Merge branch 'main' into fix-bnb-test

.github/workflows/nightly_tests.yml

@@ -180,55 +180,6 @@ jobs:
         pip install slack_sdk tabulate
         python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
 
-  run_torch_compile_tests:
-    name: PyTorch Compile CUDA tests
-
-    runs-on:
-      group: aws-g4dn-2xlarge
-
-    container:
-      image: diffusers/diffusers-pytorch-compile-cuda
-      options: --gpus 0 --shm-size "16gb" --ipc host
-
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: NVIDIA-SMI
-      run: |
-        nvidia-smi
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test,training]
-    - name: Environment
-      run: |
-        python utils/print_env.py
-    - name: Run torch compile tests on GPU
-      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-        RUN_COMPILE: yes
-      run: |
-        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: cat reports/tests_torch_compile_cuda_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: torch_compile_test_reports
-        path: reports
-
-    - name: Generate Report and Notify Channel
-      if: always()
-      run: |
-        pip install slack_sdk tabulate
-        python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
-  
   run_big_gpu_torch_tests:
     name: Torch tests on big GPU
     strategy:

.github/workflows/release_tests_fast.yml

@@ -335,7 +335,7 @@ jobs:
     - name: Environment
       run: |
         python utils/print_env.py
-    - name: Run torch compile tests on GPU
+    - name: Run example tests on GPU
       env:
         HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
         RUN_COMPILE: yes

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

@@ -28,7 +28,6 @@ LoRA is a fast and lightweight training method that inserts and trains a signifi
 - [`WanLoraLoaderMixin`] provides similar functions for [Wan](https://huggingface.co/docs/diffusers/main/en/api/pipelines/wan).
 - [`CogView4LoraLoaderMixin`] provides similar functions for [CogView4](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogview4).
 - [`AmusedLoraLoaderMixin`] is for the [`AmusedPipeline`].
-- [`HiDreamImageLoraLoaderMixin`] provides similar functions for [HiDream Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hidream)
 - [`LoraBaseMixin`] provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.
 
 <Tip>
@@ -92,10 +91,6 @@ To learn more about how to load LoRA weights, see the [LoRA](../../using-diffuse
 
 [[autodoc]] loaders.lora_pipeline.AmusedLoraLoaderMixin
 
-## HiDreamImageLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.HiDreamImageLoraLoaderMixin
-
 ## LoraBaseMixin
 
 [[autodoc]] loaders.lora_base.LoraBaseMixin

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

@@ -347,7 +347,7 @@ image = pipe(
     height=1024,
     prompt="wearing sunglasses",
     negative_prompt="",
-    true_cfg_scale=4.0,
+    true_cfg=4.0,
     generator=torch.Generator().manual_seed(4444),
     ip_adapter_image=image,
 ).images[0]

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

@@ -24,7 +24,7 @@
 
 ## Generating Videos with Wan 2.1
 
-We will first need to install some additional dependencies.
+We will first need to install some addtional dependencies.
 
 ```shell
 pip install -u ftfy imageio-ffmpeg imageio
@@ -133,60 +133,6 @@ output = pipe(
 export_to_video(output, "wan-i2v.mp4", fps=16)
 ```
 
-### First and Last Frame Interpolation
-
-```python
-import numpy as np
-import torch
-import torchvision.transforms.functional as TF
-from diffusers import AutoencoderKLWan, WanImageToVideoPipeline
-from diffusers.utils import export_to_video, load_image
-from transformers import CLIPVisionModel
-
-
-model_id = "Wan-AI/Wan2.1-FLF2V-14B-720P-diffusers"
-image_encoder = CLIPVisionModel.from_pretrained(model_id, subfolder="image_encoder", torch_dtype=torch.float32)
-vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
-pipe = WanImageToVideoPipeline.from_pretrained(
-    model_id, vae=vae, image_encoder=image_encoder, torch_dtype=torch.bfloat16
-)
-pipe.to("cuda")
-
-first_frame = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_first_frame.png")
-last_frame = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_last_frame.png")
-
-def aspect_ratio_resize(image, pipe, max_area=720 * 1280):
-    aspect_ratio = image.height / image.width
-    mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
-    height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
-    width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
-    image = image.resize((width, height))
-    return image, height, width
-
-def center_crop_resize(image, height, width):
-    # Calculate resize ratio to match first frame dimensions
-    resize_ratio = max(width / image.width, height / image.height)
-    
-    # Resize the image
-    width = round(image.width * resize_ratio)
-    height = round(image.height * resize_ratio)
-    size = [width, height]
-    image = TF.center_crop(image, size)
-    
-    return image, height, width
-
-first_frame, height, width = aspect_ratio_resize(first_frame, pipe)
-if last_frame.size != first_frame.size:
-    last_frame, _, _ = center_crop_resize(last_frame, height, width)
-
-prompt = "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird's feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective."
-
-output = pipe(
-    image=first_frame, last_image=last_frame, prompt=prompt, height=height, width=width, guidance_scale=5.5
-).frames[0]
-export_to_video(output, "output.mp4", fps=16)
-```
-
 ### Video to Video Generation
 
 ```python

docs/source/en/training/cogvideox.md

@@ -216,7 +216,7 @@ Setting the `<ID_TOKEN>` is not necessary. From some limited experimentation, we
 > - The original repository uses a `lora_alpha` of `1`. We found this not suitable in many runs, possibly due to difference in modeling backends and training settings. Our recommendation is to set to the `lora_alpha` to either `rank` or `rank // 2`.
 > - If you're training on data whose captions generate bad results with the original model, a `rank` of 64 and above is good and also the recommendation by the team behind CogVideoX. If the generations are already moderately good on your training captions, a `rank` of 16/32 should work. We found that setting the rank too low, say `4`, is not ideal and doesn't produce promising results.
 > - The authors of CogVideoX recommend 4000 training steps and 100 training videos overall to achieve the best result. While that might yield the best results, we found from our limited experimentation that 2000 steps and 25 videos could also be sufficient.
-> - When using the Prodigy optimizer for training, one can follow the recommendations from [this](https://huggingface.co/blog/sdxl_lora_advanced_script) blog. Prodigy tends to overfit quickly. From my very limited testing, I found a learning rate of `0.5` to be suitable in addition to `--prodigy_use_bias_correction`, `prodigy_safeguard_warmup` and `--prodigy_decouple`.
+> - When using the Prodigy opitimizer for training, one can follow the recommendations from [this](https://huggingface.co/blog/sdxl_lora_advanced_script) blog. Prodigy tends to overfit quickly. From my very limited testing, I found a learning rate of `0.5` to be suitable in addition to `--prodigy_use_bias_correction`, `prodigy_safeguard_warmup` and `--prodigy_decouple`.
 > - The recommended learning rate by the CogVideoX authors and from our experimentation with Adam/AdamW is between `1e-3` and `1e-4` for a dataset of 25+ videos.
 >
 > Note that our testing is not exhaustive due to limited time for exploration. Our recommendation would be to play around with the different knobs and dials to find the best settings for your data.

docs/source/en/training/dreambooth.md

@@ -589,7 +589,7 @@ For stage 2 of DeepFloyd IF with DreamBooth, pay attention to these parameters:
 
 * `--learning_rate=5e-6`, use a lower learning rate with a smaller effective batch size
 * `--resolution=256`, the expected resolution for the upscaler
-* `--train_batch_size=2` and `--gradient_accumulation_steps=6`, to effectively train on images with faces requires larger batch sizes
+* `--train_batch_size=2` and `--gradient_accumulation_steps=6`, to effectively train on images wiht faces requires larger batch sizes
 
 ```bash
 export MODEL_NAME="DeepFloyd/IF-II-L-v1.0"

docs/source/en/training/t2i_adapters.md

@@ -89,7 +89,7 @@ Many of the basic and important parameters are described in the [Text-to-image](
 
 As with the script parameters, a walkthrough of the training script is provided in the [Text-to-image](text2image#training-script) training guide. Instead, this guide takes a look at the T2I-Adapter relevant parts of the script.
 
-The training script begins by preparing the dataset. This includes [tokenizing](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/t2i_adapter/train_t2i_adapter_sdxl.py#L674) the prompt and [applying transforms](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/t2i_adapter/train_t2i_adapter_sdxl.py#L714) to the images and conditioning images.
+The training script begins by preparing the dataset. This incudes [tokenizing](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/t2i_adapter/train_t2i_adapter_sdxl.py#L674) the prompt and [applying transforms](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/t2i_adapter/train_t2i_adapter_sdxl.py#L714) to the images and conditioning images.
 
 ```py
 conditioning_image_transforms = transforms.Compose(

examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py

@@ -1915,22 +1915,17 @@ def main(args):
             free_memory()
 
     # Scheduler and math around the number of training steps.
-    # Check the PR https://github.com/huggingface/diffusers/pull/8312 for detailed explanation.
-    num_warmup_steps_for_scheduler = args.lr_warmup_steps * accelerator.num_processes
+    overrode_max_train_steps = False
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
     if args.max_train_steps is None:
-        len_train_dataloader_after_sharding = math.ceil(len(train_dataloader) / accelerator.num_processes)
-        num_update_steps_per_epoch = math.ceil(len_train_dataloader_after_sharding / args.gradient_accumulation_steps)
-        num_training_steps_for_scheduler = (
-            args.num_train_epochs * accelerator.num_processes * num_update_steps_per_epoch
-        )
-    else:
-        num_training_steps_for_scheduler = args.max_train_steps * accelerator.num_processes
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+        overrode_max_train_steps = True
 
     lr_scheduler = get_scheduler(
         args.lr_scheduler,
         optimizer=optimizer,
-        num_warmup_steps=num_warmup_steps_for_scheduler,
-        num_training_steps=num_training_steps_for_scheduler,
+        num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
+        num_training_steps=args.max_train_steps * accelerator.num_processes,
         num_cycles=args.lr_num_cycles,
         power=args.lr_power,
     )
@@ -1954,6 +1949,7 @@ def main(args):
                 lr_scheduler,
             )
         else:
+            print("I SHOULD BE HERE")
             transformer, text_encoder_one, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
                 transformer, text_encoder_one, optimizer, train_dataloader, lr_scheduler
             )
@@ -1965,14 +1961,8 @@ def main(args):
 
     # We need to recalculate our total training steps as the size of the training dataloader may have changed.
     num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if args.max_train_steps is None:
+    if overrode_max_train_steps:
         args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-        if num_training_steps_for_scheduler != args.max_train_steps:
-            logger.warning(
-                f"The length of the 'train_dataloader' after 'accelerator.prepare' ({len(train_dataloader)}) does not match "
-                f"the expected length ({len_train_dataloader_after_sharding}) when the learning rate scheduler was created. "
-                f"This inconsistency may result in the learning rate scheduler not functioning properly."
-            )
     # Afterwards we recalculate our number of training epochs
     args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
 
@@ -2181,7 +2171,7 @@ def main(args):
                 # Predict the noise residual
                 model_pred = transformer(
                     hidden_states=packed_noisy_model_input,
-                    # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transformer model (we should not keep it but I want to keep the inputs same for the model for testing)
+                    # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transforme rmodel (we should not keep it but I want to keep the inputs same for the model for testing)
                     timestep=timesteps / 1000,
                     guidance=guidance,
                     pooled_projections=pooled_prompt_embeds,

examples/community/README.md

@@ -86,7 +86,6 @@ PIXART-α Controlnet pipeline | Implementation of the controlnet model for pixar
 | Perturbed-Attention Guidance |StableDiffusionPAGPipeline is a modification of StableDiffusionPipeline to support Perturbed-Attention Guidance (PAG).|[Perturbed-Attention Guidance](#perturbed-attention-guidance)|[Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/perturbed_attention_guidance.ipynb)|[Hyoungwon Cho](https://github.com/HyoungwonCho)|
 | CogVideoX DDIM Inversion Pipeline | Implementation of DDIM inversion and guided attention-based editing denoising process on CogVideoX. | [CogVideoX DDIM Inversion Pipeline](#cogvideox-ddim-inversion-pipeline) | - | [LittleNyima](https://github.com/LittleNyima) |
 | FaithDiff Stable Diffusion XL Pipeline | Implementation of [(CVPR 2025) FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolutionUnleashing Diffusion Priors for Faithful Image Super-resolution](https://arxiv.org/abs/2411.18824) - FaithDiff is a faithful image super-resolution method that leverages latent diffusion models by actively adapting the diffusion prior and jointly fine-tuning its components (encoder and diffusion model) with an alignment module to ensure high fidelity and structural consistency. | [FaithDiff Stable Diffusion XL Pipeline](#faithdiff-stable-diffusion-xl-pipeline) | [![Hugging Face Models](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-blue)](https://huggingface.co/jychen9811/FaithDiff) | [Junyang Chen, Jinshan Pan, Jiangxin Dong, IMAG Lab, (Adapted by Eliseu Silva)](https://github.com/JyChen9811/FaithDiff) |
-| Stable Diffusion 3 InstructPix2Pix Pipeline | Implementation of Stable Diffusion 3 InstructPix2Pix Pipeline | [Stable Diffusion 3 InstructPix2Pix Pipeline](#stable-diffusion-3-instructpix2pix-pipeline) | [![Hugging Face Models](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-blue)](https://huggingface.co/BleachNick/SD3_UltraEdit_freeform) [![Hugging Face Models](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-blue)](https://huggingface.co/CaptainZZZ/sd3-instructpix2pix) | [Jiayu Zhang](https://github.com/xduzhangjiayu) and [Haozhe Zhao](https://github.com/HaozheZhao)|
 To load a custom pipeline you just need to pass the `custom_pipeline` argument to `DiffusionPipeline`, as one of the files in `diffusers/examples/community`. Feel free to send a PR with your own pipelines, we will merge them quickly.
 
 ```py
@@ -5382,7 +5381,7 @@ pipe = DiffusionPipeline.from_pretrained(
 # Here we need use pipeline internal unet model
 pipe.unet = pipe.unet_model.from_pretrained(model_id, subfolder="unet", variant="fp16", use_safetensors=True)
 
-# Load additional layers to the model
+# Load aditional layers to the model
 pipe.unet.load_additional_layers(weight_path="proc_data/faithdiff/FaithDiff.bin", dtype=dtype)
 
 # Enable vae tiling
@@ -5433,50 +5432,4 @@ cropped_image = gen_image.crop((0, 0, width_init, height_init))
 cropped_image.save("data/result.png")
 ````
 ### Result
-[<img src="https://huggingface.co/datasets/DEVAIEXP/assets/resolve/main/faithdiff_restored.PNG" width="512px" height="512px"/>](https://imgsli.com/MzY1NzE2)
-
-
-# Stable Diffusion 3 InstructPix2Pix Pipeline
-This the implementation of the Stable Diffusion 3 InstructPix2Pix Pipeline, based on the HuggingFace Diffusers.
-
-## Example Usage
-This pipeline aims to edit image based on user's instruction by using SD3
-````py
-import torch
-from diffusers import SD3Transformer2DModel
-from diffusers import DiffusionPipeline
-from diffusers.utils import load_image
-
-
-resolution = 512
-image = load_image("https://hf.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png").resize(
-    (resolution, resolution)
-)
-edit_instruction = "Turn sky into a sunny one"
-
-
-pipe = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-3-medium-diffusers", custom_pipeline="pipeline_stable_diffusion_3_instruct_pix2pix", torch_dtype=torch.float16).to('cuda')
-
-pipe.transformer = SD3Transformer2DModel.from_pretrained("CaptainZZZ/sd3-instructpix2pix",torch_dtype=torch.float16).to('cuda')
-
-edited_image = pipe(
-    prompt=edit_instruction,
-    image=image,
-    height=resolution,
-    width=resolution,
-    guidance_scale=7.5,
-    image_guidance_scale=1.5,
-    num_inference_steps=30,
-).images[0]
-
-edited_image.save("edited_image.png")
-````
-|Original|Edited|
-|---|---|
-|![Original image](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/StableDiffusion3InstructPix2Pix/mountain.png)|![Edited image](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/StableDiffusion3InstructPix2Pix/edited.png)
-
-### Note
-This model is trained on 512x512, so input size is better on 512x512.
-For better editing performance, please refer to this powerful model https://huggingface.co/BleachNick/SD3_UltraEdit_freeform and Paper "UltraEdit: Instruction-based Fine-Grained Image
-Editing at Scale", many thanks to their contribution!
+[<img src="https://huggingface.co/datasets/DEVAIEXP/assets/resolve/main/faithdiff_restored.PNG" width="512px" height="512px"/>](https://imgsli.com/MzY1NzE2)

examples/community/dps_pipeline.py

@@ -312,9 +312,9 @@ if __name__ == "__main__":
                     # These are the coordinates of the output image
                     out_coordinates = np.arange(1, out_length + 1)
 
-                    # since both scale-factor and output size can be provided simultaneously, preserving the center of the image requires shifting
-                    # the output coordinates. the deviation is because out_length doesn't necessary equal in_length*scale.
-                    # to keep the center we need to subtract half of this deviation so that we get equal margins for both sides and center is preserved.
+                    # since both scale-factor and output size can be provided simulatneously, perserving the center of the image requires shifting
+                    # the output coordinates. the deviation is because out_length doesn't necesary equal in_length*scale.
+                    # to keep the center we need to subtract half of this deivation so that we get equal margins for boths sides and center is preserved.
                     shifted_out_coordinates = out_coordinates - (out_length - in_length * scale) / 2
 
                     # These are the matching positions of the output-coordinates on the input image coordinates.

examples/community/fresco_v2v.py

@@ -351,7 +351,7 @@ def my_forward(
             cross_attention_kwargs (`dict`, *optional*):
                 A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].
             added_cond_kwargs: (`dict`, *optional*):
-                A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that
+                A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that
                 are passed along to the UNet blocks.
 
         Returns:
@@ -864,9 +864,9 @@ def get_flow_and_interframe_paras(flow_model, imgs):
 class AttentionControl:
     """
     Control FRESCO-based attention
-    * enable/disable spatial-guided attention
-    * enable/disable temporal-guided attention
-    * enable/disable cross-frame attention
+    * enable/diable spatial-guided attention
+    * enable/diable temporal-guided attention
+    * enable/diable cross-frame attention
     * collect intermediate attention feature (for spatial-guided attention)
     """
 

examples/community/hd_painter.py

@@ -34,7 +34,7 @@ class RASGAttnProcessor:
         temb: Optional[torch.Tensor] = None,
         scale: float = 1.0,
     ) -> torch.Tensor:
-        # Same as the default AttnProcessor up until the part where similarity matrix gets saved
+        # Same as the default AttnProcessor up untill the part where similarity matrix gets saved
         downscale_factor = self.mask_resoltuion // hidden_states.shape[1]
         residual = hidden_states
 

examples/community/pipeline_stable_diffusion_xl_controlnet_adapter_inpaint.py

@@ -33,6 +33,7 @@ from diffusers import DiffusionPipeline
 from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
 from diffusers.loaders import (
     FromSingleFileMixin,
+    StableDiffusionLoraLoaderMixin,
     StableDiffusionXLLoraLoaderMixin,
     TextualInversionLoaderMixin,
 )
@@ -299,7 +300,7 @@ def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
 
 
 class StableDiffusionXLControlNetAdapterInpaintPipeline(
-    DiffusionPipeline, StableDiffusionMixin, FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin
+    DiffusionPipeline, StableDiffusionMixin, FromSingleFileMixin, StableDiffusionLoraLoaderMixin
 ):
     r"""
     Pipeline for text-to-image generation using Stable Diffusion augmented with T2I-Adapter

examples/consistency_distillation/train_lcm_distill_lora_sd_wds.py

@@ -889,7 +889,7 @@ def main(args):
         mixed_precision=args.mixed_precision,
         log_with=args.report_to,
         project_config=accelerator_project_config,
-        split_batches=True,  # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be divided by the number of processes assuming batches are multiplied by the number of processes
+        split_batches=True,  # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be devide by the number of processes assuming batches are multiplied by the number of processes
     )
 
     # Make one log on every process with the configuration for debugging.

examples/consistency_distillation/train_lcm_distill_lora_sdxl.py

@@ -721,7 +721,7 @@ def main(args):
         mixed_precision=args.mixed_precision,
         log_with=args.report_to,
         project_config=accelerator_project_config,
-        split_batches=True,  # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be divided by the number of processes assuming batches are multiplied by the number of processes
+        split_batches=True,  # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be devide by the number of processes assuming batches are multiplied by the number of processes
     )
 
     # Make one log on every process with the configuration for debugging.

examples/consistency_distillation/train_lcm_distill_lora_sdxl_wds.py

@@ -884,7 +884,7 @@ def main(args):
         mixed_precision=args.mixed_precision,
         log_with=args.report_to,
         project_config=accelerator_project_config,
-        split_batches=True,  # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be divided by the number of processes assuming batches are multiplied by the number of processes
+        split_batches=True,  # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be devide by the number of processes assuming batches are multiplied by the number of processes
     )
 
     # Make one log on every process with the configuration for debugging.

examples/consistency_distillation/train_lcm_distill_sd_wds.py

@@ -854,7 +854,7 @@ def main(args):
         mixed_precision=args.mixed_precision,
         log_with=args.report_to,
         project_config=accelerator_project_config,
-        split_batches=True,  # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be divided by the number of processes assuming batches are multiplied by the number of processes
+        split_batches=True,  # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be devide by the number of processes assuming batches are multiplied by the number of processes
     )
 
     # Make one log on every process with the configuration for debugging.

examples/consistency_distillation/train_lcm_distill_sdxl_wds.py

@@ -894,7 +894,7 @@ def main(args):
         mixed_precision=args.mixed_precision,
         log_with=args.report_to,
         project_config=accelerator_project_config,
-        split_batches=True,  # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be divided by the number of processes assuming batches are multiplied by the number of processes
+        split_batches=True,  # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be devide by the number of processes assuming batches are multiplied by the number of processes
     )
 
     # Make one log on every process with the configuration for debugging.

examples/controlnet/README_flux.md

@@ -6,19 +6,7 @@ Training script provided by LibAI, which is an institution dedicated to the prog
 > [!NOTE]
 > **Memory consumption**
 >
-> Flux can be quite expensive to run on consumer hardware devices and as a result, ControlNet training of it comes with higher memory requirements than usual.
-
-Here is a gpu memory consumption for reference, tested on a single A100 with 80G.
-
-| period | GPU |
-| - | - | 
-| load as float32 | ~70G |
-| mv transformer and vae to bf16 | ~48G |
-| pre compute txt embeddings | ~62G |
-| **offload te to cpu** | ~30G |
-| training | ~58G |
-| validation | ~71G |
-
+> Flux can be quite expensive to run on consumer hardware devices and as a result, ControlNet training of it comes with higher memory requirements than usual. 
 
 > **Gated access**
 >
@@ -110,9 +98,8 @@ accelerate launch train_controlnet_flux.py \
     --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
     --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
     --train_batch_size=1 \
-    --gradient_accumulation_steps=16 \
+    --gradient_accumulation_steps=4 \
     --report_to="wandb" \
-    --lr_scheduler="cosine" \
     --num_double_layers=4 \
     --num_single_layers=0 \
     --seed=42 \

examples/controlnet/train_controlnet_flux.py

@@ -148,7 +148,7 @@ def log_validation(
                     pooled_prompt_embeds=pooled_prompt_embeds,
                     control_image=validation_image,
                     num_inference_steps=28,
-                    controlnet_conditioning_scale=1,
+                    controlnet_conditioning_scale=0.7,
                     guidance_scale=3.5,
                     generator=generator,
                 ).images[0]
@@ -639,15 +639,6 @@ def parse_args(input_args=None):
         action="store_true",
         help="Enable model cpu offload and save memory.",
     )
-    parser.add_argument(
-        "--image_interpolation_mode",
-        type=str,
-        default="lanczos",
-        choices=[
-            f.lower() for f in dir(transforms.InterpolationMode) if not f.startswith("__") and not f.endswith("__")
-        ],
-        help="The image interpolation method to use for resizing images.",
-    )
 
     if input_args is not None:
         args = parser.parse_args(input_args)
@@ -745,13 +736,9 @@ def get_train_dataset(args, accelerator):
 
 
 def prepare_train_dataset(dataset, accelerator):
-    interpolation = getattr(transforms.InterpolationMode, args.image_interpolation_mode.upper(), None)
-    if interpolation is None:
-        raise ValueError(f"Unsupported interpolation mode {interpolation=}.")
-
     image_transforms = transforms.Compose(
         [
-            transforms.Resize(args.resolution, interpolation=interpolation),
+            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
             transforms.CenterCrop(args.resolution),
             transforms.ToTensor(),
             transforms.Normalize([0.5], [0.5]),
@@ -760,7 +747,7 @@ def prepare_train_dataset(dataset, accelerator):
 
     conditioning_image_transforms = transforms.Compose(
         [
-            transforms.Resize(args.resolution, interpolation=interpolation),
+            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
             transforms.CenterCrop(args.resolution),
             transforms.ToTensor(),
             transforms.Normalize([0.5], [0.5]),
@@ -1098,6 +1085,8 @@ def main(args):
         return {"prompt_embeds": prompt_embeds, "pooled_prompt_embeds": pooled_prompt_embeds, "text_ids": text_ids}
 
     train_dataset = get_train_dataset(args, accelerator)
+    text_encoders = [text_encoder_one, text_encoder_two]
+    tokenizers = [tokenizer_one, tokenizer_two]
     compute_embeddings_fn = functools.partial(
         compute_embeddings,
         flux_controlnet_pipeline=flux_controlnet_pipeline,
@@ -1114,8 +1103,7 @@ def main(args):
             compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint, batch_size=50
         )
 
-    text_encoder_one.to("cpu")
-    text_encoder_two.to("cpu")
+    del text_encoders, tokenizers, text_encoder_one, text_encoder_two, tokenizer_one, tokenizer_two
     free_memory()
 
     # Then get the training dataset ready to be passed to the dataloader.

examples/controlnet/train_controlnet_sdxl.py

@@ -134,25 +134,7 @@ def log_validation(vae, unet, controlnet, args, accelerator, weight_dtype, step,
 
     for validation_prompt, validation_image in zip(validation_prompts, validation_images):
         validation_image = Image.open(validation_image).convert("RGB")
-
-        try:
-            interpolation = getattr(transforms.InterpolationMode, args.image_interpolation_mode.upper())
-        except (AttributeError, KeyError):
-            supported_interpolation_modes = [
-                f.lower() for f in dir(transforms.InterpolationMode) if not f.startswith("__") and not f.endswith("__")
-            ]
-            raise ValueError(
-                f"Interpolation mode {args.image_interpolation_mode} is not supported. "
-                f"Please select one of the following: {', '.join(supported_interpolation_modes)}"
-            )
-
-        transform = transforms.Compose(
-            [
-                transforms.Resize(args.resolution, interpolation=interpolation),
-                transforms.CenterCrop(args.resolution),
-            ]
-        )
-        validation_image = transform(validation_image)
+        validation_image = validation_image.resize((args.resolution, args.resolution))
 
         images = []
 
@@ -605,15 +587,6 @@ def parse_args(input_args=None):
             " more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
         ),
     )
-    parser.add_argument(
-        "--image_interpolation_mode",
-        type=str,
-        default="lanczos",
-        choices=[
-            f.lower() for f in dir(transforms.InterpolationMode) if not f.startswith("__") and not f.endswith("__")
-        ],
-        help="The image interpolation method to use for resizing images.",
-    )
 
     if input_args is not None:
         args = parser.parse_args(input_args)
@@ -759,20 +732,9 @@ def encode_prompt(prompt_batch, text_encoders, tokenizers, proportion_empty_prom
 
 
 def prepare_train_dataset(dataset, accelerator):
-    try:
-        interpolation_mode = getattr(transforms.InterpolationMode, args.image_interpolation_mode.upper())
-    except (AttributeError, KeyError):
-        supported_interpolation_modes = [
-            f.lower() for f in dir(transforms.InterpolationMode) if not f.startswith("__") and not f.endswith("__")
-        ]
-        raise ValueError(
-            f"Interpolation mode {args.image_interpolation_mode} is not supported. "
-            f"Please select one of the following: {', '.join(supported_interpolation_modes)}"
-        )
-
     image_transforms = transforms.Compose(
         [
-            transforms.Resize(args.resolution, interpolation=interpolation_mode),
+            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
             transforms.CenterCrop(args.resolution),
             transforms.ToTensor(),
             transforms.Normalize([0.5], [0.5]),
@@ -781,7 +743,7 @@ def prepare_train_dataset(dataset, accelerator):
 
     conditioning_image_transforms = transforms.Compose(
         [
-            transforms.Resize(args.resolution, interpolation=interpolation_mode),
+            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
             transforms.CenterCrop(args.resolution),
             transforms.ToTensor(),
         ]

examples/dreambooth/README_hidream.md

@@ -1,119 +0,0 @@
-# DreamBooth training example for HiDream Image
-
-[DreamBooth](https://arxiv.org/abs/2208.12242) is a method to personalize text2image models like stable diffusion given just a few (3~5) images of a subject.
-
-The `train_dreambooth_lora_hidream.py` script shows how to implement the training procedure with [LoRA](https://huggingface.co/docs/peft/conceptual_guides/adapter#low-rank-adaptation-lora) and adapt it for [HiDream Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/). 
-
-
-This will also allow us to push the trained model parameters to the Hugging Face Hub platform.
-
-## Running locally with PyTorch
-
-### Installing the dependencies
-
-Before running the scripts, make sure to install the library's training dependencies:
-
-**Important**
-
-To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
-
-```bash
-git clone https://github.com/huggingface/diffusers
-cd diffusers
-pip install -e .
-```
-
-Then cd in the `examples/dreambooth` folder and run
-```bash
-pip install -r requirements_hidream.txt
-```
-
-And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
-
-```bash
-accelerate config
-```
-
-Or for a default accelerate configuration without answering questions about your environment
-
-```bash
-accelerate config default
-```
-
-Or if your environment doesn't support an interactive shell (e.g., a notebook)
-
-```python
-from accelerate.utils import write_basic_config
-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>=0.14.0` installed in your environment.
-
-
-### 3d icon example
-
-For this example we will use some 3d icon images: https://huggingface.co/datasets/linoyts/3d_icon.
-
-This will also allow us to push the trained LoRA parameters to the Hugging Face Hub platform.
-
-Now, we can launch training using:
-> [!NOTE]
-> The following training configuration prioritizes lower memory consumption by using gradient checkpointing, 
-> 8-bit Adam optimizer, latent caching, offloading, no validation.
-> all text embeddings are pre-computed to save memory.
-```bash
-export MODEL_NAME="HiDream-ai/HiDream-I1-Dev"
-export INSTANCE_DIR="linoyts/3d_icon"
-export OUTPUT_DIR="trained-hidream-lora"
-
-accelerate launch train_dreambooth_lora_hidream.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --dataset_name=$INSTANCE_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --mixed_precision="bf16" \
-  --instance_prompt="3d icon" \
-  --caption_column="prompt"\
-  --validation_prompt="a 3dicon, a llama eating ramen" \
-  --resolution=1024 \
-  --train_batch_size=1 \
-  --gradient_accumulation_steps=4 \
-  --use_8bit_adam \
-  --rank=8 \
-  --learning_rate=2e-4 \
-  --report_to="wandb" \
-  --lr_scheduler="constant_with_warmup" \
-  --lr_warmup_steps=100 \
-  --max_train_steps=1000 \
-  --cache_latents\
-  --gradient_checkpointing \
-  --validation_epochs=25 \
-  --seed="0" \
-  --push_to_hub
-```
-
-For using `push_to_hub`, make you're logged into your Hugging Face account:
-
-```bash
-huggingface-cli login
-```
-
-To better track our training experiments, we're using the following flags in the command above:
-
-* `report_to="wandb` will ensure the training runs are tracked on [Weights and Biases](https://wandb.ai/site). To use it, be sure to install `wandb` with `pip install wandb`. Don't forget to call `wandb login <your_api_key>` before training if you haven't done it before.
-* `validation_prompt` and `validation_epochs` to allow the script to do a few validation inference runs. This allows us to qualitatively check if the training is progressing as expected.
-
-## Notes
-
-Additionally, we welcome you to explore the following CLI arguments:
-
-* `--lora_layers`: The transformer modules to apply LoRA training on. Please specify the layers in a comma seperated. E.g. - "to_k,to_q,to_v" will result in lora training of attention layers only.
-* `--rank`: The rank of the LoRA layers. The higher the rank, the more parameters are trained. The default is 16.
-
-We provide several options for optimizing memory optimization:
-
-* `--offload`: When enabled, we will offload the text encoder and VAE to CPU, when they are not used.
-* `cache_latents`: When enabled, we will pre-compute the latents from the input images with the VAE and remove the VAE from memory once done.
-* `--use_8bit_adam`: When enabled, we will use the 8bit version of AdamW provided by the `bitsandbytes` library.
-
-Refer to the [official documentation](https://huggingface.co/docs/diffusers/main/en/api/pipelines/) of the `HiDreamImagePipeline` to know more about the model.

examples/dreambooth/requirements_hidream.txt

@@ -1,8 +0,0 @@
-accelerate>=1.4.0
-torchvision
-transformers>=4.50.0
-ftfy
-tensorboard
-Jinja2
-peft>=0.14.0
-sentencepiece

examples/dreambooth/test_dreambooth_lora_hidream.py

@@ -1,220 +0,0 @@
-# coding=utf-8
-# Copyright 2024 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-import os
-import sys
-import tempfile
-
-import safetensors
-
-
-sys.path.append("..")
-from test_examples_utils import ExamplesTestsAccelerate, run_command  # noqa: E402
-
-
-logging.basicConfig(level=logging.DEBUG)
-
-logger = logging.getLogger()
-stream_handler = logging.StreamHandler(sys.stdout)
-logger.addHandler(stream_handler)
-
-
-class DreamBoothLoRAHiDreamImage(ExamplesTestsAccelerate):
-    instance_data_dir = "docs/source/en/imgs"
-    pretrained_model_name_or_path = "hf-internal-testing/tiny-hidream-i1-pipe"
-    text_encoder_4_path = "hf-internal-testing/tiny-random-LlamaForCausalLM"
-    tokenizer_4_path = "hf-internal-testing/tiny-random-LlamaForCausalLM"
-    script_path = "examples/dreambooth/train_dreambooth_lora_hidream.py"
-    transformer_layer_type = "double_stream_blocks.0.block.attn1.to_k"
-
-    def test_dreambooth_lora_hidream(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                {self.script_path}
-                --pretrained_model_name_or_path {self.pretrained_model_name_or_path}
-                --pretrained_text_encoder_4_name_or_path {self.text_encoder_4_path}
-                --pretrained_tokenizer_4_name_or_path {self.tokenizer_4_path}
-                --instance_data_dir {self.instance_data_dir}
-                --resolution 32
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --max_sequence_length 16
-                """.split()
-
-            test_args.extend(["--instance_prompt", ""])
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.safetensors")))
-
-            # make sure the state_dict has the correct naming in the parameters.
-            lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
-            is_lora = all("lora" in k for k in lora_state_dict.keys())
-            self.assertTrue(is_lora)
-
-            # when not training the text encoder, all the parameters in the state dict should start
-            # with `"transformer"` in their names.
-            starts_with_transformer = all(key.startswith("transformer") for key in lora_state_dict.keys())
-            self.assertTrue(starts_with_transformer)
-
-    def test_dreambooth_lora_latent_caching(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                {self.script_path}
-                --pretrained_model_name_or_path {self.pretrained_model_name_or_path}
-                --pretrained_text_encoder_4_name_or_path {self.text_encoder_4_path}
-                --pretrained_tokenizer_4_name_or_path {self.tokenizer_4_path}
-                --instance_data_dir {self.instance_data_dir}
-                --resolution 32
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --cache_latents
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --max_sequence_length 16
-                """.split()
-
-            test_args.extend(["--instance_prompt", ""])
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.safetensors")))
-
-            # make sure the state_dict has the correct naming in the parameters.
-            lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
-            is_lora = all("lora" in k for k in lora_state_dict.keys())
-            self.assertTrue(is_lora)
-
-            # when not training the text encoder, all the parameters in the state dict should start
-            # with `"transformer"` in their names.
-            starts_with_transformer = all(key.startswith("transformer") for key in lora_state_dict.keys())
-            self.assertTrue(starts_with_transformer)
-
-    def test_dreambooth_lora_layers(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                {self.script_path}
-                --pretrained_model_name_or_path {self.pretrained_model_name_or_path}
-                --pretrained_text_encoder_4_name_or_path {self.text_encoder_4_path}
-                --pretrained_tokenizer_4_name_or_path {self.tokenizer_4_path}
-                --instance_data_dir {self.instance_data_dir}
-                --resolution 32
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --cache_latents
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lora_layers {self.transformer_layer_type}
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --max_sequence_length 16
-                """.split()
-
-            test_args.extend(["--instance_prompt", ""])
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.safetensors")))
-
-            # make sure the state_dict has the correct naming in the parameters.
-            lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
-            is_lora = all("lora" in k for k in lora_state_dict.keys())
-            self.assertTrue(is_lora)
-
-            # when not training the text encoder, all the parameters in the state dict should start
-            # with `"transformer"` in their names. In this test, we only params of
-            # `self.transformer_layer_type` should be in the state dict.
-            starts_with_transformer = all(self.transformer_layer_type in key for key in lora_state_dict)
-            self.assertTrue(starts_with_transformer)
-
-    def test_dreambooth_lora_hidream_checkpointing_checkpoints_total_limit(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-            {self.script_path}
-            --pretrained_model_name_or_path={self.pretrained_model_name_or_path}
-            --pretrained_text_encoder_4_name_or_path {self.text_encoder_4_path}
-            --pretrained_tokenizer_4_name_or_path {self.tokenizer_4_path}
-            --instance_data_dir={self.instance_data_dir}
-            --output_dir={tmpdir}
-            --resolution=32
-            --train_batch_size=1
-            --gradient_accumulation_steps=1
-            --max_train_steps=6
-            --checkpoints_total_limit=2
-            --checkpointing_steps=2
-            --max_sequence_length 16
-            """.split()
-
-            test_args.extend(["--instance_prompt", ""])
-            run_command(self._launch_args + test_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-4", "checkpoint-6"},
-            )
-
-    def test_dreambooth_lora_hidream_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-            {self.script_path}
-            --pretrained_model_name_or_path={self.pretrained_model_name_or_path}
-            --pretrained_text_encoder_4_name_or_path {self.text_encoder_4_path}
-            --pretrained_tokenizer_4_name_or_path {self.tokenizer_4_path}
-            --instance_data_dir={self.instance_data_dir}
-            --output_dir={tmpdir}
-            --resolution=32
-            --train_batch_size=1
-            --gradient_accumulation_steps=1
-            --max_train_steps=4
-            --checkpointing_steps=2
-            --max_sequence_length 16
-            """.split()
-
-            test_args.extend(["--instance_prompt", ""])
-            run_command(self._launch_args + test_args)
-
-            self.assertEqual({x for x in os.listdir(tmpdir) if "checkpoint" in x}, {"checkpoint-2", "checkpoint-4"})
-
-            resume_run_args = f"""
-            {self.script_path}
-            --pretrained_model_name_or_path={self.pretrained_model_name_or_path}
-            --pretrained_text_encoder_4_name_or_path {self.text_encoder_4_path}
-            --pretrained_tokenizer_4_name_or_path {self.tokenizer_4_path}
-            --instance_data_dir={self.instance_data_dir}
-            --output_dir={tmpdir}
-            --resolution=32
-            --train_batch_size=1
-            --gradient_accumulation_steps=1
-            --max_train_steps=8
-            --checkpointing_steps=2
-            --resume_from_checkpoint=checkpoint-4
-            --checkpoints_total_limit=2
-            --max_sequence_length 16
-            """.split()
-
-            resume_run_args.extend(["--instance_prompt", ""])
-            run_command(self._launch_args + resume_run_args)
-
-            self.assertEqual({x for x in os.listdir(tmpdir) if "checkpoint" in x}, {"checkpoint-6", "checkpoint-8"})

examples/dreambooth/train_dreambooth_flux.py

@@ -618,15 +618,6 @@ def parse_args(input_args=None):
         ),
     )
     parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-    parser.add_argument(
-        "--image_interpolation_mode",
-        type=str,
-        default="lanczos",
-        choices=[
-            f.lower() for f in dir(transforms.InterpolationMode) if not f.startswith("__") and not f.endswith("__")
-        ],
-        help="The image interpolation method to use for resizing images.",
-    )
 
     if input_args is not None:
         args = parser.parse_args(input_args)
@@ -746,10 +737,7 @@ class DreamBoothDataset(Dataset):
             self.instance_images.extend(itertools.repeat(img, repeats))
 
         self.pixel_values = []
-        interpolation = getattr(transforms.InterpolationMode, args.image_interpolation_mode.upper(), None)
-        if interpolation is None:
-            raise ValueError(f"Unsupported interpolation mode {interpolation=}.")
-        train_resize = transforms.Resize(size, interpolation=interpolation)
+        train_resize = transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR)
         train_crop = transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size)
         train_flip = transforms.RandomHorizontalFlip(p=1.0)
         train_transforms = transforms.Compose(
@@ -1419,22 +1407,17 @@ def main(args):
                 tokens_two = torch.cat([tokens_two, class_tokens_two], dim=0)
 
     # Scheduler and math around the number of training steps.
-    # Check the PR https://github.com/huggingface/diffusers/pull/8312 for detailed explanation.
-    num_warmup_steps_for_scheduler = args.lr_warmup_steps * accelerator.num_processes
+    overrode_max_train_steps = False
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
     if args.max_train_steps is None:
-        len_train_dataloader_after_sharding = math.ceil(len(train_dataloader) / accelerator.num_processes)
-        num_update_steps_per_epoch = math.ceil(len_train_dataloader_after_sharding / args.gradient_accumulation_steps)
-        num_training_steps_for_scheduler = (
-            args.num_train_epochs * accelerator.num_processes * num_update_steps_per_epoch
-        )
-    else:
-        num_training_steps_for_scheduler = args.max_train_steps * accelerator.num_processes
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+        overrode_max_train_steps = True
 
     lr_scheduler = get_scheduler(
         args.lr_scheduler,
         optimizer=optimizer,
-        num_warmup_steps=num_warmup_steps_for_scheduler,
-        num_training_steps=num_training_steps_for_scheduler,
+        num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
+        num_training_steps=args.max_train_steps * accelerator.num_processes,
         num_cycles=args.lr_num_cycles,
         power=args.lr_power,
     )
@@ -1461,14 +1444,8 @@ def main(args):
 
     # We need to recalculate our total training steps as the size of the training dataloader may have changed.
     num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if args.max_train_steps is None:
+    if overrode_max_train_steps:
         args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-        if num_training_steps_for_scheduler != args.max_train_steps:
-            logger.warning(
-                f"The length of the 'train_dataloader' after 'accelerator.prepare' ({len(train_dataloader)}) does not match "
-                f"the expected length ({len_train_dataloader_after_sharding}) when the learning rate scheduler was created. "
-                f"This inconsistency may result in the learning rate scheduler not functioning properly."
-            )
     # Afterwards we recalculate our number of training epochs
     args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
 
@@ -1634,7 +1611,7 @@ def main(args):
                 # Predict the noise residual
                 model_pred = transformer(
                     hidden_states=packed_noisy_model_input,
-                    # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transformer model (we should not keep it but I want to keep the inputs same for the model for testing)
+                    # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transforme rmodel (we should not keep it but I want to keep the inputs same for the model for testing)
                     timestep=timesteps / 1000,
                     guidance=guidance,
                     pooled_projections=pooled_prompt_embeds,

examples/dreambooth/train_dreambooth_lora.py

@@ -524,15 +524,6 @@ def parse_args(input_args=None):
         default=4,
         help=("The dimension of the LoRA update matrices."),
     )
-    parser.add_argument(
-        "--image_interpolation_mode",
-        type=str,
-        default="lanczos",
-        choices=[
-            f.lower() for f in dir(transforms.InterpolationMode) if not f.startswith("__") and not f.endswith("__")
-        ],
-        help="The image interpolation method to use for resizing images.",
-    )
 
     if input_args is not None:
         args = parser.parse_args(input_args)
@@ -610,13 +601,9 @@ class DreamBoothDataset(Dataset):
         else:
             self.class_data_root = None
 
-        interpolation = getattr(transforms.InterpolationMode, args.image_interpolation_mode.upper(), None)
-        if interpolation is None:
-            raise ValueError(f"Unsupported interpolation mode {interpolation=}.")
-
         self.image_transforms = transforms.Compose(
             [
-                transforms.Resize(size, interpolation=interpolation),
+                transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
                 transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
                 transforms.ToTensor(),
                 transforms.Normalize([0.5], [0.5]),

examples/dreambooth/train_dreambooth_lora_flux.py

@@ -1524,22 +1524,17 @@ def main(args):
             free_memory()
 
     # Scheduler and math around the number of training steps.
-    # Check the PR https://github.com/huggingface/diffusers/pull/8312 for detailed explanation.
-    num_warmup_steps_for_scheduler = args.lr_warmup_steps * accelerator.num_processes
+    overrode_max_train_steps = False
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
     if args.max_train_steps is None:
-        len_train_dataloader_after_sharding = math.ceil(len(train_dataloader) / accelerator.num_processes)
-        num_update_steps_per_epoch = math.ceil(len_train_dataloader_after_sharding / args.gradient_accumulation_steps)
-        num_training_steps_for_scheduler = (
-            args.num_train_epochs * accelerator.num_processes * num_update_steps_per_epoch
-        )
-    else:
-        num_training_steps_for_scheduler = args.max_train_steps * accelerator.num_processes
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+        overrode_max_train_steps = True
 
     lr_scheduler = get_scheduler(
         args.lr_scheduler,
         optimizer=optimizer,
-        num_warmup_steps=num_warmup_steps_for_scheduler,
-        num_training_steps=num_training_steps_for_scheduler,
+        num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
+        num_training_steps=args.max_train_steps * accelerator.num_processes,
         num_cycles=args.lr_num_cycles,
         power=args.lr_power,
     )
@@ -1566,14 +1561,8 @@ def main(args):
 
     # We need to recalculate our total training steps as the size of the training dataloader may have changed.
     num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if args.max_train_steps is None:
+    if overrode_max_train_steps:
         args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-        if num_training_steps_for_scheduler != args.max_train_steps:
-            logger.warning(
-                f"The length of the 'train_dataloader' after 'accelerator.prepare' ({len(train_dataloader)}) does not match "
-                f"the expected length ({len_train_dataloader_after_sharding}) when the learning rate scheduler was created. "
-                f"This inconsistency may result in the learning rate scheduler not functioning properly."
-            )
     # Afterwards we recalculate our number of training epochs
     args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
 
@@ -1749,7 +1738,7 @@ def main(args):
                 # Predict the noise residual
                 model_pred = transformer(
                     hidden_states=packed_noisy_model_input,
-                    # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transformer model (we should not keep it but I want to keep the inputs same for the model for testing)
+                    # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transforme rmodel (we should not keep it but I want to keep the inputs same for the model for testing)
                     timestep=timesteps / 1000,
                     guidance=guidance,
                     pooled_projections=pooled_prompt_embeds,

examples/dreambooth/train_dreambooth_lora_sdxl.py

@@ -1523,22 +1523,17 @@ def main(args):
                 tokens_two = torch.cat([tokens_two, class_tokens_two], dim=0)
 
     # Scheduler and math around the number of training steps.
-    # Check the PR https://github.com/huggingface/diffusers/pull/8312 for detailed explanation.
-    num_warmup_steps_for_scheduler = args.lr_warmup_steps * accelerator.num_processes
+    overrode_max_train_steps = False
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
     if args.max_train_steps is None:
-        len_train_dataloader_after_sharding = math.ceil(len(train_dataloader) / accelerator.num_processes)
-        num_update_steps_per_epoch = math.ceil(len_train_dataloader_after_sharding / args.gradient_accumulation_steps)
-        num_training_steps_for_scheduler = (
-            args.num_train_epochs * accelerator.num_processes * num_update_steps_per_epoch
-        )
-    else:
-        num_training_steps_for_scheduler = args.max_train_steps * accelerator.num_processes
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+        overrode_max_train_steps = True
 
     lr_scheduler = get_scheduler(
         args.lr_scheduler,
         optimizer=optimizer,
-        num_warmup_steps=num_warmup_steps_for_scheduler,
-        num_training_steps=num_training_steps_for_scheduler,
+        num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
+        num_training_steps=args.max_train_steps * accelerator.num_processes,
         num_cycles=args.lr_num_cycles,
         power=args.lr_power,
     )
@@ -1555,14 +1550,7 @@ def main(args):
 
     # We need to recalculate our total training steps as the size of the training dataloader may have changed.
     num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if args.max_train_steps is None:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-        if num_training_steps_for_scheduler != args.max_train_steps:
-            logger.warning(
-                f"The length of the 'train_dataloader' after 'accelerator.prepare' ({len(train_dataloader)}) does not match "
-                f"the expected length ({len_train_dataloader_after_sharding}) when the learning rate scheduler was created. "
-                f"This inconsistency may result in the learning rate scheduler not functioning properly."
-            )
+    if overrode_max_train_steps:
         args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
     # Afterwards we recalculate our number of training epochs
     args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)

examples/research_projects/flux_lora_quantization/train_dreambooth_lora_flux_miniature.py

@@ -1088,7 +1088,7 @@ def main(args):
                 text_ids = batch["text_ids"].to(device=accelerator.device, dtype=weight_dtype)
                 model_pred = transformer(
                     hidden_states=packed_noisy_model_input,
-                    # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transformer model (we should not keep it but I want to keep the inputs same for the model for testing)
+                    # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transforme rmodel (we should not keep it but I want to keep the inputs same for the model for testing)
                     timestep=timesteps / 1000,
                     guidance=guidance,
                     pooled_projections=pooled_prompt_embeds,

examples/text_to_image/train_text_to_image.py

@@ -499,15 +499,6 @@ def parse_args():
             " more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
         ),
     )
-    parser.add_argument(
-        "--image_interpolation_mode",
-        type=str,
-        default="lanczos",
-        choices=[
-            f.lower() for f in dir(transforms.InterpolationMode) if not f.startswith("__") and not f.endswith("__")
-        ],
-        help="The image interpolation method to use for resizing images.",
-    )
 
     args = parser.parse_args()
     env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
@@ -796,17 +787,10 @@ def main():
         )
         return inputs.input_ids
 
-    # Get the specified interpolation method from the args
-    interpolation = getattr(transforms.InterpolationMode, args.image_interpolation_mode.upper(), None)
-
-    # Raise an error if the interpolation method is invalid
-    if interpolation is None:
-        raise ValueError(f"Unsupported interpolation mode {args.image_interpolation_mode}.")
-
-    # Data preprocessing transformations
+    # Preprocessing the datasets.
     train_transforms = transforms.Compose(
         [
-            transforms.Resize(args.resolution, interpolation=interpolation),  # Use dynamic interpolation method
+            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
             transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),
             transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),
             transforms.ToTensor(),

examples/text_to_image/train_text_to_image_lora.py

@@ -418,15 +418,6 @@ def parse_args():
         default=4,
         help=("The dimension of the LoRA update matrices."),
     )
-    parser.add_argument(
-        "--image_interpolation_mode",
-        type=str,
-        default="lanczos",
-        choices=[
-            f.lower() for f in dir(transforms.InterpolationMode) if not f.startswith("__") and not f.endswith("__")
-        ],
-        help="The image interpolation method to use for resizing images.",
-    )
 
     args = parser.parse_args()
     env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
@@ -658,17 +649,10 @@ def main():
         )
         return inputs.input_ids
 
-    # Get the specified interpolation method from the args
-    interpolation = getattr(transforms.InterpolationMode, args.image_interpolation_mode.upper(), None)
-
-    # Raise an error if the interpolation method is invalid
-    if interpolation is None:
-        raise ValueError(f"Unsupported interpolation mode {args.image_interpolation_mode}.")
-
-    # Data preprocessing transformations
+    # Preprocessing the datasets.
     train_transforms = transforms.Compose(
         [
-            transforms.Resize(args.resolution, interpolation=interpolation),  # Use dynamic interpolation method
+            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
             transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),
             transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),
             transforms.ToTensor(),

scripts/convert_wan_to_diffusers.py

@@ -39,24 +39,6 @@ TRANSFORMER_KEYS_RENAME_DICT = {
     "img_emb.proj.1": "condition_embedder.image_embedder.ff.net.0.proj",
     "img_emb.proj.3": "condition_embedder.image_embedder.ff.net.2",
     "img_emb.proj.4": "condition_embedder.image_embedder.norm2",
-    # for the FLF2V model
-    "img_emb.emb_pos": "condition_embedder.image_embedder.pos_embed",
-    # Add attention component mappings
-    "self_attn.q": "attn1.to_q",
-    "self_attn.k": "attn1.to_k",
-    "self_attn.v": "attn1.to_v",
-    "self_attn.o": "attn1.to_out.0",
-    "self_attn.norm_q": "attn1.norm_q",
-    "self_attn.norm_k": "attn1.norm_k",
-    "cross_attn.q": "attn2.to_q",
-    "cross_attn.k": "attn2.to_k",
-    "cross_attn.v": "attn2.to_v",
-    "cross_attn.o": "attn2.to_out.0",
-    "cross_attn.norm_q": "attn2.norm_q",
-    "cross_attn.norm_k": "attn2.norm_k",
-    "attn2.to_k_img": "attn2.add_k_proj",
-    "attn2.to_v_img": "attn2.add_v_proj",
-    "attn2.norm_k_img": "attn2.norm_added_k",
 }
 
 TRANSFORMER_SPECIAL_KEYS_REMAP = {}
@@ -153,28 +135,6 @@ def get_transformer_config(model_type: str) -> Dict[str, Any]:
                 "text_dim": 4096,
             },
         }
-    elif model_type == "Wan-FLF2V-14B-720P":
-        config = {
-            "model_id": "ypyp/Wan2.1-FLF2V-14B-720P",  # This is just a placeholder
-            "diffusers_config": {
-                "image_dim": 1280,
-                "added_kv_proj_dim": 5120,
-                "attention_head_dim": 128,
-                "cross_attn_norm": True,
-                "eps": 1e-06,
-                "ffn_dim": 13824,
-                "freq_dim": 256,
-                "in_channels": 36,
-                "num_attention_heads": 40,
-                "num_layers": 40,
-                "out_channels": 16,
-                "patch_size": [1, 2, 2],
-                "qk_norm": "rms_norm_across_heads",
-                "text_dim": 4096,
-                "rope_max_seq_len": 1024,
-                "pos_embed_seq_len": 257 * 2,
-            },
-        }
     return config
 
 
@@ -433,12 +393,11 @@ if __name__ == "__main__":
     vae = convert_vae()
     text_encoder = UMT5EncoderModel.from_pretrained("google/umt5-xxl")
     tokenizer = AutoTokenizer.from_pretrained("google/umt5-xxl")
-    flow_shift = 16.0 if "FLF2V" in args.model_type else 3.0
     scheduler = UniPCMultistepScheduler(
-        prediction_type="flow_prediction", use_flow_sigmas=True, num_train_timesteps=1000, flow_shift=flow_shift
+        prediction_type="flow_prediction", use_flow_sigmas=True, num_train_timesteps=1000, flow_shift=3.0
     )
 
-    if "I2V" in args.model_type or "FLF2V" in args.model_type:
+    if "I2V" in args.model_type:
         image_encoder = CLIPVisionModelWithProjection.from_pretrained(
             "laion/CLIP-ViT-H-14-laion2B-s32B-b79K", torch_dtype=torch.bfloat16
         )

src/diffusers/__init__.py

@@ -239,7 +239,6 @@ else:
             "KarrasVePipeline",
             "LDMPipeline",
             "LDMSuperResolutionPipeline",
-            "ModularPipeline",
             "PNDMPipeline",
             "RePaintPipeline",
             "ScoreSdeVePipeline",
@@ -494,12 +493,10 @@ else:
             "StableDiffusionXLImg2ImgPipeline",
             "StableDiffusionXLInpaintPipeline",
             "StableDiffusionXLInstructPix2PixPipeline",
-            "StableDiffusionXLModularPipeline",
             "StableDiffusionXLPAGImg2ImgPipeline",
             "StableDiffusionXLPAGInpaintPipeline",
             "StableDiffusionXLPAGPipeline",
             "StableDiffusionXLPipeline",
-            "StableDiffusionXLAutoPipeline",
             "StableUnCLIPImg2ImgPipeline",
             "StableUnCLIPPipeline",
             "StableVideoDiffusionPipeline",
@@ -837,7 +834,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             KarrasVePipeline,
             LDMPipeline,
             LDMSuperResolutionPipeline,
-            ModularPipeline,
             PNDMPipeline,
             RePaintPipeline,
             ScoreSdeVePipeline,
@@ -1070,12 +1066,10 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             StableDiffusionXLImg2ImgPipeline,
             StableDiffusionXLInpaintPipeline,
             StableDiffusionXLInstructPix2PixPipeline,
-            StableDiffusionXLModularPipeline,
             StableDiffusionXLPAGImg2ImgPipeline,
             StableDiffusionXLPAGInpaintPipeline,
             StableDiffusionXLPAGPipeline,
             StableDiffusionXLPipeline,
-            StableDiffusionXLAutoPipeline,
             StableUnCLIPImg2ImgPipeline,
             StableUnCLIPPipeline,
             StableVideoDiffusionPipeline,

src/diffusers/guider.py

@@ -1,745 +0,0 @@
-# Copyright 2024 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import re
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-
-from .models.attention_processor import (
-    Attention,
-    AttentionProcessor,
-    PAGCFGIdentitySelfAttnProcessor2_0,
-    PAGIdentitySelfAttnProcessor2_0,
-)
-from .utils import logging
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
-def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
-    r"""
-    Rescales `noise_cfg` tensor based on `guidance_rescale` to improve image quality and fix overexposure. Based on
-    Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
-    Flawed](https://arxiv.org/pdf/2305.08891.pdf).
-
-    Args:
-        noise_cfg (`torch.Tensor`):
-            The predicted noise tensor for the guided diffusion process.
-        noise_pred_text (`torch.Tensor`):
-            The predicted noise tensor for the text-guided diffusion process.
-        guidance_rescale (`float`, *optional*, defaults to 0.0):
-            A rescale factor applied to the noise predictions.
-
-    Returns:
-        noise_cfg (`torch.Tensor`): The rescaled noise prediction tensor.
-    """
-    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
-    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
-    # rescale the results from guidance (fixes overexposure)
-    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
-    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
-    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
-    return noise_cfg
-
-
-class CFGGuider:
-    """
-    This class is used to guide the pipeline with CFG (Classifier-Free Guidance).
-    """
-
-    @property
-    def do_classifier_free_guidance(self):
-        return self._guidance_scale > 1.0 and not self._disable_guidance
-
-    @property
-    def guidance_rescale(self):
-        return self._guidance_rescale
-
-    @property
-    def guidance_scale(self):
-        return self._guidance_scale
-
-    @property
-    def batch_size(self):
-        return self._batch_size
-
-    def set_guider(self, pipeline, guider_kwargs: Dict[str, Any]):
-        # a flag to disable CFG, e.g. we disable it for LCM and use a guidance scale embedding instead
-        disable_guidance = guider_kwargs.get("disable_guidance", False)
-        guidance_scale = guider_kwargs.get("guidance_scale", None)
-        if guidance_scale is None:
-            raise ValueError("guidance_scale is not provided in guider_kwargs")
-        guidance_rescale = guider_kwargs.get("guidance_rescale", 0.0)
-        batch_size = guider_kwargs.get("batch_size", None)
-        if batch_size is None:
-            raise ValueError("batch_size is not provided in guider_kwargs")
-        self._guidance_scale = guidance_scale
-        self._guidance_rescale = guidance_rescale
-        self._batch_size = batch_size
-        self._disable_guidance = disable_guidance
-
-    def reset_guider(self, pipeline):
-        pass
-
-    def maybe_update_guider(self, pipeline, timestep):
-        pass
-
-    def maybe_update_input(self, pipeline, cond_input):
-        pass
-
-    def _maybe_split_prepared_input(self, cond):
-        """
-        Process and potentially split the conditional input for Classifier-Free Guidance (CFG).
-
-        This method handles inputs that may already have CFG applied (i.e. when `cond` is output of `prepare_input`).
-        It determines whether to split the input based on its batch size relative to the expected batch size.
-
-        Args:
-            cond (torch.Tensor): The conditional input tensor to process.
-
-        Returns:
-            Tuple[torch.Tensor, torch.Tensor]: A tuple containing:
-                - The negative conditional input (uncond_input)
-                - The positive conditional input (cond_input)
-        """
-        if cond.shape[0] == self.batch_size * 2:
-            neg_cond = cond[0 : self.batch_size]
-            cond = cond[self.batch_size :]
-            return neg_cond, cond
-        elif cond.shape[0] == self.batch_size:
-            return cond, cond
-        else:
-            raise ValueError(f"Unsupported input shape: {cond.shape}")
-
-    def _is_prepared_input(self, cond):
-        """
-        Check if the input is already prepared for Classifier-Free Guidance (CFG).
-
-        Args:
-            cond (torch.Tensor): The conditional input tensor to check.
-
-        Returns:
-            bool: True if the input is already prepared, False otherwise.
-        """
-        cond_tensor = cond[0] if isinstance(cond, (list, tuple)) else cond
-
-        return cond_tensor.shape[0] == self.batch_size * 2
-
-    def prepare_input(
-        self,
-        cond_input: Union[torch.Tensor, List[torch.Tensor]],
-        negative_cond_input: Optional[Union[torch.Tensor, List[torch.Tensor]]] = None,
-    ) -> Union[torch.Tensor, List[torch.Tensor]]:
-        """
-        Prepare the input for CFG.
-
-        Args:
-            cond_input (Union[torch.Tensor, List[torch.Tensor]]):
-                The conditional input. It can be a single tensor or a
-            list of tensors. It must have the same length as `negative_cond_input`.
-            negative_cond_input (Union[torch.Tensor, List[torch.Tensor]]): The negative conditional input. It can be a
-                single tensor or a list of tensors. It must have the same length as `cond_input`.
-
-        Returns:
-            Union[torch.Tensor, List[torch.Tensor]]: The prepared input.
-        """
-
-        # we check if cond_input already has CFG applied, and split if it is the case.
-        if self._is_prepared_input(cond_input) and self.do_classifier_free_guidance:
-            return cond_input
-
-        if self._is_prepared_input(cond_input) and not self.do_classifier_free_guidance:
-            if isinstance(cond_input, list):
-                negative_cond_input, cond_input = zip(*[self._maybe_split_prepared_input(cond) for cond in cond_input])
-            else:
-                negative_cond_input, cond_input = self._maybe_split_prepared_input(cond_input)
-
-        if not self._is_prepared_input(cond_input) and self.do_classifier_free_guidance and negative_cond_input is None:
-            raise ValueError(
-                "`negative_cond_input` is required when cond_input does not already contains negative conditional input"
-            )
-
-        if isinstance(cond_input, (list, tuple)):
-            if not self.do_classifier_free_guidance:
-                return cond_input
-
-            if len(negative_cond_input) != len(cond_input):
-                raise ValueError("The length of negative_cond_input and cond_input must be the same.")
-            prepared_input = []
-            for neg_cond, cond in zip(negative_cond_input, cond_input):
-                if neg_cond.shape[0] != cond.shape[0]:
-                    raise ValueError("The batch size of negative_cond_input and cond_input must be the same.")
-                prepared_input.append(torch.cat([neg_cond, cond], dim=0))
-            return prepared_input
-
-        elif isinstance(cond_input, torch.Tensor):
-            if not self.do_classifier_free_guidance:
-                return cond_input
-            else:
-                return torch.cat([negative_cond_input, cond_input], dim=0)
-
-        else:
-            raise ValueError(f"Unsupported input type: {type(cond_input)}")
-
-    def apply_guidance(
-        self,
-        model_output: torch.Tensor,
-        timestep: int = None,
-        latents: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        if not self.do_classifier_free_guidance:
-            return model_output
-
-        noise_pred_uncond, noise_pred_text = model_output.chunk(2)
-        noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-        if self.guidance_rescale > 0.0:
-            # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
-            noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
-        return noise_pred
-
-
-class PAGGuider:
-    """
-    This class is used to guide the pipeline with CFG (Classifier-Free Guidance).
-    """
-
-    def __init__(
-        self,
-        pag_applied_layers: Union[str, List[str]],
-        pag_attn_processors: Tuple[AttentionProcessor, AttentionProcessor] = (
-            PAGCFGIdentitySelfAttnProcessor2_0(),
-            PAGIdentitySelfAttnProcessor2_0(),
-        ),
-    ):
-        r"""
-        Set the the self-attention layers to apply PAG. Raise ValueError if the input is invalid.
-
-        Args:
-            pag_applied_layers (`str` or `List[str]`):
-                One or more strings identifying the layer names, or a simple regex for matching multiple layers, where
-                PAG is to be applied. A few ways of expected usage are as follows:
-                  - Single layers specified as - "blocks.{layer_index}"
-                  - Multiple layers as a list - ["blocks.{layers_index_1}", "blocks.{layer_index_2}", ...]
-                  - Multiple layers as a block name - "mid"
-                  - Multiple layers as regex - "blocks.({layer_index_1}|{layer_index_2})"
-            pag_attn_processors:
-                (`Tuple[AttentionProcessor, AttentionProcessor]`, defaults to `(PAGCFGIdentitySelfAttnProcessor2_0(),
-                PAGIdentitySelfAttnProcessor2_0())`): A tuple of two attention processors. The first attention
-                processor is for PAG with Classifier-free guidance enabled (conditional and unconditional). The second
-                attention processor is for PAG with CFG disabled (unconditional only).
-        """
-
-        if not isinstance(pag_applied_layers, list):
-            pag_applied_layers = [pag_applied_layers]
-        if pag_attn_processors is not None:
-            if not isinstance(pag_attn_processors, tuple) or len(pag_attn_processors) != 2:
-                raise ValueError("Expected a tuple of two attention processors")
-
-        for i in range(len(pag_applied_layers)):
-            if not isinstance(pag_applied_layers[i], str):
-                raise ValueError(
-                    f"Expected either a string or a list of string but got type {type(pag_applied_layers[i])}"
-                )
-
-        self.pag_applied_layers = pag_applied_layers
-        self._pag_attn_processors = pag_attn_processors
-
-    def _set_pag_attn_processor(self, model, pag_applied_layers, do_classifier_free_guidance):
-        r"""
-        Set the attention processor for the PAG layers.
-        """
-        pag_attn_processors = self._pag_attn_processors
-        pag_attn_proc = pag_attn_processors[0] if do_classifier_free_guidance else pag_attn_processors[1]
-
-        def is_self_attn(module: nn.Module) -> bool:
-            r"""
-            Check if the module is self-attention module based on its name.
-            """
-            return isinstance(module, Attention) and not module.is_cross_attention
-
-        def is_fake_integral_match(layer_id, name):
-            layer_id = layer_id.split(".")[-1]
-            name = name.split(".")[-1]
-            return layer_id.isnumeric() and name.isnumeric() and layer_id == name
-
-        for layer_id in pag_applied_layers:
-            # for each PAG layer input, we find corresponding self-attention layers in the unet model
-            target_modules = []
-
-            for name, module in model.named_modules():
-                # Identify the following simple cases:
-                #   (1) Self Attention layer existing
-                #   (2) Whether the module name matches pag layer id even partially
-                #   (3) Make sure it's not a fake integral match if the layer_id ends with a number
-                #       For example, blocks.1, blocks.10 should be differentiable if layer_id="blocks.1"
-                if (
-                    is_self_attn(module)
-                    and re.search(layer_id, name) is not None
-                    and not is_fake_integral_match(layer_id, name)
-                ):
-                    logger.debug(f"Applying PAG to layer: {name}")
-                    target_modules.append(module)
-
-            if len(target_modules) == 0:
-                raise ValueError(f"Cannot find PAG layer to set attention processor for: {layer_id}")
-
-            for module in target_modules:
-                module.processor = pag_attn_proc
-
-    @property
-    def do_classifier_free_guidance(self):
-        return self._guidance_scale > 1 and not self._disable_guidance
-
-    @property
-    def do_perturbed_attention_guidance(self):
-        return self._pag_scale > 0 and not self._disable_guidance
-
-    @property
-    def do_pag_adaptive_scaling(self):
-        return self._pag_adaptive_scale > 0 and self._pag_scale > 0 and not self._disable_guidance
-
-    @property
-    def guidance_scale(self):
-        return self._guidance_scale
-
-    @property
-    def guidance_rescale(self):
-        return self._guidance_rescale
-
-    @property
-    def batch_size(self):
-        return self._batch_size
-
-    @property
-    def pag_scale(self):
-        return self._pag_scale
-
-    @property
-    def pag_adaptive_scale(self):
-        return self._pag_adaptive_scale
-
-    def set_guider(self, pipeline, guider_kwargs: Dict[str, Any]):
-        pag_scale = guider_kwargs.get("pag_scale", 3.0)
-        pag_adaptive_scale = guider_kwargs.get("pag_adaptive_scale", 0.0)
-
-        batch_size = guider_kwargs.get("batch_size", None)
-        if batch_size is None:
-            raise ValueError("batch_size is a required argument for PAGGuider")
-
-        guidance_scale = guider_kwargs.get("guidance_scale", None)
-        guidance_rescale = guider_kwargs.get("guidance_rescale", 0.0)
-        disable_guidance = guider_kwargs.get("disable_guidance", False)
-
-        if guidance_scale is None:
-            raise ValueError("guidance_scale is a required argument for PAGGuider")
-
-        self._pag_scale = pag_scale
-        self._pag_adaptive_scale = pag_adaptive_scale
-        self._guidance_scale = guidance_scale
-        self._disable_guidance = disable_guidance
-        self._guidance_rescale = guidance_rescale
-        self._batch_size = batch_size
-        if not hasattr(pipeline, "original_attn_proc") or pipeline.original_attn_proc is None:
-            pipeline.original_attn_proc = pipeline.unet.attn_processors
-            self._set_pag_attn_processor(
-                model=pipeline.unet if hasattr(pipeline, "unet") else pipeline.transformer,
-                pag_applied_layers=self.pag_applied_layers,
-                do_classifier_free_guidance=self.do_classifier_free_guidance,
-            )
-
-    def reset_guider(self, pipeline):
-        if (
-            self.do_perturbed_attention_guidance
-            and hasattr(pipeline, "original_attn_proc")
-            and pipeline.original_attn_proc is not None
-        ):
-            pipeline.unet.set_attn_processor(pipeline.original_attn_proc)
-            pipeline.original_attn_proc = None
-
-    def maybe_update_guider(self, pipeline, timestep):
-        pass
-
-    def maybe_update_input(self, pipeline, cond_input):
-        pass
-
-    def _is_prepared_input(self, cond):
-        """
-        Check if the input is already prepared for Perturbed Attention Guidance (PAG).
-
-        Args:
-            cond (torch.Tensor): The conditional input tensor to check.
-
-        Returns:
-            bool: True if the input is already prepared, False otherwise.
-        """
-        cond_tensor = cond[0] if isinstance(cond, (list, tuple)) else cond
-
-        return cond_tensor.shape[0] == self.batch_size * 3
-
-    def _maybe_split_prepared_input(self, cond):
-        """
-        Process and potentially split the conditional input for Classifier-Free Guidance (CFG).
-
-        This method handles inputs that may already have CFG applied (i.e. when `cond` is output of `prepare_input`).
-        It determines whether to split the input based on its batch size relative to the expected batch size.
-
-        Args:
-            cond (torch.Tensor): The conditional input tensor to process.
-
-        Returns:
-            Tuple[torch.Tensor, torch.Tensor]: A tuple containing:
-                - The negative conditional input (uncond_input)
-                - The positive conditional input (cond_input)
-        """
-        if cond.shape[0] == self.batch_size * 3:
-            neg_cond = cond[0 : self.batch_size]
-            cond = cond[self.batch_size : self.batch_size * 2]
-            return neg_cond, cond
-        elif cond.shape[0] == self.batch_size:
-            return cond, cond
-        else:
-            raise ValueError(f"Unsupported input shape: {cond.shape}")
-
-    def prepare_input(
-        self,
-        cond_input: Union[torch.Tensor, List[torch.Tensor], Tuple[torch.Tensor, ...]],
-        negative_cond_input: Optional[Union[torch.Tensor, List[torch.Tensor], Tuple[torch.Tensor, ...]]] = None,
-    ) -> Union[torch.Tensor, List[torch.Tensor], Tuple[torch.Tensor, ...]]:
-        """
-        Prepare the input for CFG.
-
-        Args:
-            cond_input (Union[torch.Tensor, List[torch.Tensor], Tuple[torch.Tensor, ...]]):
-                The conditional input. It can be a single tensor or a
-            list of tensors. It must have the same length as `negative_cond_input`.
-            negative_cond_input (Union[torch.Tensor, List[torch.Tensor], Tuple[torch.Tensor, ...]]):
-                The negative conditional input. It can be a single tensor or a list of tensors. It must have the same
-                length as `cond_input`.
-
-        Returns:
-            Union[torch.Tensor, List[torch.Tensor], Tuple[torch.Tensor, ...]]: The prepared input.
-        """
-
-        # we check if cond_input already has CFG applied, and split if it is the case.
-
-        if self._is_prepared_input(cond_input) and self.do_perturbed_attention_guidance:
-            return cond_input
-
-        if self._is_prepared_input(cond_input) and not self.do_perturbed_attention_guidance:
-            if isinstance(cond_input, list):
-                negative_cond_input, cond_input = zip(*[self._maybe_split_prepared_input(cond) for cond in cond_input])
-            else:
-                negative_cond_input, cond_input = self._maybe_split_prepared_input(cond_input)
-
-        if not self._is_prepared_input(cond_input) and self.do_perturbed_attention_guidance and negative_cond_input is None:
-            raise ValueError(
-                "`negative_cond_input` is required when cond_input does not already contains negative conditional input"
-            )
-
-        if isinstance(cond_input, (list, tuple)):
-            if not self.do_perturbed_attention_guidance:
-                return cond_input
-
-            if len(negative_cond_input) != len(cond_input):
-                raise ValueError("The length of negative_cond_input and cond_input must be the same.")
-
-            prepared_input = []
-            for neg_cond, cond in zip(negative_cond_input, cond_input):
-                if neg_cond.shape[0] != cond.shape[0]:
-                    raise ValueError("The batch size of negative_cond_input and cond_input must be the same.")
-
-                cond = torch.cat([cond] * 2, dim=0)
-                if self.do_classifier_free_guidance:
-                    prepared_input.append(torch.cat([neg_cond, cond], dim=0))
-                else:
-                    prepared_input.append(cond)
-
-            return prepared_input
-
-        elif isinstance(cond_input, torch.Tensor):
-            if not self.do_perturbed_attention_guidance:
-                return cond_input
-
-            cond_input = torch.cat([cond_input] * 2, dim=0)
-            if self.do_classifier_free_guidance:
-                return torch.cat([negative_cond_input, cond_input], dim=0)
-            else:
-                return cond_input
-
-        else:
-            raise ValueError(f"Unsupported input type: {type(negative_cond_input)} and {type(cond_input)}")
-
-    def apply_guidance(
-        self,
-        model_output: torch.Tensor,
-        timestep: int,
-        latents: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        if not self.do_perturbed_attention_guidance:
-            return model_output
-
-        if self.do_pag_adaptive_scaling:
-            pag_scale = max(self._pag_scale - self._pag_adaptive_scale * (1000 - timestep), 0)
-        else:
-            pag_scale = self._pag_scale
-
-        if self.do_classifier_free_guidance:
-            noise_pred_uncond, noise_pred_text, noise_pred_perturb = model_output.chunk(3)
-            noise_pred = (
-                noise_pred_uncond
-                + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
-                + pag_scale * (noise_pred_text - noise_pred_perturb)
-            )
-        else:
-            noise_pred_text, noise_pred_perturb = model_output.chunk(2)
-            noise_pred = noise_pred_text + pag_scale * (noise_pred_text - noise_pred_perturb)
-
-        if self.guidance_rescale > 0.0:
-            # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
-            noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
-
-        return noise_pred
-
-
-class MomentumBuffer:
-    def __init__(self, momentum: float):
-        self.momentum = momentum
-        self.running_average = 0
-
-    def update(self, update_value: torch.Tensor):
-        new_average = self.momentum * self.running_average
-        self.running_average = update_value + new_average
-
-
-class APGGuider:
-    """
-    This class is used to guide the pipeline with APG (Adaptive Projected Guidance).
-    """
-
-    def normalized_guidance(
-        self,
-        pred_cond: torch.Tensor,
-        pred_uncond: torch.Tensor,
-        guidance_scale: float,
-        momentum_buffer: MomentumBuffer = None,
-        norm_threshold: float = 0.0,
-        eta: float = 1.0,
-    ):
-        """
-        Based on the findings of [Eliminating Oversaturation and Artifacts of High Guidance Scales in Diffusion
-        Models](https://arxiv.org/pdf/2410.02416)
-        """
-        diff = pred_cond - pred_uncond
-        if momentum_buffer is not None:
-            momentum_buffer.update(diff)
-            diff = momentum_buffer.running_average
-        if norm_threshold > 0:
-            ones = torch.ones_like(diff)
-            diff_norm = diff.norm(p=2, dim=[-1, -2, -3], keepdim=True)
-            scale_factor = torch.minimum(ones, norm_threshold / diff_norm)
-            diff = diff * scale_factor
-        v0, v1 = diff.double(), pred_cond.double()
-        v1 = torch.nn.functional.normalize(v1, dim=[-1, -2, -3])
-        v0_parallel = (v0 * v1).sum(dim=[-1, -2, -3], keepdim=True) * v1
-        v0_orthogonal = v0 - v0_parallel
-        diff_parallel, diff_orthogonal = v0_parallel.to(diff.dtype), v0_orthogonal.to(diff.dtype)
-        normalized_update = diff_orthogonal + eta * diff_parallel
-        pred_guided = pred_cond + (guidance_scale - 1) * normalized_update
-        return pred_guided
-
-    @property
-    def adaptive_projected_guidance_momentum(self):
-        return self._adaptive_projected_guidance_momentum
-
-    @property
-    def adaptive_projected_guidance_rescale_factor(self):
-        return self._adaptive_projected_guidance_rescale_factor
-
-    @property
-    def do_classifier_free_guidance(self):
-        return self._guidance_scale > 1.0 and not self._disable_guidance
-
-    @property
-    def guidance_rescale(self):
-        return self._guidance_rescale
-
-    @property
-    def guidance_scale(self):
-        return self._guidance_scale
-
-    @property
-    def batch_size(self):
-        return self._batch_size
-
-    def set_guider(self, pipeline, guider_kwargs: Dict[str, Any]):
-        disable_guidance = guider_kwargs.get("disable_guidance", False)
-        guidance_scale = guider_kwargs.get("guidance_scale", None)
-        if guidance_scale is None:
-            raise ValueError("guidance_scale is not provided in guider_kwargs")
-        adaptive_projected_guidance_momentum = guider_kwargs.get("adaptive_projected_guidance_momentum", None)
-        adaptive_projected_guidance_rescale_factor = guider_kwargs.get(
-            "adaptive_projected_guidance_rescale_factor", 15.0
-        )
-        guidance_rescale = guider_kwargs.get("guidance_rescale", 0.0)
-        batch_size = guider_kwargs.get("batch_size", None)
-        if batch_size is None:
-            raise ValueError("batch_size is not provided in guider_kwargs")
-        self._adaptive_projected_guidance_momentum = adaptive_projected_guidance_momentum
-        self._adaptive_projected_guidance_rescale_factor = adaptive_projected_guidance_rescale_factor
-        self._guidance_scale = guidance_scale
-        self._guidance_rescale = guidance_rescale
-        self._batch_size = batch_size
-        self._disable_guidance = disable_guidance
-        if adaptive_projected_guidance_momentum is not None:
-            self.momentum_buffer = MomentumBuffer(adaptive_projected_guidance_momentum)
-        else:
-            self.momentum_buffer = None
-        self.scheduler = pipeline.scheduler
-
-    def reset_guider(self, pipeline):
-        pass
-
-    def maybe_update_guider(self, pipeline, timestep):
-        pass
-
-    def maybe_update_input(self, pipeline, cond_input):
-        pass
-
-    def _maybe_split_prepared_input(self, cond):
-        """
-        Process and potentially split the conditional input for Classifier-Free Guidance (CFG).
-
-        This method handles inputs that may already have CFG applied (i.e. when `cond` is output of `prepare_input`).
-        It determines whether to split the input based on its batch size relative to the expected batch size.
-
-        Args:
-            cond (torch.Tensor): The conditional input tensor to process.
-
-        Returns:
-            Tuple[torch.Tensor, torch.Tensor]: A tuple containing:
-                - The negative conditional input (uncond_input)
-                - The positive conditional input (cond_input)
-        """
-        if cond.shape[0] == self.batch_size * 2:
-            neg_cond = cond[0 : self.batch_size]
-            cond = cond[self.batch_size :]
-            return neg_cond, cond
-        elif cond.shape[0] == self.batch_size:
-            return cond, cond
-        else:
-            raise ValueError(f"Unsupported input shape: {cond.shape}")
-
-    def _is_prepared_input(self, cond):
-        """
-        Check if the input is already prepared for Classifier-Free Guidance (CFG).
-
-        Args:
-            cond (torch.Tensor): The conditional input tensor to check.
-
-        Returns:
-            bool: True if the input is already prepared, False otherwise.
-        """
-        cond_tensor = cond[0] if isinstance(cond, (list, tuple)) else cond
-
-        return cond_tensor.shape[0] == self.batch_size * 2
-
-    def prepare_input(
-        self,
-        cond_input: Union[torch.Tensor, List[torch.Tensor]],
-        negative_cond_input: Optional[Union[torch.Tensor, List[torch.Tensor]]] = None,
-    ) -> Union[torch.Tensor, List[torch.Tensor]]:
-        """
-        Prepare the input for CFG.
-
-        Args:
-            cond_input (Union[torch.Tensor, List[torch.Tensor]]):
-                The conditional input. It can be a single tensor or a
-            list of tensors. It must have the same length as `negative_cond_input`.
-            negative_cond_input (Union[torch.Tensor, List[torch.Tensor]]): The negative conditional input. It can be a
-                single tensor or a list of tensors. It must have the same length as `cond_input`.
-
-        Returns:
-            Union[torch.Tensor, List[torch.Tensor]]: The prepared input.
-        """
-
-        # we check if cond_input already has CFG applied, and split if it is the case.
-        if self._is_prepared_input(cond_input) and self.do_classifier_free_guidance:
-            return cond_input
-
-        if self._is_prepared_input(cond_input) and not self.do_classifier_free_guidance:
-            if isinstance(cond_input, list):
-                negative_cond_input, cond_input = zip(*[self._maybe_split_prepared_input(cond) for cond in cond_input])
-            else:
-                negative_cond_input, cond_input = self._maybe_split_prepared_input(cond_input)
-
-        if not self._is_prepared_input(cond_input) and self.do_classifier_free_guidance and negative_cond_input is None:
-            raise ValueError(
-                "`negative_cond_input` is required when cond_input does not already contains negative conditional input"
-            )
-
-        if isinstance(cond_input, (list, tuple)):
-            if not self.do_classifier_free_guidance:
-                return cond_input
-
-            if len(negative_cond_input) != len(cond_input):
-                raise ValueError("The length of negative_cond_input and cond_input must be the same.")
-            prepared_input = []
-            for neg_cond, cond in zip(negative_cond_input, cond_input):
-                if neg_cond.shape[0] != cond.shape[0]:
-                    raise ValueError("The batch size of negative_cond_input and cond_input must be the same.")
-                prepared_input.append(torch.cat([neg_cond, cond], dim=0))
-            return prepared_input
-
-        elif isinstance(cond_input, torch.Tensor):
-            if not self.do_classifier_free_guidance:
-                return cond_input
-            else:
-                return torch.cat([negative_cond_input, cond_input], dim=0)
-
-        else:
-            raise ValueError(f"Unsupported input type: {type(cond_input)}")
-
-    def apply_guidance(
-        self,
-        model_output: torch.Tensor,
-        timestep: int = None,
-        latents: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        if not self.do_classifier_free_guidance:
-            return model_output
-
-        if latents is None:
-            raise ValueError("APG requires `latents` to convert model output to denoised prediction (x0).")
-
-        sigma = self.scheduler.sigmas[self.scheduler.step_index]
-        noise_pred = latents - sigma * model_output
-        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-        noise_pred = self.normalized_guidance(
-            noise_pred_text,
-            noise_pred_uncond,
-            self.guidance_scale,
-            self.momentum_buffer,
-            self.adaptive_projected_guidance_rescale_factor,
-        )
-        noise_pred = (latents - noise_pred) / sigma
-
-        if self.guidance_rescale > 0.0:
-            # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
-            noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
-        return noise_pred

src/diffusers/hooks/group_offloading.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from contextlib import contextmanager, nullcontext
-from typing import Dict, List, Optional, Set, Tuple, Union
+from typing import Dict, List, Optional, Set, Tuple
 
 import torch
 
@@ -55,9 +55,9 @@ class ModuleGroup:
         parameters: Optional[List[torch.nn.Parameter]] = None,
         buffers: Optional[List[torch.Tensor]] = None,
         non_blocking: bool = False,
-        stream: Union[torch.cuda.Stream, torch.Stream, None] = None,
+        stream: Optional[torch.cuda.Stream] = None,
         record_stream: Optional[bool] = False,
-        low_cpu_mem_usage: bool = False,
+        low_cpu_mem_usage=False,
         onload_self: bool = True,
     ) -> None:
         self.modules = modules
@@ -115,13 +115,8 @@ class ModuleGroup:
 
     def onload_(self):
         r"""Onloads the group of modules to the onload_device."""
-        torch_accelerator_module = (
-            getattr(torch, torch.accelerator.current_accelerator().type)
-            if hasattr(torch, "accelerator")
-            else torch.cuda
-        )
-        context = nullcontext() if self.stream is None else torch_accelerator_module.stream(self.stream)
-        current_stream = torch_accelerator_module.current_stream() if self.record_stream else None
+        context = nullcontext() if self.stream is None else torch.cuda.stream(self.stream)
+        current_stream = torch.cuda.current_stream() if self.record_stream else None
 
         if self.stream is not None:
             # Wait for previous Host->Device transfer to complete
@@ -167,15 +162,9 @@ class ModuleGroup:
 
     def offload_(self):
         r"""Offloads the group of modules to the offload_device."""
-
-        torch_accelerator_module = (
-            getattr(torch, torch.accelerator.current_accelerator().type)
-            if hasattr(torch, "accelerator")
-            else torch.cuda
-        )
         if self.stream is not None:
             if not self.record_stream:
-                torch_accelerator_module.current_stream().synchronize()
+                torch.cuda.current_stream().synchronize()
             for group_module in self.modules:
                 for param in group_module.parameters():
                     param.data = self.cpu_param_dict[param]
@@ -440,10 +429,8 @@ def apply_group_offloading(
     if use_stream:
         if torch.cuda.is_available():
             stream = torch.cuda.Stream()
-        elif hasattr(torch, "xpu") and torch.xpu.is_available():
-            stream = torch.Stream()
         else:
-            raise ValueError("Using streams for data transfer requires a CUDA device, or an Intel XPU device.")
+            raise ValueError("Using streams for data transfer requires a CUDA device.")
 
     _raise_error_if_accelerate_model_or_sequential_hook_present(module)
 
@@ -481,7 +468,7 @@ def _apply_group_offloading_block_level(
     offload_device: torch.device,
     onload_device: torch.device,
     non_blocking: bool,
-    stream: Union[torch.cuda.Stream, torch.Stream, None] = None,
+    stream: Optional[torch.cuda.Stream] = None,
     record_stream: Optional[bool] = False,
     low_cpu_mem_usage: bool = False,
 ) -> None:
@@ -499,7 +486,7 @@ def _apply_group_offloading_block_level(
         non_blocking (`bool`):
             If True, offloading and onloading is done asynchronously. This can be useful for overlapping computation
             and data transfer.
-        stream (`torch.cuda.Stream`or `torch.Stream`, *optional*):
+        stream (`torch.cuda.Stream`, *optional*):
             If provided, offloading and onloading is done asynchronously using the provided stream. This can be useful
             for overlapping computation and data transfer.
         record_stream (`bool`, defaults to `False`): When enabled with `use_stream`, it marks the current tensor
@@ -511,11 +498,6 @@ def _apply_group_offloading_block_level(
             option only matters when using streamed CPU offloading (i.e. `use_stream=True`). This can be useful when
             the CPU memory is a bottleneck but may counteract the benefits of using streams.
     """
-    if stream is not None and num_blocks_per_group != 1:
-        logger.warning(
-            f"Using streams is only supported for num_blocks_per_group=1. Got {num_blocks_per_group=}. Setting it to 1."
-        )
-        num_blocks_per_group = 1
 
     # Create module groups for ModuleList and Sequential blocks
     modules_with_group_offloading = set()
@@ -539,7 +521,7 @@ def _apply_group_offloading_block_level(
                 stream=stream,
                 record_stream=record_stream,
                 low_cpu_mem_usage=low_cpu_mem_usage,
-                onload_self=True,
+                onload_self=stream is None,
             )
             matched_module_groups.append(group)
             for j in range(i, i + len(current_modules)):
@@ -547,8 +529,12 @@ def _apply_group_offloading_block_level(
 
     # Apply group offloading hooks to the module groups
     for i, group in enumerate(matched_module_groups):
+        next_group = (
+            matched_module_groups[i + 1] if i + 1 < len(matched_module_groups) and stream is not None else None
+        )
+
         for group_module in group.modules:
-            _apply_group_offloading_hook(group_module, group, None)
+            _apply_group_offloading_hook(group_module, group, next_group)
 
     # Parameters and Buffers of the top-level module need to be offloaded/onloaded separately
     # when the forward pass of this module is called. This is because the top-level module is not
@@ -574,10 +560,8 @@ def _apply_group_offloading_block_level(
         record_stream=False,
         onload_self=True,
     )
-    if stream is None:
-        _apply_group_offloading_hook(module, unmatched_group, None)
-    else:
-        _apply_lazy_group_offloading_hook(module, unmatched_group, None)
+    next_group = matched_module_groups[0] if len(matched_module_groups) > 0 else None
+    _apply_group_offloading_hook(module, unmatched_group, next_group)
 
 
 def _apply_group_offloading_leaf_level(
@@ -585,7 +569,7 @@ def _apply_group_offloading_leaf_level(
     offload_device: torch.device,
     onload_device: torch.device,
     non_blocking: bool,
-    stream: Union[torch.cuda.Stream, torch.Stream, None] = None,
+    stream: Optional[torch.cuda.Stream] = None,
     record_stream: Optional[bool] = False,
     low_cpu_mem_usage: bool = False,
 ) -> None:
@@ -605,7 +589,7 @@ def _apply_group_offloading_leaf_level(
         non_blocking (`bool`):
             If True, offloading and onloading is done asynchronously. This can be useful for overlapping computation
             and data transfer.
-        stream (`torch.cuda.Stream` or `torch.Stream`, *optional*):
+        stream (`torch.cuda.Stream`, *optional*):
             If provided, offloading and onloading is done asynchronously using the provided stream. This can be useful
             for overlapping computation and data transfer.
         record_stream (`bool`, defaults to `False`): When enabled with `use_stream`, it marks the current tensor

src/diffusers/image_processor.py

@@ -116,7 +116,6 @@ class VaeImageProcessor(ConfigMixin):
         vae_scale_factor: int = 8,
         vae_latent_channels: int = 4,
         resample: str = "lanczos",
-        reducing_gap: int = None,
         do_normalize: bool = True,
         do_binarize: bool = False,
         do_convert_rgb: bool = False,
@@ -499,11 +498,7 @@ class VaeImageProcessor(ConfigMixin):
             raise ValueError(f"Only PIL image input is supported for resize_mode {resize_mode}")
         if isinstance(image, PIL.Image.Image):
             if resize_mode == "default":
-                image = image.resize(
-                    (width, height),
-                    resample=PIL_INTERPOLATION[self.config.resample],
-                    reducing_gap=self.config.reducing_gap,
-                )
+                image = image.resize((width, height), resample=PIL_INTERPOLATION[self.config.resample])
             elif resize_mode == "fill":
                 image = self._resize_and_fill(image, width, height)
             elif resize_mode == "crop":

src/diffusers/loaders/__init__.py

@@ -77,14 +77,12 @@ if is_torch_available():
             "SanaLoraLoaderMixin",
             "Lumina2LoraLoaderMixin",
             "WanLoraLoaderMixin",
-            "HiDreamImageLoraLoaderMixin",
         ]
         _import_structure["textual_inversion"] = ["TextualInversionLoaderMixin"]
         _import_structure["ip_adapter"] = [
             "IPAdapterMixin",
             "FluxIPAdapterMixin",
             "SD3IPAdapterMixin",
-            "ModularIPAdapterMixin",
         ]
 
 _import_structure["peft"] = ["PeftAdapterMixin"]
@@ -103,7 +101,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
                 FluxIPAdapterMixin,
                 IPAdapterMixin,
                 SD3IPAdapterMixin,
-                ModularIPAdapterMixin,
             )
             from .lora_pipeline import (
                 AmusedLoraLoaderMixin,
@@ -111,7 +108,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
                 CogVideoXLoraLoaderMixin,
                 CogView4LoraLoaderMixin,
                 FluxLoraLoaderMixin,
-                HiDreamImageLoraLoaderMixin,
                 HunyuanVideoLoraLoaderMixin,
                 LoraLoaderMixin,
                 LTXVideoLoraLoaderMixin,

src/diffusers/loaders/ip_adapter.py

@@ -356,265 +356,6 @@ class IPAdapterMixin:
             )
         self.unet.set_attn_processor(attn_procs)
 
-class ModularIPAdapterMixin:
-    """Mixin for handling IP Adapters."""
-
-    @validate_hf_hub_args
-    def load_ip_adapter(
-        self,
-        pretrained_model_name_or_path_or_dict: Union[str, List[str], Dict[str, torch.Tensor]],
-        subfolder: Union[str, List[str]],
-        weight_name: Union[str, List[str]],
-        **kwargs,
-    ):
-        """
-        Parameters:
-            pretrained_model_name_or_path_or_dict (`str` or `List[str]` or `os.PathLike` or `List[os.PathLike]` or `dict` or `List[dict]`):
-                Can be either:
-
-                    - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
-                      the Hub.
-                    - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
-                      with [`ModelMixin.save_pretrained`].
-                    - A [torch state
-                      dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
-            subfolder (`str` or `List[str]`):
-                The subfolder location of a model file within a larger model repository on the Hub or locally. If a
-                list is passed, it should have the same length as `weight_name`.
-            weight_name (`str` or `List[str]`):
-                The name of the weight file to load. If a list is passed, it should have the same length as
-                `subfolder`.
-            cache_dir (`Union[str, os.PathLike]`, *optional*):
-                Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
-                is not used.
-            force_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
-                cached versions if they exist.
-
-            proxies (`Dict[str, str]`, *optional*):
-                A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
-                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
-            local_files_only (`bool`, *optional*, defaults to `False`):
-                Whether to only load local model weights and configuration files or not. If set to `True`, the model
-                won't be downloaded from the Hub.
-            token (`str` or *bool*, *optional*):
-                The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
-                `diffusers-cli login` (stored in `~/.huggingface`) is used.
-            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.
-            low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
-                Speed up model loading only loading the pretrained weights and not initializing the weights. This also
-                tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
-                Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
-                argument to `True` will raise an error.
-        """
-
-        # handle the list inputs for multiple IP Adapters
-        if not isinstance(weight_name, list):
-            weight_name = [weight_name]
-
-        if not isinstance(pretrained_model_name_or_path_or_dict, list):
-            pretrained_model_name_or_path_or_dict = [pretrained_model_name_or_path_or_dict]
-        if len(pretrained_model_name_or_path_or_dict) == 1:
-            pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict * len(weight_name)
-
-        if not isinstance(subfolder, list):
-            subfolder = [subfolder]
-        if len(subfolder) == 1:
-            subfolder = subfolder * len(weight_name)
-
-        if len(weight_name) != len(pretrained_model_name_or_path_or_dict):
-            raise ValueError("`weight_name` and `pretrained_model_name_or_path_or_dict` must have the same length.")
-
-        if len(weight_name) != len(subfolder):
-            raise ValueError("`weight_name` and `subfolder` must have the same length.")
-
-        # Load the main state dict first.
-        cache_dir = kwargs.pop("cache_dir", None)
-        force_download = kwargs.pop("force_download", False)
-        proxies = kwargs.pop("proxies", None)
-        local_files_only = kwargs.pop("local_files_only", None)
-        token = kwargs.pop("token", None)
-        revision = kwargs.pop("revision", None)
-        low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
-
-        if low_cpu_mem_usage and not is_accelerate_available():
-            low_cpu_mem_usage = False
-            logger.warning(
-                "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
-                " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
-                " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
-                " install accelerate\n```\n."
-            )
-
-        if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
-            raise NotImplementedError(
-                "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
-                " `low_cpu_mem_usage=False`."
-            )
-
-        user_agent = {
-            "file_type": "attn_procs_weights",
-            "framework": "pytorch",
-        }
-        state_dicts = []
-        for pretrained_model_name_or_path_or_dict, weight_name, subfolder in zip(
-            pretrained_model_name_or_path_or_dict, weight_name, subfolder
-        ):
-            if not isinstance(pretrained_model_name_or_path_or_dict, dict):
-                model_file = _get_model_file(
-                    pretrained_model_name_or_path_or_dict,
-                    weights_name=weight_name,
-                    cache_dir=cache_dir,
-                    force_download=force_download,
-                    proxies=proxies,
-                    local_files_only=local_files_only,
-                    token=token,
-                    revision=revision,
-                    subfolder=subfolder,
-                    user_agent=user_agent,
-                )
-                if weight_name.endswith(".safetensors"):
-                    state_dict = {"image_proj": {}, "ip_adapter": {}}
-                    with safe_open(model_file, framework="pt", device="cpu") as f:
-                        for key in f.keys():
-                            if key.startswith("image_proj."):
-                                state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key)
-                            elif key.startswith("ip_adapter."):
-                                state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key)
-                else:
-                    state_dict = load_state_dict(model_file)
-            else:
-                state_dict = pretrained_model_name_or_path_or_dict
-
-            keys = list(state_dict.keys())
-            if "image_proj" not in keys and "ip_adapter" not in keys:
-                raise ValueError("Required keys are (`image_proj` and `ip_adapter`) missing from the state dict.")
-
-            state_dicts.append(state_dict)
-
-            # create feature extractor if it has not been registered to the pipeline yet
-            if hasattr(self, "feature_extractor") and getattr(self, "feature_extractor", None) is None:
-                # FaceID IP adapters don't need the image encoder so it's not present, in this case we default to 224
-                default_clip_size = 224
-                clip_image_size = (
-                    self.image_encoder.config.image_size if self.image_encoder is not None else default_clip_size
-                )
-                feature_extractor = CLIPImageProcessor(size=clip_image_size, crop_size=clip_image_size)
-
-        unet_name = getattr(self, "unet_name", "unet")
-        unet = getattr(self, unet_name)
-        unet._load_ip_adapter_weights(state_dicts, low_cpu_mem_usage=low_cpu_mem_usage)
-
-        extra_loras = unet._load_ip_adapter_loras(state_dicts)
-        if extra_loras != {}:
-            if not USE_PEFT_BACKEND:
-                logger.warning("PEFT backend is required to load these weights.")
-            else:
-                # apply the IP Adapter Face ID LoRA weights
-                peft_config = getattr(unet, "peft_config", {})
-                for k, lora in extra_loras.items():
-                    if f"faceid_{k}" not in peft_config:
-                        self.load_lora_weights(lora, adapter_name=f"faceid_{k}")
-                        self.set_adapters([f"faceid_{k}"], adapter_weights=[1.0])
-
-    def set_ip_adapter_scale(self, scale):
-        """
-        Set IP-Adapter scales per-transformer block. Input `scale` could be a single config or a list of configs for
-        granular control over each IP-Adapter behavior. A config can be a float or a dictionary.
-
-        Example:
-
-        ```py
-        # To use original IP-Adapter
-        scale = 1.0
-        pipeline.set_ip_adapter_scale(scale)
-
-        # To use style block only
-        scale = {
-            "up": {"block_0": [0.0, 1.0, 0.0]},
-        }
-        pipeline.set_ip_adapter_scale(scale)
-
-        # To use style+layout blocks
-        scale = {
-            "down": {"block_2": [0.0, 1.0]},
-            "up": {"block_0": [0.0, 1.0, 0.0]},
-        }
-        pipeline.set_ip_adapter_scale(scale)
-
-        # To use style and layout from 2 reference images
-        scales = [{"down": {"block_2": [0.0, 1.0]}}, {"up": {"block_0": [0.0, 1.0, 0.0]}}]
-        pipeline.set_ip_adapter_scale(scales)
-        ```
-        """
-        unet_name = getattr(self, "unet_name", "unet")
-        unet = getattr(self, unet_name)
-        if not isinstance(scale, list):
-            scale = [scale]
-        scale_configs = _maybe_expand_lora_scales(unet, scale, default_scale=0.0)
-
-        for attn_name, attn_processor in unet.attn_processors.items():
-            if isinstance(
-                attn_processor, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0, IPAdapterXFormersAttnProcessor)
-            ):
-                if len(scale_configs) != len(attn_processor.scale):
-                    raise ValueError(
-                        f"Cannot assign {len(scale_configs)} scale_configs to "
-                        f"{len(attn_processor.scale)} IP-Adapter."
-                    )
-                elif len(scale_configs) == 1:
-                    scale_configs = scale_configs * len(attn_processor.scale)
-                for i, scale_config in enumerate(scale_configs):
-                    if isinstance(scale_config, dict):
-                        for k, s in scale_config.items():
-                            if attn_name.startswith(k):
-                                attn_processor.scale[i] = s
-                    else:
-                        attn_processor.scale[i] = scale_config
-
-    def unload_ip_adapter(self):
-        """
-        Unloads the IP Adapter weights
-
-        Examples:
-
-        ```python
-        >>> # Assuming `pipeline` is already loaded with the IP Adapter weights.
-        >>> pipeline.unload_ip_adapter()
-        >>> ...
-        ```
-        """
-
-        # remove hidden encoder
-        if self.unet is None:
-            return
-
-        self.unet.encoder_hid_proj = None
-        self.unet.config.encoder_hid_dim_type = None
-
-        # Kolors: restore `encoder_hid_proj` with `text_encoder_hid_proj`
-        if hasattr(self.unet, "text_encoder_hid_proj") and self.unet.text_encoder_hid_proj is not None:
-            self.unet.encoder_hid_proj = self.unet.text_encoder_hid_proj
-            self.unet.text_encoder_hid_proj = None
-            self.unet.config.encoder_hid_dim_type = "text_proj"
-
-        # restore original Unet attention processors layers
-        attn_procs = {}
-        for name, value in self.unet.attn_processors.items():
-            attn_processor_class = (
-                AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnProcessor()
-            )
-            attn_procs[name] = (
-                attn_processor_class
-                if isinstance(
-                    value, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0, IPAdapterXFormersAttnProcessor)
-                )
-                else value.__class__()
-            )
-        self.unet.set_attn_processor(attn_procs)
-
 
 class FluxIPAdapterMixin:
     """Mixin for handling Flux IP Adapters."""

src/diffusers/loaders/lora_base.py

@@ -441,7 +441,7 @@ def _func_optionally_disable_offloading(_pipeline):
     is_model_cpu_offload = False
     is_sequential_cpu_offload = False
 
-    if _pipeline is not None and hasattr(_pipeline, "hf_device_map") and _pipeline.hf_device_map is None:
+    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:
@@ -491,7 +491,6 @@ class LoraBaseMixin:
             tuple:
                 A tuple indicating if `is_model_cpu_offload` or `is_sequential_cpu_offload` is True.
         """
-
         return _func_optionally_disable_offloading(_pipeline=_pipeline)
 
     @classmethod
@@ -714,10 +713,8 @@ class LoraBaseMixin:
         # Decompose weights into weights for denoiser and text encoders.
         _component_adapter_weights = {}
         for component in self._lora_loadable_modules:
-            model = getattr(self, component, None)
-            if model is None:
-                logger.warning(f"Model {component} not found in pipeline.")
-                continue
+            model = getattr(self, component)
+
             for adapter_name, weights in zip(adapter_names, adapter_weights):
                 if isinstance(weights, dict):
                     component_adapter_weights = weights.pop(component, None)

src/diffusers/loaders/lora_conversion_utils.py

@@ -433,7 +433,7 @@ def _convert_kohya_flux_lora_to_diffusers(state_dict):
         ait_up_keys = [k + ".lora_B.weight" for k in ait_keys]
         if not is_sparse:
             # down_weight is copied to each split
-            ait_sd.update(dict.fromkeys(ait_down_keys, down_weight))
+            ait_sd.update({k: down_weight for k in ait_down_keys})
 
             # up_weight is split to each split
             ait_sd.update({k: v for k, v in zip(ait_up_keys, torch.split(up_weight, dims, dim=0))})  # noqa: C416
@@ -923,7 +923,7 @@ def _convert_xlabs_flux_lora_to_diffusers(old_state_dict):
         ait_up_keys = [k + ".lora_B.weight" for k in ait_keys]
 
         # down_weight is copied to each split
-        ait_sd.update(dict.fromkeys(ait_down_keys, down_weight))
+        ait_sd.update({k: down_weight for k in ait_down_keys})
 
         # up_weight is split to each split
         ait_sd.update({k: v for k, v in zip(ait_up_keys, torch.split(up_weight, dims, dim=0))})  # noqa: C416

src/diffusers/loaders/lora_pipeline.py

@@ -91,19 +91,18 @@ def _maybe_dequantize_weight_for_expanded_lora(model, module):
         )
 
     weight_on_cpu = False
-    if module.weight.device.type == "cpu":
+    if not module.weight.is_cuda:
         weight_on_cpu = True
 
-    device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
     if is_bnb_4bit_quantized:
         module_weight = dequantize_bnb_weight(
-            module.weight.to(device) if weight_on_cpu else module.weight,
+            module.weight.cuda() if weight_on_cpu else module.weight,
             state=module.weight.quant_state,
             dtype=model.dtype,
         ).data
     elif is_gguf_quantized:
         module_weight = dequantize_gguf_tensor(
-            module.weight.to(device) if weight_on_cpu else module.weight,
+            module.weight.cuda() if weight_on_cpu else module.weight,
         )
         module_weight = module_weight.to(model.dtype)
     else:
@@ -636,7 +635,7 @@ class StableDiffusionXLLoraLoaderMixin(LoraBaseMixin):
         # First, ensure that the checkpoint is a compatible one and can be successfully loaded.
         state_dict, network_alphas = self.lora_state_dict(
             pretrained_model_name_or_path_or_dict,
-            unet_config=self.unet.config if hasattr(self, "unet") else None,
+            unet_config=self.unet.config,
             **kwargs,
         )
 
@@ -644,40 +643,37 @@ class StableDiffusionXLLoraLoaderMixin(LoraBaseMixin):
         if not is_correct_format:
             raise ValueError("Invalid LoRA checkpoint.")
 
-        if hasattr(self, "unet"):
-            self.load_lora_into_unet(
-                state_dict,
-                network_alphas=network_alphas,
-                unet=self.unet,
-                adapter_name=adapter_name,
-                _pipeline=self,
-                low_cpu_mem_usage=low_cpu_mem_usage,
-                hotswap=hotswap,
-            )
-        if hasattr(self, "text_encoder"):
-            self.load_lora_into_text_encoder(
-                state_dict,
-                network_alphas=network_alphas,
-                text_encoder=self.text_encoder,
-                prefix=self.text_encoder_name,
-                lora_scale=self.lora_scale,
-                adapter_name=adapter_name,
-                _pipeline=self,
-                low_cpu_mem_usage=low_cpu_mem_usage,
-                hotswap=hotswap,
-            )
-        if hasattr(self, "text_encoder_2"):
-            self.load_lora_into_text_encoder(
-                state_dict,
-                network_alphas=network_alphas,
-                text_encoder=self.text_encoder_2,
-                prefix=f"{self.text_encoder_name}_2",
-                lora_scale=self.lora_scale,
-                adapter_name=adapter_name,
-                _pipeline=self,
-                low_cpu_mem_usage=low_cpu_mem_usage,
-                hotswap=hotswap,
-            )
+        self.load_lora_into_unet(
+            state_dict,
+            network_alphas=network_alphas,
+            unet=self.unet,
+            adapter_name=adapter_name,
+            _pipeline=self,
+            low_cpu_mem_usage=low_cpu_mem_usage,
+            hotswap=hotswap,
+        )
+        self.load_lora_into_text_encoder(
+            state_dict,
+            network_alphas=network_alphas,
+            text_encoder=self.text_encoder,
+            prefix=self.text_encoder_name,
+            lora_scale=self.lora_scale,
+            adapter_name=adapter_name,
+            _pipeline=self,
+            low_cpu_mem_usage=low_cpu_mem_usage,
+            hotswap=hotswap,
+        )
+        self.load_lora_into_text_encoder(
+            state_dict,
+            network_alphas=network_alphas,
+            text_encoder=self.text_encoder_2,
+            prefix=f"{self.text_encoder_name}_2",
+            lora_scale=self.lora_scale,
+            adapter_name=adapter_name,
+            _pipeline=self,
+            low_cpu_mem_usage=low_cpu_mem_usage,
+            hotswap=hotswap,
+        )
 
     @classmethod
     @validate_hf_hub_args
@@ -5364,325 +5360,6 @@ class CogView4LoraLoaderMixin(LoraBaseMixin):
         super().unfuse_lora(components=components, **kwargs)
 
 
-class HiDreamImageLoraLoaderMixin(LoraBaseMixin):
-    r"""
-    Load LoRA layers into [`HiDreamImageTransformer2DModel`]. Specific to [`HiDreamImagePipeline`].
-    """
-
-    _lora_loadable_modules = ["transformer"]
-    transformer_name = TRANSFORMER_NAME
-
-    @classmethod
-    @validate_hf_hub_args
-    # Copied from diffusers.loaders.lora_pipeline.CogVideoXLoraLoaderMixin.lora_state_dict
-    def lora_state_dict(
-        cls,
-        pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
-        **kwargs,
-    ):
-        r"""
-        Return state dict for lora weights and the network alphas.
-
-        <Tip warning={true}>
-
-        We support loading A1111 formatted LoRA checkpoints in a limited capacity.
-
-        This function is experimental and might change in the future.
-
-        </Tip>
-
-        Parameters:
-            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
-                Can be either:
-
-                    - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
-                      the Hub.
-                    - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
-                      with [`ModelMixin.save_pretrained`].
-                    - A [torch state
-                      dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
-
-            cache_dir (`Union[str, os.PathLike]`, *optional*):
-                Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
-                is not used.
-            force_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
-                cached versions if they exist.
-
-            proxies (`Dict[str, str]`, *optional*):
-                A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
-                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
-            local_files_only (`bool`, *optional*, defaults to `False`):
-                Whether to only load local model weights and configuration files or not. If set to `True`, the model
-                won't be downloaded from the Hub.
-            token (`str` or *bool*, *optional*):
-                The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
-                `diffusers-cli login` (stored in `~/.huggingface`) is used.
-            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.
-            subfolder (`str`, *optional*, defaults to `""`):
-                The subfolder location of a model file within a larger model repository on the Hub or locally.
-
-        """
-        # Load the main state dict first which has the LoRA layers for either of
-        # transformer and text encoder or both.
-        cache_dir = kwargs.pop("cache_dir", None)
-        force_download = kwargs.pop("force_download", False)
-        proxies = kwargs.pop("proxies", None)
-        local_files_only = kwargs.pop("local_files_only", None)
-        token = kwargs.pop("token", None)
-        revision = kwargs.pop("revision", None)
-        subfolder = kwargs.pop("subfolder", None)
-        weight_name = kwargs.pop("weight_name", None)
-        use_safetensors = kwargs.pop("use_safetensors", None)
-
-        allow_pickle = False
-        if use_safetensors is None:
-            use_safetensors = True
-            allow_pickle = True
-
-        user_agent = {
-            "file_type": "attn_procs_weights",
-            "framework": "pytorch",
-        }
-
-        state_dict = _fetch_state_dict(
-            pretrained_model_name_or_path_or_dict=pretrained_model_name_or_path_or_dict,
-            weight_name=weight_name,
-            use_safetensors=use_safetensors,
-            local_files_only=local_files_only,
-            cache_dir=cache_dir,
-            force_download=force_download,
-            proxies=proxies,
-            token=token,
-            revision=revision,
-            subfolder=subfolder,
-            user_agent=user_agent,
-            allow_pickle=allow_pickle,
-        )
-
-        is_dora_scale_present = any("dora_scale" in k for k in state_dict)
-        if is_dora_scale_present:
-            warn_msg = "It seems like you are using a DoRA checkpoint that is not compatible in Diffusers at the moment. So, we are going to filter out the keys associated to 'dora_scale` from the state dict. If you think this is a mistake please open an issue https://github.com/huggingface/diffusers/issues/new."
-            logger.warning(warn_msg)
-            state_dict = {k: v for k, v in state_dict.items() if "dora_scale" not in k}
-
-        return state_dict
-
-    # Copied from diffusers.loaders.lora_pipeline.CogVideoXLoraLoaderMixin.load_lora_weights
-    def load_lora_weights(
-        self,
-        pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
-        adapter_name: Optional[str] = None,
-        hotswap: bool = False,
-        **kwargs,
-    ):
-        """
-        Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.transformer` and
-        `self.text_encoder`. All kwargs are forwarded to `self.lora_state_dict`. See
-        [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`] for more details on how the state dict is loaded.
-        See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer`] for more details on how the state
-        dict is loaded into `self.transformer`.
-
-        Parameters:
-            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
-                See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
-            adapter_name (`str`, *optional*):
-                Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
-                `default_{i}` where i is the total number of adapters being loaded.
-            low_cpu_mem_usage (`bool`, *optional*):
-                Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
-                weights.
-            hotswap (`bool`, *optional*):
-                See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
-            kwargs (`dict`, *optional*):
-                See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
-        """
-        if not USE_PEFT_BACKEND:
-            raise ValueError("PEFT backend is required for this method.")
-
-        low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT_LORA)
-        if low_cpu_mem_usage and is_peft_version("<", "0.13.0"):
-            raise ValueError(
-                "`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
-            )
-
-        # if a dict is passed, copy it instead of modifying it inplace
-        if isinstance(pretrained_model_name_or_path_or_dict, dict):
-            pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict.copy()
-
-        # First, ensure that the checkpoint is a compatible one and can be successfully loaded.
-        state_dict = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)
-
-        is_correct_format = all("lora" in key for key in state_dict.keys())
-        if not is_correct_format:
-            raise ValueError("Invalid LoRA checkpoint.")
-
-        self.load_lora_into_transformer(
-            state_dict,
-            transformer=getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer,
-            adapter_name=adapter_name,
-            _pipeline=self,
-            low_cpu_mem_usage=low_cpu_mem_usage,
-            hotswap=hotswap,
-        )
-
-    @classmethod
-    # Copied from diffusers.loaders.lora_pipeline.SD3LoraLoaderMixin.load_lora_into_transformer with SD3Transformer2DModel->HiDreamImageTransformer2DModel
-    def load_lora_into_transformer(
-        cls, state_dict, transformer, adapter_name=None, _pipeline=None, low_cpu_mem_usage=False, hotswap: bool = False
-    ):
-        """
-        This will load the LoRA layers specified in `state_dict` into `transformer`.
-
-        Parameters:
-            state_dict (`dict`):
-                A standard state dict containing the lora layer parameters. The keys can either be indexed directly
-                into the unet or prefixed with an additional `unet` which can be used to distinguish between text
-                encoder lora layers.
-            transformer (`HiDreamImageTransformer2DModel`):
-                The Transformer model to load the LoRA layers into.
-            adapter_name (`str`, *optional*):
-                Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
-                `default_{i}` where i is the total number of adapters being loaded.
-            low_cpu_mem_usage (`bool`, *optional*):
-                Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
-                weights.
-            hotswap (`bool`, *optional*):
-                See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
-        """
-        if low_cpu_mem_usage and is_peft_version("<", "0.13.0"):
-            raise ValueError(
-                "`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
-            )
-
-        # Load the layers corresponding to transformer.
-        logger.info(f"Loading {cls.transformer_name}.")
-        transformer.load_lora_adapter(
-            state_dict,
-            network_alphas=None,
-            adapter_name=adapter_name,
-            _pipeline=_pipeline,
-            low_cpu_mem_usage=low_cpu_mem_usage,
-            hotswap=hotswap,
-        )
-
-    @classmethod
-    # Copied from diffusers.loaders.lora_pipeline.CogVideoXLoraLoaderMixin.save_lora_weights
-    def save_lora_weights(
-        cls,
-        save_directory: Union[str, os.PathLike],
-        transformer_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
-        is_main_process: bool = True,
-        weight_name: str = None,
-        save_function: Callable = None,
-        safe_serialization: bool = True,
-    ):
-        r"""
-        Save the LoRA parameters corresponding to the UNet and text encoder.
-
-        Arguments:
-            save_directory (`str` or `os.PathLike`):
-                Directory to save LoRA parameters to. Will be created if it doesn't exist.
-            transformer_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
-                State dict of the LoRA layers corresponding to the `transformer`.
-            is_main_process (`bool`, *optional*, defaults to `True`):
-                Whether the process calling this is the main process or not. Useful during distributed training and you
-                need to call this function on all processes. In this case, set `is_main_process=True` only on the main
-                process to avoid race conditions.
-            save_function (`Callable`):
-                The function to use to save the state dictionary. Useful during distributed training when you need to
-                replace `torch.save` with another method. Can be configured with the environment variable
-                `DIFFUSERS_SAVE_MODE`.
-            safe_serialization (`bool`, *optional*, defaults to `True`):
-                Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
-        """
-        state_dict = {}
-
-        if not transformer_lora_layers:
-            raise ValueError("You must pass `transformer_lora_layers`.")
-
-        if transformer_lora_layers:
-            state_dict.update(cls.pack_weights(transformer_lora_layers, cls.transformer_name))
-
-        # Save the model
-        cls.write_lora_layers(
-            state_dict=state_dict,
-            save_directory=save_directory,
-            is_main_process=is_main_process,
-            weight_name=weight_name,
-            save_function=save_function,
-            safe_serialization=safe_serialization,
-        )
-
-    # Copied from diffusers.loaders.lora_pipeline.SanaLoraLoaderMixin.fuse_lora
-    def fuse_lora(
-        self,
-        components: List[str] = ["transformer"],
-        lora_scale: float = 1.0,
-        safe_fusing: bool = False,
-        adapter_names: Optional[List[str]] = None,
-        **kwargs,
-    ):
-        r"""
-        Fuses the LoRA parameters into the original parameters of the corresponding blocks.
-
-        <Tip warning={true}>
-
-        This is an experimental API.
-
-        </Tip>
-
-        Args:
-            components: (`List[str]`): List of LoRA-injectable components to fuse the LoRAs into.
-            lora_scale (`float`, defaults to 1.0):
-                Controls how much to influence the outputs with the LoRA parameters.
-            safe_fusing (`bool`, defaults to `False`):
-                Whether to check fused weights for NaN values before fusing and if values are NaN not fusing them.
-            adapter_names (`List[str]`, *optional*):
-                Adapter names to be used for fusing. If nothing is passed, all active adapters will be fused.
-
-        Example:
-
-        ```py
-        from diffusers import DiffusionPipeline
-        import torch
-
-        pipeline = DiffusionPipeline.from_pretrained(
-            "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
-        ).to("cuda")
-        pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
-        pipeline.fuse_lora(lora_scale=0.7)
-        ```
-        """
-        super().fuse_lora(
-            components=components,
-            lora_scale=lora_scale,
-            safe_fusing=safe_fusing,
-            adapter_names=adapter_names,
-            **kwargs,
-        )
-
-    # Copied from diffusers.loaders.lora_pipeline.SanaLoraLoaderMixin.unfuse_lora
-    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).
-
-        <Tip warning={true}>
-
-        This is an experimental API.
-
-        </Tip>
-
-        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.
-        """
-        super().unfuse_lora(components=components, **kwargs)
-
-
 class LoraLoaderMixin(StableDiffusionLoraLoaderMixin):
     def __init__(self, *args, **kwargs):
         deprecation_message = "LoraLoaderMixin is deprecated and this will be removed in a future version. Please use `StableDiffusionLoraLoaderMixin`, instead."

src/diffusers/loaders/peft.py

@@ -56,7 +56,6 @@ _SET_ADAPTER_SCALE_FN_MAPPING = {
     "Lumina2Transformer2DModel": lambda model_cls, weights: weights,
     "WanTransformer3DModel": lambda model_cls, weights: weights,
     "CogView4Transformer2DModel": lambda model_cls, weights: weights,
-    "HiDreamImageTransformer2DModel": lambda model_cls, weights: weights,
 }
 
 

src/diffusers/loaders/unet.py

@@ -408,7 +408,6 @@ class UNet2DConditionLoadersMixin:
             tuple:
                 A tuple indicating if `is_model_cpu_offload` or `is_sequential_cpu_offload` is True.
         """
-
         return _func_optionally_disable_offloading(_pipeline=_pipeline)
 
     def save_attn_procs(

src/diffusers/models/controlnets/controlnet_flux.py

@@ -20,12 +20,12 @@ import torch.nn as nn
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import PeftAdapterMixin
+from ...models.attention_processor import AttentionProcessor
+from ...models.modeling_utils import ModelMixin
 from ...utils import USE_PEFT_BACKEND, BaseOutput, logging, scale_lora_layers, unscale_lora_layers
-from ..attention_processor import AttentionProcessor
 from ..controlnets.controlnet import ControlNetConditioningEmbedding, zero_module
 from ..embeddings import CombinedTimestepGuidanceTextProjEmbeddings, CombinedTimestepTextProjEmbeddings, FluxPosEmbed
 from ..modeling_outputs import Transformer2DModelOutput
-from ..modeling_utils import ModelMixin
 from ..transformers.transformer_flux import FluxSingleTransformerBlock, FluxTransformerBlock
 
 

src/diffusers/models/controlnets/multicontrolnet.py

@@ -4,9 +4,9 @@ from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 import torch
 from torch import nn
 
+from ...models.controlnets.controlnet import ControlNetModel, ControlNetOutput
+from ...models.modeling_utils import ModelMixin
 from ...utils import logging
-from ..controlnets.controlnet import ControlNetModel, ControlNetOutput
-from ..modeling_utils import ModelMixin
 
 
 logger = logging.get_logger(__name__)

src/diffusers/models/controlnets/multicontrolnet_union.py

@@ -4,10 +4,10 @@ from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 import torch
 from torch import nn
 
+from ...models.controlnets.controlnet import ControlNetOutput
+from ...models.controlnets.controlnet_union import ControlNetUnionModel
+from ...models.modeling_utils import ModelMixin
 from ...utils import logging
-from ..controlnets.controlnet import ControlNetOutput
-from ..controlnets.controlnet_union import ControlNetUnionModel
-from ..modeling_utils import ModelMixin
 
 
 logger = logging.get_logger(__name__)

src/diffusers/models/downsampling.py

@@ -286,7 +286,7 @@ class KDownsample2D(nn.Module):
 
 
 class CogVideoXDownsample3D(nn.Module):
-    # Todo: Wait for paper release.
+    # Todo: Wait for paper relase.
     r"""
     A 3D Downsampling layer using in [CogVideoX]() by Tsinghua University & ZhipuAI
 

src/diffusers/models/transformers/latte_transformer_3d.py

@@ -18,9 +18,10 @@ import torch
 from torch import nn
 
 from ...configuration_utils import ConfigMixin, register_to_config
+from ...models.embeddings import PixArtAlphaTextProjection, get_1d_sincos_pos_embed_from_grid
 from ..attention import BasicTransformerBlock
 from ..cache_utils import CacheMixin
-from ..embeddings import PatchEmbed, PixArtAlphaTextProjection, get_1d_sincos_pos_embed_from_grid
+from ..embeddings import PatchEmbed
 from ..modeling_outputs import Transformer2DModelOutput
 from ..modeling_utils import ModelMixin
 from ..normalization import AdaLayerNormSingle

src/diffusers/models/transformers/stable_audio_transformer.py

@@ -21,12 +21,16 @@ import torch.nn as nn
 import torch.utils.checkpoint
 
 from ...configuration_utils import ConfigMixin, register_to_config
+from ...models.attention import FeedForward
+from ...models.attention_processor import (
+    Attention,
+    AttentionProcessor,
+    StableAudioAttnProcessor2_0,
+)
+from ...models.modeling_utils import ModelMixin
+from ...models.transformers.transformer_2d import Transformer2DModelOutput
 from ...utils import logging
 from ...utils.torch_utils import maybe_allow_in_graph
-from ..attention import FeedForward
-from ..attention_processor import Attention, AttentionProcessor, StableAudioAttnProcessor2_0
-from ..modeling_utils import ModelMixin
-from ..transformers.transformer_2d import Transformer2DModelOutput
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

src/diffusers/models/transformers/transformer_cogview3plus.py

@@ -19,13 +19,18 @@ import torch
 import torch.nn as nn
 
 from ...configuration_utils import ConfigMixin, register_to_config
+from ...models.attention import FeedForward
+from ...models.attention_processor import (
+    Attention,
+    AttentionProcessor,
+    CogVideoXAttnProcessor2_0,
+)
+from ...models.modeling_utils import ModelMixin
+from ...models.normalization import AdaLayerNormContinuous
 from ...utils import logging
-from ..attention import FeedForward
-from ..attention_processor import Attention, AttentionProcessor, CogVideoXAttnProcessor2_0
 from ..embeddings import CogView3CombinedTimestepSizeEmbeddings, CogView3PlusPatchEmbed
 from ..modeling_outputs import Transformer2DModelOutput
-from ..modeling_utils import ModelMixin
-from ..normalization import AdaLayerNormContinuous, CogView3PlusAdaLayerNormZeroTextImage
+from ..normalization import CogView3PlusAdaLayerNormZeroTextImage
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

src/diffusers/models/transformers/transformer_cogview4.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
@@ -73,9 +73,8 @@ class CogView4AdaLayerNormZero(nn.Module):
     def forward(
         self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, temb: torch.Tensor
     ) -> Tuple[torch.Tensor, torch.Tensor]:
-        dtype = hidden_states.dtype
-        norm_hidden_states = self.norm(hidden_states).to(dtype=dtype)
-        norm_encoder_hidden_states = self.norm_context(encoder_hidden_states).to(dtype=dtype)
+        norm_hidden_states = self.norm(hidden_states)
+        norm_encoder_hidden_states = self.norm_context(encoder_hidden_states)
 
         emb = self.linear(temb)
         (
@@ -112,11 +111,8 @@ class CogView4AdaLayerNormZero(nn.Module):
 
 class CogView4AttnProcessor:
     """
-    Processor for implementing scaled dot-product attention for the CogView4 model. It applies a rotary embedding on
+    Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on
     query and key vectors, but does not include spatial normalization.
-
-    The processor supports passing an attention mask for text tokens. The attention mask should have shape (batch_size,
-    text_seq_length) where 1 indicates a non-padded token and 0 indicates a padded token.
     """
 
     def __init__(self):
@@ -129,10 +125,8 @@ class CogView4AttnProcessor:
         hidden_states: torch.Tensor,
         encoder_hidden_states: torch.Tensor,
         attention_mask: Optional[torch.Tensor] = None,
-        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        dtype = encoder_hidden_states.dtype
-
+        image_rotary_emb: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
         batch_size, text_seq_length, embed_dim = encoder_hidden_states.shape
         batch_size, image_seq_length, embed_dim = hidden_states.shape
         hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
@@ -148,9 +142,9 @@ class CogView4AttnProcessor:
 
         # 2. QK normalization
         if attn.norm_q is not None:
-            query = attn.norm_q(query).to(dtype=dtype)
+            query = attn.norm_q(query)
         if attn.norm_k is not None:
-            key = attn.norm_k(key).to(dtype=dtype)
+            key = attn.norm_k(key)
 
         # 3. Rotational positional embeddings applied to latent stream
         if image_rotary_emb is not None:
@@ -165,14 +159,13 @@ class CogView4AttnProcessor:
 
         # 4. Attention
         if attention_mask is not None:
-            text_attn_mask = attention_mask
-            assert text_attn_mask.dim() == 2, "the shape of text_attn_mask should be (batch_size, text_seq_length)"
-            text_attn_mask = text_attn_mask.float().to(query.device)
-            mix_attn_mask = torch.ones((batch_size, text_seq_length + image_seq_length), device=query.device)
-            mix_attn_mask[:, :text_seq_length] = text_attn_mask
-            mix_attn_mask = mix_attn_mask.unsqueeze(2)
-            attn_mask_matrix = mix_attn_mask @ mix_attn_mask.transpose(1, 2)
-            attention_mask = (attn_mask_matrix > 0).unsqueeze(1).to(query.dtype)
+            text_attention_mask = attention_mask.float().to(query.device)
+            actual_text_seq_length = text_attention_mask.size(1)
+            new_attention_mask = torch.zeros((batch_size, text_seq_length + image_seq_length), device=query.device)
+            new_attention_mask[:, :actual_text_seq_length] = text_attention_mask
+            new_attention_mask = new_attention_mask.unsqueeze(2)
+            attention_mask_matrix = new_attention_mask @ new_attention_mask.transpose(1, 2)
+            attention_mask = (attention_mask_matrix > 0).unsqueeze(1).to(query.dtype)
 
         hidden_states = F.scaled_dot_product_attention(
             query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
@@ -190,276 +183,9 @@ class CogView4AttnProcessor:
         return hidden_states, encoder_hidden_states
 
 
-class CogView4TrainingAttnProcessor:
-    """
-    Training Processor for implementing scaled dot-product attention for the CogView4 model. It applies a rotary
-    embedding on query and key vectors, but does not include spatial normalization.
-
-    This processor differs from CogView4AttnProcessor in several important ways:
-    1. It supports attention masking with variable sequence lengths for multi-resolution training
-    2. It unpacks and repacks sequences for efficient training with variable sequence lengths when batch_flag is
-       provided
-    """
-
-    def __init__(self):
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError("CogView4AttnProcessor requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0.")
-
-    def __call__(
-        self,
-        attn: Attention,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        latent_attn_mask: Optional[torch.Tensor] = None,
-        text_attn_mask: Optional[torch.Tensor] = None,
-        batch_flag: Optional[torch.Tensor] = None,
-        image_rotary_emb: Optional[
-            Union[Tuple[torch.Tensor, torch.Tensor], List[Tuple[torch.Tensor, torch.Tensor]]]
-        ] = None,
-        **kwargs,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        """
-        Args:
-            attn (`Attention`):
-                The attention module.
-            hidden_states (`torch.Tensor`):
-                The input hidden states.
-            encoder_hidden_states (`torch.Tensor`):
-                The encoder hidden states for cross-attention.
-            latent_attn_mask (`torch.Tensor`, *optional*):
-                Mask for latent tokens where 0 indicates pad token and 1 indicates non-pad token. If None, full
-                attention is used for all latent tokens. Note: the shape of latent_attn_mask is (batch_size,
-                num_latent_tokens).
-            text_attn_mask (`torch.Tensor`, *optional*):
-                Mask for text tokens where 0 indicates pad token and 1 indicates non-pad token. If None, full attention
-                is used for all text tokens.
-            batch_flag (`torch.Tensor`, *optional*):
-                Values from 0 to n-1 indicating which samples belong to the same batch. Samples with the same
-                batch_flag are packed together. Example: [0, 1, 1, 2, 2] means sample 0 forms batch0, samples 1-2 form
-                batch1, and samples 3-4 form batch2. If None, no packing is used.
-            image_rotary_emb (`Tuple[torch.Tensor, torch.Tensor]` or `list[Tuple[torch.Tensor, torch.Tensor]]`, *optional*):
-                The rotary embedding for the image part of the input.
-        Returns:
-            `Tuple[torch.Tensor, torch.Tensor]`: The processed hidden states for both image and text streams.
-        """
-
-        # Get dimensions and device info
-        batch_size, text_seq_length, embed_dim = encoder_hidden_states.shape
-        batch_size, image_seq_length, embed_dim = hidden_states.shape
-        dtype = encoder_hidden_states.dtype
-        device = encoder_hidden_states.device
-        latent_hidden_states = hidden_states
-        # Combine text and image streams for joint processing
-        mixed_hidden_states = torch.cat([encoder_hidden_states, latent_hidden_states], dim=1)
-
-        # 1. Construct attention mask and maybe packing input
-        # Create default masks if not provided
-        if text_attn_mask is None:
-            text_attn_mask = torch.ones((batch_size, text_seq_length), dtype=torch.int32, device=device)
-        if latent_attn_mask is None:
-            latent_attn_mask = torch.ones((batch_size, image_seq_length), dtype=torch.int32, device=device)
-
-        # Validate mask shapes and types
-        assert text_attn_mask.dim() == 2, "the shape of text_attn_mask should be (batch_size, text_seq_length)"
-        assert text_attn_mask.dtype == torch.int32, "the dtype of text_attn_mask should be torch.int32"
-        assert latent_attn_mask.dim() == 2, "the shape of latent_attn_mask should be (batch_size, num_latent_tokens)"
-        assert latent_attn_mask.dtype == torch.int32, "the dtype of latent_attn_mask should be torch.int32"
-
-        # Create combined mask for text and image tokens
-        mixed_attn_mask = torch.ones(
-            (batch_size, text_seq_length + image_seq_length), dtype=torch.int32, device=device
-        )
-        mixed_attn_mask[:, :text_seq_length] = text_attn_mask
-        mixed_attn_mask[:, text_seq_length:] = latent_attn_mask
-
-        # Convert mask to attention matrix format (where 1 means attend, 0 means don't attend)
-        mixed_attn_mask_input = mixed_attn_mask.unsqueeze(2).to(dtype=dtype)
-        attn_mask_matrix = mixed_attn_mask_input @ mixed_attn_mask_input.transpose(1, 2)
-
-        # Handle batch packing if enabled
-        if batch_flag is not None:
-            assert batch_flag.dim() == 1
-            # Determine packed batch size based on batch_flag
-            packing_batch_size = torch.max(batch_flag).item() + 1
-
-            # Calculate actual sequence lengths for each sample based on masks
-            text_seq_length = torch.sum(text_attn_mask, dim=1)
-            latent_seq_length = torch.sum(latent_attn_mask, dim=1)
-            mixed_seq_length = text_seq_length + latent_seq_length
-
-            # Calculate packed sequence lengths for each packed batch
-            mixed_seq_length_packed = [
-                torch.sum(mixed_attn_mask[batch_flag == batch_idx]).item() for batch_idx in range(packing_batch_size)
-            ]
-
-            assert len(mixed_seq_length_packed) == packing_batch_size
-
-            # Pack sequences by removing padding tokens
-            mixed_attn_mask_flatten = mixed_attn_mask.flatten(0, 1)
-            mixed_hidden_states_flatten = mixed_hidden_states.flatten(0, 1)
-            mixed_hidden_states_unpad = mixed_hidden_states_flatten[mixed_attn_mask_flatten == 1]
-            assert torch.sum(mixed_seq_length) == mixed_hidden_states_unpad.shape[0]
-
-            # Split the unpadded sequence into packed batches
-            mixed_hidden_states_packed = torch.split(mixed_hidden_states_unpad, mixed_seq_length_packed)
-
-            # Re-pad to create packed batches with right-side padding
-            mixed_hidden_states_packed_padded = torch.nn.utils.rnn.pad_sequence(
-                mixed_hidden_states_packed,
-                batch_first=True,
-                padding_value=0.0,
-                padding_side="right",
-            )
-
-            # Create attention mask for packed batches
-            l = mixed_hidden_states_packed_padded.shape[1]
-            attn_mask_matrix = torch.zeros(
-                (packing_batch_size, l, l),
-                dtype=dtype,
-                device=device,
-            )
-
-            # Fill attention mask with block diagonal matrices
-            # This ensures that tokens can only attend to other tokens within the same original sample
-            for idx, mask in enumerate(attn_mask_matrix):
-                seq_lengths = mixed_seq_length[batch_flag == idx]
-                offset = 0
-                for length in seq_lengths:
-                    # Create a block of 1s for each sample in the packed batch
-                    mask[offset : offset + length, offset : offset + length] = 1
-                    offset += length
-
-        attn_mask_matrix = attn_mask_matrix.to(dtype=torch.bool)
-        attn_mask_matrix = attn_mask_matrix.unsqueeze(1)  # Add attention head dim
-        attention_mask = attn_mask_matrix
-
-        # Prepare hidden states for attention computation
-        if batch_flag is None:
-            # If no packing, just combine text and image tokens
-            hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
-        else:
-            # If packing, use the packed sequence
-            hidden_states = mixed_hidden_states_packed_padded
-
-        # 2. QKV projections - convert hidden states to query, key, value
-        query = attn.to_q(hidden_states)
-        key = attn.to_k(hidden_states)
-        value = attn.to_v(hidden_states)
-
-        # Reshape for multi-head attention: [batch, seq_len, heads*dim] -> [batch, heads, seq_len, dim]
-        query = query.unflatten(2, (attn.heads, -1)).transpose(1, 2)
-        key = key.unflatten(2, (attn.heads, -1)).transpose(1, 2)
-        value = value.unflatten(2, (attn.heads, -1)).transpose(1, 2)
-
-        # 3. QK normalization - apply layer norm to queries and keys if configured
-        if attn.norm_q is not None:
-            query = attn.norm_q(query).to(dtype=dtype)
-        if attn.norm_k is not None:
-            key = attn.norm_k(key).to(dtype=dtype)
-
-        # 4. Apply rotary positional embeddings to image tokens only
-        if image_rotary_emb is not None:
-            from ..embeddings import apply_rotary_emb
-
-            if batch_flag is None:
-                # Apply RoPE only to image tokens (after text tokens)
-                query[:, :, text_seq_length:, :] = apply_rotary_emb(
-                    query[:, :, text_seq_length:, :], image_rotary_emb, use_real_unbind_dim=-2
-                )
-                key[:, :, text_seq_length:, :] = apply_rotary_emb(
-                    key[:, :, text_seq_length:, :], image_rotary_emb, use_real_unbind_dim=-2
-                )
-            else:
-                # For packed batches, need to carefully apply RoPE to appropriate tokens
-                assert query.shape[0] == packing_batch_size
-                assert key.shape[0] == packing_batch_size
-                assert len(image_rotary_emb) == batch_size
-
-                rope_idx = 0
-                for idx in range(packing_batch_size):
-                    offset = 0
-                    # Get text and image sequence lengths for samples in this packed batch
-                    text_seq_length_bi = text_seq_length[batch_flag == idx]
-                    latent_seq_length_bi = latent_seq_length[batch_flag == idx]
-
-                    # Apply RoPE to each image segment in the packed sequence
-                    for tlen, llen in zip(text_seq_length_bi, latent_seq_length_bi):
-                        mlen = tlen + llen
-                        # Apply RoPE only to image tokens (after text tokens)
-                        query[idx, :, offset + tlen : offset + mlen, :] = apply_rotary_emb(
-                            query[idx, :, offset + tlen : offset + mlen, :],
-                            image_rotary_emb[rope_idx],
-                            use_real_unbind_dim=-2,
-                        )
-                        key[idx, :, offset + tlen : offset + mlen, :] = apply_rotary_emb(
-                            key[idx, :, offset + tlen : offset + mlen, :],
-                            image_rotary_emb[rope_idx],
-                            use_real_unbind_dim=-2,
-                        )
-                        offset += mlen
-                        rope_idx += 1
-
-        hidden_states = F.scaled_dot_product_attention(
-            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
-        )
-
-        # Reshape back: [batch, heads, seq_len, dim] -> [batch, seq_len, heads*dim]
-        hidden_states = hidden_states.transpose(1, 2).flatten(2, 3)
-        hidden_states = hidden_states.type_as(query)
-
-        # 5. Output projection - project attention output to model dimension
-        hidden_states = attn.to_out[0](hidden_states)
-        hidden_states = attn.to_out[1](hidden_states)
-
-        # Split the output back into text and image streams
-        if batch_flag is None:
-            # Simple split for non-packed case
-            encoder_hidden_states, hidden_states = hidden_states.split(
-                [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1
-            )
-        else:
-            # For packed case: need to unpack, split text/image, then restore to original shapes
-            # First, unpad the sequence based on the packed sequence lengths
-            hidden_states_unpad = torch.nn.utils.rnn.unpad_sequence(
-                hidden_states,
-                lengths=torch.tensor(mixed_seq_length_packed),
-                batch_first=True,
-            )
-            # Concatenate all unpadded sequences
-            hidden_states_flatten = torch.cat(hidden_states_unpad, dim=0)
-            # Split by original sample sequence lengths
-            hidden_states_unpack = torch.split(hidden_states_flatten, mixed_seq_length.tolist())
-            assert len(hidden_states_unpack) == batch_size
-
-            # Further split each sample's sequence into text and image parts
-            hidden_states_unpack = [
-                torch.split(h, [tlen, llen])
-                for h, tlen, llen in zip(hidden_states_unpack, text_seq_length, latent_seq_length)
-            ]
-            # Separate text and image sequences
-            encoder_hidden_states_unpad = [h[0] for h in hidden_states_unpack]
-            hidden_states_unpad = [h[1] for h in hidden_states_unpack]
-
-            # Update the original tensors with the processed values, respecting the attention masks
-            for idx in range(batch_size):
-                # Place unpacked text tokens back in the encoder_hidden_states tensor
-                encoder_hidden_states[idx][text_attn_mask[idx] == 1] = encoder_hidden_states_unpad[idx]
-                # Place unpacked image tokens back in the latent_hidden_states tensor
-                latent_hidden_states[idx][latent_attn_mask[idx] == 1] = hidden_states_unpad[idx]
-
-            # Update the output hidden states
-            hidden_states = latent_hidden_states
-
-        return hidden_states, encoder_hidden_states
-
-
 class CogView4TransformerBlock(nn.Module):
     def __init__(
-        self,
-        dim: int = 2560,
-        num_attention_heads: int = 64,
-        attention_head_dim: int = 40,
-        time_embed_dim: int = 512,
+        self, dim: int = 2560, num_attention_heads: int = 64, attention_head_dim: int = 40, time_embed_dim: int = 512
     ) -> None:
         super().__init__()
 
@@ -487,11 +213,9 @@ class CogView4TransformerBlock(nn.Module):
         hidden_states: torch.Tensor,
         encoder_hidden_states: torch.Tensor,
         temb: Optional[torch.Tensor] = None,
-        image_rotary_emb: Optional[
-            Union[Tuple[torch.Tensor, torch.Tensor], List[Tuple[torch.Tensor, torch.Tensor]]]
-        ] = None,
-        attention_mask: Optional[Dict[str, torch.Tensor]] = None,
-        attention_kwargs: Optional[Dict[str, Any]] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        **kwargs,
     ) -> torch.Tensor:
         # 1. Timestep conditioning
         (
@@ -508,14 +232,12 @@ class CogView4TransformerBlock(nn.Module):
         ) = self.norm1(hidden_states, encoder_hidden_states, temb)
 
         # 2. Attention
-        if attention_kwargs is None:
-            attention_kwargs = {}
         attn_hidden_states, attn_encoder_hidden_states = self.attn1(
             hidden_states=norm_hidden_states,
             encoder_hidden_states=norm_encoder_hidden_states,
             image_rotary_emb=image_rotary_emb,
             attention_mask=attention_mask,
-            **attention_kwargs,
+            **kwargs,
         )
         hidden_states = hidden_states + attn_hidden_states * gate_msa.unsqueeze(1)
         encoder_hidden_states = encoder_hidden_states + attn_encoder_hidden_states * c_gate_msa.unsqueeze(1)
@@ -680,9 +402,7 @@ class CogView4Transformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, Cach
         attention_kwargs: Optional[Dict[str, Any]] = None,
         return_dict: bool = True,
         attention_mask: Optional[torch.Tensor] = None,
-        image_rotary_emb: Optional[
-            Union[Tuple[torch.Tensor, torch.Tensor], List[Tuple[torch.Tensor, torch.Tensor]]]
-        ] = None,
+        **kwargs,
     ) -> Union[torch.Tensor, Transformer2DModelOutput]:
         if attention_kwargs is not None:
             attention_kwargs = attention_kwargs.copy()
@@ -702,8 +422,7 @@ class CogView4Transformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, Cach
         batch_size, num_channels, height, width = hidden_states.shape
 
         # 1. RoPE
-        if image_rotary_emb is None:
-            image_rotary_emb = self.rope(hidden_states)
+        image_rotary_emb = self.rope(hidden_states)
 
         # 2. Patch & Timestep embeddings
         p = self.config.patch_size
@@ -719,22 +438,11 @@ class CogView4Transformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, Cach
         for block in self.transformer_blocks:
             if torch.is_grad_enabled() and self.gradient_checkpointing:
                 hidden_states, encoder_hidden_states = self._gradient_checkpointing_func(
-                    block,
-                    hidden_states,
-                    encoder_hidden_states,
-                    temb,
-                    image_rotary_emb,
-                    attention_mask,
-                    attention_kwargs,
+                    block, hidden_states, encoder_hidden_states, temb, image_rotary_emb, attention_mask, **kwargs
                 )
             else:
                 hidden_states, encoder_hidden_states = block(
-                    hidden_states,
-                    encoder_hidden_states,
-                    temb,
-                    image_rotary_emb,
-                    attention_mask,
-                    attention_kwargs,
+                    hidden_states, encoder_hidden_states, temb, image_rotary_emb, attention_mask, **kwargs
                 )
 
         # 4. Output norm & projection

src/diffusers/models/transformers/transformer_flux.py

@@ -21,22 +21,22 @@ import torch.nn as nn
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FluxTransformer2DLoadersMixin, FromOriginalModelMixin, PeftAdapterMixin
-from ...utils import USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers
-from ...utils.import_utils import is_torch_npu_available
-from ...utils.torch_utils import maybe_allow_in_graph
-from ..attention import FeedForward
-from ..attention_processor import (
+from ...models.attention import FeedForward
+from ...models.attention_processor import (
     Attention,
     AttentionProcessor,
     FluxAttnProcessor2_0,
     FluxAttnProcessor2_0_NPU,
     FusedFluxAttnProcessor2_0,
 )
+from ...models.modeling_utils import ModelMixin
+from ...models.normalization import AdaLayerNormContinuous, AdaLayerNormZero, AdaLayerNormZeroSingle
+from ...utils import USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers
+from ...utils.import_utils import is_torch_npu_available
+from ...utils.torch_utils import maybe_allow_in_graph
 from ..cache_utils import CacheMixin
 from ..embeddings import CombinedTimestepGuidanceTextProjEmbeddings, CombinedTimestepTextProjEmbeddings, FluxPosEmbed
 from ..modeling_outputs import Transformer2DModelOutput
-from ..modeling_utils import ModelMixin
-from ..normalization import AdaLayerNormContinuous, AdaLayerNormZero, AdaLayerNormZeroSingle
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

src/diffusers/models/transformers/transformer_hidream_image.py

@@ -275,14 +275,7 @@ class HiDreamAttnProcessor:
 
 # Modified from https://github.com/deepseek-ai/DeepSeek-V3/blob/main/inference/model.py
 class MoEGate(nn.Module):
-    def __init__(
-        self,
-        embed_dim,
-        num_routed_experts=4,
-        num_activated_experts=2,
-        aux_loss_alpha=0.01,
-        _force_inference_output=False,
-    ):
+    def __init__(self, embed_dim, num_routed_experts=4, num_activated_experts=2, aux_loss_alpha=0.01):
         super().__init__()
         self.top_k = num_activated_experts
         self.n_routed_experts = num_routed_experts
@@ -296,10 +289,9 @@ class MoEGate(nn.Module):
         self.gating_dim = embed_dim
         self.weight = nn.Parameter(torch.randn(self.n_routed_experts, self.gating_dim) / embed_dim**0.5)
 
-        self._force_inference_output = _force_inference_output
-
     def forward(self, hidden_states):
         bsz, seq_len, h = hidden_states.shape
+        # print(bsz, seq_len, h)
         ### compute gating score
         hidden_states = hidden_states.view(-1, h)
         logits = F.linear(hidden_states, self.weight, None)
@@ -317,7 +309,7 @@ class MoEGate(nn.Module):
             topk_weight = topk_weight / denominator
 
         ### expert-level computation auxiliary loss
-        if self.training and self.alpha > 0.0 and not self._force_inference_output:
+        if self.training and self.alpha > 0.0:
             scores_for_aux = scores
             aux_topk = self.top_k
             # always compute aux loss based on the naive greedy topk method
@@ -349,19 +341,14 @@ class MOEFeedForwardSwiGLU(nn.Module):
         hidden_dim: int,
         num_routed_experts: int,
         num_activated_experts: int,
-        _force_inference_output: bool = False,
     ):
         super().__init__()
         self.shared_experts = HiDreamImageFeedForwardSwiGLU(dim, hidden_dim // 2)
         self.experts = nn.ModuleList(
             [HiDreamImageFeedForwardSwiGLU(dim, hidden_dim) for i in range(num_routed_experts)]
         )
-        self._force_inference_output = _force_inference_output
         self.gate = MoEGate(
-            embed_dim=dim,
-            num_routed_experts=num_routed_experts,
-            num_activated_experts=num_activated_experts,
-            _force_inference_output=_force_inference_output,
+            embed_dim=dim, num_routed_experts=num_routed_experts, num_activated_experts=num_activated_experts
         )
         self.num_activated_experts = num_activated_experts
 
@@ -372,7 +359,7 @@ class MOEFeedForwardSwiGLU(nn.Module):
         topk_idx, topk_weight, aux_loss = self.gate(x)
         x = x.view(-1, x.shape[-1])
         flat_topk_idx = topk_idx.view(-1)
-        if self.training and not self._force_inference_output:
+        if self.training:
             x = x.repeat_interleave(self.num_activated_experts, dim=0)
             y = torch.empty_like(x, dtype=wtype)
             for i, expert in enumerate(self.experts):
@@ -426,7 +413,6 @@ class HiDreamImageSingleTransformerBlock(nn.Module):
         attention_head_dim: int,
         num_routed_experts: int = 4,
         num_activated_experts: int = 2,
-        _force_inference_output: bool = False,
     ):
         super().__init__()
         self.num_attention_heads = num_attention_heads
@@ -450,7 +436,6 @@ class HiDreamImageSingleTransformerBlock(nn.Module):
                 hidden_dim=4 * dim,
                 num_routed_experts=num_routed_experts,
                 num_activated_experts=num_activated_experts,
-                _force_inference_output=_force_inference_output,
             )
         else:
             self.ff_i = HiDreamImageFeedForwardSwiGLU(dim=dim, hidden_dim=4 * dim)
@@ -495,7 +480,6 @@ class HiDreamImageTransformerBlock(nn.Module):
         attention_head_dim: int,
         num_routed_experts: int = 4,
         num_activated_experts: int = 2,
-        _force_inference_output: bool = False,
     ):
         super().__init__()
         self.num_attention_heads = num_attention_heads
@@ -520,7 +504,6 @@ class HiDreamImageTransformerBlock(nn.Module):
                 hidden_dim=4 * dim,
                 num_routed_experts=num_routed_experts,
                 num_activated_experts=num_activated_experts,
-                _force_inference_output=_force_inference_output,
             )
         else:
             self.ff_i = HiDreamImageFeedForwardSwiGLU(dim=dim, hidden_dim=4 * dim)
@@ -623,7 +606,6 @@ class HiDreamImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
         axes_dims_rope: Tuple[int, int] = (32, 32),
         max_resolution: Tuple[int, int] = (128, 128),
         llama_layers: List[int] = None,
-        force_inference_output: bool = False,
     ):
         super().__init__()
         self.out_channels = out_channels or in_channels
@@ -647,7 +629,6 @@ class HiDreamImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
                         attention_head_dim=attention_head_dim,
                         num_routed_experts=num_routed_experts,
                         num_activated_experts=num_activated_experts,
-                        _force_inference_output=force_inference_output,
                     )
                 )
                 for _ in range(num_layers)
@@ -663,7 +644,6 @@ class HiDreamImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
                         attention_head_dim=attention_head_dim,
                         num_routed_experts=num_routed_experts,
                         num_activated_experts=num_activated_experts,
-                        _force_inference_output=force_inference_output,
                     )
                 )
                 for _ in range(num_single_layers)
@@ -682,7 +662,7 @@ class HiDreamImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
         self.gradient_checkpointing = False
 
     def unpatchify(self, x: torch.Tensor, img_sizes: List[Tuple[int, int]], is_training: bool) -> List[torch.Tensor]:
-        if is_training and not self.config.force_inference_output:
+        if is_training:
             B, S, F = x.shape
             C = F // (self.config.patch_size * self.config.patch_size)
             x = (
@@ -791,7 +771,7 @@ class HiDreamImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
 
         if encoder_hidden_states is not None:
             deprecation_message = "The `encoder_hidden_states` argument is deprecated. Please use `encoder_hidden_states_t5` and `encoder_hidden_states_llama3` instead."
-            deprecate("encoder_hidden_states", "0.35.0", deprecation_message)
+            deprecate("encoder_hidden_states", "0.34.0", deprecation_message)
             encoder_hidden_states_t5 = encoder_hidden_states[0]
             encoder_hidden_states_llama3 = encoder_hidden_states[1]
 
@@ -799,7 +779,7 @@ class HiDreamImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
             deprecation_message = (
                 "Passing `img_ids` and `img_sizes` with unpachified `hidden_states` is deprecated and will be ignored."
             )
-            deprecate("img_ids", "0.35.0", deprecation_message)
+            deprecate("img_ids", "0.34.0", deprecation_message)
 
         if hidden_states_masks is not None and (img_ids is None or img_sizes is None):
             raise ValueError("if `hidden_states_masks` is passed, `img_ids` and `img_sizes` must also be passed.")
@@ -938,5 +918,5 @@ class HiDreamImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
             unscale_lora_layers(self, lora_scale)
 
         if not return_dict:
-            return (output,)
-        return Transformer2DModelOutput(sample=output)
+            return (output, hidden_states_masks)
+        return Transformer2DModelOutput(sample=output, mask=hidden_states_masks)

src/diffusers/models/transformers/transformer_hunyuan_video.py

@@ -1068,15 +1068,17 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin,
         latent_sequence_length = hidden_states.shape[1]
         condition_sequence_length = encoder_hidden_states.shape[1]
         sequence_length = latent_sequence_length + condition_sequence_length
-        attention_mask = torch.ones(
+        attention_mask = torch.zeros(
             batch_size, sequence_length, device=hidden_states.device, dtype=torch.bool
         )  # [B, N]
+
         effective_condition_sequence_length = encoder_attention_mask.sum(dim=1, dtype=torch.int)  # [B,]
         effective_sequence_length = latent_sequence_length + effective_condition_sequence_length
-        indices = torch.arange(sequence_length, device=hidden_states.device).unsqueeze(0)  # [1, N]
-        mask_indices = indices >= effective_sequence_length.unsqueeze(1)  # [B, N]
-        attention_mask = attention_mask.masked_fill(mask_indices, False)
-        attention_mask = attention_mask.unsqueeze(1).unsqueeze(1)  # [B, 1, 1, N]
+
+        for i in range(batch_size):
+            attention_mask[i, : effective_sequence_length[i]] = True
+        # [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/transformers/transformer_sd3.py

@@ -18,19 +18,19 @@ import torch.nn as nn
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FromOriginalModelMixin, PeftAdapterMixin, SD3Transformer2DLoadersMixin
-from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
-from ...utils.torch_utils import maybe_allow_in_graph
-from ..attention import FeedForward, JointTransformerBlock
-from ..attention_processor import (
+from ...models.attention import FeedForward, JointTransformerBlock
+from ...models.attention_processor import (
     Attention,
     AttentionProcessor,
     FusedJointAttnProcessor2_0,
     JointAttnProcessor2_0,
 )
+from ...models.modeling_utils import ModelMixin
+from ...models.normalization import AdaLayerNormContinuous, AdaLayerNormZero
+from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
+from ...utils.torch_utils import maybe_allow_in_graph
 from ..embeddings import CombinedTimestepTextProjEmbeddings, PatchEmbed
 from ..modeling_outputs import Transformer2DModelOutput
-from ..modeling_utils import ModelMixin
-from ..normalization import AdaLayerNormContinuous, AdaLayerNormZero
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

src/diffusers/models/transformers/transformer_wan.py

@@ -49,10 +49,8 @@ class WanAttnProcessor2_0:
     ) -> torch.Tensor:
         encoder_hidden_states_img = None
         if attn.add_k_proj is not None:
-            # 512 is the context length of the text encoder, hardcoded for now
-            image_context_length = encoder_hidden_states.shape[1] - 512
-            encoder_hidden_states_img = encoder_hidden_states[:, :image_context_length]
-            encoder_hidden_states = encoder_hidden_states[:, image_context_length:]
+            encoder_hidden_states_img = encoder_hidden_states[:, :257]
+            encoder_hidden_states = encoder_hidden_states[:, 257:]
         if encoder_hidden_states is None:
             encoder_hidden_states = hidden_states
 
@@ -110,23 +108,14 @@ class WanAttnProcessor2_0:
 
 
 class WanImageEmbedding(torch.nn.Module):
-    def __init__(self, in_features: int, out_features: int, pos_embed_seq_len=None):
+    def __init__(self, in_features: int, out_features: int):
         super().__init__()
 
         self.norm1 = FP32LayerNorm(in_features)
         self.ff = FeedForward(in_features, out_features, mult=1, activation_fn="gelu")
         self.norm2 = FP32LayerNorm(out_features)
-        if pos_embed_seq_len is not None:
-            self.pos_embed = nn.Parameter(torch.zeros(1, pos_embed_seq_len, in_features))
-        else:
-            self.pos_embed = None
 
     def forward(self, encoder_hidden_states_image: torch.Tensor) -> torch.Tensor:
-        if self.pos_embed is not None:
-            batch_size, seq_len, embed_dim = encoder_hidden_states_image.shape
-            encoder_hidden_states_image = encoder_hidden_states_image.view(-1, 2 * seq_len, embed_dim)
-            encoder_hidden_states_image = encoder_hidden_states_image + self.pos_embed
-
         hidden_states = self.norm1(encoder_hidden_states_image)
         hidden_states = self.ff(hidden_states)
         hidden_states = self.norm2(hidden_states)
@@ -141,7 +130,6 @@ class WanTimeTextImageEmbedding(nn.Module):
         time_proj_dim: int,
         text_embed_dim: int,
         image_embed_dim: Optional[int] = None,
-        pos_embed_seq_len: Optional[int] = None,
     ):
         super().__init__()
 
@@ -153,7 +141,7 @@ class WanTimeTextImageEmbedding(nn.Module):
 
         self.image_embedder = None
         if image_embed_dim is not None:
-            self.image_embedder = WanImageEmbedding(image_embed_dim, dim, pos_embed_seq_len=pos_embed_seq_len)
+            self.image_embedder = WanImageEmbedding(image_embed_dim, dim)
 
     def forward(
         self,
@@ -362,7 +350,6 @@ class WanTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOrigi
         image_dim: Optional[int] = None,
         added_kv_proj_dim: Optional[int] = None,
         rope_max_seq_len: int = 1024,
-        pos_embed_seq_len: Optional[int] = None,
     ) -> None:
         super().__init__()
 
@@ -381,7 +368,6 @@ class WanTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOrigi
             time_proj_dim=inner_dim * 6,
             text_embed_dim=text_dim,
             image_embed_dim=image_dim,
-            pos_embed_seq_len=pos_embed_seq_len,
         )
 
         # 3. Transformer blocks

src/diffusers/models/upsampling.py

@@ -358,7 +358,7 @@ class KUpsample2D(nn.Module):
 
 class CogVideoXUpsample3D(nn.Module):
     r"""
-    A 3D Upsample layer using in CogVideoX by Tsinghua University & ZhipuAI # Todo: Wait for paper release.
+    A 3D Upsample layer using in CogVideoX by Tsinghua University & ZhipuAI # Todo: Wait for paper relase.
 
     Args:
         in_channels (`int`):

src/diffusers/pipelines/__init__.py

@@ -47,7 +47,6 @@ else:
         "AutoPipelineForInpainting",
         "AutoPipelineForText2Image",
     ]
-    _import_structure["modular_pipeline"] = ["ModularPipeline"]
     _import_structure["consistency_models"] = ["ConsistencyModelPipeline"]
     _import_structure["dance_diffusion"] = ["DanceDiffusionPipeline"]
     _import_structure["ddim"] = ["DDIMPipeline"]
@@ -330,8 +329,6 @@ else:
             "StableDiffusionXLInpaintPipeline",
             "StableDiffusionXLInstructPix2PixPipeline",
             "StableDiffusionXLPipeline",
-            "StableDiffusionXLModularPipeline",
-            "StableDiffusionXLAutoPipeline",
         ]
     )
     _import_structure["stable_diffusion_diffedit"] = ["StableDiffusionDiffEditPipeline"]
@@ -481,7 +478,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .deprecated import KarrasVePipeline, LDMPipeline, PNDMPipeline, RePaintPipeline, ScoreSdeVePipeline
         from .dit import DiTPipeline
         from .latent_diffusion import LDMSuperResolutionPipeline
-        from .modular_pipeline import ModularPipeline
         from .pipeline_utils import (
             AudioPipelineOutput,
             DiffusionPipeline,
@@ -706,9 +702,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             StableDiffusionXLImg2ImgPipeline,
             StableDiffusionXLInpaintPipeline,
             StableDiffusionXLInstructPix2PixPipeline,
-            StableDiffusionXLModularPipeline,
             StableDiffusionXLPipeline,
-            StableDiffusionXLAutoPipeline,
         )
         from .stable_video_diffusion import StableVideoDiffusionPipeline
         from .t2i_adapter import (

src/diffusers/pipelines/allegro/pipeline_allegro.py

@@ -514,7 +514,7 @@ class AllegroPipeline(DiffusionPipeline):
         # &amp
         caption = re.sub(r"&amp", "", caption)
 
-        # ip addresses:
+        # ip adresses:
         caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
 
         # article ids:

src/diffusers/pipelines/auto_pipeline.py

@@ -246,15 +246,14 @@ def _get_connected_pipeline(pipeline_cls):
         return _get_task_class(AUTO_INPAINT_PIPELINES_MAPPING, pipeline_cls.__name__, throw_error_if_not_exist=False)
 
 
-def _get_model(pipeline_class_name):
-    for task_mapping in SUPPORTED_TASKS_MAPPINGS:
-        for model_name, pipeline in task_mapping.items():
-            if pipeline.__name__ == pipeline_class_name:
-                return model_name
-
-
 def _get_task_class(mapping, pipeline_class_name, throw_error_if_not_exist: bool = True):
-    model_name = _get_model(pipeline_class_name)
+    def get_model(pipeline_class_name):
+        for task_mapping in SUPPORTED_TASKS_MAPPINGS:
+            for model_name, pipeline in task_mapping.items():
+                if pipeline.__name__ == pipeline_class_name:
+                    return model_name
+
+    model_name = get_model(pipeline_class_name)
 
     if model_name is not None:
         task_class = mapping.get(model_name, None)

src/diffusers/pipelines/components_manager.py

@@ -1,609 +0,0 @@
-# Copyright 2024 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from collections import OrderedDict
-from itertools import combinations
-from typing import List, Optional, Union, Dict, Any
-import copy
-
-import torch
-import time
-from dataclasses import dataclass
-
-from ..utils import (
-    is_accelerate_available,
-    logging,
-)
-from ..models.modeling_utils import ModelMixin
-
-
-if is_accelerate_available():
-    from accelerate.hooks import ModelHook, add_hook_to_module, remove_hook_from_module
-    from accelerate.state import PartialState
-    from accelerate.utils import send_to_device
-    from accelerate.utils.memory import clear_device_cache
-    from accelerate.utils.modeling import convert_file_size_to_int
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-# YiYi Notes: copied from modeling_utils.py (decide later where to put this)
-def get_memory_footprint(self, return_buffers=True):
-    r"""
-    Get the memory footprint of a model. This will return the memory footprint of the current model in bytes. Useful to
-    benchmark the memory footprint of the current model and design some tests. Solution inspired from the PyTorch
-    discussions: https://discuss.pytorch.org/t/gpu-memory-that-model-uses/56822/2
-
-    Arguments:
-        return_buffers (`bool`, *optional*, defaults to `True`):
-            Whether to return the size of the buffer tensors in the computation of the memory footprint. Buffers are
-            tensors that do not require gradients and not registered as parameters. E.g. mean and std in batch norm
-            layers. Please see: https://discuss.pytorch.org/t/what-pytorch-means-by-buffers/120266/2
-    """
-    mem = sum([param.nelement() * param.element_size() for param in self.parameters()])
-    if return_buffers:
-        mem_bufs = sum([buf.nelement() * buf.element_size() for buf in self.buffers()])
-        mem = mem + mem_bufs
-    return mem
-
-
-class CustomOffloadHook(ModelHook):
-    """
-    A hook that offloads a model on the CPU until its forward pass is called. It ensures the model and its inputs are
-    on the given device. Optionally offloads other models to the CPU before the forward pass is called.
-
-    Args:
-        execution_device(`str`, `int` or `torch.device`, *optional*):
-            The device on which the model should be executed. Will default to the MPS device if it's available, then
-            GPU 0 if there is a GPU, and finally to the CPU.
-    """
-
-    def __init__(
-        self,
-        execution_device: Optional[Union[str, int, torch.device]] = None,
-        other_hooks: Optional[List["UserCustomOffloadHook"]] = None,
-        offload_strategy: Optional["AutoOffloadStrategy"] = None,
-    ):
-        self.execution_device = execution_device if execution_device is not None else PartialState().default_device
-        self.other_hooks = other_hooks
-        self.offload_strategy = offload_strategy
-        self.model_id = None
-
-    def set_strategy(self, offload_strategy: "AutoOffloadStrategy"):
-        self.offload_strategy = offload_strategy
-
-    def add_other_hook(self, hook: "UserCustomOffloadHook"):
-        """
-        Add a hook to the list of hooks to consider for offloading.
-        """
-        if self.other_hooks is None:
-            self.other_hooks = []
-        self.other_hooks.append(hook)
-
-    def init_hook(self, module):
-        return module.to("cpu")
-
-    def pre_forward(self, module, *args, **kwargs):
-        if module.device != self.execution_device:
-            if self.other_hooks is not None:
-                hooks_to_offload = [hook for hook in self.other_hooks if hook.model.device == self.execution_device]
-                # offload all other hooks
-                start_time = time.perf_counter()
-                if self.offload_strategy is not None:
-                    hooks_to_offload = self.offload_strategy(
-                        hooks=hooks_to_offload,
-                        model_id=self.model_id,
-                        model=module,
-                        execution_device=self.execution_device,
-                    )
-                end_time = time.perf_counter()
-                logger.info(
-                    f" time taken to apply offload strategy for {self.model_id}: {(end_time - start_time):.2f} seconds"
-                )
-
-                for hook in hooks_to_offload:
-                    logger.info(
-                        f"moving {self.model_id} to {self.execution_device}, offloading {hook.model_id} to cpu"
-                    )
-                    hook.offload()
-
-                if hooks_to_offload:
-                    clear_device_cache()
-            module.to(self.execution_device)
-        return send_to_device(args, self.execution_device), send_to_device(kwargs, self.execution_device)
-
-
-class UserCustomOffloadHook:
-    """
-    A simple hook grouping a model and a `CustomOffloadHook`, which provides easy APIs for to call the init method of
-    the hook or remove it entirely.
-    """
-
-    def __init__(self, model_id, model, hook):
-        self.model_id = model_id
-        self.model = model
-        self.hook = hook
-
-    def offload(self):
-        self.hook.init_hook(self.model)
-
-    def attach(self):
-        add_hook_to_module(self.model, self.hook)
-        self.hook.model_id = self.model_id
-
-    def remove(self):
-        remove_hook_from_module(self.model)
-        self.hook.model_id = None
-
-    def add_other_hook(self, hook: "UserCustomOffloadHook"):
-        self.hook.add_other_hook(hook)
-
-
-def custom_offload_with_hook(
-    model_id: str,
-    model: torch.nn.Module,
-    execution_device: Union[str, int, torch.device] = None,
-    offload_strategy: Optional["AutoOffloadStrategy"] = None,
-):
-    hook = CustomOffloadHook(execution_device=execution_device, offload_strategy=offload_strategy)
-    user_hook = UserCustomOffloadHook(model_id=model_id, model=model, hook=hook)
-    user_hook.attach()
-    return user_hook
-
-
-class AutoOffloadStrategy:
-    """
-    Offload strategy that should be used with `CustomOffloadHook` to automatically offload models to the CPU based on
-    the available memory on the device.
-    """
-
-    def __init__(self, memory_reserve_margin="3GB"):
-        self.memory_reserve_margin = convert_file_size_to_int(memory_reserve_margin)
-
-    def __call__(self, hooks, model_id, model, execution_device):
-        if len(hooks) == 0:
-            return []
-
-        current_module_size = get_memory_footprint(model)
-
-        mem_on_device = torch.cuda.mem_get_info(execution_device.index)[0]
-        mem_on_device = mem_on_device - self.memory_reserve_margin
-        if current_module_size < mem_on_device:
-            return []
-
-        min_memory_offload = current_module_size - mem_on_device
-        logger.info(f" search for models to offload in order to free up {min_memory_offload / 1024**3:.2f} GB memory")
-
-        # exlucde models that's not currently loaded on the device
-        module_sizes = dict(
-            sorted(
-                {hook.model_id: get_memory_footprint(hook.model) for hook in hooks}.items(),
-                key=lambda x: x[1],
-                reverse=True,
-            )
-        )
-
-        def search_best_candidate(module_sizes, min_memory_offload):
-            """
-            search the optimal combination of models to offload to cpu, given a dictionary of module sizes and a
-            minimum memory offload size. the combination of models should add up to the smallest modulesize that is
-            larger than `min_memory_offload`
-            """
-            model_ids = list(module_sizes.keys())
-            best_candidate = None
-            best_size = float("inf")
-            for r in range(1, len(model_ids) + 1):
-                for candidate_model_ids in combinations(model_ids, r):
-                    candidate_size = sum(
-                        module_sizes[candidate_model_id] for candidate_model_id in candidate_model_ids
-                    )
-                    if candidate_size < min_memory_offload:
-                        continue
-                    else:
-                        if best_candidate is None or candidate_size < best_size:
-                            best_candidate = candidate_model_ids
-                            best_size = candidate_size
-
-            return best_candidate
-
-        best_offload_model_ids = search_best_candidate(module_sizes, min_memory_offload)
-
-        if best_offload_model_ids is None:
-            # if no combination is found, meaning that we cannot meet the memory requirement, offload all models
-            logger.warning("no combination of models to offload to cpu is found, offloading all models")
-            hooks_to_offload = hooks
-        else:
-            hooks_to_offload = [hook for hook in hooks if hook.model_id in best_offload_model_ids]
-
-        return hooks_to_offload
-
-
-class ComponentsManager:
-    def __init__(self):
-        self.components = OrderedDict()
-        self.added_time = OrderedDict()  # Store when components were added
-        self.model_hooks = None
-        self._auto_offload_enabled = False
-
-    def add(self, name, component):
-        if name in self.components:
-            logger.warning(f"Overriding existing component '{name}' in ComponentsManager")
-        self.components[name] = component
-        self.added_time[name] = time.time()
-        
-        if self._auto_offload_enabled:
-            self.enable_auto_cpu_offload(self._auto_offload_device)
-
-    def remove(self, name):
-        if name not in self.components:
-            logger.warning(f"Component '{name}' not found in ComponentsManager")
-            return
-            
-        self.components.pop(name)
-        self.added_time.pop(name)
-        
-        if self._auto_offload_enabled:
-            self.enable_auto_cpu_offload(self._auto_offload_device)
-
-    # YiYi TODO: looking into improving the search pattern
-    def get(self, names: Union[str, List[str]]):
-        """
-        Get components by name with simple pattern matching.
-        
-        Args:
-            names: Component name(s) or pattern(s)
-                Patterns:
-                - "unet" : exact match
-                - "!unet" : everything except exact match "unet"
-                - "base_*" : everything starting with "base_"
-                - "!base_*" : everything NOT starting with "base_"
-                - "*unet*" : anything containing "unet"
-                - "!*unet*" : anything NOT containing "unet"
-                - "refiner|vae|unet" : anything containing any of these terms
-                - "!refiner|vae|unet" : anything NOT containing any of these terms
-        
-        Returns:
-            Single component if names is str and matches one component,
-            dict of components if names matches multiple components or is a list
-        """
-        if isinstance(names, str):
-            # Check if this is a "not" pattern
-            is_not_pattern = names.startswith('!')
-            if is_not_pattern:
-                names = names[1:]  # Remove the ! prefix
-            
-            # Handle OR patterns (containing |)
-            if '|' in names:
-                terms = names.split('|')
-                matches = {
-                    name: comp for name, comp in self.components.items() 
-                    if any((term in name) != is_not_pattern for term in terms)  # Flip condition if not pattern
-                }
-                if is_not_pattern:
-                    logger.info(f"Getting components NOT containing any of {terms}: {list(matches.keys())}")
-                else:
-                    logger.info(f"Getting components containing any of {terms}: {list(matches.keys())}")
-            
-            # Exact match
-            elif names in self.components:
-                if is_not_pattern:
-                    matches = {
-                        name: comp for name, comp in self.components.items() 
-                        if name != names
-                    }
-                    logger.info(f"Getting all components except '{names}': {list(matches.keys())}")
-                else:
-                    logger.info(f"Getting component: {names}")
-                    return self.components[names]
-            
-            # Prefix match (ends with *)
-            elif names.endswith('*'):
-                prefix = names[:-1]
-                matches = {
-                    name: comp for name, comp in self.components.items() 
-                    if name.startswith(prefix) != is_not_pattern  # Flip condition if not pattern
-                }
-                if is_not_pattern:
-                    logger.info(f"Getting components NOT starting with '{prefix}': {list(matches.keys())}")
-                else:
-                    logger.info(f"Getting components starting with '{prefix}': {list(matches.keys())}")
-            
-            # Contains match (starts with *)
-            elif names.startswith('*'):
-                search = names[1:-1] if names.endswith('*') else names[1:]
-                matches = {
-                    name: comp for name, comp in self.components.items() 
-                    if (search in name) != is_not_pattern  # Flip condition if not pattern
-                }
-                if is_not_pattern:
-                    logger.info(f"Getting components NOT containing '{search}': {list(matches.keys())}")
-                else:
-                    logger.info(f"Getting components containing '{search}': {list(matches.keys())}")
-            
-            else:
-                raise ValueError(f"Component '{names}' not found in ComponentsManager")
-            
-            if not matches:
-                raise ValueError(f"No components found matching pattern '{names}'")
-            return matches if len(matches) > 1 else next(iter(matches.values()))
-        
-        elif isinstance(names, list):
-            results = {}
-            for name in names:
-                result = self.get(name)
-                if isinstance(result, dict):
-                    results.update(result)
-                else:
-                    results[name] = result
-            logger.info(f"Getting multiple components: {list(results.keys())}")
-            return results
-        
-        else:
-            raise ValueError(f"Invalid type for names: {type(names)}")
-
-    def enable_auto_cpu_offload(self, device: Union[str, int, torch.device]="cuda", memory_reserve_margin="3GB"):
-        for name, component in self.components.items():
-            if isinstance(component, torch.nn.Module) and hasattr(component, "_hf_hook"):
-                remove_hook_from_module(component, recurse=True)
-
-        self.disable_auto_cpu_offload()
-        offload_strategy = AutoOffloadStrategy(memory_reserve_margin=memory_reserve_margin)
-        device = torch.device(device)
-        if device.index is None:
-            device = torch.device(f"{device.type}:{0}")
-        all_hooks = []
-        for name, component in self.components.items():
-            if isinstance(component, torch.nn.Module):
-                hook = custom_offload_with_hook(name, component, device, offload_strategy=offload_strategy)
-                all_hooks.append(hook)
-
-        for hook in all_hooks:
-            other_hooks = [h for h in all_hooks if h is not hook]
-            for other_hook in other_hooks:
-                if other_hook.hook.execution_device == hook.hook.execution_device:
-                    hook.add_other_hook(other_hook)
-
-        self.model_hooks = all_hooks
-        self._auto_offload_enabled = True
-        self._auto_offload_device = device
-
-    def disable_auto_cpu_offload(self):
-        if self.model_hooks is None:
-            self._auto_offload_enabled = False
-            return
-
-        for hook in self.model_hooks:
-            hook.offload()
-            hook.remove()
-        if self.model_hooks:
-            clear_device_cache()
-        self.model_hooks = None
-        self._auto_offload_enabled = False
-
-    def get_model_info(self, name: str, fields: Optional[Union[str, List[str]]] = None) -> Optional[Dict[str, Any]]:
-        """Get comprehensive information about a component.
-        
-        Args:
-            name: Name of the component to get info for
-            fields: Optional field(s) to return. Can be a string for single field or list of fields.
-                   If None, returns all fields.
-                   
-        Returns:
-            Dictionary containing requested component metadata.
-            If fields is specified, returns only those fields.
-            If a single field is requested as string, returns just that field's value.
-        """
-        if name not in self.components:
-            raise ValueError(f"Component '{name}' not found in ComponentsManager")
-
-        component = self.components[name]
-        
-        # Build complete info dict first
-        info = {
-            "model_id": name,
-            "added_time": self.added_time[name],
-        }
-        
-        # Additional info for torch.nn.Module components
-        if isinstance(component, torch.nn.Module):
-            info.update({
-                "class_name": component.__class__.__name__,
-                "size_gb": get_memory_footprint(component) / (1024**3),
-                "adapters": None,  # Default to None
-            })
-
-            # Get adapters if applicable
-            if hasattr(component, "peft_config"):
-                info["adapters"] = list(component.peft_config.keys())
-
-            # Check for IP-Adapter scales
-            if hasattr(component, "_load_ip_adapter_weights") and hasattr(component, "attn_processors"):
-                processors = copy.deepcopy(component.attn_processors)
-                # First check if any processor is an IP-Adapter
-                processor_types = [v.__class__.__name__ for v in processors.values()]
-                if any("IPAdapter" in ptype for ptype in processor_types):
-                    # Then get scales only from IP-Adapter processors
-                    scales = {
-                        k: v.scale 
-                        for k, v in processors.items() 
-                        if hasattr(v, "scale") and "IPAdapter" in v.__class__.__name__
-                    }
-                    if scales:
-                        info["ip_adapter"] = summarize_dict_by_value_and_parts(scales)
-
-        # If fields specified, filter info
-        if fields is not None:
-            if isinstance(fields, str):
-                # Single field requested, return just that value
-                return {fields: info.get(fields)}
-            else:
-                # List of fields requested, return dict with just those fields
-                return {k: v for k, v in info.items() if k in fields}
-            
-        return info
-
-    def __repr__(self):
-        col_widths = {
-            "id": max(15, max(len(id) for id in self.components.keys())),
-            "class": max(25, max(len(component.__class__.__name__) for component in self.components.values())),
-            "device": 10,
-            "dtype": 15,
-            "size": 10,
-        }
-
-        # Create the header lines
-        sep_line = "=" * (sum(col_widths.values()) + len(col_widths) * 3 - 1) + "\n"
-        dash_line = "-" * (sum(col_widths.values()) + len(col_widths) * 3 - 1) + "\n"
-
-        output = "Components:\n" + sep_line
-
-        # Separate components into models and others
-        models = {k: v for k, v in self.components.items() if isinstance(v, torch.nn.Module)}
-        others = {k: v for k, v in self.components.items() if not isinstance(v, torch.nn.Module)}
-
-        # Models section
-        if models:
-            output += "Models:\n" + dash_line
-            # Column headers
-            output += f"{'Model ID':<{col_widths['id']}} | {'Class':<{col_widths['class']}} | "
-            output += f"{'Device':<{col_widths['device']}} | {'Dtype':<{col_widths['dtype']}} | Size (GB)\n"
-            output += dash_line
-
-            # Model entries
-            for name, component in models.items():
-                info = self.get_model_info(name)
-                device = str(getattr(component, "device", "N/A"))
-                dtype = str(component.dtype) if hasattr(component, "dtype") else "N/A"
-                output += f"{name:<{col_widths['id']}} | {info['class_name']:<{col_widths['class']}} | "
-                output += f"{device:<{col_widths['device']}} | {dtype:<{col_widths['dtype']}} | {info['size_gb']:.2f}\n"
-            output += dash_line
-
-        # Other components section
-        if others:
-            if models:  # Add extra newline if we had models section
-                output += "\n"
-            output += "Other Components:\n" + dash_line
-            # Column headers for other components
-            output += f"{'Component ID':<{col_widths['id']}} | {'Class':<{col_widths['class']}}\n"
-            output += dash_line
-
-            # Other component entries
-            for name, component in others.items():
-                output += f"{name:<{col_widths['id']}} | {component.__class__.__name__:<{col_widths['class']}}\n"
-            output += dash_line
-
-        # Add additional component info
-        output += "\nAdditional Component Info:\n" + "=" * 50 + "\n"
-        for name in self.components:
-            info = self.get_model_info(name)
-            if info is not None and (info.get("adapters") is not None or info.get("ip_adapter")):
-                output += f"\n{name}:\n"
-                if info.get("adapters") is not None:
-                    output += f"  Adapters: {info['adapters']}\n"
-                if info.get("ip_adapter"):
-                    output += f"  IP-Adapter: Enabled\n"
-                output += f"  Added Time: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(info['added_time']))}\n"
-        
-        return output
-
-    def add_from_pretrained(self, pretrained_model_name_or_path, prefix: Optional[str] = None, **kwargs):
-        """
-        Load components from a pretrained model and add them to the manager.
-        
-        Args:
-            pretrained_model_name_or_path (str): The path or identifier of the pretrained model
-            prefix (str, optional): Prefix to add to all component names loaded from this model.
-                                  If provided, components will be named as "{prefix}_{component_name}"
-            **kwargs: Additional arguments to pass to DiffusionPipeline.from_pretrained()
-        """
-        from ..pipelines.pipeline_utils import DiffusionPipeline
-
-        pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, **kwargs)
-        for name, component in pipe.components.items():
-                
-            if component is None:
-                continue
-            
-            # Add prefix if specified
-            component_name = f"{prefix}_{name}" if prefix else name
-            
-            if component_name not in self.components:
-                self.add(component_name, component)
-            else:
-                logger.warning(
-                    f"Component '{component_name}' already exists in ComponentsManager and will not be added. To add it, either:\n"
-                    f"1. remove the existing component with remove('{component_name}')\n"
-                    f"2. Use a different prefix: add_from_pretrained(..., prefix='{prefix}_2')"
-                )
-
-def summarize_dict_by_value_and_parts(d: Dict[str, Any]) -> Dict[str, Any]:
-    """Summarizes a dictionary by finding common prefixes that share the same value.
-    
-    For a dictionary with dot-separated keys like:
-    {
-        'down_blocks.1.attentions.1.transformer_blocks.0.attn2.processor': [0.6],
-        'down_blocks.1.attentions.1.transformer_blocks.1.attn2.processor': [0.6],
-        'up_blocks.1.attentions.0.transformer_blocks.0.attn2.processor': [0.3],
-    }
-    
-    Returns a dictionary where keys are the shortest common prefixes and values are their shared values:
-    {
-        'down_blocks': [0.6],
-        'up_blocks': [0.3]
-    }
-    """
-    # First group by values - convert lists to tuples to make them hashable
-    value_to_keys = {}
-    for key, value in d.items():
-        value_tuple = tuple(value) if isinstance(value, list) else value
-        if value_tuple not in value_to_keys:
-            value_to_keys[value_tuple] = []
-        value_to_keys[value_tuple].append(key)
-    
-    def find_common_prefix(keys: List[str]) -> str:
-        """Find the shortest common prefix among a list of dot-separated keys."""
-        if not keys:
-            return ""
-        if len(keys) == 1:
-            return keys[0]
-            
-        # Split all keys into parts
-        key_parts = [k.split('.') for k in keys]
-        
-        # Find how many initial parts are common
-        common_length = 0
-        for parts in zip(*key_parts):
-            if len(set(parts)) == 1:  # All parts at this position are the same
-                common_length += 1
-            else:
-                break
-                
-        if common_length == 0:
-            return ""
-            
-        # Return the common prefix
-        return '.'.join(key_parts[0][:common_length])
-
-    # Create summary by finding common prefixes for each value group
-    summary = {}
-    for value_tuple, keys in value_to_keys.items():
-        prefix = find_common_prefix(keys)
-        if prefix:  # Only add if we found a common prefix
-            # Convert tuple back to list if it was originally a list
-            value = list(value_tuple) if isinstance(d[keys[0]], list) else value_tuple
-            summary[prefix] = value
-        else:
-            summary[""] = value  # Use empty string if no common prefix
-            
-    return summary

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl_img2img.py

@@ -912,6 +912,12 @@ class StableDiffusionXLControlNetImg2ImgPipeline(
                 f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
             )
 
+        latents_mean = latents_std = None
+        if hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None:
+            latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1)
+        if hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None:
+            latents_std = torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1)
+
         # Offload text encoder if `enable_model_cpu_offload` was enabled
         if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
             self.text_encoder_2.to("cpu")
@@ -925,11 +931,6 @@ class StableDiffusionXLControlNetImg2ImgPipeline(
             init_latents = image
 
         else:
-            latents_mean = latents_std = None
-            if hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None:
-                latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1)
-            if hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None:
-                latents_std = torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1)
             # make sure the VAE is in float32 mode, as it overflows in float16
             if self.vae.config.force_upcast:
                 image = image.float()

src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py

@@ -867,6 +867,12 @@ class StableDiffusionXLControlNetUnionImg2ImgPipeline(
                 f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
             )
 
+        latents_mean = latents_std = None
+        if hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None:
+            latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1)
+        if hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None:
+            latents_std = torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1)
+
         # Offload text encoder if `enable_model_cpu_offload` was enabled
         if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
             self.text_encoder_2.to("cpu")
@@ -880,11 +886,6 @@ class StableDiffusionXLControlNetUnionImg2ImgPipeline(
             init_latents = image
 
         else:
-            latents_mean = latents_std = None
-            if hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None:
-                latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1)
-            if hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None:
-                latents_std = torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1)
             # make sure the VAE is in float32 mode, as it overflows in float16
             if self.vae.config.force_upcast:
                 image = image.float()

src/diffusers/pipelines/deepfloyd_if/pipeline_if.py

@@ -484,7 +484,7 @@ class IFPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin):
         # &amp
         caption = re.sub(r"&amp", "", caption)
 
-        # ip addresses:
+        # ip adresses:
         caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
 
         # article ids:

src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py

@@ -528,7 +528,7 @@ class IFImg2ImgPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin):
         # &amp
         caption = re.sub(r"&amp", "", caption)
 
-        # ip addresses:
+        # ip adresses:
         caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
 
         # article ids:

src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py

@@ -281,7 +281,7 @@ class IFImg2ImgSuperResolutionPipeline(DiffusionPipeline, StableDiffusionLoraLoa
         # &amp
         caption = re.sub(r"&amp", "", caption)
 
-        # ip addresses:
+        # ip adresses:
         caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
 
         # article ids:

src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py

@@ -568,7 +568,7 @@ class IFInpaintingPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin):
         # &amp
         caption = re.sub(r"&amp", "", caption)
 
-        # ip addresses:
+        # ip adresses:
         caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
 
         # article ids:

src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting_superresolution.py

@@ -283,7 +283,7 @@ class IFInpaintingSuperResolutionPipeline(DiffusionPipeline, StableDiffusionLora
         # &amp
         caption = re.sub(r"&amp", "", caption)
 
-        # ip addresses:
+        # ip adresses:
         caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
 
         # article ids:

src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py

@@ -239,7 +239,7 @@ class IFSuperResolutionPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixi
         # &amp
         caption = re.sub(r"&amp", "", caption)
 
-        # ip addresses:
+        # ip adresses:
         caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
 
         # article ids:

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

@@ -574,7 +574,7 @@ class StableDiffusionModelEditingPipeline(
                 idxs_replace.append(76)
             idxs_replaces.append(idxs_replace)
 
-        # prepare batch: for each pair of sentences, old context and new values
+        # prepare batch: for each pair of setences, old context and new values
         contexts, valuess = [], []
         for old_emb, new_emb, idxs_replace in zip(old_embs, new_embs, idxs_replaces):
             context = old_emb.detach()

src/diffusers/pipelines/flux/pipeline_flux.py

@@ -490,6 +490,14 @@ class FluxPipeline(
                 f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
             )
 
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
         if prompt_embeds is not None and pooled_prompt_embeds is None:
             raise ValueError(
                 "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
@@ -813,7 +821,7 @@ class FluxPipeline(
             (
                 negative_prompt_embeds,
                 negative_pooled_prompt_embeds,
-                negative_text_ids,
+                _,
             ) = self.encode_prompt(
                 prompt=negative_prompt,
                 prompt_2=negative_prompt_2,
@@ -930,7 +938,7 @@ class FluxPipeline(
                         guidance=guidance,
                         pooled_projections=negative_pooled_prompt_embeds,
                         encoder_hidden_states=negative_prompt_embeds,
-                        txt_ids=negative_text_ids,
+                        txt_ids=text_ids,
                         img_ids=latent_image_ids,
                         joint_attention_kwargs=self.joint_attention_kwargs,
                         return_dict=False,

src/diffusers/pipelines/flux/pipeline_flux_controlnet_image_to_image.py

@@ -800,20 +800,17 @@ class FluxControlNetImg2ImgPipeline(DiffusionPipeline, FluxLoraLoaderMixin, From
             )
             height, width = control_image.shape[-2:]
 
-            # xlab controlnet has a input_hint_block and instantx controlnet does not
-            controlnet_blocks_repeat = False if self.controlnet.input_hint_block is None else True
-            if self.controlnet.input_hint_block is None:
-                control_image = retrieve_latents(self.vae.encode(control_image), generator=generator)
-                control_image = (control_image - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+            control_image = retrieve_latents(self.vae.encode(control_image), generator=generator)
+            control_image = (control_image - self.vae.config.shift_factor) * self.vae.config.scaling_factor
 
-                height_control_image, width_control_image = control_image.shape[2:]
-                control_image = self._pack_latents(
-                    control_image,
-                    batch_size * num_images_per_prompt,
-                    num_channels_latents,
-                    height_control_image,
-                    width_control_image,
-                )
+            height_control_image, width_control_image = control_image.shape[2:]
+            control_image = self._pack_latents(
+                control_image,
+                batch_size * num_images_per_prompt,
+                num_channels_latents,
+                height_control_image,
+                width_control_image,
+            )
 
             if control_mode is not None:
                 control_mode = torch.tensor(control_mode).to(device, dtype=torch.long)
@@ -822,9 +819,7 @@ class FluxControlNetImg2ImgPipeline(DiffusionPipeline, FluxLoraLoaderMixin, From
         elif isinstance(self.controlnet, FluxMultiControlNetModel):
             control_images = []
 
-            # xlab controlnet has a input_hint_block and instantx controlnet does not
-            controlnet_blocks_repeat = False if self.controlnet.nets[0].input_hint_block is None else True
-            for i, control_image_ in enumerate(control_image):
+            for control_image_ in control_image:
                 control_image_ = self.prepare_image(
                     image=control_image_,
                     width=width,
@@ -836,18 +831,17 @@ class FluxControlNetImg2ImgPipeline(DiffusionPipeline, FluxLoraLoaderMixin, From
                 )
                 height, width = control_image_.shape[-2:]
 
-                if self.controlnet.nets[0].input_hint_block is None:
-                    control_image_ = retrieve_latents(self.vae.encode(control_image_), generator=generator)
-                    control_image_ = (control_image_ - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+                control_image_ = retrieve_latents(self.vae.encode(control_image_), generator=generator)
+                control_image_ = (control_image_ - self.vae.config.shift_factor) * self.vae.config.scaling_factor
 
-                    height_control_image, width_control_image = control_image_.shape[2:]
-                    control_image_ = self._pack_latents(
-                        control_image_,
-                        batch_size * num_images_per_prompt,
-                        num_channels_latents,
-                        height_control_image,
-                        width_control_image,
-                    )
+                height_control_image, width_control_image = control_image_.shape[2:]
+                control_image_ = self._pack_latents(
+                    control_image_,
+                    batch_size * num_images_per_prompt,
+                    num_channels_latents,
+                    height_control_image,
+                    width_control_image,
+                )
 
                 control_images.append(control_image_)
 
@@ -961,7 +955,6 @@ class FluxControlNetImg2ImgPipeline(DiffusionPipeline, FluxLoraLoaderMixin, From
                     img_ids=latent_image_ids,
                     joint_attention_kwargs=self.joint_attention_kwargs,
                     return_dict=False,
-                    controlnet_blocks_repeat=controlnet_blocks_repeat,
                 )[0]
 
                 latents_dtype = latents.dtype

src/diffusers/pipelines/hidream_image/pipeline_hidream_image.py

@@ -13,7 +13,6 @@ from transformers import (
 )
 
 from ...image_processor import VaeImageProcessor
-from ...loaders import HiDreamImageLoraLoaderMixin
 from ...models import AutoencoderKL, HiDreamImageTransformer2DModel
 from ...schedulers import FlowMatchEulerDiscreteScheduler, UniPCMultistepScheduler
 from ...utils import deprecate, is_torch_xla_available, logging, replace_example_docstring
@@ -143,7 +142,7 @@ def retrieve_timesteps(
     return timesteps, num_inference_steps
 
 
-class HiDreamImagePipeline(DiffusionPipeline, HiDreamImageLoraLoaderMixin):
+class HiDreamImagePipeline(DiffusionPipeline):
     model_cpu_offload_seq = "text_encoder->text_encoder_2->text_encoder_3->text_encoder_4->transformer->vae"
     _callback_tensor_inputs = ["latents", "prompt_embeds_t5", "prompt_embeds_llama3", "pooled_prompt_embeds"]
 
@@ -823,13 +822,13 @@ class HiDreamImagePipeline(DiffusionPipeline, HiDreamImageLoraLoaderMixin):
 
         if prompt_embeds is not None:
             deprecation_message = "The `prompt_embeds` argument is deprecated. Please use `prompt_embeds_t5` and `prompt_embeds_llama3` instead."
-            deprecate("prompt_embeds", "0.35.0", deprecation_message)
+            deprecate("prompt_embeds", "0.34.0", deprecation_message)
             prompt_embeds_t5 = prompt_embeds[0]
             prompt_embeds_llama3 = prompt_embeds[1]
 
         if negative_prompt_embeds is not None:
             deprecation_message = "The `negative_prompt_embeds` argument is deprecated. Please use `negative_prompt_embeds_t5` and `negative_prompt_embeds_llama3` instead."
-            deprecate("negative_prompt_embeds", "0.35.0", deprecation_message)
+            deprecate("negative_prompt_embeds", "0.34.0", deprecation_message)
             negative_prompt_embeds_t5 = negative_prompt_embeds[0]
             negative_prompt_embeds_llama3 = negative_prompt_embeds[1]
 

src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py

@@ -14,13 +14,14 @@
 
 from typing import Callable, List, Optional, Union
 
+import numpy as np
 import PIL.Image
 import torch
+from PIL import Image
 from transformers import (
     XLMRobertaTokenizer,
 )
 
-from ...image_processor import VaeImageProcessor
 from ...models import UNet2DConditionModel, VQModel
 from ...schedulers import DDIMScheduler
 from ...utils import (
@@ -94,6 +95,15 @@ def get_new_h_w(h, w, scale_factor=8):
     return new_h * scale_factor, new_w * scale_factor
 
 
+def prepare_image(pil_image, w=512, h=512):
+    pil_image = pil_image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)
+    arr = np.array(pil_image.convert("RGB"))
+    arr = arr.astype(np.float32) / 127.5 - 1
+    arr = np.transpose(arr, [2, 0, 1])
+    image = torch.from_numpy(arr).unsqueeze(0)
+    return image
+
+
 class KandinskyImg2ImgPipeline(DiffusionPipeline):
     """
     Pipeline for image-to-image generation using Kandinsky
@@ -133,16 +143,7 @@ class KandinskyImg2ImgPipeline(DiffusionPipeline):
             scheduler=scheduler,
             movq=movq,
         )
-        self.movq_scale_factor = (
-            2 ** (len(self.movq.config.block_out_channels) - 1) if getattr(self, "movq", None) else 8
-        )
-        movq_latent_channels = self.movq.config.latent_channels if getattr(self, "movq", None) else 4
-        self.image_processor = VaeImageProcessor(
-            vae_scale_factor=self.movq_scale_factor,
-            vae_latent_channels=movq_latent_channels,
-            resample="bicubic",
-            reducing_gap=1,
-        )
+        self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)
 
     def get_timesteps(self, num_inference_steps, strength, device):
         # get the original timestep using init_timestep
@@ -416,7 +417,7 @@ class KandinskyImg2ImgPipeline(DiffusionPipeline):
                 f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support  PIL image and pytorch tensor"
             )
 
-        image = torch.cat([self.image_processor.preprocess(i, width, height) for i in image], dim=0)
+        image = torch.cat([prepare_image(i, width, height) for i in image], dim=0)
         image = image.to(dtype=prompt_embeds.dtype, device=device)
 
         latents = self.movq.encode(image)["latents"]
@@ -497,7 +498,13 @@ class KandinskyImg2ImgPipeline(DiffusionPipeline):
         if output_type not in ["pt", "np", "pil"]:
             raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}")
 
-        image = self.image_processor.postprocess(image, output_type)
+        if output_type in ["np", "pil"]:
+            image = image * 0.5 + 0.5
+            image = image.clamp(0, 1)
+            image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
 
         if not return_dict:
             return (image,)

src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py

@@ -14,10 +14,11 @@
 
 from typing import Callable, List, Optional, Union
 
+import numpy as np
 import PIL.Image
 import torch
+from PIL import Image
 
-from ...image_processor import VaeImageProcessor
 from ...models import UNet2DConditionModel, VQModel
 from ...schedulers import DDPMScheduler
 from ...utils import (
@@ -104,6 +105,27 @@ EXAMPLE_DOC_STRING = """
 """
 
 
+# Copied from diffusers.pipelines.kandinsky2_2.pipeline_kandinsky2_2.downscale_height_and_width
+def downscale_height_and_width(height, width, scale_factor=8):
+    new_height = height // scale_factor**2
+    if height % scale_factor**2 != 0:
+        new_height += 1
+    new_width = width // scale_factor**2
+    if width % scale_factor**2 != 0:
+        new_width += 1
+    return new_height * scale_factor, new_width * scale_factor
+
+
+# Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_img2img.prepare_image
+def prepare_image(pil_image, w=512, h=512):
+    pil_image = pil_image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)
+    arr = np.array(pil_image.convert("RGB"))
+    arr = arr.astype(np.float32) / 127.5 - 1
+    arr = np.transpose(arr, [2, 0, 1])
+    image = torch.from_numpy(arr).unsqueeze(0)
+    return image
+
+
 class KandinskyV22ControlnetImg2ImgPipeline(DiffusionPipeline):
     """
     Pipeline for image-to-image generation using Kandinsky
@@ -135,14 +157,7 @@ class KandinskyV22ControlnetImg2ImgPipeline(DiffusionPipeline):
             scheduler=scheduler,
             movq=movq,
         )
-        movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1) if getattr(self, "movq", None) else 8
-        movq_latent_channels = self.movq.config.latent_channels if getattr(self, "movq", None) else 4
-        self.image_processor = VaeImageProcessor(
-            vae_scale_factor=movq_scale_factor,
-            vae_latent_channels=movq_latent_channels,
-            resample="bicubic",
-            reducing_gap=1,
-        )
+        self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)
 
     # Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_img2img.KandinskyImg2ImgPipeline.get_timesteps
     def get_timesteps(self, num_inference_steps, strength, device):
@@ -301,7 +316,7 @@ class KandinskyV22ControlnetImg2ImgPipeline(DiffusionPipeline):
                 f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support  PIL image and pytorch tensor"
             )
 
-        image = torch.cat([self.image_processor.preprocess(i, width, height) for i in image], dim=0)
+        image = torch.cat([prepare_image(i, width, height) for i in image], dim=0)
         image = image.to(dtype=image_embeds.dtype, device=device)
 
         latents = self.movq.encode(image)["latents"]
@@ -309,6 +324,7 @@ class KandinskyV22ControlnetImg2ImgPipeline(DiffusionPipeline):
         self.scheduler.set_timesteps(num_inference_steps, device=device)
         timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
         latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+        height, width = downscale_height_and_width(height, width, self.movq_scale_factor)
         latents = self.prepare_latents(
             latents, latent_timestep, batch_size, num_images_per_prompt, image_embeds.dtype, device, generator
         )
@@ -363,7 +379,13 @@ class KandinskyV22ControlnetImg2ImgPipeline(DiffusionPipeline):
         if output_type not in ["pt", "np", "pil"]:
             raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}")
 
-        image = self.image_processor.postprocess(image, output_type)
+        if output_type in ["np", "pil"]:
+            image = image * 0.5 + 0.5
+            image = image.clamp(0, 1)
+            image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
 
         if not return_dict:
             return (image,)

src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_img2img.py

@@ -14,10 +14,11 @@
 
 from typing import Callable, Dict, List, Optional, Union
 
+import numpy as np
 import PIL.Image
 import torch
+from PIL import Image
 
-from ...image_processor import VaeImageProcessor
 from ...models import UNet2DConditionModel, VQModel
 from ...schedulers import DDPMScheduler
 from ...utils import deprecate, is_torch_xla_available, logging
@@ -75,6 +76,27 @@ EXAMPLE_DOC_STRING = """
 """
 
 
+# Copied from diffusers.pipelines.kandinsky2_2.pipeline_kandinsky2_2.downscale_height_and_width
+def downscale_height_and_width(height, width, scale_factor=8):
+    new_height = height // scale_factor**2
+    if height % scale_factor**2 != 0:
+        new_height += 1
+    new_width = width // scale_factor**2
+    if width % scale_factor**2 != 0:
+        new_width += 1
+    return new_height * scale_factor, new_width * scale_factor
+
+
+# Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_img2img.prepare_image
+def prepare_image(pil_image, w=512, h=512):
+    pil_image = pil_image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)
+    arr = np.array(pil_image.convert("RGB"))
+    arr = arr.astype(np.float32) / 127.5 - 1
+    arr = np.transpose(arr, [2, 0, 1])
+    image = torch.from_numpy(arr).unsqueeze(0)
+    return image
+
+
 class KandinskyV22Img2ImgPipeline(DiffusionPipeline):
     """
     Pipeline for image-to-image generation using Kandinsky
@@ -107,14 +129,7 @@ class KandinskyV22Img2ImgPipeline(DiffusionPipeline):
             scheduler=scheduler,
             movq=movq,
         )
-        movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1) if getattr(self, "movq", None) else 8
-        movq_latent_channels = self.movq.config.latent_channels if getattr(self, "movq", None) else 4
-        self.image_processor = VaeImageProcessor(
-            vae_scale_factor=movq_scale_factor,
-            vae_latent_channels=movq_latent_channels,
-            resample="bicubic",
-            reducing_gap=1,
-        )
+        self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)
 
     # Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_img2img.KandinskyImg2ImgPipeline.get_timesteps
     def get_timesteps(self, num_inference_steps, strength, device):
@@ -304,7 +319,7 @@ class KandinskyV22Img2ImgPipeline(DiffusionPipeline):
                 f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support  PIL image and pytorch tensor"
             )
 
-        image = torch.cat([self.image_processor.preprocess(i, width, height) for i in image], dim=0)
+        image = torch.cat([prepare_image(i, width, height) for i in image], dim=0)
         image = image.to(dtype=image_embeds.dtype, device=device)
 
         latents = self.movq.encode(image)["latents"]
@@ -312,6 +327,7 @@ class KandinskyV22Img2ImgPipeline(DiffusionPipeline):
         self.scheduler.set_timesteps(num_inference_steps, device=device)
         timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
         latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+        height, width = downscale_height_and_width(height, width, self.movq_scale_factor)
         latents = self.prepare_latents(
             latents, latent_timestep, batch_size, num_images_per_prompt, image_embeds.dtype, device, generator
         )
@@ -367,9 +383,21 @@ class KandinskyV22Img2ImgPipeline(DiffusionPipeline):
             if XLA_AVAILABLE:
                 xm.mark_step()
 
+        if output_type not in ["pt", "np", "pil", "latent"]:
+            raise ValueError(
+                f"Only the output types `pt`, `pil` ,`np` and `latent` are supported not output_type={output_type}"
+            )
+
         if not output_type == "latent":
+            # post-processing
             image = self.movq.decode(latents, force_not_quantize=True)["sample"]
-            image = self.image_processor.postprocess(image, output_type)
+            if output_type in ["np", "pil"]:
+                image = image * 0.5 + 0.5
+                image = image.clamp(0, 1)
+                image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+            if output_type == "pil":
+                image = self.numpy_to_pil(image)
         else:
             image = latents
 

src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py

@@ -1,12 +1,12 @@
 import inspect
 from typing import Callable, Dict, List, Optional, Union
 
+import numpy as np
 import PIL
 import PIL.Image
 import torch
 from transformers import T5EncoderModel, T5Tokenizer
 
-from ...image_processor import VaeImageProcessor
 from ...loaders import StableDiffusionLoraLoaderMixin
 from ...models import Kandinsky3UNet, VQModel
 from ...schedulers import DDPMScheduler
@@ -53,6 +53,24 @@ EXAMPLE_DOC_STRING = """
 """
 
 
+def downscale_height_and_width(height, width, scale_factor=8):
+    new_height = height // scale_factor**2
+    if height % scale_factor**2 != 0:
+        new_height += 1
+    new_width = width // scale_factor**2
+    if width % scale_factor**2 != 0:
+        new_width += 1
+    return new_height * scale_factor, new_width * scale_factor
+
+
+def prepare_image(pil_image):
+    arr = np.array(pil_image.convert("RGB"))
+    arr = arr.astype(np.float32) / 127.5 - 1
+    arr = np.transpose(arr, [2, 0, 1])
+    image = torch.from_numpy(arr).unsqueeze(0)
+    return image
+
+
 class Kandinsky3Img2ImgPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin):
     model_cpu_offload_seq = "text_encoder->movq->unet->movq"
     _callback_tensor_inputs = [
@@ -76,14 +94,6 @@ class Kandinsky3Img2ImgPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixi
         self.register_modules(
             tokenizer=tokenizer, text_encoder=text_encoder, unet=unet, scheduler=scheduler, movq=movq
         )
-        movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1) if getattr(self, "movq", None) else 8
-        movq_latent_channels = self.movq.config.latent_channels if getattr(self, "movq", None) else 4
-        self.image_processor = VaeImageProcessor(
-            vae_scale_factor=movq_scale_factor,
-            vae_latent_channels=movq_latent_channels,
-            resample="bicubic",
-            reducing_gap=1,
-        )
 
     def get_timesteps(self, num_inference_steps, strength, device):
         # get the original timestep using init_timestep
@@ -556,7 +566,7 @@ class Kandinsky3Img2ImgPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixi
                 f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support  PIL image and pytorch tensor"
             )
 
-        image = torch.cat([self.image_processor.preprocess(i) for i in image], dim=0)
+        image = torch.cat([prepare_image(i) for i in image], dim=0)
         image = image.to(dtype=prompt_embeds.dtype, device=device)
         # 4. Prepare timesteps
         self.scheduler.set_timesteps(num_inference_steps, device=device)
@@ -620,9 +630,20 @@ class Kandinsky3Img2ImgPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixi
                     xm.mark_step()
 
             # post-processing
+            if output_type not in ["pt", "np", "pil", "latent"]:
+                raise ValueError(
+                    f"Only the output types `pt`, `pil`, `np` and `latent` are supported not output_type={output_type}"
+                )
             if not output_type == "latent":
                 image = self.movq.decode(latents, force_not_quantize=True)["sample"]
-                image = self.image_processor.postprocess(image, output_type)
+
+                if output_type in ["np", "pil"]:
+                    image = image * 0.5 + 0.5
+                    image = image.clamp(0, 1)
+                    image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+                if output_type == "pil":
+                    image = self.numpy_to_pil(image)
             else:
                 image = latents
 

src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py

@@ -609,6 +609,12 @@ class KolorsImg2ImgPipeline(DiffusionPipeline, StableDiffusionMixin, StableDiffu
                 f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
             )
 
+        latents_mean = latents_std = None
+        if hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None:
+            latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1)
+        if hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None:
+            latents_std = torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1)
+
         # Offload text encoder if `enable_model_cpu_offload` was enabled
         if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
             self.text_encoder_2.to("cpu")
@@ -622,11 +628,6 @@ class KolorsImg2ImgPipeline(DiffusionPipeline, StableDiffusionMixin, StableDiffu
             init_latents = image
 
         else:
-            latents_mean = latents_std = None
-            if hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None:
-                latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1)
-            if hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None:
-                latents_std = torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1)
             # make sure the VAE is in float32 mode, as it overflows in float16
             if self.vae.config.force_upcast:
                 image = image.float()

src/diffusers/pipelines/latte/pipeline_latte.py

@@ -501,7 +501,7 @@ class LattePipeline(DiffusionPipeline):
         # &amp
         caption = re.sub(r"&amp", "", caption)
 
-        # ip addresses:
+        # ip adresses:
         caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
 
         # article ids:

src/diffusers/pipelines/lumina/pipeline_lumina.py

@@ -534,7 +534,7 @@ class LuminaPipeline(DiffusionPipeline):
         # &amp
         caption = re.sub(r"&amp", "", caption)
 
-        # ip addresses:
+        # ip adresses:
         caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
 
         # article ids:

src/diffusers/pipelines/onnx_utils.py

@@ -75,11 +75,6 @@ class OnnxRuntimeModel:
             logger.info("No onnxruntime provider specified, using CPUExecutionProvider")
             provider = "CPUExecutionProvider"
 
-        if provider_options is None:
-            provider_options = []
-        elif not isinstance(provider_options, list):
-            provider_options = [provider_options]
-
         return ort.InferenceSession(
             path, providers=[provider], sess_options=sess_options, provider_options=provider_options
         )
@@ -179,10 +174,7 @@ class OnnxRuntimeModel:
         # load model from local directory
         if os.path.isdir(model_id):
             model = OnnxRuntimeModel.load_model(
-                Path(model_id, model_file_name).as_posix(),
-                provider=provider,
-                sess_options=sess_options,
-                provider_options=kwargs.pop("provider_options"),
+                Path(model_id, model_file_name).as_posix(), provider=provider, sess_options=sess_options
             )
             kwargs["model_save_dir"] = Path(model_id)
         # load model from hub
@@ -198,12 +190,7 @@ class OnnxRuntimeModel:
             )
             kwargs["model_save_dir"] = Path(model_cache_path).parent
             kwargs["latest_model_name"] = Path(model_cache_path).name
-            model = OnnxRuntimeModel.load_model(
-                model_cache_path,
-                provider=provider,
-                sess_options=sess_options,
-                provider_options=kwargs.pop("provider_options"),
-            )
+            model = OnnxRuntimeModel.load_model(model_cache_path, provider=provider, sess_options=sess_options)
         return cls(model=model, **kwargs)
 
     @classmethod

src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py

@@ -917,6 +917,12 @@ class StableDiffusionXLControlNetPAGImg2ImgPipeline(
                 f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
             )
 
+        latents_mean = latents_std = None
+        if hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None:
+            latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1)
+        if hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None:
+            latents_std = torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1)
+
         # Offload text encoder if `enable_model_cpu_offload` was enabled
         if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
             self.text_encoder_2.to("cpu")
@@ -930,11 +936,6 @@ class StableDiffusionXLControlNetPAGImg2ImgPipeline(
             init_latents = image
 
         else:
-            latents_mean = latents_std = None
-            if hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None:
-                latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1)
-            if hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None:
-                latents_std = torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1)
             # make sure the VAE is in float32 mode, as it overflows in float16
             if self.vae.config.force_upcast:
                 image = image.float()

src/diffusers/pipelines/pag/pipeline_pag_pixart_sigma.py

@@ -488,7 +488,7 @@ class PixArtSigmaPAGPipeline(DiffusionPipeline, PAGMixin):
         # &amp
         caption = re.sub(r"&amp", "", caption)
 
-        # ip addresses:
+        # ip adresses:
         caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
 
         # article ids:

src/diffusers/pipelines/pag/pipeline_pag_sana.py

@@ -524,7 +524,7 @@ class SanaPAGPipeline(DiffusionPipeline, PAGMixin):
         # &amp
         caption = re.sub(r"&amp", "", caption)
 
-        # ip addresses:
+        # ip adresses:
         caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
 
         # article ids:

src/diffusers/pipelines/pag/pipeline_pag_sd_xl_img2img.py

@@ -707,6 +707,12 @@ class StableDiffusionXLPAGImg2ImgPipeline(
                 f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
             )
 
+        latents_mean = latents_std = None
+        if hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None:
+            latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1)
+        if hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None:
+            latents_std = torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1)
+
         # Offload text encoder if `enable_model_cpu_offload` was enabled
         if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
             self.text_encoder_2.to("cpu")
@@ -720,11 +726,6 @@ class StableDiffusionXLPAGImg2ImgPipeline(
             init_latents = image
 
         else:
-            latents_mean = latents_std = None
-            if hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None:
-                latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1)
-            if hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None:
-                latents_std = torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1)
             # make sure the VAE is in float32 mode, as it overflows in float16
             if self.vae.config.force_upcast:
                 image = image.float()

src/diffusers/pipelines/pipeline_flax_utils.py

@@ -469,7 +469,7 @@ class FlaxDiffusionPipeline(ConfigMixin, PushToHubMixin):
                 class_obj = import_flax_or_no_model(pipeline_module, class_name)
 
                 importable_classes = ALL_IMPORTABLE_CLASSES
-                class_candidates = dict.fromkeys(importable_classes.keys(), class_obj)
+                class_candidates = {c: class_obj for c in importable_classes.keys()}
             else:
                 # else we just import it from the library.
                 library = importlib.import_module(library_name)

src/diffusers/pipelines/pipeline_loading_utils.py

@@ -341,13 +341,13 @@ def get_class_obj_and_candidates(
         pipeline_module = getattr(pipelines, library_name)
 
         class_obj = getattr(pipeline_module, class_name)
-        class_candidates = dict.fromkeys(importable_classes.keys(), class_obj)
+        class_candidates = {c: class_obj for c in importable_classes.keys()}
     elif os.path.isfile(os.path.join(component_folder, library_name + ".py")):
         # load custom component
         class_obj = get_class_from_dynamic_module(
             component_folder, module_file=library_name + ".py", class_name=class_name
         )
-        class_candidates = dict.fromkeys(importable_classes.keys(), class_obj)
+        class_candidates = {c: class_obj for c in importable_classes.keys()}
     else:
         # else we just import it from the library.
         library = importlib.import_module(library_name)
@@ -412,7 +412,7 @@ def _get_pipeline_class(
             revision=revision,
         )
 
-    if class_obj.__name__ != "DiffusionPipeline" and class_obj.__name__ != "ModularPipeline":
+    if class_obj.__name__ != "DiffusionPipeline":
         return class_obj
 
     diffusers_module = importlib.import_module(class_obj.__module__.split(".")[0])

src/diffusers/pipelines/pipeline_utils.py

@@ -58,7 +58,6 @@ from ..utils import (
     _is_valid_type,
     is_accelerate_available,
     is_accelerate_version,
-    is_hpu_available,
     is_torch_npu_available,
     is_torch_version,
     is_transformers_version,
@@ -427,7 +426,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
             module_is_sequentially_offloaded(module) for _, module in self.components.items()
         )
 
-        is_pipeline_device_mapped = hasattr(self, "hf_device_map") and self.hf_device_map is not None and len(self.hf_device_map) > 1
+        is_pipeline_device_mapped = self.hf_device_map is not None and len(self.hf_device_map) > 1
         if is_pipeline_device_mapped:
             raise ValueError(
                 "It seems like you have activated a device mapping strategy on the pipeline which doesn't allow explicit device placement using `to()`. You can call `reset_device_map()` to remove the existing device map from the pipeline."
@@ -444,7 +443,6 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
                     "You are trying to call `.to('cuda')` on a pipeline that has models quantized with `bitsandbytes`. Your current `accelerate` installation does not support it. Please upgrade the installation."
                 )
 
-
         # Display a warning in this case (the operation succeeds but the benefits are lost)
         pipeline_is_offloaded = any(module_is_offloaded(module) for _, module in self.components.items())
         if pipeline_is_offloaded and device_type in ["cuda", "xpu"]:
@@ -452,20 +450,6 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
                 f"It seems like you have activated model offloading by calling `enable_model_cpu_offload`, but are now manually moving the pipeline to GPU. It is strongly recommended against doing so as memory gains from offloading are likely to be lost. Offloading automatically takes care of moving the individual components {', '.join(self.components.keys())} to GPU when needed. To make sure offloading works as expected, you should consider moving the pipeline back to CPU: `pipeline.to('cpu')` or removing the move altogether if you use offloading."
             )
 
-        # Enable generic support for Intel Gaudi accelerator using GPU/HPU migration
-        if device_type == "hpu" and kwargs.pop("hpu_migration", True) and is_hpu_available():
-            os.environ["PT_HPU_GPU_MIGRATION"] = "1"
-            logger.debug("Environment variable set: PT_HPU_GPU_MIGRATION=1")
-
-            import habana_frameworks.torch  # noqa: F401
-
-            # HPU hardware check
-            if not (hasattr(torch, "hpu") and torch.hpu.is_available()):
-                raise ValueError("You are trying to call `.to('hpu')` but HPU device is unavailable.")
-
-            os.environ["PT_HPU_MAX_COMPOUND_OP_SIZE"] = "1"
-            logger.debug("Environment variable set: PT_HPU_MAX_COMPOUND_OP_SIZE=1")
-
         module_names, _ = self._get_signature_keys(self)
         modules = [getattr(self, n, None) for n in module_names]
         modules = [m for m in modules if isinstance(m, torch.nn.Module)]
@@ -1120,11 +1104,9 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
                 The PyTorch device type of the accelerator that shall be used in inference. If not specified, it will
                 automatically detect the available accelerator and use.
         """
- 
         self._maybe_raise_error_if_group_offload_active(raise_error=True)
 
-        is_pipeline_device_mapped = hasattr(self, "hf_device_map") and self.hf_device_map is not None and len(self.hf_device_map) > 1
-
+        is_pipeline_device_mapped = self.hf_device_map is not None and len(self.hf_device_map) > 1
         if is_pipeline_device_mapped:
             raise ValueError(
                 "It seems like you have activated a device mapping strategy on the pipeline so calling `enable_model_cpu_offload() isn't allowed. You can call `reset_device_map()` first and then call `enable_model_cpu_offload()`."
@@ -1248,7 +1230,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
             raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")
         self.remove_all_hooks()
 
-        is_pipeline_device_mapped = hasattr(self, "hf_device_map") and self.hf_device_map is not None and len(self.hf_device_map) > 1
+        is_pipeline_device_mapped = self.hf_device_map is not None and len(self.hf_device_map) > 1
         if is_pipeline_device_mapped:
             raise ValueError(
                 "It seems like you have activated a device mapping strategy on the pipeline so calling `enable_sequential_cpu_offload() isn't allowed. You can call `reset_device_map()` first and then call `enable_sequential_cpu_offload()`."

src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py

@@ -598,7 +598,7 @@ class PixArtAlphaPipeline(DiffusionPipeline):
         # &amp
         caption = re.sub(r"&amp", "", caption)
 
-        # ip addresses:
+        # ip adresses:
         caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
 
         # article ids:

src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py

@@ -525,7 +525,7 @@ class PixArtSigmaPipeline(DiffusionPipeline):
         # &amp
         caption = re.sub(r"&amp", "", caption)
 
-        # ip addresses:
+        # ip adresses:
         caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
 
         # article ids:

src/diffusers/pipelines/sana/pipeline_sana.py

@@ -600,7 +600,7 @@ class SanaPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
         # &amp
         caption = re.sub(r"&amp", "", caption)
 
-        # ip addresses:
+        # ip adresses:
         caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
 
         # article ids:

src/diffusers/pipelines/sana/pipeline_sana_controlnet.py

@@ -615,7 +615,7 @@ class SanaControlNetPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
         # &amp
         caption = re.sub(r"&amp", "", caption)
 
-        # ip addresses:
+        # ip adresses:
         caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
 
         # article ids: