict
-                text_encoder_lora_state_dict = convert_state_dict_to_peft(text_encoder_lora_state_dict)
-
-                for name, _ in text_encoder_attn_modules(text_encoder):
-                    for module in ("out_proj", "q_proj", "k_proj", "v_proj"):
-                        rank_key = f"{name}.{module}.lora_B.weight"
-                        if rank_key not in text_encoder_lora_state_dict:
-                            continue
-                        rank[rank_key] = text_encoder_lora_state_dict[rank_key].shape[1]
-
-                for name, _ in text_encoder_mlp_modules(text_encoder):
-                    for module in ("fc1", "fc2"):
-                        rank_key = f"{name}.{module}.lora_B.weight"
-                        if rank_key not in text_encoder_lora_state_dict:
-                            continue
-                        rank[rank_key] = text_encoder_lora_state_dict[rank_key].shape[1]
-
-                if network_alphas is not None:
-                    alpha_keys = [
-                        k for k in network_alphas.keys() if k.startswith(prefix) and k.split(".")[0] == prefix
-                    ]
-                    network_alphas = {
-                        k.replace(f"{prefix}.", ""): v for k, v in network_alphas.items() if k in alpha_keys
-                    }
-
-                lora_config_kwargs = get_peft_kwargs(rank, network_alphas, text_encoder_lora_state_dict, is_unet=False)
-
-                if "use_dora" in lora_config_kwargs:
-                    if lora_config_kwargs["use_dora"]:
-                        if is_peft_version("<", "0.9.0"):
-                            raise ValueError(
-                                "You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`."
-                            )
-                    else:
-                        if is_peft_version("<", "0.9.0"):
-                            lora_config_kwargs.pop("use_dora")
-
-                if "lora_bias" in lora_config_kwargs:
-                    if lora_config_kwargs["lora_bias"]:
-                        if is_peft_version("<=", "0.13.2"):
-                            raise ValueError(
-                                "You need `peft` 0.14.0 at least to use `bias` in LoRAs. Please upgrade your installation of `peft`."
-                            )
-                    else:
-                        if is_peft_version("<=", "0.13.2"):
-                            lora_config_kwargs.pop("lora_bias")
-
-                lora_config = LoraConfig(**lora_config_kwargs)
-
-                # adapter_name
-                if adapter_name is None:
-                    adapter_name = get_adapter_name(text_encoder)
-
-                is_model_cpu_offload, is_sequential_cpu_offload = cls._optionally_disable_offloading(_pipeline)
-
-                # inject LoRA layers and load the state dict
-                # in transformers we automatically check whether the adapter name is already in use or not
-                text_encoder.load_adapter(
-                    adapter_name=adapter_name,
-                    adapter_state_dict=text_encoder_lora_state_dict,
-                    peft_config=lora_config,
-                    **peft_kwargs,
-                )
-
-                # scale LoRA layers with `lora_scale`
-                scale_lora_layers(text_encoder, weight=lora_scale)
-
-                text_encoder.to(device=text_encoder.device, dtype=text_encoder.dtype)
-
-                # Offload back.
-                if is_model_cpu_offload:
-                    _pipeline.enable_model_cpu_offload()
-                elif is_sequential_cpu_offload:
-                    _pipeline.enable_sequential_cpu_offload()
-                # Unsafe code />
+        _load_lora_into_text_encoder(
+            state_dict=state_dict,
+            network_alphas=network_alphas,
+            lora_scale=lora_scale,
+            text_encoder=text_encoder,
+            prefix=prefix,
+            text_encoder_name=cls.text_encoder_name,
+            adapter_name=adapter_name,
+            _pipeline=_pipeline,
+            low_cpu_mem_usage=low_cpu_mem_usage,
+        )
 
     @classmethod
     def save_lora_weights(
@@ -3008,10 +2496,9 @@ class CogVideoXLoraLoaderMixin(LoraBaseMixin):
             safe_serialization=safe_serialization,
         )
 
-    # Copied from diffusers.loaders.lora_pipeline.StableDiffusionLoraLoaderMixin.fuse_lora with unet->transformer
     def fuse_lora(
         self,
-        components: List[str] = ["transformer", "text_encoder"],
+        components: List[str] = ["transformer"],
         lora_scale: float = 1.0,
         safe_fusing: bool = False,
         adapter_names: Optional[List[str]] = None,
@@ -3052,8 +2539,7 @@ class CogVideoXLoraLoaderMixin(LoraBaseMixin):
             components=components, lora_scale=lora_scale, safe_fusing=safe_fusing, adapter_names=adapter_names
         )
 
-    # Copied from diffusers.loaders.lora_pipeline.StableDiffusionLoraLoaderMixin.unfuse_lora with unet->transformer
-    def unfuse_lora(self, components: List[str] = ["transformer", "text_encoder"], **kwargs):
+    def unfuse_lora(self, components: List[str] = ["transformer"], **kwargs):
         r"""
         Reverses the effect of
         [`pipe.fuse_lora()`](https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora).
@@ -3067,9 +2553,6 @@ class CogVideoXLoraLoaderMixin(LoraBaseMixin):
         Args:
             components (`List[str]`): List of LoRA-injectable components to unfuse LoRA from.
             unfuse_transformer (`bool`, defaults to `True`): Whether to unfuse the UNet LoRA parameters.
-            unfuse_text_encoder (`bool`, defaults to `True`):
-                Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn't monkey-patched with the
-                LoRA parameters then it won't have any effect.
         """
         super().unfuse_lora(components=components)
 
@@ -3316,10 +2799,9 @@ class Mochi1LoraLoaderMixin(LoraBaseMixin):
             safe_serialization=safe_serialization,
         )
 
-    # Copied from diffusers.loaders.lora_pipeline.StableDiffusionLoraLoaderMixin.fuse_lora with unet->transformer
     def fuse_lora(
         self,
-        components: List[str] = ["transformer", "text_encoder"],
+        components: List[str] = ["transformer"],
         lora_scale: float = 1.0,
         safe_fusing: bool = False,
         adapter_names: Optional[List[str]] = None,
@@ -3360,8 +2842,7 @@ class Mochi1LoraLoaderMixin(LoraBaseMixin):
             components=components, lora_scale=lora_scale, safe_fusing=safe_fusing, adapter_names=adapter_names
         )
 
-    # Copied from diffusers.loaders.lora_pipeline.StableDiffusionLoraLoaderMixin.unfuse_lora with unet->transformer
-    def unfuse_lora(self, components: List[str] = ["transformer", "text_encoder"], **kwargs):
+    def unfuse_lora(self, components: List[str] = ["transformer"], **kwargs):
         r"""
         Reverses the effect of
         [`pipe.fuse_lora()`](https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora).
@@ -3375,9 +2856,6 @@ class Mochi1LoraLoaderMixin(LoraBaseMixin):
         Args:
             components (`List[str]`): List of LoRA-injectable components to unfuse LoRA from.
             unfuse_transformer (`bool`, defaults to `True`): Whether to unfuse the UNet LoRA parameters.
-            unfuse_text_encoder (`bool`, defaults to `True`):
-                Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn't monkey-patched with the
-                LoRA parameters then it won't have any effect.
         """
         super().unfuse_lora(components=components)
 
@@ -3624,10 +3102,9 @@ class LTXVideoLoraLoaderMixin(LoraBaseMixin):
             safe_serialization=safe_serialization,
         )
 
-    # Copied from diffusers.loaders.lora_pipeline.StableDiffusionLoraLoaderMixin.fuse_lora with unet->transformer
     def fuse_lora(
         self,
-        components: List[str] = ["transformer", "text_encoder"],
+        components: List[str] = ["transformer"],
         lora_scale: float = 1.0,
         safe_fusing: bool = False,
         adapter_names: Optional[List[str]] = None,
@@ -3668,8 +3145,7 @@ class LTXVideoLoraLoaderMixin(LoraBaseMixin):
             components=components, lora_scale=lora_scale, safe_fusing=safe_fusing, adapter_names=adapter_names
         )
 
-    # Copied from diffusers.loaders.lora_pipeline.StableDiffusionLoraLoaderMixin.unfuse_lora with unet->transformer
-    def unfuse_lora(self, components: List[str] = ["transformer", "text_encoder"], **kwargs):
+    def unfuse_lora(self, components: List[str] = ["transformer"], **kwargs):
         r"""
         Reverses the effect of
         [`pipe.fuse_lora()`](https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora).
@@ -3683,9 +3159,6 @@ class LTXVideoLoraLoaderMixin(LoraBaseMixin):
         Args:
             components (`List[str]`): List of LoRA-injectable components to unfuse LoRA from.
             unfuse_transformer (`bool`, defaults to `True`): Whether to unfuse the UNet LoRA parameters.
-            unfuse_text_encoder (`bool`, defaults to `True`):
-                Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn't monkey-patched with the
-                LoRA parameters then it won't have any effect.
         """
         super().unfuse_lora(components=components)
 
@@ -3932,10 +3405,9 @@ class SanaLoraLoaderMixin(LoraBaseMixin):
             safe_serialization=safe_serialization,
         )
 
-    # Copied from diffusers.loaders.lora_pipeline.StableDiffusionLoraLoaderMixin.fuse_lora with unet->transformer
     def fuse_lora(
         self,
-        components: List[str] = ["transformer", "text_encoder"],
+        components: List[str] = ["transformer"],
         lora_scale: float = 1.0,
         safe_fusing: bool = False,
         adapter_names: Optional[List[str]] = None,
@@ -3976,8 +3448,7 @@ class SanaLoraLoaderMixin(LoraBaseMixin):
             components=components, lora_scale=lora_scale, safe_fusing=safe_fusing, adapter_names=adapter_names
         )
 
-    # Copied from diffusers.loaders.lora_pipeline.StableDiffusionLoraLoaderMixin.unfuse_lora with unet->transformer
-    def unfuse_lora(self, components: List[str] = ["transformer", "text_encoder"], **kwargs):
+    def unfuse_lora(self, components: List[str] = ["transformer"], **kwargs):
         r"""
         Reverses the effect of
         [`pipe.fuse_lora()`](https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora).
@@ -3991,9 +3462,6 @@ class SanaLoraLoaderMixin(LoraBaseMixin):
         Args:
             components (`List[str]`): List of LoRA-injectable components to unfuse LoRA from.
             unfuse_transformer (`bool`, defaults to `True`): Whether to unfuse the UNet LoRA parameters.
-            unfuse_text_encoder (`bool`, defaults to `True`):
-                Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn't monkey-patched with the
-                LoRA parameters then it won't have any effect.
         """
         super().unfuse_lora(components=components)
 
@@ -4300,9 +3768,6 @@ class HunyuanVideoLoraLoaderMixin(LoraBaseMixin):
         Args:
             components (`List[str]`): List of LoRA-injectable components to unfuse LoRA from.
             unfuse_transformer (`bool`, defaults to `True`): Whether to unfuse the UNet LoRA parameters.
-            unfuse_text_encoder (`bool`, defaults to `True`):
-                Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn't monkey-patched with the
-                LoRA parameters then it won't have any effect.
         """
         super().unfuse_lora(components=components)
 

src/diffusers/loaders/peft.py

@@ -20,7 +20,6 @@ from typing import Dict, List, Optional, Union
 
 import safetensors
 import torch
-import torch.nn as nn
 
 from ..utils import (
     MIN_PEFT_VERSION,
@@ -30,20 +29,16 @@ from ..utils import (
     delete_adapter_layers,
     get_adapter_name,
     get_peft_kwargs,
-    is_accelerate_available,
     is_peft_available,
     is_peft_version,
     logging,
     set_adapter_layers,
     set_weights_and_activate_adapters,
 )
-from .lora_base import _fetch_state_dict
+from .lora_base import _fetch_state_dict, _func_optionally_disable_offloading
 from .unet_loader_utils import _maybe_expand_lora_scales
 
 
-if is_accelerate_available():
-    from accelerate.hooks import AlignDevicesHook, CpuOffload, remove_hook_from_module
-
 logger = logging.get_logger(__name__)
 
 _SET_ADAPTER_SCALE_FN_MAPPING = {
@@ -140,27 +135,7 @@ class PeftAdapterMixin:
             tuple:
                 A tuple indicating if `is_model_cpu_offload` or `is_sequential_cpu_offload` is True.
         """
-        is_model_cpu_offload = False
-        is_sequential_cpu_offload = False
-
-        if _pipeline is not None and _pipeline.hf_device_map is None:
-            for _, component in _pipeline.components.items():
-                if isinstance(component, nn.Module) and hasattr(component, "_hf_hook"):
-                    if not is_model_cpu_offload:
-                        is_model_cpu_offload = isinstance(component._hf_hook, CpuOffload)
-                    if not is_sequential_cpu_offload:
-                        is_sequential_cpu_offload = (
-                            isinstance(component._hf_hook, AlignDevicesHook)
-                            or hasattr(component._hf_hook, "hooks")
-                            and isinstance(component._hf_hook.hooks[0], AlignDevicesHook)
-                        )
-
-                    logger.info(
-                        "Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again."
-                    )
-                    remove_hook_from_module(component, recurse=is_sequential_cpu_offload)
-
-        return (is_model_cpu_offload, is_sequential_cpu_offload)
+        return _func_optionally_disable_offloading(_pipeline=_pipeline)
 
     def load_lora_adapter(self, pretrained_model_name_or_path_or_dict, prefix="transformer", **kwargs):
         r"""
@@ -325,15 +300,17 @@ class PeftAdapterMixin:
             try:
                 inject_adapter_in_model(lora_config, self, adapter_name=adapter_name, **peft_kwargs)
                 incompatible_keys = set_peft_model_state_dict(self, state_dict, adapter_name, **peft_kwargs)
-            except RuntimeError as e:
-                for module in self.modules():
-                    if isinstance(module, BaseTunerLayer):
-                        active_adapters = module.active_adapters
-                        for active_adapter in active_adapters:
-                            if adapter_name in active_adapter:
-                                module.delete_adapter(adapter_name)
+            except Exception as e:
+                # In case `inject_adapter_in_model()` was unsuccessful even before injecting the `peft_config`.
+                if hasattr(self, "peft_config"):
+                    for module in self.modules():
+                        if isinstance(module, BaseTunerLayer):
+                            active_adapters = module.active_adapters
+                            for active_adapter in active_adapters:
+                                if adapter_name in active_adapter:
+                                    module.delete_adapter(adapter_name)
 
-                self.peft_config.pop(adapter_name)
+                    self.peft_config.pop(adapter_name)
                 logger.error(f"Loading {adapter_name} was unsucessful with the following error: \n{e}")
                 raise
 

src/diffusers/loaders/single_file.py

@@ -60,6 +60,7 @@ def load_single_file_sub_model(
     local_files_only=False,
     torch_dtype=None,
     is_legacy_loading=False,
+    disable_mmap=False,
     **kwargs,
 ):
     if is_pipeline_module:
@@ -106,6 +107,7 @@ def load_single_file_sub_model(
             subfolder=name,
             torch_dtype=torch_dtype,
             local_files_only=local_files_only,
+            disable_mmap=disable_mmap,
             **kwargs,
         )
 
@@ -308,6 +310,9 @@ class FromSingleFileMixin:
                       hosted on the Hub.
                     - A path to a *directory* (for example `./my_pipeline_directory/`) containing the pipeline
                       component configs in Diffusers format.
+            disable_mmap ('bool', *optional*, defaults to 'False'):
+                Whether to disable mmap when loading a Safetensors model. This option can perform better when the model
+                is on a network mount or hard drive.
             kwargs (remaining dictionary of keyword arguments, *optional*):
                 Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline
                 class). The overwritten components are passed directly to the pipelines `__init__` method. See example
@@ -355,6 +360,7 @@ class FromSingleFileMixin:
         local_files_only = kwargs.pop("local_files_only", False)
         revision = kwargs.pop("revision", None)
         torch_dtype = kwargs.pop("torch_dtype", None)
+        disable_mmap = kwargs.pop("disable_mmap", False)
 
         is_legacy_loading = False
 
@@ -383,6 +389,7 @@ class FromSingleFileMixin:
             cache_dir=cache_dir,
             local_files_only=local_files_only,
             revision=revision,
+            disable_mmap=disable_mmap,
         )
 
         if config is None:
@@ -504,6 +511,7 @@ class FromSingleFileMixin:
                         original_config=original_config,
                         local_files_only=local_files_only,
                         is_legacy_loading=is_legacy_loading,
+                        disable_mmap=disable_mmap,
                         **kwargs,
                     )
                 except SingleFileComponentError as e:

src/diffusers/loaders/single_file_model.py

@@ -25,6 +25,7 @@ from ..utils import deprecate, is_accelerate_available, logging
 from .single_file_utils import (
     SingleFileComponentError,
     convert_animatediff_checkpoint_to_diffusers,
+    convert_auraflow_transformer_checkpoint_to_diffusers,
     convert_autoencoder_dc_checkpoint_to_diffusers,
     convert_controlnet_checkpoint,
     convert_flux_transformer_checkpoint_to_diffusers,
@@ -106,6 +107,10 @@ SINGLE_FILE_LOADABLE_CLASSES = {
         "checkpoint_mapping_fn": convert_hunyuan_video_transformer_to_diffusers,
         "default_subfolder": "transformer",
     },
+    "AuraFlowTransformer2DModel": {
+        "checkpoint_mapping_fn": convert_auraflow_transformer_checkpoint_to_diffusers,
+        "default_subfolder": "transformer",
+    },
 }
 
 
@@ -182,6 +187,9 @@ class FromOriginalModelMixin:
             revision (`str`, *optional*, defaults to `"main"`):
                 The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
                 allowed by Git.
+            disable_mmap ('bool', *optional*, defaults to 'False'):
+                Whether to disable mmap when loading a Safetensors model. This option can perform better when the model
+                is on a network mount or hard drive, which may not handle the seeky-ness of mmap very well.
             kwargs (remaining dictionary of keyword arguments, *optional*):
                 Can be used to overwrite load and saveable variables (for example the pipeline components of the
                 specific pipeline class). The overwritten components are directly passed to the pipelines `__init__`
@@ -229,6 +237,7 @@ class FromOriginalModelMixin:
         torch_dtype = kwargs.pop("torch_dtype", None)
         quantization_config = kwargs.pop("quantization_config", None)
         device = kwargs.pop("device", None)
+        disable_mmap = kwargs.pop("disable_mmap", False)
 
         if isinstance(pretrained_model_link_or_path_or_dict, dict):
             checkpoint = pretrained_model_link_or_path_or_dict
@@ -241,6 +250,7 @@ class FromOriginalModelMixin:
                 cache_dir=cache_dir,
                 local_files_only=local_files_only,
                 revision=revision,
+                disable_mmap=disable_mmap,
             )
         if quantization_config is not None:
             hf_quantizer = DiffusersAutoQuantizer.from_config(quantization_config)

src/diffusers/loaders/single_file_utils.py

@@ -94,6 +94,12 @@ CHECKPOINT_KEY_NAMES = {
     "animatediff_sdxl_beta": "up_blocks.2.motion_modules.0.temporal_transformer.norm.weight",
     "animatediff_scribble": "controlnet_cond_embedding.conv_in.weight",
     "animatediff_rgb": "controlnet_cond_embedding.weight",
+    "auraflow": [
+        "double_layers.0.attn.w2q.weight",
+        "double_layers.0.attn.w1q.weight",
+        "cond_seq_linear.weight",
+        "t_embedder.mlp.0.weight",
+    ],
     "flux": [
         "double_blocks.0.img_attn.norm.key_norm.scale",
         "model.diffusion_model.double_blocks.0.img_attn.norm.key_norm.scale",
@@ -154,6 +160,7 @@ DIFFUSERS_DEFAULT_PIPELINE_PATHS = {
     "animatediff_sdxl_beta": {"pretrained_model_name_or_path": "guoyww/animatediff-motion-adapter-sdxl-beta"},
     "animatediff_scribble": {"pretrained_model_name_or_path": "guoyww/animatediff-sparsectrl-scribble"},
     "animatediff_rgb": {"pretrained_model_name_or_path": "guoyww/animatediff-sparsectrl-rgb"},
+    "auraflow": {"pretrained_model_name_or_path": "fal/AuraFlow-v0.3"},
     "flux-dev": {"pretrained_model_name_or_path": "black-forest-labs/FLUX.1-dev"},
     "flux-fill": {"pretrained_model_name_or_path": "black-forest-labs/FLUX.1-Fill-dev"},
     "flux-depth": {"pretrained_model_name_or_path": "black-forest-labs/FLUX.1-Depth-dev"},
@@ -179,6 +186,7 @@ DIFFUSERS_TO_LDM_DEFAULT_IMAGE_SIZE_MAP = {
     "inpainting": 512,
     "inpainting_v2": 512,
     "controlnet": 512,
+    "instruct-pix2pix": 512,
     "v2": 768,
     "v1": 512,
 }
@@ -380,6 +388,7 @@ def load_single_file_checkpoint(
     cache_dir=None,
     local_files_only=None,
     revision=None,
+    disable_mmap=False,
 ):
     if os.path.isfile(pretrained_model_link_or_path):
         pretrained_model_link_or_path = pretrained_model_link_or_path
@@ -397,7 +406,7 @@ def load_single_file_checkpoint(
             revision=revision,
         )
 
-    checkpoint = load_state_dict(pretrained_model_link_or_path)
+    checkpoint = load_state_dict(pretrained_model_link_or_path, disable_mmap=disable_mmap)
 
     # some checkpoints contain the model state dict under a "state_dict" key
     while "state_dict" in checkpoint:
@@ -597,10 +606,14 @@ def infer_diffusers_model_type(checkpoint):
         if any(
             g in checkpoint for g in ["guidance_in.in_layer.bias", "model.diffusion_model.guidance_in.in_layer.bias"]
         ):
-            if checkpoint["img_in.weight"].shape[1] == 384:
-                model_type = "flux-fill"
+            if "model.diffusion_model.img_in.weight" in checkpoint:
+                key = "model.diffusion_model.img_in.weight"
+            else:
+                key = "img_in.weight"
 
-            elif checkpoint["img_in.weight"].shape[1] == 128:
+            if checkpoint[key].shape[1] == 384:
+                model_type = "flux-fill"
+            elif checkpoint[key].shape[1] == 128:
                 model_type = "flux-depth"
             else:
                 model_type = "flux-dev"
@@ -635,6 +648,9 @@ def infer_diffusers_model_type(checkpoint):
     elif CHECKPOINT_KEY_NAMES["hunyuan-video"] in checkpoint:
         model_type = "hunyuan-video"
 
+    elif all(key in checkpoint for key in CHECKPOINT_KEY_NAMES["auraflow"]):
+        model_type = "auraflow"
+
     elif (
         CHECKPOINT_KEY_NAMES["instruct-pix2pix"] in checkpoint
         and checkpoint[CHECKPOINT_KEY_NAMES["instruct-pix2pix"]].shape[1] == 8
@@ -2090,6 +2106,7 @@ def convert_animatediff_checkpoint_to_diffusers(checkpoint, **kwargs):
 def convert_flux_transformer_checkpoint_to_diffusers(checkpoint, **kwargs):
     converted_state_dict = {}
     keys = list(checkpoint.keys())
+
     for k in keys:
         if "model.diffusion_model." in k:
             checkpoint[k.replace("model.diffusion_model.", "")] = checkpoint.pop(k)
@@ -2689,3 +2706,95 @@ def convert_hunyuan_video_transformer_to_diffusers(checkpoint, **kwargs):
             handler_fn_inplace(key, checkpoint)
 
     return checkpoint
+
+
+def convert_auraflow_transformer_checkpoint_to_diffusers(checkpoint, **kwargs):
+    converted_state_dict = {}
+    state_dict_keys = list(checkpoint.keys())
+
+    # Handle register tokens and positional embeddings
+    converted_state_dict["register_tokens"] = checkpoint.pop("register_tokens", None)
+
+    # Handle time step projection
+    converted_state_dict["time_step_proj.linear_1.weight"] = checkpoint.pop("t_embedder.mlp.0.weight", None)
+    converted_state_dict["time_step_proj.linear_1.bias"] = checkpoint.pop("t_embedder.mlp.0.bias", None)
+    converted_state_dict["time_step_proj.linear_2.weight"] = checkpoint.pop("t_embedder.mlp.2.weight", None)
+    converted_state_dict["time_step_proj.linear_2.bias"] = checkpoint.pop("t_embedder.mlp.2.bias", None)
+
+    # Handle context embedder
+    converted_state_dict["context_embedder.weight"] = checkpoint.pop("cond_seq_linear.weight", None)
+
+    # Calculate the number of layers
+    def calculate_layers(keys, key_prefix):
+        layers = set()
+        for k in keys:
+            if key_prefix in k:
+                layer_num = int(k.split(".")[1])  # get the layer number
+                layers.add(layer_num)
+        return len(layers)
+
+    mmdit_layers = calculate_layers(state_dict_keys, key_prefix="double_layers")
+    single_dit_layers = calculate_layers(state_dict_keys, key_prefix="single_layers")
+
+    # MMDiT blocks
+    for i in range(mmdit_layers):
+        # Feed-forward
+        path_mapping = {"mlpX": "ff", "mlpC": "ff_context"}
+        weight_mapping = {"c_fc1": "linear_1", "c_fc2": "linear_2", "c_proj": "out_projection"}
+        for orig_k, diffuser_k in path_mapping.items():
+            for k, v in weight_mapping.items():
+                converted_state_dict[f"joint_transformer_blocks.{i}.{diffuser_k}.{v}.weight"] = checkpoint.pop(
+                    f"double_layers.{i}.{orig_k}.{k}.weight", None
+                )
+
+        # Norms
+        path_mapping = {"modX": "norm1", "modC": "norm1_context"}
+        for orig_k, diffuser_k in path_mapping.items():
+            converted_state_dict[f"joint_transformer_blocks.{i}.{diffuser_k}.linear.weight"] = checkpoint.pop(
+                f"double_layers.{i}.{orig_k}.1.weight", None
+            )
+
+        # Attentions
+        x_attn_mapping = {"w2q": "to_q", "w2k": "to_k", "w2v": "to_v", "w2o": "to_out.0"}
+        context_attn_mapping = {"w1q": "add_q_proj", "w1k": "add_k_proj", "w1v": "add_v_proj", "w1o": "to_add_out"}
+        for attn_mapping in [x_attn_mapping, context_attn_mapping]:
+            for k, v in attn_mapping.items():
+                converted_state_dict[f"joint_transformer_blocks.{i}.attn.{v}.weight"] = checkpoint.pop(
+                    f"double_layers.{i}.attn.{k}.weight", None
+                )
+
+    # Single-DiT blocks
+    for i in range(single_dit_layers):
+        # Feed-forward
+        mapping = {"c_fc1": "linear_1", "c_fc2": "linear_2", "c_proj": "out_projection"}
+        for k, v in mapping.items():
+            converted_state_dict[f"single_transformer_blocks.{i}.ff.{v}.weight"] = checkpoint.pop(
+                f"single_layers.{i}.mlp.{k}.weight", None
+            )
+
+        # Norms
+        converted_state_dict[f"single_transformer_blocks.{i}.norm1.linear.weight"] = checkpoint.pop(
+            f"single_layers.{i}.modCX.1.weight", None
+        )
+
+        # Attentions
+        x_attn_mapping = {"w1q": "to_q", "w1k": "to_k", "w1v": "to_v", "w1o": "to_out.0"}
+        for k, v in x_attn_mapping.items():
+            converted_state_dict[f"single_transformer_blocks.{i}.attn.{v}.weight"] = checkpoint.pop(
+                f"single_layers.{i}.attn.{k}.weight", None
+            )
+    # Final blocks
+    converted_state_dict["proj_out.weight"] = checkpoint.pop("final_linear.weight", None)
+
+    # Handle the final norm layer
+    norm_weight = checkpoint.pop("modF.1.weight", None)
+    if norm_weight is not None:
+        converted_state_dict["norm_out.linear.weight"] = swap_scale_shift(norm_weight, dim=None)
+    else:
+        converted_state_dict["norm_out.linear.weight"] = None
+
+    converted_state_dict["pos_embed.pos_embed"] = checkpoint.pop("positional_encoding")
+    converted_state_dict["pos_embed.proj.weight"] = checkpoint.pop("init_x_linear.weight")
+    converted_state_dict["pos_embed.proj.bias"] = checkpoint.pop("init_x_linear.bias")
+
+    return converted_state_dict

src/diffusers/loaders/unet.py

@@ -21,7 +21,6 @@ import safetensors
 import torch
 import torch.nn.functional as F
 from huggingface_hub.utils import validate_hf_hub_args
-from torch import nn
 
 from ..models.embeddings import (
     ImageProjection,
@@ -44,13 +43,11 @@ from ..utils import (
     is_torch_version,
     logging,
 )
+from .lora_base import _func_optionally_disable_offloading
 from .lora_pipeline import LORA_WEIGHT_NAME, LORA_WEIGHT_NAME_SAFE, TEXT_ENCODER_NAME, UNET_NAME
 from .utils import AttnProcsLayers
 
 
-if is_accelerate_available():
-    from accelerate.hooks import AlignDevicesHook, CpuOffload, remove_hook_from_module
-
 logger = logging.get_logger(__name__)
 
 
@@ -411,27 +408,7 @@ class UNet2DConditionLoadersMixin:
             tuple:
                 A tuple indicating if `is_model_cpu_offload` or `is_sequential_cpu_offload` is True.
         """
-        is_model_cpu_offload = False
-        is_sequential_cpu_offload = False
-
-        if _pipeline is not None and _pipeline.hf_device_map is None:
-            for _, component in _pipeline.components.items():
-                if isinstance(component, nn.Module) and hasattr(component, "_hf_hook"):
-                    if not is_model_cpu_offload:
-                        is_model_cpu_offload = isinstance(component._hf_hook, CpuOffload)
-                    if not is_sequential_cpu_offload:
-                        is_sequential_cpu_offload = (
-                            isinstance(component._hf_hook, AlignDevicesHook)
-                            or hasattr(component._hf_hook, "hooks")
-                            and isinstance(component._hf_hook.hooks[0], AlignDevicesHook)
-                        )
-
-                    logger.info(
-                        "Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again."
-                    )
-                    remove_hook_from_module(component, recurse=is_sequential_cpu_offload)
-
-        return (is_model_cpu_offload, is_sequential_cpu_offload)
+        return _func_optionally_disable_offloading(_pipeline=_pipeline)
 
     def save_attn_procs(
         self,

src/diffusers/models/autoencoders/autoencoder_dc.py

@@ -486,6 +486,9 @@ class AutoencoderDC(ModelMixin, ConfigMixin, FromOriginalModelMixin):
         self.tile_sample_stride_height = 448
         self.tile_sample_stride_width = 448
 
+        self.tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
+        self.tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
+
     def enable_tiling(
         self,
         tile_sample_min_height: Optional[int] = None,
@@ -515,6 +518,8 @@ class AutoencoderDC(ModelMixin, ConfigMixin, FromOriginalModelMixin):
         self.tile_sample_min_width = tile_sample_min_width or self.tile_sample_min_width
         self.tile_sample_stride_height = tile_sample_stride_height or self.tile_sample_stride_height
         self.tile_sample_stride_width = tile_sample_stride_width or self.tile_sample_stride_width
+        self.tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
+        self.tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
 
     def disable_tiling(self) -> None:
         r"""
@@ -606,11 +611,106 @@ class AutoencoderDC(ModelMixin, ConfigMixin, FromOriginalModelMixin):
             return (decoded,)
         return DecoderOutput(sample=decoded)
 
+    def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
+        blend_extent = min(a.shape[2], b.shape[2], blend_extent)
+        for y in range(blend_extent):
+            b[:, :, y, :] = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
+        return b
+
+    def blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
+        blend_extent = min(a.shape[3], b.shape[3], blend_extent)
+        for x in range(blend_extent):
+            b[:, :, :, x] = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
+        return b
+
     def tiled_encode(self, x: torch.Tensor, return_dict: bool = True) -> torch.Tensor:
-        raise NotImplementedError("`tiled_encode` has not been implemented for AutoencoderDC.")
+        batch_size, num_channels, height, width = x.shape
+        latent_height = height // self.spatial_compression_ratio
+        latent_width = width // self.spatial_compression_ratio
+
+        tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
+        tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
+        tile_latent_stride_height = self.tile_sample_stride_height // self.spatial_compression_ratio
+        tile_latent_stride_width = self.tile_sample_stride_width // self.spatial_compression_ratio
+        blend_height = tile_latent_min_height - tile_latent_stride_height
+        blend_width = tile_latent_min_width - tile_latent_stride_width
+
+        # Split x into overlapping tiles and encode them separately.
+        # The tiles have an overlap to avoid seams between tiles.
+        rows = []
+        for i in range(0, x.shape[2], self.tile_sample_stride_height):
+            row = []
+            for j in range(0, x.shape[3], self.tile_sample_stride_width):
+                tile = x[:, :, i : i + self.tile_sample_min_height, j : j + self.tile_sample_min_width]
+                if (
+                    tile.shape[2] % self.spatial_compression_ratio != 0
+                    or tile.shape[3] % self.spatial_compression_ratio != 0
+                ):
+                    pad_h = (self.spatial_compression_ratio - tile.shape[2]) % self.spatial_compression_ratio
+                    pad_w = (self.spatial_compression_ratio - tile.shape[3]) % self.spatial_compression_ratio
+                    tile = F.pad(tile, (0, pad_w, 0, pad_h))
+                tile = self.encoder(tile)
+                row.append(tile)
+            rows.append(row)
+        result_rows = []
+        for i, row in enumerate(rows):
+            result_row = []
+            for j, tile in enumerate(row):
+                # blend the above tile and the left tile
+                # to the current tile and add the current tile to the result row
+                if i > 0:
+                    tile = self.blend_v(rows[i - 1][j], tile, blend_height)
+                if j > 0:
+                    tile = self.blend_h(row[j - 1], tile, blend_width)
+                result_row.append(tile[:, :, :tile_latent_stride_height, :tile_latent_stride_width])
+            result_rows.append(torch.cat(result_row, dim=3))
+
+        encoded = torch.cat(result_rows, dim=2)[:, :, :latent_height, :latent_width]
+
+        if not return_dict:
+            return (encoded,)
+        return EncoderOutput(latent=encoded)
 
     def tiled_decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
-        raise NotImplementedError("`tiled_decode` has not been implemented for AutoencoderDC.")
+        batch_size, num_channels, height, width = z.shape
+
+        tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
+        tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
+        tile_latent_stride_height = self.tile_sample_stride_height // self.spatial_compression_ratio
+        tile_latent_stride_width = self.tile_sample_stride_width // self.spatial_compression_ratio
+
+        blend_height = self.tile_sample_min_height - self.tile_sample_stride_height
+        blend_width = self.tile_sample_min_width - self.tile_sample_stride_width
+
+        # Split z into overlapping tiles and decode them separately.
+        # The tiles have an overlap to avoid seams between tiles.
+        rows = []
+        for i in range(0, height, tile_latent_stride_height):
+            row = []
+            for j in range(0, width, tile_latent_stride_width):
+                tile = z[:, :, i : i + tile_latent_min_height, j : j + tile_latent_min_width]
+                decoded = self.decoder(tile)
+                row.append(decoded)
+            rows.append(row)
+
+        result_rows = []
+        for i, row in enumerate(rows):
+            result_row = []
+            for j, tile in enumerate(row):
+                # blend the above tile and the left tile
+                # to the current tile and add the current tile to the result row
+                if i > 0:
+                    tile = self.blend_v(rows[i - 1][j], tile, blend_height)
+                if j > 0:
+                    tile = self.blend_h(row[j - 1], tile, blend_width)
+                result_row.append(tile[:, :, : self.tile_sample_stride_height, : self.tile_sample_stride_width])
+            result_rows.append(torch.cat(result_row, dim=3))
+
+        decoded = torch.cat(result_rows, dim=2)
+
+        if not return_dict:
+            return (decoded,)
+        return DecoderOutput(sample=decoded)
 
     def forward(self, sample: torch.Tensor, return_dict: bool = True) -> torch.Tensor:
         encoded = self.encode(sample, return_dict=False)[0]

src/diffusers/models/autoencoders/autoencoder_kl_ltx.py

@@ -1010,10 +1010,12 @@ class AutoencoderKLLTXVideo(ModelMixin, ConfigMixin, FromOriginalModelMixin):
         # The minimal tile height and width for spatial tiling to be used
         self.tile_sample_min_height = 512
         self.tile_sample_min_width = 512
+        self.tile_sample_min_num_frames = 16
 
         # The minimal distance between two spatial tiles
         self.tile_sample_stride_height = 448
         self.tile_sample_stride_width = 448
+        self.tile_sample_stride_num_frames = 8
 
     def _set_gradient_checkpointing(self, module, value=False):
         if isinstance(module, (LTXVideoEncoder3d, LTXVideoDecoder3d)):
@@ -1023,8 +1025,10 @@ class AutoencoderKLLTXVideo(ModelMixin, ConfigMixin, FromOriginalModelMixin):
         self,
         tile_sample_min_height: Optional[int] = None,
         tile_sample_min_width: Optional[int] = None,
+        tile_sample_min_num_frames: Optional[int] = None,
         tile_sample_stride_height: Optional[float] = None,
         tile_sample_stride_width: Optional[float] = None,
+        tile_sample_stride_num_frames: Optional[float] = None,
     ) -> None:
         r"""
         Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
@@ -1046,8 +1050,10 @@ class AutoencoderKLLTXVideo(ModelMixin, ConfigMixin, FromOriginalModelMixin):
         self.use_tiling = True
         self.tile_sample_min_height = tile_sample_min_height or self.tile_sample_min_height
         self.tile_sample_min_width = tile_sample_min_width or self.tile_sample_min_width
+        self.tile_sample_min_num_frames = tile_sample_min_num_frames or self.tile_sample_min_num_frames
         self.tile_sample_stride_height = tile_sample_stride_height or self.tile_sample_stride_height
         self.tile_sample_stride_width = tile_sample_stride_width or self.tile_sample_stride_width
+        self.tile_sample_stride_num_frames = tile_sample_stride_num_frames or self.tile_sample_stride_num_frames
 
     def disable_tiling(self) -> None:
         r"""
@@ -1073,18 +1079,13 @@ class AutoencoderKLLTXVideo(ModelMixin, ConfigMixin, FromOriginalModelMixin):
     def _encode(self, x: torch.Tensor) -> torch.Tensor:
         batch_size, num_channels, num_frames, height, width = x.shape
 
+        if self.use_framewise_decoding and num_frames > self.tile_sample_min_num_frames:
+            return self._temporal_tiled_encode(x)
+
         if self.use_tiling and (width > self.tile_sample_min_width or height > self.tile_sample_min_height):
             return self.tiled_encode(x)
 
-        if self.use_framewise_encoding:
-            # TODO(aryan): requires investigation
-            raise NotImplementedError(
-                "Frame-wise encoding has not been implemented for AutoencoderKLLTXVideo, at the moment, due to "
-                "quality issues caused by splitting inference across frame dimension. If you believe this "
-                "should be possible, please submit a PR to https://github.com/huggingface/diffusers/pulls."
-            )
-        else:
-            enc = self.encoder(x)
+        enc = self.encoder(x)
 
         return enc
 
@@ -1121,19 +1122,15 @@ class AutoencoderKLLTXVideo(ModelMixin, ConfigMixin, FromOriginalModelMixin):
         batch_size, num_channels, num_frames, height, width = z.shape
         tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
         tile_latent_min_width = self.tile_sample_stride_width // self.spatial_compression_ratio
+        tile_latent_min_num_frames = self.tile_sample_min_num_frames // self.temporal_compression_ratio
+
+        if self.use_framewise_decoding and num_frames > tile_latent_min_num_frames:
+            return self._temporal_tiled_decode(z, temb, return_dict=return_dict)
 
         if self.use_tiling and (width > tile_latent_min_width or height > tile_latent_min_height):
             return self.tiled_decode(z, temb, return_dict=return_dict)
 
-        if self.use_framewise_decoding:
-            # TODO(aryan): requires investigation
-            raise NotImplementedError(
-                "Frame-wise decoding has not been implemented for AutoencoderKLLTXVideo, at the moment, due to "
-                "quality issues caused by splitting inference across frame dimension. If you believe this "
-                "should be possible, please submit a PR to https://github.com/huggingface/diffusers/pulls."
-            )
-        else:
-            dec = self.decoder(z, temb)
+        dec = self.decoder(z, temb)
 
         if not return_dict:
             return (dec,)
@@ -1189,6 +1186,14 @@ class AutoencoderKLLTXVideo(ModelMixin, ConfigMixin, FromOriginalModelMixin):
             )
         return b
 
+    def blend_t(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
+        blend_extent = min(a.shape[-3], b.shape[-3], blend_extent)
+        for x in range(blend_extent):
+            b[:, :, x, :, :] = a[:, :, -blend_extent + x, :, :] * (1 - x / blend_extent) + b[:, :, x, :, :] * (
+                x / blend_extent
+            )
+        return b
+
     def tiled_encode(self, x: torch.Tensor) -> torch.Tensor:
         r"""Encode a batch of images using a tiled encoder.
 
@@ -1217,17 +1222,9 @@ class AutoencoderKLLTXVideo(ModelMixin, ConfigMixin, FromOriginalModelMixin):
         for i in range(0, height, self.tile_sample_stride_height):
             row = []
             for j in range(0, width, self.tile_sample_stride_width):
-                if self.use_framewise_encoding:
-                    # TODO(aryan): requires investigation
-                    raise NotImplementedError(
-                        "Frame-wise encoding has not been implemented for AutoencoderKLLTXVideo, at the moment, due to "
-                        "quality issues caused by splitting inference across frame dimension. If you believe this "
-                        "should be possible, please submit a PR to https://github.com/huggingface/diffusers/pulls."
-                    )
-                else:
-                    time = self.encoder(
-                        x[:, :, :, i : i + self.tile_sample_min_height, j : j + self.tile_sample_min_width]
-                    )
+                time = self.encoder(
+                    x[:, :, :, i : i + self.tile_sample_min_height, j : j + self.tile_sample_min_width]
+                )
 
                 row.append(time)
             rows.append(row)
@@ -1283,17 +1280,7 @@ class AutoencoderKLLTXVideo(ModelMixin, ConfigMixin, FromOriginalModelMixin):
         for i in range(0, height, tile_latent_stride_height):
             row = []
             for j in range(0, width, tile_latent_stride_width):
-                if self.use_framewise_decoding:
-                    # TODO(aryan): requires investigation
-                    raise NotImplementedError(
-                        "Frame-wise decoding has not been implemented for AutoencoderKLLTXVideo, at the moment, due to "
-                        "quality issues caused by splitting inference across frame dimension. If you believe this "
-                        "should be possible, please submit a PR to https://github.com/huggingface/diffusers/pulls."
-                    )
-                else:
-                    time = self.decoder(
-                        z[:, :, :, i : i + tile_latent_min_height, j : j + tile_latent_min_width], temb
-                    )
+                time = self.decoder(z[:, :, :, i : i + tile_latent_min_height, j : j + tile_latent_min_width], temb)
 
                 row.append(time)
             rows.append(row)
@@ -1318,6 +1305,74 @@ class AutoencoderKLLTXVideo(ModelMixin, ConfigMixin, FromOriginalModelMixin):
 
         return DecoderOutput(sample=dec)
 
+    def _temporal_tiled_encode(self, x: torch.Tensor) -> AutoencoderKLOutput:
+        batch_size, num_channels, num_frames, height, width = x.shape
+        latent_num_frames = (num_frames - 1) // self.temporal_compression_ratio + 1
+
+        tile_latent_min_num_frames = self.tile_sample_min_num_frames // self.temporal_compression_ratio
+        tile_latent_stride_num_frames = self.tile_sample_stride_num_frames // self.temporal_compression_ratio
+        blend_num_frames = tile_latent_min_num_frames - tile_latent_stride_num_frames
+
+        row = []
+        for i in range(0, num_frames, self.tile_sample_stride_num_frames):
+            tile = x[:, :, i : i + self.tile_sample_min_num_frames + 1, :, :]
+            if self.use_tiling and (height > self.tile_sample_min_height or width > self.tile_sample_min_width):
+                tile = self.tiled_encode(tile)
+            else:
+                tile = self.encoder(tile)
+            if i > 0:
+                tile = tile[:, :, 1:, :, :]
+            row.append(tile)
+
+        result_row = []
+        for i, tile in enumerate(row):
+            if i > 0:
+                tile = self.blend_t(row[i - 1], tile, blend_num_frames)
+                result_row.append(tile[:, :, :tile_latent_stride_num_frames, :, :])
+            else:
+                result_row.append(tile[:, :, : tile_latent_stride_num_frames + 1, :, :])
+
+        enc = torch.cat(result_row, dim=2)[:, :, :latent_num_frames]
+        return enc
+
+    def _temporal_tiled_decode(
+        self, z: torch.Tensor, temb: Optional[torch.Tensor], return_dict: bool = True
+    ) -> Union[DecoderOutput, torch.Tensor]:
+        batch_size, num_channels, num_frames, height, width = z.shape
+        num_sample_frames = (num_frames - 1) * self.temporal_compression_ratio + 1
+
+        tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
+        tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
+        tile_latent_min_num_frames = self.tile_sample_min_num_frames // self.temporal_compression_ratio
+        tile_latent_stride_num_frames = self.tile_sample_stride_num_frames // self.temporal_compression_ratio
+        blend_num_frames = self.tile_sample_min_num_frames - self.tile_sample_stride_num_frames
+
+        row = []
+        for i in range(0, num_frames, tile_latent_stride_num_frames):
+            tile = z[:, :, i : i + tile_latent_min_num_frames + 1, :, :]
+            if self.use_tiling and (tile.shape[-1] > tile_latent_min_width or tile.shape[-2] > tile_latent_min_height):
+                decoded = self.tiled_decode(tile, temb, return_dict=True).sample
+            else:
+                decoded = self.decoder(tile, temb)
+            if i > 0:
+                decoded = decoded[:, :, :-1, :, :]
+            row.append(decoded)
+
+        result_row = []
+        for i, tile in enumerate(row):
+            if i > 0:
+                tile = self.blend_t(row[i - 1], tile, blend_num_frames)
+                tile = tile[:, :, : self.tile_sample_stride_num_frames, :, :]
+                result_row.append(tile)
+            else:
+                result_row.append(tile[:, :, : self.tile_sample_stride_num_frames + 1, :, :])
+
+        dec = torch.cat(result_row, dim=2)[:, :, :num_sample_frames]
+
+        if not return_dict:
+            return (dec,)
+        return DecoderOutput(sample=dec)
+
     def forward(
         self,
         sample: torch.Tensor,
@@ -1334,5 +1389,5 @@ class AutoencoderKLLTXVideo(ModelMixin, ConfigMixin, FromOriginalModelMixin):
             z = posterior.mode()
         dec = self.decode(z, temb)
         if not return_dict:
-            return (dec,)
+            return (dec.sample,)
         return dec

src/diffusers/models/model_loading_utils.py

@@ -131,7 +131,9 @@ def _fetch_remapped_cls_from_config(config, old_class):
         return old_class
 
 
-def load_state_dict(checkpoint_file: Union[str, os.PathLike], variant: Optional[str] = None):
+def load_state_dict(
+    checkpoint_file: Union[str, os.PathLike], variant: Optional[str] = None, disable_mmap: bool = False
+):
     """
     Reads a checkpoint file, returning properly formatted errors if they arise.
     """
@@ -142,7 +144,10 @@ def load_state_dict(checkpoint_file: Union[str, os.PathLike], variant: Optional[
     try:
         file_extension = os.path.basename(checkpoint_file).split(".")[-1]
         if file_extension == SAFETENSORS_FILE_EXTENSION:
-            return safetensors.torch.load_file(checkpoint_file, device="cpu")
+            if disable_mmap:
+                return safetensors.torch.load(open(checkpoint_file, "rb").read())
+            else:
+                return safetensors.torch.load_file(checkpoint_file, device="cpu")
         elif file_extension == GGUF_FILE_EXTENSION:
             return load_gguf_checkpoint(checkpoint_file)
         else:

src/diffusers/models/modeling_utils.py

@@ -559,6 +559,9 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                 If set to `None`, the `safetensors` weights are downloaded if they're available **and** if the
                 `safetensors` library is installed. If set to `True`, the model is forcibly loaded from `safetensors`
                 weights. If set to `False`, `safetensors` weights are not loaded.
+            disable_mmap ('bool', *optional*, defaults to 'False'):
+                Whether to disable mmap when loading a Safetensors model. This option can perform better when the model
+                is on a network mount or hard drive, which may not handle the seeky-ness of mmap very well.
 
         <Tip>
 
@@ -604,6 +607,7 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
         variant = kwargs.pop("variant", None)
         use_safetensors = kwargs.pop("use_safetensors", None)
         quantization_config = kwargs.pop("quantization_config", None)
+        disable_mmap = kwargs.pop("disable_mmap", False)
 
         allow_pickle = False
         if use_safetensors is None:
@@ -883,7 +887,7 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                     # TODO (sayakpaul,  SunMarc): remove this after model loading refactor
                     else:
                         param_device = torch.device(torch.cuda.current_device())
-                    state_dict = load_state_dict(model_file, variant=variant)
+                    state_dict = load_state_dict(model_file, variant=variant, disable_mmap=disable_mmap)
                     model._convert_deprecated_attention_blocks(state_dict)
 
                     # move the params from meta device to cpu
@@ -920,14 +924,12 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                 else:  # else let accelerate handle loading and dispatching.
                     # Load weights and dispatch according to the device_map
                     # by default the device_map is None and the weights are loaded on the CPU
-                    force_hook = True
                     device_map = _determine_device_map(
                         model, device_map, max_memory, torch_dtype, keep_in_fp32_modules, hf_quantizer
                     )
                     if device_map is None and is_sharded:
                         # we load the parameters on the cpu
                         device_map = {"": "cpu"}
-                        force_hook = False
                     try:
                         accelerate.load_checkpoint_and_dispatch(
                             model,
@@ -937,7 +939,6 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                             offload_folder=offload_folder,
                             offload_state_dict=offload_state_dict,
                             dtype=torch_dtype,
-                            force_hooks=force_hook,
                             strict=True,
                         )
                     except AttributeError as e:
@@ -967,7 +968,6 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                                 offload_folder=offload_folder,
                                 offload_state_dict=offload_state_dict,
                                 dtype=torch_dtype,
-                                force_hooks=force_hook,
                                 strict=True,
                             )
                             model._undo_temp_convert_self_to_deprecated_attention_blocks()
@@ -983,7 +983,7 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
             else:
                 model = cls.from_config(config, **unused_kwargs)
 
-                state_dict = load_state_dict(model_file, variant=variant)
+                state_dict = load_state_dict(model_file, variant=variant, disable_mmap=disable_mmap)
                 model._convert_deprecated_attention_blocks(state_dict)
 
                 model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model(

src/diffusers/models/transformers/auraflow_transformer_2d.py

@@ -20,6 +20,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 from ...configuration_utils import ConfigMixin, register_to_config
+from ...loaders import FromOriginalModelMixin
 from ...utils import is_torch_version, logging
 from ...utils.torch_utils import maybe_allow_in_graph
 from ..attention_processor import (
@@ -253,7 +254,7 @@ class AuraFlowJointTransformerBlock(nn.Module):
         return encoder_hidden_states, hidden_states
 
 
-class AuraFlowTransformer2DModel(ModelMixin, ConfigMixin):
+class AuraFlowTransformer2DModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
     r"""
     A 2D Transformer model as introduced in AuraFlow (https://blog.fal.ai/auraflow/).
 

src/diffusers/models/transformers/cogvideox_transformer_3d.py

@@ -120,8 +120,10 @@ class CogVideoXBlock(nn.Module):
         encoder_hidden_states: torch.Tensor,
         temb: torch.Tensor,
         image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
     ) -> torch.Tensor:
         text_seq_length = encoder_hidden_states.size(1)
+        attention_kwargs = attention_kwargs or {}
 
         # norm & modulate
         norm_hidden_states, norm_encoder_hidden_states, gate_msa, enc_gate_msa = self.norm1(
@@ -133,6 +135,7 @@ class CogVideoXBlock(nn.Module):
             hidden_states=norm_hidden_states,
             encoder_hidden_states=norm_encoder_hidden_states,
             image_rotary_emb=image_rotary_emb,
+            **attention_kwargs,
         )
 
         hidden_states = hidden_states + gate_msa * attn_hidden_states
@@ -498,6 +501,7 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
                     encoder_hidden_states,
                     emb,
                     image_rotary_emb,
+                    attention_kwargs,
                     **ckpt_kwargs,
                 )
             else:
@@ -506,6 +510,7 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
                     encoder_hidden_states=encoder_hidden_states,
                     temb=emb,
                     image_rotary_emb=image_rotary_emb,
+                    attention_kwargs=attention_kwargs,
                 )
 
         if not self.config.use_rotary_positional_embeddings:

src/diffusers/models/transformers/transformer_hunyuan_video.py

@@ -727,7 +727,8 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin,
 
         for i in range(batch_size):
             attention_mask[i, : effective_sequence_length[i]] = True
-        attention_mask = attention_mask.unsqueeze(1)  # [B, 1, N], for broadcasting across attention heads
+        # [B, 1, 1, N], for broadcasting across attention heads
+        attention_mask = attention_mask.unsqueeze(1).unsqueeze(1)
 
         # 4. Transformer blocks
         if torch.is_grad_enabled() and self.gradient_checkpointing:

src/diffusers/models/unets/unet_2d.py

@@ -58,7 +58,7 @@ class UNet2DModel(ModelMixin, ConfigMixin):
         down_block_types (`Tuple[str]`, *optional*, defaults to `("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")`):
             Tuple of downsample block types.
         mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2D"`):
-            Block type for middle of UNet, it can be either `UNetMidBlock2D` or `UnCLIPUNetMidBlock2D`.
+            Block type for middle of UNet, it can be either `UNetMidBlock2D` or `None`.
         up_block_types (`Tuple[str]`, *optional*, defaults to `("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")`):
             Tuple of upsample block types.
         block_out_channels (`Tuple[int]`, *optional*, defaults to `(224, 448, 672, 896)`):
@@ -103,6 +103,7 @@ class UNet2DModel(ModelMixin, ConfigMixin):
         freq_shift: int = 0,
         flip_sin_to_cos: bool = True,
         down_block_types: Tuple[str, ...] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D"),
+        mid_block_type: Optional[str] = "UNetMidBlock2D",
         up_block_types: Tuple[str, ...] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D"),
         block_out_channels: Tuple[int, ...] = (224, 448, 672, 896),
         layers_per_block: int = 2,
@@ -194,19 +195,22 @@ class UNet2DModel(ModelMixin, ConfigMixin):
             self.down_blocks.append(down_block)
 
         # mid
-        self.mid_block = UNetMidBlock2D(
-            in_channels=block_out_channels[-1],
-            temb_channels=time_embed_dim,
-            dropout=dropout,
-            resnet_eps=norm_eps,
-            resnet_act_fn=act_fn,
-            output_scale_factor=mid_block_scale_factor,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-            attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1],
-            resnet_groups=norm_num_groups,
-            attn_groups=attn_norm_num_groups,
-            add_attention=add_attention,
-        )
+        if mid_block_type is None:
+            self.mid_block = None
+        else:
+            self.mid_block = UNetMidBlock2D(
+                in_channels=block_out_channels[-1],
+                temb_channels=time_embed_dim,
+                dropout=dropout,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1],
+                resnet_groups=norm_num_groups,
+                attn_groups=attn_norm_num_groups,
+                add_attention=add_attention,
+            )
 
         # up
         reversed_block_out_channels = list(reversed(block_out_channels))
@@ -322,7 +326,8 @@ class UNet2DModel(ModelMixin, ConfigMixin):
             down_block_res_samples += res_samples
 
         # 4. mid
-        sample = self.mid_block(sample, emb)
+        if self.mid_block is not None:
+            sample = self.mid_block(sample, emb)
 
         # 5. up
         skip_sample = None

src/diffusers/pipelines/allegro/pipeline_allegro.py

@@ -33,6 +33,7 @@ from ...utils import (
     deprecate,
     is_bs4_available,
     is_ftfy_available,
+    is_torch_xla_available,
     logging,
     replace_example_docstring,
 )
@@ -41,6 +42,14 @@ from ...video_processor import VideoProcessor
 from .pipeline_output import AllegroPipelineOutput
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+
 logger = logging.get_logger(__name__)
 
 if is_bs4_available():
@@ -921,6 +930,9 @@ class AllegroPipeline(DiffusionPipeline):
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         if not output_type == "latent":
             latents = latents.to(self.vae.dtype)
             video = self.decode_latents(latents)

src/diffusers/pipelines/amused/pipeline_amused.py

@@ -20,10 +20,18 @@ from transformers import CLIPTextModelWithProjection, CLIPTokenizer
 from ...image_processor import VaeImageProcessor
 from ...models import UVit2DModel, VQModel
 from ...schedulers import AmusedScheduler
-from ...utils import replace_example_docstring
+from ...utils import is_torch_xla_available, replace_example_docstring
 from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+
 EXAMPLE_DOC_STRING = """
     Examples:
         ```py
@@ -299,6 +307,9 @@ class AmusedPipeline(DiffusionPipeline):
                         step_idx = i // getattr(self.scheduler, "order", 1)
                         callback(step_idx, timestep, latents)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         if output_type == "latent":
             output = latents
         else:

src/diffusers/pipelines/amused/pipeline_amused_img2img.py

@@ -20,10 +20,18 @@ from transformers import CLIPTextModelWithProjection, CLIPTokenizer
 from ...image_processor import PipelineImageInput, VaeImageProcessor
 from ...models import UVit2DModel, VQModel
 from ...schedulers import AmusedScheduler
-from ...utils import replace_example_docstring
+from ...utils import is_torch_xla_available, replace_example_docstring
 from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+
 EXAMPLE_DOC_STRING = """
     Examples:
         ```py
@@ -325,6 +333,9 @@ class AmusedImg2ImgPipeline(DiffusionPipeline):
                         step_idx = i // getattr(self.scheduler, "order", 1)
                         callback(step_idx, timestep, latents)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         if output_type == "latent":
             output = latents
         else:

src/diffusers/pipelines/amused/pipeline_amused_inpaint.py

@@ -21,10 +21,18 @@ from transformers import CLIPTextModelWithProjection, CLIPTokenizer
 from ...image_processor import PipelineImageInput, VaeImageProcessor
 from ...models import UVit2DModel, VQModel
 from ...schedulers import AmusedScheduler
-from ...utils import replace_example_docstring
+from ...utils import is_torch_xla_available, replace_example_docstring
 from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+
 EXAMPLE_DOC_STRING = """
     Examples:
         ```py
@@ -356,6 +364,9 @@ class AmusedInpaintPipeline(DiffusionPipeline):
                         step_idx = i // getattr(self.scheduler, "order", 1)
                         callback(step_idx, timestep, latents)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         if output_type == "latent":
             output = latents
         else:

src/diffusers/pipelines/animatediff/pipeline_animatediff.py

@@ -34,6 +34,7 @@ from ...schedulers import (
 from ...utils import (
     USE_PEFT_BACKEND,
     deprecate,
+    is_torch_xla_available,
     logging,
     replace_example_docstring,
     scale_lora_layers,
@@ -47,8 +48,16 @@ from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin
 from .pipeline_output import AnimateDiffPipelineOutput
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
+
 EXAMPLE_DOC_STRING = """
     Examples:
         ```py
@@ -844,6 +853,9 @@ class AnimateDiffPipeline(
                         if callback is not None and i % callback_steps == 0:
                             callback(i, t, latents)
 
+                    if XLA_AVAILABLE:
+                        xm.mark_step()
+
         # 9. Post processing
         if output_type == "latent":
             video = latents

src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.py

@@ -32,7 +32,7 @@ from ...models import (
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...models.unets.unet_motion_model import MotionAdapter
 from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
+from ...utils import USE_PEFT_BACKEND, is_torch_xla_available, logging, scale_lora_layers, unscale_lora_layers
 from ...utils.torch_utils import is_compiled_module, randn_tensor
 from ...video_processor import VideoProcessor
 from ..free_init_utils import FreeInitMixin
@@ -41,8 +41,16 @@ from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin
 from .pipeline_output import AnimateDiffPipelineOutput
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
+
 EXAMPLE_DOC_STRING = """
     Examples:
         ```py
@@ -1090,6 +1098,9 @@ class AnimateDiffControlNetPipeline(
                     if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                         progress_bar.update()
 
+                    if XLA_AVAILABLE:
+                        xm.mark_step()
+
         # 9. Post processing
         if output_type == "latent":
             video = latents

src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py

@@ -48,6 +48,7 @@ from ...schedulers import (
 )
 from ...utils import (
     USE_PEFT_BACKEND,
+    is_torch_xla_available,
     logging,
     replace_example_docstring,
     scale_lora_layers,
@@ -60,8 +61,16 @@ from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin
 from .pipeline_output import AnimateDiffPipelineOutput
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
+
 EXAMPLE_DOC_STRING = """
     Examples:
         ```py
@@ -310,7 +319,11 @@ class AnimateDiffSDXLPipeline(
         self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
         self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor)
 
-        self.default_sample_size = self.unet.config.sample_size
+        self.default_sample_size = (
+            self.unet.config.sample_size
+            if hasattr(self, "unet") and self.unet is not None and hasattr(self.unet.config, "sample_size")
+            else 128
+        )
 
     # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt with num_images_per_prompt->num_videos_per_prompt
     def encode_prompt(
@@ -438,7 +451,9 @@ class AnimateDiffSDXLPipeline(
                 prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
 
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                pooled_prompt_embeds = prompt_embeds[0]
+                if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
+                    pooled_prompt_embeds = prompt_embeds[0]
+
                 if clip_skip is None:
                     prompt_embeds = prompt_embeds.hidden_states[-2]
                 else:
@@ -497,8 +512,10 @@ class AnimateDiffSDXLPipeline(
                     uncond_input.input_ids.to(device),
                     output_hidden_states=True,
                 )
+
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
+                    negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                 negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
 
                 negative_prompt_embeds_list.append(negative_prompt_embeds)
@@ -1261,6 +1278,9 @@ class AnimateDiffSDXLPipeline(
 
                     progress_bar.update()
 
+                    if XLA_AVAILABLE:
+                        xm.mark_step()
+
         # make sure the VAE is in float32 mode, as it overflows in float16
         needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 

src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py

@@ -30,6 +30,7 @@ from ...models.unets.unet_motion_model import MotionAdapter
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
     USE_PEFT_BACKEND,
+    is_torch_xla_available,
     logging,
     replace_example_docstring,
     scale_lora_layers,
@@ -42,8 +43,16 @@ from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin
 from .pipeline_output import AnimateDiffPipelineOutput
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
+
 EXAMPLE_DOC_STRING = """
     Examples:
         ```python
@@ -994,6 +1003,9 @@ class AnimateDiffSparseControlNetPipeline(
                     if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                         progress_bar.update()
 
+                    if XLA_AVAILABLE:
+                        xm.mark_step()
+
         # 11. Post processing
         if output_type == "latent":
             video = latents

src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py

@@ -31,7 +31,7 @@ from ...schedulers import (
     LMSDiscreteScheduler,
     PNDMScheduler,
 )
-from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
+from ...utils import USE_PEFT_BACKEND, is_torch_xla_available, logging, scale_lora_layers, unscale_lora_layers
 from ...utils.torch_utils import randn_tensor
 from ...video_processor import VideoProcessor
 from ..free_init_utils import FreeInitMixin
@@ -40,8 +40,16 @@ from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin
 from .pipeline_output import AnimateDiffPipelineOutput
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
+
 EXAMPLE_DOC_STRING = """
     Examples:
         ```py
@@ -1037,6 +1045,9 @@ class AnimateDiffVideoToVideoPipeline(
                     if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                         progress_bar.update()
 
+                    if XLA_AVAILABLE:
+                        xm.mark_step()
+
         # 10. Post-processing
         if output_type == "latent":
             video = latents

src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video_controlnet.py

@@ -39,7 +39,7 @@ from ...schedulers import (
     LMSDiscreteScheduler,
     PNDMScheduler,
 )
-from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
+from ...utils import USE_PEFT_BACKEND, is_torch_xla_available, logging, scale_lora_layers, unscale_lora_layers
 from ...utils.torch_utils import is_compiled_module, randn_tensor
 from ...video_processor import VideoProcessor
 from ..free_init_utils import FreeInitMixin
@@ -48,8 +48,16 @@ from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin
 from .pipeline_output import AnimateDiffPipelineOutput
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
+
 EXAMPLE_DOC_STRING = """
     Examples:
         ```py
@@ -1325,6 +1333,9 @@ class AnimateDiffVideoToVideoControlNetPipeline(
                     if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                         progress_bar.update()
 
+                    if XLA_AVAILABLE:
+                        xm.mark_step()
+
         # 11. Post-processing
         if output_type == "latent":
             video = latents

src/diffusers/pipelines/audioldm/pipeline_audioldm.py

@@ -22,13 +22,21 @@ from transformers import ClapTextModelWithProjection, RobertaTokenizer, RobertaT
 
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import logging, replace_example_docstring
+from ...utils import is_torch_xla_available, logging, replace_example_docstring
 from ...utils.torch_utils import randn_tensor
 from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline, StableDiffusionMixin
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
+
 EXAMPLE_DOC_STRING = """
     Examples:
         ```py
@@ -530,6 +538,9 @@ class AudioLDMPipeline(DiffusionPipeline, StableDiffusionMixin):
                         step_idx = i // getattr(self.scheduler, "order", 1)
                         callback(step_idx, t, latents)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         # 8. Post-processing
         mel_spectrogram = self.decode_latents(latents)
 

src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py

@@ -48,8 +48,20 @@ from .modeling_audioldm2 import AudioLDM2ProjectionModel, AudioLDM2UNet2DConditi
 if is_librosa_available():
     import librosa
 
+
+from ...utils import is_torch_xla_available
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
+
 EXAMPLE_DOC_STRING = """
     Examples:
         ```py
@@ -225,7 +237,7 @@ class AudioLDM2Pipeline(DiffusionPipeline):
         """
         self.vae.disable_slicing()
 
-    def enable_model_cpu_offload(self, gpu_id=0):
+    def enable_model_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"):
         r"""
         Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
         to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
@@ -237,11 +249,23 @@ class AudioLDM2Pipeline(DiffusionPipeline):
         else:
             raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
 
-        device = torch.device(f"cuda:{gpu_id}")
+        torch_device = torch.device(device)
+        device_index = torch_device.index
+
+        if gpu_id is not None and device_index is not None:
+            raise ValueError(
+                f"You have passed both `gpu_id`={gpu_id} and an index as part of the passed device `device`={device}"
+                f"Cannot pass both. Please make sure to either not define `gpu_id` or not pass the index as part of the device: `device`={torch_device.type}"
+            )
+
+        device_type = torch_device.type
+        device = torch.device(f"{device_type}:{gpu_id or torch_device.index}")
 
         if self.device.type != "cpu":
             self.to("cpu", silence_dtype_warnings=True)
-            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
+            device_mod = getattr(torch, device.type, None)
+            if hasattr(device_mod, "empty_cache") and device_mod.is_available():
+                device_mod.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
 
         model_sequence = [
             self.text_encoder.text_model,
@@ -1033,6 +1057,9 @@ class AudioLDM2Pipeline(DiffusionPipeline):
                         step_idx = i // getattr(self.scheduler, "order", 1)
                         callback(step_idx, t, latents)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         self.maybe_free_model_hooks()
 
         # 8. Post-processing

src/diffusers/pipelines/auto_pipeline.py

@@ -68,6 +68,7 @@ from .lumina import LuminaText2ImgPipeline
 from .pag import (
     HunyuanDiTPAGPipeline,
     PixArtSigmaPAGPipeline,
+    SanaPAGPipeline,
     StableDiffusion3PAGImg2ImgPipeline,
     StableDiffusion3PAGPipeline,
     StableDiffusionControlNetPAGInpaintPipeline,
@@ -82,6 +83,7 @@ from .pag import (
     StableDiffusionXLPAGPipeline,
 )
 from .pixart_alpha import PixArtAlphaPipeline, PixArtSigmaPipeline
+from .sana import SanaPipeline
 from .stable_cascade import StableCascadeCombinedPipeline, StableCascadeDecoderPipeline
 from .stable_diffusion import (
     StableDiffusionImg2ImgPipeline,
@@ -121,6 +123,8 @@ AUTO_TEXT2IMAGE_PIPELINES_MAPPING = OrderedDict(
         ("lcm", LatentConsistencyModelPipeline),
         ("pixart-alpha", PixArtAlphaPipeline),
         ("pixart-sigma", PixArtSigmaPipeline),
+        ("sana", SanaPipeline),
+        ("sana-pag", SanaPAGPipeline),
         ("stable-diffusion-pag", StableDiffusionPAGPipeline),
         ("stable-diffusion-controlnet-pag", StableDiffusionControlNetPAGPipeline),
         ("stable-diffusion-xl-pag", StableDiffusionXLPAGPipeline),

src/diffusers/pipelines/blip_diffusion/pipeline_blip_diffusion.py

@@ -20,6 +20,7 @@ from transformers import CLIPTokenizer
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...schedulers import PNDMScheduler
 from ...utils import (
+    is_torch_xla_available,
     logging,
     replace_example_docstring,
 )
@@ -30,8 +31,16 @@ from .modeling_blip2 import Blip2QFormerModel
 from .modeling_ctx_clip import ContextCLIPTextModel
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
+
 EXAMPLE_DOC_STRING = """
     Examples:
         ```py
@@ -336,6 +345,9 @@ class BlipDiffusionPipeline(DiffusionPipeline):
                 latents,
             )["prev_sample"]
 
+            if XLA_AVAILABLE:
+                xm.mark_step()
+
         image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
         image = self.image_processor.postprocess(image, output_type=output_type)
 

src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py

@@ -26,12 +26,19 @@ from ...models import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel
 from ...models.embeddings import get_3d_rotary_pos_embed
 from ...pipelines.pipeline_utils import DiffusionPipeline
 from ...schedulers import CogVideoXDDIMScheduler, CogVideoXDPMScheduler
-from ...utils import logging, replace_example_docstring
+from ...utils import is_torch_xla_available, logging, replace_example_docstring
 from ...utils.torch_utils import randn_tensor
 from ...video_processor import VideoProcessor
 from .pipeline_output import CogVideoXPipelineOutput
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -753,6 +760,9 @@ class CogVideoXPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         if not output_type == "latent":
             # Discard any padding frames that were added for CogVideoX 1.5
             latents = latents[:, additional_frames:]

src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.py

@@ -27,12 +27,19 @@ from ...models import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel
 from ...models.embeddings import get_3d_rotary_pos_embed
 from ...pipelines.pipeline_utils import DiffusionPipeline
 from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import logging, replace_example_docstring
+from ...utils import is_torch_xla_available, logging, replace_example_docstring
 from ...utils.torch_utils import randn_tensor
 from ...video_processor import VideoProcessor
 from .pipeline_output import CogVideoXPipelineOutput
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -808,6 +815,9 @@ class CogVideoXFunControlPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         if not output_type == "latent":
             video = self.decode_latents(latents)
             video = self.video_processor.postprocess_video(video=video, output_type=output_type)

src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py

@@ -29,6 +29,7 @@ from ...models.embeddings import get_3d_rotary_pos_embed
 from ...pipelines.pipeline_utils import DiffusionPipeline
 from ...schedulers import CogVideoXDDIMScheduler, CogVideoXDPMScheduler
 from ...utils import (
+    is_torch_xla_available,
     logging,
     replace_example_docstring,
 )
@@ -37,6 +38,13 @@ from ...video_processor import VideoProcessor
 from .pipeline_output import CogVideoXPipelineOutput
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -866,6 +874,9 @@ class CogVideoXImageToVideoPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin)
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         if not output_type == "latent":
             # Discard any padding frames that were added for CogVideoX 1.5
             latents = latents[:, additional_frames:]

src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py

@@ -27,12 +27,19 @@ from ...models import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel
 from ...models.embeddings import get_3d_rotary_pos_embed
 from ...pipelines.pipeline_utils import DiffusionPipeline
 from ...schedulers import CogVideoXDDIMScheduler, CogVideoXDPMScheduler
-from ...utils import logging, replace_example_docstring
+from ...utils import is_torch_xla_available, logging, replace_example_docstring
 from ...utils.torch_utils import randn_tensor
 from ...video_processor import VideoProcessor
 from .pipeline_output import CogVideoXPipelineOutput
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -834,6 +841,9 @@ class CogVideoXVideoToVideoPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin)
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         if not output_type == "latent":
             video = self.decode_latents(latents)
             video = self.video_processor.postprocess_video(video=video, output_type=output_type)

src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py

@@ -24,11 +24,18 @@ from ...image_processor import VaeImageProcessor
 from ...models import AutoencoderKL, CogView3PlusTransformer2DModel
 from ...pipelines.pipeline_utils import DiffusionPipeline
 from ...schedulers import CogVideoXDDIMScheduler, CogVideoXDPMScheduler
-from ...utils import logging, replace_example_docstring
+from ...utils import is_torch_xla_available, logging, replace_example_docstring
 from ...utils.torch_utils import randn_tensor
 from .pipeline_output import CogView3PipelineOutput
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -654,6 +661,9 @@ class CogView3PlusPipeline(DiffusionPipeline):
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         if not output_type == "latent":
             image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[
                 0

src/diffusers/pipelines/consistency_models/pipeline_consistency_models.py

@@ -19,6 +19,7 @@ import torch
 from ...models import UNet2DModel
 from ...schedulers import CMStochasticIterativeScheduler
 from ...utils import (
+    is_torch_xla_available,
     logging,
     replace_example_docstring,
 )
@@ -26,6 +27,13 @@ from ...utils.torch_utils import randn_tensor
 from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -263,6 +271,9 @@ class ConsistencyModelPipeline(DiffusionPipeline):
                 if callback is not None and i % callback_steps == 0:
                     callback(i, t, sample)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         # 6. Post-process image sample
         image = self.postprocess_image(sample, output_type=output_type)
 

src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py

@@ -21,6 +21,7 @@ from transformers import CLIPTokenizer
 from ...models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
 from ...schedulers import PNDMScheduler
 from ...utils import (
+    is_torch_xla_available,
     logging,
     replace_example_docstring,
 )
@@ -31,8 +32,16 @@ from ..blip_diffusion.modeling_ctx_clip import ContextCLIPTextModel
 from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
+
 EXAMPLE_DOC_STRING = """
     Examples:
         ```py
@@ -401,6 +410,10 @@ class BlipDiffusionControlNetPipeline(DiffusionPipeline):
                 t,
                 latents,
             )["prev_sample"]
+
+            if XLA_AVAILABLE:
+                xm.mark_step()
+
         image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
         image = self.image_processor.postprocess(image, output_type=output_type)
 

src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py

@@ -30,6 +30,7 @@ from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
     USE_PEFT_BACKEND,
     deprecate,
+    is_torch_xla_available,
     logging,
     replace_example_docstring,
     scale_lora_layers,
@@ -41,6 +42,13 @@ from ..stable_diffusion import StableDiffusionPipelineOutput
 from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -1294,6 +1302,9 @@ class StableDiffusionControlNetImg2ImgPipeline(
                         step_idx = i // getattr(self.scheduler, "order", 1)
                         callback(step_idx, t, latents)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         # If we do sequential model offloading, let's offload unet and controlnet
         # manually for max memory savings
         if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:

src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py

@@ -32,6 +32,7 @@ from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
     USE_PEFT_BACKEND,
     deprecate,
+    is_torch_xla_available,
     logging,
     replace_example_docstring,
     scale_lora_layers,
@@ -43,6 +44,13 @@ from ..stable_diffusion import StableDiffusionPipelineOutput
 from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -1476,6 +1484,9 @@ class StableDiffusionControlNetInpaintPipeline(
                         step_idx = i // getattr(self.scheduler, "order", 1)
                         callback(step_idx, t, latents)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         # If we do sequential model offloading, let's offload unet and controlnet
         # manually for max memory savings
         if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:

src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py

@@ -60,6 +60,16 @@ if is_invisible_watermark_available():
     from diffusers.pipelines.stable_diffusion_xl.watermark import StableDiffusionXLWatermarker
 
 
+from ...utils import is_torch_xla_available
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -406,7 +416,9 @@ class StableDiffusionXLControlNetInpaintPipeline(
                 prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
 
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                pooled_prompt_embeds = prompt_embeds[0]
+                if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
+                    pooled_prompt_embeds = prompt_embeds[0]
+
                 if clip_skip is None:
                     prompt_embeds = prompt_embeds.hidden_states[-2]
                 else:
@@ -465,8 +477,10 @@ class StableDiffusionXLControlNetInpaintPipeline(
                     uncond_input.input_ids.to(device),
                     output_hidden_states=True,
                 )
+
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
+                    negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                 negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
 
                 negative_prompt_embeds_list.append(negative_prompt_embeds)
@@ -1829,6 +1843,9 @@ class StableDiffusionXLControlNetInpaintPipeline(
                         step_idx = i // getattr(self.scheduler, "order", 1)
                         callback(step_idx, t, latents)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         # make sure the VAE is in float32 mode, as it overflows in float16
         if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
             self.upcast_vae()

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py

@@ -62,6 +62,16 @@ if is_invisible_watermark_available():
     from ..stable_diffusion_xl.watermark import StableDiffusionXLWatermarker
 
 
+from ...utils import is_torch_xla_available
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -415,7 +425,9 @@ class StableDiffusionXLControlNetPipeline(
                 prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
 
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                pooled_prompt_embeds = prompt_embeds[0]
+                if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
+                    pooled_prompt_embeds = prompt_embeds[0]
+
                 if clip_skip is None:
                     prompt_embeds = prompt_embeds.hidden_states[-2]
                 else:
@@ -474,8 +486,10 @@ class StableDiffusionXLControlNetPipeline(
                     uncond_input.input_ids.to(device),
                     output_hidden_states=True,
                 )
+
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
+                    negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                 negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
 
                 negative_prompt_embeds_list.append(negative_prompt_embeds)
@@ -1548,6 +1562,9 @@ class StableDiffusionXLControlNetPipeline(
                         step_idx = i // getattr(self.scheduler, "order", 1)
                         callback(step_idx, t, latents)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         if not output_type == "latent":
             # make sure the VAE is in float32 mode, as it overflows in float16
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl_img2img.py

@@ -62,6 +62,16 @@ if is_invisible_watermark_available():
     from ..stable_diffusion_xl.watermark import StableDiffusionXLWatermarker
 
 
+from ...utils import is_torch_xla_available
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -408,7 +418,9 @@ class StableDiffusionXLControlNetImg2ImgPipeline(
                 prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
 
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                pooled_prompt_embeds = prompt_embeds[0]
+                if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
+                    pooled_prompt_embeds = prompt_embeds[0]
+
                 if clip_skip is None:
                     prompt_embeds = prompt_embeds.hidden_states[-2]
                 else:
@@ -467,8 +479,10 @@ class StableDiffusionXLControlNetImg2ImgPipeline(
                     uncond_input.input_ids.to(device),
                     output_hidden_states=True,
                 )
+
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
+                    negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                 negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
 
                 negative_prompt_embeds_list.append(negative_prompt_embeds)
@@ -1608,6 +1622,9 @@ class StableDiffusionXLControlNetImg2ImgPipeline(
                         step_idx = i // getattr(self.scheduler, "order", 1)
                         callback(step_idx, t, latents)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         # If we do sequential model offloading, let's offload unet and controlnet
         # manually for max memory savings
         if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:

src/diffusers/pipelines/controlnet/pipeline_controlnet_union_inpaint_sd_xl.py

@@ -60,6 +60,16 @@ if is_invisible_watermark_available():
     from diffusers.pipelines.stable_diffusion_xl.watermark import StableDiffusionXLWatermarker
 
 
+from ...utils import is_torch_xla_available
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -388,7 +398,9 @@ class StableDiffusionXLControlNetUnionInpaintPipeline(
                 prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
 
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                pooled_prompt_embeds = prompt_embeds[0]
+                if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
+                    pooled_prompt_embeds = prompt_embeds[0]
+
                 if clip_skip is None:
                     prompt_embeds = prompt_embeds.hidden_states[-2]
                 else:
@@ -447,8 +459,10 @@ class StableDiffusionXLControlNetUnionInpaintPipeline(
                     uncond_input.input_ids.to(device),
                     output_hidden_states=True,
                 )
+
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
+                    negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                 negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
 
                 negative_prompt_embeds_list.append(negative_prompt_embeds)
@@ -1755,6 +1769,9 @@ class StableDiffusionXLControlNetUnionInpaintPipeline(
                         step_idx = i // getattr(self.scheduler, "order", 1)
                         callback(step_idx, t, latents)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         # make sure the VAE is in float32 mode, as it overflows in float16
         if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
             self.upcast_vae()

src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl.py

@@ -60,6 +60,17 @@ from ..stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutpu
 if is_invisible_watermark_available():
     from ..stable_diffusion_xl.watermark import StableDiffusionXLWatermarker
 
+
+from ...utils import is_torch_xla_available
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -397,7 +408,9 @@ class StableDiffusionXLControlNetUnionPipeline(
                 prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
 
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                pooled_prompt_embeds = prompt_embeds[0]
+                if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
+                    pooled_prompt_embeds = prompt_embeds[0]
+
                 if clip_skip is None:
                     prompt_embeds = prompt_embeds.hidden_states[-2]
                 else:
@@ -456,8 +469,10 @@ class StableDiffusionXLControlNetUnionPipeline(
                     uncond_input.input_ids.to(device),
                     output_hidden_states=True,
                 )
+
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
+                    negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                 negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
 
                 negative_prompt_embeds_list.append(negative_prompt_embeds)
@@ -1454,6 +1469,9 @@ class StableDiffusionXLControlNetUnionPipeline(
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         if not output_type == "latent":
             # make sure the VAE is in float32 mode, as it overflows in float16
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast

src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py

@@ -61,6 +61,17 @@ from ..stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutpu
 if is_invisible_watermark_available():
     from ..stable_diffusion_xl.watermark import StableDiffusionXLWatermarker
 
+
+from ...utils import is_torch_xla_available
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -422,7 +433,9 @@ class StableDiffusionXLControlNetUnionImg2ImgPipeline(
                 prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
 
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                pooled_prompt_embeds = prompt_embeds[0]
+                if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
+                    pooled_prompt_embeds = prompt_embeds[0]
+
                 if clip_skip is None:
                     prompt_embeds = prompt_embeds.hidden_states[-2]
                 else:
@@ -481,8 +494,10 @@ class StableDiffusionXLControlNetUnionImg2ImgPipeline(
                     uncond_input.input_ids.to(device),
                     output_hidden_states=True,
                 )
+
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
+                    negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                 negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
 
                 negative_prompt_embeds_list.append(negative_prompt_embeds)
@@ -1573,6 +1588,9 @@ class StableDiffusionXLControlNetUnionImg2ImgPipeline(
                         step_idx = i // getattr(self.scheduler, "order", 1)
                         callback(step_idx, t, latents)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         # If we do sequential model offloading, let's offload unet and controlnet
         # manually for max memory savings
         if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:

src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py

@@ -404,9 +404,9 @@ class StableDiffusion3ControlNetPipeline(
             negative_prompt_2 (`str` or `List[str]`, *optional*):
                 The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
                 `text_encoder_2`. If not defined, `negative_prompt` is used in all the text-encoders.
-            negative_prompt_2 (`str` or `List[str]`, *optional*):
+            negative_prompt_3 (`str` or `List[str]`, *optional*):
                 The prompt or prompts not to guide the image generation to be sent to `tokenizer_3` and
-                `text_encoder_3`. If not defined, `negative_prompt` is used in both text-encoders
+                `text_encoder_3`. If not defined, `negative_prompt` is used in all the text-encoders.
             prompt_embeds (`torch.FloatTensor`, *optional*):
                 Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                 provided, text embeddings will be generated from `prompt` input argument.

src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py

@@ -17,14 +17,16 @@ from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import torch
 from transformers import (
+    BaseImageProcessor,
     CLIPTextModelWithProjection,
     CLIPTokenizer,
+    PreTrainedModel,
     T5EncoderModel,
     T5TokenizerFast,
 )
 
 from ...image_processor import PipelineImageInput, VaeImageProcessor
-from ...loaders import FromSingleFileMixin, SD3LoraLoaderMixin
+from ...loaders import FromSingleFileMixin, SD3IPAdapterMixin, SD3LoraLoaderMixin
 from ...models.autoencoders import AutoencoderKL
 from ...models.controlnets.controlnet_sd3 import SD3ControlNetModel, SD3MultiControlNetModel
 from ...models.transformers import SD3Transformer2DModel
@@ -159,7 +161,9 @@ def retrieve_timesteps(
     return timesteps, num_inference_steps
 
 
-class StableDiffusion3ControlNetInpaintingPipeline(DiffusionPipeline, SD3LoraLoaderMixin, FromSingleFileMixin):
+class StableDiffusion3ControlNetInpaintingPipeline(
+    DiffusionPipeline, SD3LoraLoaderMixin, FromSingleFileMixin, SD3IPAdapterMixin
+):
     r"""
     Args:
         transformer ([`SD3Transformer2DModel`]):
@@ -192,13 +196,17 @@ class StableDiffusion3ControlNetInpaintingPipeline(DiffusionPipeline, SD3LoraLoa
             Tokenizer of class
             [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
         controlnet ([`SD3ControlNetModel`] or `List[SD3ControlNetModel]` or [`SD3MultiControlNetModel`]):
-            Provides additional conditioning to the `unet` during the denoising process. If you set multiple
+            Provides additional conditioning to the `transformer` during the denoising process. If you set multiple
             ControlNets as a list, the outputs from each ControlNet are added together to create one combined
             additional conditioning.
+        image_encoder (`PreTrainedModel`, *optional*):
+            Pre-trained Vision Model for IP Adapter.
+        feature_extractor (`BaseImageProcessor`, *optional*):
+            Image processor for IP Adapter.
     """
 
-    model_cpu_offload_seq = "text_encoder->text_encoder_2->text_encoder_3->transformer->vae"
-    _optional_components = []
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->text_encoder_3->image_encoder->transformer->vae"
+    _optional_components = ["image_encoder", "feature_extractor"]
     _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds", "negative_pooled_prompt_embeds"]
 
     def __init__(
@@ -215,6 +223,8 @@ class StableDiffusion3ControlNetInpaintingPipeline(DiffusionPipeline, SD3LoraLoa
         controlnet: Union[
             SD3ControlNetModel, List[SD3ControlNetModel], Tuple[SD3ControlNetModel], SD3MultiControlNetModel
         ],
+        image_encoder: PreTrainedModel = None,
+        feature_extractor: BaseImageProcessor = None,
     ):
         super().__init__()
 
@@ -229,6 +239,8 @@ class StableDiffusion3ControlNetInpaintingPipeline(DiffusionPipeline, SD3LoraLoa
             transformer=transformer,
             scheduler=scheduler,
             controlnet=controlnet,
+            image_encoder=image_encoder,
+            feature_extractor=feature_extractor,
         )
         self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
         self.image_processor = VaeImageProcessor(
@@ -410,9 +422,9 @@ class StableDiffusion3ControlNetInpaintingPipeline(DiffusionPipeline, SD3LoraLoa
             negative_prompt_2 (`str` or `List[str]`, *optional*):
                 The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
                 `text_encoder_2`. If not defined, `negative_prompt` is used in all the text-encoders.
-            negative_prompt_2 (`str` or `List[str]`, *optional*):
+            negative_prompt_3 (`str` or `List[str]`, *optional*):
                 The prompt or prompts not to guide the image generation to be sent to `tokenizer_3` and
-                `text_encoder_3`. If not defined, `negative_prompt` is used in both text-encoders
+                `text_encoder_3`. If not defined, `negative_prompt` is used in all the text-encoders.
             prompt_embeds (`torch.FloatTensor`, *optional*):
                 Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                 provided, text embeddings will be generated from `prompt` input argument.
@@ -775,6 +787,84 @@ class StableDiffusion3ControlNetInpaintingPipeline(DiffusionPipeline, SD3LoraLoa
     def interrupt(self):
         return self._interrupt
 
+    # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline.encode_image
+    def encode_image(self, image: PipelineImageInput, device: torch.device) -> torch.Tensor:
+        """Encodes the given image into a feature representation using a pre-trained image encoder.
+
+        Args:
+            image (`PipelineImageInput`):
+                Input image to be encoded.
+            device: (`torch.device`):
+                Torch device.
+
+        Returns:
+            `torch.Tensor`: The encoded image feature representation.
+        """
+        if not isinstance(image, torch.Tensor):
+            image = self.feature_extractor(image, return_tensors="pt").pixel_values
+
+        image = image.to(device=device, dtype=self.dtype)
+
+        return self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
+
+    # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline.prepare_ip_adapter_image_embeds
+    def prepare_ip_adapter_image_embeds(
+        self,
+        ip_adapter_image: Optional[PipelineImageInput] = None,
+        ip_adapter_image_embeds: Optional[torch.Tensor] = None,
+        device: Optional[torch.device] = None,
+        num_images_per_prompt: int = 1,
+        do_classifier_free_guidance: bool = True,
+    ) -> torch.Tensor:
+        """Prepares image embeddings for use in the IP-Adapter.
+
+        Either `ip_adapter_image` or `ip_adapter_image_embeds` must be passed.
+
+        Args:
+            ip_adapter_image (`PipelineImageInput`, *optional*):
+                The input image to extract features from for IP-Adapter.
+            ip_adapter_image_embeds (`torch.Tensor`, *optional*):
+                Precomputed image embeddings.
+            device: (`torch.device`, *optional*):
+                Torch device.
+            num_images_per_prompt (`int`, defaults to 1):
+                Number of images that should be generated per prompt.
+            do_classifier_free_guidance (`bool`, defaults to True):
+                Whether to use classifier free guidance or not.
+        """
+        device = device or self._execution_device
+
+        if ip_adapter_image_embeds is not None:
+            if do_classifier_free_guidance:
+                single_negative_image_embeds, single_image_embeds = ip_adapter_image_embeds.chunk(2)
+            else:
+                single_image_embeds = ip_adapter_image_embeds
+        elif ip_adapter_image is not None:
+            single_image_embeds = self.encode_image(ip_adapter_image, device)
+            if do_classifier_free_guidance:
+                single_negative_image_embeds = torch.zeros_like(single_image_embeds)
+        else:
+            raise ValueError("Neither `ip_adapter_image_embeds` or `ip_adapter_image_embeds` were provided.")
+
+        image_embeds = torch.cat([single_image_embeds] * num_images_per_prompt, dim=0)
+
+        if do_classifier_free_guidance:
+            negative_image_embeds = torch.cat([single_negative_image_embeds] * num_images_per_prompt, dim=0)
+            image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0)
+
+        return image_embeds.to(device=device)
+
+    # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline.enable_sequential_cpu_offload
+    def enable_sequential_cpu_offload(self, *args, **kwargs):
+        if self.image_encoder is not None and "image_encoder" not in self._exclude_from_cpu_offload:
+            logger.warning(
+                "`pipe.enable_sequential_cpu_offload()` might fail for `image_encoder` if it uses "
+                "`torch.nn.MultiheadAttention`. You can exclude `image_encoder` from CPU offloading by calling "
+                "`pipe._exclude_from_cpu_offload.append('image_encoder')` before `pipe.enable_sequential_cpu_offload()`."
+            )
+
+        super().enable_sequential_cpu_offload(*args, **kwargs)
+
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)
     def __call__(
@@ -803,6 +893,8 @@ class StableDiffusion3ControlNetInpaintingPipeline(DiffusionPipeline, SD3LoraLoa
         negative_prompt_embeds: Optional[torch.FloatTensor] = None,
         pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
         negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        ip_adapter_image: Optional[PipelineImageInput] = None,
+        ip_adapter_image_embeds: Optional[torch.Tensor] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
         joint_attention_kwargs: Optional[Dict[str, Any]] = None,
@@ -896,6 +988,12 @@ class StableDiffusion3ControlNetInpaintingPipeline(DiffusionPipeline, SD3LoraLoa
                 Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                 weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
                 input argument.
+            ip_adapter_image (`PipelineImageInput`, *optional*):
+                Optional image input to work with IP Adapters.
+            ip_adapter_image_embeds (`torch.Tensor`, *optional*):
+                Pre-generated image embeddings for IP-Adapter. Should be a tensor of shape `(batch_size, num_images,
+                emb_dim)`. It should contain the negative image embedding if `do_classifier_free_guidance` is set to
+                `True`. If not provided, embeddings are computed from the `ip_adapter_image` input argument.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generate image. Choose between
                 [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
@@ -1057,7 +1155,22 @@ class StableDiffusion3ControlNetInpaintingPipeline(DiffusionPipeline, SD3LoraLoa
             ]
             controlnet_keep.append(keeps[0] if isinstance(self.controlnet, SD3ControlNetModel) else keeps)
 
-        # 7. Denoising loop
+        # 7. Prepare image embeddings
+        if (ip_adapter_image is not None and self.is_ip_adapter_active) or ip_adapter_image_embeds is not None:
+            ip_adapter_image_embeds = self.prepare_ip_adapter_image_embeds(
+                ip_adapter_image,
+                ip_adapter_image_embeds,
+                device,
+                batch_size * num_images_per_prompt,
+                self.do_classifier_free_guidance,
+            )
+
+            if self.joint_attention_kwargs is None:
+                self._joint_attention_kwargs = {"ip_adapter_image_embeds": ip_adapter_image_embeds}
+            else:
+                self._joint_attention_kwargs.update(ip_adapter_image_embeds=ip_adapter_image_embeds)
+
+        # 8. Denoising loop
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):
                 if self.interrupt:

src/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs.py

@@ -30,6 +30,7 @@ from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
     USE_PEFT_BACKEND,
     deprecate,
+    is_torch_xla_available,
     logging,
     replace_example_docstring,
     scale_lora_layers,
@@ -41,6 +42,13 @@ from ..stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
 from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -884,6 +892,9 @@ class StableDiffusionControlNetXSPipeline(
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         # If we do sequential model offloading, let's offload unet and controlnet
         # manually for max memory savings
         if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:

src/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs_sd_xl.py

@@ -54,6 +54,16 @@ if is_invisible_watermark_available():
     from ..stable_diffusion_xl.watermark import StableDiffusionXLWatermarker
 
 
+from ...utils import is_torch_xla_available
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -336,7 +346,9 @@ class StableDiffusionXLControlNetXSPipeline(
                 prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
 
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                pooled_prompt_embeds = prompt_embeds[0]
+                if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
+                    pooled_prompt_embeds = prompt_embeds[0]
+
                 if clip_skip is None:
                     prompt_embeds = prompt_embeds.hidden_states[-2]
                 else:
@@ -395,8 +407,10 @@ class StableDiffusionXLControlNetXSPipeline(
                     uncond_input.input_ids.to(device),
                     output_hidden_states=True,
                 )
+
                 # We are only ALWAYS interested in the pooled output of the final text encoder
-                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
+                    negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                 negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
 
                 negative_prompt_embeds_list.append(negative_prompt_embeds)
@@ -1074,6 +1088,9 @@ class StableDiffusionXLControlNetXSPipeline(
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         # manually for max memory savings
         if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
             self.upcast_vae()

src/diffusers/pipelines/dance_diffusion/pipeline_dance_diffusion.py

@@ -17,11 +17,18 @@ from typing import List, Optional, Tuple, Union
 
 import torch
 
-from ...utils import logging
+from ...utils import is_torch_xla_available, logging
 from ...utils.torch_utils import randn_tensor
 from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -146,6 +153,9 @@ class DanceDiffusionPipeline(DiffusionPipeline):
             # 2. compute previous audio sample: x_t -> t_t-1
             audio = self.scheduler.step(model_output, t, audio).prev_sample
 
+            if XLA_AVAILABLE:
+                xm.mark_step()
+
         audio = audio.clamp(-1, 1).float().cpu().numpy()
 
         audio = audio[:, :, :original_sample_size]

src/diffusers/pipelines/ddim/pipeline_ddim.py

@@ -17,10 +17,19 @@ from typing import List, Optional, Tuple, Union
 import torch
 
 from ...schedulers import DDIMScheduler
+from ...utils import is_torch_xla_available
 from ...utils.torch_utils import randn_tensor
 from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+
 class DDIMPipeline(DiffusionPipeline):
     r"""
     Pipeline for image generation.
@@ -143,6 +152,9 @@ class DDIMPipeline(DiffusionPipeline):
                 model_output, t, image, eta=eta, use_clipped_model_output=use_clipped_model_output, generator=generator
             ).prev_sample
 
+            if XLA_AVAILABLE:
+                xm.mark_step()
+
         image = (image / 2 + 0.5).clamp(0, 1)
         image = image.cpu().permute(0, 2, 3, 1).numpy()
         if output_type == "pil":

src/diffusers/pipelines/ddpm/pipeline_ddpm.py

@@ -17,10 +17,19 @@ from typing import List, Optional, Tuple, Union
 
 import torch
 
+from ...utils import is_torch_xla_available
 from ...utils.torch_utils import randn_tensor
 from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+
 class DDPMPipeline(DiffusionPipeline):
     r"""
     Pipeline for image generation.
@@ -116,6 +125,9 @@ class DDPMPipeline(DiffusionPipeline):
             # 2. compute previous image: x_t -> x_t-1
             image = self.scheduler.step(model_output, t, image, generator=generator).prev_sample
 
+            if XLA_AVAILABLE:
+                xm.mark_step()
+
         image = (image / 2 + 0.5).clamp(0, 1)
         image = image.cpu().permute(0, 2, 3, 1).numpy()
         if output_type == "pil":

src/diffusers/pipelines/deepfloyd_if/pipeline_if.py

@@ -14,6 +14,7 @@ from ...utils import (
     BACKENDS_MAPPING,
     is_bs4_available,
     is_ftfy_available,
+    is_torch_xla_available,
     logging,
     replace_example_docstring,
 )
@@ -24,8 +25,16 @@ from .safety_checker import IFSafetyChecker
 from .watermark import IFWatermarker
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
+
 if is_bs4_available():
     from bs4 import BeautifulSoup
 
@@ -735,6 +744,9 @@ class IFPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin):
                     if callback is not None and i % callback_steps == 0:
                         callback(i, t, intermediate_images)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         image = intermediate_images
 
         if output_type == "pil":

src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py

@@ -17,6 +17,7 @@ from ...utils import (
     PIL_INTERPOLATION,
     is_bs4_available,
     is_ftfy_available,
+    is_torch_xla_available,
     logging,
     replace_example_docstring,
 )
@@ -27,8 +28,16 @@ from .safety_checker import IFSafetyChecker
 from .watermark import IFWatermarker
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
+
 if is_bs4_available():
     from bs4 import BeautifulSoup
 
@@ -856,6 +865,9 @@ class IFImg2ImgPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin):
                     if callback is not None and i % callback_steps == 0:
                         callback(i, t, intermediate_images)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         image = intermediate_images
 
         if output_type == "pil":

src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py

@@ -35,6 +35,16 @@ if is_ftfy_available():
     import ftfy
 
 
+from ...utils import is_torch_xla_available
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -174,7 +184,7 @@ class IFImg2ImgSuperResolutionPipeline(DiffusionPipeline, StableDiffusionLoraLoa
                 " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
             )
 
-        if unet.config.in_channels != 6:
+        if unet is not None and unet.config.in_channels != 6:
             logger.warning(
                 "It seems like you have loaded a checkpoint that shall not be used for super resolution from {unet.config._name_or_path} as it accepts {unet.config.in_channels} input channels instead of 6. Please make sure to pass a super resolution checkpoint as the `'unet'`: IFSuperResolutionPipeline.from_pretrained(unet=super_resolution_unet, ...)`."
             )
@@ -974,6 +984,9 @@ class IFImg2ImgSuperResolutionPipeline(DiffusionPipeline, StableDiffusionLoraLoa
                     if callback is not None and i % callback_steps == 0:
                         callback(i, t, intermediate_images)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         image = intermediate_images
 
         if output_type == "pil":

src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py

@@ -17,6 +17,7 @@ from ...utils import (
     PIL_INTERPOLATION,
     is_bs4_available,
     is_ftfy_available,
+    is_torch_xla_available,
     logging,
     replace_example_docstring,
 )
@@ -27,8 +28,16 @@ from .safety_checker import IFSafetyChecker
 from .watermark import IFWatermarker
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
+
 if is_bs4_available():
     from bs4 import BeautifulSoup
 
@@ -975,6 +984,9 @@ class IFInpaintingPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin):
                     if callback is not None and i % callback_steps == 0:
                         callback(i, t, intermediate_images)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         image = intermediate_images
 
         if output_type == "pil":

src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting_superresolution.py

@@ -35,6 +35,16 @@ if is_ftfy_available():
     import ftfy
 
 
+from ...utils import is_torch_xla_available
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -176,7 +186,7 @@ class IFInpaintingSuperResolutionPipeline(DiffusionPipeline, StableDiffusionLora
                 " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
             )
 
-        if unet.config.in_channels != 6:
+        if unet is not None and unet.config.in_channels != 6:
             logger.warning(
                 "It seems like you have loaded a checkpoint that shall not be used for super resolution from {unet.config._name_or_path} as it accepts {unet.config.in_channels} input channels instead of 6. Please make sure to pass a super resolution checkpoint as the `'unet'`: IFSuperResolutionPipeline.from_pretrained(unet=super_resolution_unet, ...)`."
             )
@@ -1085,6 +1095,9 @@ class IFInpaintingSuperResolutionPipeline(DiffusionPipeline, StableDiffusionLora
                     if callback is not None and i % callback_steps == 0:
                         callback(i, t, intermediate_images)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         image = intermediate_images
 
         if output_type == "pil":