update

.github/workflows/benchmark.yml

@@ -7,7 +7,7 @@ on:
 
 env:
   DIFFUSERS_IS_CI: yes
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
   HF_HOME: /mnt/cache
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8

.github/workflows/build_docker_images.yml

@@ -42,39 +42,18 @@ jobs:
           CHANGED_FILES: ${{ steps.file_changes.outputs.all }}
         run: |
           echo "$CHANGED_FILES"
-          ALLOWED_IMAGES=(
-            diffusers-pytorch-cpu
-            diffusers-pytorch-cuda
-            diffusers-pytorch-xformers-cuda
-            diffusers-pytorch-minimum-cuda
-            diffusers-doc-builder
-          )
-
-          declare -A IMAGES_TO_BUILD=()
-
-          for FILE in $CHANGED_FILES; do
+          for FILE in $CHANGED_FILES; do 
             # skip anything that isn't still on disk
-            if [[ ! -e "$FILE" ]]; then
+            if [[ ! -f "$FILE" ]]; then
               echo "Skipping removed file $FILE"
               continue
+            fi           
+            if [[ "$FILE" == docker/*Dockerfile ]]; then
+              DOCKER_PATH="${FILE%/Dockerfile}"
+              DOCKER_TAG=$(basename "$DOCKER_PATH")
+              echo "Building Docker image for $DOCKER_TAG"
+              docker build -t "$DOCKER_TAG" "$DOCKER_PATH"
             fi
-
-            for IMAGE in "${ALLOWED_IMAGES[@]}"; do
-              if [[ "$FILE" == docker/${IMAGE}/* ]]; then
-                IMAGES_TO_BUILD["$IMAGE"]=1
-              fi
-            done
-          done
-
-          if [[ ${#IMAGES_TO_BUILD[@]} -eq 0 ]]; then
-            echo "No relevant Docker changes detected."
-            exit 0
-          fi
-
-          for IMAGE in "${!IMAGES_TO_BUILD[@]}"; do
-            DOCKER_PATH="docker/${IMAGE}"
-            echo "Building Docker image for $IMAGE"
-            docker build -t "$IMAGE" "$DOCKER_PATH"
           done
         if: steps.file_changes.outputs.all != ''
 

.github/workflows/nightly_tests.yml

@@ -7,7 +7,7 @@ on:
 
 env:
   DIFFUSERS_IS_CI: yes
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
   PYTEST_TIMEOUT: 600

.github/workflows/pr_modular_tests.yml

@@ -26,7 +26,7 @@ concurrency:
 
 env:
   DIFFUSERS_IS_CI: yes
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
   OMP_NUM_THREADS: 4
   MKL_NUM_THREADS: 4
   PYTEST_TIMEOUT: 60

.github/workflows/pr_tests.yml

@@ -22,7 +22,7 @@ concurrency:
 
 env:
   DIFFUSERS_IS_CI: yes
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
   OMP_NUM_THREADS: 4
   MKL_NUM_THREADS: 4
   PYTEST_TIMEOUT: 60

.github/workflows/pr_tests_gpu.yml

@@ -24,7 +24,7 @@ env:
   DIFFUSERS_IS_CI: yes
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
   PYTEST_TIMEOUT: 600
   PIPELINE_USAGE_CUTOFF: 1000000000 # set high cutoff so that only always-test pipelines run
 

.github/workflows/push_tests.yml

@@ -14,7 +14,7 @@ env:
   DIFFUSERS_IS_CI: yes
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
   PYTEST_TIMEOUT: 600
   PIPELINE_USAGE_CUTOFF: 50000
 

.github/workflows/push_tests_fast.yml

@@ -18,7 +18,7 @@ env:
   HF_HOME: /mnt/cache
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
   PYTEST_TIMEOUT: 600
   RUN_SLOW: no
 

.github/workflows/push_tests_mps.yml

@@ -8,7 +8,7 @@ env:
   HF_HOME: /mnt/cache
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
   PYTEST_TIMEOUT: 600
   RUN_SLOW: no
 

docker/diffusers-doc-builder/Dockerfile

@@ -33,7 +33,7 @@ RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.
 RUN uv pip install --no-cache-dir \
     accelerate \
     numpy==1.26.4 \
-    hf_xet \
+    hf_transfer \
     setuptools==69.5.1 \
     bitsandbytes \
     torchao \

docker/diffusers-onnxruntime-cpu/Dockerfile

@@ -44,6 +44,6 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
         scipy \
         tensorboard \
         transformers \
-        hf_xet
+        hf_transfer
 
 CMD ["/bin/bash"]

docker/diffusers-onnxruntime-cuda/Dockerfile

@@ -38,12 +38,13 @@ RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
         datasets \
         hf-doc-builder \
         huggingface-hub \
-        hf_xet \
+        hf_transfer \
         Jinja2 \
         librosa \
         numpy==1.26.4 \
         scipy \
         tensorboard \
-        transformers
+        transformers \
+        hf_transfer
 
 CMD ["/bin/bash"]

docker/diffusers-pytorch-cpu/Dockerfile

@@ -31,7 +31,7 @@ RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.
 RUN uv pip install --no-cache-dir \
     accelerate \
     numpy==1.26.4 \
-    hf_xet
+    hf_transfer
 
 RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean
 

docker/diffusers-pytorch-cuda/Dockerfile

@@ -44,6 +44,6 @@ RUN uv pip install --no-cache-dir \
     accelerate \
     numpy==1.26.4 \
     pytorch-lightning \
-    hf_xet
+    hf_transfer
 
 CMD ["/bin/bash"]

docker/diffusers-pytorch-minimum-cuda/Dockerfile

@@ -47,6 +47,6 @@ RUN uv pip install --no-cache-dir \
     accelerate \
     numpy==1.26.4 \
     pytorch-lightning \
-    hf_xet
+    hf_transfer
 
 CMD ["/bin/bash"]

docker/diffusers-pytorch-xformers-cuda/Dockerfile

@@ -44,7 +44,7 @@ RUN uv pip install --no-cache-dir \
     accelerate \
     numpy==1.26.4 \
     pytorch-lightning \
-    hf_xet \
+    hf_transfer \
     xformers
 
 CMD ["/bin/bash"]

docs/source/en/_toctree.yml

@@ -323,8 +323,6 @@
         title: AllegroTransformer3DModel
       - local: api/models/aura_flow_transformer2d
         title: AuraFlowTransformer2DModel
-      - local: api/models/transformer_bria_fibo
-        title: BriaFiboTransformer2DModel
       - local: api/models/bria_transformer
         title: BriaTransformer2DModel
       - local: api/models/chroma_transformer
@@ -349,8 +347,6 @@
         title: HiDreamImageTransformer2DModel
       - local: api/models/hunyuan_transformer2d
         title: HunyuanDiT2DModel
-      - local: api/models/hunyuanimage_transformer_2d
-        title: HunyuanImageTransformer2DModel
       - local: api/models/hunyuan_video_transformer_3d
         title: HunyuanVideoTransformer3DModel
       - local: api/models/latte_transformer3d
@@ -373,8 +369,6 @@
         title: QwenImageTransformer2DModel
       - local: api/models/sana_transformer2d
         title: SanaTransformer2DModel
-      - local: api/models/sana_video_transformer3d
-        title: SanaVideoTransformer3DModel
       - local: api/models/sd3_transformer2d
         title: SD3Transformer2DModel
       - local: api/models/skyreels_v2_transformer_3d
@@ -417,10 +411,6 @@
         title: AutoencoderKLCogVideoX
       - local: api/models/autoencoderkl_cosmos
         title: AutoencoderKLCosmos
-      - local: api/models/autoencoder_kl_hunyuanimage
-        title: AutoencoderKLHunyuanImage
-      - local: api/models/autoencoder_kl_hunyuanimage_refiner
-        title: AutoencoderKLHunyuanImageRefiner
       - local: api/models/autoencoder_kl_hunyuan_video
         title: AutoencoderKLHunyuanVideo
       - local: api/models/autoencoderkl_ltx_video
@@ -473,8 +463,6 @@
         title: BLIP-Diffusion
       - local: api/pipelines/bria_3_2
         title: Bria 3.2
-      - local: api/pipelines/bria_fibo
-        title: Bria Fibo
       - local: api/pipelines/chroma
         title: Chroma
       - local: api/pipelines/cogview3
@@ -557,16 +545,12 @@
         title: PixArt-α
       - local: api/pipelines/pixart_sigma
         title: PixArt-Σ
-      - local: api/pipelines/prx
-        title: PRX
       - local: api/pipelines/qwenimage
         title: QwenImage
       - local: api/pipelines/sana
         title: Sana
       - local: api/pipelines/sana_sprint
         title: Sana Sprint
-      - local: api/pipelines/sana_video
-        title: Sana Video
       - local: api/pipelines/self_attention_guidance
         title: Self-Attention Guidance
       - local: api/pipelines/semantic_stable_diffusion
@@ -634,14 +618,10 @@
         title: ConsisID
       - local: api/pipelines/framepack
         title: Framepack
-      - local: api/pipelines/hunyuanimage21
-        title: HunyuanImage2.1
       - local: api/pipelines/hunyuan_video
         title: HunyuanVideo
       - local: api/pipelines/i2vgenxl
         title: I2VGen-XL
-      - local: api/pipelines/kandinsky5_video
-        title: Kandinsky 5.0 Video
       - local: api/pipelines/latte
         title: Latte
       - local: api/pipelines/ltx_video

docs/source/en/api/models/autoencoder_kl_hunyuanimage.md

@@ -1,32 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# AutoencoderKLHunyuanImage
-
-The 2D variational autoencoder (VAE) model with KL loss used in [HunyuanImage2.1].
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import AutoencoderKLHunyuanImage
-
-vae = AutoencoderKLHunyuanImage.from_pretrained("hunyuanvideo-community/HunyuanImage-2.1-Diffusers", subfolder="vae", torch_dtype=torch.bfloat16)
-```
-
-## AutoencoderKLHunyuanImage
-
-[[autodoc]] AutoencoderKLHunyuanImage
-  - decode
-  - all
-
-## DecoderOutput
-
-[[autodoc]] models.autoencoders.vae.DecoderOutput

docs/source/en/api/models/autoencoder_kl_hunyuanimage_refiner.md

@@ -1,32 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# AutoencoderKLHunyuanImageRefiner
-
-The 3D variational autoencoder (VAE) model with KL loss used in [HunyuanImage2.1](https://github.com/Tencent-Hunyuan/HunyuanImage-2.1) for its refiner pipeline.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import AutoencoderKLHunyuanImageRefiner
-
-vae = AutoencoderKLHunyuanImageRefiner.from_pretrained("hunyuanvideo-community/HunyuanImage-2.1-Refiner-Diffusers", subfolder="vae", torch_dtype=torch.bfloat16)
-```
-
-## AutoencoderKLHunyuanImageRefiner
-
-[[autodoc]] AutoencoderKLHunyuanImageRefiner
-  - decode
-  - all
-
-## DecoderOutput
-
-[[autodoc]] models.autoencoders.vae.DecoderOutput

docs/source/en/api/models/chroma_transformer.md

@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
 
 # ChromaTransformer2DModel
 
-A modified flux Transformer model from [Chroma](https://huggingface.co/lodestones/Chroma1-HD)
+A modified flux Transformer model from [Chroma](https://huggingface.co/lodestones/Chroma)
 
 ## ChromaTransformer2DModel
 

docs/source/en/api/models/hunyuanimage_transformer_2d.md

@@ -1,30 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# HunyuanImageTransformer2DModel
-
-A Diffusion Transformer model for [HunyuanImage2.1](https://github.com/Tencent-Hunyuan/HunyuanImage-2.1).
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import HunyuanImageTransformer2DModel
-
-transformer = HunyuanImageTransformer2DModel.from_pretrained("hunyuanvideo-community/HunyuanImage-2.1-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
-```
-
-## HunyuanImageTransformer2DModel
-
-[[autodoc]] HunyuanImageTransformer2DModel
-
-## Transformer2DModelOutput
-
-[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

docs/source/en/api/models/sana_video_transformer3d.md

@@ -1,36 +0,0 @@
-<!-- Copyright 2025 The SANA-Video Authors and 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. -->
-
-# SanaVideoTransformer3DModel
-
-A Diffusion Transformer model for 3D data (video) from [SANA-Video: Efficient Video Generation with Block Linear Diffusion Transformer](https://huggingface.co/papers/2509.24695) from NVIDIA and MIT HAN Lab, by Junsong Chen, Yuyang Zhao, Jincheng Yu, Ruihang Chu, Junyu Chen, Shuai Yang, Xianbang Wang, Yicheng Pan, Daquan Zhou, Huan Ling, Haozhe Liu, Hongwei Yi, Hao Zhang, Muyang Li, Yukang Chen, Han Cai, Sanja Fidler, Ping Luo, Song Han, Enze Xie.
-
-The abstract from the paper is:
-
-*We introduce SANA-Video, a small diffusion model that can efficiently generate videos up to 720x1280 resolution and minute-length duration. SANA-Video synthesizes high-resolution, high-quality and long videos with strong text-video alignment at a remarkably fast speed, deployable on RTX 5090 GPU. Two core designs ensure our efficient, effective and long video generation: (1) Linear DiT: We leverage linear attention as the core operation, which is more efficient than vanilla attention given the large number of tokens processed in video generation. (2) Constant-Memory KV cache for Block Linear Attention: we design block-wise autoregressive approach for long video generation by employing a constant-memory state, derived from the cumulative properties of linear attention. This KV cache provides the Linear DiT with global context at a fixed memory cost, eliminating the need for a traditional KV cache and enabling efficient, minute-long video generation. In addition, we explore effective data filters and model training strategies, narrowing the training cost to 12 days on 64 H100 GPUs, which is only 1% of the cost of MovieGen. Given its low cost, SANA-Video achieves competitive performance compared to modern state-of-the-art small diffusion models (e.g., Wan 2.1-1.3B and SkyReel-V2-1.3B) while being 16x faster in measured latency. Moreover, SANA-Video can be deployed on RTX 5090 GPUs with NVFP4 precision, accelerating the inference speed of generating a 5-second 720p video from 71s to 29s (2.4x speedup). In summary, SANA-Video enables low-cost, high-quality video generation.*
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import SanaVideoTransformer3DModel
-import torch
-
-transformer = SanaVideoTransformer3DModel.from_pretrained("Efficient-Large-Model/SANA-Video_2B_480p_diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
-```
-
-## SanaVideoTransformer3DModel
-
-[[autodoc]] SanaVideoTransformer3DModel
-
-## Transformer2DModelOutput
-
-[[autodoc]] models.modeling_outputs.Transformer2DModelOutput
-

docs/source/en/api/models/transformer_bria_fibo.md

@@ -1,19 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# BriaFiboTransformer2DModel
-
-A modified flux Transformer model from [Bria](https://huggingface.co/briaai/FIBO)
-
-## BriaFiboTransformer2DModel
-
-[[autodoc]] BriaFiboTransformer2DModel

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

@@ -1,45 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Bria Fibo
-
-Text-to-image models have mastered imagination - but not control. FIBO changes that.
-
-FIBO is trained on structured JSON captions up to 1,000+ words and designed to understand and control different visual parameters such as lighting, composition, color, and camera settings, enabling precise and reproducible outputs.
-
-With only 8 billion parameters, FIBO provides a new level of image quality, prompt adherence and proffesional control.
-
-FIBO is trained exclusively on a structured prompt and will not work with freeform text prompts.
-you can use the [FIBO-VLM-prompt-to-JSON](https://huggingface.co/briaai/FIBO-VLM-prompt-to-JSON) model or the [FIBO-gemini-prompt-to-JSON](https://huggingface.co/briaai/FIBO-gemini-prompt-to-JSON)  to convert your freeform text prompt to a structured JSON prompt.
-
-its not recommended to use freeform text prompts directly with FIBO, as it will not produce the best results.
-
-you can learn more about FIBO in  [Bria Fibo Hugging Face page](https://huggingface.co/briaai/FIBO).
-
-
-## Usage
-
-_As the model is gated, before using it with diffusers you first need to go to the [Bria Fibo Hugging Face page](https://huggingface.co/briaai/FIBO), fill in the form and accept the gate. Once you are in, you need to login so that your system knows you’ve accepted the gate._
-
-Use the command below to log in:
-
-```bash
-hf auth login
-```
-
-
-## BriaPipeline
-
-[[autodoc]] BriaPipeline
-	- all
-	- __call__
-

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

@@ -19,21 +19,20 @@ specific language governing permissions and limitations under the License.
 
 Chroma is a text to image generation model based on Flux.
 
-Original model checkpoints for Chroma can be found here:
-* High-resolution finetune: [lodestones/Chroma1-HD](https://huggingface.co/lodestones/Chroma1-HD)
-* Base model: [lodestones/Chroma1-Base](https://huggingface.co/lodestones/Chroma1-Base)
-* Original repo with progress checkpoints: [lodestones/Chroma](https://huggingface.co/lodestones/Chroma) (loading this repo with `from_pretrained` will load a Diffusers-compatible version of the `unlocked-v37` checkpoint)
+Original model checkpoints for Chroma can be found [here](https://huggingface.co/lodestones/Chroma).
 
 > [!TIP]
 > Chroma can use all the same optimizations as Flux.
 
 ## Inference
 
+The Diffusers version of Chroma is based on the [`unlocked-v37`](https://huggingface.co/lodestones/Chroma/blob/main/chroma-unlocked-v37.safetensors) version of the original model, which is available in the [Chroma repository](https://huggingface.co/lodestones/Chroma).
+
 ```python
 import torch
 from diffusers import ChromaPipeline
 
-pipe = ChromaPipeline.from_pretrained("lodestones/Chroma1-HD", torch_dtype=torch.bfloat16)
+pipe = ChromaPipeline.from_pretrained("lodestones/Chroma", torch_dtype=torch.bfloat16)
 pipe.enable_model_cpu_offload()
 
 prompt = [
@@ -64,10 +63,10 @@ Then run the following example
 import torch
 from diffusers import ChromaTransformer2DModel, ChromaPipeline
 
-model_id = "lodestones/Chroma1-HD"
+model_id = "lodestones/Chroma"
 dtype = torch.bfloat16
 
-transformer = ChromaTransformer2DModel.from_single_file("https://huggingface.co/lodestones/Chroma1-HD/blob/main/Chroma1-HD.safetensors", torch_dtype=dtype)
+transformer = ChromaTransformer2DModel.from_single_file("https://huggingface.co/lodestones/Chroma/blob/main/chroma-unlocked-v37.safetensors", torch_dtype=dtype)
 
 pipe = ChromaPipeline.from_pretrained(model_id, transformer=transformer, torch_dtype=dtype)
 pipe.enable_model_cpu_offload()

docs/source/en/api/pipelines/hunyuanimage21.md

@@ -1,152 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License. -->
-
-# HunyuanImage2.1
-
-
-HunyuanImage-2.1 is a 17B text-to-image model that is capable of generating 2K (2048 x 2048) resolution images
-
-HunyuanImage-2.1 comes in the following variants:
-
-| model type | model id |
-|:----------:|:--------:|
-| HunyuanImage-2.1 | [hunyuanvideo-community/HunyuanImage-2.1-Diffusers](https://huggingface.co/hunyuanvideo-community/HunyuanImage-2.1-Diffusers) |
-| HunyuanImage-2.1-Distilled | [hunyuanvideo-community/HunyuanImage-2.1-Distilled-Diffusers](https://huggingface.co/hunyuanvideo-community/HunyuanImage-2.1-Distilled-Diffusers) |
-| HunyuanImage-2.1-Refiner | [hunyuanvideo-community/HunyuanImage-2.1-Refiner-Diffusers](https://huggingface.co/hunyuanvideo-community/HunyuanImage-2.1-Refiner-Diffusers) |
-
-> [!TIP]
-> [Caching](../../optimization/cache) may also speed up inference by storing and reusing intermediate outputs.
-
-## HunyuanImage-2.1
-
-HunyuanImage-2.1 applies [Adaptive Projected Guidance (APG)](https://huggingface.co/papers/2410.02416) combined with Classifier-Free Guidance (CFG) in the denoising loop. `HunyuanImagePipeline` has a `guider` component (read more about [Guider](../modular_diffusers/guiders.md)) and does not take a `guidance_scale` parameter at runtime. To change guider-related parameters, e.g., `guidance_scale`, you can update the `guider` configuration instead.
-
-```python
-import torch
-from diffusers import HunyuanImagePipeline
-
-pipe = HunyuanImagePipeline.from_pretrained(
-    "hunyuanvideo-community/HunyuanImage-2.1-Diffusers", 
-    torch_dtype=torch.bfloat16
-)
-pipe = pipe.to("cuda")
-``` 
-
-You can inspect the `guider` object:
-
-```py
->>> pipe.guider
-AdaptiveProjectedMixGuidance {
-  "_class_name": "AdaptiveProjectedMixGuidance",
-  "_diffusers_version": "0.36.0.dev0",
-  "adaptive_projected_guidance_momentum": -0.5,
-  "adaptive_projected_guidance_rescale": 10.0,
-  "adaptive_projected_guidance_scale": 10.0,
-  "adaptive_projected_guidance_start_step": 5,
-  "enabled": true,
-  "eta": 0.0,
-  "guidance_rescale": 0.0,
-  "guidance_scale": 3.5,
-  "start": 0.0,
-  "stop": 1.0,
-  "use_original_formulation": false
-}
-
-State:
-  step: None
-  num_inference_steps: None
-  timestep: None
-  count_prepared: 0
-  enabled: True
-  num_conditions: 2
-  momentum_buffer: None
-  is_apg_enabled: False
-  is_cfg_enabled: True
-```
-
-To update the guider with a different configuration, use the `new()` method. For example, to generate an image with `guidance_scale=5.0` while keeping all other default guidance parameters:
-
-```py
-import torch
-from diffusers import HunyuanImagePipeline
-
-pipe = HunyuanImagePipeline.from_pretrained(
-    "hunyuanvideo-community/HunyuanImage-2.1-Diffusers", 
-    torch_dtype=torch.bfloat16
-)
-pipe = pipe.to("cuda")
-
-# Update the guider configuration
-pipe.guider = pipe.guider.new(guidance_scale=5.0)
-
-prompt = (
-    "A cute, cartoon-style anthropomorphic penguin plush toy with fluffy fur, standing in a painting studio, "
-    "wearing a red knitted scarf and a red beret with the word 'Tencent' on it, holding a paintbrush with a "
-    "focused expression as it paints an oil painting of the Mona Lisa, rendered in a photorealistic photographic style."
-)
-
-image = pipe(
-    prompt=prompt, 
-    num_inference_steps=50, 
-    height=2048, 
-    width=2048,
-).images[0]
-image.save("image.png")
-```
-
-
-## HunyuanImage-2.1-Distilled
-
-use `distilled_guidance_scale` with the guidance-distilled checkpoint, 
-
-```py
-import torch
-from diffusers import HunyuanImagePipeline
-pipe = HunyuanImagePipeline.from_pretrained("hunyuanvideo-community/HunyuanImage-2.1-Distilled-Diffusers", torch_dtype=torch.bfloat16)
-pipe = pipe.to("cuda")
-
-prompt = (
-    "A cute, cartoon-style anthropomorphic penguin plush toy with fluffy fur, standing in a painting studio, "
-    "wearing a red knitted scarf and a red beret with the word 'Tencent' on it, holding a paintbrush with a "
-    "focused expression as it paints an oil painting of the Mona Lisa, rendered in a photorealistic photographic style."
-)
-
-out = pipe(
-    prompt,
-    num_inference_steps=8,
-    distilled_guidance_scale=3.25,
-    height=2048,
-    width=2048,
-    generator=generator,
-).images[0]
-
-```
-
-
-## HunyuanImagePipeline
-
-[[autodoc]] HunyuanImagePipeline
-  - all
-  - __call__
-
-## HunyuanImageRefinerPipeline
-
-[[autodoc]] HunyuanImageRefinerPipeline
-  - all
-  - __call__
-
-
-## HunyuanImagePipelineOutput
-
-[[autodoc]] pipelines.hunyuan_image.pipeline_output.HunyuanImagePipelineOutput

docs/source/en/api/pipelines/kandinsky5_video.md

@@ -1,149 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-http://www.apache.org/licenses/LICENSE-2.0
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Kandinsky 5.0 Video
-
-Kandinsky 5.0 Video is created by the Kandinsky team: Alexey Letunovskiy, Maria Kovaleva, Ivan Kirillov, Lev Novitskiy, Denis Koposov, Dmitrii Mikhailov, Anna Averchenkova, Andrey Shutkin, Julia Agafonova, Olga Kim, Anastasiia Kargapoltseva, Nikita Kiselev, Anna Dmitrienko,  Anastasia Maltseva, Kirill Chernyshev, Ilia Vasiliev, Viacheslav Vasilev, Vladimir Polovnikov, Yury Kolabushin, Alexander Belykh, Mikhail Mamaev, Anastasia Aliaskina, Tatiana Nikulina, Polina Gavrilova, Vladimir Arkhipkin, Vladimir Korviakov, Nikolai Gerasimenko, Denis Parkhomenko, Denis Dimitrov
-
-
-Kandinsky 5.0 is a family of diffusion models for Video & Image generation. Kandinsky 5.0 T2V Lite is a lightweight video generation model (2B parameters) that ranks #1 among open-source models in its class. It outperforms larger models and offers the best understanding of Russian concepts in the open-source ecosystem.
-
-The model introduces several key innovations:
-- **Latent diffusion pipeline** with **Flow Matching** for improved training stability
-- **Diffusion Transformer (DiT)** as the main generative backbone with cross-attention to text embeddings
-- Dual text encoding using **Qwen2.5-VL** and **CLIP** for comprehensive text understanding
-- **HunyuanVideo 3D VAE** for efficient video encoding and decoding
-- **Sparse attention mechanisms** (NABLA) for efficient long-sequence processing
-
-The original codebase can be found at [ai-forever/Kandinsky-5](https://github.com/ai-forever/Kandinsky-5).
-
-> [!TIP]
-> Check out the [AI Forever](https://huggingface.co/ai-forever) organization on the Hub for the official model checkpoints for text-to-video generation, including pretrained, SFT, no-CFG, and distilled variants.
-
-## Available Models
-
-Kandinsky 5.0 T2V Lite comes in several variants optimized for different use cases:
-
-| model_id | Description | Use Cases |
-|------------|-------------|-----------|
-| **ai-forever/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers** | 5 second Supervised Fine-Tuned model | Highest generation quality |
-| **ai-forever/Kandinsky-5.0-T2V-Lite-sft-10s-Diffusers** | 10 second Supervised Fine-Tuned model | Highest generation quality |
-| **ai-forever/Kandinsky-5.0-T2V-Lite-nocfg-5s-Diffusers** | 5 second Classifier-Free Guidance distilled | 2× faster inference |
-| **ai-forever/Kandinsky-5.0-T2V-Lite-nocfg-10s-Diffusers** | 10 second Classifier-Free Guidance distilled | 2× faster inference |
-| **ai-forever/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers** | 5 second Diffusion distilled to 16 steps | 6× faster inference, minimal quality loss |
-| **ai-forever/Kandinsky-5.0-T2V-Lite-distilled16steps-10s-Diffusers** | 10 second Diffusion distilled to 16 steps | 6× faster inference, minimal quality loss |
-| **ai-forever/Kandinsky-5.0-T2V-Lite-pretrain-5s-Diffusers** | 5 second Base pretrained model | Research and fine-tuning |
-| **ai-forever/Kandinsky-5.0-T2V-Lite-pretrain-10s-Diffusers** | 10 second Base pretrained model | Research and fine-tuning |
-
-All models are available in 5-second and 10-second video generation versions.
-
-## Kandinsky5T2VPipeline
-
-[[autodoc]] Kandinsky5T2VPipeline
-    - all
-    - __call__
-
-## Usage Examples
-
-### Basic Text-to-Video Generation
-
-```python
-import torch
-from diffusers import Kandinsky5T2VPipeline
-from diffusers.utils import export_to_video
-
-# Load the pipeline
-model_id = "ai-forever/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers"
-pipe = Kandinsky5T2VPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
-pipe = pipe.to("cuda")
-
-# Generate video
-prompt = "A cat and a dog baking a cake together in a kitchen."
-negative_prompt = "Static, 2D cartoon, cartoon, 2d animation, paintings, images, worst quality, low quality, ugly, deformed, walking backwards"
-
-output = pipe(
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    height=512,
-    width=768,
-    num_frames=121,  # ~5 seconds at 24fps
-    num_inference_steps=50,
-    guidance_scale=5.0,
-).frames[0]
-
-export_to_video(output, "output.mp4", fps=24, quality=9)
-```
-
-### 10 second Models
-**⚠️ Warning!** all 10 second models should be used with Flex attention and max-autotune-no-cudagraphs compilation:
-
-```python
-pipe = Kandinsky5T2VPipeline.from_pretrained(
-    "ai-forever/Kandinsky-5.0-T2V-Lite-sft-10s-Diffusers", 
-    torch_dtype=torch.bfloat16
-)
-pipe = pipe.to("cuda")
-
-pipe.transformer.set_attention_backend(
-    "flex"
-)                                       # <--- Sett attention bakend to Flex
-pipe.transformer.compile(
-    mode="max-autotune-no-cudagraphs", 
-    dynamic=True
-)                                       # <--- Compile with max-autotune-no-cudagraphs
-
-prompt = "A cat and a dog baking a cake together in a kitchen."
-negative_prompt = "Static, 2D cartoon, cartoon, 2d animation, paintings, images, worst quality, low quality, ugly, deformed, walking backwards"
-
-output = pipe(
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    height=512,
-    width=768,
-    num_frames=241,
-    num_inference_steps=50,
-    guidance_scale=5.0,
-).frames[0]
-
-export_to_video(output, "output.mp4", fps=24, quality=9)
-```
-
-### Diffusion Distilled model
-**⚠️ Warning!** all nocfg and diffusion distilled models should be infered wothout CFG (```guidance_scale=1.0```):
-
-```python
-model_id = "ai-forever/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers"
-pipe = Kandinsky5T2VPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
-pipe = pipe.to("cuda")
-
-output = pipe(
-    prompt="A beautiful sunset over mountains",
-    num_inference_steps=16,  # <--- Model is distilled in 16 steps
-    guidance_scale=1.0,      # <--- no CFG
-).frames[0]
-
-export_to_video(output, "output.mp4", fps=24, quality=9)
-```
-
-
-## Citation
-```bibtex
-@misc{kandinsky2025,
-    author = {Alexey Letunovskiy and Maria Kovaleva and Ivan Kirillov and Lev Novitskiy and Denis Koposov and
-              Dmitrii Mikhailov and Anna Averchenkova and Andrey Shutkin and Julia Agafonova and Olga Kim and
-              Anastasiia Kargapoltseva and Nikita Kiselev and Vladimir Arkhipkin and Vladimir Korviakov and
-              Nikolai Gerasimenko and Denis Parkhomenko and Anna Dmitrienko and Anastasia Maltseva and
-              Kirill Chernyshev and Ilia Vasiliev and Viacheslav Vasilev and Vladimir Polovnikov and
-              Yury Kolabushin and Alexander Belykh and Mikhail Mamaev and Anastasia Aliaskina and
-              Tatiana Nikulina and Polina Gavrilova and Denis Dimitrov},
-    title = {Kandinsky 5.0: A family of diffusion models for Video & Image generation},
-    howpublished = {\url{https://github.com/ai-forever/Kandinsky-5}},
-    year = 2025
-}
-```

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

@@ -1,131 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License. -->
-
-# PRX
-
-
-PRX generates high-quality images from text using a simplified MMDIT architecture where text tokens don't update through transformer blocks. It employs flow matching with discrete scheduling for efficient sampling and uses Google's T5Gemma-2B-2B-UL2 model for multi-language text encoding. The ~1.3B parameter transformer delivers fast inference without sacrificing quality. You can choose between Flux VAE (8x compression, 16 latent channels) for balanced quality and speed or DC-AE (32x compression, 32 latent channels) for latent compression and faster processing.
-
-## Available models
-
-PRX offers multiple variants with different VAE configurations, each optimized for specific resolutions. Base models excel with detailed prompts, capturing complex compositions and subtle details. Fine-tuned models trained on the [Alchemist dataset](https://huggingface.co/datasets/yandex/alchemist) improve aesthetic quality, especially with simpler prompts.
-
-
-| Model | Resolution | Fine-tuned | Distilled | Description | Suggested prompts | Suggested parameters | Recommended dtype |
-|:-----:|:-----------------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|
-| [`Photoroom/prx-256-t2i`](https://huggingface.co/Photoroom/prx-256-t2i)| 256 | No | No | Base model pre-trained at 256 with Flux VAE|Works best with detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` |
-| [`Photoroom/prx-256-t2i-sft`](https://huggingface.co/Photoroom/prx-256-t2i-sft)| 512 | Yes | No | Fine-tuned on the [Alchemist dataset](https://huggingface.co/datasets/yandex/alchemist) dataset with Flux VAE | Can handle less detailed prompts|28 steps, cfg=5.0| `torch.bfloat16` |
-| [`Photoroom/prx-512-t2i`](https://huggingface.co/Photoroom/prx-512-t2i)| 512 | No | No | Base model pre-trained at 512 with Flux VAE |Works best with detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` |
-| [`Photoroom/prx-512-t2i-sft`](https://huggingface.co/Photoroom/prx-512-t2i-sft)| 512 | Yes | No | Fine-tuned on the [Alchemist dataset](https://huggingface.co/datasets/yandex/alchemist) dataset with Flux VAE | Can handle less detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` |
-| [`Photoroom/prx-512-t2i-sft-distilled`](https://huggingface.co/Photoroom/prx-512-t2i-sft-distilled)| 512 | Yes | Yes | 8-step distilled model from [`Photoroom/prx-512-t2i-sft`](https://huggingface.co/Photoroom/prx-512-t2i-sft) | Can handle less detailed prompts in natural language|8 steps, cfg=1.0| `torch.bfloat16` |
-| [`Photoroom/prx-512-t2i-dc-ae`](https://huggingface.co/Photoroom/prx-512-t2i-dc-ae)| 512 | No | No | Base model pre-trained at 512 with [Deep Compression Autoencoder (DC-AE)](https://hanlab.mit.edu/projects/dc-ae)|Works best with detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` |
-| [`Photoroom/prx-512-t2i-dc-ae-sft`](https://huggingface.co/Photoroom/prx-512-t2i-dc-ae-sft)| 512 | Yes | No | Fine-tuned on the [Alchemist dataset](https://huggingface.co/datasets/yandex/alchemist) dataset with [Deep Compression Autoencoder (DC-AE)](https://hanlab.mit.edu/projects/dc-ae) | Can handle less detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` |
-| [`Photoroom/prx-512-t2i-dc-ae-sft-distilled`](https://huggingface.co/Photoroom/prx-512-t2i-dc-ae-sft-distilled)| 512 | Yes | Yes | 8-step distilled model from [`Photoroom/prx-512-t2i-dc-ae-sft-distilled`](https://huggingface.co/Photoroom/prx-512-t2i-dc-ae-sft-distilled) | Can handle less detailed prompts in natural language|8 steps, cfg=1.0| `torch.bfloat16` |s
-
-Refer to [this](https://huggingface.co/collections/Photoroom/prx-models-68e66254c202ebfab99ad38e) collection for more information.
-
-## Loading the pipeline
-
-Load the pipeline with [`~DiffusionPipeline.from_pretrained`].
-
-```py
-from diffusers.pipelines.prx import PRXPipeline
-
-# Load pipeline - VAE and text encoder will be loaded from HuggingFace
-pipe = PRXPipeline.from_pretrained("Photoroom/prx-512-t2i-sft", torch_dtype=torch.bfloat16)
-pipe.to("cuda")
-
-prompt = "A front-facing portrait of a lion the golden savanna at sunset."
-image = pipe(prompt, num_inference_steps=28, guidance_scale=5.0).images[0]
-image.save("prx_output.png")
-```
-
-### Manual Component Loading
-
-Load components individually to customize the pipeline for instance to use quantized models.
-
-```py
-import torch
-from diffusers.pipelines.prx import PRXPipeline
-from diffusers.models import AutoencoderKL, AutoencoderDC
-from diffusers.models.transformers.transformer_prx import PRXTransformer2DModel
-from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
-from transformers import T5GemmaModel, GemmaTokenizerFast
-from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig
-from transformers import BitsAndBytesConfig as BitsAndBytesConfig
-
-quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
-# Load transformer
-transformer = PRXTransformer2DModel.from_pretrained(
-    "checkpoints/prx-512-t2i-sft",
-    subfolder="transformer",
-    quantization_config=quant_config,
-    torch_dtype=torch.bfloat16,
-)
-
-# Load scheduler
-scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
-    "checkpoints/prx-512-t2i-sft", subfolder="scheduler"
-)
-
-# Load T5Gemma text encoder
-t5gemma_model = T5GemmaModel.from_pretrained("google/t5gemma-2b-2b-ul2",
-                                            quantization_config=quant_config,
-                                            torch_dtype=torch.bfloat16)
-text_encoder = t5gemma_model.encoder.to(dtype=torch.bfloat16)
-tokenizer = GemmaTokenizerFast.from_pretrained("google/t5gemma-2b-2b-ul2")
-tokenizer.model_max_length = 256
-
-# Load VAE - choose either Flux VAE or DC-AE
-# Flux VAE
-vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev",
-                                    subfolder="vae",
-                                    quantization_config=quant_config,
-                                    torch_dtype=torch.bfloat16)
-
-pipe = PRXPipeline(
-    transformer=transformer,
-    scheduler=scheduler,
-    text_encoder=text_encoder,
-    tokenizer=tokenizer,
-    vae=vae
-)
-pipe.to("cuda")
-```
-
-
-## Memory Optimization
-
-For memory-constrained environments:
-
-```py
-import torch
-from diffusers.pipelines.prx import PRXPipeline
-
-pipe = PRXPipeline.from_pretrained("Photoroom/prx-512-t2i-sft", torch_dtype=torch.bfloat16)
-pipe.enable_model_cpu_offload()  # Offload components to CPU when not in use
-
-# Or use sequential CPU offload for even lower memory
-pipe.enable_sequential_cpu_offload()
-```
-
-## PRXPipeline
-
-[[autodoc]] PRXPipeline
-  - all
-  - __call__
-
-## PRXPipelineOutput
-
-[[autodoc]] pipelines.prx.pipeline_output.PRXPipelineOutput

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

@@ -24,6 +24,9 @@ The abstract from the paper is:
 
 *This paper presents SANA-Sprint, an efficient diffusion model for ultra-fast text-to-image (T2I) generation. SANA-Sprint is built on a pre-trained foundation model and augmented with hybrid distillation, dramatically reducing inference steps from 20 to 1-4. We introduce three key innovations: (1) We propose a training-free approach that transforms a pre-trained flow-matching model for continuous-time consistency distillation (sCM), eliminating costly training from scratch and achieving high training efficiency. Our hybrid distillation strategy combines sCM with latent adversarial distillation (LADD): sCM ensures alignment with the teacher model, while LADD enhances single-step generation fidelity. (2) SANA-Sprint is a unified step-adaptive model that achieves high-quality generation in 1-4 steps, eliminating step-specific training and improving efficiency. (3) We integrate ControlNet with SANA-Sprint for real-time interactive image generation, enabling instant visual feedback for user interaction. SANA-Sprint establishes a new Pareto frontier in speed-quality tradeoffs, achieving state-of-the-art performance with 7.59 FID and 0.74 GenEval in only 1 step — outperforming FLUX-schnell (7.94 FID / 0.71 GenEval) while being 10× faster (0.1s vs 1.1s on H100). It also achieves 0.1s (T2I) and 0.25s (ControlNet) latency for 1024×1024 images on H100, and 0.31s (T2I) on an RTX 4090, showcasing its exceptional efficiency and potential for AI-powered consumer applications (AIPC). Code and pre-trained models will be open-sourced.*
 
+> [!TIP]
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+
 This pipeline was contributed by [lawrence-cj](https://github.com/lawrence-cj), [shuchen Xue](https://github.com/scxue) and [Enze Xie](https://github.com/xieenze). The original codebase can be found [here](https://github.com/NVlabs/Sana). The original weights can be found under [hf.co/Efficient-Large-Model](https://huggingface.co/Efficient-Large-Model/).
 
 Available models:

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

@@ -1,102 +0,0 @@
-<!-- Copyright 2025 The SANA-Video Authors and 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. -->
-
-# SanaVideoPipeline
-
-<div class="flex flex-wrap space-x-1">
-  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
-  <img alt="MPS" src="https://img.shields.io/badge/MPS-000000?style=flat&logo=apple&logoColor=white%22">
-</div>
-
-[SANA-Video: Efficient Video Generation with Block Linear Diffusion Transformer](https://huggingface.co/papers/2509.24695) from NVIDIA and MIT HAN Lab, by Junsong Chen, Yuyang Zhao, Jincheng Yu, Ruihang Chu, Junyu Chen, Shuai Yang, Xianbang Wang, Yicheng Pan, Daquan Zhou, Huan Ling, Haozhe Liu, Hongwei Yi, Hao Zhang, Muyang Li, Yukang Chen, Han Cai, Sanja Fidler, Ping Luo, Song Han, Enze Xie.
-
-The abstract from the paper is:
-
-*We introduce SANA-Video, a small diffusion model that can efficiently generate videos up to 720x1280 resolution and minute-length duration. SANA-Video synthesizes high-resolution, high-quality and long videos with strong text-video alignment at a remarkably fast speed, deployable on RTX 5090 GPU. Two core designs ensure our efficient, effective and long video generation: (1) Linear DiT: We leverage linear attention as the core operation, which is more efficient than vanilla attention given the large number of tokens processed in video generation. (2) Constant-Memory KV cache for Block Linear Attention: we design block-wise autoregressive approach for long video generation by employing a constant-memory state, derived from the cumulative properties of linear attention. This KV cache provides the Linear DiT with global context at a fixed memory cost, eliminating the need for a traditional KV cache and enabling efficient, minute-long video generation. In addition, we explore effective data filters and model training strategies, narrowing the training cost to 12 days on 64 H100 GPUs, which is only 1% of the cost of MovieGen. Given its low cost, SANA-Video achieves competitive performance compared to modern state-of-the-art small diffusion models (e.g., Wan 2.1-1.3B and SkyReel-V2-1.3B) while being 16x faster in measured latency. Moreover, SANA-Video can be deployed on RTX 5090 GPUs with NVFP4 precision, accelerating the inference speed of generating a 5-second 720p video from 71s to 29s (2.4x speedup). In summary, SANA-Video enables low-cost, high-quality video generation. [this https URL](https://github.com/NVlabs/SANA).*
-
-This pipeline was contributed by SANA Team. The original codebase can be found [here](https://github.com/NVlabs/Sana). The original weights can be found under [hf.co/Efficient-Large-Model](https://hf.co/collections/Efficient-Large-Model/sana-video).
-
-Available models:
-
-| Model | Recommended dtype |
-|:-----:|:-----------------:|
-| [`Efficient-Large-Model/SANA-Video_2B_480p_diffusers`](https://huggingface.co/Efficient-Large-Model/ANA-Video_2B_480p_diffusers) | `torch.bfloat16` |
-
-Refer to [this](https://huggingface.co/collections/Efficient-Large-Model/sana-video) collection for more information.
-
-Note: The recommended dtype mentioned is for the transformer weights. The text encoder and VAE weights must stay in `torch.bfloat16` or `torch.float32` for the model to work correctly. Please refer to the inference example below to see how to load the model with the recommended dtype. 
-
-## Quantization
-
-Quantization helps reduce the memory requirements of very large models by storing model weights in a lower precision data type. However, quantization may have varying impact on video quality depending on the video model.
-
-Refer to the [Quantization](../../quantization/overview) overview to learn more about supported quantization backends and selecting a quantization backend that supports your use case. The example below demonstrates how to load a quantized [`SanaVideoPipeline`] for inference with bitsandbytes.
-
-```py
-import torch
-from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, SanaVideoTransformer3DModel, SanaVideoPipeline
-from transformers import BitsAndBytesConfig as BitsAndBytesConfig, AutoModel
-
-quant_config = BitsAndBytesConfig(load_in_8bit=True)
-text_encoder_8bit = AutoModel.from_pretrained(
-    "Efficient-Large-Model/SANA-Video_2B_480p_diffusers",
-    subfolder="text_encoder",
-    quantization_config=quant_config,
-    torch_dtype=torch.float16,
-)
-
-quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
-transformer_8bit = SanaVideoTransformer3DModel.from_pretrained(
-    "Efficient-Large-Model/SANA-Video_2B_480p_diffusers",
-    subfolder="transformer",
-    quantization_config=quant_config,
-    torch_dtype=torch.float16,
-)
-
-pipeline = SanaVideoPipeline.from_pretrained(
-    "Efficient-Large-Model/SANA-Video_2B_480p_diffusers",
-    text_encoder=text_encoder_8bit,
-    transformer=transformer_8bit,
-    torch_dtype=torch.float16,
-    device_map="balanced",
-)
-
-model_score = 30
-prompt = "Evening, backlight, side lighting, soft light, high contrast, mid-shot, centered composition, clean solo shot, warm color. A young Caucasian man stands in a forest, golden light glimmers on his hair as sunlight filters through the leaves. He wears a light shirt, wind gently blowing his hair and collar, light dances across his face with his movements. The background is blurred, with dappled light and soft tree shadows in the distance. The camera focuses on his lifted gaze, clear and emotional."
-negative_prompt = "A chaotic sequence with misshapen, deformed limbs in heavy motion blur, sudden disappearance, jump cuts, jerky movements, rapid shot changes, frames out of sync, inconsistent character shapes, temporal artifacts, jitter, and ghosting effects, creating a disorienting visual experience."
-motion_prompt = f" motion score: {model_score}."
-prompt = prompt + motion_prompt
-
-output = pipeline(
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    height=480,
-    width=832,
-    num_frames=81,
-    guidance_scale=6.0,
-    num_inference_steps=50
-).frames[0]
-export_to_video(output, "sana-video-output.mp4", fps=16)
-```
-
-## SanaVideoPipeline
-
-[[autodoc]] SanaVideoPipeline
-  - all
-  - __call__
-
-
-## SanaVideoPipelineOutput
-
-[[autodoc]] pipelines.sana.pipeline_sana_video.SanaVideoPipelineOutput

docs/source/en/modular_diffusers/loop_sequential_pipeline_blocks.md

@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
 
 # LoopSequentialPipelineBlocks
 
-[`~modular_pipelines.LoopSequentialPipelineBlocks`] are a multi-block type that composes other [`~modular_pipelines.ModularPipelineBlocks`] together in a loop. Data flows circularly, using `inputs` and `intermediate_outputs`, and each block is run iteratively. This is typically used to create a denoising loop which is iterative by default.
+[`~modular_pipelines.LoopSequentialPipelineBlocks`] are a multi-block type that composes other [`~modular_pipelines.ModularPipelineBlocks`] together in a loop. Data flows circularly, using `intermediate_inputs` and `intermediate_outputs`, and each block is run iteratively. This is typically used to create a denoising loop which is iterative by default.
 
 This guide shows you how to create [`~modular_pipelines.LoopSequentialPipelineBlocks`].
 
@@ -21,6 +21,7 @@ This guide shows you how to create [`~modular_pipelines.LoopSequentialPipelineBl
 [`~modular_pipelines.LoopSequentialPipelineBlocks`], is also known as the *loop wrapper* because it defines the loop structure, iteration variables, and configuration. Within the loop wrapper, you need the following variables.
 
 - `loop_inputs` are user provided values and equivalent to [`~modular_pipelines.ModularPipelineBlocks.inputs`].
+- `loop_intermediate_inputs` are intermediate variables from the [`~modular_pipelines.PipelineState`] and equivalent to [`~modular_pipelines.ModularPipelineBlocks.intermediate_inputs`].
 - `loop_intermediate_outputs` are new intermediate variables created by the block and added to the [`~modular_pipelines.PipelineState`]. It is equivalent to [`~modular_pipelines.ModularPipelineBlocks.intermediate_outputs`].
 - `__call__` method defines the loop structure and iteration logic.
 
@@ -89,4 +90,4 @@ Add more loop blocks to run within each iteration with [`~modular_pipelines.Loop
 
 ```py
 loop = LoopWrapper.from_blocks_dict({"block1": LoopBlock(), "block2": LoopBlock})
-```
+```

docs/source/en/modular_diffusers/pipeline_block.md

@@ -37,7 +37,17 @@ A [`~modular_pipelines.ModularPipelineBlocks`] requires `inputs`, and `intermedi
     ]
     ```
 
-- `intermediate_outputs` are new values created by a block and added to the [`~modular_pipelines.PipelineState`]. The `intermediate_outputs` are available as `inputs` for subsequent blocks or available as the final output from running the pipeline.
+- `intermediate_inputs` are values typically created from a previous block but it can also be directly provided if no preceding block generates them. Unlike `inputs`, `intermediate_inputs` can be modified.
+
+    Use `InputParam` to define `intermediate_inputs`.
+
+    ```py
+    user_intermediate_inputs = [
+        InputParam(name="processed_image", type_hint="torch.Tensor", description="image that has been preprocessed and normalized"),
+    ]
+    ```
+
+- `intermediate_outputs` are new values created by a block and added to the [`~modular_pipelines.PipelineState`]. The `intermediate_outputs` are available as `intermediate_inputs` for subsequent blocks or available as the final output from running the pipeline.
 
     Use `OutputParam` to define `intermediate_outputs`.
 
@@ -55,8 +65,8 @@ The intermediate inputs and outputs share data to connect blocks. They are acces
 
 The computation a block performs is defined in the `__call__` method and it follows a specific structure.
 
-1. Retrieve the [`~modular_pipelines.BlockState`] to get a local view of the `inputs`
-2. Implement the computation logic on the `inputs`.
+1. Retrieve the [`~modular_pipelines.BlockState`] to get a local view of the `inputs` and `intermediate_inputs`.
+2. Implement the computation logic on the `inputs` and `intermediate_inputs`.
 3. Update [`~modular_pipelines.PipelineState`] to push changes from the local [`~modular_pipelines.BlockState`] back to the global [`~modular_pipelines.PipelineState`].
 4. Return the components and state which becomes available to the next block.
 
@@ -66,7 +76,7 @@ def __call__(self, components, state):
     block_state = self.get_block_state(state)
 
     # Your computation logic here
-    # block_state contains all your inputs
+    # block_state contains all your inputs and intermediate_inputs
     # Access them like: block_state.image, block_state.processed_image
 
     # Update the pipeline state with your updated block_states
@@ -102,4 +112,4 @@ def __call__(self, components, state):
     unet = components.unet
     vae = components.vae
     scheduler = components.scheduler
-```
+```

docs/source/en/modular_diffusers/quickstart.md

@@ -183,7 +183,7 @@ from diffusers.modular_pipelines import ComponentsManager
 components = ComponentManager()
 
 dd_pipeline = dd_blocks.init_pipeline("YiYiXu/modular-demo-auto", components_manager=components, collection="diffdiff")
-dd_pipeline.load_componenets(torch_dtype=torch.float16)
+dd_pipeline.load_default_componenets(torch_dtype=torch.float16)
 dd_pipeline.to("cuda")
 ```
 

docs/source/en/modular_diffusers/sequential_pipeline_blocks.md

@@ -12,11 +12,11 @@ specific language governing permissions and limitations under the License.
 
 # SequentialPipelineBlocks
 
-[`~modular_pipelines.SequentialPipelineBlocks`] are a multi-block type that composes other [`~modular_pipelines.ModularPipelineBlocks`] together in a sequence. Data flows linearly from one block to the next using `inputs` and `intermediate_outputs`. Each block in [`~modular_pipelines.SequentialPipelineBlocks`] usually represents a step in the pipeline, and by combining them, you gradually build a pipeline.
+[`~modular_pipelines.SequentialPipelineBlocks`] are a multi-block type that composes other [`~modular_pipelines.ModularPipelineBlocks`] together in a sequence. Data flows linearly from one block to the next using `intermediate_inputs` and `intermediate_outputs`. Each block in [`~modular_pipelines.SequentialPipelineBlocks`] usually represents a step in the pipeline, and by combining them, you gradually build a pipeline.
 
 This guide shows you how to connect two blocks into a [`~modular_pipelines.SequentialPipelineBlocks`].
 
-Create two [`~modular_pipelines.ModularPipelineBlocks`]. The first block, `InputBlock`, outputs a `batch_size` value and the second block, `ImageEncoderBlock` uses `batch_size` as `inputs`.
+Create two [`~modular_pipelines.ModularPipelineBlocks`]. The first block, `InputBlock`, outputs a `batch_size` value and the second block, `ImageEncoderBlock` uses `batch_size` as `intermediate_inputs`.
 
 <hfoptions id="sequential">
 <hfoption id="InputBlock">
@@ -110,4 +110,4 @@ Inspect the sub-blocks in [`~modular_pipelines.SequentialPipelineBlocks`] by cal
 ```py
 print(blocks)
 print(blocks.doc)
-```
+```

docs/source/en/optimization/attention_backends.md

@@ -21,7 +21,6 @@ Refer to the table below for an overview of the available attention families and
 | attention family | main feature |
 |---|---|
 | FlashAttention | minimizes memory reads/writes through tiling and recomputation |
-| AI Tensor Engine for ROCm | FlashAttention implementation optimized for AMD ROCm accelerators |
 | SageAttention | quantizes attention to int8 |
 | PyTorch native | built-in PyTorch implementation using [scaled_dot_product_attention](./fp16#scaled-dot-product-attention) |
 | xFormers | memory-efficient attention with support for various attention kernels |
@@ -140,7 +139,6 @@ Refer to the table below for a complete list of available attention backends and
 | `_native_xla` | [PyTorch native](https://docs.pytorch.org/docs/stable/generated/torch.nn.attention.SDPBackend.html#torch.nn.attention.SDPBackend) | XLA-optimized attention |
 | `flash` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | FlashAttention-2 |
 | `flash_varlen` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | Variable length FlashAttention |
-| `aiter` | [AI Tensor Engine for ROCm](https://github.com/ROCm/aiter) | FlashAttention for AMD ROCm |
 | `_flash_3` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | FlashAttention-3 |
 | `_flash_varlen_3` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | Variable length FlashAttention-3 |
 | `_flash_3_hub` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | FlashAttention-3 from kernels |

examples/unconditional_image_generation/README.md

@@ -104,8 +104,6 @@ To use your own dataset, there are 2 ways:
 - you can either provide your own folder as `--train_data_dir`
 - or you can upload your dataset to the hub (possibly as a private repo, if you prefer so), and simply pass the `--dataset_name` argument.
 
-If your dataset contains 16 or 32-bit channels (for example, medical TIFFs), add the `--preserve_input_precision` flag so the preprocessing keeps the original precision while still training a 3-channel model. Precision still depends on the decoder: Pillow keeps 16-bit grayscale and float inputs, but many 16-bit RGB files are decoded as 8-bit RGB, and the flag cannot recover precision lost at load time.
-
 Below, we explain both in more detail.
 
 #### Provide the dataset as a folder

examples/unconditional_image_generation/train_unconditional.py

@@ -52,24 +52,6 @@ def _extract_into_tensor(arr, timesteps, broadcast_shape):
     return res.expand(broadcast_shape)
 
 
-def _ensure_three_channels(tensor: torch.Tensor) -> torch.Tensor:
-    """
-    Ensure the tensor has exactly three channels (C, H, W) by repeating or truncating channels when needed.
-    """
-    if tensor.ndim == 2:
-        tensor = tensor.unsqueeze(0)
-    channels = tensor.shape[0]
-    if channels == 3:
-        return tensor
-    if channels == 1:
-        return tensor.repeat(3, 1, 1)
-    if channels == 2:
-        return torch.cat([tensor, tensor[:1]], dim=0)
-    if channels > 3:
-        return tensor[:3]
-    raise ValueError(f"Unsupported number of channels: {channels}")
-
-
 def parse_args():
     parser = argparse.ArgumentParser(description="Simple example of a training script.")
     parser.add_argument(
@@ -278,11 +260,6 @@ def parse_args():
     parser.add_argument(
         "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
     )
-    parser.add_argument(
-        "--preserve_input_precision",
-        action="store_true",
-        help="Preserve 16/32-bit image precision by avoiding 8-bit RGB conversion while still producing 3-channel tensors.",
-    )
 
     args = parser.parse_args()
     env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
@@ -476,41 +453,19 @@ def main(args):
         # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
 
     # Preprocessing the datasets and DataLoaders creation.
-    spatial_augmentations = [
-        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),
-    ]
-
     augmentations = transforms.Compose(
-        spatial_augmentations
-        + [
+        [
+            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(),
             transforms.Normalize([0.5], [0.5]),
         ]
     )
 
-    precision_augmentations = transforms.Compose(
-        [
-            transforms.PILToTensor(),
-            transforms.Lambda(_ensure_three_channels),
-            transforms.ConvertImageDtype(torch.float32),
-        ]
-        + spatial_augmentations
-        + [transforms.Normalize([0.5], [0.5])]
-    )
-
     def transform_images(examples):
-        processed = []
-        for image in examples["image"]:
-            if not args.preserve_input_precision:
-                processed.append(augmentations(image.convert("RGB")))
-            else:
-                precise_image = image
-                if precise_image.mode == "P":
-                    precise_image = precise_image.convert("RGB")
-                processed.append(precision_augmentations(precise_image))
-        return {"input": processed}
+        images = [augmentations(image.convert("RGB")) for image in examples["image"]]
+        return {"input": images}
 
     logger.info(f"Dataset size: {len(dataset)}")
 

scripts/convert_prx_to_diffusers.py

@@ -1,345 +0,0 @@
-#!/usr/bin/env python3
-"""
-Script to convert PRX checkpoint from original codebase to diffusers format.
-"""
-
-import argparse
-import json
-import os
-import sys
-from dataclasses import asdict, dataclass
-from typing import Dict, Tuple
-
-import torch
-from safetensors.torch import save_file
-
-from diffusers.models.transformers.transformer_prx import PRXTransformer2DModel
-from diffusers.pipelines.prx import PRXPipeline
-
-
-DEFAULT_RESOLUTION = 512
-
-
-@dataclass(frozen=True)
-class PRXBase:
-    context_in_dim: int = 2304
-    hidden_size: int = 1792
-    mlp_ratio: float = 3.5
-    num_heads: int = 28
-    depth: int = 16
-    axes_dim: Tuple[int, int] = (32, 32)
-    theta: int = 10_000
-    time_factor: float = 1000.0
-    time_max_period: int = 10_000
-
-
-@dataclass(frozen=True)
-class PRXFlux(PRXBase):
-    in_channels: int = 16
-    patch_size: int = 2
-
-
-@dataclass(frozen=True)
-class PRXDCAE(PRXBase):
-    in_channels: int = 32
-    patch_size: int = 1
-
-
-def build_config(vae_type: str) -> Tuple[dict, int]:
-    if vae_type == "flux":
-        cfg = PRXFlux()
-    elif vae_type == "dc-ae":
-        cfg = PRXDCAE()
-    else:
-        raise ValueError(f"Unsupported VAE type: {vae_type}. Use 'flux' or 'dc-ae'")
-
-    config_dict = asdict(cfg)
-    config_dict["axes_dim"] = list(config_dict["axes_dim"])  # type: ignore[index]
-    return config_dict
-
-
-def create_parameter_mapping(depth: int) -> dict:
-    """Create mapping from old parameter names to new diffusers names."""
-
-    # Key mappings for structural changes
-    mapping = {}
-
-    # Map old structure (layers in PRXBlock) to new structure (layers in PRXAttention)
-    for i in range(depth):
-        # QKV projections moved to attention module
-        mapping[f"blocks.{i}.img_qkv_proj.weight"] = f"blocks.{i}.attention.img_qkv_proj.weight"
-        mapping[f"blocks.{i}.txt_kv_proj.weight"] = f"blocks.{i}.attention.txt_kv_proj.weight"
-
-        # QK norm moved to attention module and renamed to match Attention's qk_norm structure
-        mapping[f"blocks.{i}.qk_norm.query_norm.scale"] = f"blocks.{i}.attention.norm_q.weight"
-        mapping[f"blocks.{i}.qk_norm.key_norm.scale"] = f"blocks.{i}.attention.norm_k.weight"
-        mapping[f"blocks.{i}.qk_norm.query_norm.weight"] = f"blocks.{i}.attention.norm_q.weight"
-        mapping[f"blocks.{i}.qk_norm.key_norm.weight"] = f"blocks.{i}.attention.norm_k.weight"
-
-        # K norm for text tokens moved to attention module
-        mapping[f"blocks.{i}.k_norm.scale"] = f"blocks.{i}.attention.norm_added_k.weight"
-        mapping[f"blocks.{i}.k_norm.weight"] = f"blocks.{i}.attention.norm_added_k.weight"
-
-        # Attention output projection
-        mapping[f"blocks.{i}.attn_out.weight"] = f"blocks.{i}.attention.to_out.0.weight"
-
-    return mapping
-
-
-def convert_checkpoint_parameters(old_state_dict: Dict[str, torch.Tensor], depth: int) -> Dict[str, torch.Tensor]:
-    """Convert old checkpoint parameters to new diffusers format."""
-
-    print("Converting checkpoint parameters...")
-
-    mapping = create_parameter_mapping(depth)
-    converted_state_dict = {}
-
-    for key, value in old_state_dict.items():
-        new_key = key
-
-        # Apply specific mappings if needed
-        if key in mapping:
-            new_key = mapping[key]
-            print(f"  Mapped: {key} -> {new_key}")
-
-        converted_state_dict[new_key] = value
-
-    print(f"✓ Converted {len(converted_state_dict)} parameters")
-    return converted_state_dict
-
-
-def create_transformer_from_checkpoint(checkpoint_path: str, config: dict) -> PRXTransformer2DModel:
-    """Create and load PRXTransformer2DModel from old checkpoint."""
-
-    print(f"Loading checkpoint from: {checkpoint_path}")
-
-    # Load old checkpoint
-    if not os.path.exists(checkpoint_path):
-        raise FileNotFoundError(f"Checkpoint not found: {checkpoint_path}")
-
-    old_checkpoint = torch.load(checkpoint_path, map_location="cpu")
-
-    # Handle different checkpoint formats
-    if isinstance(old_checkpoint, dict):
-        if "model" in old_checkpoint:
-            state_dict = old_checkpoint["model"]
-        elif "state_dict" in old_checkpoint:
-            state_dict = old_checkpoint["state_dict"]
-        else:
-            state_dict = old_checkpoint
-    else:
-        state_dict = old_checkpoint
-
-    print(f"✓ Loaded checkpoint with {len(state_dict)} parameters")
-
-    # Convert parameter names if needed
-    model_depth = int(config.get("depth", 16))
-    converted_state_dict = convert_checkpoint_parameters(state_dict, depth=model_depth)
-
-    # Create transformer with config
-    print("Creating PRXTransformer2DModel...")
-    transformer = PRXTransformer2DModel(**config)
-
-    # Load state dict
-    print("Loading converted parameters...")
-    missing_keys, unexpected_keys = transformer.load_state_dict(converted_state_dict, strict=False)
-
-    if missing_keys:
-        print(f"⚠ Missing keys: {missing_keys}")
-    if unexpected_keys:
-        print(f"⚠ Unexpected keys: {unexpected_keys}")
-
-    if not missing_keys and not unexpected_keys:
-        print("✓ All parameters loaded successfully!")
-
-    return transformer
-
-
-def create_scheduler_config(output_path: str, shift: float):
-    """Create FlowMatchEulerDiscreteScheduler config."""
-
-    scheduler_config = {"_class_name": "FlowMatchEulerDiscreteScheduler", "num_train_timesteps": 1000, "shift": shift}
-
-    scheduler_path = os.path.join(output_path, "scheduler")
-    os.makedirs(scheduler_path, exist_ok=True)
-
-    with open(os.path.join(scheduler_path, "scheduler_config.json"), "w") as f:
-        json.dump(scheduler_config, f, indent=2)
-
-    print("✓ Created scheduler config")
-
-
-def download_and_save_vae(vae_type: str, output_path: str):
-    """Download and save VAE to local directory."""
-    from diffusers import AutoencoderDC, AutoencoderKL
-
-    vae_path = os.path.join(output_path, "vae")
-    os.makedirs(vae_path, exist_ok=True)
-
-    if vae_type == "flux":
-        print("Downloading FLUX VAE from black-forest-labs/FLUX.1-dev...")
-        vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae")
-    else:  # dc-ae
-        print("Downloading DC-AE VAE from mit-han-lab/dc-ae-f32c32-sana-1.1-diffusers...")
-        vae = AutoencoderDC.from_pretrained("mit-han-lab/dc-ae-f32c32-sana-1.1-diffusers")
-
-    vae.save_pretrained(vae_path)
-    print(f"✓ Saved VAE to {vae_path}")
-
-
-def download_and_save_text_encoder(output_path: str):
-    """Download and save T5Gemma text encoder and tokenizer."""
-    from transformers import GemmaTokenizerFast
-    from transformers.models.t5gemma.modeling_t5gemma import T5GemmaModel
-
-    text_encoder_path = os.path.join(output_path, "text_encoder")
-    tokenizer_path = os.path.join(output_path, "tokenizer")
-    os.makedirs(text_encoder_path, exist_ok=True)
-    os.makedirs(tokenizer_path, exist_ok=True)
-
-    print("Downloading T5Gemma model from google/t5gemma-2b-2b-ul2...")
-    t5gemma_model = T5GemmaModel.from_pretrained("google/t5gemma-2b-2b-ul2")
-
-    # Extract and save only the encoder
-    t5gemma_encoder = t5gemma_model.encoder
-    t5gemma_encoder.save_pretrained(text_encoder_path)
-    print(f"✓ Saved T5GemmaEncoder to {text_encoder_path}")
-
-    print("Downloading tokenizer from google/t5gemma-2b-2b-ul2...")
-    tokenizer = GemmaTokenizerFast.from_pretrained("google/t5gemma-2b-2b-ul2")
-    tokenizer.model_max_length = 256
-    tokenizer.save_pretrained(tokenizer_path)
-    print(f"✓ Saved tokenizer to {tokenizer_path}")
-
-
-def create_model_index(vae_type: str, default_image_size: int, output_path: str):
-    """Create model_index.json for the pipeline."""
-
-    if vae_type == "flux":
-        vae_class = "AutoencoderKL"
-    else:  # dc-ae
-        vae_class = "AutoencoderDC"
-
-    model_index = {
-        "_class_name": "PRXPipeline",
-        "_diffusers_version": "0.31.0.dev0",
-        "_name_or_path": os.path.basename(output_path),
-        "default_sample_size": default_image_size,
-        "scheduler": ["diffusers", "FlowMatchEulerDiscreteScheduler"],
-        "text_encoder": ["prx", "T5GemmaEncoder"],
-        "tokenizer": ["transformers", "GemmaTokenizerFast"],
-        "transformer": ["diffusers", "PRXTransformer2DModel"],
-        "vae": ["diffusers", vae_class],
-    }
-
-    model_index_path = os.path.join(output_path, "model_index.json")
-    with open(model_index_path, "w") as f:
-        json.dump(model_index, f, indent=2)
-
-
-def main(args):
-    # Validate inputs
-    if not os.path.exists(args.checkpoint_path):
-        raise FileNotFoundError(f"Checkpoint not found: {args.checkpoint_path}")
-
-    config = build_config(args.vae_type)
-
-    # Create output directory
-    os.makedirs(args.output_path, exist_ok=True)
-    print(f"✓ Output directory: {args.output_path}")
-
-    # Create transformer from checkpoint
-    transformer = create_transformer_from_checkpoint(args.checkpoint_path, config)
-
-    # Save transformer
-    transformer_path = os.path.join(args.output_path, "transformer")
-    os.makedirs(transformer_path, exist_ok=True)
-
-    # Save config
-    with open(os.path.join(transformer_path, "config.json"), "w") as f:
-        json.dump(config, f, indent=2)
-
-    # Save model weights as safetensors
-    state_dict = transformer.state_dict()
-    save_file(state_dict, os.path.join(transformer_path, "diffusion_pytorch_model.safetensors"))
-    print(f"✓ Saved transformer to {transformer_path}")
-
-    # Create scheduler config
-    create_scheduler_config(args.output_path, args.shift)
-
-    download_and_save_vae(args.vae_type, args.output_path)
-    download_and_save_text_encoder(args.output_path)
-
-    # Create model_index.json
-    create_model_index(args.vae_type, args.resolution, args.output_path)
-
-    # Verify the pipeline can be loaded
-    try:
-        pipeline = PRXPipeline.from_pretrained(args.output_path)
-        print("Pipeline loaded successfully!")
-        print(f"Transformer: {type(pipeline.transformer).__name__}")
-        print(f"VAE: {type(pipeline.vae).__name__}")
-        print(f"Text Encoder: {type(pipeline.text_encoder).__name__}")
-        print(f"Scheduler: {type(pipeline.scheduler).__name__}")
-
-        # Display model info
-        num_params = sum(p.numel() for p in pipeline.transformer.parameters())
-        print(f"✓ Transformer parameters: {num_params:,}")
-
-    except Exception as e:
-        print(f"Pipeline verification failed: {e}")
-        return False
-
-    print("Conversion completed successfully!")
-    print(f"Converted pipeline saved to: {args.output_path}")
-    print(f"VAE type: {args.vae_type}")
-
-    return True
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Convert PRX checkpoint to diffusers format")
-
-    parser.add_argument(
-        "--checkpoint_path", type=str, required=True, help="Path to the original PRX checkpoint (.pth file )"
-    )
-
-    parser.add_argument(
-        "--output_path", type=str, required=True, help="Output directory for the converted diffusers pipeline"
-    )
-
-    parser.add_argument(
-        "--vae_type",
-        type=str,
-        choices=["flux", "dc-ae"],
-        required=True,
-        help="VAE type to use: 'flux' for AutoencoderKL (16 channels) or 'dc-ae' for AutoencoderDC (32 channels)",
-    )
-
-    parser.add_argument(
-        "--resolution",
-        type=int,
-        choices=[256, 512, 1024],
-        default=DEFAULT_RESOLUTION,
-        help="Target resolution for the model (256, 512, or 1024). Affects the transformer's sample_size.",
-    )
-
-    parser.add_argument(
-        "--shift",
-        type=float,
-        default=3.0,
-        help="Shift for the scheduler",
-    )
-
-    args = parser.parse_args()
-
-    try:
-        success = main(args)
-        if not success:
-            sys.exit(1)
-    except Exception as e:
-        print(f"Conversion failed: {e}")
-        import traceback
-
-        traceback.print_exc()
-        sys.exit(1)

scripts/convert_sana_video_to_diffusers.py

@@ -1,324 +0,0 @@
-#!/usr/bin/env python
-from __future__ import annotations
-
-import argparse
-import os
-from contextlib import nullcontext
-
-import torch
-from accelerate import init_empty_weights
-from huggingface_hub import hf_hub_download, snapshot_download
-from termcolor import colored
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-from diffusers import (
-    AutoencoderKLWan,
-    DPMSolverMultistepScheduler,
-    FlowMatchEulerDiscreteScheduler,
-    SanaVideoPipeline,
-    SanaVideoTransformer3DModel,
-    UniPCMultistepScheduler,
-)
-from diffusers.utils.import_utils import is_accelerate_available
-
-
-CTX = init_empty_weights if is_accelerate_available else nullcontext
-
-ckpt_ids = ["Efficient-Large-Model/SANA-Video_2B_480p/checkpoints/SANA_Video_2B_480p.pth"]
-# https://github.com/NVlabs/Sana/blob/main/inference_video_scripts/inference_sana_video.py
-
-
-def main(args):
-    cache_dir_path = os.path.expanduser("~/.cache/huggingface/hub")
-
-    if args.orig_ckpt_path is None or args.orig_ckpt_path in ckpt_ids:
-        ckpt_id = args.orig_ckpt_path or ckpt_ids[0]
-        snapshot_download(
-            repo_id=f"{'/'.join(ckpt_id.split('/')[:2])}",
-            cache_dir=cache_dir_path,
-            repo_type="model",
-        )
-        file_path = hf_hub_download(
-            repo_id=f"{'/'.join(ckpt_id.split('/')[:2])}",
-            filename=f"{'/'.join(ckpt_id.split('/')[2:])}",
-            cache_dir=cache_dir_path,
-            repo_type="model",
-        )
-    else:
-        file_path = args.orig_ckpt_path
-
-    print(colored(f"Loading checkpoint from {file_path}", "green", attrs=["bold"]))
-    all_state_dict = torch.load(file_path, weights_only=True)
-    state_dict = all_state_dict.pop("state_dict")
-    converted_state_dict = {}
-
-    # Patch embeddings.
-    converted_state_dict["patch_embedding.weight"] = state_dict.pop("x_embedder.proj.weight")
-    converted_state_dict["patch_embedding.bias"] = state_dict.pop("x_embedder.proj.bias")
-
-    # Caption projection.
-    converted_state_dict["caption_projection.linear_1.weight"] = state_dict.pop("y_embedder.y_proj.fc1.weight")
-    converted_state_dict["caption_projection.linear_1.bias"] = state_dict.pop("y_embedder.y_proj.fc1.bias")
-    converted_state_dict["caption_projection.linear_2.weight"] = state_dict.pop("y_embedder.y_proj.fc2.weight")
-    converted_state_dict["caption_projection.linear_2.bias"] = state_dict.pop("y_embedder.y_proj.fc2.bias")
-
-    converted_state_dict["time_embed.emb.timestep_embedder.linear_1.weight"] = state_dict.pop(
-        "t_embedder.mlp.0.weight"
-    )
-    converted_state_dict["time_embed.emb.timestep_embedder.linear_1.bias"] = state_dict.pop("t_embedder.mlp.0.bias")
-    converted_state_dict["time_embed.emb.timestep_embedder.linear_2.weight"] = state_dict.pop(
-        "t_embedder.mlp.2.weight"
-    )
-    converted_state_dict["time_embed.emb.timestep_embedder.linear_2.bias"] = state_dict.pop("t_embedder.mlp.2.bias")
-
-    # Shared norm.
-    converted_state_dict["time_embed.linear.weight"] = state_dict.pop("t_block.1.weight")
-    converted_state_dict["time_embed.linear.bias"] = state_dict.pop("t_block.1.bias")
-
-    # y norm
-    converted_state_dict["caption_norm.weight"] = state_dict.pop("attention_y_norm.weight")
-
-    # scheduler
-    flow_shift = 8.0
-
-    # model config
-    layer_num = 20
-    # Positional embedding interpolation scale.
-    qk_norm = True
-
-    # sample size
-    if args.video_size == 480:
-        sample_size = 30  # Wan-VAE: 8xp2 downsample factor
-        patch_size = (1, 2, 2)
-    elif args.video_size == 720:
-        sample_size = 22  # Wan-VAE: 32xp1 downsample factor
-        patch_size = (1, 1, 1)
-    else:
-        raise ValueError(f"Video size {args.video_size} is not supported.")
-
-    for depth in range(layer_num):
-        # Transformer blocks.
-        converted_state_dict[f"transformer_blocks.{depth}.scale_shift_table"] = state_dict.pop(
-            f"blocks.{depth}.scale_shift_table"
-        )
-
-        # Linear Attention is all you need 🤘
-        # Self attention.
-        q, k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.attn.qkv.weight"), 3, dim=0)
-        converted_state_dict[f"transformer_blocks.{depth}.attn1.to_q.weight"] = q
-        converted_state_dict[f"transformer_blocks.{depth}.attn1.to_k.weight"] = k
-        converted_state_dict[f"transformer_blocks.{depth}.attn1.to_v.weight"] = v
-        if qk_norm is not None:
-            # Add Q/K normalization for self-attention (attn1) - needed for Sana-Sprint and Sana-1.5
-            converted_state_dict[f"transformer_blocks.{depth}.attn1.norm_q.weight"] = state_dict.pop(
-                f"blocks.{depth}.attn.q_norm.weight"
-            )
-            converted_state_dict[f"transformer_blocks.{depth}.attn1.norm_k.weight"] = state_dict.pop(
-                f"blocks.{depth}.attn.k_norm.weight"
-            )
-        # Projection.
-        converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.weight"] = state_dict.pop(
-            f"blocks.{depth}.attn.proj.weight"
-        )
-        converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.bias"] = state_dict.pop(
-            f"blocks.{depth}.attn.proj.bias"
-        )
-
-        # Feed-forward.
-        converted_state_dict[f"transformer_blocks.{depth}.ff.conv_inverted.weight"] = state_dict.pop(
-            f"blocks.{depth}.mlp.inverted_conv.conv.weight"
-        )
-        converted_state_dict[f"transformer_blocks.{depth}.ff.conv_inverted.bias"] = state_dict.pop(
-            f"blocks.{depth}.mlp.inverted_conv.conv.bias"
-        )
-        converted_state_dict[f"transformer_blocks.{depth}.ff.conv_depth.weight"] = state_dict.pop(
-            f"blocks.{depth}.mlp.depth_conv.conv.weight"
-        )
-        converted_state_dict[f"transformer_blocks.{depth}.ff.conv_depth.bias"] = state_dict.pop(
-            f"blocks.{depth}.mlp.depth_conv.conv.bias"
-        )
-        converted_state_dict[f"transformer_blocks.{depth}.ff.conv_point.weight"] = state_dict.pop(
-            f"blocks.{depth}.mlp.point_conv.conv.weight"
-        )
-        converted_state_dict[f"transformer_blocks.{depth}.ff.conv_temp.weight"] = state_dict.pop(
-            f"blocks.{depth}.mlp.t_conv.weight"
-        )
-
-        # Cross-attention.
-        q = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.weight")
-        q_bias = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.bias")
-        k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.cross_attn.kv_linear.weight"), 2, dim=0)
-        k_bias, v_bias = torch.chunk(state_dict.pop(f"blocks.{depth}.cross_attn.kv_linear.bias"), 2, dim=0)
-
-        converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.weight"] = q
-        converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.bias"] = q_bias
-        converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.weight"] = k
-        converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.bias"] = k_bias
-        converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.weight"] = v
-        converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.bias"] = v_bias
-        if qk_norm is not None:
-            # Add Q/K normalization for cross-attention (attn2) - needed for Sana-Sprint and Sana-1.5
-            converted_state_dict[f"transformer_blocks.{depth}.attn2.norm_q.weight"] = state_dict.pop(
-                f"blocks.{depth}.cross_attn.q_norm.weight"
-            )
-            converted_state_dict[f"transformer_blocks.{depth}.attn2.norm_k.weight"] = state_dict.pop(
-                f"blocks.{depth}.cross_attn.k_norm.weight"
-            )
-
-        converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.weight"] = state_dict.pop(
-            f"blocks.{depth}.cross_attn.proj.weight"
-        )
-        converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.bias"] = state_dict.pop(
-            f"blocks.{depth}.cross_attn.proj.bias"
-        )
-
-    # Final block.
-    converted_state_dict["proj_out.weight"] = state_dict.pop("final_layer.linear.weight")
-    converted_state_dict["proj_out.bias"] = state_dict.pop("final_layer.linear.bias")
-    converted_state_dict["scale_shift_table"] = state_dict.pop("final_layer.scale_shift_table")
-
-    # Transformer
-    with CTX():
-        transformer_kwargs = {
-            "in_channels": 16,
-            "out_channels": 16,
-            "num_attention_heads": 20,
-            "attention_head_dim": 112,
-            "num_layers": 20,
-            "num_cross_attention_heads": 20,
-            "cross_attention_head_dim": 112,
-            "cross_attention_dim": 2240,
-            "caption_channels": 2304,
-            "mlp_ratio": 3.0,
-            "attention_bias": False,
-            "sample_size": sample_size,
-            "patch_size": patch_size,
-            "norm_elementwise_affine": False,
-            "norm_eps": 1e-6,
-            "qk_norm": "rms_norm_across_heads",
-            "rope_max_seq_len": 1024,
-        }
-
-        transformer = SanaVideoTransformer3DModel(**transformer_kwargs)
-
-    transformer.load_state_dict(converted_state_dict, strict=True, assign=True)
-
-    try:
-        state_dict.pop("y_embedder.y_embedding")
-        state_dict.pop("pos_embed")
-        state_dict.pop("logvar_linear.weight")
-        state_dict.pop("logvar_linear.bias")
-    except KeyError:
-        print("y_embedder.y_embedding or pos_embed not found in the state_dict")
-
-    assert len(state_dict) == 0, f"State dict is not empty, {state_dict.keys()}"
-
-    num_model_params = sum(p.numel() for p in transformer.parameters())
-    print(f"Total number of transformer parameters: {num_model_params}")
-
-    transformer = transformer.to(weight_dtype)
-
-    if not args.save_full_pipeline:
-        print(
-            colored(
-                f"Only saving transformer model of {args.model_type}. "
-                f"Set --save_full_pipeline to save the whole Pipeline",
-                "green",
-                attrs=["bold"],
-            )
-        )
-        transformer.save_pretrained(
-            os.path.join(args.dump_path, "transformer"), safe_serialization=True, max_shard_size="5GB"
-        )
-    else:
-        print(colored(f"Saving the whole Pipeline containing {args.model_type}", "green", attrs=["bold"]))
-        # VAE
-        vae = AutoencoderKLWan.from_pretrained(
-            "Wan-AI/Wan2.1-T2V-1.3B-Diffusers", subfolder="vae", torch_dtype=torch.float32
-        )
-
-        # Text Encoder
-        text_encoder_model_path = "Efficient-Large-Model/gemma-2-2b-it"
-        tokenizer = AutoTokenizer.from_pretrained(text_encoder_model_path)
-        tokenizer.padding_side = "right"
-        text_encoder = AutoModelForCausalLM.from_pretrained(
-            text_encoder_model_path, torch_dtype=torch.bfloat16
-        ).get_decoder()
-
-        # Choose the appropriate pipeline and scheduler based on model type
-        # Original Sana scheduler
-        if args.scheduler_type == "flow-dpm_solver":
-            scheduler = DPMSolverMultistepScheduler(
-                flow_shift=flow_shift,
-                use_flow_sigmas=True,
-                prediction_type="flow_prediction",
-            )
-        elif args.scheduler_type == "flow-euler":
-            scheduler = FlowMatchEulerDiscreteScheduler(shift=flow_shift)
-        elif args.scheduler_type == "uni-pc":
-            scheduler = UniPCMultistepScheduler(
-                prediction_type="flow_prediction",
-                use_flow_sigmas=True,
-                num_train_timesteps=1000,
-                flow_shift=flow_shift,
-            )
-        else:
-            raise ValueError(f"Scheduler type {args.scheduler_type} is not supported")
-
-        pipe = SanaVideoPipeline(
-            tokenizer=tokenizer,
-            text_encoder=text_encoder,
-            transformer=transformer,
-            vae=vae,
-            scheduler=scheduler,
-        )
-
-        pipe.save_pretrained(args.dump_path, safe_serialization=True, max_shard_size="5GB")
-
-
-DTYPE_MAPPING = {
-    "fp32": torch.float32,
-    "fp16": torch.float16,
-    "bf16": torch.bfloat16,
-}
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--orig_ckpt_path", default=None, type=str, required=False, help="Path to the checkpoint to convert."
-    )
-    parser.add_argument(
-        "--video_size",
-        default=480,
-        type=int,
-        choices=[480, 720],
-        required=False,
-        help="Video size of pretrained model, 480 or 720.",
-    )
-    parser.add_argument(
-        "--model_type",
-        default="SanaVideo",
-        type=str,
-        choices=[
-            "SanaVideo",
-        ],
-    )
-    parser.add_argument(
-        "--scheduler_type",
-        default="flow-dpm_solver",
-        type=str,
-        choices=["flow-dpm_solver", "flow-euler", "uni-pc"],
-        help="Scheduler type to use.",
-    )
-    parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output pipeline.")
-    parser.add_argument("--save_full_pipeline", action="store_true", help="save all the pipeline elements in one.")
-    parser.add_argument("--dtype", default="fp32", type=str, choices=["fp32", "fp16", "bf16"], help="Weight dtype.")
-
-    args = parser.parse_args()
-
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    weight_dtype = DTYPE_MAPPING[args.dtype]
-
-    main(args)

src/diffusers/__init__.py

@@ -149,9 +149,7 @@ else:
     _import_structure["guiders"].extend(
         [
             "AdaptiveProjectedGuidance",
-            "AdaptiveProjectedMixGuidance",
             "AutoGuidance",
-            "BaseGuidance",
             "ClassifierFreeGuidance",
             "ClassifierFreeZeroStarGuidance",
             "FrequencyDecoupledGuidance",
@@ -186,8 +184,6 @@ else:
             "AutoencoderKLAllegro",
             "AutoencoderKLCogVideoX",
             "AutoencoderKLCosmos",
-            "AutoencoderKLHunyuanImage",
-            "AutoencoderKLHunyuanImageRefiner",
             "AutoencoderKLHunyuanVideo",
             "AutoencoderKLLTXVideo",
             "AutoencoderKLMagvit",
@@ -198,7 +194,6 @@ else:
             "AutoencoderOobleck",
             "AutoencoderTiny",
             "AutoModel",
-            "BriaFiboTransformer2DModel",
             "BriaTransformer2DModel",
             "CacheMixin",
             "ChromaTransformer2DModel",
@@ -221,7 +216,6 @@ else:
             "HunyuanDiT2DControlNetModel",
             "HunyuanDiT2DModel",
             "HunyuanDiT2DMultiControlNetModel",
-            "HunyuanImageTransformer2DModel",
             "HunyuanVideoFramepackTransformer3DModel",
             "HunyuanVideoTransformer3DModel",
             "I2VGenXLUNet",
@@ -240,13 +234,11 @@ else:
             "ParallelConfig",
             "PixArtTransformer2DModel",
             "PriorTransformer",
-            "PRXTransformer2DModel",
             "QwenImageControlNetModel",
             "QwenImageMultiControlNetModel",
             "QwenImageTransformer2DModel",
             "SanaControlNetModel",
             "SanaTransformer2DModel",
-            "SanaVideoTransformer3DModel",
             "SD3ControlNetModel",
             "SD3MultiControlNetModel",
             "SD3Transformer2DModel",
@@ -432,7 +424,6 @@ else:
             "AuraFlowPipeline",
             "BlipDiffusionControlNetPipeline",
             "BlipDiffusionPipeline",
-            "BriaFiboPipeline",
             "BriaPipeline",
             "ChromaImg2ImgPipeline",
             "ChromaPipeline",
@@ -470,8 +461,6 @@ else:
             "HunyuanDiTControlNetPipeline",
             "HunyuanDiTPAGPipeline",
             "HunyuanDiTPipeline",
-            "HunyuanImagePipeline",
-            "HunyuanImageRefinerPipeline",
             "HunyuanSkyreelsImageToVideoPipeline",
             "HunyuanVideoFramepackPipeline",
             "HunyuanVideoImageToVideoPipeline",
@@ -530,7 +519,6 @@ else:
             "PixArtAlphaPipeline",
             "PixArtSigmaPAGPipeline",
             "PixArtSigmaPipeline",
-            "PRXPipeline",
             "QwenImageControlNetInpaintPipeline",
             "QwenImageControlNetPipeline",
             "QwenImageEditInpaintPipeline",
@@ -545,7 +533,6 @@ else:
             "SanaPipeline",
             "SanaSprintImg2ImgPipeline",
             "SanaSprintPipeline",
-            "SanaVideoPipeline",
             "SemanticStableDiffusionPipeline",
             "ShapEImg2ImgPipeline",
             "ShapEPipeline",
@@ -860,9 +847,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     else:
         from .guiders import (
             AdaptiveProjectedGuidance,
-            AdaptiveProjectedMixGuidance,
             AutoGuidance,
-            BaseGuidance,
             ClassifierFreeGuidance,
             ClassifierFreeZeroStarGuidance,
             FrequencyDecoupledGuidance,
@@ -893,8 +878,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             AutoencoderKLAllegro,
             AutoencoderKLCogVideoX,
             AutoencoderKLCosmos,
-            AutoencoderKLHunyuanImage,
-            AutoencoderKLHunyuanImageRefiner,
             AutoencoderKLHunyuanVideo,
             AutoencoderKLLTXVideo,
             AutoencoderKLMagvit,
@@ -905,7 +888,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             AutoencoderOobleck,
             AutoencoderTiny,
             AutoModel,
-            BriaFiboTransformer2DModel,
             BriaTransformer2DModel,
             CacheMixin,
             ChromaTransformer2DModel,
@@ -928,7 +910,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             HunyuanDiT2DControlNetModel,
             HunyuanDiT2DModel,
             HunyuanDiT2DMultiControlNetModel,
-            HunyuanImageTransformer2DModel,
             HunyuanVideoFramepackTransformer3DModel,
             HunyuanVideoTransformer3DModel,
             I2VGenXLUNet,
@@ -947,13 +928,11 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             ParallelConfig,
             PixArtTransformer2DModel,
             PriorTransformer,
-            PRXTransformer2DModel,
             QwenImageControlNetModel,
             QwenImageMultiControlNetModel,
             QwenImageTransformer2DModel,
             SanaControlNetModel,
             SanaTransformer2DModel,
-            SanaVideoTransformer3DModel,
             SD3ControlNetModel,
             SD3MultiControlNetModel,
             SD3Transformer2DModel,
@@ -1109,7 +1088,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             AudioLDM2UNet2DConditionModel,
             AudioLDMPipeline,
             AuraFlowPipeline,
-            BriaFiboPipeline,
             BriaPipeline,
             ChromaImg2ImgPipeline,
             ChromaPipeline,
@@ -1147,8 +1125,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             HunyuanDiTControlNetPipeline,
             HunyuanDiTPAGPipeline,
             HunyuanDiTPipeline,
-            HunyuanImagePipeline,
-            HunyuanImageRefinerPipeline,
             HunyuanSkyreelsImageToVideoPipeline,
             HunyuanVideoFramepackPipeline,
             HunyuanVideoImageToVideoPipeline,
@@ -1207,7 +1183,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             PixArtAlphaPipeline,
             PixArtSigmaPAGPipeline,
             PixArtSigmaPipeline,
-            PRXPipeline,
             QwenImageControlNetInpaintPipeline,
             QwenImageControlNetPipeline,
             QwenImageEditInpaintPipeline,
@@ -1222,7 +1197,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             SanaPipeline,
             SanaSprintImg2ImgPipeline,
             SanaSprintPipeline,
-            SanaVideoPipeline,
             SemanticStableDiffusionPipeline,
             ShapEImg2ImgPipeline,
             ShapEPipeline,

src/diffusers/guiders/__init__.py

@@ -14,18 +14,28 @@
 
 from typing import Union
 
-from ..utils import is_torch_available, logging
+from ..utils import is_torch_available
 
 
 if is_torch_available():
     from .adaptive_projected_guidance import AdaptiveProjectedGuidance
-    from .adaptive_projected_guidance_mix import AdaptiveProjectedMixGuidance
     from .auto_guidance import AutoGuidance
     from .classifier_free_guidance import ClassifierFreeGuidance
     from .classifier_free_zero_star_guidance import ClassifierFreeZeroStarGuidance
     from .frequency_decoupled_guidance import FrequencyDecoupledGuidance
-    from .guider_utils import BaseGuidance
     from .perturbed_attention_guidance import PerturbedAttentionGuidance
     from .skip_layer_guidance import SkipLayerGuidance
     from .smoothed_energy_guidance import SmoothedEnergyGuidance
     from .tangential_classifier_free_guidance import TangentialClassifierFreeGuidance
+
+    GuiderType = Union[
+        AdaptiveProjectedGuidance,
+        AutoGuidance,
+        ClassifierFreeGuidance,
+        ClassifierFreeZeroStarGuidance,
+        FrequencyDecoupledGuidance,
+        PerturbedAttentionGuidance,
+        SkipLayerGuidance,
+        SmoothedEnergyGuidance,
+        TangentialClassifierFreeGuidance,
+    ]

src/diffusers/guiders/adaptive_projected_guidance.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import math
-from typing import TYPE_CHECKING, Dict, List, Optional, Tuple
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
 
 import torch
 
@@ -65,9 +65,8 @@ class AdaptiveProjectedGuidance(BaseGuidance):
         use_original_formulation: bool = False,
         start: float = 0.0,
         stop: float = 1.0,
-        enabled: bool = True,
     ):
-        super().__init__(start, stop, enabled)
+        super().__init__(start, stop)
 
         self.guidance_scale = guidance_scale
         self.adaptive_projected_guidance_momentum = adaptive_projected_guidance_momentum
@@ -77,14 +76,19 @@ class AdaptiveProjectedGuidance(BaseGuidance):
         self.use_original_formulation = use_original_formulation
         self.momentum_buffer = None
 
-    def prepare_inputs(self, data: Dict[str, Tuple[torch.Tensor, torch.Tensor]]) -> List["BlockState"]:
+    def prepare_inputs(
+        self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None
+    ) -> List["BlockState"]:
+        if input_fields is None:
+            input_fields = self._input_fields
+
         if self._step == 0:
             if self.adaptive_projected_guidance_momentum is not None:
                 self.momentum_buffer = MomentumBuffer(self.adaptive_projected_guidance_momentum)
         tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
         data_batches = []
-        for tuple_idx, input_prediction in zip(tuple_indices, self._input_predictions):
-            data_batch = self._prepare_batch(data, tuple_idx, input_prediction)
+        for i in range(self.num_conditions):
+            data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], self._input_predictions[i])
             data_batches.append(data_batch)
         return data_batches
 
@@ -148,44 +152,6 @@ class MomentumBuffer:
         new_average = self.momentum * self.running_average
         self.running_average = update_value + new_average
 
-    def __repr__(self) -> str:
-        """
-        Returns a string representation showing momentum, shape, statistics, and a slice of the running_average.
-        """
-        if isinstance(self.running_average, torch.Tensor):
-            shape = tuple(self.running_average.shape)
-
-            # Calculate statistics
-            with torch.no_grad():
-                stats = {
-                    "mean": self.running_average.mean().item(),
-                    "std": self.running_average.std().item(),
-                    "min": self.running_average.min().item(),
-                    "max": self.running_average.max().item(),
-                }
-
-            # Get a slice (max 3 elements per dimension)
-            slice_indices = tuple(slice(None, min(3, dim)) for dim in shape)
-            sliced_data = self.running_average[slice_indices]
-
-            # Format the slice for display (convert to float32 for numpy compatibility with bfloat16)
-            slice_str = str(sliced_data.detach().float().cpu().numpy())
-            if len(slice_str) > 200:  # Truncate if too long
-                slice_str = slice_str[:200] + "..."
-
-            stats_str = ", ".join([f"{k}={v:.4f}" for k, v in stats.items()])
-
-            return (
-                f"MomentumBuffer(\n"
-                f"  momentum={self.momentum},\n"
-                f"  shape={shape},\n"
-                f"  stats=[{stats_str}],\n"
-                f"  slice={slice_str}\n"
-                f")"
-            )
-        else:
-            return f"MomentumBuffer(momentum={self.momentum}, running_average={self.running_average})"
-
 
 def normalized_guidance(
     pred_cond: torch.Tensor,

src/diffusers/guiders/adaptive_projected_guidance_mix.py

@@ -1,284 +0,0 @@
-# Copyright 2025 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import math
-from typing import TYPE_CHECKING, Dict, List, Optional, Tuple
-
-import torch
-
-from ..configuration_utils import register_to_config
-from .guider_utils import BaseGuidance, GuiderOutput, rescale_noise_cfg
-
-
-if TYPE_CHECKING:
-    from ..modular_pipelines.modular_pipeline import BlockState
-
-
-class AdaptiveProjectedMixGuidance(BaseGuidance):
-    """
-    Adaptive Projected Guidance (APG) https://huggingface.co/papers/2410.02416 combined with Classifier-Free Guidance
-    (CFG). This guider is used in HunyuanImage2.1 https://github.com/Tencent-Hunyuan/HunyuanImage-2.1
-
-    Args:
-        guidance_scale (`float`, defaults to `7.5`):
-            The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text
-            prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and
-            deterioration of image quality.
-        adaptive_projected_guidance_momentum (`float`, defaults to `None`):
-            The momentum parameter for the adaptive projected guidance. Disabled if set to `None`.
-        adaptive_projected_guidance_rescale (`float`, defaults to `15.0`):
-            The rescale factor applied to the noise predictions for adaptive projected guidance. This is used to
-            improve image quality and fix
-        guidance_rescale (`float`, defaults to `0.0`):
-            The rescale factor applied to the noise predictions for classifier-free guidance. This is used to improve
-            image quality and fix overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample
-            Steps are Flawed](https://huggingface.co/papers/2305.08891).
-        use_original_formulation (`bool`, defaults to `False`):
-            Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default,
-            we use the diffusers-native implementation that has been in the codebase for a long time. See
-            [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details.
-        start (`float`, defaults to `0.0`):
-            The fraction of the total number of denoising steps after which the classifier-free guidance starts.
-        stop (`float`, defaults to `1.0`):
-            The fraction of the total number of denoising steps after which the classifier-free guidance stops.
-        adaptive_projected_guidance_start_step (`int`, defaults to `5`):
-            The step at which the adaptive projected guidance starts (before this step, classifier-free guidance is
-            used, and momentum buffer is updated).
-        enabled (`bool`, defaults to `True`):
-            Whether this guidance is enabled.
-    """
-
-    _input_predictions = ["pred_cond", "pred_uncond"]
-
-    @register_to_config
-    def __init__(
-        self,
-        guidance_scale: float = 3.5,
-        guidance_rescale: float = 0.0,
-        adaptive_projected_guidance_scale: float = 10.0,
-        adaptive_projected_guidance_momentum: float = -0.5,
-        adaptive_projected_guidance_rescale: float = 10.0,
-        eta: float = 0.0,
-        use_original_formulation: bool = False,
-        start: float = 0.0,
-        stop: float = 1.0,
-        adaptive_projected_guidance_start_step: int = 5,
-        enabled: bool = True,
-    ):
-        super().__init__(start, stop, enabled)
-
-        self.guidance_scale = guidance_scale
-        self.guidance_rescale = guidance_rescale
-        self.adaptive_projected_guidance_scale = adaptive_projected_guidance_scale
-        self.adaptive_projected_guidance_momentum = adaptive_projected_guidance_momentum
-        self.adaptive_projected_guidance_rescale = adaptive_projected_guidance_rescale
-        self.eta = eta
-        self.adaptive_projected_guidance_start_step = adaptive_projected_guidance_start_step
-        self.use_original_formulation = use_original_formulation
-        self.momentum_buffer = None
-
-    def prepare_inputs(self, data: Dict[str, Tuple[torch.Tensor, torch.Tensor]]) -> List["BlockState"]:
-        if self._step == 0:
-            if self.adaptive_projected_guidance_momentum is not None:
-                self.momentum_buffer = MomentumBuffer(self.adaptive_projected_guidance_momentum)
-        tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
-        data_batches = []
-        for tuple_idx, input_prediction in zip(tuple_indices, self._input_predictions):
-            data_batch = self._prepare_batch(data, tuple_idx, input_prediction)
-            data_batches.append(data_batch)
-        return data_batches
-
-    def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] = None) -> GuiderOutput:
-        pred = None
-
-        # no guidance
-        if not self._is_cfg_enabled():
-            pred = pred_cond
-
-        # CFG + update momentum buffer
-        elif not self._is_apg_enabled():
-            if self.momentum_buffer is not None:
-                update_momentum_buffer(pred_cond, pred_uncond, self.momentum_buffer)
-            # CFG + update momentum buffer
-            shift = pred_cond - pred_uncond
-            pred = pred_cond if self.use_original_formulation else pred_uncond
-            pred = pred + self.guidance_scale * shift
-
-        # APG
-        elif self._is_apg_enabled():
-            pred = normalized_guidance(
-                pred_cond,
-                pred_uncond,
-                self.adaptive_projected_guidance_scale,
-                self.momentum_buffer,
-                self.eta,
-                self.adaptive_projected_guidance_rescale,
-                self.use_original_formulation,
-            )
-
-        if self.guidance_rescale > 0.0:
-            pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)
-
-        return GuiderOutput(pred=pred, pred_cond=pred_cond, pred_uncond=pred_uncond)
-
-    @property
-    def is_conditional(self) -> bool:
-        return self._count_prepared == 1
-
-    @property
-    def num_conditions(self) -> int:
-        num_conditions = 1
-        if self._is_apg_enabled() or self._is_cfg_enabled():
-            num_conditions += 1
-        return num_conditions
-
-    # Copied from diffusers.guiders.classifier_free_guidance.ClassifierFreeGuidance._is_cfg_enabled
-    def _is_cfg_enabled(self) -> bool:
-        if not self._enabled:
-            return False
-
-        is_within_range = True
-        if self._num_inference_steps is not None:
-            skip_start_step = int(self._start * self._num_inference_steps)
-            skip_stop_step = int(self._stop * self._num_inference_steps)
-            is_within_range = skip_start_step <= self._step < skip_stop_step
-
-        is_close = False
-        if self.use_original_formulation:
-            is_close = math.isclose(self.guidance_scale, 0.0)
-        else:
-            is_close = math.isclose(self.guidance_scale, 1.0)
-
-        return is_within_range and not is_close
-
-    def _is_apg_enabled(self) -> bool:
-        if not self._enabled:
-            return False
-
-        if not self._is_cfg_enabled():
-            return False
-
-        is_within_range = False
-        if self._step is not None:
-            is_within_range = self._step > self.adaptive_projected_guidance_start_step
-
-        is_close = False
-        if self.use_original_formulation:
-            is_close = math.isclose(self.adaptive_projected_guidance_scale, 0.0)
-        else:
-            is_close = math.isclose(self.adaptive_projected_guidance_scale, 1.0)
-
-        return is_within_range and not is_close
-
-    def get_state(self):
-        state = super().get_state()
-        state["momentum_buffer"] = self.momentum_buffer
-        state["is_apg_enabled"] = self._is_apg_enabled()
-        state["is_cfg_enabled"] = self._is_cfg_enabled()
-        return state
-
-
-# Copied from diffusers.guiders.adaptive_projected_guidance.MomentumBuffer
-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
-
-    def __repr__(self) -> str:
-        """
-        Returns a string representation showing momentum, shape, statistics, and a slice of the running_average.
-        """
-        if isinstance(self.running_average, torch.Tensor):
-            shape = tuple(self.running_average.shape)
-
-            # Calculate statistics
-            with torch.no_grad():
-                stats = {
-                    "mean": self.running_average.mean().item(),
-                    "std": self.running_average.std().item(),
-                    "min": self.running_average.min().item(),
-                    "max": self.running_average.max().item(),
-                }
-
-            # Get a slice (max 3 elements per dimension)
-            slice_indices = tuple(slice(None, min(3, dim)) for dim in shape)
-            sliced_data = self.running_average[slice_indices]
-
-            # Format the slice for display (convert to float32 for numpy compatibility with bfloat16)
-            slice_str = str(sliced_data.detach().float().cpu().numpy())
-            if len(slice_str) > 200:  # Truncate if too long
-                slice_str = slice_str[:200] + "..."
-
-            stats_str = ", ".join([f"{k}={v:.4f}" for k, v in stats.items()])
-
-            return (
-                f"MomentumBuffer(\n"
-                f"  momentum={self.momentum},\n"
-                f"  shape={shape},\n"
-                f"  stats=[{stats_str}],\n"
-                f"  slice={slice_str}\n"
-                f")"
-            )
-        else:
-            return f"MomentumBuffer(momentum={self.momentum}, running_average={self.running_average})"
-
-
-def update_momentum_buffer(
-    pred_cond: torch.Tensor,
-    pred_uncond: torch.Tensor,
-    momentum_buffer: Optional[MomentumBuffer] = None,
-):
-    diff = pred_cond - pred_uncond
-    if momentum_buffer is not None:
-        momentum_buffer.update(diff)
-
-
-def normalized_guidance(
-    pred_cond: torch.Tensor,
-    pred_uncond: torch.Tensor,
-    guidance_scale: float,
-    momentum_buffer: Optional[MomentumBuffer] = None,
-    eta: float = 1.0,
-    norm_threshold: float = 0.0,
-    use_original_formulation: bool = False,
-):
-    if momentum_buffer is not None:
-        update_momentum_buffer(pred_cond, pred_uncond, momentum_buffer)
-        diff = momentum_buffer.running_average
-    else:
-        diff = pred_cond - pred_uncond
-
-    dim = [-i for i in range(1, len(diff.shape))]
-
-    if norm_threshold > 0:
-        ones = torch.ones_like(diff)
-        diff_norm = diff.norm(p=2, dim=dim, 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=dim)
-    v0_parallel = (v0 * v1).sum(dim=dim, keepdim=True) * v1
-    v0_orthogonal = v0 - v0_parallel
-    diff_parallel, diff_orthogonal = v0_parallel.type_as(diff), v0_orthogonal.type_as(diff)
-    normalized_update = diff_orthogonal + eta * diff_parallel
-
-    pred = pred_cond if use_original_formulation else pred_uncond
-    pred = pred + guidance_scale * normalized_update
-
-    return pred

src/diffusers/guiders/auto_guidance.py

@@ -72,9 +72,8 @@ class AutoGuidance(BaseGuidance):
         use_original_formulation: bool = False,
         start: float = 0.0,
         stop: float = 1.0,
-        enabled: bool = True,
     ):
-        super().__init__(start, stop, enabled)
+        super().__init__(start, stop)
 
         self.guidance_scale = guidance_scale
         self.auto_guidance_layers = auto_guidance_layers
@@ -133,11 +132,16 @@ class AutoGuidance(BaseGuidance):
                 registry = HookRegistry.check_if_exists_or_initialize(denoiser)
                 registry.remove_hook(name, recurse=True)
 
-    def prepare_inputs(self, data: Dict[str, Tuple[torch.Tensor, torch.Tensor]]) -> List["BlockState"]:
+    def prepare_inputs(
+        self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None
+    ) -> List["BlockState"]:
+        if input_fields is None:
+            input_fields = self._input_fields
+
         tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
         data_batches = []
-        for tuple_idx, input_prediction in zip(tuple_indices, self._input_predictions):
-            data_batch = self._prepare_batch(data, tuple_idx, input_prediction)
+        for i in range(self.num_conditions):
+            data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], self._input_predictions[i])
             data_batches.append(data_batch)
         return data_batches
 

src/diffusers/guiders/classifier_free_guidance.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import math
-from typing import TYPE_CHECKING, Dict, List, Optional, Tuple
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
 
 import torch
 
@@ -27,50 +27,43 @@ if TYPE_CHECKING:
 
 class ClassifierFreeGuidance(BaseGuidance):
     """
-    Implements Classifier-Free Guidance (CFG) for diffusion models.
+    Classifier-free guidance (CFG): https://huggingface.co/papers/2207.12598
 
-    Reference: https://huggingface.co/papers/2207.12598
+    CFG is a technique used to improve generation quality and condition-following in diffusion models. It works by
+    jointly training a model on both conditional and unconditional data, and using a weighted sum of the two during
+    inference. This allows the model to tradeoff between generation quality and sample diversity. The original paper
+    proposes scaling and shifting the conditional distribution based on the difference between conditional and
+    unconditional predictions. [x_pred = x_cond + scale * (x_cond - x_uncond)]
 
-    CFG improves generation quality and prompt adherence by jointly training models on both conditional and
-    unconditional data, then combining predictions during inference. This allows trading off between quality (high
-    guidance) and diversity (low guidance).
+    Diffusers implemented the scaling and shifting on the unconditional prediction instead based on the [Imagen
+    paper](https://huggingface.co/papers/2205.11487), which is equivalent to what the original paper proposed in
+    theory. [x_pred = x_uncond + scale * (x_cond - x_uncond)]
 
-    **Two CFG Formulations:**
+    The intution behind the original formulation can be thought of as moving the conditional distribution estimates
+    further away from the unconditional distribution estimates, while the diffusers-native implementation can be
+    thought of as moving the unconditional distribution towards the conditional distribution estimates to get rid of
+    the unconditional predictions (usually negative features like "bad quality, bad anotomy, watermarks", etc.)
 
-    1. **Original formulation** (from paper):
-       ```
-       x_pred = x_cond + guidance_scale * (x_cond - x_uncond)
-       ```
-       Moves conditional predictions further from unconditional ones.
-
-    2. **Diffusers-native formulation** (default, from Imagen paper):
-       ```
-       x_pred = x_uncond + guidance_scale * (x_cond - x_uncond)
-       ```
-       Moves unconditional predictions toward conditional ones, effectively suppressing negative features (e.g., "bad
-       quality", "watermarks"). Equivalent in theory but more intuitive.
-
-    Use `use_original_formulation=True` to switch to the original formulation.
+    The `use_original_formulation` argument can be set to `True` to use the original CFG formulation mentioned in the
+    paper. By default, we use the diffusers-native implementation that has been in the codebase for a long time.
 
     Args:
         guidance_scale (`float`, defaults to `7.5`):
-            CFG scale applied by this guider during post-processing. Higher values = stronger prompt conditioning but
-            may reduce quality. Typical range: 1.0-20.0.
+            The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text
+            prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and
+            deterioration of image quality.
         guidance_rescale (`float`, defaults to `0.0`):
-            Rescaling factor to prevent overexposure from high guidance scales. Based on [Common Diffusion Noise
-            Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891). Range: 0.0 (no rescaling)
-            to 1.0 (full rescaling).
+            The rescale factor applied to the noise predictions. This is used to improve image quality and fix
+            overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
+            Flawed](https://huggingface.co/papers/2305.08891).
         use_original_formulation (`bool`, defaults to `False`):
-            If `True`, uses the original CFG formulation from the paper. If `False` (default), uses the
-            diffusers-native formulation from the Imagen paper.
+            Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default,
+            we use the diffusers-native implementation that has been in the codebase for a long time. See
+            [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details.
         start (`float`, defaults to `0.0`):
-            Fraction of denoising steps (0.0-1.0) after which CFG starts. Use > 0.0 to disable CFG in early denoising
-            steps.
+            The fraction of the total number of denoising steps after which guidance starts.
         stop (`float`, defaults to `1.0`):
-            Fraction of denoising steps (0.0-1.0) after which CFG stops. Use < 1.0 to disable CFG in late denoising
-            steps.
-        enabled (`bool`, defaults to `True`):
-            Whether CFG is enabled. Set to `False` to disable CFG entirely (uses only conditional predictions).
+            The fraction of the total number of denoising steps after which guidance stops.
     """
 
     _input_predictions = ["pred_cond", "pred_uncond"]
@@ -83,19 +76,23 @@ class ClassifierFreeGuidance(BaseGuidance):
         use_original_formulation: bool = False,
         start: float = 0.0,
         stop: float = 1.0,
-        enabled: bool = True,
     ):
-        super().__init__(start, stop, enabled)
+        super().__init__(start, stop)
 
         self.guidance_scale = guidance_scale
         self.guidance_rescale = guidance_rescale
         self.use_original_formulation = use_original_formulation
 
-    def prepare_inputs(self, data: Dict[str, Tuple[torch.Tensor, torch.Tensor]]) -> List["BlockState"]:
+    def prepare_inputs(
+        self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None
+    ) -> List["BlockState"]:
+        if input_fields is None:
+            input_fields = self._input_fields
+
         tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
         data_batches = []
-        for tuple_idx, input_prediction in zip(tuple_indices, self._input_predictions):
-            data_batch = self._prepare_batch(data, tuple_idx, input_prediction)
+        for i in range(self.num_conditions):
+            data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], self._input_predictions[i])
             data_batches.append(data_batch)
         return data_batches
 

src/diffusers/guiders/classifier_free_zero_star_guidance.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import math
-from typing import TYPE_CHECKING, Dict, List, Optional, Tuple
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
 
 import torch
 
@@ -68,31 +68,31 @@ class ClassifierFreeZeroStarGuidance(BaseGuidance):
         use_original_formulation: bool = False,
         start: float = 0.0,
         stop: float = 1.0,
-        enabled: bool = True,
     ):
-        super().__init__(start, stop, enabled)
+        super().__init__(start, stop)
 
         self.guidance_scale = guidance_scale
         self.zero_init_steps = zero_init_steps
         self.guidance_rescale = guidance_rescale
         self.use_original_formulation = use_original_formulation
 
-    def prepare_inputs(self, data: Dict[str, Tuple[torch.Tensor, torch.Tensor]]) -> List["BlockState"]:
+    def prepare_inputs(
+        self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None
+    ) -> List["BlockState"]:
+        if input_fields is None:
+            input_fields = self._input_fields
+
         tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
         data_batches = []
-        for tuple_idx, input_prediction in zip(tuple_indices, self._input_predictions):
-            data_batch = self._prepare_batch(data, tuple_idx, input_prediction)
+        for i in range(self.num_conditions):
+            data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], self._input_predictions[i])
             data_batches.append(data_batch)
         return data_batches
 
     def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] = None) -> GuiderOutput:
         pred = None
 
-        # YiYi Notes: add default behavior for self._enabled == False
-        if not self._enabled:
-            pred = pred_cond
-
-        elif self._step < self.zero_init_steps:
+        if self._step < self.zero_init_steps:
             pred = torch.zeros_like(pred_cond)
         elif not self._is_cfg_enabled():
             pred = pred_cond

src/diffusers/guiders/frequency_decoupled_guidance.py

@@ -149,7 +149,6 @@ class FrequencyDecoupledGuidance(BaseGuidance):
         stop: Union[float, List[float], Tuple[float]] = 1.0,
         guidance_rescale_space: str = "data",
         upcast_to_double: bool = True,
-        enabled: bool = True,
     ):
         if not _CAN_USE_KORNIA:
             raise ImportError(
@@ -161,7 +160,7 @@ class FrequencyDecoupledGuidance(BaseGuidance):
         # Set start to earliest start for any freq component and stop to latest stop for any freq component
         min_start = start if isinstance(start, float) else min(start)
         max_stop = stop if isinstance(stop, float) else max(stop)
-        super().__init__(min_start, max_stop, enabled)
+        super().__init__(min_start, max_stop)
 
         self.guidance_scales = guidance_scales
         self.levels = len(guidance_scales)
@@ -218,11 +217,16 @@ class FrequencyDecoupledGuidance(BaseGuidance):
                 f"({len(self.guidance_scales)})"
             )
 
-    def prepare_inputs(self, data: Dict[str, Tuple[torch.Tensor, torch.Tensor]]) -> List["BlockState"]:
+    def prepare_inputs(
+        self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None
+    ) -> List["BlockState"]:
+        if input_fields is None:
+            input_fields = self._input_fields
+
         tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
         data_batches = []
-        for tuple_idx, input_prediction in zip(tuple_indices, self._input_predictions):
-            data_batch = self._prepare_batch(data, tuple_idx, input_prediction)
+        for i in range(self.num_conditions):
+            data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], self._input_predictions[i])
             data_batches.append(data_batch)
         return data_batches
 

src/diffusers/guiders/guider_utils.py

@@ -40,11 +40,7 @@ class BaseGuidance(ConfigMixin, PushToHubMixin):
     _input_predictions = None
     _identifier_key = "__guidance_identifier__"
 
-    def __init__(self, start: float = 0.0, stop: float = 1.0, enabled: bool = True):
-        logger.warning(
-            "Guiders are currently an experimental feature under active development. The API is subject to breaking changes in future releases."
-        )
-
+    def __init__(self, start: float = 0.0, stop: float = 1.0):
         self._start = start
         self._stop = stop
         self._step: int = None
@@ -52,7 +48,7 @@ class BaseGuidance(ConfigMixin, PushToHubMixin):
         self._timestep: torch.LongTensor = None
         self._count_prepared = 0
         self._input_fields: Dict[str, Union[str, Tuple[str, str]]] = None
-        self._enabled = enabled
+        self._enabled = True
 
         if not (0.0 <= start < 1.0):
             raise ValueError(f"Expected `start` to be between 0.0 and 1.0, but got {start}.")
@@ -64,31 +60,6 @@ class BaseGuidance(ConfigMixin, PushToHubMixin):
                 "`_input_predictions` must be a list of required prediction names for the guidance technique."
             )
 
-    def new(self, **kwargs):
-        """
-        Creates a copy of this guider instance, optionally with modified configuration parameters.
-
-        Args:
-            **kwargs: Configuration parameters to override in the new instance. If no kwargs are provided,
-                returns an exact copy with the same configuration.
-
-        Returns:
-            A new guider instance with the same (or updated) configuration.
-
-        Example:
-            ```python
-            # Create a CFG guider
-            guider = ClassifierFreeGuidance(guidance_scale=3.5)
-
-            # Create an exact copy
-            same_guider = guider.new()
-
-            # Create a copy with different start step, keeping other config the same
-            new_guider = guider.new(guidance_scale=5)
-            ```
-        """
-        return self.__class__.from_config(self.config, **kwargs)
-
     def disable(self):
         self._enabled = False
 
@@ -101,52 +72,42 @@ class BaseGuidance(ConfigMixin, PushToHubMixin):
         self._timestep = timestep
         self._count_prepared = 0
 
-    def get_state(self) -> Dict[str, Any]:
+    def set_input_fields(self, **kwargs: Dict[str, Union[str, Tuple[str, str]]]) -> None:
         """
-        Returns the current state of the guidance technique as a dictionary. The state variables will be included in
-        the __repr__ method. Returns:
-            `Dict[str, Any]`: A dictionary containing the current state variables including:
-                - step: Current inference step
-                - num_inference_steps: Total number of inference steps
-                - timestep: Current timestep tensor
-                - count_prepared: Number of times prepare_models has been called
-                - enabled: Whether the guidance is enabled
-                - num_conditions: Number of conditions
+        Set the input fields for the guidance technique. The input fields are used to specify the names of the returned
+        attributes containing the prepared data after `prepare_inputs` is called. The prepared data is obtained from
+        the values of the provided keyword arguments to this method.
+
+        Args:
+            **kwargs (`Dict[str, Union[str, Tuple[str, str]]]`):
+                A dictionary where the keys are the names of the fields that will be used to store the data once it is
+                prepared with `prepare_inputs`. The values can be either a string or a tuple of length 2, which is used
+                to look up the required data provided for preparation.
+
+                If a string is provided, it will be used as the conditional data (or unconditional if used with a
+                guidance method that requires it). If a tuple of length 2 is provided, the first element must be the
+                conditional data identifier and the second element must be the unconditional data identifier or None.
+
+                Example:
+                ```
+                data = {"prompt_embeds": <some tensor>, "negative_prompt_embeds": <some tensor>, "latents": <some tensor>}
+
+                BaseGuidance.set_input_fields(
+                    latents="latents",
+                    prompt_embeds=("prompt_embeds", "negative_prompt_embeds"),
+                )
+                ```
         """
-        state = {
-            "step": self._step,
-            "num_inference_steps": self._num_inference_steps,
-            "timestep": self._timestep,
-            "count_prepared": self._count_prepared,
-            "enabled": self._enabled,
-            "num_conditions": self.num_conditions,
-        }
-        return state
-
-    def __repr__(self) -> str:
-        """
-        Returns a string representation of the guidance object including both config and current state.
-        """
-        # Get ConfigMixin's __repr__
-        str_repr = super().__repr__()
-
-        # Get current state
-        state = self.get_state()
-
-        # Format each state variable on its own line with indentation
-        state_lines = []
-        for k, v in state.items():
-            # Convert value to string and handle multi-line values
-            v_str = str(v)
-            if "\n" in v_str:
-                # For multi-line values (like MomentumBuffer), indent subsequent lines
-                v_lines = v_str.split("\n")
-                v_str = v_lines[0] + "\n" + "\n".join(["    " + line for line in v_lines[1:]])
-            state_lines.append(f"  {k}: {v_str}")
-
-        state_str = "\n".join(state_lines)
-
-        return f"{str_repr}\nState:\n{state_str}"
+        for key, value in kwargs.items():
+            is_string = isinstance(value, str)
+            is_tuple_of_str_with_len_2 = (
+                isinstance(value, tuple) and len(value) == 2 and all(isinstance(v, str) for v in value)
+            )
+            if not (is_string or is_tuple_of_str_with_len_2):
+                raise ValueError(
+                    f"Expected `set_input_fields` to be called with a string or a tuple of string with length 2, but got {type(value)} for key {key}."
+                )
+        self._input_fields = kwargs
 
     def prepare_models(self, denoiser: torch.nn.Module) -> None:
         """
@@ -194,7 +155,8 @@ class BaseGuidance(ConfigMixin, PushToHubMixin):
     @classmethod
     def _prepare_batch(
         cls,
-        data: Dict[str, Tuple[torch.Tensor, torch.Tensor]],
+        input_fields: Dict[str, Union[str, Tuple[str, str]]],
+        data: "BlockState",
         tuple_index: int,
         identifier: str,
     ) -> "BlockState":
@@ -220,16 +182,21 @@ class BaseGuidance(ConfigMixin, PushToHubMixin):
         """
         from ..modular_pipelines.modular_pipeline import BlockState
 
+        if input_fields is None:
+            raise ValueError(
+                "Input fields cannot be None. Please pass `input_fields` to `prepare_inputs` or call `set_input_fields` before preparing inputs."
+            )
         data_batch = {}
-        for key, value in data.items():
+        for key, value in input_fields.items():
             try:
-                if isinstance(value, torch.Tensor):
-                    data_batch[key] = value
+                if isinstance(value, str):
+                    data_batch[key] = getattr(data, value)
                 elif isinstance(value, tuple):
-                    data_batch[key] = value[tuple_index]
+                    data_batch[key] = getattr(data, value[tuple_index])
                 else:
-                    raise ValueError(f"Invalid value type: {type(value)}")
-            except ValueError:
+                    # We've already checked that value is a string or a tuple of strings with length 2
+                    pass
+            except AttributeError:
                 logger.debug(f"`data` does not have attribute(s) {value}, skipping.")
         data_batch[cls._identifier_key] = identifier
         return BlockState(**data_batch)

src/diffusers/guiders/perturbed_attention_guidance.py

@@ -98,9 +98,8 @@ class PerturbedAttentionGuidance(BaseGuidance):
         use_original_formulation: bool = False,
         start: float = 0.0,
         stop: float = 1.0,
-        enabled: bool = True,
     ):
-        super().__init__(start, stop, enabled)
+        super().__init__(start, stop)
 
         self.guidance_scale = guidance_scale
         self.skip_layer_guidance_scale = perturbed_guidance_scale
@@ -169,7 +168,12 @@ class PerturbedAttentionGuidance(BaseGuidance):
                 registry.remove_hook(hook_name, recurse=True)
 
     # Copied from diffusers.guiders.skip_layer_guidance.SkipLayerGuidance.prepare_inputs
-    def prepare_inputs(self, data: Dict[str, Tuple[torch.Tensor, torch.Tensor]]) -> List["BlockState"]:
+    def prepare_inputs(
+        self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None
+    ) -> List["BlockState"]:
+        if input_fields is None:
+            input_fields = self._input_fields
+
         if self.num_conditions == 1:
             tuple_indices = [0]
             input_predictions = ["pred_cond"]
@@ -182,8 +186,8 @@ class PerturbedAttentionGuidance(BaseGuidance):
             tuple_indices = [0, 1, 0]
             input_predictions = ["pred_cond", "pred_uncond", "pred_cond_skip"]
         data_batches = []
-        for tuple_idx, input_prediction in zip(tuple_indices, input_predictions):
-            data_batch = self._prepare_batch(data, tuple_idx, input_prediction)
+        for i in range(self.num_conditions):
+            data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], input_predictions[i])
             data_batches.append(data_batch)
         return data_batches
 

src/diffusers/guiders/skip_layer_guidance.py

@@ -100,9 +100,8 @@ class SkipLayerGuidance(BaseGuidance):
         use_original_formulation: bool = False,
         start: float = 0.0,
         stop: float = 1.0,
-        enabled: bool = True,
     ):
-        super().__init__(start, stop, enabled)
+        super().__init__(start, stop)
 
         self.guidance_scale = guidance_scale
         self.skip_layer_guidance_scale = skip_layer_guidance_scale
@@ -165,7 +164,12 @@ class SkipLayerGuidance(BaseGuidance):
             for hook_name in self._skip_layer_hook_names:
                 registry.remove_hook(hook_name, recurse=True)
 
-    def prepare_inputs(self, data: Dict[str, Tuple[torch.Tensor, torch.Tensor]]) -> List["BlockState"]:
+    def prepare_inputs(
+        self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None
+    ) -> List["BlockState"]:
+        if input_fields is None:
+            input_fields = self._input_fields
+
         if self.num_conditions == 1:
             tuple_indices = [0]
             input_predictions = ["pred_cond"]
@@ -178,8 +182,8 @@ class SkipLayerGuidance(BaseGuidance):
             tuple_indices = [0, 1, 0]
             input_predictions = ["pred_cond", "pred_uncond", "pred_cond_skip"]
         data_batches = []
-        for tuple_idx, input_prediction in zip(tuple_indices, input_predictions):
-            data_batch = self._prepare_batch(data, tuple_idx, input_prediction)
+        for i in range(self.num_conditions):
+            data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], input_predictions[i])
             data_batches.append(data_batch)
         return data_batches
 

src/diffusers/guiders/smoothed_energy_guidance.py

@@ -92,9 +92,8 @@ class SmoothedEnergyGuidance(BaseGuidance):
         use_original_formulation: bool = False,
         start: float = 0.0,
         stop: float = 1.0,
-        enabled: bool = True,
     ):
-        super().__init__(start, stop, enabled)
+        super().__init__(start, stop)
 
         self.guidance_scale = guidance_scale
         self.seg_guidance_scale = seg_guidance_scale
@@ -154,7 +153,12 @@ class SmoothedEnergyGuidance(BaseGuidance):
             for hook_name in self._seg_layer_hook_names:
                 registry.remove_hook(hook_name, recurse=True)
 
-    def prepare_inputs(self, data: Dict[str, Tuple[torch.Tensor, torch.Tensor]]) -> List["BlockState"]:
+    def prepare_inputs(
+        self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None
+    ) -> List["BlockState"]:
+        if input_fields is None:
+            input_fields = self._input_fields
+
         if self.num_conditions == 1:
             tuple_indices = [0]
             input_predictions = ["pred_cond"]
@@ -167,8 +171,8 @@ class SmoothedEnergyGuidance(BaseGuidance):
             tuple_indices = [0, 1, 0]
             input_predictions = ["pred_cond", "pred_uncond", "pred_cond_seg"]
         data_batches = []
-        for tuple_idx, input_prediction in zip(tuple_indices, input_predictions):
-            data_batch = self._prepare_batch(data, tuple_idx, input_prediction)
+        for i in range(self.num_conditions):
+            data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], input_predictions[i])
             data_batches.append(data_batch)
         return data_batches
 

src/diffusers/guiders/tangential_classifier_free_guidance.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import math
-from typing import TYPE_CHECKING, Dict, List, Optional, Tuple
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
 
 import torch
 
@@ -58,19 +58,23 @@ class TangentialClassifierFreeGuidance(BaseGuidance):
         use_original_formulation: bool = False,
         start: float = 0.0,
         stop: float = 1.0,
-        enabled: bool = True,
     ):
-        super().__init__(start, stop, enabled)
+        super().__init__(start, stop)
 
         self.guidance_scale = guidance_scale
         self.guidance_rescale = guidance_rescale
         self.use_original_formulation = use_original_formulation
 
-    def prepare_inputs(self, data: Dict[str, Tuple[torch.Tensor, torch.Tensor]]) -> List["BlockState"]:
+    def prepare_inputs(
+        self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None
+    ) -> List["BlockState"]:
+        if input_fields is None:
+            input_fields = self._input_fields
+
         tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
         data_batches = []
-        for tuple_idx, input_prediction in zip(tuple_indices, self._input_predictions):
-            data_batch = self._prepare_batch(data, tuple_idx, input_prediction)
+        for i in range(self.num_conditions):
+            data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], self._input_predictions[i])
             data_batches.append(data_batch)
         return data_batches
 

src/diffusers/hooks/_helpers.py

@@ -108,7 +108,6 @@ def _register_attention_processors_metadata():
     from ..models.attention_processor import AttnProcessor2_0
     from ..models.transformers.transformer_cogview4 import CogView4AttnProcessor
     from ..models.transformers.transformer_flux import FluxAttnProcessor
-    from ..models.transformers.transformer_hunyuanimage import HunyuanImageAttnProcessor
     from ..models.transformers.transformer_qwenimage import QwenDoubleStreamAttnProcessor2_0
     from ..models.transformers.transformer_wan import WanAttnProcessor2_0
 
@@ -150,14 +149,6 @@ def _register_attention_processors_metadata():
         ),
     )
 
-    # HunyuanImageAttnProcessor
-    AttentionProcessorRegistry.register(
-        model_class=HunyuanImageAttnProcessor,
-        metadata=AttentionProcessorMetadata(
-            skip_processor_output_fn=_skip_proc_output_fn_Attention_HunyuanImageAttnProcessor,
-        ),
-    )
-
 
 def _register_transformer_blocks_metadata():
     from ..models.attention import BasicTransformerBlock
@@ -171,10 +162,6 @@ def _register_transformer_blocks_metadata():
         HunyuanVideoTokenReplaceTransformerBlock,
         HunyuanVideoTransformerBlock,
     )
-    from ..models.transformers.transformer_hunyuanimage import (
-        HunyuanImageSingleTransformerBlock,
-        HunyuanImageTransformerBlock,
-    )
     from ..models.transformers.transformer_ltx import LTXVideoTransformerBlock
     from ..models.transformers.transformer_mochi import MochiTransformerBlock
     from ..models.transformers.transformer_qwenimage import QwenImageTransformerBlock
@@ -296,22 +283,6 @@ def _register_transformer_blocks_metadata():
         ),
     )
 
-    # HunyuanImage2.1
-    TransformerBlockRegistry.register(
-        model_class=HunyuanImageTransformerBlock,
-        metadata=TransformerBlockMetadata(
-            return_hidden_states_index=0,
-            return_encoder_hidden_states_index=1,
-        ),
-    )
-    TransformerBlockRegistry.register(
-        model_class=HunyuanImageSingleTransformerBlock,
-        metadata=TransformerBlockMetadata(
-            return_hidden_states_index=0,
-            return_encoder_hidden_states_index=1,
-        ),
-    )
-
 
 # fmt: off
 def _skip_attention___ret___hidden_states(self, *args, **kwargs):
@@ -337,5 +308,4 @@ _skip_proc_output_fn_Attention_WanAttnProcessor2_0 = _skip_attention___ret___hid
 # not sure what this is yet.
 _skip_proc_output_fn_Attention_FluxAttnProcessor = _skip_attention___ret___hidden_states
 _skip_proc_output_fn_Attention_QwenDoubleStreamAttnProcessor2_0 = _skip_attention___ret___hidden_states
-_skip_proc_output_fn_Attention_HunyuanImageAttnProcessor = _skip_attention___ret___hidden_states
 # fmt: on

src/diffusers/hooks/context_parallel.py

@@ -203,12 +203,10 @@ class ContextParallelSplitHook(ModelHook):
 
     def _prepare_cp_input(self, x: torch.Tensor, cp_input: ContextParallelInput) -> torch.Tensor:
         if cp_input.expected_dims is not None and x.dim() != cp_input.expected_dims:
-            logger.warning_once(
-                f"Expected input tensor to have {cp_input.expected_dims} dimensions, but got {x.dim()} dimensions, split will not be applied."
+            raise ValueError(
+                f"Expected input tensor to have {cp_input.expected_dims} dimensions, but got {x.dim()} dimensions."
             )
-            return x
-        else:
-            return EquipartitionSharder.shard(x, cp_input.split_dim, self.parallel_config._flattened_mesh)
+        return EquipartitionSharder.shard(x, cp_input.split_dim, self.parallel_config._flattened_mesh)
 
 
 class ContextParallelGatherHook(ModelHook):

src/diffusers/image_processor.py

@@ -1045,39 +1045,16 @@ class VaeImageProcessorLDM3D(VaeImageProcessor):
     def rgblike_to_depthmap(image: Union[np.ndarray, torch.Tensor]) -> Union[np.ndarray, torch.Tensor]:
         r"""
         Convert an RGB-like depth image to a depth map.
+
+        Args:
+            image (`Union[np.ndarray, torch.Tensor]`):
+                The RGB-like depth image to convert.
+
+        Returns:
+            `Union[np.ndarray, torch.Tensor]`:
+                The corresponding depth map.
         """
-        # 1. Cast the tensor to a larger integer type (e.g., int32)
-        #    to safely perform the multiplication by 256.
-        # 2. Perform the 16-bit combination: High-byte * 256 + Low-byte.
-        # 3. Cast the final result to the desired depth map type (uint16) if needed
-        #    before returning, though leaving it as int32/int64 is often safer
-        #    for return value from a library function.
-
-        if isinstance(image, torch.Tensor):
-            # Cast to a safe dtype (e.g., int32 or int64) for the calculation
-            original_dtype = image.dtype
-            image_safe = image.to(torch.int32)
-
-            # Calculate the depth map
-            depth_map = image_safe[:, :, 1] * 256 + image_safe[:, :, 2]
-
-            # You may want to cast the final result to uint16, but casting to a
-            # larger int type (like int32) is sufficient to fix the overflow.
-            # depth_map = depth_map.to(torch.uint16) # Uncomment if uint16 is strictly required
-            return depth_map.to(original_dtype)
-
-        elif isinstance(image, np.ndarray):
-            # NumPy equivalent: Cast to a safe dtype (e.g., np.int32)
-            original_dtype = image.dtype
-            image_safe = image.astype(np.int32)
-
-            # Calculate the depth map
-            depth_map = image_safe[:, :, 1] * 256 + image_safe[:, :, 2]
-
-            # depth_map = depth_map.astype(np.uint16) # Uncomment if uint16 is strictly required
-            return depth_map.astype(original_dtype)
-        else:
-            raise TypeError("Input image must be a torch.Tensor or np.ndarray")
+        return image[:, :, 1] * 2**8 + image[:, :, 2]
 
     def numpy_to_depth(self, images: np.ndarray) -> List[PIL.Image.Image]:
         r"""

src/diffusers/loaders/lora_conversion_utils.py

@@ -1977,34 +1977,14 @@ def _convert_non_diffusers_wan_lora_to_diffusers(state_dict):
                     "time_projection.1.diff_b"
                 )
 
-        if any("head.head" in k for k in original_state_dict):
-            if any(f"head.head.{lora_down_key}.weight" in k for k in state_dict):
-                converted_state_dict["proj_out.lora_A.weight"] = original_state_dict.pop(
-                    f"head.head.{lora_down_key}.weight"
-                )
-            if any(f"head.head.{lora_up_key}.weight" in k for k in state_dict):
-                converted_state_dict["proj_out.lora_B.weight"] = original_state_dict.pop(
-                    f"head.head.{lora_up_key}.weight"
-                )
+        if any("head.head" in k for k in state_dict):
+            converted_state_dict["proj_out.lora_A.weight"] = original_state_dict.pop(
+                f"head.head.{lora_down_key}.weight"
+            )
+            converted_state_dict["proj_out.lora_B.weight"] = original_state_dict.pop(f"head.head.{lora_up_key}.weight")
             if "head.head.diff_b" in original_state_dict:
                 converted_state_dict["proj_out.lora_B.bias"] = original_state_dict.pop("head.head.diff_b")
 
-            # Notes: https://huggingface.co/lightx2v/Wan2.2-Distill-Loras
-            # This is my (sayakpaul) assumption that this particular key belongs to the down matrix.
-            # Since for this particular LoRA, we don't have the corresponding up matrix, I will use
-            # an identity.
-            if any("head.head" in k and k.endswith(".diff") for k in state_dict):
-                if f"head.head.{lora_down_key}.weight" in state_dict:
-                    logger.info(
-                        f"The state dict seems to be have both `head.head.diff` and `head.head.{lora_down_key}.weight` keys, which is unexpected."
-                    )
-                converted_state_dict["proj_out.lora_A.weight"] = original_state_dict.pop("head.head.diff")
-                down_matrix_head = converted_state_dict["proj_out.lora_A.weight"]
-                up_matrix_shape = (down_matrix_head.shape[0], converted_state_dict["proj_out.lora_B.bias"].shape[0])
-                converted_state_dict["proj_out.lora_B.weight"] = torch.eye(
-                    *up_matrix_shape, dtype=down_matrix_head.dtype, device=down_matrix_head.device
-                ).T
-
         for text_time in ["text_embedding", "time_embedding"]:
             if any(text_time in k for k in original_state_dict):
                 for b_n in [0, 2]:
@@ -2213,10 +2193,6 @@ def _convert_non_diffusers_qwen_lora_to_diffusers(state_dict):
 
         state_dict = {convert_key(k): v for k, v in state_dict.items()}
 
-    has_default = any("default." in k for k in state_dict)
-    if has_default:
-        state_dict = {k.replace("default.", ""): v for k, v in state_dict.items()}
-
     converted_state_dict = {}
     all_keys = list(state_dict.keys())
     down_key = ".lora_down.weight"

src/diffusers/loaders/lora_pipeline.py

@@ -4940,8 +4940,7 @@ class QwenImageLoraLoaderMixin(LoraBaseMixin):
         has_alphas_in_sd = any(k.endswith(".alpha") for k in state_dict)
         has_lora_unet = any(k.startswith("lora_unet_") for k in state_dict)
         has_diffusion_model = any(k.startswith("diffusion_model.") for k in state_dict)
-        has_default = any("default." in k for k in state_dict)
-        if has_alphas_in_sd or has_lora_unet or has_diffusion_model or has_default:
+        if has_alphas_in_sd or has_lora_unet or has_diffusion_model:
             state_dict = _convert_non_diffusers_qwen_lora_to_diffusers(state_dict)
 
         out = (state_dict, metadata) if return_lora_metadata else state_dict

src/diffusers/models/__init__.py

@@ -36,8 +36,6 @@ if is_torch_available():
     _import_structure["autoencoders.autoencoder_kl_cogvideox"] = ["AutoencoderKLCogVideoX"]
     _import_structure["autoencoders.autoencoder_kl_cosmos"] = ["AutoencoderKLCosmos"]
     _import_structure["autoencoders.autoencoder_kl_hunyuan_video"] = ["AutoencoderKLHunyuanVideo"]
-    _import_structure["autoencoders.autoencoder_kl_hunyuanimage"] = ["AutoencoderKLHunyuanImage"]
-    _import_structure["autoencoders.autoencoder_kl_hunyuanimage_refiner"] = ["AutoencoderKLHunyuanImageRefiner"]
     _import_structure["autoencoders.autoencoder_kl_ltx"] = ["AutoencoderKLLTXVideo"]
     _import_structure["autoencoders.autoencoder_kl_magvit"] = ["AutoencoderKLMagvit"]
     _import_structure["autoencoders.autoencoder_kl_mochi"] = ["AutoencoderKLMochi"]
@@ -84,7 +82,6 @@ if is_torch_available():
     _import_structure["transformers.transformer_2d"] = ["Transformer2DModel"]
     _import_structure["transformers.transformer_allegro"] = ["AllegroTransformer3DModel"]
     _import_structure["transformers.transformer_bria"] = ["BriaTransformer2DModel"]
-    _import_structure["transformers.transformer_bria_fibo"] = ["BriaFiboTransformer2DModel"]
     _import_structure["transformers.transformer_chroma"] = ["ChromaTransformer2DModel"]
     _import_structure["transformers.transformer_cogview3plus"] = ["CogView3PlusTransformer2DModel"]
     _import_structure["transformers.transformer_cogview4"] = ["CogView4Transformer2DModel"]
@@ -94,15 +91,12 @@ if is_torch_available():
     _import_structure["transformers.transformer_hidream_image"] = ["HiDreamImageTransformer2DModel"]
     _import_structure["transformers.transformer_hunyuan_video"] = ["HunyuanVideoTransformer3DModel"]
     _import_structure["transformers.transformer_hunyuan_video_framepack"] = ["HunyuanVideoFramepackTransformer3DModel"]
-    _import_structure["transformers.transformer_hunyuanimage"] = ["HunyuanImageTransformer2DModel"]
     _import_structure["transformers.transformer_kandinsky"] = ["Kandinsky5Transformer3DModel"]
     _import_structure["transformers.transformer_ltx"] = ["LTXVideoTransformer3DModel"]
     _import_structure["transformers.transformer_lumina2"] = ["Lumina2Transformer2DModel"]
     _import_structure["transformers.transformer_mochi"] = ["MochiTransformer3DModel"]
     _import_structure["transformers.transformer_omnigen"] = ["OmniGenTransformer2DModel"]
-    _import_structure["transformers.transformer_prx"] = ["PRXTransformer2DModel"]
     _import_structure["transformers.transformer_qwenimage"] = ["QwenImageTransformer2DModel"]
-    _import_structure["transformers.transformer_sana_video"] = ["SanaVideoTransformer3DModel"]
     _import_structure["transformers.transformer_sd3"] = ["SD3Transformer2DModel"]
     _import_structure["transformers.transformer_skyreels_v2"] = ["SkyReelsV2Transformer3DModel"]
     _import_structure["transformers.transformer_temporal"] = ["TransformerTemporalModel"]
@@ -138,8 +132,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             AutoencoderKLAllegro,
             AutoencoderKLCogVideoX,
             AutoencoderKLCosmos,
-            AutoencoderKLHunyuanImage,
-            AutoencoderKLHunyuanImageRefiner,
             AutoencoderKLHunyuanVideo,
             AutoencoderKLLTXVideo,
             AutoencoderKLMagvit,
@@ -176,7 +168,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .transformers import (
             AllegroTransformer3DModel,
             AuraFlowTransformer2DModel,
-            BriaFiboTransformer2DModel,
             BriaTransformer2DModel,
             ChromaTransformer2DModel,
             CogVideoXTransformer3DModel,
@@ -190,7 +181,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             FluxTransformer2DModel,
             HiDreamImageTransformer2DModel,
             HunyuanDiT2DModel,
-            HunyuanImageTransformer2DModel,
             HunyuanVideoFramepackTransformer3DModel,
             HunyuanVideoTransformer3DModel,
             Kandinsky5Transformer3DModel,
@@ -202,10 +192,8 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             OmniGenTransformer2DModel,
             PixArtTransformer2DModel,
             PriorTransformer,
-            PRXTransformer2DModel,
             QwenImageTransformer2DModel,
             SanaTransformer2DModel,
-            SanaVideoTransformer3DModel,
             SD3Transformer2DModel,
             SkyReelsV2Transformer3DModel,
             StableAudioDiTModel,

src/diffusers/models/attention_dispatch.py

@@ -27,8 +27,6 @@ if torch.distributed.is_available():
 
 from ..utils import (
     get_logger,
-    is_aiter_available,
-    is_aiter_version,
     is_flash_attn_3_available,
     is_flash_attn_available,
     is_flash_attn_version,
@@ -49,7 +47,6 @@ if TYPE_CHECKING:
     from ._modeling_parallel import ParallelConfig
 
 _REQUIRED_FLASH_VERSION = "2.6.3"
-_REQUIRED_AITER_VERSION = "0.1.5"
 _REQUIRED_SAGE_VERSION = "2.1.1"
 _REQUIRED_FLEX_VERSION = "2.5.0"
 _REQUIRED_XLA_VERSION = "2.2"
@@ -57,7 +54,6 @@ _REQUIRED_XFORMERS_VERSION = "0.0.29"
 
 _CAN_USE_FLASH_ATTN = is_flash_attn_available() and is_flash_attn_version(">=", _REQUIRED_FLASH_VERSION)
 _CAN_USE_FLASH_ATTN_3 = is_flash_attn_3_available()
-_CAN_USE_AITER_ATTN = is_aiter_available() and is_aiter_version(">=", _REQUIRED_AITER_VERSION)
 _CAN_USE_SAGE_ATTN = is_sageattention_available() and is_sageattention_version(">=", _REQUIRED_SAGE_VERSION)
 _CAN_USE_FLEX_ATTN = is_torch_version(">=", _REQUIRED_FLEX_VERSION)
 _CAN_USE_NPU_ATTN = is_torch_npu_available()
@@ -82,12 +78,6 @@ else:
     flash_attn_3_func = None
     flash_attn_3_varlen_func = None
 
-
-if _CAN_USE_AITER_ATTN:
-    from aiter import flash_attn_func as aiter_flash_attn_func
-else:
-    aiter_flash_attn_func = None
-
 if DIFFUSERS_ENABLE_HUB_KERNELS:
     if not is_kernels_available():
         raise ImportError(
@@ -188,9 +178,6 @@ class AttentionBackendName(str, Enum):
     _FLASH_3_HUB = "_flash_3_hub"
     # _FLASH_VARLEN_3_HUB = "_flash_varlen_3_hub"  # not supported yet.
 
-    # `aiter`
-    AITER = "aiter"
-
     # PyTorch native
     FLEX = "flex"
     NATIVE = "native"
@@ -427,12 +414,6 @@ def _check_attention_backend_requirements(backend: AttentionBackendName) -> None
                 f"Flash Attention 3 Hub backend '{backend.value}' is not usable because the `kernels` package isn't available. Please install it with `pip install kernels`."
             )
 
-    elif backend == AttentionBackendName.AITER:
-        if not _CAN_USE_AITER_ATTN:
-            raise RuntimeError(
-                f"Aiter Attention backend '{backend.value}' is not usable because of missing package or the version is too old. Please install `aiter>={_REQUIRED_AITER_VERSION}`."
-            )
-
     elif backend in [
         AttentionBackendName.SAGE,
         AttentionBackendName.SAGE_VARLEN,
@@ -649,86 +630,6 @@ def _(
 # ===== Helper functions to use attention backends with templated CP autograd functions =====
 
 
-def _native_attention_forward_op(
-    ctx: torch.autograd.function.FunctionCtx,
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attn_mask: Optional[torch.Tensor] = None,
-    dropout_p: float = 0.0,
-    is_causal: bool = False,
-    scale: Optional[float] = None,
-    enable_gqa: bool = False,
-    return_lse: bool = False,
-    _save_ctx: bool = True,
-    _parallel_config: Optional["ParallelConfig"] = None,
-):
-    # Native attention does not return_lse
-    if return_lse:
-        raise ValueError("Native attention does not support return_lse=True")
-
-    # used for backward pass
-    if _save_ctx:
-        ctx.save_for_backward(query, key, value)
-        ctx.attn_mask = attn_mask
-        ctx.dropout_p = dropout_p
-        ctx.is_causal = is_causal
-        ctx.scale = scale
-        ctx.enable_gqa = enable_gqa
-
-    query, key, value = (x.permute(0, 2, 1, 3) for x in (query, key, value))
-    out = torch.nn.functional.scaled_dot_product_attention(
-        query=query,
-        key=key,
-        value=value,
-        attn_mask=attn_mask,
-        dropout_p=dropout_p,
-        is_causal=is_causal,
-        scale=scale,
-        enable_gqa=enable_gqa,
-    )
-    out = out.permute(0, 2, 1, 3)
-
-    return out
-
-
-def _native_attention_backward_op(
-    ctx: torch.autograd.function.FunctionCtx,
-    grad_out: torch.Tensor,
-    *args,
-    **kwargs,
-):
-    query, key, value = ctx.saved_tensors
-
-    query.requires_grad_(True)
-    key.requires_grad_(True)
-    value.requires_grad_(True)
-
-    query_t, key_t, value_t = (x.permute(0, 2, 1, 3) for x in (query, key, value))
-    out = torch.nn.functional.scaled_dot_product_attention(
-        query=query_t,
-        key=key_t,
-        value=value_t,
-        attn_mask=ctx.attn_mask,
-        dropout_p=ctx.dropout_p,
-        is_causal=ctx.is_causal,
-        scale=ctx.scale,
-        enable_gqa=ctx.enable_gqa,
-    )
-    out = out.permute(0, 2, 1, 3)
-
-    grad_out_t = grad_out.permute(0, 2, 1, 3)
-    grad_query_t, grad_key_t, grad_value_t = torch.autograd.grad(
-        outputs=out, inputs=[query_t, key_t, value_t], grad_outputs=grad_out_t, retain_graph=False
-    )
-
-    grad_query = grad_query_t.permute(0, 2, 1, 3)
-    grad_key = grad_key_t.permute(0, 2, 1, 3)
-    grad_value = grad_value_t.permute(0, 2, 1, 3)
-
-    return grad_query, grad_key, grad_value
-
-
 # https://github.com/pytorch/pytorch/blob/8904ba638726f8c9a5aff5977c4aa76c9d2edfa6/aten/src/ATen/native/native_functions.yaml#L14958
 # forward declaration:
 #   aten::_scaled_dot_product_cudnn_attention(Tensor query, Tensor key, Tensor value, Tensor? attn_bias, bool compute_log_sumexp, float dropout_p=0., bool is_causal=False, bool return_debug_mask=False, *, float? scale=None) -> (Tensor output, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, Tensor philox_seed, Tensor philox_offset, Tensor debug_attn_mask)
@@ -1496,47 +1397,6 @@ def _flash_varlen_attention_3(
     return (out, lse) if return_lse else out
 
 
-@_AttentionBackendRegistry.register(
-    AttentionBackendName.AITER,
-    constraints=[_check_device_cuda, _check_qkv_dtype_bf16_or_fp16, _check_shape],
-)
-def _aiter_flash_attention(
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    dropout_p: float = 0.0,
-    is_causal: bool = False,
-    scale: Optional[float] = None,
-    return_lse: bool = False,
-    _parallel_config: Optional["ParallelConfig"] = None,
-) -> torch.Tensor:
-    if not return_lse and torch.is_grad_enabled():
-        # aiter requires return_lse=True by assertion when gradients are enabled.
-        out, lse, *_ = aiter_flash_attn_func(
-            q=query,
-            k=key,
-            v=value,
-            dropout_p=dropout_p,
-            softmax_scale=scale,
-            causal=is_causal,
-            return_lse=True,
-        )
-    else:
-        out = aiter_flash_attn_func(
-            q=query,
-            k=key,
-            v=value,
-            dropout_p=dropout_p,
-            softmax_scale=scale,
-            causal=is_causal,
-            return_lse=return_lse,
-        )
-        if return_lse:
-            out, lse, *_ = out
-
-    return (out, lse) if return_lse else out
-
-
 @_AttentionBackendRegistry.register(
     AttentionBackendName.FLEX,
     constraints=[_check_attn_mask_or_causal, _check_device, _check_shape],
@@ -1603,7 +1463,6 @@ def _native_flex_attention(
 @_AttentionBackendRegistry.register(
     AttentionBackendName.NATIVE,
     constraints=[_check_device, _check_shape],
-    supports_context_parallel=True,
 )
 def _native_attention(
     query: torch.Tensor,
@@ -1619,35 +1478,18 @@ def _native_attention(
 ) -> torch.Tensor:
     if return_lse:
         raise ValueError("Native attention backend does not support setting `return_lse=True`.")
-    if _parallel_config is None:
-        query, key, value = (x.permute(0, 2, 1, 3) for x in (query, key, value))
-        out = torch.nn.functional.scaled_dot_product_attention(
-            query=query,
-            key=key,
-            value=value,
-            attn_mask=attn_mask,
-            dropout_p=dropout_p,
-            is_causal=is_causal,
-            scale=scale,
-            enable_gqa=enable_gqa,
-        )
-        out = out.permute(0, 2, 1, 3)
-    else:
-        out = _templated_context_parallel_attention(
-            query,
-            key,
-            value,
-            attn_mask,
-            dropout_p,
-            is_causal,
-            scale,
-            enable_gqa,
-            return_lse,
-            forward_op=_native_attention_forward_op,
-            backward_op=_native_attention_backward_op,
-            _parallel_config=_parallel_config,
-        )
-
+    query, key, value = (x.permute(0, 2, 1, 3) for x in (query, key, value))
+    out = torch.nn.functional.scaled_dot_product_attention(
+        query=query,
+        key=key,
+        value=value,
+        attn_mask=attn_mask,
+        dropout_p=dropout_p,
+        is_causal=is_causal,
+        scale=scale,
+        enable_gqa=enable_gqa,
+    )
+    out = out.permute(0, 2, 1, 3)
     return out
 
 

src/diffusers/models/auto_model.py

@@ -147,13 +147,14 @@ class AutoModel(ConfigMixin):
             "force_download",
             "local_files_only",
             "proxies",
+            "resume_download",
             "revision",
             "token",
         ]
         hub_kwargs = {name: kwargs.pop(name, None) for name in hub_kwargs_names}
 
         # load_config_kwargs uses the same hub kwargs minus subfolder and resume_download
-        load_config_kwargs = {k: v for k, v in hub_kwargs.items() if k not in ["subfolder"]}
+        load_config_kwargs = {k: v for k, v in hub_kwargs.items() if k not in ["subfolder", "resume_download"]}
 
         library = None
         orig_class_name = None
@@ -204,6 +205,7 @@ class AutoModel(ConfigMixin):
                 module_file=module_file,
                 class_name=class_name,
                 **hub_kwargs,
+                **kwargs,
             )
         else:
             from ..pipelines.pipeline_loading_utils import ALL_IMPORTABLE_CLASSES, get_class_obj_and_candidates

src/diffusers/models/autoencoders/__init__.py

@@ -5,8 +5,6 @@ from .autoencoder_kl_allegro import AutoencoderKLAllegro
 from .autoencoder_kl_cogvideox import AutoencoderKLCogVideoX
 from .autoencoder_kl_cosmos import AutoencoderKLCosmos
 from .autoencoder_kl_hunyuan_video import AutoencoderKLHunyuanVideo
-from .autoencoder_kl_hunyuanimage import AutoencoderKLHunyuanImage
-from .autoencoder_kl_hunyuanimage_refiner import AutoencoderKLHunyuanImageRefiner
 from .autoencoder_kl_ltx import AutoencoderKLLTXVideo
 from .autoencoder_kl_magvit import AutoencoderKLMagvit
 from .autoencoder_kl_mochi import AutoencoderKLMochi

src/diffusers/models/autoencoders/autoencoder_asym_kl.py

@@ -20,10 +20,10 @@ from ...configuration_utils import ConfigMixin, register_to_config
 from ...utils.accelerate_utils import apply_forward_hook
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
-from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution, Encoder, MaskConditionDecoder
+from .vae import DecoderOutput, DiagonalGaussianDistribution, Encoder, MaskConditionDecoder
 
 
-class AsymmetricAutoencoderKL(ModelMixin, AutoencoderMixin, ConfigMixin):
+class AsymmetricAutoencoderKL(ModelMixin, ConfigMixin):
     r"""
     Designing a Better Asymmetric VQGAN for StableDiffusion https://huggingface.co/papers/2306.04632 . A VAE model with
     KL loss for encoding images into latents and decoding latent representations into images.
@@ -107,6 +107,9 @@ class AsymmetricAutoencoderKL(ModelMixin, AutoencoderMixin, ConfigMixin):
         self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1)
         self.post_quant_conv = nn.Conv2d(latent_channels, latent_channels, 1)
 
+        self.use_slicing = False
+        self.use_tiling = False
+
         self.register_to_config(block_out_channels=up_block_out_channels)
         self.register_to_config(force_upcast=False)
 

src/diffusers/models/autoencoders/autoencoder_dc.py

@@ -27,7 +27,7 @@ from ..attention_processor import SanaMultiscaleLinearAttention
 from ..modeling_utils import ModelMixin
 from ..normalization import RMSNorm, get_normalization
 from ..transformers.sana_transformer import GLUMBConv
-from .vae import AutoencoderMixin, DecoderOutput, EncoderOutput
+from .vae import DecoderOutput, EncoderOutput
 
 
 class ResBlock(nn.Module):
@@ -378,7 +378,7 @@ class Decoder(nn.Module):
         return hidden_states
 
 
-class AutoencoderDC(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalModelMixin):
+class AutoencoderDC(ModelMixin, ConfigMixin, FromOriginalModelMixin):
     r"""
     An Autoencoder model introduced in [DCAE](https://huggingface.co/papers/2410.10733) and used in
     [SANA](https://huggingface.co/papers/2410.10629).
@@ -536,6 +536,27 @@ class AutoencoderDC(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalModel
         self.tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
         self.tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
 
+    def disable_tiling(self) -> None:
+        r"""
+        Disable tiled AE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_tiling = False
+
+    def enable_slicing(self) -> None:
+        r"""
+        Enable sliced AE decoding. When this option is enabled, the AE will split the input tensor in slices to compute
+        decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.use_slicing = True
+
+    def disable_slicing(self) -> None:
+        r"""
+        Disable sliced AE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_slicing = False
+
     def _encode(self, x: torch.Tensor) -> torch.Tensor:
         batch_size, num_channels, height, width = x.shape
 

src/diffusers/models/autoencoders/autoencoder_kl.py

@@ -32,10 +32,10 @@ from ..attention_processor import (
 )
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
-from .vae import AutoencoderMixin, Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
+from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
 
 
-class AutoencoderKL(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalModelMixin, PeftAdapterMixin):
+class AutoencoderKL(ModelMixin, ConfigMixin, FromOriginalModelMixin, PeftAdapterMixin):
     r"""
     A VAE model with KL loss for encoding images into latents and decoding latent representations into images.
 
@@ -138,6 +138,35 @@ class AutoencoderKL(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalModel
         self.tile_latent_min_size = int(sample_size / (2 ** (len(self.config.block_out_channels) - 1)))
         self.tile_overlap_factor = 0.25
 
+    def enable_tiling(self, use_tiling: bool = True):
+        r"""
+        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+        processing larger images.
+        """
+        self.use_tiling = use_tiling
+
+    def disable_tiling(self):
+        r"""
+        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.enable_tiling(False)
+
+    def enable_slicing(self):
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.use_slicing = True
+
+    def disable_slicing(self):
+        r"""
+        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_slicing = False
+
     @property
     # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
     def attn_processors(self) -> Dict[str, AttentionProcessor]:

src/diffusers/models/autoencoders/autoencoder_kl_allegro.py

@@ -28,7 +28,6 @@ from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
 from ..resnet import ResnetBlock2D
 from ..upsampling import Upsample2D
-from .vae import AutoencoderMixin
 
 
 class AllegroTemporalConvLayer(nn.Module):
@@ -674,7 +673,7 @@ class AllegroDecoder3D(nn.Module):
         return sample
 
 
-class AutoencoderKLAllegro(ModelMixin, AutoencoderMixin, ConfigMixin):
+class AutoencoderKLAllegro(ModelMixin, ConfigMixin):
     r"""
     A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos. Used in
     [Allegro](https://github.com/rhymes-ai/Allegro).
@@ -796,6 +795,35 @@ class AutoencoderKLAllegro(ModelMixin, AutoencoderMixin, ConfigMixin):
             sample_size - self.tile_overlap_w,
         )
 
+    def enable_tiling(self) -> None:
+        r"""
+        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+        processing larger images.
+        """
+        self.use_tiling = True
+
+    def disable_tiling(self) -> None:
+        r"""
+        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_tiling = False
+
+    def enable_slicing(self) -> None:
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.use_slicing = True
+
+    def disable_slicing(self) -> None:
+        r"""
+        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_slicing = False
+
     def _encode(self, x: torch.Tensor) -> torch.Tensor:
         # TODO(aryan)
         # if self.use_tiling and (width > self.tile_sample_min_width or height > self.tile_sample_min_height):

src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py

@@ -29,7 +29,7 @@ from ..downsampling import CogVideoXDownsample3D
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
 from ..upsampling import CogVideoXUpsample3D
-from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution
+from .vae import DecoderOutput, DiagonalGaussianDistribution
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
@@ -955,7 +955,7 @@ class CogVideoXDecoder3D(nn.Module):
         return hidden_states, new_conv_cache
 
 
-class AutoencoderKLCogVideoX(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalModelMixin):
+class AutoencoderKLCogVideoX(ModelMixin, ConfigMixin, FromOriginalModelMixin):
     r"""
     A VAE model with KL loss for encoding images into latents and decoding latent representations into images. Used in
     [CogVideoX](https://github.com/THUDM/CogVideo).
@@ -1124,6 +1124,27 @@ class AutoencoderKLCogVideoX(ModelMixin, AutoencoderMixin, ConfigMixin, FromOrig
         self.tile_overlap_factor_height = tile_overlap_factor_height or self.tile_overlap_factor_height
         self.tile_overlap_factor_width = tile_overlap_factor_width or self.tile_overlap_factor_width
 
+    def disable_tiling(self) -> None:
+        r"""
+        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_tiling = False
+
+    def enable_slicing(self) -> None:
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.use_slicing = True
+
+    def disable_slicing(self) -> None:
+        r"""
+        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_slicing = False
+
     def _encode(self, x: torch.Tensor) -> torch.Tensor:
         batch_size, num_channels, num_frames, height, width = x.shape
 

src/diffusers/models/autoencoders/autoencoder_kl_cosmos.py

@@ -24,7 +24,7 @@ from ...utils import get_logger
 from ...utils.accelerate_utils import apply_forward_hook
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
-from .vae import AutoencoderMixin, DecoderOutput, IdentityDistribution
+from .vae import DecoderOutput, IdentityDistribution
 
 
 logger = get_logger(__name__)
@@ -875,7 +875,7 @@ class CosmosDecoder3d(nn.Module):
         return hidden_states
 
 
-class AutoencoderKLCosmos(ModelMixin, AutoencoderMixin, ConfigMixin):
+class AutoencoderKLCosmos(ModelMixin, ConfigMixin):
     r"""
     Autoencoder used in [Cosmos](https://huggingface.co/papers/2501.03575).
 
@@ -1031,6 +1031,27 @@ class AutoencoderKLCosmos(ModelMixin, AutoencoderMixin, ConfigMixin):
         self.tile_sample_stride_width = tile_sample_stride_width or self.tile_sample_stride_width
         self.tile_sample_stride_num_frames = tile_sample_stride_num_frames or self.tile_sample_stride_num_frames
 
+    def disable_tiling(self) -> None:
+        r"""
+        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_tiling = False
+
+    def enable_slicing(self) -> None:
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.use_slicing = True
+
+    def disable_slicing(self) -> None:
+        r"""
+        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_slicing = False
+
     def _encode(self, x: torch.Tensor) -> torch.Tensor:
         x = self.encoder(x)
         enc = self.quant_conv(x)

src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py

@@ -26,7 +26,7 @@ from ..activations import get_activation
 from ..attention_processor import Attention
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
-from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution
+from .vae import DecoderOutput, DiagonalGaussianDistribution
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
@@ -624,7 +624,7 @@ class HunyuanVideoDecoder3D(nn.Module):
         return hidden_states
 
 
-class AutoencoderKLHunyuanVideo(ModelMixin, AutoencoderMixin, ConfigMixin):
+class AutoencoderKLHunyuanVideo(ModelMixin, ConfigMixin):
     r"""
     A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos.
     Introduced in [HunyuanVideo](https://huggingface.co/papers/2412.03603).
@@ -763,6 +763,27 @@ class AutoencoderKLHunyuanVideo(ModelMixin, AutoencoderMixin, ConfigMixin):
         self.tile_sample_stride_width = tile_sample_stride_width or self.tile_sample_stride_width
         self.tile_sample_stride_num_frames = tile_sample_stride_num_frames or self.tile_sample_stride_num_frames
 
+    def disable_tiling(self) -> None:
+        r"""
+        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_tiling = False
+
+    def enable_slicing(self) -> None:
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.use_slicing = True
+
+    def disable_slicing(self) -> None:
+        r"""
+        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_slicing = False
+
     def _encode(self, x: torch.Tensor) -> torch.Tensor:
         batch_size, num_channels, num_frames, height, width = x.shape
 

src/diffusers/models/autoencoders/autoencoder_kl_hunyuanimage.py

@@ -1,709 +0,0 @@
-# Copyright 2025 The Hunyuan Team and 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 typing import Optional, Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint
-
-from ...configuration_utils import ConfigMixin, register_to_config
-from ...loaders import FromOriginalModelMixin
-from ...utils import logging
-from ...utils.accelerate_utils import apply_forward_hook
-from ..activations import get_activation
-from ..modeling_outputs import AutoencoderKLOutput
-from ..modeling_utils import ModelMixin
-from .vae import DecoderOutput, DiagonalGaussianDistribution
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class HunyuanImageResnetBlock(nn.Module):
-    r"""
-    Residual block with two convolutions and optional channel change.
-
-    Args:
-        in_channels (int): Number of input channels.
-        out_channels (int): Number of output channels.
-        non_linearity (str, optional): Type of non-linearity to use. Default is "silu".
-    """
-
-    def __init__(self, in_channels: int, out_channels: int, non_linearity: str = "silu") -> None:
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.nonlinearity = get_activation(non_linearity)
-
-        # layers
-        self.norm1 = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
-        self.norm2 = nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=1e-6, affine=True)
-        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
-        if in_channels != out_channels:
-            self.conv_shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
-        else:
-            self.conv_shortcut = None
-
-    def forward(self, x):
-        # Apply shortcut connection
-        residual = x
-
-        # First normalization and activation
-        x = self.norm1(x)
-        x = self.nonlinearity(x)
-
-        x = self.conv1(x)
-        x = self.norm2(x)
-        x = self.nonlinearity(x)
-        x = self.conv2(x)
-
-        if self.conv_shortcut is not None:
-            x = self.conv_shortcut(x)
-        # Add residual connection
-        return x + residual
-
-
-class HunyuanImageAttentionBlock(nn.Module):
-    r"""
-    Self-attention with a single head.
-
-    Args:
-        in_channels (int): The number of channels in the input tensor.
-    """
-
-    def __init__(self, in_channels: int):
-        super().__init__()
-
-        # layers
-        self.norm = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-        self.to_q = nn.Conv2d(in_channels, in_channels, 1)
-        self.to_k = nn.Conv2d(in_channels, in_channels, 1)
-        self.to_v = nn.Conv2d(in_channels, in_channels, 1)
-        self.proj = nn.Conv2d(in_channels, in_channels, 1)
-
-    def forward(self, x):
-        identity = x
-        x = self.norm(x)
-
-        # compute query, key, value
-        query = self.to_q(x)
-        key = self.to_k(x)
-        value = self.to_v(x)
-
-        batch_size, channels, height, width = query.shape
-        query = query.permute(0, 2, 3, 1).reshape(batch_size, height * width, channels).contiguous()
-        key = key.permute(0, 2, 3, 1).reshape(batch_size, height * width, channels).contiguous()
-        value = value.permute(0, 2, 3, 1).reshape(batch_size, height * width, channels).contiguous()
-
-        # apply attention
-        x = F.scaled_dot_product_attention(query, key, value)
-
-        x = x.reshape(batch_size, height, width, channels).permute(0, 3, 1, 2)
-        # output projection
-        x = self.proj(x)
-
-        return x + identity
-
-
-class HunyuanImageDownsample(nn.Module):
-    """
-    Downsampling block for spatial reduction.
-
-    Args:
-        in_channels (int): Number of input channels.
-        out_channels (int): Number of output channels.
-    """
-
-    def __init__(self, in_channels: int, out_channels: int):
-        super().__init__()
-        factor = 4
-        if out_channels % factor != 0:
-            raise ValueError(f"out_channels % factor != 0: {out_channels % factor}")
-
-        self.conv = nn.Conv2d(in_channels, out_channels // factor, kernel_size=3, stride=1, padding=1)
-        self.group_size = factor * in_channels // out_channels
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        h = self.conv(x)
-
-        B, C, H, W = h.shape
-        h = h.reshape(B, C, H // 2, 2, W // 2, 2)
-        h = h.permute(0, 3, 5, 1, 2, 4)  # b, r1, r2, c, h, w
-        h = h.reshape(B, 4 * C, H // 2, W // 2)
-
-        B, C, H, W = x.shape
-        shortcut = x.reshape(B, C, H // 2, 2, W // 2, 2)
-        shortcut = shortcut.permute(0, 3, 5, 1, 2, 4)  # b, r1, r2, c, h, w
-        shortcut = shortcut.reshape(B, 4 * C, H // 2, W // 2)
-
-        B, C, H, W = shortcut.shape
-        shortcut = shortcut.view(B, h.shape[1], self.group_size, H, W).mean(dim=2)
-        return h + shortcut
-
-
-class HunyuanImageUpsample(nn.Module):
-    """
-    Upsampling block for spatial expansion.
-
-    Args:
-        in_channels (int): Number of input channels.
-        out_channels (int): Number of output channels.
-    """
-
-    def __init__(self, in_channels: int, out_channels: int):
-        super().__init__()
-        factor = 4
-        self.conv = nn.Conv2d(in_channels, out_channels * factor, kernel_size=3, stride=1, padding=1)
-        self.repeats = factor * out_channels // in_channels
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        h = self.conv(x)
-
-        B, C, H, W = h.shape
-        h = h.reshape(B, 2, 2, C // 4, H, W)  # b, r1, r2, c, h, w
-        h = h.permute(0, 3, 4, 1, 5, 2)  # b, c, h, r1, w, r2
-        h = h.reshape(B, C // 4, H * 2, W * 2)
-
-        shortcut = x.repeat_interleave(repeats=self.repeats, dim=1)
-
-        B, C, H, W = shortcut.shape
-        shortcut = shortcut.reshape(B, 2, 2, C // 4, H, W)  # b, r1, r2, c, h, w
-        shortcut = shortcut.permute(0, 3, 4, 1, 5, 2)  # b, c, h, r1, w, r2
-        shortcut = shortcut.reshape(B, C // 4, H * 2, W * 2)
-        return h + shortcut
-
-
-class HunyuanImageMidBlock(nn.Module):
-    """
-    Middle block for HunyuanImageVAE encoder and decoder.
-
-    Args:
-        in_channels (int): Number of input channels.
-        num_layers (int): Number of layers.
-    """
-
-    def __init__(self, in_channels: int, num_layers: int = 1):
-        super().__init__()
-
-        resnets = [HunyuanImageResnetBlock(in_channels=in_channels, out_channels=in_channels)]
-
-        attentions = []
-        for _ in range(num_layers):
-            attentions.append(HunyuanImageAttentionBlock(in_channels))
-            resnets.append(HunyuanImageResnetBlock(in_channels=in_channels, out_channels=in_channels))
-
-        self.resnets = nn.ModuleList(resnets)
-        self.attentions = nn.ModuleList(attentions)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.resnets[0](x)
-
-        for attn, resnet in zip(self.attentions, self.resnets[1:]):
-            x = attn(x)
-            x = resnet(x)
-
-        return x
-
-
-class HunyuanImageEncoder2D(nn.Module):
-    r"""
-    Encoder network that compresses input to latent representation.
-
-    Args:
-        in_channels (int): Number of input channels.
-        z_channels (int): Number of latent channels.
-        block_out_channels (list of int): Output channels for each block.
-        num_res_blocks (int): Number of residual blocks per block.
-        spatial_compression_ratio (int): Spatial downsampling factor.
-        non_linearity (str): Type of non-linearity to use. Default is "silu".
-        downsample_match_channel (bool): Whether to match channels during downsampling.
-    """
-
-    def __init__(
-        self,
-        in_channels: int,
-        z_channels: int,
-        block_out_channels: Tuple[int, ...],
-        num_res_blocks: int,
-        spatial_compression_ratio: int,
-        non_linearity: str = "silu",
-        downsample_match_channel: bool = True,
-    ):
-        super().__init__()
-        if block_out_channels[-1] % (2 * z_channels) != 0:
-            raise ValueError(
-                f"block_out_channels[-1 has to be divisible by 2 * out_channels, you have block_out_channels = {block_out_channels[-1]} and out_channels = {z_channels}"
-            )
-
-        self.in_channels = in_channels
-        self.z_channels = z_channels
-        self.block_out_channels = block_out_channels
-        self.num_res_blocks = num_res_blocks
-        self.spatial_compression_ratio = spatial_compression_ratio
-
-        self.group_size = block_out_channels[-1] // (2 * z_channels)
-        self.nonlinearity = get_activation(non_linearity)
-
-        # init block
-        self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, stride=1, padding=1)
-
-        # downsample blocks
-        self.down_blocks = nn.ModuleList([])
-
-        block_in_channel = block_out_channels[0]
-        for i in range(len(block_out_channels)):
-            block_out_channel = block_out_channels[i]
-            # residual blocks
-            for _ in range(num_res_blocks):
-                self.down_blocks.append(
-                    HunyuanImageResnetBlock(in_channels=block_in_channel, out_channels=block_out_channel)
-                )
-                block_in_channel = block_out_channel
-
-            # downsample block
-            if i < np.log2(spatial_compression_ratio) and i != len(block_out_channels) - 1:
-                if downsample_match_channel:
-                    block_out_channel = block_out_channels[i + 1]
-                self.down_blocks.append(
-                    HunyuanImageDownsample(in_channels=block_in_channel, out_channels=block_out_channel)
-                )
-                block_in_channel = block_out_channel
-
-        # middle blocks
-        self.mid_block = HunyuanImageMidBlock(in_channels=block_out_channels[-1], num_layers=1)
-
-        # output blocks
-        # Output layers
-        self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_out_channels[-1], eps=1e-6, affine=True)
-        self.conv_out = nn.Conv2d(block_out_channels[-1], 2 * z_channels, kernel_size=3, stride=1, padding=1)
-
-        self.gradient_checkpointing = False
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.conv_in(x)
-
-        ## downsamples
-        for down_block in self.down_blocks:
-            if torch.is_grad_enabled() and self.gradient_checkpointing:
-                x = self._gradient_checkpointing_func(down_block, x)
-            else:
-                x = down_block(x)
-
-        ## middle
-        if torch.is_grad_enabled() and self.gradient_checkpointing:
-            x = self._gradient_checkpointing_func(self.mid_block, x)
-        else:
-            x = self.mid_block(x)
-
-        ## head
-        B, C, H, W = x.shape
-        residual = x.view(B, C // self.group_size, self.group_size, H, W).mean(dim=2)
-
-        x = self.norm_out(x)
-        x = self.nonlinearity(x)
-        x = self.conv_out(x)
-        return x + residual
-
-
-class HunyuanImageDecoder2D(nn.Module):
-    r"""
-    Decoder network that reconstructs output from latent representation.
-
-    Args:
-    z_channels : int
-        Number of latent channels.
-    out_channels : int
-        Number of output channels.
-    block_out_channels : Tuple[int, ...]
-        Output channels for each block.
-    num_res_blocks : int
-        Number of residual blocks per block.
-    spatial_compression_ratio : int
-        Spatial upsampling factor.
-    upsample_match_channel : bool
-        Whether to match channels during upsampling.
-    non_linearity (str): Type of non-linearity to use. Default is "silu".
-    """
-
-    def __init__(
-        self,
-        z_channels: int,
-        out_channels: int,
-        block_out_channels: Tuple[int, ...],
-        num_res_blocks: int,
-        spatial_compression_ratio: int,
-        upsample_match_channel: bool = True,
-        non_linearity: str = "silu",
-    ):
-        super().__init__()
-        if block_out_channels[0] % z_channels != 0:
-            raise ValueError(
-                f"block_out_channels[0] should be divisible by z_channels but has block_out_channels[0] = {block_out_channels[0]} and z_channels = {z_channels}"
-            )
-
-        self.z_channels = z_channels
-        self.block_out_channels = block_out_channels
-        self.num_res_blocks = num_res_blocks
-        self.repeat = block_out_channels[0] // z_channels
-        self.spatial_compression_ratio = spatial_compression_ratio
-        self.nonlinearity = get_activation(non_linearity)
-
-        self.conv_in = nn.Conv2d(z_channels, block_out_channels[0], kernel_size=3, stride=1, padding=1)
-
-        # Middle blocks with attention
-        self.mid_block = HunyuanImageMidBlock(in_channels=block_out_channels[0], num_layers=1)
-
-        # Upsampling blocks
-        block_in_channel = block_out_channels[0]
-        self.up_blocks = nn.ModuleList()
-        for i in range(len(block_out_channels)):
-            block_out_channel = block_out_channels[i]
-            for _ in range(self.num_res_blocks + 1):
-                self.up_blocks.append(
-                    HunyuanImageResnetBlock(in_channels=block_in_channel, out_channels=block_out_channel)
-                )
-                block_in_channel = block_out_channel
-
-            if i < np.log2(spatial_compression_ratio) and i != len(block_out_channels) - 1:
-                if upsample_match_channel:
-                    block_out_channel = block_out_channels[i + 1]
-                self.up_blocks.append(HunyuanImageUpsample(block_in_channel, block_out_channel))
-                block_in_channel = block_out_channel
-
-        # Output layers
-        self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_out_channels[-1], eps=1e-6, affine=True)
-        self.conv_out = nn.Conv2d(block_out_channels[-1], out_channels, kernel_size=3, stride=1, padding=1)
-
-        self.gradient_checkpointing = False
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        h = self.conv_in(x) + x.repeat_interleave(repeats=self.repeat, dim=1)
-
-        if torch.is_grad_enabled() and self.gradient_checkpointing:
-            h = self._gradient_checkpointing_func(self.mid_block, h)
-        else:
-            h = self.mid_block(h)
-
-        for up_block in self.up_blocks:
-            if torch.is_grad_enabled() and self.gradient_checkpointing:
-                h = self._gradient_checkpointing_func(up_block, h)
-            else:
-                h = up_block(h)
-        h = self.norm_out(h)
-        h = self.nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-
-class AutoencoderKLHunyuanImage(ModelMixin, ConfigMixin, FromOriginalModelMixin):
-    r"""
-    A VAE model for 2D images with spatial tiling support.
-
-    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
-    for all models (such as downloading or saving).
-    """
-
-    _supports_gradient_checkpointing = False
-
-    # fmt: off
-    @register_to_config
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        latent_channels: int,
-        block_out_channels: Tuple[int, ...],
-        layers_per_block: int,
-        spatial_compression_ratio: int,
-        sample_size: int,
-        scaling_factor: float = None,
-        downsample_match_channel: bool = True,
-        upsample_match_channel: bool = True,
-    ) -> None:
-    # fmt: on
-        super().__init__()
-
-        self.encoder = HunyuanImageEncoder2D(
-            in_channels=in_channels,
-            z_channels=latent_channels,
-            block_out_channels=block_out_channels,
-            num_res_blocks=layers_per_block,
-            spatial_compression_ratio=spatial_compression_ratio,
-            downsample_match_channel=downsample_match_channel,
-        )
-
-        self.decoder = HunyuanImageDecoder2D(
-            z_channels=latent_channels,
-            out_channels=out_channels,
-            block_out_channels=list(reversed(block_out_channels)),
-            num_res_blocks=layers_per_block,
-            spatial_compression_ratio=spatial_compression_ratio,
-            upsample_match_channel=upsample_match_channel,
-        )
-
-        # Tiling and slicing configuration
-        self.use_slicing = False
-        self.use_tiling = False
-
-        # Tiling parameters
-        self.tile_sample_min_size = sample_size
-        self.tile_latent_min_size = sample_size // spatial_compression_ratio
-        self.tile_overlap_factor = 0.25
-
-    def enable_tiling(
-        self,
-        tile_sample_min_size: Optional[int] = None,
-        tile_overlap_factor: Optional[float] = None,
-    ) -> None:
-        r"""
-        Enable spatial tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles
-        to compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to
-        allow processing larger images.
-
-        Args:
-            tile_sample_min_size (`int`, *optional*):
-                The minimum size required for a sample to be separated into tiles across the spatial dimension.
-            tile_overlap_factor (`float`, *optional*):
-                The overlap factor required for a latent to be separated into tiles across the spatial dimension.
-        """
-        self.use_tiling = True
-        self.tile_sample_min_size = tile_sample_min_size or self.tile_sample_min_size
-        self.tile_overlap_factor = tile_overlap_factor or self.tile_overlap_factor
-        self.tile_latent_min_size = self.tile_sample_min_size // self.config.spatial_compression_ratio
-
-    def disable_tiling(self) -> None:
-        r"""
-        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_tiling = False
-
-    def enable_slicing(self) -> None:
-        r"""
-        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
-        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.use_slicing = True
-
-    def disable_slicing(self) -> None:
-        r"""
-        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_slicing = False
-
-    def _encode(self, x: torch.Tensor):
-
-        batch_size, num_channels, height, width = x.shape
-
-        if self.use_tiling and (width > self.tile_sample_min_size or height > self.tile_sample_min_size):
-            return self.tiled_encode(x)
-
-        enc = self.encoder(x)
-
-        return enc
-
-    @apply_forward_hook
-    def encode(
-        self, x: torch.Tensor, return_dict: bool = True
-    ) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]:
-        r"""
-        Encode a batch of images into latents.
-
-        Args:
-            x (`torch.Tensor`): Input batch of images.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.
-
-        Returns:
-                The latent representations of the encoded videos. If `return_dict` is True, a
-                [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
-        """
-        if self.use_slicing and x.shape[0] > 1:
-            encoded_slices = [self._encode(x_slice) for x_slice in x.split(1)]
-            h = torch.cat(encoded_slices)
-        else:
-            h = self._encode(x)
-        posterior = DiagonalGaussianDistribution(h)
-
-        if not return_dict:
-            return (posterior,)
-        return AutoencoderKLOutput(latent_dist=posterior)
-
-    def _decode(self, z: torch.Tensor, return_dict: bool = True):
-
-        batch_size, num_channels, height, width = z.shape
-
-        if self.use_tiling and (width > self.tile_latent_min_size or height > self.tile_latent_min_size):
-            return self.tiled_decode(z, return_dict=return_dict)
-
-        dec = self.decoder(z)
-
-        if not return_dict:
-            return (dec,)
-
-        return DecoderOutput(sample=dec)
-
-    @apply_forward_hook
-    def decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
-        r"""
-        Decode a batch of images.
-
-        Args:
-            z (`torch.Tensor`): Input batch of latent vectors.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.
-
-        Returns:
-            [`~models.vae.DecoderOutput`] or `tuple`:
-                If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
-                returned.
-        """
-        if self.use_slicing and z.shape[0] > 1:
-            decoded_slices = [self._decode(z_slice).sample for z_slice in z.split(1)]
-            decoded = torch.cat(decoded_slices)
-        else:
-            decoded = self._decode(z).sample
-
-        if not return_dict:
-            return (decoded,)
-        return DecoderOutput(sample=decoded)
-
-
-    def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
-        blend_extent = min(a.shape[-2], b.shape[-2], blend_extent)
-        for y in range(blend_extent):
-            b[:, :, y, :] = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (
-                y / blend_extent
-            )
-        return b
-
-    def blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
-        blend_extent = min(a.shape[-1], b.shape[-1], blend_extent)
-        for x in range(blend_extent):
-            b[:, :, :, x] = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (
-                x / blend_extent
-            )
-        return b
-
-    def tiled_encode(self, x: torch.Tensor) -> torch.Tensor:
-        """
-        Encode input using spatial tiling strategy.
-
-        Args:
-            x (`torch.Tensor`): Input tensor of shape (B, C, T, H, W).
-
-        Returns:
-            `torch.Tensor`:
-                The latent representation of the encoded images.
-        """
-        _, _, _, height, width = x.shape
-        overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor))
-        blend_extent = int(self.tile_latent_min_size * self.tile_overlap_factor)
-        row_limit = self.tile_latent_min_size - blend_extent
-
-        rows = []
-        for i in range(0, height, overlap_size):
-            row = []
-            for j in range(0, width, overlap_size):
-                tile = x[:, :, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
-                tile = self.encoder(tile)
-                row.append(tile)
-            rows.append(row)
-
-        result_rows = []
-        for i, row in enumerate(rows):
-            result_row = []
-            for j, tile in enumerate(row):
-                if i > 0:
-                    tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
-                if j > 0:
-                    tile = self.blend_h(row[j - 1], tile, blend_extent)
-                result_row.append(tile[:, :, :, :row_limit, :row_limit])
-            result_rows.append(torch.cat(result_row, dim=-1))
-
-        moments = torch.cat(result_rows, dim=-2)
-
-        return moments
-
-    def tiled_decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
-        """
-        Decode latent using spatial tiling strategy.
-
-        Args:
-            z (`torch.Tensor`): Latent tensor of shape (B, C, H, W).
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.
-
-        Returns:
-            [`~models.vae.DecoderOutput`] or `tuple`:
-                If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
-                returned.
-        """
-        _, _, height, width = z.shape
-        overlap_size = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor))
-        blend_extent = int(self.tile_sample_min_size * self.tile_overlap_factor)
-        row_limit = self.tile_sample_min_size - blend_extent
-
-        rows = []
-        for i in range(0, height, overlap_size):
-            row = []
-            for j in range(0, width, overlap_size):
-                tile = z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
-                decoded = self.decoder(tile)
-                row.append(decoded)
-            rows.append(row)
-
-        result_rows = []
-        for i, row in enumerate(rows):
-            result_row = []
-            for j, tile in enumerate(row):
-                if i > 0:
-                    tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
-                if j > 0:
-                    tile = self.blend_h(row[j - 1], tile, blend_extent)
-                result_row.append(tile[:, :, :row_limit, :row_limit])
-            result_rows.append(torch.cat(result_row, dim=-1))
-
-        dec = torch.cat(result_rows, dim=-2)
-        if not return_dict:
-            return (dec,)
-        return DecoderOutput(sample=dec)
-
-
-    def forward(
-        self,
-        sample: torch.Tensor,
-        sample_posterior: bool = False,
-        return_dict: bool = True,
-        generator: Optional[torch.Generator] = None,
-    ) -> Union[DecoderOutput, torch.Tensor]:
-        """
-        Args:
-            sample (`torch.Tensor`): Input sample.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
-        """
-        posterior = self.encode(sample).latent_dist
-        if sample_posterior:
-            z = posterior.sample(generator=generator)
-        else:
-            z = posterior.mode()
-        dec = self.decode(z, return_dict=return_dict)
-
-        return dec

src/diffusers/models/autoencoders/autoencoder_kl_hunyuanimage_refiner.py

@@ -1,934 +0,0 @@
-# Copyright 2025 The Hunyuan Team and 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 typing import Optional, Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint
-
-from ...configuration_utils import ConfigMixin, register_to_config
-from ...utils import logging
-from ...utils.accelerate_utils import apply_forward_hook
-from ..activations import get_activation
-from ..modeling_outputs import AutoencoderKLOutput
-from ..modeling_utils import ModelMixin
-from .vae import DecoderOutput, DiagonalGaussianDistribution
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class HunyuanImageRefinerCausalConv3d(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        kernel_size: Union[int, Tuple[int, int, int]] = 3,
-        stride: Union[int, Tuple[int, int, int]] = 1,
-        padding: Union[int, Tuple[int, int, int]] = 0,
-        dilation: Union[int, Tuple[int, int, int]] = 1,
-        bias: bool = True,
-        pad_mode: str = "replicate",
-    ) -> None:
-        super().__init__()
-
-        kernel_size = (kernel_size, kernel_size, kernel_size) if isinstance(kernel_size, int) else kernel_size
-
-        self.pad_mode = pad_mode
-        self.time_causal_padding = (
-            kernel_size[0] // 2,
-            kernel_size[0] // 2,
-            kernel_size[1] // 2,
-            kernel_size[1] // 2,
-            kernel_size[2] - 1,
-            0,
-        )
-
-        self.conv = nn.Conv3d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias=bias)
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = F.pad(hidden_states, self.time_causal_padding, mode=self.pad_mode)
-        return self.conv(hidden_states)
-
-
-class HunyuanImageRefinerRMS_norm(nn.Module):
-    r"""
-    A custom RMS normalization layer.
-
-    Args:
-        dim (int): The number of dimensions to normalize over.
-        channel_first (bool, optional): Whether the input tensor has channels as the first dimension.
-            Default is True.
-        images (bool, optional): Whether the input represents image data. Default is True.
-        bias (bool, optional): Whether to include a learnable bias term. Default is False.
-    """
-
-    def __init__(self, dim: int, channel_first: bool = True, images: bool = True, bias: bool = False) -> None:
-        super().__init__()
-        broadcastable_dims = (1, 1, 1) if not images else (1, 1)
-        shape = (dim, *broadcastable_dims) if channel_first else (dim,)
-
-        self.channel_first = channel_first
-        self.scale = dim**0.5
-        self.gamma = nn.Parameter(torch.ones(shape))
-        self.bias = nn.Parameter(torch.zeros(shape)) if bias else 0.0
-
-    def forward(self, x):
-        return F.normalize(x, dim=(1 if self.channel_first else -1)) * self.scale * self.gamma + self.bias
-
-
-class HunyuanImageRefinerAttnBlock(nn.Module):
-    def __init__(self, in_channels: int):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = HunyuanImageRefinerRMS_norm(in_channels, images=False)
-
-        self.to_q = nn.Conv3d(in_channels, in_channels, kernel_size=1)
-        self.to_k = nn.Conv3d(in_channels, in_channels, kernel_size=1)
-        self.to_v = nn.Conv3d(in_channels, in_channels, kernel_size=1)
-        self.proj_out = nn.Conv3d(in_channels, in_channels, kernel_size=1)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        identity = x
-
-        x = self.norm(x)
-
-        query = self.to_q(x)
-        key = self.to_k(x)
-        value = self.to_v(x)
-
-        batch_size, channels, frames, height, width = query.shape
-
-        query = query.reshape(batch_size, channels, frames * height * width).permute(0, 2, 1).unsqueeze(1).contiguous()
-        key = key.reshape(batch_size, channels, frames * height * width).permute(0, 2, 1).unsqueeze(1).contiguous()
-        value = value.reshape(batch_size, channels, frames * height * width).permute(0, 2, 1).unsqueeze(1).contiguous()
-
-        x = nn.functional.scaled_dot_product_attention(query, key, value, attn_mask=None)
-
-        # batch_size, 1, frames * height * width, channels
-
-        x = x.squeeze(1).reshape(batch_size, frames, height, width, channels).permute(0, 4, 1, 2, 3)
-        x = self.proj_out(x)
-
-        return x + identity
-
-
-class HunyuanImageRefinerUpsampleDCAE(nn.Module):
-    def __init__(self, in_channels: int, out_channels: int, add_temporal_upsample: bool = True):
-        super().__init__()
-        factor = 2 * 2 * 2 if add_temporal_upsample else 1 * 2 * 2
-        self.conv = HunyuanImageRefinerCausalConv3d(in_channels, out_channels * factor, kernel_size=3)
-
-        self.add_temporal_upsample = add_temporal_upsample
-        self.repeats = factor * out_channels // in_channels
-
-    @staticmethod
-    def _dcae_upsample_rearrange(tensor, r1=1, r2=2, r3=2):
-        """
-        Convert (b, r1*r2*r3*c, f, h, w) -> (b, c, r1*f, r2*h, r3*w)
-
-        Args:
-            tensor: Input tensor of shape (b, r1*r2*r3*c, f, h, w)
-            r1: temporal upsampling factor
-            r2: height upsampling factor
-            r3: width upsampling factor
-        """
-        b, packed_c, f, h, w = tensor.shape
-        factor = r1 * r2 * r3
-        c = packed_c // factor
-
-        tensor = tensor.view(b, r1, r2, r3, c, f, h, w)
-        tensor = tensor.permute(0, 4, 5, 1, 6, 2, 7, 3)
-        return tensor.reshape(b, c, f * r1, h * r2, w * r3)
-
-    def forward(self, x: torch.Tensor):
-        r1 = 2 if self.add_temporal_upsample else 1
-        h = self.conv(x)
-        if self.add_temporal_upsample:
-            h = self._dcae_upsample_rearrange(h, r1=1, r2=2, r3=2)
-            h = h[:, : h.shape[1] // 2]
-
-            # shortcut computation
-            shortcut = self._dcae_upsample_rearrange(x, r1=1, r2=2, r3=2)
-            shortcut = shortcut.repeat_interleave(repeats=self.repeats // 2, dim=1)
-
-        else:
-            h = self._dcae_upsample_rearrange(h, r1=r1, r2=2, r3=2)
-            shortcut = x.repeat_interleave(repeats=self.repeats, dim=1)
-            shortcut = self._dcae_upsample_rearrange(shortcut, r1=r1, r2=2, r3=2)
-        return h + shortcut
-
-
-class HunyuanImageRefinerDownsampleDCAE(nn.Module):
-    def __init__(self, in_channels: int, out_channels: int, add_temporal_downsample: bool = True):
-        super().__init__()
-        factor = 2 * 2 * 2 if add_temporal_downsample else 1 * 2 * 2
-        assert out_channels % factor == 0
-        # self.conv = Conv3d(in_channels, out_channels // factor, kernel_size=3, stride=1, padding=1)
-        self.conv = HunyuanImageRefinerCausalConv3d(in_channels, out_channels // factor, kernel_size=3)
-
-        self.add_temporal_downsample = add_temporal_downsample
-        self.group_size = factor * in_channels // out_channels
-
-    @staticmethod
-    def _dcae_downsample_rearrange(tensor, r1=1, r2=2, r3=2):
-        """
-        Convert (b, c, r1*f, r2*h, r3*w) -> (b, r1*r2*r3*c, f, h, w)
-
-        This packs spatial/temporal dimensions into channels (opposite of upsample)
-        """
-        b, c, packed_f, packed_h, packed_w = tensor.shape
-        f, h, w = packed_f // r1, packed_h // r2, packed_w // r3
-
-        tensor = tensor.view(b, c, f, r1, h, r2, w, r3)
-        tensor = tensor.permute(0, 3, 5, 7, 1, 2, 4, 6)
-        return tensor.reshape(b, r1 * r2 * r3 * c, f, h, w)
-
-    def forward(self, x: torch.Tensor):
-        r1 = 2 if self.add_temporal_downsample else 1
-        h = self.conv(x)
-        if self.add_temporal_downsample:
-            # h = rearrange(h, "b c f (h r2) (w r3) -> b (r2 r3 c) f h w", r2=2, r3=2)
-            h = self._dcae_downsample_rearrange(h, r1=1, r2=2, r3=2)
-            h = torch.cat([h, h], dim=1)
-            # shortcut computation
-            # shortcut = rearrange(x, "b c f (h r2) (w r3) -> b (r2 r3 c) f h w", r2=2, r3=2)
-            shortcut = self._dcae_downsample_rearrange(x, r1=1, r2=2, r3=2)
-            B, C, T, H, W = shortcut.shape
-            shortcut = shortcut.view(B, h.shape[1], self.group_size // 2, T, H, W).mean(dim=2)
-        else:
-            # h = rearrange(h, "b c (f r1) (h r2) (w r3) -> b (r1 r2 r3 c) f h w", r1=r1, r2=2, r3=2)
-            h = self._dcae_downsample_rearrange(h, r1=r1, r2=2, r3=2)
-            # shortcut = rearrange(x, "b c (f r1) (h r2) (w r3) -> b (r1 r2 r3 c) f h w", r1=r1, r2=2, r3=2)
-            shortcut = self._dcae_downsample_rearrange(x, r1=r1, r2=2, r3=2)
-            B, C, T, H, W = shortcut.shape
-            shortcut = shortcut.view(B, h.shape[1], self.group_size, T, H, W).mean(dim=2)
-
-        return h + shortcut
-
-
-class HunyuanImageRefinerResnetBlock(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: Optional[int] = None,
-        non_linearity: str = "swish",
-    ) -> None:
-        super().__init__()
-        out_channels = out_channels or in_channels
-
-        self.nonlinearity = get_activation(non_linearity)
-
-        self.norm1 = HunyuanImageRefinerRMS_norm(in_channels, images=False)
-        self.conv1 = HunyuanImageRefinerCausalConv3d(in_channels, out_channels, kernel_size=3)
-
-        self.norm2 = HunyuanImageRefinerRMS_norm(out_channels, images=False)
-        self.conv2 = HunyuanImageRefinerCausalConv3d(out_channels, out_channels, kernel_size=3)
-
-        self.conv_shortcut = None
-        if in_channels != out_channels:
-            self.conv_shortcut = nn.Conv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        residual = hidden_states
-
-        hidden_states = self.norm1(hidden_states)
-        hidden_states = self.nonlinearity(hidden_states)
-        hidden_states = self.conv1(hidden_states)
-
-        hidden_states = self.norm2(hidden_states)
-        hidden_states = self.nonlinearity(hidden_states)
-        hidden_states = self.conv2(hidden_states)
-
-        if self.conv_shortcut is not None:
-            residual = self.conv_shortcut(residual)
-
-        return hidden_states + residual
-
-
-class HunyuanImageRefinerMidBlock(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        num_layers: int = 1,
-        add_attention: bool = True,
-    ) -> None:
-        super().__init__()
-        self.add_attention = add_attention
-
-        # There is always at least one resnet
-        resnets = [
-            HunyuanImageRefinerResnetBlock(
-                in_channels=in_channels,
-                out_channels=in_channels,
-            )
-        ]
-        attentions = []
-
-        for _ in range(num_layers):
-            if self.add_attention:
-                attentions.append(HunyuanImageRefinerAttnBlock(in_channels))
-            else:
-                attentions.append(None)
-
-            resnets.append(
-                HunyuanImageRefinerResnetBlock(
-                    in_channels=in_channels,
-                    out_channels=in_channels,
-                )
-            )
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-        self.gradient_checkpointing = False
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.resnets[0](hidden_states)
-
-        for attn, resnet in zip(self.attentions, self.resnets[1:]):
-            if attn is not None:
-                hidden_states = attn(hidden_states)
-            hidden_states = resnet(hidden_states)
-
-        return hidden_states
-
-
-class HunyuanImageRefinerDownBlock3D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        num_layers: int = 1,
-        downsample_out_channels: Optional[int] = None,
-        add_temporal_downsample: int = True,
-    ) -> None:
-        super().__init__()
-        resnets = []
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            resnets.append(
-                HunyuanImageRefinerResnetBlock(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                )
-            )
-
-        self.resnets = nn.ModuleList(resnets)
-
-        if downsample_out_channels is not None:
-            self.downsamplers = nn.ModuleList(
-                [
-                    HunyuanImageRefinerDownsampleDCAE(
-                        out_channels,
-                        out_channels=downsample_out_channels,
-                        add_temporal_downsample=add_temporal_downsample,
-                    )
-                ]
-            )
-        else:
-            self.downsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        for resnet in self.resnets:
-            hidden_states = resnet(hidden_states)
-
-        if self.downsamplers is not None:
-            for downsampler in self.downsamplers:
-                hidden_states = downsampler(hidden_states)
-
-        return hidden_states
-
-
-class HunyuanImageRefinerUpBlock3D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        num_layers: int = 1,
-        upsample_out_channels: Optional[int] = None,
-        add_temporal_upsample: bool = True,
-    ) -> None:
-        super().__init__()
-        resnets = []
-
-        for i in range(num_layers):
-            input_channels = in_channels if i == 0 else out_channels
-
-            resnets.append(
-                HunyuanImageRefinerResnetBlock(
-                    in_channels=input_channels,
-                    out_channels=out_channels,
-                )
-            )
-
-        self.resnets = nn.ModuleList(resnets)
-
-        if upsample_out_channels is not None:
-            self.upsamplers = nn.ModuleList(
-                [
-                    HunyuanImageRefinerUpsampleDCAE(
-                        out_channels,
-                        out_channels=upsample_out_channels,
-                        add_temporal_upsample=add_temporal_upsample,
-                    )
-                ]
-            )
-        else:
-            self.upsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for resnet in self.resnets:
-                hidden_states = self._gradient_checkpointing_func(resnet, hidden_states)
-
-        else:
-            for resnet in self.resnets:
-                hidden_states = resnet(hidden_states)
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states)
-
-        return hidden_states
-
-
-class HunyuanImageRefinerEncoder3D(nn.Module):
-    r"""
-    3D vae encoder for HunyuanImageRefiner.
-    """
-
-    def __init__(
-        self,
-        in_channels: int = 3,
-        out_channels: int = 64,
-        block_out_channels: Tuple[int, ...] = (128, 256, 512, 1024, 1024),
-        layers_per_block: int = 2,
-        temporal_compression_ratio: int = 4,
-        spatial_compression_ratio: int = 16,
-        downsample_match_channel: bool = True,
-    ) -> None:
-        super().__init__()
-
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.group_size = block_out_channels[-1] // self.out_channels
-
-        self.conv_in = HunyuanImageRefinerCausalConv3d(in_channels, block_out_channels[0], kernel_size=3)
-        self.mid_block = None
-        self.down_blocks = nn.ModuleList([])
-
-        input_channel = block_out_channels[0]
-        for i in range(len(block_out_channels)):
-            add_spatial_downsample = i < np.log2(spatial_compression_ratio)
-            output_channel = block_out_channels[i]
-            if not add_spatial_downsample:
-                down_block = HunyuanImageRefinerDownBlock3D(
-                    num_layers=layers_per_block,
-                    in_channels=input_channel,
-                    out_channels=output_channel,
-                    downsample_out_channels=None,
-                    add_temporal_downsample=False,
-                )
-                input_channel = output_channel
-            else:
-                add_temporal_downsample = i >= np.log2(spatial_compression_ratio // temporal_compression_ratio)
-                downsample_out_channels = block_out_channels[i + 1] if downsample_match_channel else output_channel
-                down_block = HunyuanImageRefinerDownBlock3D(
-                    num_layers=layers_per_block,
-                    in_channels=input_channel,
-                    out_channels=output_channel,
-                    downsample_out_channels=downsample_out_channels,
-                    add_temporal_downsample=add_temporal_downsample,
-                )
-                input_channel = downsample_out_channels
-
-            self.down_blocks.append(down_block)
-
-        self.mid_block = HunyuanImageRefinerMidBlock(in_channels=block_out_channels[-1])
-
-        self.norm_out = HunyuanImageRefinerRMS_norm(block_out_channels[-1], images=False)
-        self.conv_act = nn.SiLU()
-        self.conv_out = HunyuanImageRefinerCausalConv3d(block_out_channels[-1], out_channels, kernel_size=3)
-
-        self.gradient_checkpointing = False
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.conv_in(hidden_states)
-
-        if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for down_block in self.down_blocks:
-                hidden_states = self._gradient_checkpointing_func(down_block, hidden_states)
-
-            hidden_states = self._gradient_checkpointing_func(self.mid_block, hidden_states)
-        else:
-            for down_block in self.down_blocks:
-                hidden_states = down_block(hidden_states)
-
-            hidden_states = self.mid_block(hidden_states)
-
-        # short_cut = rearrange(hidden_states, "b (c r) f h w -> b c r f h w", r=self.group_size).mean(dim=2)
-        batch_size, _, frame, height, width = hidden_states.shape
-        short_cut = hidden_states.view(batch_size, -1, self.group_size, frame, height, width).mean(dim=2)
-
-        hidden_states = self.norm_out(hidden_states)
-        hidden_states = self.conv_act(hidden_states)
-        hidden_states = self.conv_out(hidden_states)
-
-        hidden_states += short_cut
-
-        return hidden_states
-
-
-class HunyuanImageRefinerDecoder3D(nn.Module):
-    r"""
-    Causal decoder for 3D video-like data used for HunyuanImage-2.1 Refiner.
-    """
-
-    def __init__(
-        self,
-        in_channels: int = 32,
-        out_channels: int = 3,
-        block_out_channels: Tuple[int, ...] = (1024, 1024, 512, 256, 128),
-        layers_per_block: int = 2,
-        spatial_compression_ratio: int = 16,
-        temporal_compression_ratio: int = 4,
-        upsample_match_channel: bool = True,
-    ):
-        super().__init__()
-        self.layers_per_block = layers_per_block
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.repeat = block_out_channels[0] // self.in_channels
-
-        self.conv_in = HunyuanImageRefinerCausalConv3d(self.in_channels, block_out_channels[0], kernel_size=3)
-        self.up_blocks = nn.ModuleList([])
-
-        # mid
-        self.mid_block = HunyuanImageRefinerMidBlock(in_channels=block_out_channels[0])
-
-        # up
-        input_channel = block_out_channels[0]
-        for i in range(len(block_out_channels)):
-            output_channel = block_out_channels[i]
-
-            add_spatial_upsample = i < np.log2(spatial_compression_ratio)
-            add_temporal_upsample = i < np.log2(temporal_compression_ratio)
-            if add_spatial_upsample or add_temporal_upsample:
-                upsample_out_channels = block_out_channels[i + 1] if upsample_match_channel else output_channel
-                up_block = HunyuanImageRefinerUpBlock3D(
-                    num_layers=self.layers_per_block + 1,
-                    in_channels=input_channel,
-                    out_channels=output_channel,
-                    upsample_out_channels=upsample_out_channels,
-                    add_temporal_upsample=add_temporal_upsample,
-                )
-                input_channel = upsample_out_channels
-            else:
-                up_block = HunyuanImageRefinerUpBlock3D(
-                    num_layers=self.layers_per_block + 1,
-                    in_channels=input_channel,
-                    out_channels=output_channel,
-                    upsample_out_channels=None,
-                    add_temporal_upsample=False,
-                )
-                input_channel = output_channel
-
-            self.up_blocks.append(up_block)
-
-        # out
-        self.norm_out = HunyuanImageRefinerRMS_norm(block_out_channels[-1], images=False)
-        self.conv_act = nn.SiLU()
-        self.conv_out = HunyuanImageRefinerCausalConv3d(block_out_channels[-1], out_channels, kernel_size=3)
-
-        self.gradient_checkpointing = False
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.conv_in(hidden_states) + hidden_states.repeat_interleave(repeats=self.repeat, dim=1)
-
-        if torch.is_grad_enabled() and self.gradient_checkpointing:
-            hidden_states = self._gradient_checkpointing_func(self.mid_block, hidden_states)
-
-            for up_block in self.up_blocks:
-                hidden_states = self._gradient_checkpointing_func(up_block, hidden_states)
-        else:
-            hidden_states = self.mid_block(hidden_states)
-
-            for up_block in self.up_blocks:
-                hidden_states = up_block(hidden_states)
-
-        # post-process
-        hidden_states = self.norm_out(hidden_states)
-        hidden_states = self.conv_act(hidden_states)
-        hidden_states = self.conv_out(hidden_states)
-        return hidden_states
-
-
-class AutoencoderKLHunyuanImageRefiner(ModelMixin, ConfigMixin):
-    r"""
-    A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos. Used for
-    HunyuanImage-2.1 Refiner.
-
-    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
-    for all models (such as downloading or saving).
-    """
-
-    _supports_gradient_checkpointing = True
-
-    @register_to_config
-    def __init__(
-        self,
-        in_channels: int = 3,
-        out_channels: int = 3,
-        latent_channels: int = 32,
-        block_out_channels: Tuple[int] = (128, 256, 512, 1024, 1024),
-        layers_per_block: int = 2,
-        spatial_compression_ratio: int = 16,
-        temporal_compression_ratio: int = 4,
-        downsample_match_channel: bool = True,
-        upsample_match_channel: bool = True,
-        scaling_factor: float = 1.03682,
-    ) -> None:
-        super().__init__()
-
-        self.encoder = HunyuanImageRefinerEncoder3D(
-            in_channels=in_channels,
-            out_channels=latent_channels * 2,
-            block_out_channels=block_out_channels,
-            layers_per_block=layers_per_block,
-            temporal_compression_ratio=temporal_compression_ratio,
-            spatial_compression_ratio=spatial_compression_ratio,
-            downsample_match_channel=downsample_match_channel,
-        )
-
-        self.decoder = HunyuanImageRefinerDecoder3D(
-            in_channels=latent_channels,
-            out_channels=out_channels,
-            block_out_channels=list(reversed(block_out_channels)),
-            layers_per_block=layers_per_block,
-            temporal_compression_ratio=temporal_compression_ratio,
-            spatial_compression_ratio=spatial_compression_ratio,
-            upsample_match_channel=upsample_match_channel,
-        )
-
-        self.spatial_compression_ratio = spatial_compression_ratio
-        self.temporal_compression_ratio = temporal_compression_ratio
-
-        # When decoding a batch of video latents at a time, one can save memory by slicing across the batch dimension
-        # to perform decoding of a single video latent at a time.
-        self.use_slicing = False
-
-        # When decoding spatially large video latents, the memory requirement is very high. By breaking the video latent
-        # frames spatially into smaller tiles and performing multiple forward passes for decoding, and then blending the
-        # intermediate tiles together, the memory requirement can be lowered.
-        self.use_tiling = False
-
-        # The minimal tile height and width for spatial tiling to be used
-        self.tile_sample_min_height = 256
-        self.tile_sample_min_width = 256
-
-        # The minimal distance between two spatial tiles
-        self.tile_sample_stride_height = 192
-        self.tile_sample_stride_width = 192
-
-        self.tile_overlap_factor = 0.25
-
-    def enable_tiling(
-        self,
-        tile_sample_min_height: Optional[int] = None,
-        tile_sample_min_width: Optional[int] = None,
-        tile_sample_stride_height: Optional[float] = None,
-        tile_sample_stride_width: Optional[float] = None,
-        tile_overlap_factor: Optional[float] = None,
-    ) -> None:
-        r"""
-        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
-        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
-        processing larger images.
-
-        Args:
-            tile_sample_min_height (`int`, *optional*):
-                The minimum height required for a sample to be separated into tiles across the height dimension.
-            tile_sample_min_width (`int`, *optional*):
-                The minimum width required for a sample to be separated into tiles across the width dimension.
-            tile_sample_stride_height (`int`, *optional*):
-                The minimum amount of overlap between two consecutive vertical tiles. This is to ensure that there are
-                no tiling artifacts produced across the height dimension.
-            tile_sample_stride_width (`int`, *optional*):
-                The stride between two consecutive horizontal tiles. This is to ensure that there are no tiling
-                artifacts produced across the width dimension.
-        """
-        self.use_tiling = True
-        self.tile_sample_min_height = tile_sample_min_height or self.tile_sample_min_height
-        self.tile_sample_min_width = tile_sample_min_width or self.tile_sample_min_width
-        self.tile_sample_stride_height = tile_sample_stride_height or self.tile_sample_stride_height
-        self.tile_sample_stride_width = tile_sample_stride_width or self.tile_sample_stride_width
-        self.tile_overlap_factor = tile_overlap_factor or self.tile_overlap_factor
-
-    def disable_tiling(self) -> None:
-        r"""
-        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_tiling = False
-
-    def enable_slicing(self) -> None:
-        r"""
-        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
-        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.use_slicing = True
-
-    def disable_slicing(self) -> None:
-        r"""
-        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_slicing = False
-
-    def _encode(self, x: torch.Tensor) -> torch.Tensor:
-        _, _, _, height, width = x.shape
-
-        if self.use_tiling and (width > self.tile_sample_min_width or height > self.tile_sample_min_height):
-            return self.tiled_encode(x)
-
-        x = self.encoder(x)
-        return x
-
-    @apply_forward_hook
-    def encode(
-        self, x: torch.Tensor, return_dict: bool = True
-    ) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]:
-        r"""
-        Encode a batch of images into latents.
-
-        Args:
-            x (`torch.Tensor`): Input batch of images.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.
-
-        Returns:
-                The latent representations of the encoded videos. If `return_dict` is True, a
-                [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
-        """
-        if self.use_slicing and x.shape[0] > 1:
-            encoded_slices = [self._encode(x_slice) for x_slice in x.split(1)]
-            h = torch.cat(encoded_slices)
-        else:
-            h = self._encode(x)
-
-        posterior = DiagonalGaussianDistribution(h)
-
-        if not return_dict:
-            return (posterior,)
-        return AutoencoderKLOutput(latent_dist=posterior)
-
-    def _decode(self, z: torch.Tensor) -> torch.Tensor:
-        _, _, _, height, width = z.shape
-        tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
-        tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
-
-        if self.use_tiling and (width > tile_latent_min_width or height > tile_latent_min_height):
-            return self.tiled_decode(z)
-
-        dec = self.decoder(z)
-
-        return dec
-
-    @apply_forward_hook
-    def decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
-        r"""
-        Decode a batch of images.
-
-        Args:
-            z (`torch.Tensor`): Input batch of latent vectors.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.
-
-        Returns:
-            [`~models.vae.DecoderOutput`] or `tuple`:
-                If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
-                returned.
-        """
-        if self.use_slicing and z.shape[0] > 1:
-            decoded_slices = [self._decode(z_slice) for z_slice in z.split(1)]
-            decoded = torch.cat(decoded_slices)
-        else:
-            decoded = self._decode(z)
-
-        if not return_dict:
-            return (decoded,)
-
-        return DecoderOutput(sample=decoded)
-
-    def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
-        blend_extent = min(a.shape[-2], b.shape[-2], blend_extent)
-        for y in range(blend_extent):
-            b[:, :, :, y, :] = a[:, :, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, :, y, :] * (
-                y / blend_extent
-            )
-        return b
-
-    def blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
-        blend_extent = min(a.shape[-1], b.shape[-1], blend_extent)
-        for x in range(blend_extent):
-            b[:, :, :, :, x] = a[:, :, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, :, x] * (
-                x / blend_extent
-            )
-        return b
-
-    def blend_t(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
-        blend_extent = min(a.shape[-3], b.shape[-3], blend_extent)
-        for x in range(blend_extent):
-            b[:, :, x, :, :] = a[:, :, -blend_extent + x, :, :] * (1 - x / blend_extent) + b[:, :, x, :, :] * (
-                x / blend_extent
-            )
-        return b
-
-    def tiled_encode(self, x: torch.Tensor) -> torch.Tensor:
-        r"""Encode a batch of images using a tiled encoder.
-
-        Args:
-            x (`torch.Tensor`): Input batch of videos.
-
-        Returns:
-            `torch.Tensor`:
-                The latent representation of the encoded videos.
-        """
-        _, _, _, height, width = x.shape
-
-        tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
-        tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
-        overlap_height = int(tile_latent_min_height * (1 - self.tile_overlap_factor))  # 256 * (1 - 0.25) = 192
-        overlap_width = int(tile_latent_min_width * (1 - self.tile_overlap_factor))  # 256 * (1 - 0.25) = 192
-        blend_height = int(tile_latent_min_height * self.tile_overlap_factor)  # 8 * 0.25 = 2
-        blend_width = int(tile_latent_min_width * self.tile_overlap_factor)  # 8 * 0.25 = 2
-        row_limit_height = tile_latent_min_height - blend_height  # 8 - 2 = 6
-        row_limit_width = tile_latent_min_width - blend_width  # 8 - 2 = 6
-
-        rows = []
-        for i in range(0, height, overlap_height):
-            row = []
-            for j in range(0, width, overlap_width):
-                tile = x[
-                    :,
-                    :,
-                    :,
-                    i : i + self.tile_sample_min_height,
-                    j : j + self.tile_sample_min_width,
-                ]
-                tile = self.encoder(tile)
-                row.append(tile)
-            rows.append(row)
-
-        result_rows = []
-        for i, row in enumerate(rows):
-            result_row = []
-            for j, tile in enumerate(row):
-                if i > 0:
-                    tile = self.blend_v(rows[i - 1][j], tile, blend_height)
-                if j > 0:
-                    tile = self.blend_h(row[j - 1], tile, blend_width)
-                result_row.append(tile[:, :, :, :row_limit_height, :row_limit_width])
-            result_rows.append(torch.cat(result_row, dim=-1))
-        moments = torch.cat(result_rows, dim=-2)
-
-        return moments
-
-    def tiled_decode(self, z: torch.Tensor) -> torch.Tensor:
-        r"""
-        Decode a batch of images using a tiled decoder.
-
-        Args:
-            z (`torch.Tensor`): Input batch of latent vectors.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.
-
-        Returns:
-            [`~models.vae.DecoderOutput`] or `tuple`:
-                If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
-                returned.
-        """
-
-        _, _, _, height, width = z.shape
-        tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
-        tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
-        overlap_height = int(tile_latent_min_height * (1 - self.tile_overlap_factor))  # 8 * (1 - 0.25) = 6
-        overlap_width = int(tile_latent_min_width * (1 - self.tile_overlap_factor))  # 8 * (1 - 0.25) = 6
-        blend_height = int(tile_latent_min_height * self.tile_overlap_factor)  # 256 * 0.25 = 64
-        blend_width = int(tile_latent_min_width * self.tile_overlap_factor)  # 256 * 0.25 = 64
-        row_limit_height = tile_latent_min_height - blend_height  # 256 - 64 = 192
-        row_limit_width = tile_latent_min_width - blend_width  # 256 - 64 = 192
-
-        rows = []
-        for i in range(0, height, overlap_height):
-            row = []
-            for j in range(0, width, overlap_width):
-                tile = z[
-                    :,
-                    :,
-                    :,
-                    i : i + tile_latent_min_height,
-                    j : j + tile_latent_min_width,
-                ]
-                decoded = self.decoder(tile)
-                row.append(decoded)
-            rows.append(row)
-
-        result_rows = []
-        for i, row in enumerate(rows):
-            result_row = []
-            for j, tile in enumerate(row):
-                if i > 0:
-                    tile = self.blend_v(rows[i - 1][j], tile, blend_height)
-                if j > 0:
-                    tile = self.blend_h(row[j - 1], tile, blend_width)
-                result_row.append(tile[:, :, :, :row_limit_height, :row_limit_width])
-            result_rows.append(torch.cat(result_row, dim=-1))
-        dec = torch.cat(result_rows, dim=-2)
-
-        return dec
-
-    def forward(
-        self,
-        sample: torch.Tensor,
-        sample_posterior: bool = False,
-        return_dict: bool = True,
-        generator: Optional[torch.Generator] = None,
-    ) -> Union[DecoderOutput, torch.Tensor]:
-        r"""
-        Args:
-            sample (`torch.Tensor`): Input sample.
-            sample_posterior (`bool`, *optional*, defaults to `False`):
-                Whether to sample from the posterior.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
-        """
-        x = sample
-        posterior = self.encode(x).latent_dist
-        if sample_posterior:
-            z = posterior.sample(generator=generator)
-        else:
-            z = posterior.mode()
-        dec = self.decode(z, return_dict=return_dict)
-        return dec

src/diffusers/models/autoencoders/autoencoder_kl_ltx.py

@@ -26,7 +26,7 @@ from ..embeddings import PixArtAlphaCombinedTimestepSizeEmbeddings
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
 from ..normalization import RMSNorm
-from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution
+from .vae import DecoderOutput, DiagonalGaussianDistribution
 
 
 class LTXVideoCausalConv3d(nn.Module):
@@ -1034,7 +1034,7 @@ class LTXVideoDecoder3d(nn.Module):
         return hidden_states
 
 
-class AutoencoderKLLTXVideo(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalModelMixin):
+class AutoencoderKLLTXVideo(ModelMixin, ConfigMixin, FromOriginalModelMixin):
     r"""
     A VAE model with KL loss for encoding images into latents and decoding latent representations into images. Used in
     [LTX](https://huggingface.co/Lightricks/LTX-Video).
@@ -1219,6 +1219,27 @@ class AutoencoderKLLTXVideo(ModelMixin, AutoencoderMixin, ConfigMixin, FromOrigi
         self.tile_sample_stride_width = tile_sample_stride_width or self.tile_sample_stride_width
         self.tile_sample_stride_num_frames = tile_sample_stride_num_frames or self.tile_sample_stride_num_frames
 
+    def disable_tiling(self) -> None:
+        r"""
+        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_tiling = False
+
+    def enable_slicing(self) -> None:
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.use_slicing = True
+
+    def disable_slicing(self) -> None:
+        r"""
+        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_slicing = False
+
     def _encode(self, x: torch.Tensor) -> torch.Tensor:
         batch_size, num_channels, num_frames, height, width = x.shape
 

src/diffusers/models/autoencoders/autoencoder_kl_magvit.py

@@ -26,7 +26,7 @@ from ...utils.accelerate_utils import apply_forward_hook
 from ..activations import get_activation
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
-from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution
+from .vae import DecoderOutput, DiagonalGaussianDistribution
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
@@ -663,7 +663,7 @@ class EasyAnimateDecoder(nn.Module):
         return hidden_states
 
 
-class AutoencoderKLMagvit(ModelMixin, AutoencoderMixin, ConfigMixin):
+class AutoencoderKLMagvit(ModelMixin, ConfigMixin):
     r"""
     A VAE model with KL loss for encoding images into latents and decoding latent representations into images. This
     model is used in [EasyAnimate](https://huggingface.co/papers/2405.18991).
@@ -805,6 +805,27 @@ class AutoencoderKLMagvit(ModelMixin, AutoencoderMixin, ConfigMixin):
         self.tile_sample_stride_width = tile_sample_stride_width or self.tile_sample_stride_width
         self.tile_sample_stride_num_frames = tile_sample_stride_num_frames or self.tile_sample_stride_num_frames
 
+    def disable_tiling(self) -> None:
+        r"""
+        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_tiling = False
+
+    def enable_slicing(self) -> None:
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.use_slicing = True
+
+    def disable_slicing(self) -> None:
+        r"""
+        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_slicing = False
+
     @apply_forward_hook
     def _encode(
         self, x: torch.Tensor, return_dict: bool = True

src/diffusers/models/autoencoders/autoencoder_kl_mochi.py

@@ -27,7 +27,7 @@ from ..attention_processor import Attention, MochiVaeAttnProcessor2_0
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
 from .autoencoder_kl_cogvideox import CogVideoXCausalConv3d
-from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution
+from .vae import DecoderOutput, DiagonalGaussianDistribution
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
@@ -657,7 +657,7 @@ class MochiDecoder3D(nn.Module):
         return hidden_states, new_conv_cache
 
 
-class AutoencoderKLMochi(ModelMixin, AutoencoderMixin, ConfigMixin):
+class AutoencoderKLMochi(ModelMixin, ConfigMixin):
     r"""
     A VAE model with KL loss for encoding images into latents and decoding latent representations into images. Used in
     [Mochi 1 preview](https://github.com/genmoai/models).
@@ -818,6 +818,27 @@ class AutoencoderKLMochi(ModelMixin, AutoencoderMixin, ConfigMixin):
         self.tile_sample_stride_height = tile_sample_stride_height or self.tile_sample_stride_height
         self.tile_sample_stride_width = tile_sample_stride_width or self.tile_sample_stride_width
 
+    def disable_tiling(self) -> None:
+        r"""
+        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_tiling = False
+
+    def enable_slicing(self) -> None:
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.use_slicing = True
+
+    def disable_slicing(self) -> None:
+        r"""
+        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_slicing = False
+
     def _enable_framewise_encoding(self):
         r"""
         Enables the framewise VAE encoding implementation with past latent padding. By default, Diffusers uses the

src/diffusers/models/autoencoders/autoencoder_kl_qwenimage.py

@@ -31,7 +31,7 @@ from ...utils.accelerate_utils import apply_forward_hook
 from ..activations import get_activation
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
-from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution
+from .vae import DecoderOutput, DiagonalGaussianDistribution
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
@@ -663,7 +663,7 @@ class QwenImageDecoder3d(nn.Module):
         return x
 
 
-class AutoencoderKLQwenImage(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalModelMixin):
+class AutoencoderKLQwenImage(ModelMixin, ConfigMixin, FromOriginalModelMixin):
     r"""
     A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos.
 
@@ -763,6 +763,27 @@ class AutoencoderKLQwenImage(ModelMixin, AutoencoderMixin, ConfigMixin, FromOrig
         self.tile_sample_stride_height = tile_sample_stride_height or self.tile_sample_stride_height
         self.tile_sample_stride_width = tile_sample_stride_width or self.tile_sample_stride_width
 
+    def disable_tiling(self) -> None:
+        r"""
+        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_tiling = False
+
+    def enable_slicing(self) -> None:
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.use_slicing = True
+
+    def disable_slicing(self) -> None:
+        r"""
+        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_slicing = False
+
     def clear_cache(self):
         def _count_conv3d(model):
             count = 0

src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py

@@ -23,7 +23,7 @@ from ..attention_processor import CROSS_ATTENTION_PROCESSORS, AttentionProcessor
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
 from ..unets.unet_3d_blocks import MidBlockTemporalDecoder, UpBlockTemporalDecoder
-from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution, Encoder
+from .vae import DecoderOutput, DiagonalGaussianDistribution, Encoder
 
 
 class TemporalDecoder(nn.Module):
@@ -135,7 +135,7 @@ class TemporalDecoder(nn.Module):
         return sample
 
 
-class AutoencoderKLTemporalDecoder(ModelMixin, AutoencoderMixin, ConfigMixin):
+class AutoencoderKLTemporalDecoder(ModelMixin, ConfigMixin):
     r"""
     A VAE model with KL loss for encoding images into latents and decoding latent representations into images.
 

src/diffusers/models/autoencoders/autoencoder_kl_wan.py

@@ -25,7 +25,7 @@ from ...utils.accelerate_utils import apply_forward_hook
 from ..activations import get_activation
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
-from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution
+from .vae import DecoderOutput, DiagonalGaussianDistribution
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
@@ -453,14 +453,14 @@ class WanMidBlock(nn.Module):
 
     def forward(self, x, feat_cache=None, feat_idx=[0]):
         # First residual block
-        x = self.resnets[0](x, feat_cache=feat_cache, feat_idx=feat_idx)
+        x = self.resnets[0](x, feat_cache, feat_idx)
 
         # Process through attention and residual blocks
         for attn, resnet in zip(self.attentions, self.resnets[1:]):
             if attn is not None:
                 x = attn(x)
 
-            x = resnet(x, feat_cache=feat_cache, feat_idx=feat_idx)
+            x = resnet(x, feat_cache, feat_idx)
 
         return x
 
@@ -494,9 +494,9 @@ class WanResidualDownBlock(nn.Module):
     def forward(self, x, feat_cache=None, feat_idx=[0]):
         x_copy = x.clone()
         for resnet in self.resnets:
-            x = resnet(x, feat_cache=feat_cache, feat_idx=feat_idx)
+            x = resnet(x, feat_cache, feat_idx)
         if self.downsampler is not None:
-            x = self.downsampler(x, feat_cache=feat_cache, feat_idx=feat_idx)
+            x = self.downsampler(x, feat_cache, feat_idx)
 
         return x + self.avg_shortcut(x_copy)
 
@@ -598,12 +598,12 @@ class WanEncoder3d(nn.Module):
         ## downsamples
         for layer in self.down_blocks:
             if feat_cache is not None:
-                x = layer(x, feat_cache=feat_cache, feat_idx=feat_idx)
+                x = layer(x, feat_cache, feat_idx)
             else:
                 x = layer(x)
 
         ## middle
-        x = self.mid_block(x, feat_cache=feat_cache, feat_idx=feat_idx)
+        x = self.mid_block(x, feat_cache, feat_idx)
 
         ## head
         x = self.norm_out(x)
@@ -694,13 +694,13 @@ class WanResidualUpBlock(nn.Module):
 
         for resnet in self.resnets:
             if feat_cache is not None:
-                x = resnet(x, feat_cache=feat_cache, feat_idx=feat_idx)
+                x = resnet(x, feat_cache, feat_idx)
             else:
                 x = resnet(x)
 
         if self.upsampler is not None:
             if feat_cache is not None:
-                x = self.upsampler(x, feat_cache=feat_cache, feat_idx=feat_idx)
+                x = self.upsampler(x, feat_cache, feat_idx)
             else:
                 x = self.upsampler(x)
 
@@ -767,13 +767,13 @@ class WanUpBlock(nn.Module):
         """
         for resnet in self.resnets:
             if feat_cache is not None:
-                x = resnet(x, feat_cache=feat_cache, feat_idx=feat_idx)
+                x = resnet(x, feat_cache, feat_idx)
             else:
                 x = resnet(x)
 
         if self.upsamplers is not None:
             if feat_cache is not None:
-                x = self.upsamplers[0](x, feat_cache=feat_cache, feat_idx=feat_idx)
+                x = self.upsamplers[0](x, feat_cache, feat_idx)
             else:
                 x = self.upsamplers[0](x)
         return x
@@ -885,11 +885,11 @@ class WanDecoder3d(nn.Module):
             x = self.conv_in(x)
 
         ## middle
-        x = self.mid_block(x, feat_cache=feat_cache, feat_idx=feat_idx)
+        x = self.mid_block(x, feat_cache, feat_idx)
 
         ## upsamples
         for up_block in self.up_blocks:
-            x = up_block(x, feat_cache=feat_cache, feat_idx=feat_idx, first_chunk=first_chunk)
+            x = up_block(x, feat_cache, feat_idx, first_chunk=first_chunk)
 
         ## head
         x = self.norm_out(x)
@@ -951,7 +951,7 @@ def unpatchify(x, patch_size):
     return x
 
 
-class AutoencoderKLWan(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalModelMixin):
+class AutoencoderKLWan(ModelMixin, ConfigMixin, FromOriginalModelMixin):
     r"""
     A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos.
     Introduced in [Wan 2.1].
@@ -961,9 +961,6 @@ class AutoencoderKLWan(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalMo
     """
 
     _supports_gradient_checkpointing = False
-    # keys toignore when AlignDeviceHook moves inputs/outputs between devices
-    # these are shared mutable state modified in-place
-    _skip_keys = ["feat_cache", "feat_idx"]
 
     @register_to_config
     def __init__(
@@ -1113,6 +1110,27 @@ class AutoencoderKLWan(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalMo
         self.tile_sample_stride_height = tile_sample_stride_height or self.tile_sample_stride_height
         self.tile_sample_stride_width = tile_sample_stride_width or self.tile_sample_stride_width
 
+    def disable_tiling(self) -> None:
+        r"""
+        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_tiling = False
+
+    def enable_slicing(self) -> None:
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.use_slicing = True
+
+    def disable_slicing(self) -> None:
+        r"""
+        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_slicing = False
+
     def clear_cache(self):
         # Use cached conv counts for decoder and encoder to avoid re-iterating modules each call
         self._conv_num = self._cached_conv_counts["decoder"]
@@ -1337,18 +1355,9 @@ class AutoencoderKLWan(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalMo
         tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
         tile_latent_stride_height = self.tile_sample_stride_height // self.spatial_compression_ratio
         tile_latent_stride_width = self.tile_sample_stride_width // self.spatial_compression_ratio
-        tile_sample_stride_height = self.tile_sample_stride_height
-        tile_sample_stride_width = self.tile_sample_stride_width
-        if self.config.patch_size is not None:
-            sample_height = sample_height // self.config.patch_size
-            sample_width = sample_width // self.config.patch_size
-            tile_sample_stride_height = tile_sample_stride_height // self.config.patch_size
-            tile_sample_stride_width = tile_sample_stride_width // self.config.patch_size
-            blend_height = self.tile_sample_min_height // self.config.patch_size - tile_sample_stride_height
-            blend_width = self.tile_sample_min_width // self.config.patch_size - tile_sample_stride_width
-        else:
-            blend_height = self.tile_sample_min_height - tile_sample_stride_height
-            blend_width = self.tile_sample_min_width - tile_sample_stride_width
+
+        blend_height = self.tile_sample_min_height - self.tile_sample_stride_height
+        blend_width = self.tile_sample_min_width - self.tile_sample_stride_width
 
         # Split z into overlapping tiles and decode them separately.
         # The tiles have an overlap to avoid seams between tiles.
@@ -1362,9 +1371,7 @@ class AutoencoderKLWan(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalMo
                     self._conv_idx = [0]
                     tile = z[:, :, k : k + 1, i : i + tile_latent_min_height, j : j + tile_latent_min_width]
                     tile = self.post_quant_conv(tile)
-                    decoded = self.decoder(
-                        tile, feat_cache=self._feat_map, feat_idx=self._conv_idx, first_chunk=(k == 0)
-                    )
+                    decoded = self.decoder(tile, feat_cache=self._feat_map, feat_idx=self._conv_idx)
                     time.append(decoded)
                 row.append(torch.cat(time, dim=2))
             rows.append(row)
@@ -1380,15 +1387,11 @@ class AutoencoderKLWan(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalMo
                     tile = self.blend_v(rows[i - 1][j], tile, blend_height)
                 if j > 0:
                     tile = self.blend_h(row[j - 1], tile, blend_width)
-                result_row.append(tile[:, :, :, :tile_sample_stride_height, :tile_sample_stride_width])
+                result_row.append(tile[:, :, :, : self.tile_sample_stride_height, : self.tile_sample_stride_width])
             result_rows.append(torch.cat(result_row, dim=-1))
+
         dec = torch.cat(result_rows, dim=3)[:, :, :, :sample_height, :sample_width]
 
-        if self.config.patch_size is not None:
-            dec = unpatchify(dec, patch_size=self.config.patch_size)
-
-        dec = torch.clamp(dec, min=-1.0, max=1.0)
-
         if not return_dict:
             return (dec,)
         return DecoderOutput(sample=dec)

src/diffusers/models/autoencoders/autoencoder_oobleck.py

@@ -25,7 +25,6 @@ from ...utils import BaseOutput
 from ...utils.accelerate_utils import apply_forward_hook
 from ...utils.torch_utils import randn_tensor
 from ..modeling_utils import ModelMixin
-from .vae import AutoencoderMixin
 
 
 class Snake1d(nn.Module):
@@ -292,7 +291,7 @@ class OobleckDecoder(nn.Module):
         return hidden_state
 
 
-class AutoencoderOobleck(ModelMixin, AutoencoderMixin, ConfigMixin):
+class AutoencoderOobleck(ModelMixin, ConfigMixin):
     r"""
     An autoencoder for encoding waveforms into latents and decoding latent representations into waveforms. First
     introduced in Stable Audio.
@@ -357,6 +356,20 @@ class AutoencoderOobleck(ModelMixin, AutoencoderMixin, ConfigMixin):
 
         self.use_slicing = False
 
+    def enable_slicing(self):
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.use_slicing = True
+
+    def disable_slicing(self):
+        r"""
+        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_slicing = False
+
     @apply_forward_hook
     def encode(
         self, x: torch.Tensor, return_dict: bool = True

src/diffusers/models/autoencoders/autoencoder_tiny.py

@@ -22,7 +22,7 @@ from ...configuration_utils import ConfigMixin, register_to_config
 from ...utils import BaseOutput
 from ...utils.accelerate_utils import apply_forward_hook
 from ..modeling_utils import ModelMixin
-from .vae import AutoencoderMixin, DecoderOutput, DecoderTiny, EncoderTiny
+from .vae import DecoderOutput, DecoderTiny, EncoderTiny
 
 
 @dataclass
@@ -38,7 +38,7 @@ class AutoencoderTinyOutput(BaseOutput):
     latents: torch.Tensor
 
 
-class AutoencoderTiny(ModelMixin, AutoencoderMixin, ConfigMixin):
+class AutoencoderTiny(ModelMixin, ConfigMixin):
     r"""
     A tiny distilled VAE model for encoding images into latents and decoding latent representations into images.
 
@@ -162,6 +162,35 @@ class AutoencoderTiny(ModelMixin, AutoencoderMixin, ConfigMixin):
         """[0, 1] -> raw latents"""
         return x.sub(self.latent_shift).mul(2 * self.latent_magnitude)
 
+    def enable_slicing(self) -> None:
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.use_slicing = True
+
+    def disable_slicing(self) -> None:
+        r"""
+        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_slicing = False
+
+    def enable_tiling(self, use_tiling: bool = True) -> None:
+        r"""
+        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+        processing larger images.
+        """
+        self.use_tiling = use_tiling
+
+    def disable_tiling(self) -> None:
+        r"""
+        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.enable_tiling(False)
+
     def _tiled_encode(self, x: torch.Tensor) -> torch.Tensor:
         r"""Encode a batch of images using a tiled encoder.
 

src/diffusers/models/autoencoders/consistency_decoder_vae.py

@@ -32,7 +32,7 @@ from ..attention_processor import (
 )
 from ..modeling_utils import ModelMixin
 from ..unets.unet_2d import UNet2DModel
-from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution, Encoder
+from .vae import DecoderOutput, DiagonalGaussianDistribution, Encoder
 
 
 @dataclass
@@ -49,7 +49,7 @@ class ConsistencyDecoderVAEOutput(BaseOutput):
     latent_dist: "DiagonalGaussianDistribution"
 
 
-class ConsistencyDecoderVAE(ModelMixin, AutoencoderMixin, ConfigMixin):
+class ConsistencyDecoderVAE(ModelMixin, ConfigMixin):
     r"""
     The consistency decoder used with DALL-E 3.
 
@@ -167,6 +167,39 @@ class ConsistencyDecoderVAE(ModelMixin, AutoencoderMixin, ConfigMixin):
         self.tile_latent_min_size = int(sample_size / (2 ** (len(self.config.block_out_channels) - 1)))
         self.tile_overlap_factor = 0.25
 
+    # Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.enable_tiling
+    def enable_tiling(self, use_tiling: bool = True):
+        r"""
+        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+        processing larger images.
+        """
+        self.use_tiling = use_tiling
+
+    # Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.disable_tiling
+    def disable_tiling(self):
+        r"""
+        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.enable_tiling(False)
+
+    # Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.enable_slicing
+    def enable_slicing(self):
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.use_slicing = True
+
+    # Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.disable_slicing
+    def disable_slicing(self):
+        r"""
+        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_slicing = False
+
     @property
     # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
     def attn_processors(self) -> Dict[str, AttentionProcessor]:

src/diffusers/models/autoencoders/vae.py

@@ -286,9 +286,11 @@ class Decoder(nn.Module):
 
         sample = self.conv_in(sample)
 
+        upscale_dtype = next(iter(self.up_blocks.parameters())).dtype
         if torch.is_grad_enabled() and self.gradient_checkpointing:
             # middle
             sample = self._gradient_checkpointing_func(self.mid_block, sample, latent_embeds)
+            sample = sample.to(upscale_dtype)
 
             # up
             for up_block in self.up_blocks:
@@ -296,6 +298,7 @@ class Decoder(nn.Module):
         else:
             # middle
             sample = self.mid_block(sample, latent_embeds)
+            sample = sample.to(upscale_dtype)
 
             # up
             for up_block in self.up_blocks:
@@ -891,38 +894,3 @@ class DecoderTiny(nn.Module):
 
         # scale image from [0, 1] to [-1, 1] to match diffusers convention
         return x.mul(2).sub(1)
-
-
-class AutoencoderMixin:
-    def enable_tiling(self):
-        r"""
-        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
-        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
-        processing larger images.
-        """
-        if not hasattr(self, "use_tiling"):
-            raise NotImplementedError(f"Tiling doesn't seem to be implemented for {self.__class__.__name__}.")
-        self.use_tiling = True
-
-    def disable_tiling(self):
-        r"""
-        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_tiling = False
-
-    def enable_slicing(self):
-        r"""
-        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
-        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        if not hasattr(self, "use_slicing"):
-            raise NotImplementedError(f"Slicing doesn't seem to be implemented for {self.__class__.__name__}.")
-        self.use_slicing = True
-
-    def disable_slicing(self):
-        r"""
-        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_slicing = False

src/diffusers/models/autoencoders/vq_model.py

@@ -22,7 +22,6 @@ from ...utils import BaseOutput
 from ...utils.accelerate_utils import apply_forward_hook
 from ..autoencoders.vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
 from ..modeling_utils import ModelMixin
-from .vae import AutoencoderMixin
 
 
 @dataclass
@@ -38,7 +37,7 @@ class VQEncoderOutput(BaseOutput):
     latents: torch.Tensor
 
 
-class VQModel(ModelMixin, AutoencoderMixin, ConfigMixin):
+class VQModel(ModelMixin, ConfigMixin):
     r"""
     A VQ-VAE model for decoding latent representations.
 

src/diffusers/models/embeddings.py

@@ -319,17 +319,13 @@ def get_2d_sincos_pos_embed_from_grid(embed_dim, grid, output_type="np"):
     return emb
 
 
-def get_1d_sincos_pos_embed_from_grid(embed_dim, pos, output_type="np", flip_sin_to_cos=False, dtype=None):
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos, output_type="np", flip_sin_to_cos=False):
     """
     This function generates 1D positional embeddings from a grid.
 
     Args:
         embed_dim (`int`): The embedding dimension `D`
         pos (`torch.Tensor`): 1D tensor of positions with shape `(M,)`
-        output_type (`str`, *optional*, defaults to `"np"`): Output type. Use `"pt"` for PyTorch tensors.
-        flip_sin_to_cos (`bool`, *optional*, defaults to `False`): Whether to flip sine and cosine embeddings.
-        dtype (`torch.dtype`, *optional*): Data type for frequency calculations. If `None`, defaults to
-            `torch.float32` on MPS devices (which don't support `torch.float64`) and `torch.float64` on other devices.
 
     Returns:
         `torch.Tensor`: Sinusoidal positional embeddings of shape `(M, D)`.
@@ -345,11 +341,7 @@ def get_1d_sincos_pos_embed_from_grid(embed_dim, pos, output_type="np", flip_sin
     if embed_dim % 2 != 0:
         raise ValueError("embed_dim must be divisible by 2")
 
-    # Auto-detect appropriate dtype if not specified
-    if dtype is None:
-        dtype = torch.float32 if pos.device.type == "mps" else torch.float64
-
-    omega = torch.arange(embed_dim // 2, device=pos.device, dtype=dtype)
+    omega = torch.arange(embed_dim // 2, device=pos.device, dtype=torch.float64)
     omega /= embed_dim / 2.0
     omega = 1.0 / 10000**omega  # (D/2,)
 

src/diffusers/models/modeling_utils.py

@@ -251,7 +251,6 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
     _repeated_blocks = []
     _parallel_config = None
     _cp_plan = None
-    _skip_keys = None
 
     def __init__(self):
         super().__init__()

src/diffusers/models/transformers/__init__.py

@@ -18,7 +18,6 @@ if is_torch_available():
     from .transformer_2d import Transformer2DModel
     from .transformer_allegro import AllegroTransformer3DModel
     from .transformer_bria import BriaTransformer2DModel
-    from .transformer_bria_fibo import BriaFiboTransformer2DModel
     from .transformer_chroma import ChromaTransformer2DModel
     from .transformer_cogview3plus import CogView3PlusTransformer2DModel
     from .transformer_cogview4 import CogView4Transformer2DModel
@@ -28,15 +27,12 @@ if is_torch_available():
     from .transformer_hidream_image import HiDreamImageTransformer2DModel
     from .transformer_hunyuan_video import HunyuanVideoTransformer3DModel
     from .transformer_hunyuan_video_framepack import HunyuanVideoFramepackTransformer3DModel
-    from .transformer_hunyuanimage import HunyuanImageTransformer2DModel
     from .transformer_kandinsky import Kandinsky5Transformer3DModel
     from .transformer_ltx import LTXVideoTransformer3DModel
     from .transformer_lumina2 import Lumina2Transformer2DModel
     from .transformer_mochi import MochiTransformer3DModel
     from .transformer_omnigen import OmniGenTransformer2DModel
-    from .transformer_prx import PRXTransformer2DModel
     from .transformer_qwenimage import QwenImageTransformer2DModel
-    from .transformer_sana_video import SanaVideoTransformer3DModel
     from .transformer_sd3 import SD3Transformer2DModel
     from .transformer_skyreels_v2 import SkyReelsV2Transformer3DModel
     from .transformer_temporal import TransformerTemporalModel

src/diffusers/models/transformers/transformer_bria_fibo.py

@@ -1,655 +0,0 @@
-# Copyright (c) Bria.ai. All rights reserved.
-#
-# This file is licensed under the Creative Commons Attribution-NonCommercial 4.0 International Public License (CC-BY-NC-4.0).
-# You may obtain a copy of the license at https://creativecommons.org/licenses/by-nc/4.0/
-#
-# You are free to share and adapt this material for non-commercial purposes provided you give appropriate credit,
-# indicate if changes were made, and do not use the material for commercial purposes.
-#
-# See the license for further details.
-import inspect
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ...configuration_utils import ConfigMixin, register_to_config
-from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
-from ...models.attention_processor import Attention
-from ...models.embeddings import TimestepEmbedding, apply_rotary_emb, get_1d_rotary_pos_embed, get_timestep_embedding
-from ...models.modeling_outputs import Transformer2DModelOutput
-from ...models.modeling_utils import ModelMixin
-from ...models.transformers.transformer_bria import BriaAttnProcessor
-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 AttentionModuleMixin, FeedForward
-from ..attention_dispatch import dispatch_attention_fn
-from ..normalization import AdaLayerNormContinuous, AdaLayerNormZero, AdaLayerNormZeroSingle
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-def _get_projections(attn: "BriaFiboAttention", hidden_states, encoder_hidden_states=None):
-    query = attn.to_q(hidden_states)
-    key = attn.to_k(hidden_states)
-    value = attn.to_v(hidden_states)
-
-    encoder_query = encoder_key = encoder_value = None
-    if encoder_hidden_states is not None and attn.added_kv_proj_dim is not None:
-        encoder_query = attn.add_q_proj(encoder_hidden_states)
-        encoder_key = attn.add_k_proj(encoder_hidden_states)
-        encoder_value = attn.add_v_proj(encoder_hidden_states)
-
-    return query, key, value, encoder_query, encoder_key, encoder_value
-
-
-def _get_fused_projections(attn: "BriaFiboAttention", hidden_states, encoder_hidden_states=None):
-    query, key, value = attn.to_qkv(hidden_states).chunk(3, dim=-1)
-
-    encoder_query = encoder_key = encoder_value = (None,)
-    if encoder_hidden_states is not None and hasattr(attn, "to_added_qkv"):
-        encoder_query, encoder_key, encoder_value = attn.to_added_qkv(encoder_hidden_states).chunk(3, dim=-1)
-
-    return query, key, value, encoder_query, encoder_key, encoder_value
-
-
-def _get_qkv_projections(attn: "BriaFiboAttention", hidden_states, encoder_hidden_states=None):
-    if attn.fused_projections:
-        return _get_fused_projections(attn, hidden_states, encoder_hidden_states)
-    return _get_projections(attn, hidden_states, encoder_hidden_states)
-
-
-# Copied from diffusers.models.transformers.transformer_flux.FluxAttnProcessor with FluxAttnProcessor->BriaFiboAttnProcessor, FluxAttention->BriaFiboAttention
-class BriaFiboAttnProcessor:
-    _attention_backend = None
-    _parallel_config = None
-
-    def __init__(self):
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError(f"{self.__class__.__name__} requires PyTorch 2.0. Please upgrade your pytorch version.")
-
-    def __call__(
-        self,
-        attn: "BriaFiboAttention",
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        image_rotary_emb: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        query, key, value, encoder_query, encoder_key, encoder_value = _get_qkv_projections(
-            attn, hidden_states, encoder_hidden_states
-        )
-
-        query = query.unflatten(-1, (attn.heads, -1))
-        key = key.unflatten(-1, (attn.heads, -1))
-        value = value.unflatten(-1, (attn.heads, -1))
-
-        query = attn.norm_q(query)
-        key = attn.norm_k(key)
-
-        if attn.added_kv_proj_dim is not None:
-            encoder_query = encoder_query.unflatten(-1, (attn.heads, -1))
-            encoder_key = encoder_key.unflatten(-1, (attn.heads, -1))
-            encoder_value = encoder_value.unflatten(-1, (attn.heads, -1))
-
-            encoder_query = attn.norm_added_q(encoder_query)
-            encoder_key = attn.norm_added_k(encoder_key)
-
-            query = torch.cat([encoder_query, query], dim=1)
-            key = torch.cat([encoder_key, key], dim=1)
-            value = torch.cat([encoder_value, value], dim=1)
-
-        if image_rotary_emb is not None:
-            query = apply_rotary_emb(query, image_rotary_emb, sequence_dim=1)
-            key = apply_rotary_emb(key, image_rotary_emb, sequence_dim=1)
-
-        hidden_states = dispatch_attention_fn(
-            query,
-            key,
-            value,
-            attn_mask=attention_mask,
-            backend=self._attention_backend,
-            parallel_config=self._parallel_config,
-        )
-        hidden_states = hidden_states.flatten(2, 3)
-        hidden_states = hidden_states.to(query.dtype)
-
-        if encoder_hidden_states is not None:
-            encoder_hidden_states, hidden_states = hidden_states.split_with_sizes(
-                [encoder_hidden_states.shape[1], hidden_states.shape[1] - encoder_hidden_states.shape[1]], dim=1
-            )
-            hidden_states = attn.to_out[0](hidden_states)
-            hidden_states = attn.to_out[1](hidden_states)
-            encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
-
-            return hidden_states, encoder_hidden_states
-        else:
-            return hidden_states
-
-
-# Based on https://github.com/huggingface/diffusers/blob/55d49d4379007740af20629bb61aba9546c6b053/src/diffusers/models/transformers/transformer_flux.py
-class BriaFiboAttention(torch.nn.Module, AttentionModuleMixin):
-    _default_processor_cls = BriaFiboAttnProcessor
-    _available_processors = [BriaFiboAttnProcessor]
-
-    def __init__(
-        self,
-        query_dim: int,
-        heads: int = 8,
-        dim_head: int = 64,
-        dropout: float = 0.0,
-        bias: bool = False,
-        added_kv_proj_dim: Optional[int] = None,
-        added_proj_bias: Optional[bool] = True,
-        out_bias: bool = True,
-        eps: float = 1e-5,
-        out_dim: int = None,
-        context_pre_only: Optional[bool] = None,
-        pre_only: bool = False,
-        elementwise_affine: bool = True,
-        processor=None,
-    ):
-        super().__init__()
-
-        self.head_dim = dim_head
-        self.inner_dim = out_dim if out_dim is not None else dim_head * heads
-        self.query_dim = query_dim
-        self.use_bias = bias
-        self.dropout = dropout
-        self.out_dim = out_dim if out_dim is not None else query_dim
-        self.context_pre_only = context_pre_only
-        self.pre_only = pre_only
-        self.heads = out_dim // dim_head if out_dim is not None else heads
-        self.added_kv_proj_dim = added_kv_proj_dim
-        self.added_proj_bias = added_proj_bias
-
-        self.norm_q = torch.nn.RMSNorm(dim_head, eps=eps, elementwise_affine=elementwise_affine)
-        self.norm_k = torch.nn.RMSNorm(dim_head, eps=eps, elementwise_affine=elementwise_affine)
-        self.to_q = torch.nn.Linear(query_dim, self.inner_dim, bias=bias)
-        self.to_k = torch.nn.Linear(query_dim, self.inner_dim, bias=bias)
-        self.to_v = torch.nn.Linear(query_dim, self.inner_dim, bias=bias)
-
-        if not self.pre_only:
-            self.to_out = torch.nn.ModuleList([])
-            self.to_out.append(torch.nn.Linear(self.inner_dim, self.out_dim, bias=out_bias))
-            self.to_out.append(torch.nn.Dropout(dropout))
-
-        if added_kv_proj_dim is not None:
-            self.norm_added_q = torch.nn.RMSNorm(dim_head, eps=eps)
-            self.norm_added_k = torch.nn.RMSNorm(dim_head, eps=eps)
-            self.add_q_proj = torch.nn.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias)
-            self.add_k_proj = torch.nn.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias)
-            self.add_v_proj = torch.nn.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias)
-            self.to_add_out = torch.nn.Linear(self.inner_dim, query_dim, bias=out_bias)
-
-        if processor is None:
-            processor = self._default_processor_cls()
-        self.set_processor(processor)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        image_rotary_emb: Optional[torch.Tensor] = None,
-        **kwargs,
-    ) -> torch.Tensor:
-        attn_parameters = set(inspect.signature(self.processor.__call__).parameters.keys())
-        quiet_attn_parameters = {"ip_adapter_masks", "ip_hidden_states"}
-        unused_kwargs = [k for k, _ in kwargs.items() if k not in attn_parameters and k not in quiet_attn_parameters]
-        if len(unused_kwargs) > 0:
-            logger.warning(
-                f"joint_attention_kwargs {unused_kwargs} are not expected by {self.processor.__class__.__name__} and will be ignored."
-            )
-        kwargs = {k: w for k, w in kwargs.items() if k in attn_parameters}
-        return self.processor(self, hidden_states, encoder_hidden_states, attention_mask, image_rotary_emb, **kwargs)
-
-
-class BriaFiboEmbedND(torch.nn.Module):
-    # modified from https://github.com/black-forest-labs/flux/blob/c00d7c60b085fce8058b9df845e036090873f2ce/src/flux/modules/layers.py#L11
-    def __init__(self, theta: int, axes_dim: List[int]):
-        super().__init__()
-        self.theta = theta
-        self.axes_dim = axes_dim
-
-    def forward(self, ids: torch.Tensor) -> torch.Tensor:
-        n_axes = ids.shape[-1]
-        cos_out = []
-        sin_out = []
-        pos = ids.float()
-        is_mps = ids.device.type == "mps"
-        freqs_dtype = torch.float32 if is_mps else torch.float64
-        for i in range(n_axes):
-            cos, sin = get_1d_rotary_pos_embed(
-                self.axes_dim[i],
-                pos[:, i],
-                theta=self.theta,
-                repeat_interleave_real=True,
-                use_real=True,
-                freqs_dtype=freqs_dtype,
-            )
-            cos_out.append(cos)
-            sin_out.append(sin)
-        freqs_cos = torch.cat(cos_out, dim=-1).to(ids.device)
-        freqs_sin = torch.cat(sin_out, dim=-1).to(ids.device)
-        return freqs_cos, freqs_sin
-
-
-@maybe_allow_in_graph
-class BriaFiboSingleTransformerBlock(nn.Module):
-    def __init__(self, dim: int, num_attention_heads: int, attention_head_dim: int, mlp_ratio: float = 4.0):
-        super().__init__()
-        self.mlp_hidden_dim = int(dim * mlp_ratio)
-
-        self.norm = AdaLayerNormZeroSingle(dim)
-        self.proj_mlp = nn.Linear(dim, self.mlp_hidden_dim)
-        self.act_mlp = nn.GELU(approximate="tanh")
-        self.proj_out = nn.Linear(dim + self.mlp_hidden_dim, dim)
-
-        processor = BriaAttnProcessor()
-
-        self.attn = Attention(
-            query_dim=dim,
-            cross_attention_dim=None,
-            dim_head=attention_head_dim,
-            heads=num_attention_heads,
-            out_dim=dim,
-            bias=True,
-            processor=processor,
-            qk_norm="rms_norm",
-            eps=1e-6,
-            pre_only=True,
-        )
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        temb: torch.Tensor,
-        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
-    ) -> torch.Tensor:
-        residual = hidden_states
-        norm_hidden_states, gate = self.norm(hidden_states, emb=temb)
-        mlp_hidden_states = self.act_mlp(self.proj_mlp(norm_hidden_states))
-        joint_attention_kwargs = joint_attention_kwargs or {}
-        attn_output = self.attn(
-            hidden_states=norm_hidden_states,
-            image_rotary_emb=image_rotary_emb,
-            **joint_attention_kwargs,
-        )
-
-        hidden_states = torch.cat([attn_output, mlp_hidden_states], dim=2)
-        gate = gate.unsqueeze(1)
-        hidden_states = gate * self.proj_out(hidden_states)
-        hidden_states = residual + hidden_states
-        if hidden_states.dtype == torch.float16:
-            hidden_states = hidden_states.clip(-65504, 65504)
-
-        return hidden_states
-
-
-class BriaFiboTextProjection(nn.Module):
-    def __init__(self, in_features, hidden_size):
-        super().__init__()
-        self.linear = nn.Linear(in_features=in_features, out_features=hidden_size, bias=False)
-
-    def forward(self, caption):
-        hidden_states = self.linear(caption)
-        return hidden_states
-
-
-@maybe_allow_in_graph
-# Based on from diffusers.models.transformers.transformer_flux.FluxTransformerBlock
-class BriaFiboTransformerBlock(nn.Module):
-    def __init__(
-        self, dim: int, num_attention_heads: int, attention_head_dim: int, qk_norm: str = "rms_norm", eps: float = 1e-6
-    ):
-        super().__init__()
-
-        self.norm1 = AdaLayerNormZero(dim)
-        self.norm1_context = AdaLayerNormZero(dim)
-
-        self.attn = BriaFiboAttention(
-            query_dim=dim,
-            added_kv_proj_dim=dim,
-            dim_head=attention_head_dim,
-            heads=num_attention_heads,
-            out_dim=dim,
-            context_pre_only=False,
-            bias=True,
-            processor=BriaFiboAttnProcessor(),
-            eps=eps,
-        )
-
-        self.norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
-        self.ff = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate")
-
-        self.norm2_context = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
-        self.ff_context = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate")
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        temb: torch.Tensor,
-        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)
-
-        norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context(
-            encoder_hidden_states, emb=temb
-        )
-        joint_attention_kwargs = joint_attention_kwargs or {}
-
-        # Attention.
-        attention_outputs = self.attn(
-            hidden_states=norm_hidden_states,
-            encoder_hidden_states=norm_encoder_hidden_states,
-            image_rotary_emb=image_rotary_emb,
-            **joint_attention_kwargs,
-        )
-
-        if len(attention_outputs) == 2:
-            attn_output, context_attn_output = attention_outputs
-        elif len(attention_outputs) == 3:
-            attn_output, context_attn_output, ip_attn_output = attention_outputs
-
-        # Process attention outputs for the `hidden_states`.
-        attn_output = gate_msa.unsqueeze(1) * attn_output
-        hidden_states = hidden_states + attn_output
-
-        norm_hidden_states = self.norm2(hidden_states)
-        norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
-
-        ff_output = self.ff(norm_hidden_states)
-        ff_output = gate_mlp.unsqueeze(1) * ff_output
-
-        hidden_states = hidden_states + ff_output
-        if len(attention_outputs) == 3:
-            hidden_states = hidden_states + ip_attn_output
-
-        # Process attention outputs for the `encoder_hidden_states`.
-        context_attn_output = c_gate_msa.unsqueeze(1) * context_attn_output
-        encoder_hidden_states = encoder_hidden_states + context_attn_output
-
-        norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
-        norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
-
-        context_ff_output = self.ff_context(norm_encoder_hidden_states)
-        encoder_hidden_states = encoder_hidden_states + c_gate_mlp.unsqueeze(1) * context_ff_output
-        if encoder_hidden_states.dtype == torch.float16:
-            encoder_hidden_states = encoder_hidden_states.clip(-65504, 65504)
-
-        return encoder_hidden_states, hidden_states
-
-
-class BriaFiboTimesteps(nn.Module):
-    def __init__(
-        self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, scale: int = 1, time_theta=10000
-    ):
-        super().__init__()
-        self.num_channels = num_channels
-        self.flip_sin_to_cos = flip_sin_to_cos
-        self.downscale_freq_shift = downscale_freq_shift
-        self.scale = scale
-        self.time_theta = time_theta
-
-    def forward(self, timesteps):
-        t_emb = get_timestep_embedding(
-            timesteps,
-            self.num_channels,
-            flip_sin_to_cos=self.flip_sin_to_cos,
-            downscale_freq_shift=self.downscale_freq_shift,
-            scale=self.scale,
-            max_period=self.time_theta,
-        )
-        return t_emb
-
-
-class BriaFiboTimestepProjEmbeddings(nn.Module):
-    def __init__(self, embedding_dim, time_theta):
-        super().__init__()
-
-        self.time_proj = BriaFiboTimesteps(
-            num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0, time_theta=time_theta
-        )
-        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
-
-    def forward(self, timestep, dtype):
-        timesteps_proj = self.time_proj(timestep)
-        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=dtype))  # (N, D)
-        return timesteps_emb
-
-
-class BriaFiboTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
-    """
-    Parameters:
-        patch_size (`int`): Patch size to turn the input data into small patches.
-        in_channels (`int`, *optional*, defaults to 16): The number of channels in the input.
-        num_layers (`int`, *optional*, defaults to 18): The number of layers of MMDiT blocks to use.
-        num_single_layers (`int`, *optional*, defaults to 18): The number of layers of single DiT blocks to use.
-        attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.
-        num_attention_heads (`int`, *optional*, defaults to 18): The number of heads to use for multi-head attention.
-        joint_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
-        pooled_projection_dim (`int`): Number of dimensions to use when projecting the `pooled_projections`.
-        guidance_embeds (`bool`, defaults to False): Whether to use guidance embeddings.
-        ...
-    """
-
-    _supports_gradient_checkpointing = True
-
-    @register_to_config
-    def __init__(
-        self,
-        patch_size: int = 1,
-        in_channels: int = 64,
-        num_layers: int = 19,
-        num_single_layers: int = 38,
-        attention_head_dim: int = 128,
-        num_attention_heads: int = 24,
-        joint_attention_dim: int = 4096,
-        pooled_projection_dim: int = None,
-        guidance_embeds: bool = False,
-        axes_dims_rope: List[int] = [16, 56, 56],
-        rope_theta=10000,
-        time_theta=10000,
-        text_encoder_dim: int = 2048,
-    ):
-        super().__init__()
-        self.out_channels = in_channels
-        self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim
-
-        self.pos_embed = BriaFiboEmbedND(theta=rope_theta, axes_dim=axes_dims_rope)
-
-        self.time_embed = BriaFiboTimestepProjEmbeddings(embedding_dim=self.inner_dim, time_theta=time_theta)
-
-        if guidance_embeds:
-            self.guidance_embed = BriaFiboTimestepProjEmbeddings(embedding_dim=self.inner_dim)
-
-        self.context_embedder = nn.Linear(self.config.joint_attention_dim, self.inner_dim)
-        self.x_embedder = torch.nn.Linear(self.config.in_channels, self.inner_dim)
-
-        self.transformer_blocks = nn.ModuleList(
-            [
-                BriaFiboTransformerBlock(
-                    dim=self.inner_dim,
-                    num_attention_heads=self.config.num_attention_heads,
-                    attention_head_dim=self.config.attention_head_dim,
-                )
-                for i in range(self.config.num_layers)
-            ]
-        )
-
-        self.single_transformer_blocks = nn.ModuleList(
-            [
-                BriaFiboSingleTransformerBlock(
-                    dim=self.inner_dim,
-                    num_attention_heads=self.config.num_attention_heads,
-                    attention_head_dim=self.config.attention_head_dim,
-                )
-                for i in range(self.config.num_single_layers)
-            ]
-        )
-
-        self.norm_out = AdaLayerNormContinuous(self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6)
-        self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True)
-
-        self.gradient_checkpointing = False
-
-        caption_projection = [
-            BriaFiboTextProjection(in_features=text_encoder_dim, hidden_size=self.inner_dim // 2)
-            for i in range(self.config.num_layers + self.config.num_single_layers)
-        ]
-        self.caption_projection = nn.ModuleList(caption_projection)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor = None,
-        text_encoder_layers: list = None,
-        pooled_projections: torch.Tensor = None,
-        timestep: torch.LongTensor = None,
-        img_ids: torch.Tensor = None,
-        txt_ids: torch.Tensor = None,
-        guidance: torch.Tensor = None,
-        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
-        return_dict: bool = True,
-    ) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
-        """
-
-        Args:
-            hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
-                Input `hidden_states`.
-            encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
-                Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
-            pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
-                from the embeddings of input conditions.
-            timestep ( `torch.LongTensor`):
-                Used to indicate denoising step.
-            joint_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
-                `self.processor` in
-                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
-                tuple.
-        Returns:
-            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
-            `tuple` where the first element is the sample tensor.
-        """
-        if joint_attention_kwargs is not None:
-            joint_attention_kwargs = joint_attention_kwargs.copy()
-            lora_scale = joint_attention_kwargs.pop("scale", 1.0)
-        else:
-            lora_scale = 1.0
-
-        if USE_PEFT_BACKEND:
-            # weight the lora layers by setting `lora_scale` for each PEFT layer
-            scale_lora_layers(self, lora_scale)
-        else:
-            if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
-                logger.warning(
-                    "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
-                )
-        hidden_states = self.x_embedder(hidden_states)
-
-        timestep = timestep.to(hidden_states.dtype)
-        if guidance is not None:
-            guidance = guidance.to(hidden_states.dtype)
-        else:
-            guidance = None
-
-        temb = self.time_embed(timestep, dtype=hidden_states.dtype)
-
-        if guidance:
-            temb += self.guidance_embed(guidance, dtype=hidden_states.dtype)
-
-        encoder_hidden_states = self.context_embedder(encoder_hidden_states)
-
-        if len(txt_ids.shape) == 3:
-            txt_ids = txt_ids[0]
-
-        if len(img_ids.shape) == 3:
-            img_ids = img_ids[0]
-
-        ids = torch.cat((txt_ids, img_ids), dim=0)
-        image_rotary_emb = self.pos_embed(ids)
-
-        new_text_encoder_layers = []
-        for i, text_encoder_layer in enumerate(text_encoder_layers):
-            text_encoder_layer = self.caption_projection[i](text_encoder_layer)
-            new_text_encoder_layers.append(text_encoder_layer)
-        text_encoder_layers = new_text_encoder_layers
-
-        block_id = 0
-        for index_block, block in enumerate(self.transformer_blocks):
-            current_text_encoder_layer = text_encoder_layers[block_id]
-            encoder_hidden_states = torch.cat(
-                [encoder_hidden_states[:, :, : self.inner_dim // 2], current_text_encoder_layer], dim=-1
-            )
-            block_id += 1
-            if torch.is_grad_enabled() and self.gradient_checkpointing:
-                encoder_hidden_states, hidden_states = self._gradient_checkpointing_func(
-                    block,
-                    hidden_states,
-                    encoder_hidden_states,
-                    temb,
-                    image_rotary_emb,
-                    joint_attention_kwargs,
-                )
-
-            else:
-                encoder_hidden_states, hidden_states = block(
-                    hidden_states=hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    temb=temb,
-                    image_rotary_emb=image_rotary_emb,
-                    joint_attention_kwargs=joint_attention_kwargs,
-                )
-
-        for index_block, block in enumerate(self.single_transformer_blocks):
-            current_text_encoder_layer = text_encoder_layers[block_id]
-            encoder_hidden_states = torch.cat(
-                [encoder_hidden_states[:, :, : self.inner_dim // 2], current_text_encoder_layer], dim=-1
-            )
-            block_id += 1
-            hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
-            if torch.is_grad_enabled() and self.gradient_checkpointing:
-                hidden_states = self._gradient_checkpointing_func(
-                    block,
-                    hidden_states,
-                    temb,
-                    image_rotary_emb,
-                    joint_attention_kwargs,
-                )
-
-            else:
-                hidden_states = block(
-                    hidden_states=hidden_states,
-                    temb=temb,
-                    image_rotary_emb=image_rotary_emb,
-                    joint_attention_kwargs=joint_attention_kwargs,
-                )
-
-            encoder_hidden_states = hidden_states[:, : encoder_hidden_states.shape[1], ...]
-            hidden_states = hidden_states[:, encoder_hidden_states.shape[1] :, ...]
-
-        hidden_states = self.norm_out(hidden_states, temb)
-        output = self.proj_out(hidden_states)
-
-        if USE_PEFT_BACKEND:
-            # remove `lora_scale` from each PEFT layer
-            unscale_lora_layers(self, lora_scale)
-
-        if not return_dict:
-            return (output,)
-
-        return Transformer2DModelOutput(sample=output)

src/diffusers/models/transformers/transformer_chroma.py

@@ -379,7 +379,7 @@ class ChromaTransformer2DModel(
     """
     The Transformer model introduced in Flux, modified for Chroma.
 
-    Reference: https://huggingface.co/lodestones/Chroma1-HD
+    Reference: https://huggingface.co/lodestones/Chroma
 
     Args:
         patch_size (`int`, defaults to `1`):

src/diffusers/models/transformers/transformer_flux.py

@@ -22,7 +22,7 @@ import torch.nn.functional as F
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FluxTransformer2DLoadersMixin, FromOriginalModelMixin, PeftAdapterMixin
-from ...utils import USE_PEFT_BACKEND, is_torch_npu_available, logging, scale_lora_layers, unscale_lora_layers
+from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
 from ...utils.torch_utils import maybe_allow_in_graph
 from .._modeling_parallel import ContextParallelInput, ContextParallelOutput
 from ..attention import AttentionMixin, AttentionModuleMixin, FeedForward
@@ -717,11 +717,7 @@ class FluxTransformer2DModel(
             img_ids = img_ids[0]
 
         ids = torch.cat((txt_ids, img_ids), dim=0)
-        if is_torch_npu_available():
-            freqs_cos, freqs_sin = self.pos_embed(ids.cpu())
-            image_rotary_emb = (freqs_cos.npu(), freqs_sin.npu())
-        else:
-            image_rotary_emb = self.pos_embed(ids)
+        image_rotary_emb = self.pos_embed(ids)
 
         if joint_attention_kwargs is not None and "ip_adapter_image_embeds" in joint_attention_kwargs:
             ip_adapter_image_embeds = joint_attention_kwargs.pop("ip_adapter_image_embeds")

src/diffusers/models/transformers/transformer_hunyuan_video.py

@@ -24,7 +24,6 @@ from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import PeftAdapterMixin
 from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
 from ..attention import FeedForward
-from ..attention_dispatch import dispatch_attention_fn
 from ..attention_processor import Attention, AttentionProcessor
 from ..cache_utils import CacheMixin
 from ..embeddings import (
@@ -43,9 +42,6 @@ logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
 class HunyuanVideoAttnProcessor2_0:
-    _attention_backend = None
-    _parallel_config = None
-
     def __init__(self):
         if not hasattr(F, "scaled_dot_product_attention"):
             raise ImportError(
@@ -68,9 +64,9 @@ class HunyuanVideoAttnProcessor2_0:
         key = attn.to_k(hidden_states)
         value = attn.to_v(hidden_states)
 
-        query = query.unflatten(2, (attn.heads, -1))
-        key = key.unflatten(2, (attn.heads, -1))
-        value = value.unflatten(2, (attn.heads, -1))
+        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)
 
         # 2. QK normalization
         if attn.norm_q is not None:
@@ -85,29 +81,21 @@ class HunyuanVideoAttnProcessor2_0:
             if attn.add_q_proj is None and encoder_hidden_states is not None:
                 query = torch.cat(
                     [
-                        apply_rotary_emb(
-                            query[:, : -encoder_hidden_states.shape[1]],
-                            image_rotary_emb,
-                            sequence_dim=1,
-                        ),
-                        query[:, -encoder_hidden_states.shape[1] :],
+                        apply_rotary_emb(query[:, :, : -encoder_hidden_states.shape[1]], image_rotary_emb),
+                        query[:, :, -encoder_hidden_states.shape[1] :],
                     ],
-                    dim=1,
+                    dim=2,
                 )
                 key = torch.cat(
                     [
-                        apply_rotary_emb(
-                            key[:, : -encoder_hidden_states.shape[1]],
-                            image_rotary_emb,
-                            sequence_dim=1,
-                        ),
-                        key[:, -encoder_hidden_states.shape[1] :],
+                        apply_rotary_emb(key[:, :, : -encoder_hidden_states.shape[1]], image_rotary_emb),
+                        key[:, :, -encoder_hidden_states.shape[1] :],
                     ],
-                    dim=1,
+                    dim=2,
                 )
             else:
-                query = apply_rotary_emb(query, image_rotary_emb, sequence_dim=1)
-                key = apply_rotary_emb(key, image_rotary_emb, sequence_dim=1)
+                query = apply_rotary_emb(query, image_rotary_emb)
+                key = apply_rotary_emb(key, image_rotary_emb)
 
         # 4. Encoder condition QKV projection and normalization
         if attn.add_q_proj is not None and encoder_hidden_states is not None:
@@ -115,31 +103,24 @@ class HunyuanVideoAttnProcessor2_0:
             encoder_key = attn.add_k_proj(encoder_hidden_states)
             encoder_value = attn.add_v_proj(encoder_hidden_states)
 
-            encoder_query = encoder_query.unflatten(2, (attn.heads, -1))
-            encoder_key = encoder_key.unflatten(2, (attn.heads, -1))
-            encoder_value = encoder_value.unflatten(2, (attn.heads, -1))
+            encoder_query = encoder_query.unflatten(2, (attn.heads, -1)).transpose(1, 2)
+            encoder_key = encoder_key.unflatten(2, (attn.heads, -1)).transpose(1, 2)
+            encoder_value = encoder_value.unflatten(2, (attn.heads, -1)).transpose(1, 2)
 
             if attn.norm_added_q is not None:
                 encoder_query = attn.norm_added_q(encoder_query)
             if attn.norm_added_k is not None:
                 encoder_key = attn.norm_added_k(encoder_key)
 
-            query = torch.cat([query, encoder_query], dim=1)
-            key = torch.cat([key, encoder_key], dim=1)
-            value = torch.cat([value, encoder_value], dim=1)
+            query = torch.cat([query, encoder_query], dim=2)
+            key = torch.cat([key, encoder_key], dim=2)
+            value = torch.cat([value, encoder_value], dim=2)
 
         # 5. Attention
-        hidden_states = dispatch_attention_fn(
-            query,
-            key,
-            value,
-            attn_mask=attention_mask,
-            dropout_p=0.0,
-            is_causal=False,
-            backend=self._attention_backend,
-            parallel_config=self._parallel_config,
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
         )
-        hidden_states = hidden_states.flatten(2, 3)
+        hidden_states = hidden_states.transpose(1, 2).flatten(2, 3)
         hidden_states = hidden_states.to(query.dtype)
 
         # 6. Output projection

src/diffusers/models/transformers/transformer_hunyuanimage.py

@@ -1,971 +0,0 @@
-# Copyright 2025 The Hunyuan Team and 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 math
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from diffusers.loaders import FromOriginalModelMixin
-
-from ...configuration_utils import ConfigMixin, register_to_config
-from ...loaders import PeftAdapterMixin
-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
-from ..attention_dispatch import dispatch_attention_fn
-from ..attention_processor import Attention, AttentionProcessor
-from ..cache_utils import CacheMixin
-from ..embeddings import (
-    CombinedTimestepTextProjEmbeddings,
-    TimestepEmbedding,
-    Timesteps,
-    get_1d_rotary_pos_embed,
-)
-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
-
-
-class HunyuanImageAttnProcessor:
-    _attention_backend = None
-    _parallel_config = None
-
-    def __init__(self):
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError(
-                "HunyuanImageAttnProcessor 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: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        image_rotary_emb: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        if attn.add_q_proj is None and encoder_hidden_states is not None:
-            hidden_states = torch.cat([hidden_states, encoder_hidden_states], dim=1)
-
-        # 1. QKV projections
-        query = attn.to_q(hidden_states)
-        key = attn.to_k(hidden_states)
-        value = attn.to_v(hidden_states)
-
-        query = query.unflatten(2, (attn.heads, -1))  # batch_size, seq_len, heads, head_dim
-        key = key.unflatten(2, (attn.heads, -1))
-        value = value.unflatten(2, (attn.heads, -1))
-
-        # 2. QK normalization
-        if attn.norm_q is not None:
-            query = attn.norm_q(query)
-        if attn.norm_k is not None:
-            key = attn.norm_k(key)
-
-        # 3. Rotational positional embeddings applied to latent stream
-        if image_rotary_emb is not None:
-            from ..embeddings import apply_rotary_emb
-
-            if attn.add_q_proj is None and encoder_hidden_states is not None:
-                query = torch.cat(
-                    [
-                        apply_rotary_emb(
-                            query[:, : -encoder_hidden_states.shape[1]], image_rotary_emb, sequence_dim=1
-                        ),
-                        query[:, -encoder_hidden_states.shape[1] :],
-                    ],
-                    dim=1,
-                )
-                key = torch.cat(
-                    [
-                        apply_rotary_emb(key[:, : -encoder_hidden_states.shape[1]], image_rotary_emb, sequence_dim=1),
-                        key[:, -encoder_hidden_states.shape[1] :],
-                    ],
-                    dim=1,
-                )
-            else:
-                query = apply_rotary_emb(query, image_rotary_emb, sequence_dim=1)
-                key = apply_rotary_emb(key, image_rotary_emb, sequence_dim=1)
-
-        # 4. Encoder condition QKV projection and normalization
-        if attn.add_q_proj is not None and encoder_hidden_states is not None:
-            encoder_query = attn.add_q_proj(encoder_hidden_states)
-            encoder_key = attn.add_k_proj(encoder_hidden_states)
-            encoder_value = attn.add_v_proj(encoder_hidden_states)
-
-            encoder_query = encoder_query.unflatten(2, (attn.heads, -1))
-            encoder_key = encoder_key.unflatten(2, (attn.heads, -1))
-            encoder_value = encoder_value.unflatten(2, (attn.heads, -1))
-
-            if attn.norm_added_q is not None:
-                encoder_query = attn.norm_added_q(encoder_query)
-            if attn.norm_added_k is not None:
-                encoder_key = attn.norm_added_k(encoder_key)
-
-            query = torch.cat([query, encoder_query], dim=1)
-            key = torch.cat([key, encoder_key], dim=1)
-            value = torch.cat([value, encoder_value], dim=1)
-
-        # 5. Attention
-        hidden_states = dispatch_attention_fn(
-            query,
-            key,
-            value,
-            attn_mask=attention_mask,
-            dropout_p=0.0,
-            is_causal=False,
-            backend=self._attention_backend,
-            parallel_config=self._parallel_config,
-        )
-        hidden_states = hidden_states.flatten(2, 3)
-        hidden_states = hidden_states.to(query.dtype)
-
-        # 6. Output projection
-        if encoder_hidden_states is not None:
-            hidden_states, encoder_hidden_states = (
-                hidden_states[:, : -encoder_hidden_states.shape[1]],
-                hidden_states[:, -encoder_hidden_states.shape[1] :],
-            )
-
-            if getattr(attn, "to_out", None) is not None:
-                hidden_states = attn.to_out[0](hidden_states)
-                hidden_states = attn.to_out[1](hidden_states)
-
-            if getattr(attn, "to_add_out", None) is not None:
-                encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
-
-        return hidden_states, encoder_hidden_states
-
-
-class HunyuanImagePatchEmbed(nn.Module):
-    def __init__(
-        self,
-        patch_size: Union[Tuple[int, int], Tuple[int, int, int]] = (16, 16),
-        in_chans: int = 3,
-        embed_dim: int = 768,
-    ) -> None:
-        super().__init__()
-
-        self.patch_size = patch_size
-
-        if len(patch_size) == 2:
-            self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
-        elif len(patch_size) == 3:
-            self.proj = nn.Conv3d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
-        else:
-            raise ValueError(f"patch_size must be a tuple of length 2 or 3, got {len(patch_size)}")
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.proj(hidden_states)
-        hidden_states = hidden_states.flatten(2).transpose(1, 2)
-        return hidden_states
-
-
-class HunyuanImageByT5TextProjection(nn.Module):
-    def __init__(self, in_features: int, hidden_size: int, out_features: int):
-        super().__init__()
-        self.norm = nn.LayerNorm(in_features)
-        self.linear_1 = nn.Linear(in_features, hidden_size)
-        self.linear_2 = nn.Linear(hidden_size, hidden_size)
-        self.linear_3 = nn.Linear(hidden_size, out_features)
-        self.act_fn = nn.GELU()
-
-    def forward(self, encoder_hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.norm(encoder_hidden_states)
-        hidden_states = self.linear_1(hidden_states)
-        hidden_states = self.act_fn(hidden_states)
-        hidden_states = self.linear_2(hidden_states)
-        hidden_states = self.act_fn(hidden_states)
-        hidden_states = self.linear_3(hidden_states)
-        return hidden_states
-
-
-class HunyuanImageAdaNorm(nn.Module):
-    def __init__(self, in_features: int, out_features: Optional[int] = None) -> None:
-        super().__init__()
-
-        out_features = out_features or 2 * in_features
-        self.linear = nn.Linear(in_features, out_features)
-        self.nonlinearity = nn.SiLU()
-
-    def forward(
-        self, temb: torch.Tensor
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
-        temb = self.linear(self.nonlinearity(temb))
-        gate_msa, gate_mlp = temb.chunk(2, dim=1)
-        gate_msa, gate_mlp = gate_msa.unsqueeze(1), gate_mlp.unsqueeze(1)
-        return gate_msa, gate_mlp
-
-
-class HunyuanImageCombinedTimeGuidanceEmbedding(nn.Module):
-    def __init__(
-        self,
-        embedding_dim: int,
-        guidance_embeds: bool = False,
-        use_meanflow: bool = False,
-    ):
-        super().__init__()
-
-        self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
-        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
-
-        self.use_meanflow = use_meanflow
-
-        self.time_proj_r = None
-        self.timestep_embedder_r = None
-        if use_meanflow:
-            self.time_proj_r = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
-            self.timestep_embedder_r = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
-
-        self.guidance_embedder = None
-        if guidance_embeds:
-            self.guidance_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
-
-    def forward(
-        self,
-        timestep: torch.Tensor,
-        timestep_r: Optional[torch.Tensor] = None,
-        guidance: Optional[torch.Tensor] = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        timesteps_proj = self.time_proj(timestep)
-        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=timestep.dtype))
-
-        if timestep_r is not None:
-            timesteps_proj_r = self.time_proj_r(timestep_r)
-            timesteps_emb_r = self.timestep_embedder_r(timesteps_proj_r.to(dtype=timestep.dtype))
-            timesteps_emb = (timesteps_emb + timesteps_emb_r) / 2
-
-        if self.guidance_embedder is not None:
-            guidance_proj = self.time_proj(guidance)
-            guidance_emb = self.guidance_embedder(guidance_proj.to(dtype=timestep.dtype))
-            conditioning = timesteps_emb + guidance_emb
-        else:
-            conditioning = timesteps_emb
-
-        return conditioning
-
-
-# IndividualTokenRefinerBlock
-@maybe_allow_in_graph
-class HunyuanImageIndividualTokenRefinerBlock(nn.Module):
-    def __init__(
-        self,
-        num_attention_heads: int,  # 28
-        attention_head_dim: int,  # 128
-        mlp_width_ratio: str = 4.0,
-        mlp_drop_rate: float = 0.0,
-        attention_bias: bool = True,
-    ) -> None:
-        super().__init__()
-
-        hidden_size = num_attention_heads * attention_head_dim
-
-        self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6)
-        self.attn = Attention(
-            query_dim=hidden_size,
-            cross_attention_dim=None,
-            heads=num_attention_heads,
-            dim_head=attention_head_dim,
-            bias=attention_bias,
-        )
-
-        self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6)
-        self.ff = FeedForward(hidden_size, mult=mlp_width_ratio, activation_fn="linear-silu", dropout=mlp_drop_rate)
-
-        self.norm_out = HunyuanImageAdaNorm(hidden_size, 2 * hidden_size)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        temb: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        norm_hidden_states = self.norm1(hidden_states)
-
-        attn_output = self.attn(
-            hidden_states=norm_hidden_states,
-            encoder_hidden_states=None,
-            attention_mask=attention_mask,
-        )
-
-        gate_msa, gate_mlp = self.norm_out(temb)
-        hidden_states = hidden_states + attn_output * gate_msa
-
-        ff_output = self.ff(self.norm2(hidden_states))
-        hidden_states = hidden_states + ff_output * gate_mlp
-
-        return hidden_states
-
-
-class HunyuanImageIndividualTokenRefiner(nn.Module):
-    def __init__(
-        self,
-        num_attention_heads: int,
-        attention_head_dim: int,
-        num_layers: int,
-        mlp_width_ratio: float = 4.0,
-        mlp_drop_rate: float = 0.0,
-        attention_bias: bool = True,
-    ) -> None:
-        super().__init__()
-
-        self.refiner_blocks = nn.ModuleList(
-            [
-                HunyuanImageIndividualTokenRefinerBlock(
-                    num_attention_heads=num_attention_heads,
-                    attention_head_dim=attention_head_dim,
-                    mlp_width_ratio=mlp_width_ratio,
-                    mlp_drop_rate=mlp_drop_rate,
-                    attention_bias=attention_bias,
-                )
-                for _ in range(num_layers)
-            ]
-        )
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        temb: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-    ) -> None:
-        self_attn_mask = None
-        if attention_mask is not None:
-            batch_size = attention_mask.shape[0]
-            seq_len = attention_mask.shape[1]
-            attention_mask = attention_mask.to(hidden_states.device)
-            self_attn_mask_1 = attention_mask.view(batch_size, 1, 1, seq_len).repeat(1, 1, seq_len, 1)
-            self_attn_mask_2 = self_attn_mask_1.transpose(2, 3)
-            self_attn_mask = (self_attn_mask_1 & self_attn_mask_2).bool()
-            self_attn_mask[:, :, :, 0] = True
-
-        for block in self.refiner_blocks:
-            hidden_states = block(hidden_states, temb, self_attn_mask)
-
-        return hidden_states
-
-
-# txt_in
-class HunyuanImageTokenRefiner(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        num_attention_heads: int,
-        attention_head_dim: int,
-        num_layers: int,
-        mlp_ratio: float = 4.0,
-        mlp_drop_rate: float = 0.0,
-        attention_bias: bool = True,
-    ) -> None:
-        super().__init__()
-
-        hidden_size = num_attention_heads * attention_head_dim
-
-        self.time_text_embed = CombinedTimestepTextProjEmbeddings(
-            embedding_dim=hidden_size, pooled_projection_dim=in_channels
-        )
-        self.proj_in = nn.Linear(in_channels, hidden_size, bias=True)
-        self.token_refiner = HunyuanImageIndividualTokenRefiner(
-            num_attention_heads=num_attention_heads,
-            attention_head_dim=attention_head_dim,
-            num_layers=num_layers,
-            mlp_width_ratio=mlp_ratio,
-            mlp_drop_rate=mlp_drop_rate,
-            attention_bias=attention_bias,
-        )
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        timestep: torch.LongTensor,
-        attention_mask: Optional[torch.LongTensor] = None,
-    ) -> torch.Tensor:
-        if attention_mask is None:
-            pooled_hidden_states = hidden_states.mean(dim=1)
-        else:
-            original_dtype = hidden_states.dtype
-            mask_float = attention_mask.float().unsqueeze(-1)
-            pooled_hidden_states = (hidden_states * mask_float).sum(dim=1) / mask_float.sum(dim=1)
-            pooled_hidden_states = pooled_hidden_states.to(original_dtype)
-
-        temb = self.time_text_embed(timestep, pooled_hidden_states)
-        hidden_states = self.proj_in(hidden_states)
-        hidden_states = self.token_refiner(hidden_states, temb, attention_mask)
-
-        return hidden_states
-
-
-class HunyuanImageRotaryPosEmbed(nn.Module):
-    def __init__(
-        self, patch_size: Union[Tuple, List[int]], rope_dim: Union[Tuple, List[int]], theta: float = 256.0
-    ) -> None:
-        super().__init__()
-
-        if not isinstance(patch_size, (tuple, list)) or len(patch_size) not in [2, 3]:
-            raise ValueError(f"patch_size must be a tuple or list of length 2 or 3, got {patch_size}")
-
-        if not isinstance(rope_dim, (tuple, list)) or len(rope_dim) not in [2, 3]:
-            raise ValueError(f"rope_dim must be a tuple or list of length 2 or 3, got {rope_dim}")
-
-        if not len(patch_size) == len(rope_dim):
-            raise ValueError(f"patch_size and rope_dim must have the same length, got {patch_size} and {rope_dim}")
-
-        self.patch_size = patch_size
-        self.rope_dim = rope_dim
-        self.theta = theta
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        if hidden_states.ndim == 5:
-            _, _, frame, height, width = hidden_states.shape
-            patch_size_frame, patch_size_height, patch_size_width = self.patch_size
-            rope_sizes = [frame // patch_size_frame, height // patch_size_height, width // patch_size_width]
-        elif hidden_states.ndim == 4:
-            _, _, height, width = hidden_states.shape
-            patch_size_height, patch_size_width = self.patch_size
-            rope_sizes = [height // patch_size_height, width // patch_size_width]
-        else:
-            raise ValueError(f"hidden_states must be a 4D or 5D tensor, got {hidden_states.shape}")
-
-        axes_grids = []
-        for i in range(len(rope_sizes)):
-            grid = torch.arange(0, rope_sizes[i], device=hidden_states.device, dtype=torch.float32)
-            axes_grids.append(grid)
-        grid = torch.meshgrid(*axes_grids, indexing="ij")  # dim x [H, W]
-        grid = torch.stack(grid, dim=0)  # [2, H, W]
-
-        freqs = []
-        for i in range(len(rope_sizes)):
-            freq = get_1d_rotary_pos_embed(self.rope_dim[i], grid[i].reshape(-1), self.theta, use_real=True)
-            freqs.append(freq)
-
-        freqs_cos = torch.cat([f[0] for f in freqs], dim=1)  # (W * H * T, D / 2)
-        freqs_sin = torch.cat([f[1] for f in freqs], dim=1)  # (W * H * T, D / 2)
-        return freqs_cos, freqs_sin
-
-
-@maybe_allow_in_graph
-class HunyuanImageSingleTransformerBlock(nn.Module):
-    def __init__(
-        self,
-        num_attention_heads: int,
-        attention_head_dim: int,
-        mlp_ratio: float = 4.0,
-        qk_norm: str = "rms_norm",
-    ) -> None:
-        super().__init__()
-
-        hidden_size = num_attention_heads * attention_head_dim
-        mlp_dim = int(hidden_size * mlp_ratio)
-
-        self.attn = Attention(
-            query_dim=hidden_size,
-            cross_attention_dim=None,
-            dim_head=attention_head_dim,
-            heads=num_attention_heads,
-            out_dim=hidden_size,
-            bias=True,
-            processor=HunyuanImageAttnProcessor(),
-            qk_norm=qk_norm,
-            eps=1e-6,
-            pre_only=True,
-        )
-
-        self.norm = AdaLayerNormZeroSingle(hidden_size, norm_type="layer_norm")
-        self.proj_mlp = nn.Linear(hidden_size, mlp_dim)
-        self.act_mlp = nn.GELU(approximate="tanh")
-        self.proj_out = nn.Linear(hidden_size + mlp_dim, hidden_size)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        temb: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-        *args,
-        **kwargs,
-    ) -> torch.Tensor:
-        text_seq_length = encoder_hidden_states.shape[1]
-        hidden_states = torch.cat([hidden_states, encoder_hidden_states], dim=1)
-
-        residual = hidden_states
-
-        # 1. Input normalization
-        norm_hidden_states, gate = self.norm(hidden_states, emb=temb)
-        mlp_hidden_states = self.act_mlp(self.proj_mlp(norm_hidden_states))
-
-        norm_hidden_states, norm_encoder_hidden_states = (
-            norm_hidden_states[:, :-text_seq_length, :],
-            norm_hidden_states[:, -text_seq_length:, :],
-        )
-
-        # 2. Attention
-        attn_output, context_attn_output = self.attn(
-            hidden_states=norm_hidden_states,
-            encoder_hidden_states=norm_encoder_hidden_states,
-            attention_mask=attention_mask,
-            image_rotary_emb=image_rotary_emb,
-        )
-        attn_output = torch.cat([attn_output, context_attn_output], dim=1)
-
-        # 3. Modulation and residual connection
-        hidden_states = torch.cat([attn_output, mlp_hidden_states], dim=2)
-        hidden_states = gate.unsqueeze(1) * self.proj_out(hidden_states)
-        hidden_states = hidden_states + residual
-
-        hidden_states, encoder_hidden_states = (
-            hidden_states[:, :-text_seq_length, :],
-            hidden_states[:, -text_seq_length:, :],
-        )
-        return hidden_states, encoder_hidden_states
-
-
-@maybe_allow_in_graph
-class HunyuanImageTransformerBlock(nn.Module):
-    def __init__(
-        self,
-        num_attention_heads: int,
-        attention_head_dim: int,
-        mlp_ratio: float,
-        qk_norm: str = "rms_norm",
-    ) -> None:
-        super().__init__()
-
-        hidden_size = num_attention_heads * attention_head_dim
-
-        self.norm1 = AdaLayerNormZero(hidden_size, norm_type="layer_norm")
-        self.norm1_context = AdaLayerNormZero(hidden_size, norm_type="layer_norm")
-
-        self.attn = Attention(
-            query_dim=hidden_size,
-            cross_attention_dim=None,
-            added_kv_proj_dim=hidden_size,
-            dim_head=attention_head_dim,
-            heads=num_attention_heads,
-            out_dim=hidden_size,
-            context_pre_only=False,
-            bias=True,
-            processor=HunyuanImageAttnProcessor(),
-            qk_norm=qk_norm,
-            eps=1e-6,
-        )
-
-        self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.ff = FeedForward(hidden_size, mult=mlp_ratio, activation_fn="gelu-approximate")
-
-        self.norm2_context = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.ff_context = FeedForward(hidden_size, mult=mlp_ratio, activation_fn="gelu-approximate")
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        temb: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-        *args,
-        **kwargs,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        # 1. Input normalization
-        norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)
-        norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context(
-            encoder_hidden_states, emb=temb
-        )
-
-        # 2. Joint attention
-        attn_output, context_attn_output = self.attn(
-            hidden_states=norm_hidden_states,
-            encoder_hidden_states=norm_encoder_hidden_states,
-            attention_mask=attention_mask,
-            image_rotary_emb=image_rotary_emb,
-        )
-
-        # 3. Modulation and residual connection
-        hidden_states = hidden_states + attn_output * gate_msa.unsqueeze(1)
-        encoder_hidden_states = encoder_hidden_states + context_attn_output * c_gate_msa.unsqueeze(1)
-
-        norm_hidden_states = self.norm2(hidden_states)
-        norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
-
-        norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
-        norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
-
-        # 4. Feed-forward
-        ff_output = self.ff(norm_hidden_states)
-        context_ff_output = self.ff_context(norm_encoder_hidden_states)
-
-        hidden_states = hidden_states + gate_mlp.unsqueeze(1) * ff_output
-        encoder_hidden_states = encoder_hidden_states + c_gate_mlp.unsqueeze(1) * context_ff_output
-
-        return hidden_states, encoder_hidden_states
-
-
-class HunyuanImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, CacheMixin):
-    r"""
-    The Transformer model used in [HunyuanImage-2.1](https://github.com/Tencent-Hunyuan/HunyuanImage-2.1).
-
-    Args:
-        in_channels (`int`, defaults to `16`):
-            The number of channels in the input.
-        out_channels (`int`, defaults to `16`):
-            The number of channels in the output.
-        num_attention_heads (`int`, defaults to `24`):
-            The number of heads to use for multi-head attention.
-        attention_head_dim (`int`, defaults to `128`):
-            The number of channels in each head.
-        num_layers (`int`, defaults to `20`):
-            The number of layers of dual-stream blocks to use.
-        num_single_layers (`int`, defaults to `40`):
-            The number of layers of single-stream blocks to use.
-        num_refiner_layers (`int`, defaults to `2`):
-            The number of layers of refiner blocks to use.
-        mlp_ratio (`float`, defaults to `4.0`):
-            The ratio of the hidden layer size to the input size in the feedforward network.
-        patch_size (`int`, defaults to `2`):
-            The size of the spatial patches to use in the patch embedding layer.
-        patch_size_t (`int`, defaults to `1`):
-            The size of the tmeporal patches to use in the patch embedding layer.
-        qk_norm (`str`, defaults to `rms_norm`):
-            The normalization to use for the query and key projections in the attention layers.
-        guidance_embeds (`bool`, defaults to `True`):
-            Whether to use guidance embeddings in the model.
-        text_embed_dim (`int`, defaults to `4096`):
-            Input dimension of text embeddings from the text encoder.
-        pooled_projection_dim (`int`, defaults to `768`):
-            The dimension of the pooled projection of the text embeddings.
-        rope_theta (`float`, defaults to `256.0`):
-            The value of theta to use in the RoPE layer.
-        rope_axes_dim (`Tuple[int]`, defaults to `(16, 56, 56)`):
-            The dimensions of the axes to use in the RoPE layer.
-        image_condition_type (`str`, *optional*, defaults to `None`):
-            The type of image conditioning to use. If `None`, no image conditioning is used. If `latent_concat`, the
-            image is concatenated to the latent stream. If `token_replace`, the image is used to replace first-frame
-            tokens in the latent stream and apply conditioning.
-    """
-
-    _supports_gradient_checkpointing = True
-    _skip_layerwise_casting_patterns = ["x_embedder", "context_embedder", "norm"]
-    _no_split_modules = [
-        "HunyuanImageTransformerBlock",
-        "HunyuanImageSingleTransformerBlock",
-        "HunyuanImagePatchEmbed",
-        "HunyuanImageTokenRefiner",
-    ]
-    _repeated_blocks = [
-        "HunyuanImageTransformerBlock",
-        "HunyuanImageSingleTransformerBlock",
-    ]
-
-    @register_to_config
-    def __init__(
-        self,
-        in_channels: int = 64,
-        out_channels: int = 64,
-        num_attention_heads: int = 28,
-        attention_head_dim: int = 128,
-        num_layers: int = 20,
-        num_single_layers: int = 40,
-        num_refiner_layers: int = 2,
-        mlp_ratio: float = 4.0,
-        patch_size: Tuple[int, int] = (1, 1),
-        qk_norm: str = "rms_norm",
-        guidance_embeds: bool = False,
-        text_embed_dim: int = 3584,
-        text_embed_2_dim: Optional[int] = None,
-        rope_theta: float = 256.0,
-        rope_axes_dim: Tuple[int] = (64, 64),
-        use_meanflow: bool = False,
-    ) -> None:
-        super().__init__()
-
-        if not (isinstance(patch_size, (tuple, list)) and len(patch_size) in [2, 3]):
-            raise ValueError(f"patch_size must be a tuple of length 2 or 3, got {patch_size}")
-
-        inner_dim = num_attention_heads * attention_head_dim
-        out_channels = out_channels or in_channels
-
-        # 1. Latent and condition embedders
-        self.x_embedder = HunyuanImagePatchEmbed(patch_size, in_channels, inner_dim)
-        self.context_embedder = HunyuanImageTokenRefiner(
-            text_embed_dim, num_attention_heads, attention_head_dim, num_layers=num_refiner_layers
-        )
-
-        if text_embed_2_dim is not None:
-            self.context_embedder_2 = HunyuanImageByT5TextProjection(text_embed_2_dim, 2048, inner_dim)
-        else:
-            self.context_embedder_2 = None
-
-        self.time_guidance_embed = HunyuanImageCombinedTimeGuidanceEmbedding(inner_dim, guidance_embeds, use_meanflow)
-
-        # 2. RoPE
-        self.rope = HunyuanImageRotaryPosEmbed(patch_size, rope_axes_dim, rope_theta)
-
-        # 3. Dual stream transformer blocks
-
-        self.transformer_blocks = nn.ModuleList(
-            [
-                HunyuanImageTransformerBlock(
-                    num_attention_heads, attention_head_dim, mlp_ratio=mlp_ratio, qk_norm=qk_norm
-                )
-                for _ in range(num_layers)
-            ]
-        )
-
-        # 4. Single stream transformer blocks
-        self.single_transformer_blocks = nn.ModuleList(
-            [
-                HunyuanImageSingleTransformerBlock(
-                    num_attention_heads, attention_head_dim, mlp_ratio=mlp_ratio, qk_norm=qk_norm
-                )
-                for _ in range(num_single_layers)
-            ]
-        )
-
-        # 5. Output projection
-        self.norm_out = AdaLayerNormContinuous(inner_dim, inner_dim, elementwise_affine=False, eps=1e-6)
-        self.proj_out = nn.Linear(inner_dim, math.prod(patch_size) * out_channels)
-
-        self.gradient_checkpointing = False
-
-    @property
-    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
-    def attn_processors(self) -> Dict[str, AttentionProcessor]:
-        r"""
-        Returns:
-            `dict` of attention processors: A dictionary containing all attention processors used in the model with
-            indexed by its weight name.
-        """
-        # set recursively
-        processors = {}
-
-        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
-            if hasattr(module, "get_processor"):
-                processors[f"{name}.processor"] = module.get_processor()
-
-            for sub_name, child in module.named_children():
-                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
-
-            return processors
-
-        for name, module in self.named_children():
-            fn_recursive_add_processors(name, module, processors)
-
-        return processors
-
-    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
-    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
-        r"""
-        Sets the attention processor to use to compute attention.
-
-        Parameters:
-            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
-                The instantiated processor class or a dictionary of processor classes that will be set as the processor
-                for **all** `Attention` layers.
-
-                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
-                processor. This is strongly recommended when setting trainable attention processors.
-
-        """
-        count = len(self.attn_processors.keys())
-
-        if isinstance(processor, dict) and len(processor) != count:
-            raise ValueError(
-                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
-                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
-            )
-
-        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
-            if hasattr(module, "set_processor"):
-                if not isinstance(processor, dict):
-                    module.set_processor(processor)
-                else:
-                    module.set_processor(processor.pop(f"{name}.processor"))
-
-            for sub_name, child in module.named_children():
-                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
-
-        for name, module in self.named_children():
-            fn_recursive_attn_processor(name, module, processor)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        timestep: torch.LongTensor,
-        encoder_hidden_states: torch.Tensor,
-        encoder_attention_mask: torch.Tensor,
-        timestep_r: Optional[torch.LongTensor] = None,
-        encoder_hidden_states_2: Optional[torch.Tensor] = None,
-        encoder_attention_mask_2: Optional[torch.Tensor] = None,
-        guidance: Optional[torch.Tensor] = None,
-        attention_kwargs: Optional[Dict[str, Any]] = None,
-        return_dict: bool = True,
-    ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
-        if attention_kwargs is not None:
-            attention_kwargs = attention_kwargs.copy()
-            lora_scale = attention_kwargs.pop("scale", 1.0)
-        else:
-            lora_scale = 1.0
-
-        if USE_PEFT_BACKEND:
-            # weight the lora layers by setting `lora_scale` for each PEFT layer
-            scale_lora_layers(self, lora_scale)
-        else:
-            if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None:
-                logger.warning(
-                    "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective."
-                )
-
-        if hidden_states.ndim == 4:
-            batch_size, channels, height, width = hidden_states.shape
-            sizes = (height, width)
-        elif hidden_states.ndim == 5:
-            batch_size, channels, frame, height, width = hidden_states.shape
-            sizes = (frame, height, width)
-        else:
-            raise ValueError(f"hidden_states must be a 4D or 5D tensor, got {hidden_states.shape}")
-
-        post_patch_sizes = tuple(d // p for d, p in zip(sizes, self.config.patch_size))
-
-        # 1. RoPE
-        image_rotary_emb = self.rope(hidden_states)
-
-        # 2. Conditional embeddings
-        encoder_attention_mask = encoder_attention_mask.bool()
-        temb = self.time_guidance_embed(timestep, guidance=guidance, timestep_r=timestep_r)
-        hidden_states = self.x_embedder(hidden_states)
-        encoder_hidden_states = self.context_embedder(encoder_hidden_states, timestep, encoder_attention_mask)
-
-        if self.context_embedder_2 is not None and encoder_hidden_states_2 is not None:
-            encoder_hidden_states_2 = self.context_embedder_2(encoder_hidden_states_2)
-
-            encoder_attention_mask_2 = encoder_attention_mask_2.bool()
-
-            # reorder and combine text tokens: combine valid tokens first, then padding
-            new_encoder_hidden_states = []
-            new_encoder_attention_mask = []
-
-            for text, text_mask, text_2, text_mask_2 in zip(
-                encoder_hidden_states, encoder_attention_mask, encoder_hidden_states_2, encoder_attention_mask_2
-            ):
-                # Concatenate: [valid_mllm, valid_byt5, invalid_mllm, invalid_byt5]
-                new_encoder_hidden_states.append(
-                    torch.cat(
-                        [
-                            text_2[text_mask_2],  # valid byt5
-                            text[text_mask],  # valid mllm
-                            text_2[~text_mask_2],  # invalid byt5
-                            text[~text_mask],  # invalid mllm
-                        ],
-                        dim=0,
-                    )
-                )
-
-                # Apply same reordering to attention masks
-                new_encoder_attention_mask.append(
-                    torch.cat(
-                        [
-                            text_mask_2[text_mask_2],
-                            text_mask[text_mask],
-                            text_mask_2[~text_mask_2],
-                            text_mask[~text_mask],
-                        ],
-                        dim=0,
-                    )
-                )
-
-            encoder_hidden_states = torch.stack(new_encoder_hidden_states)
-            encoder_attention_mask = torch.stack(new_encoder_attention_mask)
-
-        attention_mask = torch.nn.functional.pad(encoder_attention_mask, (hidden_states.shape[1], 0), value=True)
-        attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
-        # 3. Transformer blocks
-        if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for block in self.transformer_blocks:
-                hidden_states, encoder_hidden_states = self._gradient_checkpointing_func(
-                    block,
-                    hidden_states,
-                    encoder_hidden_states,
-                    temb,
-                    attention_mask=attention_mask,
-                    image_rotary_emb=image_rotary_emb,
-                )
-
-            for block in self.single_transformer_blocks:
-                hidden_states, encoder_hidden_states = self._gradient_checkpointing_func(
-                    block,
-                    hidden_states,
-                    encoder_hidden_states,
-                    temb,
-                    attention_mask=attention_mask,
-                    image_rotary_emb=image_rotary_emb,
-                )
-
-        else:
-            for block in self.transformer_blocks:
-                hidden_states, encoder_hidden_states = block(
-                    hidden_states,
-                    encoder_hidden_states,
-                    temb,
-                    attention_mask=attention_mask,
-                    image_rotary_emb=image_rotary_emb,
-                )
-
-            for block in self.single_transformer_blocks:
-                hidden_states, encoder_hidden_states = block(
-                    hidden_states,
-                    encoder_hidden_states,
-                    temb,
-                    attention_mask=attention_mask,
-                    image_rotary_emb=image_rotary_emb,
-                )
-
-        # 4. Output projection
-        hidden_states = self.norm_out(hidden_states, temb)
-        hidden_states = self.proj_out(hidden_states)
-
-        # 5. unpatchify
-        # reshape: [batch_size, *post_patch_dims, channels, *patch_size]
-        out_channels = self.config.out_channels
-        reshape_dims = [batch_size] + list(post_patch_sizes) + [out_channels] + list(self.config.patch_size)
-        hidden_states = hidden_states.reshape(*reshape_dims)
-
-        # create permutation pattern: batch, channels, then interleave post_patch and patch dims
-        # For 4D: [0, 3, 1, 4, 2, 5] -> batch, channels, post_patch_height, patch_size_height, post_patch_width, patch_size_width
-        # For 5D: [0, 4, 1, 5, 2, 6, 3, 7] -> batch, channels, post_patch_frame, patch_size_frame, post_patch_height, patch_size_height, post_patch_width, patch_size_width
-        ndim = len(post_patch_sizes)
-        permute_pattern = [0, ndim + 1]  # batch, channels
-        for i in range(ndim):
-            permute_pattern.extend([i + 1, ndim + 2 + i])  # post_patch_sizes[i], patch_sizes[i]
-        hidden_states = hidden_states.permute(*permute_pattern)
-
-        # flatten patch dimensions: flatten each (post_patch_size, patch_size) pair
-        # batch_size, channels, post_patch_sizes[0] * patch_sizes[0], post_patch_sizes[1] * patch_sizes[1], ...
-        final_dims = [batch_size, out_channels] + [
-            post_patch * patch for post_patch, patch in zip(post_patch_sizes, self.config.patch_size)
-        ]
-        hidden_states = hidden_states.reshape(*final_dims)
-
-        if USE_PEFT_BACKEND:
-            # remove `lora_scale` from each PEFT layer
-            unscale_lora_layers(self, lora_scale)
-
-        if not return_dict:
-            return (hidden_states,)
-
-        return Transformer2DModelOutput(sample=hidden_states)

src/diffusers/models/transformers/transformer_kandinsky.py

@@ -324,7 +324,6 @@ class Kandinsky5AttnProcessor:
                 sparse_params["sta_mask"],
                 thr=sparse_params["P"],
             )
-
         else:
             attn_mask = None
 
@@ -336,7 +335,6 @@ class Kandinsky5AttnProcessor:
             backend=self._attention_backend,
             parallel_config=self._parallel_config,
         )
-
         hidden_states = hidden_states.flatten(-2, -1)
 
         attn_out = attn.out_layer(hidden_states)

src/diffusers/models/transformers/transformer_prx.py

@@ -1,770 +0,0 @@
-# Copyright 2025 The Photoroom and The HuggingFace Teams. 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 typing import Any, Dict, List, Optional, Tuple, Union
-
-import torch
-from torch import nn
-from torch.nn.functional import fold, unfold
-
-from ...configuration_utils import ConfigMixin, register_to_config
-from ...utils import logging
-from ..attention import AttentionMixin, AttentionModuleMixin
-from ..attention_dispatch import dispatch_attention_fn
-from ..embeddings import get_timestep_embedding
-from ..modeling_outputs import Transformer2DModelOutput
-from ..modeling_utils import ModelMixin
-from ..normalization import RMSNorm
-
-
-logger = logging.get_logger(__name__)
-
-
-def get_image_ids(batch_size: int, height: int, width: int, patch_size: int, device: torch.device) -> torch.Tensor:
-    r"""
-    Generates 2D patch coordinate indices for a batch of images.
-
-    Args:
-        batch_size (`int`):
-            Number of images in the batch.
-        height (`int`):
-            Height of the input images (in pixels).
-        width (`int`):
-            Width of the input images (in pixels).
-        patch_size (`int`):
-            Size of the square patches that the image is divided into.
-        device (`torch.device`):
-            The device on which to create the tensor.
-
-    Returns:
-        `torch.Tensor`:
-            Tensor of shape `(batch_size, num_patches, 2)` containing the (row, col) coordinates of each patch in the
-            image grid.
-    """
-
-    img_ids = torch.zeros(height // patch_size, width // patch_size, 2, device=device)
-    img_ids[..., 0] = torch.arange(height // patch_size, device=device)[:, None]
-    img_ids[..., 1] = torch.arange(width // patch_size, device=device)[None, :]
-    return img_ids.reshape((height // patch_size) * (width // patch_size), 2).unsqueeze(0).repeat(batch_size, 1, 1)
-
-
-def apply_rope(xq: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
-    r"""
-    Applies rotary positional embeddings (RoPE) to a query tensor.
-
-    Args:
-        xq (`torch.Tensor`):
-            Input tensor of shape `(..., dim)` representing the queries.
-        freqs_cis (`torch.Tensor`):
-            Precomputed rotary frequency components of shape `(..., dim/2, 2)` containing cosine and sine pairs.
-
-    Returns:
-        `torch.Tensor`:
-            Tensor of the same shape as `xq` with rotary embeddings applied.
-    """
-    xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
-    # Ensure freqs_cis is on the same device as queries to avoid device mismatches with offloading
-    freqs_cis = freqs_cis.to(device=xq.device, dtype=xq_.dtype)
-    xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
-    return xq_out.reshape(*xq.shape).type_as(xq)
-
-
-class PRXAttnProcessor2_0:
-    r"""
-    Processor for implementing PRX-style attention with multi-source tokens and RoPE. Supports multiple attention
-    backends (Flash Attention, Sage Attention, etc.) via dispatch_attention_fn.
-    """
-
-    _attention_backend = None
-    _parallel_config = None
-
-    def __init__(self):
-        if not hasattr(torch.nn.functional, "scaled_dot_product_attention"):
-            raise ImportError("PRXAttnProcessor2_0 requires PyTorch 2.0, please upgrade PyTorch to 2.0.")
-
-    def __call__(
-        self,
-        attn: "PRXAttention",
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        image_rotary_emb: Optional[torch.Tensor] = None,
-        **kwargs,
-    ) -> torch.Tensor:
-        """
-        Apply PRX attention using PRXAttention module.
-
-        Args:
-            attn: PRXAttention module containing projection layers
-            hidden_states: Image tokens [B, L_img, D]
-            encoder_hidden_states: Text tokens [B, L_txt, D]
-            attention_mask: Boolean mask for text tokens [B, L_txt]
-            image_rotary_emb: Rotary positional embeddings [B, 1, L_img, head_dim//2, 2, 2]
-        """
-
-        if encoder_hidden_states is None:
-            raise ValueError("PRXAttnProcessor2_0 requires 'encoder_hidden_states' containing text tokens.")
-
-        # Project image tokens to Q, K, V
-        img_qkv = attn.img_qkv_proj(hidden_states)
-        B, L_img, _ = img_qkv.shape
-        img_qkv = img_qkv.reshape(B, L_img, 3, attn.heads, attn.head_dim)
-        img_qkv = img_qkv.permute(2, 0, 3, 1, 4)  # [3, B, H, L_img, D]
-        img_q, img_k, img_v = img_qkv[0], img_qkv[1], img_qkv[2]
-
-        # Apply QK normalization to image tokens
-        img_q = attn.norm_q(img_q)
-        img_k = attn.norm_k(img_k)
-
-        # Project text tokens to K, V
-        txt_kv = attn.txt_kv_proj(encoder_hidden_states)
-        B, L_txt, _ = txt_kv.shape
-        txt_kv = txt_kv.reshape(B, L_txt, 2, attn.heads, attn.head_dim)
-        txt_kv = txt_kv.permute(2, 0, 3, 1, 4)  # [2, B, H, L_txt, D]
-        txt_k, txt_v = txt_kv[0], txt_kv[1]
-
-        # Apply K normalization to text tokens
-        txt_k = attn.norm_added_k(txt_k)
-
-        # Apply RoPE to image queries and keys
-        if image_rotary_emb is not None:
-            img_q = apply_rope(img_q, image_rotary_emb)
-            img_k = apply_rope(img_k, image_rotary_emb)
-
-        # Concatenate text and image keys/values
-        k = torch.cat((txt_k, img_k), dim=2)  # [B, H, L_txt + L_img, D]
-        v = torch.cat((txt_v, img_v), dim=2)  # [B, H, L_txt + L_img, D]
-
-        # Build attention mask if provided
-        attn_mask_tensor = None
-        if attention_mask is not None:
-            bs, _, l_img, _ = img_q.shape
-            l_txt = txt_k.shape[2]
-
-            if attention_mask.dim() != 2:
-                raise ValueError(f"Unsupported attention_mask shape: {attention_mask.shape}")
-            if attention_mask.shape[-1] != l_txt:
-                raise ValueError(f"attention_mask last dim {attention_mask.shape[-1]} must equal text length {l_txt}")
-
-            device = img_q.device
-            ones_img = torch.ones((bs, l_img), dtype=torch.bool, device=device)
-            attention_mask = attention_mask.to(device=device, dtype=torch.bool)
-            joint_mask = torch.cat([attention_mask, ones_img], dim=-1)
-            attn_mask_tensor = joint_mask[:, None, None, :].expand(-1, attn.heads, l_img, -1)
-
-        # Apply attention using dispatch_attention_fn for backend support
-        # Reshape to match dispatch_attention_fn expectations: [B, L, H, D]
-        query = img_q.transpose(1, 2)  # [B, L_img, H, D]
-        key = k.transpose(1, 2)  # [B, L_txt + L_img, H, D]
-        value = v.transpose(1, 2)  # [B, L_txt + L_img, H, D]
-
-        attn_output = dispatch_attention_fn(
-            query,
-            key,
-            value,
-            attn_mask=attn_mask_tensor,
-            backend=self._attention_backend,
-            parallel_config=self._parallel_config,
-        )
-
-        # Reshape from [B, L_img, H, D] to [B, L_img, H*D]
-        batch_size, seq_len, num_heads, head_dim = attn_output.shape
-        attn_output = attn_output.reshape(batch_size, seq_len, num_heads * head_dim)
-
-        # Apply output projection
-        attn_output = attn.to_out[0](attn_output)
-        if len(attn.to_out) > 1:
-            attn_output = attn.to_out[1](attn_output)  # dropout if present
-
-        return attn_output
-
-
-class PRXAttention(nn.Module, AttentionModuleMixin):
-    r"""
-    PRX-style attention module that handles multi-source tokens and RoPE. Similar to FluxAttention but adapted for
-    PRX's architecture.
-    """
-
-    _default_processor_cls = PRXAttnProcessor2_0
-    _available_processors = [PRXAttnProcessor2_0]
-
-    def __init__(
-        self,
-        query_dim: int,
-        heads: int = 8,
-        dim_head: int = 64,
-        bias: bool = False,
-        out_bias: bool = False,
-        eps: float = 1e-6,
-        processor=None,
-    ):
-        super().__init__()
-
-        self.heads = heads
-        self.head_dim = dim_head
-        self.inner_dim = dim_head * heads
-        self.query_dim = query_dim
-
-        self.img_qkv_proj = nn.Linear(query_dim, query_dim * 3, bias=bias)
-
-        self.norm_q = RMSNorm(self.head_dim, eps=eps, elementwise_affine=True)
-        self.norm_k = RMSNorm(self.head_dim, eps=eps, elementwise_affine=True)
-
-        self.txt_kv_proj = nn.Linear(query_dim, query_dim * 2, bias=bias)
-        self.norm_added_k = RMSNorm(self.head_dim, eps=eps, elementwise_affine=True)
-
-        self.to_out = nn.ModuleList([])
-        self.to_out.append(nn.Linear(self.inner_dim, query_dim, bias=out_bias))
-        self.to_out.append(nn.Dropout(0.0))
-
-        if processor is None:
-            processor = self._default_processor_cls()
-        self.set_processor(processor)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        image_rotary_emb: Optional[torch.Tensor] = None,
-        **kwargs,
-    ) -> torch.Tensor:
-        return self.processor(
-            self,
-            hidden_states,
-            encoder_hidden_states=encoder_hidden_states,
-            attention_mask=attention_mask,
-            image_rotary_emb=image_rotary_emb,
-            **kwargs,
-        )
-
-
-# inspired from https://github.com/black-forest-labs/flux/blob/main/src/flux/modules/layers.py
-class PRXEmbedND(nn.Module):
-    r"""
-    N-dimensional rotary positional embedding.
-
-    This module creates rotary embeddings (RoPE) across multiple axes, where each axis can have its own embedding
-    dimension. The embeddings are combined and returned as a single tensor
-
-    Args:
-        dim (int):
-        Base embedding dimension (must be even).
-        theta (int):
-        Scaling factor that controls the frequency spectrum of the rotary embeddings.
-        axes_dim (list[int]):
-        List of embedding dimensions for each axis (each must be even).
-    """
-
-    def __init__(self, dim: int, theta: int, axes_dim: List[int]):
-        super().__init__()
-        self.dim = dim
-        self.theta = theta
-        self.axes_dim = axes_dim
-
-    def rope(self, pos: torch.Tensor, dim: int, theta: int) -> torch.Tensor:
-        assert dim % 2 == 0
-        scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
-        omega = 1.0 / (theta**scale)
-        out = pos.unsqueeze(-1) * omega.unsqueeze(0)
-        out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)
-        # Native PyTorch equivalent of: Rearrange("b n d (i j) -> b n d i j", i=2, j=2)
-        # out shape: (b, n, d, 4) -> reshape to (b, n, d, 2, 2)
-        out = out.reshape(*out.shape[:-1], 2, 2)
-        return out.float()
-
-    def forward(self, ids: torch.Tensor) -> torch.Tensor:
-        n_axes = ids.shape[-1]
-        emb = torch.cat(
-            [self.rope(ids[:, :, i], self.axes_dim[i], self.theta) for i in range(n_axes)],
-            dim=-3,
-        )
-        return emb.unsqueeze(1)
-
-
-class MLPEmbedder(nn.Module):
-    r"""
-    A simple 2-layer MLP used for embedding inputs.
-
-    Args:
-        in_dim (`int`):
-            Dimensionality of the input features.
-        hidden_dim (`int`):
-            Dimensionality of the hidden and output embedding space.
-
-    Returns:
-        `torch.Tensor`:
-            Tensor of shape `(..., hidden_dim)` containing the embedded representations.
-    """
-
-    def __init__(self, in_dim: int, hidden_dim: int):
-        super().__init__()
-        self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True)
-        self.silu = nn.SiLU()
-        self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return self.out_layer(self.silu(self.in_layer(x)))
-
-
-class Modulation(nn.Module):
-    r"""
-    Modulation network that generates scale, shift, and gating parameters.
-
-    Given an input vector, the module projects it through a linear layer to produce six chunks, which are grouped into
-    two tuples `(shift, scale, gate)`.
-
-    Args:
-        dim (`int`):
-            Dimensionality of the input vector. The output will have `6 * dim` features internally.
-
-    Returns:
-        ((`torch.Tensor`, `torch.Tensor`, `torch.Tensor`), (`torch.Tensor`, `torch.Tensor`, `torch.Tensor`)):
-            Two tuples `(shift, scale, gate)`.
-    """
-
-    def __init__(self, dim: int):
-        super().__init__()
-        self.lin = nn.Linear(dim, 6 * dim, bias=True)
-        nn.init.constant_(self.lin.weight, 0)
-        nn.init.constant_(self.lin.bias, 0)
-
-    def forward(
-        self, vec: torch.Tensor
-    ) -> Tuple[Tuple[torch.Tensor, torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor, torch.Tensor]]:
-        out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(6, dim=-1)
-        return tuple(out[:3]), tuple(out[3:])
-
-
-class PRXBlock(nn.Module):
-    r"""
-    Multimodal transformer block with text–image cross-attention, modulation, and MLP.
-
-    Args:
-        hidden_size (`int`):
-            Dimension of the hidden representations.
-        num_heads (`int`):
-            Number of attention heads.
-        mlp_ratio (`float`, *optional*, defaults to 4.0):
-            Expansion ratio for the hidden dimension inside the MLP.
-        qk_scale (`float`, *optional*):
-            Scale factor for queries and keys. If not provided, defaults to ``head_dim**-0.5``.
-
-    Attributes:
-        img_pre_norm (`nn.LayerNorm`):
-            Pre-normalization applied to image tokens before attention.
-        attention (`PRXAttention`):
-            Multi-head attention module with built-in QKV projections and normalizations for cross-attention between
-            image and text tokens.
-        post_attention_layernorm (`nn.LayerNorm`):
-            Normalization applied after attention.
-        gate_proj / up_proj / down_proj (`nn.Linear`):
-            Feedforward layers forming the gated MLP.
-        mlp_act (`nn.GELU`):
-            Nonlinear activation used in the MLP.
-        modulation (`Modulation`):
-            Produces scale/shift/gating parameters for modulated layers.
-
-        Methods:
-            The forward method performs cross-attention and the MLP with modulation.
-    """
-
-    def __init__(
-        self,
-        hidden_size: int,
-        num_heads: int,
-        mlp_ratio: float = 4.0,
-        qk_scale: Optional[float] = None,
-    ):
-        super().__init__()
-
-        self.hidden_dim = hidden_size
-        self.num_heads = num_heads
-        self.head_dim = hidden_size // num_heads
-        self.scale = qk_scale or self.head_dim**-0.5
-
-        self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
-        self.hidden_size = hidden_size
-
-        # Pre-attention normalization for image tokens
-        self.img_pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-
-        # PRXAttention module with built-in projections and norms
-        self.attention = PRXAttention(
-            query_dim=hidden_size,
-            heads=num_heads,
-            dim_head=self.head_dim,
-            bias=False,
-            out_bias=False,
-            eps=1e-6,
-            processor=PRXAttnProcessor2_0(),
-        )
-
-        # mlp
-        self.post_attention_layernorm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.gate_proj = nn.Linear(hidden_size, self.mlp_hidden_dim, bias=False)
-        self.up_proj = nn.Linear(hidden_size, self.mlp_hidden_dim, bias=False)
-        self.down_proj = nn.Linear(self.mlp_hidden_dim, hidden_size, bias=False)
-        self.mlp_act = nn.GELU(approximate="tanh")
-
-        self.modulation = Modulation(hidden_size)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        temb: torch.Tensor,
-        image_rotary_emb: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        **kwargs: Dict[str, Any],
-    ) -> torch.Tensor:
-        r"""
-        Runs modulation-gated cross-attention and MLP, with residual connections.
-
-        Args:
-            hidden_states (`torch.Tensor`):
-                Image tokens of shape `(B, L_img, hidden_size)`.
-            encoder_hidden_states (`torch.Tensor`):
-                Text tokens of shape `(B, L_txt, hidden_size)`.
-            temb (`torch.Tensor`):
-                Conditioning vector used by `Modulation` to produce scale/shift/gates, shape `(B, hidden_size)` (or
-                broadcastable).
-            image_rotary_emb (`torch.Tensor`):
-                Rotary positional embeddings applied inside attention.
-            attention_mask (`torch.Tensor`, *optional*):
-                Boolean mask for text tokens of shape `(B, L_txt)`, where `0` marks padding.
-            **kwargs:
-                Additional keyword arguments for API compatibility.
-
-        Returns:
-            `torch.Tensor`:
-                Updated image tokens of shape `(B, L_img, hidden_size)`.
-        """
-
-        mod_attn, mod_mlp = self.modulation(temb)
-        attn_shift, attn_scale, attn_gate = mod_attn
-        mlp_shift, mlp_scale, mlp_gate = mod_mlp
-
-        hidden_states_mod = (1 + attn_scale) * self.img_pre_norm(hidden_states) + attn_shift
-
-        attn_out = self.attention(
-            hidden_states=hidden_states_mod,
-            encoder_hidden_states=encoder_hidden_states,
-            attention_mask=attention_mask,
-            image_rotary_emb=image_rotary_emb,
-        )
-
-        hidden_states = hidden_states + attn_gate * attn_out
-
-        x = (1 + mlp_scale) * self.post_attention_layernorm(hidden_states) + mlp_shift
-        hidden_states = hidden_states + mlp_gate * (self.down_proj(self.mlp_act(self.gate_proj(x)) * self.up_proj(x)))
-        return hidden_states
-
-
-class FinalLayer(nn.Module):
-    r"""
-    Final projection layer with adaptive LayerNorm modulation.
-
-    This layer applies a normalized and modulated transformation to input tokens and projects them into patch-level
-    outputs.
-
-    Args:
-        hidden_size (`int`):
-            Dimensionality of the input tokens.
-        patch_size (`int`):
-            Size of the square image patches.
-        out_channels (`int`):
-            Number of output channels per pixel (e.g. RGB = 3).
-
-    Forward Inputs:
-        x (`torch.Tensor`):
-            Input tokens of shape `(B, L, hidden_size)`, where `L` is the number of patches.
-        vec (`torch.Tensor`):
-            Conditioning vector of shape `(B, hidden_size)` used to generate shift and scale parameters for adaptive
-            LayerNorm.
-
-    Returns:
-        `torch.Tensor`:
-            Projected patch outputs of shape `(B, L, patch_size * patch_size * out_channels)`.
-    """
-
-    def __init__(self, hidden_size: int, patch_size: int, out_channels: int):
-        super().__init__()
-        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
-        self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
-
-    def forward(self, x: torch.Tensor, vec: torch.Tensor) -> torch.Tensor:
-        shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
-        x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
-        x = self.linear(x)
-        return x
-
-
-def img2seq(img: torch.Tensor, patch_size: int) -> torch.Tensor:
-    r"""
-    Flattens an image tensor into a sequence of non-overlapping patches.
-
-    Args:
-        img (`torch.Tensor`):
-            Input image tensor of shape `(B, C, H, W)`.
-        patch_size (`int`):
-            Size of each square patch. Must evenly divide both `H` and `W`.
-
-    Returns:
-        `torch.Tensor`:
-            Flattened patch sequence of shape `(B, L, C * patch_size * patch_size)`, where `L = (H // patch_size) * (W
-            // patch_size)` is the number of patches.
-    """
-    return unfold(img, kernel_size=patch_size, stride=patch_size).transpose(1, 2)
-
-
-def seq2img(seq: torch.Tensor, patch_size: int, shape: torch.Tensor) -> torch.Tensor:
-    r"""
-    Reconstructs an image tensor from a sequence of patches (inverse of `img2seq`).
-
-    Args:
-        seq (`torch.Tensor`):
-            Patch sequence of shape `(B, L, C * patch_size * patch_size)`, where `L = (H // patch_size) * (W //
-            patch_size)`.
-        patch_size (`int`):
-            Size of each square patch.
-        shape (`tuple` or `torch.Tensor`):
-            The original image spatial shape `(H, W)`. If a tensor is provided, the first two values are interpreted as
-            height and width.
-
-    Returns:
-        `torch.Tensor`:
-            Reconstructed image tensor of shape `(B, C, H, W)`.
-    """
-    if isinstance(shape, tuple):
-        shape = shape[-2:]
-    elif isinstance(shape, torch.Tensor):
-        shape = (int(shape[0]), int(shape[1]))
-    else:
-        raise NotImplementedError(f"shape type {type(shape)} not supported")
-    return fold(seq.transpose(1, 2), shape, kernel_size=patch_size, stride=patch_size)
-
-
-class PRXTransformer2DModel(ModelMixin, ConfigMixin, AttentionMixin):
-    r"""
-    Transformer-based 2D model for text to image generation.
-
-    Args:
-        in_channels (`int`, *optional*, defaults to 16):
-            Number of input channels in the latent image.
-        patch_size (`int`, *optional*, defaults to 2):
-            Size of the square patches used to flatten the input image.
-        context_in_dim (`int`, *optional*, defaults to 2304):
-            Dimensionality of the text conditioning input.
-        hidden_size (`int`, *optional*, defaults to 1792):
-            Dimension of the hidden representation.
-        mlp_ratio (`float`, *optional*, defaults to 3.5):
-            Expansion ratio for the hidden dimension inside MLP blocks.
-        num_heads (`int`, *optional*, defaults to 28):
-            Number of attention heads.
-        depth (`int`, *optional*, defaults to 16):
-            Number of transformer blocks.
-        axes_dim (`list[int]`, *optional*):
-            List of dimensions for each positional embedding axis. Defaults to `[32, 32]`.
-        theta (`int`, *optional*, defaults to 10000):
-            Frequency scaling factor for rotary embeddings.
-        time_factor (`float`, *optional*, defaults to 1000.0):
-            Scaling factor applied in timestep embeddings.
-        time_max_period (`int`, *optional*, defaults to 10000):
-            Maximum frequency period for timestep embeddings.
-
-    Attributes:
-        pe_embedder (`EmbedND`):
-            Multi-axis rotary embedding generator for positional encodings.
-        img_in (`nn.Linear`):
-            Projection layer for image patch tokens.
-        time_in (`MLPEmbedder`):
-            Embedding layer for timestep embeddings.
-        txt_in (`nn.Linear`):
-            Projection layer for text conditioning.
-        blocks (`nn.ModuleList`):
-            Stack of transformer blocks (`PRXBlock`).
-        final_layer (`LastLayer`):
-            Projection layer mapping hidden tokens back to patch outputs.
-
-    Methods:
-        attn_processors:
-            Returns a dictionary of all attention processors in the model.
-        set_attn_processor(processor):
-            Replaces attention processors across all attention layers.
-        process_inputs(image_latent, txt):
-            Converts inputs into patch tokens, encodes text, and produces positional encodings.
-        compute_timestep_embedding(timestep, dtype):
-            Creates a timestep embedding of dimension 256, scaled and projected.
-        forward_transformers(image_latent, cross_attn_conditioning, timestep, time_embedding, attention_mask,
-        **block_kwargs):
-            Runs the sequence of transformer blocks over image and text tokens.
-        forward(image_latent, timestep, cross_attn_conditioning, micro_conditioning, cross_attn_mask=None,
-        attention_kwargs=None, return_dict=True):
-            Full forward pass from latent input to reconstructed output image.
-
-    Returns:
-        `Transformer2DModelOutput` if `return_dict=True` (default), otherwise a tuple containing:
-            - `sample` (`torch.Tensor`): Reconstructed image of shape `(B, C, H, W)`.
-    """
-
-    config_name = "config.json"
-    _supports_gradient_checkpointing = True
-
-    @register_to_config
-    def __init__(
-        self,
-        in_channels: int = 16,
-        patch_size: int = 2,
-        context_in_dim: int = 2304,
-        hidden_size: int = 1792,
-        mlp_ratio: float = 3.5,
-        num_heads: int = 28,
-        depth: int = 16,
-        axes_dim: list = None,
-        theta: int = 10000,
-        time_factor: float = 1000.0,
-        time_max_period: int = 10000,
-    ):
-        super().__init__()
-
-        if axes_dim is None:
-            axes_dim = [32, 32]
-
-        # Store parameters directly
-        self.in_channels = in_channels
-        self.patch_size = patch_size
-        self.out_channels = self.in_channels * self.patch_size**2
-
-        self.time_factor = time_factor
-        self.time_max_period = time_max_period
-
-        if hidden_size % num_heads != 0:
-            raise ValueError(f"Hidden size {hidden_size} must be divisible by num_heads {num_heads}")
-
-        pe_dim = hidden_size // num_heads
-
-        if sum(axes_dim) != pe_dim:
-            raise ValueError(f"Got {axes_dim} but expected positional dim {pe_dim}")
-
-        self.hidden_size = hidden_size
-        self.num_heads = num_heads
-        self.pe_embedder = PRXEmbedND(dim=pe_dim, theta=theta, axes_dim=axes_dim)
-        self.img_in = nn.Linear(self.in_channels * self.patch_size**2, self.hidden_size, bias=True)
-        self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
-        self.txt_in = nn.Linear(context_in_dim, self.hidden_size)
-
-        self.blocks = nn.ModuleList(
-            [
-                PRXBlock(
-                    self.hidden_size,
-                    self.num_heads,
-                    mlp_ratio=mlp_ratio,
-                )
-                for i in range(depth)
-            ]
-        )
-
-        self.final_layer = FinalLayer(self.hidden_size, 1, self.out_channels)
-
-        self.gradient_checkpointing = False
-
-    def _compute_timestep_embedding(self, timestep: torch.Tensor, dtype: torch.dtype) -> torch.Tensor:
-        return self.time_in(
-            get_timestep_embedding(
-                timesteps=timestep,
-                embedding_dim=256,
-                max_period=self.time_max_period,
-                scale=self.time_factor,
-                flip_sin_to_cos=True,  # Match original cos, sin order
-            ).to(dtype)
-        )
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        timestep: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        attention_kwargs: Optional[Dict[str, Any]] = None,
-        return_dict: bool = True,
-    ) -> Union[Tuple[torch.Tensor, ...], Transformer2DModelOutput]:
-        r"""
-        Forward pass of the PRXTransformer2DModel.
-
-        The latent image is split into patch tokens, combined with text conditioning, and processed through a stack of
-        transformer blocks modulated by the timestep. The output is reconstructed into the latent image space.
-
-        Args:
-            hidden_states (`torch.Tensor`):
-                Input latent image tensor of shape `(B, C, H, W)`.
-            timestep (`torch.Tensor`):
-                Timestep tensor of shape `(B,)` or `(1,)`, used for temporal conditioning.
-            encoder_hidden_states (`torch.Tensor`):
-                Text conditioning tensor of shape `(B, L_txt, context_in_dim)`.
-            attention_mask (`torch.Tensor`, *optional*):
-                Boolean mask of shape `(B, L_txt)`, where `0` marks padding in the text sequence.
-            attention_kwargs (`dict`, *optional*):
-                Additional arguments passed to attention layers.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether to return a `Transformer2DModelOutput` or a tuple.
-
-        Returns:
-            `Transformer2DModelOutput` if `return_dict=True`, otherwise a tuple:
-
-                - `sample` (`torch.Tensor`): Output latent image of shape `(B, C, H, W)`.
-        """
-        # Process text conditioning
-        txt = self.txt_in(encoder_hidden_states)
-
-        # Convert image to sequence and embed
-        img = img2seq(hidden_states, self.patch_size)
-        img = self.img_in(img)
-
-        # Generate positional embeddings
-        bs, _, h, w = hidden_states.shape
-        img_ids = get_image_ids(bs, h, w, patch_size=self.patch_size, device=hidden_states.device)
-        pe = self.pe_embedder(img_ids)
-
-        # Compute time embedding
-        vec = self._compute_timestep_embedding(timestep, dtype=img.dtype)
-
-        # Apply transformer blocks
-        for block in self.blocks:
-            if torch.is_grad_enabled() and self.gradient_checkpointing:
-                img = self._gradient_checkpointing_func(
-                    block.__call__,
-                    img,
-                    txt,
-                    vec,
-                    pe,
-                    attention_mask,
-                )
-            else:
-                img = block(
-                    hidden_states=img,
-                    encoder_hidden_states=txt,
-                    temb=vec,
-                    image_rotary_emb=pe,
-                    attention_mask=attention_mask,
-                )
-
-        # Final layer and convert back to image
-        img = self.final_layer(img, vec)
-        output = seq2img(img, self.patch_size, hidden_states.shape)
-
-        if not return_dict:
-            return (output,)
-        return Transformer2DModelOutput(sample=output)

src/diffusers/models/transformers/transformer_sana_video.py

@@ -1,703 +0,0 @@
-# Copyright 2025 The HuggingFace Team and SANA-Video 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 math
-from typing import Any, Dict, Optional, Tuple, Union
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from ...configuration_utils import ConfigMixin, register_to_config
-from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
-from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
-from ..attention import AttentionMixin
-from ..attention_dispatch import dispatch_attention_fn
-from ..attention_processor import Attention
-from ..embeddings import PixArtAlphaTextProjection, TimestepEmbedding, Timesteps, get_1d_rotary_pos_embed
-from ..modeling_outputs import Transformer2DModelOutput
-from ..modeling_utils import ModelMixin
-from ..normalization import AdaLayerNormSingle, RMSNorm
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class GLUMBTempConv(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        expand_ratio: float = 4,
-        norm_type: Optional[str] = None,
-        residual_connection: bool = True,
-    ) -> None:
-        super().__init__()
-
-        hidden_channels = int(expand_ratio * in_channels)
-        self.norm_type = norm_type
-        self.residual_connection = residual_connection
-
-        self.nonlinearity = nn.SiLU()
-        self.conv_inverted = nn.Conv2d(in_channels, hidden_channels * 2, 1, 1, 0)
-        self.conv_depth = nn.Conv2d(hidden_channels * 2, hidden_channels * 2, 3, 1, 1, groups=hidden_channels * 2)
-        self.conv_point = nn.Conv2d(hidden_channels, out_channels, 1, 1, 0, bias=False)
-
-        self.norm = None
-        if norm_type == "rms_norm":
-            self.norm = RMSNorm(out_channels, eps=1e-5, elementwise_affine=True, bias=True)
-
-        self.conv_temp = nn.Conv2d(
-            out_channels, out_channels, kernel_size=(3, 1), stride=1, padding=(1, 0), bias=False
-        )
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        if self.residual_connection:
-            residual = hidden_states
-        batch_size, num_frames, height, width, num_channels = hidden_states.shape
-        hidden_states = hidden_states.view(batch_size * num_frames, height, width, num_channels).permute(0, 3, 1, 2)
-
-        hidden_states = self.conv_inverted(hidden_states)
-        hidden_states = self.nonlinearity(hidden_states)
-
-        hidden_states = self.conv_depth(hidden_states)
-        hidden_states, gate = torch.chunk(hidden_states, 2, dim=1)
-        hidden_states = hidden_states * self.nonlinearity(gate)
-
-        hidden_states = self.conv_point(hidden_states)
-
-        # Temporal aggregation
-        hidden_states_temporal = hidden_states.view(batch_size, num_frames, num_channels, height * width).permute(
-            0, 2, 1, 3
-        )
-        hidden_states = hidden_states_temporal + self.conv_temp(hidden_states_temporal)
-        hidden_states = hidden_states.permute(0, 2, 3, 1).view(batch_size, num_frames, height, width, num_channels)
-
-        if self.norm_type == "rms_norm":
-            # move channel to the last dimension so we apply RMSnorm across channel dimension
-            hidden_states = self.norm(hidden_states.movedim(1, -1)).movedim(-1, 1)
-
-        if self.residual_connection:
-            hidden_states = hidden_states + residual
-
-        return hidden_states
-
-
-class SanaLinearAttnProcessor3_0:
-    r"""
-    Processor for implementing scaled dot-product linear attention.
-    """
-
-    def __call__(
-        self,
-        attn: Attention,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        rotary_emb: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        original_dtype = hidden_states.dtype
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-
-        query = attn.to_q(hidden_states)
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
-
-        if attn.norm_q is not None:
-            query = attn.norm_q(query)
-        if attn.norm_k is not None:
-            key = attn.norm_k(key)
-
-        query = query.unflatten(2, (attn.heads, -1))
-        key = key.unflatten(2, (attn.heads, -1))
-        value = value.unflatten(2, (attn.heads, -1))
-        # B,N,H,C
-
-        query = F.relu(query)
-        key = F.relu(key)
-
-        if rotary_emb is not None:
-
-            def apply_rotary_emb(
-                hidden_states: torch.Tensor,
-                freqs_cos: torch.Tensor,
-                freqs_sin: torch.Tensor,
-            ):
-                x1, x2 = hidden_states.unflatten(-1, (-1, 2)).unbind(-1)
-                cos = freqs_cos[..., 0::2]
-                sin = freqs_sin[..., 1::2]
-                out = torch.empty_like(hidden_states)
-                out[..., 0::2] = x1 * cos - x2 * sin
-                out[..., 1::2] = x1 * sin + x2 * cos
-                return out.type_as(hidden_states)
-
-            query_rotate = apply_rotary_emb(query, *rotary_emb)
-            key_rotate = apply_rotary_emb(key, *rotary_emb)
-
-        # B,H,C,N
-        query = query.permute(0, 2, 3, 1)
-        key = key.permute(0, 2, 3, 1)
-        query_rotate = query_rotate.permute(0, 2, 3, 1)
-        key_rotate = key_rotate.permute(0, 2, 3, 1)
-        value = value.permute(0, 2, 3, 1)
-
-        query_rotate, key_rotate, value = query_rotate.float(), key_rotate.float(), value.float()
-
-        z = 1 / (key.sum(dim=-1, keepdim=True).transpose(-2, -1) @ query + 1e-15)
-
-        scores = torch.matmul(value, key_rotate.transpose(-1, -2))
-        hidden_states = torch.matmul(scores, query_rotate)
-
-        hidden_states = hidden_states * z
-        # B,H,C,N
-        hidden_states = hidden_states.flatten(1, 2).transpose(1, 2)
-        hidden_states = hidden_states.to(original_dtype)
-
-        hidden_states = attn.to_out[0](hidden_states)
-        hidden_states = attn.to_out[1](hidden_states)
-
-        return hidden_states
-
-
-# Copied from diffusers.models.transformers.transformer_wan.WanRotaryPosEmbed
-class WanRotaryPosEmbed(nn.Module):
-    def __init__(
-        self,
-        attention_head_dim: int,
-        patch_size: Tuple[int, int, int],
-        max_seq_len: int,
-        theta: float = 10000.0,
-    ):
-        super().__init__()
-
-        self.attention_head_dim = attention_head_dim
-        self.patch_size = patch_size
-        self.max_seq_len = max_seq_len
-
-        h_dim = w_dim = 2 * (attention_head_dim // 6)
-        t_dim = attention_head_dim - h_dim - w_dim
-        freqs_dtype = torch.float32 if torch.backends.mps.is_available() else torch.float64
-
-        freqs_cos = []
-        freqs_sin = []
-
-        for dim in [t_dim, h_dim, w_dim]:
-            freq_cos, freq_sin = get_1d_rotary_pos_embed(
-                dim,
-                max_seq_len,
-                theta,
-                use_real=True,
-                repeat_interleave_real=True,
-                freqs_dtype=freqs_dtype,
-            )
-            freqs_cos.append(freq_cos)
-            freqs_sin.append(freq_sin)
-
-        self.register_buffer("freqs_cos", torch.cat(freqs_cos, dim=1), persistent=False)
-        self.register_buffer("freqs_sin", torch.cat(freqs_sin, dim=1), persistent=False)
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        batch_size, num_channels, num_frames, height, width = hidden_states.shape
-        p_t, p_h, p_w = self.patch_size
-        ppf, pph, ppw = num_frames // p_t, height // p_h, width // p_w
-
-        split_sizes = [
-            self.attention_head_dim - 2 * (self.attention_head_dim // 3),
-            self.attention_head_dim // 3,
-            self.attention_head_dim // 3,
-        ]
-
-        freqs_cos = self.freqs_cos.split(split_sizes, dim=1)
-        freqs_sin = self.freqs_sin.split(split_sizes, dim=1)
-
-        freqs_cos_f = freqs_cos[0][:ppf].view(ppf, 1, 1, -1).expand(ppf, pph, ppw, -1)
-        freqs_cos_h = freqs_cos[1][:pph].view(1, pph, 1, -1).expand(ppf, pph, ppw, -1)
-        freqs_cos_w = freqs_cos[2][:ppw].view(1, 1, ppw, -1).expand(ppf, pph, ppw, -1)
-
-        freqs_sin_f = freqs_sin[0][:ppf].view(ppf, 1, 1, -1).expand(ppf, pph, ppw, -1)
-        freqs_sin_h = freqs_sin[1][:pph].view(1, pph, 1, -1).expand(ppf, pph, ppw, -1)
-        freqs_sin_w = freqs_sin[2][:ppw].view(1, 1, ppw, -1).expand(ppf, pph, ppw, -1)
-
-        freqs_cos = torch.cat([freqs_cos_f, freqs_cos_h, freqs_cos_w], dim=-1).reshape(1, ppf * pph * ppw, 1, -1)
-        freqs_sin = torch.cat([freqs_sin_f, freqs_sin_h, freqs_sin_w], dim=-1).reshape(1, ppf * pph * ppw, 1, -1)
-
-        return freqs_cos, freqs_sin
-
-
-# Copied from diffusers.models.transformers.sana_transformer.SanaModulatedNorm
-class SanaModulatedNorm(nn.Module):
-    def __init__(self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6):
-        super().__init__()
-        self.norm = nn.LayerNorm(dim, elementwise_affine=elementwise_affine, eps=eps)
-
-    def forward(
-        self, hidden_states: torch.Tensor, temb: torch.Tensor, scale_shift_table: torch.Tensor
-    ) -> torch.Tensor:
-        hidden_states = self.norm(hidden_states)
-        shift, scale = (scale_shift_table[None] + temb[:, None].to(scale_shift_table.device)).chunk(2, dim=1)
-        hidden_states = hidden_states * (1 + scale) + shift
-        return hidden_states
-
-
-class SanaCombinedTimestepGuidanceEmbeddings(nn.Module):
-    def __init__(self, embedding_dim):
-        super().__init__()
-        self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
-        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
-
-        self.guidance_condition_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
-        self.guidance_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
-
-        self.silu = nn.SiLU()
-        self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True)
-
-    def forward(self, timestep: torch.Tensor, guidance: torch.Tensor = None, hidden_dtype: torch.dtype = None):
-        timesteps_proj = self.time_proj(timestep)
-        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype))  # (N, D)
-
-        guidance_proj = self.guidance_condition_proj(guidance)
-        guidance_emb = self.guidance_embedder(guidance_proj.to(dtype=hidden_dtype))
-        conditioning = timesteps_emb + guidance_emb
-
-        return self.linear(self.silu(conditioning)), conditioning
-
-
-class SanaAttnProcessor2_0:
-    r"""
-    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
-    """
-
-    _attention_backend = None
-    _parallel_config = None
-
-    def __init__(self):
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError("SanaAttnProcessor2_0 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: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-
-        if attention_mask is not None:
-            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-            # scaled_dot_product_attention expects attention_mask shape to be
-            # (batch, heads, source_length, target_length)
-            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-
-        query = attn.to_q(hidden_states)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
-
-        if attn.norm_q is not None:
-            query = attn.norm_q(query)
-        if attn.norm_k is not None:
-            key = attn.norm_k(key)
-
-        inner_dim = key.shape[-1]
-        head_dim = inner_dim // attn.heads
-
-        query = query.view(batch_size, -1, attn.heads, head_dim)
-        key = key.view(batch_size, -1, attn.heads, head_dim)
-        value = value.view(batch_size, -1, attn.heads, head_dim)
-
-        # the output of sdp = (batch, num_heads, seq_len, head_dim)
-        hidden_states = dispatch_attention_fn(
-            query,
-            key,
-            value,
-            attn_mask=attention_mask,
-            dropout_p=0.0,
-            is_causal=False,
-            backend=self._attention_backend,
-            parallel_config=self._parallel_config,
-        )
-        hidden_states = hidden_states.flatten(2, 3)
-        hidden_states = hidden_states.type_as(query)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        hidden_states = hidden_states / attn.rescale_output_factor
-
-        return hidden_states
-
-
-class SanaVideoTransformerBlock(nn.Module):
-    r"""
-    Transformer block introduced in [Sana-Video](https://huggingface.co/papers/2509.24695).
-    """
-
-    def __init__(
-        self,
-        dim: int = 2240,
-        num_attention_heads: int = 20,
-        attention_head_dim: int = 112,
-        dropout: float = 0.0,
-        num_cross_attention_heads: Optional[int] = 20,
-        cross_attention_head_dim: Optional[int] = 112,
-        cross_attention_dim: Optional[int] = 2240,
-        attention_bias: bool = True,
-        norm_elementwise_affine: bool = False,
-        norm_eps: float = 1e-6,
-        attention_out_bias: bool = True,
-        mlp_ratio: float = 3.0,
-        qk_norm: Optional[str] = "rms_norm_across_heads",
-        rope_max_seq_len: int = 1024,
-    ) -> None:
-        super().__init__()
-
-        # 1. Self Attention
-        self.norm1 = nn.LayerNorm(dim, elementwise_affine=False, eps=norm_eps)
-        self.attn1 = Attention(
-            query_dim=dim,
-            heads=num_attention_heads,
-            dim_head=attention_head_dim,
-            kv_heads=num_attention_heads if qk_norm is not None else None,
-            qk_norm=qk_norm,
-            dropout=dropout,
-            bias=attention_bias,
-            cross_attention_dim=None,
-            processor=SanaLinearAttnProcessor3_0(),
-        )
-
-        # 2. Cross Attention
-        if cross_attention_dim is not None:
-            self.norm2 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
-            self.attn2 = Attention(
-                query_dim=dim,
-                qk_norm=qk_norm,
-                kv_heads=num_cross_attention_heads if qk_norm is not None else None,
-                cross_attention_dim=cross_attention_dim,
-                heads=num_cross_attention_heads,
-                dim_head=cross_attention_head_dim,
-                dropout=dropout,
-                bias=True,
-                out_bias=attention_out_bias,
-                processor=SanaAttnProcessor2_0(),
-            )
-
-        # 3. Feed-forward
-        self.ff = GLUMBTempConv(dim, dim, mlp_ratio, norm_type=None, residual_connection=False)
-
-        self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        encoder_hidden_states: Optional[torch.Tensor] = None,
-        encoder_attention_mask: Optional[torch.Tensor] = None,
-        timestep: Optional[torch.LongTensor] = None,
-        frames: int = None,
-        height: int = None,
-        width: int = None,
-        rotary_emb: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        batch_size = hidden_states.shape[0]
-
-        # 1. Modulation
-        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
-            self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)
-        ).chunk(6, dim=1)
-
-        # 2. Self Attention
-        norm_hidden_states = self.norm1(hidden_states)
-        norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa
-        norm_hidden_states = norm_hidden_states.to(hidden_states.dtype)
-
-        attn_output = self.attn1(norm_hidden_states, rotary_emb=rotary_emb)
-        hidden_states = hidden_states + gate_msa * attn_output
-
-        # 3. Cross Attention
-        if self.attn2 is not None:
-            attn_output = self.attn2(
-                hidden_states,
-                encoder_hidden_states=encoder_hidden_states,
-                attention_mask=encoder_attention_mask,
-            )
-            hidden_states = attn_output + hidden_states
-
-        # 4. Feed-forward
-        norm_hidden_states = self.norm2(hidden_states)
-        norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp
-
-        norm_hidden_states = norm_hidden_states.unflatten(1, (frames, height, width))
-        ff_output = self.ff(norm_hidden_states)
-        ff_output = ff_output.flatten(1, 3)
-        hidden_states = hidden_states + gate_mlp * ff_output
-
-        return hidden_states
-
-
-class SanaVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, AttentionMixin):
-    r"""
-    A 3D Transformer model introduced in [Sana-Video](https://huggingface.co/papers/2509.24695) family of models.
-
-    Args:
-        in_channels (`int`, defaults to `16`):
-            The number of channels in the input.
-        out_channels (`int`, *optional*, defaults to `16`):
-            The number of channels in the output.
-        num_attention_heads (`int`, defaults to `20`):
-            The number of heads to use for multi-head attention.
-        attention_head_dim (`int`, defaults to `112`):
-            The number of channels in each head.
-        num_layers (`int`, defaults to `20`):
-            The number of layers of Transformer blocks to use.
-        num_cross_attention_heads (`int`, *optional*, defaults to `20`):
-            The number of heads to use for cross-attention.
-        cross_attention_head_dim (`int`, *optional*, defaults to `112`):
-            The number of channels in each head for cross-attention.
-        cross_attention_dim (`int`, *optional*, defaults to `2240`):
-            The number of channels in the cross-attention output.
-        caption_channels (`int`, defaults to `2304`):
-            The number of channels in the caption embeddings.
-        mlp_ratio (`float`, defaults to `2.5`):
-            The expansion ratio to use in the GLUMBConv layer.
-        dropout (`float`, defaults to `0.0`):
-            The dropout probability.
-        attention_bias (`bool`, defaults to `False`):
-            Whether to use bias in the attention layer.
-        sample_size (`int`, defaults to `32`):
-            The base size of the input latent.
-        patch_size (`int`, defaults to `1`):
-            The size of the patches to use in the patch embedding layer.
-        norm_elementwise_affine (`bool`, defaults to `False`):
-            Whether to use elementwise affinity in the normalization layer.
-        norm_eps (`float`, defaults to `1e-6`):
-            The epsilon value for the normalization layer.
-        qk_norm (`str`, *optional*, defaults to `None`):
-            The normalization to use for the query and key.
-    """
-
-    _supports_gradient_checkpointing = True
-    _no_split_modules = ["SanaVideoTransformerBlock", "SanaModulatedNorm"]
-    _skip_layerwise_casting_patterns = ["patch_embedding", "norm"]
-
-    @register_to_config
-    def __init__(
-        self,
-        in_channels: int = 16,
-        out_channels: Optional[int] = 16,
-        num_attention_heads: int = 20,
-        attention_head_dim: int = 112,
-        num_layers: int = 20,
-        num_cross_attention_heads: Optional[int] = 20,
-        cross_attention_head_dim: Optional[int] = 112,
-        cross_attention_dim: Optional[int] = 2240,
-        caption_channels: int = 2304,
-        mlp_ratio: float = 2.5,
-        dropout: float = 0.0,
-        attention_bias: bool = False,
-        sample_size: int = 30,
-        patch_size: Tuple[int, int, int] = (1, 2, 2),
-        norm_elementwise_affine: bool = False,
-        norm_eps: float = 1e-6,
-        interpolation_scale: Optional[int] = None,
-        guidance_embeds: bool = False,
-        guidance_embeds_scale: float = 0.1,
-        qk_norm: Optional[str] = "rms_norm_across_heads",
-        rope_max_seq_len: int = 1024,
-    ) -> None:
-        super().__init__()
-
-        out_channels = out_channels or in_channels
-        inner_dim = num_attention_heads * attention_head_dim
-
-        # 1. Patch & position embedding
-        self.rope = WanRotaryPosEmbed(attention_head_dim, patch_size, rope_max_seq_len)
-        self.patch_embedding = nn.Conv3d(in_channels, inner_dim, kernel_size=patch_size, stride=patch_size)
-
-        # 2. Additional condition embeddings
-        if guidance_embeds:
-            self.time_embed = SanaCombinedTimestepGuidanceEmbeddings(inner_dim)
-        else:
-            self.time_embed = AdaLayerNormSingle(inner_dim)
-
-        self.caption_projection = PixArtAlphaTextProjection(in_features=caption_channels, hidden_size=inner_dim)
-        self.caption_norm = RMSNorm(inner_dim, eps=1e-5, elementwise_affine=True)
-
-        # 3. Transformer blocks
-        self.transformer_blocks = nn.ModuleList(
-            [
-                SanaVideoTransformerBlock(
-                    inner_dim,
-                    num_attention_heads,
-                    attention_head_dim,
-                    dropout=dropout,
-                    num_cross_attention_heads=num_cross_attention_heads,
-                    cross_attention_head_dim=cross_attention_head_dim,
-                    cross_attention_dim=cross_attention_dim,
-                    attention_bias=attention_bias,
-                    norm_elementwise_affine=norm_elementwise_affine,
-                    norm_eps=norm_eps,
-                    mlp_ratio=mlp_ratio,
-                    qk_norm=qk_norm,
-                )
-                for _ in range(num_layers)
-            ]
-        )
-
-        # 4. Output blocks
-        self.scale_shift_table = nn.Parameter(torch.randn(2, inner_dim) / inner_dim**0.5)
-        self.norm_out = SanaModulatedNorm(inner_dim, elementwise_affine=False, eps=1e-6)
-        self.proj_out = nn.Linear(inner_dim, math.prod(patch_size) * out_channels)
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        timestep: torch.Tensor,
-        guidance: Optional[torch.Tensor] = None,
-        encoder_attention_mask: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        attention_kwargs: Optional[Dict[str, Any]] = None,
-        controlnet_block_samples: Optional[Tuple[torch.Tensor]] = None,
-        return_dict: bool = True,
-    ) -> Union[Tuple[torch.Tensor, ...], Transformer2DModelOutput]:
-        if attention_kwargs is not None:
-            attention_kwargs = attention_kwargs.copy()
-            lora_scale = attention_kwargs.pop("scale", 1.0)
-        else:
-            lora_scale = 1.0
-
-        if USE_PEFT_BACKEND:
-            # weight the lora layers by setting `lora_scale` for each PEFT layer
-            scale_lora_layers(self, lora_scale)
-        else:
-            if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None:
-                logger.warning(
-                    "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective."
-                )
-
-        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
-        #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
-        #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
-        # expects mask of shape:
-        #   [batch, key_tokens]
-        # adds singleton query_tokens dimension:
-        #   [batch,                    1, key_tokens]
-        # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
-        #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
-        #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
-        if attention_mask is not None and attention_mask.ndim == 2:
-            # assume that mask is expressed as:
-            #   (1 = keep,      0 = discard)
-            # convert mask into a bias that can be added to attention scores:
-            #       (keep = +0,     discard = -10000.0)
-            attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
-            attention_mask = attention_mask.unsqueeze(1)
-
-        # convert encoder_attention_mask to a bias the same way we do for attention_mask
-        if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
-            encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0
-            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
-
-        # 1. Input
-        batch_size, num_channels, num_frames, height, width = hidden_states.shape
-        p_t, p_h, p_w = self.config.patch_size
-        post_patch_num_frames = num_frames // p_t
-        post_patch_height = height // p_h
-        post_patch_width = width // p_w
-
-        rotary_emb = self.rope(hidden_states)
-
-        hidden_states = self.patch_embedding(hidden_states)
-        hidden_states = hidden_states.flatten(2).transpose(1, 2)
-
-        if guidance is not None:
-            timestep, embedded_timestep = self.time_embed(
-                timestep, guidance=guidance, hidden_dtype=hidden_states.dtype
-            )
-        else:
-            timestep, embedded_timestep = self.time_embed(
-                timestep, batch_size=batch_size, hidden_dtype=hidden_states.dtype
-            )
-
-        encoder_hidden_states = self.caption_projection(encoder_hidden_states)
-        encoder_hidden_states = encoder_hidden_states.view(batch_size, -1, hidden_states.shape[-1])
-
-        encoder_hidden_states = self.caption_norm(encoder_hidden_states)
-
-        # 2. Transformer blocks
-        if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for index_block, block in enumerate(self.transformer_blocks):
-                hidden_states = self._gradient_checkpointing_func(
-                    block,
-                    hidden_states,
-                    attention_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    timestep,
-                    post_patch_num_frames,
-                    post_patch_height,
-                    post_patch_width,
-                    rotary_emb,
-                )
-                if controlnet_block_samples is not None and 0 < index_block <= len(controlnet_block_samples):
-                    hidden_states = hidden_states + controlnet_block_samples[index_block - 1]
-
-        else:
-            for index_block, block in enumerate(self.transformer_blocks):
-                hidden_states = block(
-                    hidden_states,
-                    attention_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    timestep,
-                    post_patch_num_frames,
-                    post_patch_height,
-                    post_patch_width,
-                    rotary_emb,
-                )
-                if controlnet_block_samples is not None and 0 < index_block <= len(controlnet_block_samples):
-                    hidden_states = hidden_states + controlnet_block_samples[index_block - 1]
-
-        # 3. Normalization
-        hidden_states = self.norm_out(hidden_states, embedded_timestep, self.scale_shift_table)
-
-        hidden_states = self.proj_out(hidden_states)
-
-        # 5. Unpatchify
-        hidden_states = hidden_states.reshape(
-            batch_size, post_patch_num_frames, post_patch_height, post_patch_width, p_t, p_h, p_w, -1
-        )
-        hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
-        output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)
-
-        if USE_PEFT_BACKEND:
-            # remove `lora_scale` from each PEFT layer
-            unscale_lora_layers(self, lora_scale)
-
-        if not return_dict:
-            return (output,)
-
-        return Transformer2DModelOutput(sample=output)

src/diffusers/models/transformers/transformer_wan.py

@@ -555,9 +555,6 @@ class WanTransformer3DModel(
             "encoder_hidden_states": ContextParallelInput(split_dim=1, expected_dims=3, split_output=False),
         },
         "proj_out": ContextParallelOutput(gather_dim=1, expected_dims=3),
-        "": {
-            "timestep": ContextParallelInput(split_dim=1, expected_dims=2, split_output=False),
-        },
     }
 
     @register_to_config

src/diffusers/modular_pipelines/components_manager.py

@@ -164,11 +164,7 @@ class AutoOffloadStrategy:
 
         device_type = execution_device.type
         device_module = getattr(torch, device_type, torch.cuda)
-        try:
-            mem_on_device = device_module.mem_get_info(execution_device.index)[0]
-        except AttributeError:
-            raise AttributeError(f"Do not know how to obtain obtain memory info for {str(device_module)}.")
-
+        mem_on_device = device_module.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 []
@@ -703,8 +699,6 @@ class ComponentsManager:
         if not is_accelerate_available():
             raise ImportError("Make sure to install accelerate to use auto_cpu_offload")
 
-        # TODO: add a warning if mem_get_info isn't available on `device`.
-
         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)

src/diffusers/modular_pipelines/flux/before_denoise.py

@@ -598,7 +598,7 @@ class FluxKontextRoPEInputsStep(ModularPipelineBlocks):
             and getattr(block_state, "image_width", None) is not None
         ):
             image_latent_height = 2 * (int(block_state.image_height) // (components.vae_scale_factor * 2))
-            image_latent_width = 2 * (int(block_state.image_width) // (components.vae_scale_factor * 2))
+            image_latent_width = 2 * (int(block_state.width) // (components.vae_scale_factor * 2))
             img_ids = FluxPipeline._prepare_latent_image_ids(
                 None, image_latent_height // 2, image_latent_width // 2, device, dtype
             )

src/diffusers/modular_pipelines/flux/denoise.py

@@ -59,7 +59,7 @@ class FluxLoopDenoiser(ModularPipelineBlocks):
             ),
             InputParam(
                 "guidance",
-                required=False,
+                required=True,
                 type_hint=torch.Tensor,
                 description="Guidance scale as a tensor",
             ),
@@ -141,7 +141,7 @@ class FluxKontextLoopDenoiser(ModularPipelineBlocks):
             ),
             InputParam(
                 "guidance",
-                required=False,
+                required=True,
                 type_hint=torch.Tensor,
                 description="Guidance scale as a tensor",
             ),

src/diffusers/modular_pipelines/flux/encoders.py

@@ -95,7 +95,7 @@ class FluxProcessImagesInputStep(ModularPipelineBlocks):
             ComponentSpec(
                 "image_processor",
                 VaeImageProcessor,
-                config=FrozenDict({"vae_scale_factor": 16, "vae_latent_channels": 16}),
+                config=FrozenDict({"vae_scale_factor": 16}),
                 default_creation_method="from_config",
             ),
         ]
@@ -143,6 +143,10 @@ class FluxProcessImagesInputStep(ModularPipelineBlocks):
 class FluxKontextProcessImagesInputStep(ModularPipelineBlocks):
     model_name = "flux-kontext"
 
+    def __init__(self, _auto_resize=True):
+        self._auto_resize = _auto_resize
+        super().__init__()
+
     @property
     def description(self) -> str:
         return (
@@ -163,7 +167,7 @@ class FluxKontextProcessImagesInputStep(ModularPipelineBlocks):
 
     @property
     def inputs(self) -> List[InputParam]:
-        return [InputParam("image"), InputParam("_auto_resize", type_hint=bool, default=True)]
+        return [InputParam("image")]
 
     @property
     def intermediate_outputs(self) -> List[OutputParam]:
@@ -191,8 +195,7 @@ class FluxKontextProcessImagesInputStep(ModularPipelineBlocks):
             img = images[0]
             image_height, image_width = components.image_processor.get_default_height_width(img)
             aspect_ratio = image_width / image_height
-            _auto_resize = block_state._auto_resize
-            if _auto_resize:
+            if self._auto_resize:
                 # Kontext is trained on specific resolutions, using one of them is recommended
                 _, image_width, image_height = min(
                     (abs(aspect_ratio - w / h), w, h) for w, h in PREFERRED_KONTEXT_RESOLUTIONS

src/diffusers/modular_pipelines/flux/inputs.py

@@ -112,10 +112,6 @@ class FluxTextInputStep(ModularPipelineBlocks):
         block_state.prompt_embeds = block_state.prompt_embeds.view(
             block_state.batch_size * block_state.num_images_per_prompt, seq_len, -1
         )
-        pooled_prompt_embeds = block_state.pooled_prompt_embeds.repeat(1, block_state.num_images_per_prompt)
-        block_state.pooled_prompt_embeds = pooled_prompt_embeds.view(
-            block_state.batch_size * block_state.num_images_per_prompt, -1
-        )
         self.set_block_state(state, block_state)
 
         return components, state

src/diffusers/modular_pipelines/modular_pipeline.py

@@ -130,14 +130,8 @@ class PipelineState:
         Allow attribute access to intermediate values. If an attribute is not found in the object, look for it in the
         intermediates dict.
         """
-        # Use object.__getattribute__ to avoid infinite recursion during deepcopy
-        try:
-            values = object.__getattribute__(self, "values")
-        except AttributeError:
-            raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{name}'")
-
-        if name in values:
-            return values[name]
+        if name in self.values:
+            return self.values[name]
         raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{name}'")
 
     def __repr__(self):
@@ -305,15 +299,15 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
             "cache_dir",
             "force_download",
             "local_files_only",
-            "local_dir",
             "proxies",
+            "resume_download",
             "revision",
             "subfolder",
             "token",
         ]
         hub_kwargs = {name: kwargs.pop(name) for name in hub_kwargs_names if name in kwargs}
 
-        config = cls.load_config(pretrained_model_name_or_path, **hub_kwargs)
+        config = cls.load_config(pretrained_model_name_or_path)
         has_remote_code = "auto_map" in config and cls.__name__ in config["auto_map"]
         trust_remote_code = resolve_trust_remote_code(
             trust_remote_code, pretrained_model_name_or_path, has_remote_code
@@ -331,10 +325,11 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
             module_file=module_file,
             class_name=class_name,
             **hub_kwargs,
+            **kwargs,
         )
         expected_kwargs, optional_kwargs = block_cls._get_signature_keys(block_cls)
         block_kwargs = {
-            name: kwargs.get(name) for name in kwargs if name in expected_kwargs or name in optional_kwargs
+            name: kwargs.pop(name) for name in kwargs if name in expected_kwargs or name in optional_kwargs
         }
 
         return block_cls(**block_kwargs)
@@ -2130,13 +2125,8 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
                         component_load_kwargs[key] = value["default"]
             try:
                 components_to_register[name] = spec.load(**component_load_kwargs)
-            except Exception:
-                logger.warning(
-                    f"\nFailed to create component {name}:\n"
-                    f"- Component spec: {spec}\n"
-                    f"- load() called with kwargs: {component_load_kwargs}\n\n"
-                    f"{traceback.format_exc()}"
-                )
+            except Exception as e:
+                logger.warning(f"Failed to create component '{name}': {e}")
 
         # Register all components at once
         self.register_components(**components_to_register)
@@ -2502,8 +2492,6 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
         """
         if state is None:
             state = PipelineState()
-        else:
-            state = deepcopy(state)
 
         # Make a copy of the input kwargs
         passed_kwargs = kwargs.copy()