styling issues.

.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
@@ -84,7 +84,7 @@ jobs:
           CUBLAS_WORKSPACE_CONFIG: :16:8
         run: |
           pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -k "not Flax and not Onnx" \
+            -s -v -k "not Flax and not Onnx" \
             --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
             --report-log=tests_pipeline_${{ matrix.module }}_cuda.log \
             tests/pipelines/${{ matrix.module }}
@@ -138,7 +138,7 @@ jobs:
         CUBLAS_WORKSPACE_CONFIG: :16:8
       run: |
         pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -k "not Flax and not Onnx" \
+          -s -v -k "not Flax and not Onnx" \
           --make-reports=tests_torch_${{ matrix.module }}_cuda \
           --report-log=tests_torch_${{ matrix.module }}_cuda.log \
           tests/${{ matrix.module }}
@@ -151,7 +151,7 @@ jobs:
         CUBLAS_WORKSPACE_CONFIG: :16:8
       run: |
         pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          --make-reports=examples_torch_cuda \
+          -s -v --make-reports=examples_torch_cuda \
           --report-log=examples_torch_cuda.log \
           examples/
 
@@ -198,7 +198,7 @@ jobs:
         HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
         RUN_COMPILE: yes
       run: |
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile -k "compile" --make-reports=tests_torch_compile_cuda tests/
+        pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
     - name: Failure short reports
       if: ${{ failure() }}
       run: cat reports/tests_torch_compile_cuda_failures_short.txt
@@ -293,7 +293,7 @@ jobs:
           CUBLAS_WORKSPACE_CONFIG: :16:8
         run: |
           pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -k "not Flax and not Onnx" \
+            -s -v -k "not Flax and not Onnx" \
             --make-reports=tests_torch_minimum_version_cuda \
             tests/models/test_modeling_common.py \
             tests/pipelines/test_pipelines_common.py \
@@ -531,7 +531,7 @@ jobs:
 #          HF_HOME: /System/Volumes/Data/mnt/cache
 #          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
 #        run: |
-#          ${CONDA_RUN} pytest -n 1 --make-reports=tests_torch_mps \
+#          ${CONDA_RUN} pytest -n 1 -s -v --make-reports=tests_torch_mps \
 #            --report-log=tests_torch_mps.log \
 #            tests/
 #      - name: Failure short reports
@@ -587,7 +587,7 @@ jobs:
 #          HF_HOME: /System/Volumes/Data/mnt/cache
 #          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
 #        run: |
-#          ${CONDA_RUN} pytest -n 1 --make-reports=tests_torch_mps \
+#          ${CONDA_RUN} pytest -n 1 -s -v --make-reports=tests_torch_mps \
 #            --report-log=tests_torch_mps.log \
 #            tests/
 #      - name: Failure short reports

.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
@@ -120,7 +120,7 @@ jobs:
       if: ${{ matrix.config.framework == 'pytorch_pipelines' }}
       run: |
         pytest -n 8 --max-worker-restart=0 --dist=loadfile \
-          -k "not Flax and not Onnx" \
+          -s -v -k "not Flax and not Onnx" \
           --make-reports=tests_${{ matrix.config.report }} \
           tests/modular_pipelines
 

.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
@@ -126,7 +126,7 @@ jobs:
       if: ${{ matrix.config.framework == 'pytorch_pipelines' }}
       run: |
         pytest -n 8 --max-worker-restart=0 --dist=loadfile \
-          -k "not Flax and not Onnx" \
+          -s -v -k "not Flax and not Onnx" \
           --make-reports=tests_${{ matrix.config.report }} \
           tests/pipelines
 
@@ -134,7 +134,7 @@ jobs:
       if: ${{ matrix.config.framework == 'pytorch_models' }}
       run: |
         pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-          -k "not Flax and not Onnx and not Dependency" \
+          -s -v -k "not Flax and not Onnx and not Dependency" \
           --make-reports=tests_${{ matrix.config.report }} \
           tests/models tests/schedulers tests/others
 
@@ -255,11 +255,11 @@ jobs:
     - name: Run fast PyTorch LoRA tests with PEFT
       run: |
         pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-          \
+          -s -v \
           --make-reports=tests_peft_main \
           tests/lora/
         pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-          \
+          -s -v \
           --make-reports=tests_models_lora_peft_main \
           tests/models/ -k "lora"
 

.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
 
@@ -151,13 +151,13 @@ jobs:
         run: |
           if [ "${{ matrix.module }}" = "ip_adapters" ]; then 
               pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-              -k "not Flax and not Onnx" \
+              -s -v -k "not Flax and not Onnx" \
               --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
               tests/pipelines/${{ matrix.module }}
           else 
               pattern=$(cat ${{ steps.extract_tests.outputs.pattern_file }})
               pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-              -k "not Flax and not Onnx and $pattern" \
+              -s -v -k "not Flax and not Onnx and $pattern" \
               --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
               tests/pipelines/${{ matrix.module }}
           fi 
@@ -222,10 +222,10 @@ jobs:
       run: |
         pattern=$(cat ${{ steps.extract_tests.outputs.pattern_file }})
         if [ -z "$pattern" ]; then
-          pytest -n 1  --max-worker-restart=0 --dist=loadfile -k "not Flax and not Onnx" tests/${{ matrix.module }} \
+          pytest -n 1 -sv --max-worker-restart=0 --dist=loadfile -k "not Flax and not Onnx" tests/${{ matrix.module }} \
           --make-reports=tests_torch_cuda_${{ matrix.module }}  
         else
-          pytest -n 1  --max-worker-restart=0 --dist=loadfile -k "not Flax and not Onnx and $pattern" tests/${{ matrix.module }} \
+          pytest -n 1 -sv --max-worker-restart=0 --dist=loadfile -k "not Flax and not Onnx and $pattern" tests/${{ matrix.module }} \
           --make-reports=tests_torch_cuda_${{ matrix.module }}  
         fi
 
@@ -274,7 +274,7 @@ jobs:
         HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
       run: |
         uv pip install ".[training]"
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile --make-reports=examples_torch_cuda examples/
+        pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_cuda examples/
 
     - name: Failure short reports
       if: ${{ failure() }}

.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
 
@@ -76,7 +76,6 @@ jobs:
         run: |
           uv pip install -e ".[quality]"
           uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-          uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
       - name: Environment
         run: |
           python utils/print_env.py
@@ -87,7 +86,7 @@ jobs:
           CUBLAS_WORKSPACE_CONFIG: :16:8
         run: |
           pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -k "not Flax and not Onnx" \
+            -s -v -k "not Flax and not Onnx" \
             --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
             tests/pipelines/${{ matrix.module }}
       - name: Failure short reports
@@ -128,7 +127,6 @@ jobs:
         uv pip install -e ".[quality]"
         uv pip install peft@git+https://github.com/huggingface/peft.git
         uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-        uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
 
     - name: Environment
       run: |
@@ -141,7 +139,7 @@ jobs:
         CUBLAS_WORKSPACE_CONFIG: :16:8
       run: |
         pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -k "not Flax and not Onnx" \
+          -s -v -k "not Flax and not Onnx" \
           --make-reports=tests_torch_cuda_${{ matrix.module }} \
           tests/${{ matrix.module }}
 
@@ -180,7 +178,6 @@ jobs:
     - name: Install dependencies
       run: |
         uv pip install -e ".[quality,training]"
-        uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
     - name: Environment
       run: |
         python utils/print_env.py
@@ -189,7 +186,7 @@ jobs:
         HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
         RUN_COMPILE: yes
       run: |
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile -k "compile" --make-reports=tests_torch_compile_cuda tests/
+        pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
     - name: Failure short reports
       if: ${{ failure() }}
       run: cat reports/tests_torch_compile_cuda_failures_short.txt
@@ -230,7 +227,7 @@ jobs:
       env:
         HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
       run: |
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile -k "xformers" --make-reports=tests_torch_xformers_cuda tests/
+        pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "xformers" --make-reports=tests_torch_xformers_cuda tests/
     - name: Failure short reports
       if: ${{ failure() }}
       run: cat reports/tests_torch_xformers_cuda_failures_short.txt
@@ -273,7 +270,7 @@ jobs:
         HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
       run: |
         uv pip install ".[training]"
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile --make-reports=examples_torch_cuda examples/
+        pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_cuda examples/
 
     - name: Failure short reports
       if: ${{ failure() }}

.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
 
@@ -70,7 +70,7 @@ jobs:
       if: ${{ matrix.config.framework == 'pytorch' }}
       run: |
         pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-          -k "not Flax and not Onnx" \
+          -s -v -k "not Flax and not Onnx" \
           --make-reports=tests_${{ matrix.config.report }} \
           tests/
 

.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
 
@@ -57,7 +57,7 @@ jobs:
         HF_HOME: /System/Volumes/Data/mnt/cache
         HF_TOKEN: ${{ secrets.HF_TOKEN }}
       run: |
-        ${CONDA_RUN} python -m pytest -n 0 --make-reports=tests_torch_mps tests/
+        ${CONDA_RUN} python -m pytest -n 0 -s -v --make-reports=tests_torch_mps tests/
 
     - name: Failure short reports
       if: ${{ failure() }}

.github/workflows/release_tests_fast.yml

@@ -84,7 +84,7 @@ jobs:
           CUBLAS_WORKSPACE_CONFIG: :16:8
         run: |
           pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -k "not Flax and not Onnx" \
+            -s -v -k "not Flax and not Onnx" \
             --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
             tests/pipelines/${{ matrix.module }}
       - name: Failure short reports
@@ -137,7 +137,7 @@ jobs:
         CUBLAS_WORKSPACE_CONFIG: :16:8
       run: |
         pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -k "not Flax and not Onnx" \
+          -s -v -k "not Flax and not Onnx" \
           --make-reports=tests_torch_${{ matrix.module }}_cuda \
           tests/${{ matrix.module }}
 
@@ -187,7 +187,7 @@ jobs:
           CUBLAS_WORKSPACE_CONFIG: :16:8
         run: |
           pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -k "not Flax and not Onnx" \
+            -s -v -k "not Flax and not Onnx" \
             --make-reports=tests_torch_minimum_cuda \
             tests/models/test_modeling_common.py \
             tests/pipelines/test_pipelines_common.py \
@@ -240,7 +240,7 @@ jobs:
         HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
         RUN_COMPILE: yes
       run: |
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile -k "compile" --make-reports=tests_torch_compile_cuda tests/
+        pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
     - name: Failure short reports
       if: ${{ failure() }}
       run: cat reports/tests_torch_compile_cuda_failures_short.txt
@@ -281,7 +281,7 @@ jobs:
       env:
         HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
       run: |
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile -k "xformers" --make-reports=tests_torch_xformers_cuda tests/
+        pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "xformers" --make-reports=tests_torch_xformers_cuda tests/
     - name: Failure short reports
       if: ${{ failure() }}
       run: cat reports/tests_torch_xformers_cuda_failures_short.txt
@@ -326,7 +326,7 @@ jobs:
         HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
       run: |
         uv pip install ".[training]"
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile --make-reports=examples_torch_cuda examples/
+        pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_cuda examples/
 
     - name: Failure short reports
       if: ${{ failure() }}

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

@@ -22,8 +22,6 @@
     title: Reproducibility
   - local: using-diffusers/schedulers
     title: Schedulers
-  - local: using-diffusers/automodel
-    title: AutoModel
   - local: using-diffusers/other-formats
     title: Model formats
   - local: using-diffusers/push_to_hub
@@ -121,8 +119,6 @@
     title: ComponentsManager
   - local: modular_diffusers/guiders
     title: Guiders
-  - local: modular_diffusers/custom_blocks
-    title: Building Custom Blocks
   title: Modular Diffusers
 - isExpanded: false
   sections:
@@ -327,14 +323,10 @@
         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
         title: ChromaTransformer2DModel
-      - local: api/models/chronoedit_transformer_3d
-        title: ChronoEditTransformer3DModel
       - local: api/models/cogvideox_transformer3d
         title: CogVideoXTransformer3DModel
       - local: api/models/cogview3plus_transformer2d
@@ -355,8 +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
@@ -379,8 +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
@@ -391,8 +379,6 @@
         title: Transformer2DModel
       - local: api/models/transformer_temporal
         title: TransformerTemporalModel
-      - local: api/models/wan_animate_transformer_3d
-        title: WanAnimateTransformer3DModel
       - local: api/models/wan_transformer_3d
         title: WanTransformer3DModel
       title: Transformers
@@ -425,10 +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
@@ -454,8 +436,6 @@
   - sections:
     - local: api/pipelines/overview
       title: Overview
-    - local: api/pipelines/auto_pipeline
-      title: AutoPipeline
     - sections:
       - local: api/pipelines/audioldm
         title: AudioLDM
@@ -468,6 +448,8 @@
       - local: api/pipelines/stable_audio
         title: Stable Audio
       title: Audio
+    - local: api/pipelines/auto_pipeline
+      title: AutoPipeline
     - sections:
       - local: api/pipelines/amused
         title: aMUSEd
@@ -481,8 +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
@@ -531,8 +511,6 @@
         title: HiDream-I1
       - local: api/pipelines/hunyuandit
         title: Hunyuan-DiT
-      - local: api/pipelines/hunyuanimage21
-        title: HunyuanImage2.1
       - local: api/pipelines/pix2pix
         title: InstructPix2Pix
       - local: api/pipelines/kandinsky
@@ -567,16 +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
@@ -638,8 +612,6 @@
     - sections:
       - local: api/pipelines/allegro
         title: Allegro
-      - local: api/pipelines/chronoedit
-        title: ChronoEdit
       - local: api/pipelines/cogvideox
         title: CogVideoX
       - local: api/pipelines/consisid
@@ -650,8 +622,6 @@
         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/auto_model.md

@@ -12,7 +12,15 @@ specific language governing permissions and limitations under the License.
 
 # AutoModel
 
-[`AutoModel`] automatically retrieves the correct model class from the checkpoint `config.json` file.
+The `AutoModel` is designed to make it easy to load a checkpoint without needing to know the specific model class. `AutoModel` automatically retrieves the correct model class from the checkpoint `config.json` file.
+
+```python
+from diffusers import AutoModel, AutoPipelineForText2Image
+
+unet = AutoModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="unet")
+pipe = AutoPipelineForText2Image.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", unet=unet)
+```
+
 
 ## AutoModel
 

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/chronoedit_transformer_3d.md

@@ -1,32 +0,0 @@
-<!-- Copyright 2025 The ChronoEdit Team 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. -->
-
-# ChronoEditTransformer3DModel
-
-A Diffusion Transformer model for 3D video-like data from [ChronoEdit: Towards Temporal Reasoning for Image Editing and World Simulation](https://huggingface.co/papers/2510.04290) from NVIDIA and University of Toronto, by Jay Zhangjie Wu, Xuanchi Ren, Tianchang Shen, Tianshi Cao, Kai He, Yifan Lu, Ruiyuan Gao, Enze Xie, Shiyi Lan, Jose M. Alvarez, Jun Gao, Sanja Fidler, Zian Wang, Huan Ling.
-
-> **TL;DR:** ChronoEdit reframes image editing as a video generation task, using input and edited images as start/end frames to leverage pretrained video models with temporal consistency. A temporal reasoning stage introduces reasoning tokens to ensure physically plausible edits and visualize the editing trajectory.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import ChronoEditTransformer3DModel
-
-transformer = ChronoEditTransformer3DModel.from_pretrained("nvidia/ChronoEdit-14B-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
-```
-
-## ChronoEditTransformer3DModel
-
-[[autodoc]] ChronoEditTransformer3DModel
-
-## Transformer2DModelOutput
-
-[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

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/models/wan_animate_transformer_3d.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. -->
-
-# WanAnimateTransformer3DModel
-
-A Diffusion Transformer model for 3D video-like data was introduced in [Wan Animate](https://github.com/Wan-Video/Wan2.2) by the Alibaba Wan Team.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import WanAnimateTransformer3DModel
-
-transformer = WanAnimateTransformer3DModel.from_pretrained("Wan-AI/Wan2.2-Animate-14B-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
-```
-
-## WanAnimateTransformer3DModel
-
-[[autodoc]] WanAnimateTransformer3DModel
-
-## Transformer2DModelOutput
-
-[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

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/chronoedit.md

@@ -1,156 +0,0 @@
-<!-- Copyright 2025 The ChronoEdit Team 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. -->
-
-<div style="float: right;">
-  <div class="flex flex-wrap space-x-1">
-    <a href="https://huggingface.co/docs/diffusers/main/en/tutorials/using_peft_for_inference" target="_blank" rel="noopener">
-      <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
-    </a>
-  </div>
-</div>
-
-# ChronoEdit
-
-[ChronoEdit: Towards Temporal Reasoning for Image Editing and World Simulation](https://huggingface.co/papers/2510.04290) from NVIDIA and University of Toronto, by Jay Zhangjie Wu, Xuanchi Ren, Tianchang Shen, Tianshi Cao, Kai He, Yifan Lu, Ruiyuan Gao, Enze Xie, Shiyi Lan, Jose M. Alvarez, Jun Gao, Sanja Fidler, Zian Wang, Huan Ling.
-
-> **TL;DR:** ChronoEdit reframes image editing as a video generation task, using input and edited images as start/end frames to leverage pretrained video models with temporal consistency. A temporal reasoning stage introduces reasoning tokens to ensure physically plausible edits and visualize the editing trajectory.
-
-*Recent advances in large generative models have greatly enhanced both image editing and in-context image generation, yet a critical gap remains in ensuring physical consistency, where edited objects must remain coherent. This capability is especially vital for world simulation related tasks. In this paper, we present ChronoEdit, a framework that reframes image editing as a video generation problem. First, ChronoEdit treats the input and edited images as the first and last frames of a video, allowing it to leverage large pretrained video generative models that capture not only object appearance but also the implicit physics of motion and interaction through learned temporal consistency. Second, ChronoEdit introduces a temporal reasoning stage that explicitly performs editing at inference time. Under this setting, target frame is jointly denoised with reasoning tokens to imagine a plausible editing trajectory that constrains the solution space to physically viable transformations. The reasoning tokens are then dropped after a few steps to avoid the high computational cost of rendering a full video. To validate ChronoEdit, we introduce PBench-Edit, a new benchmark of image-prompt pairs for contexts that require physical consistency, and demonstrate that ChronoEdit surpasses state-of-the-art baselines in both visual fidelity and physical plausibility. Project page for code and models: [this https URL](https://research.nvidia.com/labs/toronto-ai/chronoedit).*
-
-The ChronoEdit pipeline is developed by the ChronoEdit Team. The original code is available on [GitHub](https://github.com/nv-tlabs/ChronoEdit), and pretrained models can be found in the [nvidia/ChronoEdit](https://huggingface.co/collections/nvidia/chronoedit) collection on Hugging Face.
-
-
-### Image Editing
-
-```py
-import torch
-import numpy as np
-from diffusers import AutoencoderKLWan, ChronoEditTransformer3DModel, ChronoEditPipeline
-from diffusers.utils import export_to_video, load_image
-from transformers import CLIPVisionModel
-from PIL import Image
-
-model_id = "nvidia/ChronoEdit-14B-Diffusers"
-image_encoder = CLIPVisionModel.from_pretrained(model_id, subfolder="image_encoder", torch_dtype=torch.float32)
-vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
-transformer = ChronoEditTransformer3DModel.from_pretrained(model_id, subfolder="transformer", torch_dtype=torch.bfloat16)
-pipe = ChronoEditPipeline.from_pretrained(model_id, image_encoder=image_encoder, transformer=transformer, vae=vae, torch_dtype=torch.bfloat16)
-pipe.to("cuda")
-
-image = load_image(
-    "https://huggingface.co/spaces/nvidia/ChronoEdit/resolve/main/examples/3.png"
-)
-max_area = 720 * 1280
-aspect_ratio = image.height / image.width
-mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
-height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
-width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
-print("width", width, "height", height)
-image = image.resize((width, height))
-prompt = (
-    "The user wants to transform the image by adding a small, cute mouse sitting inside the floral teacup, enjoying a spa bath. The mouse should appear relaxed and cheerful, with a tiny white bath towel draped over its head like a turban. It should be positioned comfortably in the cup’s liquid, with gentle steam rising around it to blend with the cozy atmosphere. "
-    "The mouse’s pose should be natural—perhaps sitting upright with paws resting lightly on the rim or submerged in the tea. The teacup’s floral design, gold trim, and warm lighting must remain unchanged to preserve the original aesthetic. The steam should softly swirl around the mouse, enhancing the spa-like, whimsical mood."
-)
-
-output = pipe(
-    image=image,
-    prompt=prompt,
-    height=height,
-    width=width,
-    num_frames=5,
-    num_inference_steps=50,
-    guidance_scale=5.0,
-    enable_temporal_reasoning=False,
-    num_temporal_reasoning_steps=0,
-).frames[0]
-Image.fromarray((output[-1] * 255).clip(0, 255).astype("uint8")).save("output.png")
-```
-
-Optionally, enable **temporal reasoning** for improved physical consistency:
-```py
-output = pipe(
-    image=image,
-    prompt=prompt,
-    height=height,
-    width=width,
-    num_frames=29,
-    num_inference_steps=50,
-    guidance_scale=5.0,
-    enable_temporal_reasoning=True,
-    num_temporal_reasoning_steps=50,
-).frames[0]
-export_to_video(output, "output.mp4", fps=16)
-Image.fromarray((output[-1] * 255).clip(0, 255).astype("uint8")).save("output.png")
-```
-
-### Inference with 8-Step Distillation Lora
-
-```py
-import torch
-import numpy as np
-from diffusers import AutoencoderKLWan, ChronoEditTransformer3DModel, ChronoEditPipeline
-from diffusers.utils import export_to_video, load_image
-from transformers import CLIPVisionModel
-from PIL import Image
-
-model_id = "nvidia/ChronoEdit-14B-Diffusers"
-image_encoder = CLIPVisionModel.from_pretrained(model_id, subfolder="image_encoder", torch_dtype=torch.float32)
-vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
-transformer = ChronoEditTransformer3DModel.from_pretrained(model_id, subfolder="transformer", torch_dtype=torch.bfloat16)
-pipe = ChronoEditPipeline.from_pretrained(model_id, image_encoder=image_encoder, transformer=transformer, vae=vae, torch_dtype=torch.bfloat16)
-lora_path = hf_hub_download(repo_id=model_id, filename="lora/chronoedit_distill_lora.safetensors")
-pipe.load_lora_weights(lora_path)
-pipe.fuse_lora(lora_scale=1.0)
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=2.0)
-pipe.to("cuda")
-
-image = load_image(
-    "https://huggingface.co/spaces/nvidia/ChronoEdit/resolve/main/examples/3.png"
-)
-max_area = 720 * 1280
-aspect_ratio = image.height / image.width
-mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
-height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
-width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
-print("width", width, "height", height)
-image = image.resize((width, height))
-prompt = (
-    "The user wants to transform the image by adding a small, cute mouse sitting inside the floral teacup, enjoying a spa bath. The mouse should appear relaxed and cheerful, with a tiny white bath towel draped over its head like a turban. It should be positioned comfortably in the cup’s liquid, with gentle steam rising around it to blend with the cozy atmosphere. "
-    "The mouse’s pose should be natural—perhaps sitting upright with paws resting lightly on the rim or submerged in the tea. The teacup’s floral design, gold trim, and warm lighting must remain unchanged to preserve the original aesthetic. The steam should softly swirl around the mouse, enhancing the spa-like, whimsical mood."
-)
-
-output = pipe(
-    image=image,
-    prompt=prompt,
-    height=height,
-    width=width,
-    num_frames=5,
-    num_inference_steps=8,
-    guidance_scale=1.0,
-    enable_temporal_reasoning=False,
-    num_temporal_reasoning_steps=0,
-).frames[0]
-export_to_video(output, "output.mp4", fps=16)
-Image.fromarray((output[-1] * 255).clip(0, 255).astype("uint8")).save("output.png")
-```
-
-## ChronoEditPipeline
-
-[[autodoc]] ChronoEditPipeline
-  - all
-  - __call__
-
-## ChronoEditPipelineOutput
-
-[[autodoc]] pipelines.chronoedit.pipeline_output.ChronoEditPipelineOutput

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/api/pipelines/wan.md

@@ -40,7 +40,6 @@ The following Wan models are supported in Diffusers:
 - [Wan 2.2 T2V 14B](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B-Diffusers)
 - [Wan 2.2 I2V 14B](https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers)
 - [Wan 2.2 TI2V 5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers)
-- [Wan 2.2 Animate 14B](https://huggingface.co/Wan-AI/Wan2.2-Animate-14B-Diffusers)
 
 > [!TIP]
 > Click on the Wan models in the right sidebar for more examples of video generation.
@@ -96,15 +95,15 @@ pipeline = WanPipeline.from_pretrained(
 pipeline.to("cuda")
 
 prompt = """
-The camera rushes from far to near in a low-angle shot,
-revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in
-for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground.
-Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts dynamic
+The camera rushes from far to near in a low-angle shot, 
+revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in 
+for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground. 
+Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts dynamic 
 shadows and warm highlights. Medium composition, front view, low angle, with depth of field.
 """
 negative_prompt = """
-Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality,
-low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured,
+Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, 
+low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, 
 misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards
 """
 
@@ -151,15 +150,15 @@ pipeline.transformer = torch.compile(
 )
 
 prompt = """
-The camera rushes from far to near in a low-angle shot,
-revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in
-for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground.
-Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts dynamic
+The camera rushes from far to near in a low-angle shot, 
+revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in 
+for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground. 
+Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts dynamic 
 shadows and warm highlights. Medium composition, front view, low angle, with depth of field.
 """
 negative_prompt = """
-Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality,
-low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured,
+Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, 
+low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, 
 misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards
 """
 
@@ -250,208 +249,6 @@ The code snippets available in [this](https://github.com/huggingface/diffusers/p
 
 The general rule of thumb to keep in mind when preparing inputs for the VACE pipeline is that the input images, or frames of a video that you want to use for conditioning, should have a corresponding mask that is black in color. The black mask signifies that the model will not generate new content for that area, and only use those parts for conditioning the generation process. For parts/frames that should be generated by the model, the mask should be white in color.
 
-</hfoption>
-</hfoptions>
-
-### Wan-Animate: Unified Character Animation and Replacement with Holistic Replication
-
-[Wan-Animate](https://huggingface.co/papers/2509.14055) by the Wan Team.
-
-*We introduce Wan-Animate, a unified framework for character animation and replacement. Given a character image and a reference video, Wan-Animate can animate the character by precisely replicating the expressions and movements of the character in the video to generate high-fidelity character videos. Alternatively, it can integrate the animated character into the reference video to replace the original character, replicating the scene's lighting and color tone to achieve seamless environmental integration. Wan-Animate is built upon the Wan model. To adapt it for character animation tasks, we employ a modified input paradigm to differentiate between reference conditions and regions for generation. This design unifies multiple tasks into a common symbolic representation. We use spatially-aligned skeleton signals to replicate body motion and implicit facial features extracted from source images to reenact expressions, enabling the generation of character videos with high controllability and expressiveness. Furthermore, to enhance environmental integration during character replacement, we develop an auxiliary Relighting LoRA. This module preserves the character's appearance consistency while applying the appropriate environmental lighting and color tone. Experimental results demonstrate that Wan-Animate achieves state-of-the-art performance. We are committed to open-sourcing the model weights and its source code.*
-
-The project page: https://humanaigc.github.io/wan-animate
-
-This model was mostly contributed by [M. Tolga Cangöz](https://github.com/tolgacangoz).
-
-#### Usage
-
-The Wan-Animate pipeline supports two modes of operation:
-
-1. **Animation Mode** (default): Animates a character image based on motion and expression from reference videos
-2. **Replacement Mode**: Replaces a character in a background video with a new character while preserving the scene
-
-##### Prerequisites
-
-Before using the pipeline, you need to preprocess your reference video to extract:
-- **Pose video**: Contains skeletal keypoints representing body motion
-- **Face video**: Contains facial feature representations for expression control
-
-For replacement mode, you additionally need:
-- **Background video**: The original video containing the scene
-- **Mask video**: A mask indicating where to generate content (white) vs. preserve original (black)
-
-> [!NOTE]
-> Raw videos should not be used for inputs such as `pose_video`, which the pipeline expects to be preprocessed to extract the proper information. Preprocessing scripts to prepare these inputs are available in the [original Wan-Animate repository](https://github.com/Wan-Video/Wan2.2?tab=readme-ov-file#1-preprocessing). Integration of these preprocessing steps into Diffusers is planned for a future release.
-
-The example below demonstrates how to use the Wan-Animate pipeline:
-
-<hfoptions id="Animate usage">
-<hfoption id="Animation mode">
-
-```python
-import numpy as np
-import torch
-from diffusers import AutoencoderKLWan, WanAnimatePipeline
-from diffusers.utils import export_to_video, load_image, load_video
-
-model_id = "Wan-AI/Wan2.2-Animate-14B-Diffusers"
-vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
-pipe = WanAnimatePipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
-pipe.to("cuda")
-
-# Load character image and preprocessed videos
-image = load_image("path/to/character.jpg")
-pose_video = load_video("path/to/pose_video.mp4")  # Preprocessed skeletal keypoints
-face_video = load_video("path/to/face_video.mp4")  # Preprocessed facial features
-
-# Resize image to match VAE constraints
-def aspect_ratio_resize(image, pipe, max_area=720 * 1280):
-    aspect_ratio = image.height / image.width
-    mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
-    height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
-    width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
-    image = image.resize((width, height))
-    return image, height, width
-
-image, height, width = aspect_ratio_resize(image, pipe)
-
-prompt = "A person dancing energetically in a studio with dynamic lighting and professional camera work"
-negative_prompt = "blurry, low quality, distorted, deformed, static, poorly drawn"
-
-# Generate animated video
-output = pipe(
-    image=image,
-    pose_video=pose_video,
-    face_video=face_video,
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    height=height,
-    width=width,
-    segment_frame_length=77,
-    guidance_scale=1.0,
-    mode="animate",  # Animation mode (default)
-).frames[0]
-export_to_video(output, "animated_character.mp4", fps=30)
-```
-
-</hfoption>
-<hfoption id="Replacement mode">
-
-```python
-import numpy as np
-import torch
-from diffusers import AutoencoderKLWan, WanAnimatePipeline
-from diffusers.utils import export_to_video, load_image, load_video
-
-model_id = "Wan-AI/Wan2.2-Animate-14B-Diffusers"
-vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
-pipe = WanAnimatePipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
-pipe.to("cuda")
-
-# Load all required inputs for replacement mode
-image = load_image("path/to/new_character.jpg")
-pose_video = load_video("path/to/pose_video.mp4")  # Preprocessed skeletal keypoints
-face_video = load_video("path/to/face_video.mp4")  # Preprocessed facial features
-background_video = load_video("path/to/background_video.mp4")  # Original scene
-mask_video = load_video("path/to/mask_video.mp4")  # Black: preserve, White: generate
-
-# Resize image to match video dimensions
-def aspect_ratio_resize(image, pipe, max_area=720 * 1280):
-    aspect_ratio = image.height / image.width
-    mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
-    height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
-    width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
-    image = image.resize((width, height))
-    return image, height, width
-
-image, height, width = aspect_ratio_resize(image, pipe)
-
-prompt = "A person seamlessly integrated into the scene with consistent lighting and environment"
-negative_prompt = "blurry, low quality, inconsistent lighting, floating, disconnected from scene"
-
-# Replace character in background video
-output = pipe(
-    image=image,
-    pose_video=pose_video,
-    face_video=face_video,
-    background_video=background_video,
-    mask_video=mask_video,
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    height=height,
-    width=width,
-    segment_frame_lengths=77,
-    guidance_scale=1.0,
-    mode="replace",  # Replacement mode
-).frames[0]
-export_to_video(output, "character_replaced.mp4", fps=30)
-```
-
-</hfoption>
-<hfoption id="Advanced options">
-
-```python
-import numpy as np
-import torch
-from diffusers import AutoencoderKLWan, WanAnimatePipeline
-from diffusers.utils import export_to_video, load_image, load_video
-
-model_id = "Wan-AI/Wan2.2-Animate-14B-Diffusers"
-vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
-pipe = WanAnimatePipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
-pipe.to("cuda")
-
-image = load_image("path/to/character.jpg")
-pose_video = load_video("path/to/pose_video.mp4")
-face_video = load_video("path/to/face_video.mp4")
-
-def aspect_ratio_resize(image, pipe, max_area=720 * 1280):
-    aspect_ratio = image.height / image.width
-    mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
-    height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
-    width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
-    image = image.resize((width, height))
-    return image, height, width
-
-image, height, width = aspect_ratio_resize(image, pipe)
-
-prompt = "A person dancing energetically in a studio"
-negative_prompt = "blurry, low quality"
-
-# Advanced: Use temporal guidance and custom callback
-def callback_fn(pipe, step_index, timestep, callback_kwargs):
-    # You can modify latents or other tensors here
-    print(f"Step {step_index}, Timestep {timestep}")
-    return callback_kwargs
-
-output = pipe(
-    image=image,
-    pose_video=pose_video,
-    face_video=face_video,
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    height=height,
-    width=width,
-    segment_frame_length=77,
-    num_inference_steps=50,
-    guidance_scale=5.0,
-    prev_segment_conditioning_frames=5,  # Use 5 frames for temporal guidance (1 or 5 recommended)
-    callback_on_step_end=callback_fn,
-    callback_on_step_end_tensor_inputs=["latents"],
-).frames[0]
-export_to_video(output, "animated_advanced.mp4", fps=30)
-```
-
-</hfoption>
-</hfoptions>
-
-#### Key Parameters
-
-- **mode**: Choose between `"animate"` (default) or `"replace"`
-- **prev_segment_conditioning_frames**: Number of frames for temporal guidance (1 or 5 recommended). Using 5 provides better temporal consistency but requires more memory
-- **guidance_scale**: Controls how closely the output follows the text prompt. Higher values (5-7) produce results more aligned with the prompt. For Wan-Animate, CFG is disabled by default (`guidance_scale=1.0`) but can be enabled to support negative prompts and finer control over facial expressions. (Note that CFG will only target the text prompt and face conditioning.)
-
-
 ## Notes
 
 - Wan2.1 supports LoRAs with [`~loaders.WanLoraLoaderMixin.load_lora_weights`].
@@ -484,10 +281,10 @@ export_to_video(output, "animated_advanced.mp4", fps=30)
 
   # use "steamboat willie style" to trigger the LoRA
   prompt = """
-  steamboat willie style, golden era animation, The camera rushes from far to near in a low-angle shot,
-  revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in
-  for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground.
-  Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts dynamic
+  steamboat willie style, golden era animation, The camera rushes from far to near in a low-angle shot, 
+  revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in 
+  for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground. 
+  Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts dynamic 
   shadows and warm highlights. Medium composition, front view, low angle, with depth of field.
   """
 
@@ -562,12 +359,6 @@ export_to_video(output, "animated_advanced.mp4", fps=30)
   - all
   - __call__
 
-## WanAnimatePipeline
-
-[[autodoc]] WanAnimatePipeline
-  - all
-  - __call__
-
 ## WanPipelineOutput
 
-[[autodoc]] pipelines.wan.pipeline_output.WanPipelineOutput
+[[autodoc]] pipelines.wan.pipeline_output.WanPipelineOutput

docs/source/en/modular_diffusers/custom_blocks.md

@@ -1,492 +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.
--->
-
-
-# Building Custom Blocks
-
-[ModularPipelineBlocks](./pipeline_block) are the fundamental building blocks of a [`ModularPipeline`]. You can create custom blocks by defining their inputs, outputs, and computation logic. This guide demonstrates how to create and use a custom block.
-
-> [!TIP]
-> Explore the [Modular Diffusers Custom Blocks](https://huggingface.co/collections/diffusers/modular-diffusers-custom-blocks) collection for official custom modular blocks like Nano Banana.
-
-## Project Structure
-
-Your custom block project should use the following structure:
-
-```shell
-.
-├── block.py
-└── modular_config.json
-```
-
-- `block.py` contains the custom block implementation
-- `modular_config.json` contains the metadata needed to load the block
-
-## Example: Florence 2 Inpainting Block
-
-In this example we will create a custom block that uses the [Florence 2](https://huggingface.co/docs/transformers/model_doc/florence2) model to process an input image and generate a mask for inpainting.
-
-The first step is to define the components that the block will use. In this case, we will need to use the `Florence2ForConditionalGeneration` model and its corresponding processor `AutoProcessor`. When defining components, we must specify the name of the component within our pipeline, model class via `type_hint`, and provide a `pretrained_model_name_or_path` for the component if we intend to load the model weights from a specific repository on the Hub.
-
-```py
-# Inside block.py
-from diffusers.modular_pipelines import (
-    ModularPipelineBlocks,
-    ComponentSpec,
-)
-from transformers import AutoProcessor, Florence2ForConditionalGeneration
-
-
-class Florence2ImageAnnotatorBlock(ModularPipelineBlocks):
-
-    @property
-    def expected_components(self):
-        return [
-            ComponentSpec(
-                name="image_annotator",
-                type_hint=Florence2ForConditionalGeneration,
-                pretrained_model_name_or_path="florence-community/Florence-2-base-ft",
-            ),
-            ComponentSpec(
-                name="image_annotator_processor",
-                type_hint=AutoProcessor,
-                pretrained_model_name_or_path="florence-community/Florence-2-base-ft",
-            ),
-        ]
-```
-
-Next, we define the inputs and outputs of the block. The inputs include the image to be annotated, the annotation task, and the annotation prompt. The outputs include the generated mask image and annotations.
-
-```py
-from typing import List, Union
-from PIL import Image, ImageDraw
-import torch
-import numpy as np
-
-from diffusers.modular_pipelines import (
-    PipelineState,
-    ModularPipelineBlocks,
-    InputParam,
-    ComponentSpec,
-    OutputParam,
-)
-from transformers import AutoProcessor, Florence2ForConditionalGeneration
-
-
-class Florence2ImageAnnotatorBlock(ModularPipelineBlocks):
-
-    @property
-    def expected_components(self):
-        return [
-            ComponentSpec(
-                name="image_annotator",
-                type_hint=Florence2ForConditionalGeneration,
-                pretrained_model_name_or_path="florence-community/Florence-2-base-ft",
-            ),
-            ComponentSpec(
-                name="image_annotator_processor",
-                type_hint=AutoProcessor,
-                pretrained_model_name_or_path="florence-community/Florence-2-base-ft",
-            ),
-        ]
-
-    @property
-    def inputs(self) -> List[InputParam]:
-        return [
-            InputParam(
-                "image",
-                type_hint=Union[Image.Image, List[Image.Image]],
-                required=True,
-                description="Image(s) to annotate",
-            ),
-            InputParam(
-                "annotation_task",
-                type_hint=Union[str, List[str]],
-                required=True,
-                default="<REFERRING_EXPRESSION_SEGMENTATION>",
-                description="""Annotation Task to perform on the image.
-                Supported Tasks:
-
-                <OD>
-                <REFERRING_EXPRESSION_SEGMENTATION>
-                <CAPTION>
-                <DETAILED_CAPTION>
-                <MORE_DETAILED_CAPTION>
-                <DENSE_REGION_CAPTION>
-                <CAPTION_TO_PHRASE_GROUNDING>
-                <OPEN_VOCABULARY_DETECTION>
-
-                """,
-            ),
-            InputParam(
-                "annotation_prompt",
-                type_hint=Union[str, List[str]],
-                required=True,
-                description="""Annotation Prompt to provide more context to the task.
-                Can be used to detect or segment out specific elements in the image
-                """,
-            ),
-            InputParam(
-                "annotation_output_type",
-                type_hint=str,
-                required=True,
-                default="mask_image",
-                description="""Output type from annotation predictions. Availabe options are
-                mask_image:
-                    -black and white mask image for the given image based on the task type
-                mask_overlay:
-                    - mask overlayed on the original image
-                bounding_box:
-                    - bounding boxes drawn on the original image
-                """,
-            ),
-            InputParam(
-                "annotation_overlay",
-                type_hint=bool,
-                required=True,
-                default=False,
-                description="",
-            ),
-        ]
-
-    @property
-    def intermediate_outputs(self) -> List[OutputParam]:
-        return [
-            OutputParam(
-                "mask_image",
-                type_hint=Image,
-                description="Inpainting Mask for input Image(s)",
-            ),
-            OutputParam(
-                "annotations",
-                type_hint=dict,
-                description="Annotations Predictions for input Image(s)",
-            ),
-            OutputParam(
-                "image",
-                type_hint=Image,
-                description="Annotated input Image(s)",
-            ),
-        ]
-
-```
-
-Now we implement the `__call__` method, which contains the logic for processing the input image and generating the mask.
-
-```py
-from typing import List, Union
-from PIL import Image, ImageDraw
-import torch
-import numpy as np
-
-from diffusers.modular_pipelines import (
-    PipelineState,
-    ModularPipelineBlocks,
-    InputParam,
-    ComponentSpec,
-    OutputParam,
-)
-from transformers import AutoProcessor, Florence2ForConditionalGeneration
-
-
-class Florence2ImageAnnotatorBlock(ModularPipelineBlocks):
-
-    @property
-    def expected_components(self):
-        return [
-            ComponentSpec(
-                name="image_annotator",
-                type_hint=Florence2ForConditionalGeneration,
-                pretrained_model_name_or_path="florence-community/Florence-2-base-ft",
-            ),
-            ComponentSpec(
-                name="image_annotator_processor",
-                type_hint=AutoProcessor,
-                pretrained_model_name_or_path="florence-community/Florence-2-base-ft",
-            ),
-        ]
-
-    @property
-    def inputs(self) -> List[InputParam]:
-        return [
-            InputParam(
-                "image",
-                type_hint=Union[Image.Image, List[Image.Image]],
-                required=True,
-                description="Image(s) to annotate",
-            ),
-            InputParam(
-                "annotation_task",
-                type_hint=Union[str, List[str]],
-                required=True,
-                default="<REFERRING_EXPRESSION_SEGMENTATION>",
-                description="""Annotation Task to perform on the image.
-                Supported Tasks:
-
-                <OD>
-                <REFERRING_EXPRESSION_SEGMENTATION>
-                <CAPTION>
-                <DETAILED_CAPTION>
-                <MORE_DETAILED_CAPTION>
-                <DENSE_REGION_CAPTION>
-                <CAPTION_TO_PHRASE_GROUNDING>
-                <OPEN_VOCABULARY_DETECTION>
-
-                """,
-            ),
-            InputParam(
-                "annotation_prompt",
-                type_hint=Union[str, List[str]],
-                required=True,
-                description="""Annotation Prompt to provide more context to the task.
-                Can be used to detect or segment out specific elements in the image
-                """,
-            ),
-            InputParam(
-                "annotation_output_type",
-                type_hint=str,
-                required=True,
-                default="mask_image",
-                description="""Output type from annotation predictions. Availabe options are
-                mask_image:
-                    -black and white mask image for the given image based on the task type
-                mask_overlay:
-                    - mask overlayed on the original image
-                bounding_box:
-                    - bounding boxes drawn on the original image
-                """,
-            ),
-            InputParam(
-                "annotation_overlay",
-                type_hint=bool,
-                required=True,
-                default=False,
-                description="",
-            ),
-        ]
-
-    @property
-    def intermediate_outputs(self) -> List[OutputParam]:
-        return [
-            OutputParam(
-                "mask_image",
-                type_hint=Image,
-                description="Inpainting Mask for input Image(s)",
-            ),
-            OutputParam(
-                "annotations",
-                type_hint=dict,
-                description="Annotations Predictions for input Image(s)",
-            ),
-            OutputParam(
-                "image",
-                type_hint=Image,
-                description="Annotated input Image(s)",
-            ),
-        ]
-
-    def get_annotations(self, components, images, prompts, task):
-        task_prompts = [task + prompt for prompt in prompts]
-
-        inputs = components.image_annotator_processor(
-            text=task_prompts, images=images, return_tensors="pt"
-        ).to(components.image_annotator.device, components.image_annotator.dtype)
-
-        generated_ids = components.image_annotator.generate(
-            input_ids=inputs["input_ids"],
-            pixel_values=inputs["pixel_values"],
-            max_new_tokens=1024,
-            early_stopping=False,
-            do_sample=False,
-            num_beams=3,
-        )
-        annotations = components.image_annotator_processor.batch_decode(
-            generated_ids, skip_special_tokens=False
-        )
-        outputs = []
-        for image, annotation in zip(images, annotations):
-            outputs.append(
-                components.image_annotator_processor.post_process_generation(
-                    annotation, task=task, image_size=(image.width, image.height)
-                )
-            )
-        return outputs
-
-    def prepare_mask(self, images, annotations, overlay=False, fill="white"):
-        masks = []
-        for image, annotation in zip(images, annotations):
-            mask_image = image.copy() if overlay else Image.new("L", image.size, 0)
-            draw = ImageDraw.Draw(mask_image)
-
-            for _, _annotation in annotation.items():
-                if "polygons" in _annotation:
-                    for polygon in _annotation["polygons"]:
-                        polygon = np.array(polygon).reshape(-1, 2)
-                        if len(polygon) < 3:
-                            continue
-                        polygon = polygon.reshape(-1).tolist()
-                        draw.polygon(polygon, fill=fill)
-
-                elif "bbox" in _annotation:
-                    bbox = _annotation["bbox"]
-                    draw.rectangle(bbox, fill="white")
-
-            masks.append(mask_image)
-
-        return masks
-
-    def prepare_bounding_boxes(self, images, annotations):
-        outputs = []
-        for image, annotation in zip(images, annotations):
-            image_copy = image.copy()
-            draw = ImageDraw.Draw(image_copy)
-            for _, _annotation in annotation.items():
-                bbox = _annotation["bbox"]
-                label = _annotation["label"]
-
-                draw.rectangle(bbox, outline="red", width=3)
-                draw.text((bbox[0], bbox[1] - 20), label, fill="red")
-
-            outputs.append(image_copy)
-
-        return outputs
-
-    def prepare_inputs(self, images, prompts):
-        prompts = prompts or ""
-
-        if isinstance(images, Image.Image):
-            images = [images]
-        if isinstance(prompts, str):
-            prompts = [prompts]
-
-        if len(images) != len(prompts):
-            raise ValueError("Number of images and annotation prompts must match.")
-
-        return images, prompts
-
-    @torch.no_grad()
-    def __call__(self, components, state: PipelineState) -> PipelineState:
-        block_state = self.get_block_state(state)
-        images, annotation_task_prompt = self.prepare_inputs(
-            block_state.image, block_state.annotation_prompt
-        )
-        task = block_state.annotation_task
-        fill = block_state.fill
-
-        annotations = self.get_annotations(
-            components, images, annotation_task_prompt, task
-        )
-        block_state.annotations = annotations
-        if block_state.annotation_output_type == "mask_image":
-            block_state.mask_image = self.prepare_mask(images, annotations)
-        else:
-            block_state.mask_image = None
-
-        if block_state.annotation_output_type == "mask_overlay":
-            block_state.image = self.prepare_mask(images, annotations, overlay=True, fill=fill)
-
-        elif block_state.annotation_output_type == "bounding_box":
-            block_state.image = self.prepare_bounding_boxes(images, annotations)
-
-        self.set_block_state(state, block_state)
-
-        return components, state
-
-```
-
-Once we have defined our custom block, we can save it to the Hub, using either the CLI or the [`push_to_hub`] method. This will make it easy to share and reuse our custom block with other pipelines.
-
-<hfoptions id="share">
-<hfoption id="hf CLI">
-
-```shell
-# In the folder with the `block.py` file, run:
-diffusers-cli custom_block
-```
-
-Then upload the block to the Hub:
-
-```shell
-hf upload <your repo id> . .
-```
-</hfoption>
-<hfoption id="push_to_hub">
-
-```py
-from block import Florence2ImageAnnotatorBlock
-block = Florence2ImageAnnotatorBlock()
-block.push_to_hub("<your repo id>")
-```
-
-</hfoption>
-</hfoptions>
-
-## Using Custom Blocks
-
-Load the custom block with [`~ModularPipelineBlocks.from_pretrained`] and set `trust_remote_code=True`.
-
-```py
-import torch
-from diffusers.modular_pipelines import ModularPipelineBlocks, SequentialPipelineBlocks
-from diffusers.modular_pipelines.stable_diffusion_xl import INPAINT_BLOCKS
-from diffusers.utils import load_image
-
-# Fetch the Florence2 image annotator block that will create our mask
-image_annotator_block = ModularPipelineBlocks.from_pretrained("diffusers/florence-2-custom-block", trust_remote_code=True)
-
-my_blocks = INPAINT_BLOCKS.copy()
-# insert the annotation block before the image encoding step
-my_blocks.insert("image_annotator", image_annotator_block, 1)
-
-# Create our initial set of inpainting blocks
-blocks = SequentialPipelineBlocks.from_blocks_dict(my_blocks)
-
-repo_id = "diffusers/modular-stable-diffusion-xl-base-1.0"
-pipe = blocks.init_pipeline(repo_id)
-pipe.load_components(torch_dtype=torch.float16, device_map="cuda", trust_remote_code=True)
-
-image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true")
-image = image.resize((1024, 1024))
-
-prompt = ["A red car"]
-annotation_task = "<REFERRING_EXPRESSION_SEGMENTATION>"
-annotation_prompt = ["the car"]
-
-output = pipe(
-    prompt=prompt,
-    image=image,
-    annotation_task=annotation_task,
-    annotation_prompt=annotation_prompt,
-    annotation_output_type="mask_image",
-    num_inference_steps=35,
-    guidance_scale=7.5,
-    strength=0.95,
-    output="images"
-)
-output[0].save("florence-inpainting.png")
-```
-
-## Editing Custom Blocks
-
-By default, custom blocks are saved in your cache directory. Use the `local_dir` argument to download and edit a custom block in a specific folder.
-
-```py
-import torch
-from diffusers.modular_pipelines import ModularPipelineBlocks, SequentialPipelineBlocks
-from diffusers.modular_pipelines.stable_diffusion_xl import INPAINT_BLOCKS
-from diffusers.utils import load_image
-
-# Fetch the Florence2 image annotator block that will create our mask
-image_annotator_block = ModularPipelineBlocks.from_pretrained("diffusers/florence-2-custom-block", trust_remote_code=True, local_dir="/my-local-folder")
-```
-
-Any changes made to the block files in this folder will be reflected when you load the block again.

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 |

docs/source/en/using-diffusers/automodel.md

@@ -1,46 +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.
--->
-
-# AutoModel
-
-The [`AutoModel`] class automatically detects and loads the correct model class (UNet, transformer, VAE) from a `config.json` file. You don't need to know the specific model class name ahead of time. It supports data types and device placement, and works across model types and libraries.
-
-The example below loads a transformer from Diffusers and a text encoder from Transformers. Use the `subfolder` parameter to specify where to load the `config.json` file from.
-
-```py
-import torch
-from diffusers import AutoModel, DiffusionPipeline
-
-transformer = AutoModel.from_pretrained(
-    "Qwen/Qwen-Image", subfolder="transformer", torch_dtype=torch.bfloat16, device_map="cuda"
-)
-
-text_encoder = AutoModel.from_pretrained(
-    "Qwen/Qwen-Image", subfolder="text_encoder", torch_dtype=torch.bfloat16, device_map="cuda"
-)
-```
-
-[`AutoModel`] also loads models from the [Hub](https://huggingface.co/models) that aren't included in Diffusers. Set `trust_remote_code=True` in [`AutoModel.from_pretrained`] to load custom models.
-
-```py
-import torch
-from diffusers import AutoModel
-
-transformer = AutoModel.from_pretrained(
-    "custom/custom-transformer-model", trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="cuda"
-)
-```
-
-If the custom model inherits from the [`ModelMixin`] class, it gets access to the same features as Diffusers model classes, like [regional compilation](../optimization/fp16#regional-compilation) and [group offloading](../optimization/memory#group-offloading).
-
-> [!NOTE]
-> Learn more about implementing custom models in the [Community components](../using-diffusers/custom_pipeline_overview#community-components) guide.

examples/community/README.md

@@ -5488,7 +5488,7 @@ Editing at Scale", many thanks to their contribution!
 
 This implementation of Flux Kontext allows users to pass multiple reference images. Each image is encoded separately, and the resulting latent vectors are concatenated.
 
-As explained in Section 3 of [the paper](https://huggingface.co/papers/2506.15742), the model's sequence concatenation mechanism can extend its capabilities to handle multiple reference images. However, note that the current version of Flux Kontext was not trained for this use case. In practice, stacking along the first axis does not yield correct results, while stacking along the other two axes appears to work.
+As explained in Section 3 of [the paper](https://arxiv.org/pdf/2506.15742), the model's sequence concatenation mechanism can extend its capabilities to handle multiple reference images. However, note that the current version of Flux Kontext was not trained for this use case. In practice, stacking along the first axis does not yield correct results, while stacking along the other two axes appears to work.
 
 ## Example Usage
 

examples/community/img2img_inpainting.py

@@ -45,7 +45,7 @@ def check_size(image, height, width):
         raise ValueError(f"Image size should be {height}x{width}, but got {h}x{w}")
 
 
-def overlay_inner_image(image, inner_image, paste_offset: Tuple[int, ...] = (0, 0)):
+def overlay_inner_image(image, inner_image, paste_offset: Tuple[int] = (0, 0)):
     inner_image = inner_image.convert("RGBA")
     image = image.convert("RGB")
 

examples/community/matryoshka.py

@@ -1966,21 +1966,16 @@ class MatryoshkaUNet2DConditionModel(
         center_input_sample: bool = False,
         flip_sin_to_cos: bool = True,
         freq_shift: int = 0,
-        down_block_types: Tuple[str, ...] = (
+        down_block_types: Tuple[str] = (
             "CrossAttnDownBlock2D",
             "CrossAttnDownBlock2D",
             "CrossAttnDownBlock2D",
             "DownBlock2D",
         ),
         mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
-        up_block_types: Tuple[str, ...] = (
-            "UpBlock2D",
-            "CrossAttnUpBlock2D",
-            "CrossAttnUpBlock2D",
-            "CrossAttnUpBlock2D",
-        ),
+        up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
         only_cross_attention: Union[bool, Tuple[bool]] = False,
-        block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
+        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
         layers_per_block: Union[int, Tuple[int]] = 2,
         downsample_padding: int = 1,
         mid_block_scale_factor: float = 1,
@@ -2299,10 +2294,10 @@ class MatryoshkaUNet2DConditionModel(
 
     def _check_config(
         self,
-        down_block_types: Tuple[str, ...],
-        up_block_types: Tuple[str, ...],
+        down_block_types: Tuple[str],
+        up_block_types: Tuple[str],
         only_cross_attention: Union[bool, Tuple[bool]],
-        block_out_channels: Tuple[int, ...],
+        block_out_channels: Tuple[int],
         layers_per_block: Union[int, Tuple[int]],
         cross_attention_dim: Union[int, Tuple[int]],
         transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple[int]]],

examples/community/pipeline_faithdiff_stable_diffusion_xl.py

@@ -438,21 +438,16 @@ class UNet2DConditionModel(OriginalUNet2DConditionModel, ConfigMixin, UNet2DCond
         center_input_sample: bool = False,
         flip_sin_to_cos: bool = True,
         freq_shift: int = 0,
-        down_block_types: Tuple[str, ...] = (
+        down_block_types: Tuple[str] = (
             "CrossAttnDownBlock2D",
             "CrossAttnDownBlock2D",
             "CrossAttnDownBlock2D",
             "DownBlock2D",
         ),
         mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
-        up_block_types: Tuple[str, ...] = (
-            "UpBlock2D",
-            "CrossAttnUpBlock2D",
-            "CrossAttnUpBlock2D",
-            "CrossAttnUpBlock2D",
-        ),
+        up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
         only_cross_attention: Union[bool, Tuple[bool]] = False,
-        block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
+        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
         layers_per_block: Union[int, Tuple[int]] = 2,
         downsample_padding: int = 1,
         mid_block_scale_factor: float = 1,

examples/community/regional_prompting_stable_diffusion.py

@@ -490,7 +490,7 @@ class RegionalPromptingStableDiffusionPipeline(
     def prepare_extra_step_kwargs(self, generator, eta):
         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://huggingface.co/papers/2010.02502
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
         # and should be between [0, 1]
 
         accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
@@ -841,7 +841,7 @@ class RegionalPromptingStableDiffusionPipeline(
             num_images_per_prompt (`int`, *optional*, defaults to 1):
                 The number of images to generate per prompt.
             eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) from the [DDIM](https://huggingface.co/papers/2010.02502) paper. Only applies
+                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
                 to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
             generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
                 A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
@@ -872,7 +872,7 @@ class RegionalPromptingStableDiffusionPipeline(
                 [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
             guidance_rescale (`float`, *optional*, defaults to 0.0):
                 Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are
-                Flawed](https://huggingface.co/papers/2305.08891). Guidance rescale factor should fix overexposure when
+                Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when
                 using zero terminal SNR.
             clip_skip (`int`, *optional*):
                 Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
@@ -1062,7 +1062,7 @@ class RegionalPromptingStableDiffusionPipeline(
                     noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
 
                 if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
-                    # Based on 3.4. in https://huggingface.co/papers/2305.08891
+                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
                     noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
 
                 # compute the previous noisy sample x_t -> x_t-1
@@ -1668,7 +1668,7 @@ def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
     r"""
     Rescales `noise_cfg` tensor based on `guidance_rescale` to improve image quality and fix overexposure. Based on
     Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
-    Flawed](https://huggingface.co/papers/2305.08891).
+    Flawed](https://arxiv.org/pdf/2305.08891.pdf).
 
     Args:
         noise_cfg (`torch.Tensor`):

examples/dreambooth/README_flux.md

@@ -268,11 +268,12 @@ provide a simple script for LoRA fine-tuning Kontext in [train_dreambooth_lora_f
 **important**
 
 > [!NOTE] 
-> To make sure you can successfully run the latest version of the kontext example script, we highly recommend installing from source.
+> To make sure you can successfully run the latest version of the kontext example script, we highly recommend installing from source, specifically from the commit mentioned below.
 > To do this, execute the following steps in a new virtual environment:
 > ```
 > git clone https://github.com/huggingface/diffusers
 > cd diffusers
+> git checkout 05e7a854d0a5661f5b433f6dd5954c224b104f0b
 > pip install -e .
 > ```
 

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_controlnet_to_diffusers.py

@@ -10,7 +10,7 @@ from accelerate import init_empty_weights
 from diffusers import (
     SanaControlNetModel,
 )
-from diffusers.models.model_loading_utils import load_model_dict_into_meta
+from diffusers.models.modeling_utils import load_model_dict_into_meta
 from diffusers.utils.import_utils import is_accelerate_available
 
 

scripts/convert_sana_to_diffusers.py

@@ -20,7 +20,7 @@ from diffusers import (
     SanaTransformer2DModel,
     SCMScheduler,
 )
-from diffusers.models.model_loading_utils import load_model_dict_into_meta
+from diffusers.models.modeling_utils import load_model_dict_into_meta
 from diffusers.utils.import_utils import is_accelerate_available
 
 

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)

scripts/convert_sd3_to_diffusers.py

@@ -7,7 +7,7 @@ from accelerate import init_empty_weights
 
 from diffusers import AutoencoderKL, SD3Transformer2DModel
 from diffusers.loaders.single_file_utils import convert_ldm_vae_checkpoint
-from diffusers.models.model_loading_utils import load_model_dict_into_meta
+from diffusers.models.modeling_utils import load_model_dict_into_meta
 from diffusers.utils.import_utils import is_accelerate_available
 
 

scripts/convert_stable_audio.py

@@ -18,7 +18,7 @@ from diffusers import (
     StableAudioPipeline,
     StableAudioProjectionModel,
 )
-from diffusers.models.model_loading_utils import load_model_dict_into_meta
+from diffusers.models.modeling_utils import load_model_dict_into_meta
 from diffusers.utils import is_accelerate_available
 
 

scripts/convert_stable_cascade.py

@@ -20,7 +20,7 @@ from diffusers import (
 )
 from diffusers.loaders.single_file_utils import convert_stable_cascade_unet_single_file_to_diffusers
 from diffusers.models import StableCascadeUNet
-from diffusers.models.model_loading_utils import load_model_dict_into_meta
+from diffusers.models.modeling_utils import load_model_dict_into_meta
 from diffusers.pipelines.wuerstchen import PaellaVQModel
 from diffusers.utils import is_accelerate_available
 

scripts/convert_stable_cascade_lite.py

@@ -20,7 +20,7 @@ from diffusers import (
 )
 from diffusers.loaders.single_file_utils import convert_stable_cascade_unet_single_file_to_diffusers
 from diffusers.models import StableCascadeUNet
-from diffusers.models.model_loading_utils import load_model_dict_into_meta
+from diffusers.models.modeling_utils import load_model_dict_into_meta
 from diffusers.pipelines.wuerstchen import PaellaVQModel
 from diffusers.utils import is_accelerate_available
 

scripts/convert_wan_to_diffusers.py

@@ -6,20 +6,11 @@ import torch
 from accelerate import init_empty_weights
 from huggingface_hub import hf_hub_download, snapshot_download
 from safetensors.torch import load_file
-from transformers import (
-    AutoProcessor,
-    AutoTokenizer,
-    CLIPImageProcessor,
-    CLIPVisionModel,
-    CLIPVisionModelWithProjection,
-    UMT5EncoderModel,
-)
+from transformers import AutoProcessor, AutoTokenizer, CLIPVisionModelWithProjection, UMT5EncoderModel
 
 from diffusers import (
     AutoencoderKLWan,
     UniPCMultistepScheduler,
-    WanAnimatePipeline,
-    WanAnimateTransformer3DModel,
     WanImageToVideoPipeline,
     WanPipeline,
     WanTransformer3DModel,
@@ -114,203 +105,8 @@ VACE_TRANSFORMER_KEYS_RENAME_DICT = {
     "after_proj": "proj_out",
 }
 
-ANIMATE_TRANSFORMER_KEYS_RENAME_DICT = {
-    "time_embedding.0": "condition_embedder.time_embedder.linear_1",
-    "time_embedding.2": "condition_embedder.time_embedder.linear_2",
-    "text_embedding.0": "condition_embedder.text_embedder.linear_1",
-    "text_embedding.2": "condition_embedder.text_embedder.linear_2",
-    "time_projection.1": "condition_embedder.time_proj",
-    "head.modulation": "scale_shift_table",
-    "head.head": "proj_out",
-    "modulation": "scale_shift_table",
-    "ffn.0": "ffn.net.0.proj",
-    "ffn.2": "ffn.net.2",
-    # Hack to swap the layer names
-    # The original model calls the norms in following order: norm1, norm3, norm2
-    # We convert it to: norm1, norm2, norm3
-    "norm2": "norm__placeholder",
-    "norm3": "norm2",
-    "norm__placeholder": "norm3",
-    "img_emb.proj.0": "condition_embedder.image_embedder.norm1",
-    "img_emb.proj.1": "condition_embedder.image_embedder.ff.net.0.proj",
-    "img_emb.proj.3": "condition_embedder.image_embedder.ff.net.2",
-    "img_emb.proj.4": "condition_embedder.image_embedder.norm2",
-    # Add attention component mappings
-    "self_attn.q": "attn1.to_q",
-    "self_attn.k": "attn1.to_k",
-    "self_attn.v": "attn1.to_v",
-    "self_attn.o": "attn1.to_out.0",
-    "self_attn.norm_q": "attn1.norm_q",
-    "self_attn.norm_k": "attn1.norm_k",
-    "cross_attn.q": "attn2.to_q",
-    "cross_attn.k": "attn2.to_k",
-    "cross_attn.v": "attn2.to_v",
-    "cross_attn.o": "attn2.to_out.0",
-    "cross_attn.norm_q": "attn2.norm_q",
-    "cross_attn.norm_k": "attn2.norm_k",
-    "cross_attn.k_img": "attn2.to_k_img",
-    "cross_attn.v_img": "attn2.to_v_img",
-    "cross_attn.norm_k_img": "attn2.norm_k_img",
-    # After cross_attn -> attn2 rename, we need to rename the img keys
-    "attn2.to_k_img": "attn2.add_k_proj",
-    "attn2.to_v_img": "attn2.add_v_proj",
-    "attn2.norm_k_img": "attn2.norm_added_k",
-    # Wan Animate-specific mappings (motion encoder, face encoder, face adapter)
-    # Motion encoder mappings
-    # The name mapping is complicated for the convolutional part so we handle that in its own function
-    "motion_encoder.enc.fc": "motion_encoder.motion_network",
-    "motion_encoder.dec.direction.weight": "motion_encoder.motion_synthesis_weight",
-    # Face encoder mappings - CausalConv1d has a .conv submodule that we need to flatten
-    "face_encoder.conv1_local.conv": "face_encoder.conv1_local",
-    "face_encoder.conv2.conv": "face_encoder.conv2",
-    "face_encoder.conv3.conv": "face_encoder.conv3",
-    # Face adapter mappings are handled in a separate function
-}
-
-
-# TODO: Verify this and simplify if possible.
-def convert_animate_motion_encoder_weights(key: str, state_dict: Dict[str, Any], final_conv_idx: int = 8) -> None:
-    """
-    Convert all motion encoder weights for Animate model.
-
-    In the original model:
-    - All Linear layers in fc use EqualLinear
-    - All Conv2d layers in convs use EqualConv2d (except blur_conv which is initialized separately)
-    - Blur kernels are stored as buffers in Sequential modules
-    - ConvLayer is nn.Sequential with indices: [Blur (optional), EqualConv2d, FusedLeakyReLU (optional)]
-
-    Conversion strategy:
-    1. Drop .kernel buffers (blur kernels)
-    2. Rename sequential indices to named components (e.g., 0 -> conv2d, 1 -> bias_leaky_relu)
-    """
-    # Skip if not a weight, bias, or kernel
-    if ".weight" not in key and ".bias" not in key and ".kernel" not in key:
-        return
-
-    # Handle Blur kernel buffers from original implementation.
-    # After renaming, these appear under: motion_encoder.res_blocks.*.conv{2,skip}.blur_kernel
-    # Diffusers constructs blur kernels as a non-persistent buffer so we must drop these keys
-    if ".kernel" in key and "motion_encoder" in key:
-        # Remove unexpected blur kernel buffers to avoid strict load errors
-        state_dict.pop(key, None)
-        return
-
-    # Rename Sequential indices to named components in ConvLayer and ResBlock
-    if ".enc.net_app.convs." in key and (".weight" in key or ".bias" in key):
-        parts = key.split(".")
-
-        # Find the sequential index (digit) after convs or after conv1/conv2/skip
-        # Examples:
-        # - enc.net_app.convs.0.0.weight -> conv_in.weight (initial conv layer weight)
-        # - enc.net_app.convs.0.1.bias -> conv_in.act_fn.bias (initial conv layer bias)
-        # - enc.net_app.convs.{n:1-7}.conv1.0.weight -> res_blocks.{(n-1):0-6}.conv1.weight (conv1 weight)
-        #     - e.g. enc.net_app.convs.1.conv1.0.weight -> res_blocks.0.conv1.weight
-        # - enc.net_app.convs.{n:1-7}.conv1.1.bias -> res_blocks.{(n-1):0-6}.conv1.act_fn.bias (conv1 bias)
-        #     - e.g. enc.net_app.convs.1.conv1.1.bias -> res_blocks.0.conv1.act_fn.bias
-        # - enc.net_app.convs.{n:1-7}.conv2.1.weight -> res_blocks.{(n-1):0-6}.conv2.weight (conv2 weight)
-        # - enc.net_app.convs.1.conv2.2.bias -> res_blocks.0.conv2.act_fn.bias (conv2 bias)
-        # - enc.net_app.convs.{n:1-7}.skip.1.weight -> res_blocks.{(n-1):0-6}.conv_skip.weight (skip conv weight)
-        # - enc.net_app.convs.8 -> conv_out (final conv layer)
-
-        convs_idx = parts.index("convs") if "convs" in parts else -1
-        if convs_idx >= 0 and len(parts) - convs_idx >= 2:
-            bias = False
-            # The nn.Sequential index will always follow convs
-            sequential_idx = int(parts[convs_idx + 1])
-            if sequential_idx == 0:
-                if key.endswith(".weight"):
-                    new_key = "motion_encoder.conv_in.weight"
-                elif key.endswith(".bias"):
-                    new_key = "motion_encoder.conv_in.act_fn.bias"
-                    bias = True
-            elif sequential_idx == final_conv_idx:
-                if key.endswith(".weight"):
-                    new_key = "motion_encoder.conv_out.weight"
-            else:
-                # Intermediate .convs. layers, which get mapped to .res_blocks.
-                prefix = "motion_encoder.res_blocks."
-
-                layer_name = parts[convs_idx + 2]
-                if layer_name == "skip":
-                    layer_name = "conv_skip"
-
-                if key.endswith(".weight"):
-                    param_name = "weight"
-                elif key.endswith(".bias"):
-                    param_name = "act_fn.bias"
-                    bias = True
-
-                suffix_parts = [str(sequential_idx - 1), layer_name, param_name]
-                suffix = ".".join(suffix_parts)
-                new_key = prefix + suffix
-
-            param = state_dict.pop(key)
-            if bias:
-                param = param.squeeze()
-            state_dict[new_key] = param
-            return
-        return
-    return
-
-
-def convert_animate_face_adapter_weights(key: str, state_dict: Dict[str, Any]) -> None:
-    """
-    Convert face adapter weights for the Animate model.
-
-    The original model uses a fused KV projection but the diffusers models uses separate K and V projections.
-    """
-    # Skip if not a weight or bias
-    if ".weight" not in key and ".bias" not in key:
-        return
-
-    prefix = "face_adapter."
-    if ".fuser_blocks." in key:
-        parts = key.split(".")
-
-        module_list_idx = parts.index("fuser_blocks") if "fuser_blocks" in parts else -1
-        if module_list_idx >= 0 and (len(parts) - 1) - module_list_idx == 3:
-            block_idx = parts[module_list_idx + 1]
-            layer_name = parts[module_list_idx + 2]
-            param_name = parts[module_list_idx + 3]
-
-            if layer_name == "linear1_kv":
-                layer_name_k = "to_k"
-                layer_name_v = "to_v"
-
-                suffix_k = ".".join([block_idx, layer_name_k, param_name])
-                suffix_v = ".".join([block_idx, layer_name_v, param_name])
-                new_key_k = prefix + suffix_k
-                new_key_v = prefix + suffix_v
-
-                kv_proj = state_dict.pop(key)
-                k_proj, v_proj = torch.chunk(kv_proj, 2, dim=0)
-                state_dict[new_key_k] = k_proj
-                state_dict[new_key_v] = v_proj
-                return
-            else:
-                if layer_name == "q_norm":
-                    new_layer_name = "norm_q"
-                elif layer_name == "k_norm":
-                    new_layer_name = "norm_k"
-                elif layer_name == "linear1_q":
-                    new_layer_name = "to_q"
-                elif layer_name == "linear2":
-                    new_layer_name = "to_out"
-
-                suffix_parts = [block_idx, new_layer_name, param_name]
-                suffix = ".".join(suffix_parts)
-                new_key = prefix + suffix
-                state_dict[new_key] = state_dict.pop(key)
-                return
-    return
-
-
 TRANSFORMER_SPECIAL_KEYS_REMAP = {}
 VACE_TRANSFORMER_SPECIAL_KEYS_REMAP = {}
-ANIMATE_TRANSFORMER_SPECIAL_KEYS_REMAP = {
-    "motion_encoder": convert_animate_motion_encoder_weights,
-    "face_adapter": convert_animate_face_adapter_weights,
-}
 
 
 def update_state_dict_(state_dict: Dict[str, Any], old_key: str, new_key: str) -> Dict[str, Any]:
@@ -568,37 +364,6 @@ def get_transformer_config(model_type: str) -> Tuple[Dict[str, Any], ...]:
         }
         RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT
         SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP
-    elif model_type == "Wan2.2-Animate-14B":
-        config = {
-            "model_id": "Wan-AI/Wan2.2-Animate-14B",
-            "diffusers_config": {
-                "image_dim": 1280,
-                "added_kv_proj_dim": 5120,
-                "attention_head_dim": 128,
-                "cross_attn_norm": True,
-                "eps": 1e-06,
-                "ffn_dim": 13824,
-                "freq_dim": 256,
-                "in_channels": 36,
-                "num_attention_heads": 40,
-                "num_layers": 40,
-                "out_channels": 16,
-                "patch_size": (1, 2, 2),
-                "qk_norm": "rms_norm_across_heads",
-                "text_dim": 4096,
-                "rope_max_seq_len": 1024,
-                "pos_embed_seq_len": None,
-                "motion_encoder_size": 512,  # Start of Wan Animate-specific configs
-                "motion_style_dim": 512,
-                "motion_dim": 20,
-                "motion_encoder_dim": 512,
-                "face_encoder_hidden_dim": 1024,
-                "face_encoder_num_heads": 4,
-                "inject_face_latents_blocks": 5,
-            },
-        }
-        RENAME_DICT = ANIMATE_TRANSFORMER_KEYS_RENAME_DICT
-        SPECIAL_KEYS_REMAP = ANIMATE_TRANSFORMER_SPECIAL_KEYS_REMAP
     return config, RENAME_DICT, SPECIAL_KEYS_REMAP
 
 
@@ -615,12 +380,10 @@ def convert_transformer(model_type: str, stage: str = None):
     original_state_dict = load_sharded_safetensors(model_dir)
 
     with init_empty_weights():
-        if "Animate" in model_type:
-            transformer = WanAnimateTransformer3DModel.from_config(diffusers_config)
-        elif "VACE" in model_type:
-            transformer = WanVACETransformer3DModel.from_config(diffusers_config)
-        else:
+        if "VACE" not in model_type:
             transformer = WanTransformer3DModel.from_config(diffusers_config)
+        else:
+            transformer = WanVACETransformer3DModel.from_config(diffusers_config)
 
     for key in list(original_state_dict.keys()):
         new_key = key[:]
@@ -634,12 +397,7 @@ def convert_transformer(model_type: str, stage: str = None):
                 continue
             handler_fn_inplace(key, original_state_dict)
 
-    # Load state dict into the meta model, which will materialize the tensors
     transformer.load_state_dict(original_state_dict, strict=True, assign=True)
-
-    # Move to CPU to ensure all tensors are materialized
-    transformer = transformer.to("cpu")
-
     return transformer
 
 
@@ -1168,7 +926,7 @@ DTYPE_MAPPING = {
 if __name__ == "__main__":
     args = get_args()
 
-    if "Wan2.2" in args.model_type and "TI2V" not in args.model_type and "Animate" not in args.model_type:
+    if "Wan2.2" in args.model_type and "TI2V" not in args.model_type:
         transformer = convert_transformer(args.model_type, stage="high_noise_model")
         transformer_2 = convert_transformer(args.model_type, stage="low_noise_model")
     else:
@@ -1184,7 +942,7 @@ if __name__ == "__main__":
     tokenizer = AutoTokenizer.from_pretrained("google/umt5-xxl")
     if "FLF2V" in args.model_type:
         flow_shift = 16.0
-    elif "TI2V" in args.model_type or "Animate" in args.model_type:
+    elif "TI2V" in args.model_type:
         flow_shift = 5.0
     else:
         flow_shift = 3.0
@@ -1196,8 +954,6 @@ if __name__ == "__main__":
     if args.dtype != "none":
         dtype = DTYPE_MAPPING[args.dtype]
         transformer.to(dtype)
-        if transformer_2 is not None:
-            transformer_2.to(dtype)
 
     if "Wan2.2" and "I2V" in args.model_type and "TI2V" not in args.model_type:
         pipe = WanImageToVideoPipeline(
@@ -1260,21 +1016,6 @@ if __name__ == "__main__":
             vae=vae,
             scheduler=scheduler,
         )
-    elif "Animate" in args.model_type:
-        image_encoder = CLIPVisionModel.from_pretrained(
-            "laion/CLIP-ViT-H-14-laion2B-s32B-b79K", torch_dtype=torch.bfloat16
-        )
-        image_processor = CLIPImageProcessor.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K")
-
-        pipe = WanAnimatePipeline(
-            transformer=transformer,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            vae=vae,
-            scheduler=scheduler,
-            image_encoder=image_encoder,
-            image_processor=image_processor,
-        )
     else:
         pipe = WanPipeline(
             transformer=transformer,

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",
@@ -198,11 +194,9 @@ else:
             "AutoencoderOobleck",
             "AutoencoderTiny",
             "AutoModel",
-            "BriaFiboTransformer2DModel",
             "BriaTransformer2DModel",
             "CacheMixin",
             "ChromaTransformer2DModel",
-            "ChronoEditTransformer3DModel",
             "CogVideoXTransformer3DModel",
             "CogView3PlusTransformer2DModel",
             "CogView4Transformer2DModel",
@@ -222,7 +216,6 @@ else:
             "HunyuanDiT2DControlNetModel",
             "HunyuanDiT2DModel",
             "HunyuanDiT2DMultiControlNetModel",
-            "HunyuanImageTransformer2DModel",
             "HunyuanVideoFramepackTransformer3DModel",
             "HunyuanVideoTransformer3DModel",
             "I2VGenXLUNet",
@@ -241,13 +234,11 @@ else:
             "ParallelConfig",
             "PixArtTransformer2DModel",
             "PriorTransformer",
-            "PRXTransformer2DModel",
             "QwenImageControlNetModel",
             "QwenImageMultiControlNetModel",
             "QwenImageTransformer2DModel",
             "SanaControlNetModel",
             "SanaTransformer2DModel",
-            "SanaVideoTransformer3DModel",
             "SD3ControlNetModel",
             "SD3MultiControlNetModel",
             "SD3Transformer2DModel",
@@ -268,7 +259,6 @@ else:
             "UNetSpatioTemporalConditionModel",
             "UVit2DModel",
             "VQModel",
-            "WanAnimateTransformer3DModel",
             "WanTransformer3DModel",
             "WanVACETransformer3DModel",
             "attention_backend",
@@ -408,7 +398,6 @@ else:
             "QwenImageModularPipeline",
             "StableDiffusionXLAutoBlocks",
             "StableDiffusionXLModularPipeline",
-            "Wan22AutoBlocks",
             "WanAutoBlocks",
             "WanModularPipeline",
         ]
@@ -435,11 +424,9 @@ else:
             "AuraFlowPipeline",
             "BlipDiffusionControlNetPipeline",
             "BlipDiffusionPipeline",
-            "BriaFiboPipeline",
             "BriaPipeline",
             "ChromaImg2ImgPipeline",
             "ChromaPipeline",
-            "ChronoEditPipeline",
             "CLIPImageProjection",
             "CogVideoXFunControlPipeline",
             "CogVideoXImageToVideoPipeline",
@@ -474,8 +461,6 @@ else:
             "HunyuanDiTControlNetPipeline",
             "HunyuanDiTPAGPipeline",
             "HunyuanDiTPipeline",
-            "HunyuanImagePipeline",
-            "HunyuanImageRefinerPipeline",
             "HunyuanSkyreelsImageToVideoPipeline",
             "HunyuanVideoFramepackPipeline",
             "HunyuanVideoImageToVideoPipeline",
@@ -534,7 +519,6 @@ else:
             "PixArtAlphaPipeline",
             "PixArtSigmaPAGPipeline",
             "PixArtSigmaPipeline",
-            "PRXPipeline",
             "QwenImageControlNetInpaintPipeline",
             "QwenImageControlNetPipeline",
             "QwenImageEditInpaintPipeline",
@@ -549,7 +533,6 @@ else:
             "SanaPipeline",
             "SanaSprintImg2ImgPipeline",
             "SanaSprintPipeline",
-            "SanaVideoPipeline",
             "SemanticStableDiffusionPipeline",
             "ShapEImg2ImgPipeline",
             "ShapEPipeline",
@@ -637,7 +620,6 @@ else:
             "VisualClozeGenerationPipeline",
             "VisualClozePipeline",
             "VQDiffusionPipeline",
-            "WanAnimatePipeline",
             "WanImageToVideoPipeline",
             "WanPipeline",
             "WanVACEPipeline",
@@ -865,9 +847,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     else:
         from .guiders import (
             AdaptiveProjectedGuidance,
-            AdaptiveProjectedMixGuidance,
             AutoGuidance,
-            BaseGuidance,
             ClassifierFreeGuidance,
             ClassifierFreeZeroStarGuidance,
             FrequencyDecoupledGuidance,
@@ -898,8 +878,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             AutoencoderKLAllegro,
             AutoencoderKLCogVideoX,
             AutoencoderKLCosmos,
-            AutoencoderKLHunyuanImage,
-            AutoencoderKLHunyuanImageRefiner,
             AutoencoderKLHunyuanVideo,
             AutoencoderKLLTXVideo,
             AutoencoderKLMagvit,
@@ -910,11 +888,9 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             AutoencoderOobleck,
             AutoencoderTiny,
             AutoModel,
-            BriaFiboTransformer2DModel,
             BriaTransformer2DModel,
             CacheMixin,
             ChromaTransformer2DModel,
-            ChronoEditTransformer3DModel,
             CogVideoXTransformer3DModel,
             CogView3PlusTransformer2DModel,
             CogView4Transformer2DModel,
@@ -934,7 +910,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             HunyuanDiT2DControlNetModel,
             HunyuanDiT2DModel,
             HunyuanDiT2DMultiControlNetModel,
-            HunyuanImageTransformer2DModel,
             HunyuanVideoFramepackTransformer3DModel,
             HunyuanVideoTransformer3DModel,
             I2VGenXLUNet,
@@ -953,13 +928,11 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             ParallelConfig,
             PixArtTransformer2DModel,
             PriorTransformer,
-            PRXTransformer2DModel,
             QwenImageControlNetModel,
             QwenImageMultiControlNetModel,
             QwenImageTransformer2DModel,
             SanaControlNetModel,
             SanaTransformer2DModel,
-            SanaVideoTransformer3DModel,
             SD3ControlNetModel,
             SD3MultiControlNetModel,
             SD3Transformer2DModel,
@@ -979,7 +952,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             UNetSpatioTemporalConditionModel,
             UVit2DModel,
             VQModel,
-            WanAnimateTransformer3DModel,
             WanTransformer3DModel,
             WanVACETransformer3DModel,
             attention_backend,
@@ -1094,7 +1066,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             QwenImageModularPipeline,
             StableDiffusionXLAutoBlocks,
             StableDiffusionXLModularPipeline,
-            Wan22AutoBlocks,
             WanAutoBlocks,
             WanModularPipeline,
         )
@@ -1117,11 +1088,9 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             AudioLDM2UNet2DConditionModel,
             AudioLDMPipeline,
             AuraFlowPipeline,
-            BriaFiboPipeline,
             BriaPipeline,
             ChromaImg2ImgPipeline,
             ChromaPipeline,
-            ChronoEditPipeline,
             CLIPImageProjection,
             CogVideoXFunControlPipeline,
             CogVideoXImageToVideoPipeline,
@@ -1156,8 +1125,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             HunyuanDiTControlNetPipeline,
             HunyuanDiTPAGPipeline,
             HunyuanDiTPipeline,
-            HunyuanImagePipeline,
-            HunyuanImageRefinerPipeline,
             HunyuanSkyreelsImageToVideoPipeline,
             HunyuanVideoFramepackPipeline,
             HunyuanVideoImageToVideoPipeline,
@@ -1216,7 +1183,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             PixArtAlphaPipeline,
             PixArtSigmaPAGPipeline,
             PixArtSigmaPipeline,
-            PRXPipeline,
             QwenImageControlNetInpaintPipeline,
             QwenImageControlNetPipeline,
             QwenImageEditInpaintPipeline,
@@ -1231,7 +1197,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             SanaPipeline,
             SanaSprintImg2ImgPipeline,
             SanaSprintPipeline,
-            SanaVideoPipeline,
             SemanticStableDiffusionPipeline,
             ShapEImg2ImgPipeline,
             ShapEPipeline,
@@ -1318,7 +1283,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             VisualClozeGenerationPipeline,
             VisualClozePipeline,
             VQDiffusionPipeline,
-            WanAnimatePipeline,
             WanImageToVideoPipeline,
             WanPipeline,
             WanVACEPipeline,

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

@@ -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
@@ -77,27 +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"]:
-        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 prepare_inputs_from_block_state(
-        self, data: "BlockState", input_fields: Dict[str, Union[str, Tuple[str, str]]]
+    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_from_block_state(input_fields, 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
 
@@ -161,44 +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

@@ -1,297 +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, Union
-
-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 prepare_inputs_from_block_state(
-        self, data: "BlockState", input_fields: Dict[str, Union[str, Tuple[str, str]]]
-    ) -> 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_from_block_state(input_fields, 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
@@ -133,21 +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"]:
-        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 prepare_inputs_from_block_state(
-        self, data: "BlockState", input_fields: Dict[str, Union[str, Tuple[str, str]]]
+    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_from_block_state(input_fields, 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

@@ -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"]
@@ -83,29 +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"]:
-        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 prepare_inputs_from_block_state(
-        self, data: "BlockState", input_fields: Dict[str, Union[str, Tuple[str, str]]]
+    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_from_block_state(input_fields, 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

@@ -68,41 +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"]:
-        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 prepare_inputs_from_block_state(
-        self, data: "BlockState", input_fields: Dict[str, Union[str, Tuple[str, str]]]
+    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_from_block_state(input_fields, 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)
@@ -218,21 +217,16 @@ class FrequencyDecoupledGuidance(BaseGuidance):
                 f"({len(self.guidance_scales)})"
             )
 
-    def prepare_inputs(self, data: Dict[str, Tuple[torch.Tensor, torch.Tensor]]) -> List["BlockState"]:
-        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 prepare_inputs_from_block_state(
-        self, data: "BlockState", input_fields: Dict[str, Union[str, Tuple[str, str]]]
+    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_from_block_state(input_fields, 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:
         """
@@ -166,11 +127,6 @@ class BaseGuidance(ConfigMixin, PushToHubMixin):
     def prepare_inputs(self, data: "BlockState") -> List["BlockState"]:
         raise NotImplementedError("BaseGuidance::prepare_inputs must be implemented in subclasses.")
 
-    def prepare_inputs_from_block_state(
-        self, data: "BlockState", input_fields: Dict[str, Union[str, Tuple[str, str]]]
-    ) -> List["BlockState"]:
-        raise NotImplementedError("BaseGuidance::prepare_inputs_from_block_state must be implemented in subclasses.")
-
     def __call__(self, data: List["BlockState"]) -> Any:
         if not all(hasattr(d, "noise_pred") for d in data):
             raise ValueError("Expected all data to have `noise_pred` attribute.")
@@ -198,49 +154,6 @@ class BaseGuidance(ConfigMixin, PushToHubMixin):
 
     @classmethod
     def _prepare_batch(
-        cls,
-        data: Dict[str, Tuple[torch.Tensor, torch.Tensor]],
-        tuple_index: int,
-        identifier: str,
-    ) -> "BlockState":
-        """
-        Prepares a batch of data for the guidance technique. This method is used in the `prepare_inputs` method of the
-        `BaseGuidance` class. It prepares the batch based on the provided tuple index.
-
-        Args:
-            input_fields (`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.
-            data (`BlockState`):
-                The input data to be prepared.
-            tuple_index (`int`):
-                The index to use when accessing input fields that are tuples.
-
-        Returns:
-            `BlockState`: The prepared batch of data.
-        """
-        from ..modular_pipelines.modular_pipeline import BlockState
-
-        data_batch = {}
-        for key, value in data.items():
-            try:
-                if isinstance(value, torch.Tensor):
-                    data_batch[key] = value
-                elif isinstance(value, tuple):
-                    data_batch[key] = value[tuple_index]
-                else:
-                    raise ValueError(f"Invalid value type: {type(value)}")
-            except ValueError:
-                logger.debug(f"`data` does not have attribute(s) {value}, skipping.")
-        data_batch[cls._identifier_key] = identifier
-        return BlockState(**data_batch)
-
-    @classmethod
-    def _prepare_batch_from_block_state(
         cls,
         input_fields: Dict[str, Union[str, Tuple[str, str]]],
         data: "BlockState",
@@ -269,6 +182,10 @@ 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 input_fields.items():
             try:
@@ -373,7 +290,7 @@ def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
     r"""
     Rescales `noise_cfg` tensor based on `guidance_rescale` to improve image quality and fix overexposure. Based on
     Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
-    Flawed](https://huggingface.co/papers/2305.08891).
+    Flawed](https://arxiv.org/pdf/2305.08891.pdf).
 
     Args:
         noise_cfg (`torch.Tensor`):

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
@@ -169,27 +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"]:
-        if self.num_conditions == 1:
-            tuple_indices = [0]
-            input_predictions = ["pred_cond"]
-        elif self.num_conditions == 2:
-            tuple_indices = [0, 1]
-            input_predictions = (
-                ["pred_cond", "pred_uncond"] if self._is_cfg_enabled() else ["pred_cond", "pred_cond_skip"]
-            )
-        else:
-            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)
-            data_batches.append(data_batch)
-        return data_batches
-
-    def prepare_inputs_from_block_state(
-        self, data: "BlockState", input_fields: Dict[str, Union[str, Tuple[str, str]]]
+    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"]
@@ -202,8 +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_from_block_state(input_fields, 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
@@ -165,27 +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"]:
-        if self.num_conditions == 1:
-            tuple_indices = [0]
-            input_predictions = ["pred_cond"]
-        elif self.num_conditions == 2:
-            tuple_indices = [0, 1]
-            input_predictions = (
-                ["pred_cond", "pred_uncond"] if self._is_cfg_enabled() else ["pred_cond", "pred_cond_skip"]
-            )
-        else:
-            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)
-            data_batches.append(data_batch)
-        return data_batches
-
-    def prepare_inputs_from_block_state(
-        self, data: "BlockState", input_fields: Dict[str, Union[str, Tuple[str, str]]]
+    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"]
@@ -198,8 +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_from_block_state(input_fields, 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
@@ -154,27 +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"]:
-        if self.num_conditions == 1:
-            tuple_indices = [0]
-            input_predictions = ["pred_cond"]
-        elif self.num_conditions == 2:
-            tuple_indices = [0, 1]
-            input_predictions = (
-                ["pred_cond", "pred_uncond"] if self._is_cfg_enabled() else ["pred_cond", "pred_cond_seg"]
-            )
-        else:
-            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)
-            data_batches.append(data_batch)
-        return data_batches
-
-    def prepare_inputs_from_block_state(
-        self, data: "BlockState", input_fields: Dict[str, Union[str, Tuple[str, str]]]
+    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"]
@@ -187,8 +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_from_block_state(input_fields, 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

@@ -58,29 +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"]:
-        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 prepare_inputs_from_block_state(
-        self, data: "BlockState", input_fields: Dict[str, Union[str, Tuple[str, str]]]
+    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_from_block_state(input_fields, 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

@@ -409,7 +409,7 @@ class VaeImageProcessor(ConfigMixin):
         src_w = width if ratio < src_ratio else image.width * height // image.height
         src_h = height if ratio >= src_ratio else image.height * width // image.width
 
-        resized = image.resize((src_w, src_h), resample=PIL_INTERPOLATION[self.config.resample])
+        resized = image.resize((src_w, src_h), resample=PIL_INTERPOLATION["lanczos"])
         res = Image.new("RGB", (width, height))
         res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
 
@@ -460,7 +460,7 @@ class VaeImageProcessor(ConfigMixin):
         src_w = width if ratio > src_ratio else image.width * height // image.height
         src_h = height if ratio <= src_ratio else image.height * width // image.width
 
-        resized = image.resize((src_w, src_h), resample=PIL_INTERPOLATION[self.config.resample])
+        resized = image.resize((src_w, src_h), resample=PIL_INTERPOLATION["lanczos"])
         res = Image.new("RGB", (width, height))
         res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
         return res
@@ -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"]
@@ -84,9 +82,7 @@ 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_chronoedit"] = ["ChronoEditTransformer3DModel"]
     _import_structure["transformers.transformer_cogview3plus"] = ["CogView3PlusTransformer2DModel"]
     _import_structure["transformers.transformer_cogview4"] = ["CogView4Transformer2DModel"]
     _import_structure["transformers.transformer_cosmos"] = ["CosmosTransformer3DModel"]
@@ -95,20 +91,16 @@ 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"]
     _import_structure["transformers.transformer_wan"] = ["WanTransformer3DModel"]
-    _import_structure["transformers.transformer_wan_animate"] = ["WanAnimateTransformer3DModel"]
     _import_structure["transformers.transformer_wan_vace"] = ["WanVACETransformer3DModel"]
     _import_structure["unets.unet_1d"] = ["UNet1DModel"]
     _import_structure["unets.unet_2d"] = ["UNet2DModel"]
@@ -140,8 +132,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             AutoencoderKLAllegro,
             AutoencoderKLCogVideoX,
             AutoencoderKLCosmos,
-            AutoencoderKLHunyuanImage,
-            AutoencoderKLHunyuanImageRefiner,
             AutoencoderKLHunyuanVideo,
             AutoencoderKLLTXVideo,
             AutoencoderKLMagvit,
@@ -178,10 +168,8 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .transformers import (
             AllegroTransformer3DModel,
             AuraFlowTransformer2DModel,
-            BriaFiboTransformer2DModel,
             BriaTransformer2DModel,
             ChromaTransformer2DModel,
-            ChronoEditTransformer3DModel,
             CogVideoXTransformer3DModel,
             CogView3PlusTransformer2DModel,
             CogView4Transformer2DModel,
@@ -193,7 +181,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             FluxTransformer2DModel,
             HiDreamImageTransformer2DModel,
             HunyuanDiT2DModel,
-            HunyuanImageTransformer2DModel,
             HunyuanVideoFramepackTransformer3DModel,
             HunyuanVideoTransformer3DModel,
             Kandinsky5Transformer3DModel,
@@ -205,17 +192,14 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             OmniGenTransformer2DModel,
             PixArtTransformer2DModel,
             PriorTransformer,
-            PRXTransformer2DModel,
             QwenImageTransformer2DModel,
             SanaTransformer2DModel,
-            SanaVideoTransformer3DModel,
             SD3Transformer2DModel,
             SkyReelsV2Transformer3DModel,
             StableAudioDiTModel,
             T5FilmDecoder,
             Transformer2DModel,
             TransformerTemporalModel,
-            WanAnimateTransformer3DModel,
             WanTransformer3DModel,
             WanVACETransformer3DModel,
         )

src/diffusers/models/_modeling_parallel.py

@@ -44,16 +44,11 @@ class ContextParallelConfig:
 
     Args:
         ring_degree (`int`, *optional*, defaults to `1`):
-            Number of devices to use for Ring Attention. Sequence is split across devices. Each device computes
-            attention between its local Q and KV chunks passed sequentially around ring. Lower memory (only holds 1/N
-            of KV at a time), overlaps compute with communication, but requires N iterations to see all tokens. Best
-            for long sequences with limited memory/bandwidth. Number of devices to use for ring attention within a
-            context parallel region. Must be a divisor of the total number of devices in the context parallel mesh.
+            Number of devices to use for ring attention within a context parallel region. Must be a divisor of the
+            total number of devices in the context parallel mesh.
         ulysses_degree (`int`, *optional*, defaults to `1`):
-            Number of devices to use for Ulysses Attention. Sequence split is across devices. Each device computes
-            local QKV, then all-gathers all KV chunks to compute full attention in one pass. Higher memory (stores all
-            KV), requires high-bandwidth all-to-all communication, but lower latency. Best for moderate sequences with
-            good interconnect bandwidth.
+            Number of devices to use for ulysses attention within a context parallel region. Must be a divisor of the
+            total number of devices in the context parallel mesh.
         convert_to_fp32 (`bool`, *optional*, defaults to `True`):
             Whether to convert output and LSE to float32 for ring attention numerical stability.
         rotate_method (`str`, *optional*, defaults to `"allgather"`):
@@ -84,46 +79,29 @@ class ContextParallelConfig:
         if self.ulysses_degree is None:
             self.ulysses_degree = 1
 
-        if self.ring_degree == 1 and self.ulysses_degree == 1:
-            raise ValueError(
-                "Either ring_degree or ulysses_degree must be greater than 1 in order to use context parallel inference"
-            )
-        if self.ring_degree < 1 or self.ulysses_degree < 1:
-            raise ValueError("`ring_degree` and `ulysses_degree` must be greater than or equal to 1.")
-        if self.ring_degree > 1 and self.ulysses_degree > 1:
-            raise ValueError(
-                "Unified Ulysses-Ring attention is not yet supported. Please set either `ring_degree` or `ulysses_degree` to 1."
-            )
-        if self.rotate_method != "allgather":
-            raise NotImplementedError(
-                f"Only rotate_method='allgather' is supported for now, but got {self.rotate_method}."
-            )
-
-    @property
-    def mesh_shape(self) -> Tuple[int, int]:
-        return (self.ring_degree, self.ulysses_degree)
-
-    @property
-    def mesh_dim_names(self) -> Tuple[str, str]:
-        """Dimension names for the device mesh."""
-        return ("ring", "ulysses")
-
     def setup(self, rank: int, world_size: int, device: torch.device, mesh: torch.distributed.device_mesh.DeviceMesh):
         self._rank = rank
         self._world_size = world_size
         self._device = device
         self._mesh = mesh
-
-        if self.ulysses_degree * self.ring_degree > world_size:
-            raise ValueError(
-                f"The product of `ring_degree` ({self.ring_degree}) and `ulysses_degree` ({self.ulysses_degree}) must not exceed the world size ({world_size})."
+        if self.ring_degree is None:
+            self.ring_degree = 1
+        if self.ulysses_degree is None:
+            self.ulysses_degree = 1
+        if self.rotate_method != "allgather":
+            raise NotImplementedError(
+                f"Only rotate_method='allgather' is supported for now, but got {self.rotate_method}."
             )
-
-        self._flattened_mesh = self._mesh._flatten()
-        self._ring_mesh = self._mesh["ring"]
-        self._ulysses_mesh = self._mesh["ulysses"]
-        self._ring_local_rank = self._ring_mesh.get_local_rank()
-        self._ulysses_local_rank = self._ulysses_mesh.get_local_rank()
+        if self._flattened_mesh is None:
+            self._flattened_mesh = self._mesh._flatten()
+        if self._ring_mesh is None:
+            self._ring_mesh = self._mesh["ring"]
+        if self._ulysses_mesh is None:
+            self._ulysses_mesh = self._mesh["ulysses"]
+        if self._ring_local_rank is None:
+            self._ring_local_rank = self._ring_mesh.get_local_rank()
+        if self._ulysses_local_rank is None:
+            self._ulysses_local_rank = self._ulysses_mesh.get_local_rank()
 
 
 @dataclass
@@ -141,7 +119,7 @@ class ParallelConfig:
     _rank: int = None
     _world_size: int = None
     _device: torch.device = None
-    _mesh: torch.distributed.device_mesh.DeviceMesh = None
+    _cp_mesh: torch.distributed.device_mesh.DeviceMesh = None
 
     def setup(
         self,
@@ -149,14 +127,14 @@ class ParallelConfig:
         world_size: int,
         device: torch.device,
         *,
-        mesh: Optional[torch.distributed.device_mesh.DeviceMesh] = None,
+        cp_mesh: Optional[torch.distributed.device_mesh.DeviceMesh] = None,
     ):
         self._rank = rank
         self._world_size = world_size
         self._device = device
-        self._mesh = mesh
+        self._cp_mesh = cp_mesh
         if self.context_parallel_config is not None:
-            self.context_parallel_config.setup(rank, world_size, device, mesh)
+            self.context_parallel_config.setup(rank, world_size, device, cp_mesh)
 
 
 @dataclass(frozen=True)

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"
@@ -220,7 +207,7 @@ class _AttentionBackendRegistry:
     _backends = {}
     _constraints = {}
     _supported_arg_names = {}
-    _supports_context_parallel = set()
+    _supports_context_parallel = {}
     _active_backend = AttentionBackendName(DIFFUSERS_ATTN_BACKEND)
     _checks_enabled = DIFFUSERS_ATTN_CHECKS
 
@@ -237,9 +224,7 @@ class _AttentionBackendRegistry:
             cls._backends[backend] = func
             cls._constraints[backend] = constraints or []
             cls._supported_arg_names[backend] = set(inspect.signature(func).parameters.keys())
-            if supports_context_parallel:
-                cls._supports_context_parallel.add(backend.value)
-
+            cls._supports_context_parallel[backend] = supports_context_parallel
             return func
 
         return decorator
@@ -253,12 +238,15 @@ class _AttentionBackendRegistry:
         return list(cls._backends.keys())
 
     @classmethod
-    def _is_context_parallel_available(
-        cls,
-        backend: AttentionBackendName,
+    def _is_context_parallel_enabled(
+        cls, backend: AttentionBackendName, parallel_config: Optional["ParallelConfig"]
     ) -> bool:
-        supports_context_parallel = backend.value in cls._supports_context_parallel
-        return supports_context_parallel
+        supports_context_parallel = backend in cls._supports_context_parallel
+        is_degree_greater_than_1 = parallel_config is not None and (
+            parallel_config.context_parallel_config.ring_degree > 1
+            or parallel_config.context_parallel_config.ulysses_degree > 1
+        )
+        return supports_context_parallel and is_degree_greater_than_1
 
 
 @contextlib.contextmanager
@@ -305,6 +293,14 @@ def dispatch_attention_fn(
         backend_name = AttentionBackendName(backend)
         backend_fn = _AttentionBackendRegistry._backends.get(backend_name)
 
+    if parallel_config is not None and not _AttentionBackendRegistry._is_context_parallel_enabled(
+        backend_name, parallel_config
+    ):
+        raise ValueError(
+            f"Backend {backend_name} either does not support context parallelism or context parallelism "
+            f"was enabled with a world size of 1."
+        )
+
     kwargs = {
         "query": query,
         "key": key,
@@ -383,18 +379,12 @@ def _check_shape(
     attn_mask: Optional[torch.Tensor] = None,
     **kwargs,
 ) -> None:
-    # Expected shapes:
-    # query: (batch_size, seq_len_q, num_heads, head_dim)
-    # key:   (batch_size, seq_len_kv, num_heads, head_dim)
-    # value: (batch_size, seq_len_kv, num_heads, head_dim)
-    # attn_mask: (seq_len_q, seq_len_kv) or (batch_size, seq_len_q, seq_len_kv)
-    #            or (batch_size, num_heads, seq_len_q, seq_len_kv)
     if query.shape[-1] != key.shape[-1]:
-        raise ValueError("Query and key must have the same head dimension.")
-    if key.shape[-3] != value.shape[-3]:
-        raise ValueError("Key and value must have the same sequence length.")
-    if attn_mask is not None and attn_mask.shape[-1] != key.shape[-3]:
-        raise ValueError("Attention mask must match the key's sequence length.")
+        raise ValueError("Query and key must have the same last dimension.")
+    if query.shape[-2] != value.shape[-2]:
+        raise ValueError("Query and value must have the same second to last dimension.")
+    if attn_mask is not None and attn_mask.shape[-1] != key.shape[-2]:
+        raise ValueError("Attention mask must match the key's second to last dimension.")
 
 
 # ===== Helper functions =====
@@ -424,12 +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,
@@ -646,86 +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)
@@ -1493,47 +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],
@@ -1600,7 +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,
@@ -1616,35 +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):
@@ -102,7 +102,7 @@ def get_block(
     attention_head_dim: int,
     norm_type: str,
     act_fn: str,
-    qkv_mutliscales: Tuple[int, ...] = (),
+    qkv_mutliscales: Tuple[int] = (),
 ):
     if block_type == "ResBlock":
         block = ResBlock(in_channels, out_channels, norm_type, act_fn)
@@ -206,8 +206,8 @@ class Encoder(nn.Module):
         latent_channels: int,
         attention_head_dim: int = 32,
         block_type: Union[str, Tuple[str]] = "ResBlock",
-        block_out_channels: Tuple[int, ...] = (128, 256, 512, 512, 1024, 1024),
-        layers_per_block: Tuple[int, ...] = (2, 2, 2, 2, 2, 2),
+        block_out_channels: Tuple[int] = (128, 256, 512, 512, 1024, 1024),
+        layers_per_block: Tuple[int] = (2, 2, 2, 2, 2, 2),
         qkv_multiscales: Tuple[Tuple[int, ...], ...] = ((), (), (), (5,), (5,), (5,)),
         downsample_block_type: str = "pixel_unshuffle",
         out_shortcut: bool = True,
@@ -292,8 +292,8 @@ class Decoder(nn.Module):
         latent_channels: int,
         attention_head_dim: int = 32,
         block_type: Union[str, Tuple[str]] = "ResBlock",
-        block_out_channels: Tuple[int, ...] = (128, 256, 512, 512, 1024, 1024),
-        layers_per_block: Tuple[int, ...] = (2, 2, 2, 2, 2, 2),
+        block_out_channels: Tuple[int] = (128, 256, 512, 512, 1024, 1024),
+        layers_per_block: Tuple[int] = (2, 2, 2, 2, 2, 2),
         qkv_multiscales: Tuple[Tuple[int, ...], ...] = ((), (), (), (5,), (5,), (5,)),
         norm_type: Union[str, Tuple[str]] = "rms_norm",
         act_fn: Union[str, Tuple[str]] = "silu",
@@ -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).
@@ -440,8 +440,8 @@ class AutoencoderDC(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalModel
         decoder_block_types: Union[str, Tuple[str]] = "ResBlock",
         encoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 512, 1024, 1024),
         decoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 512, 1024, 1024),
-        encoder_layers_per_block: Tuple[int, ...] = (2, 2, 2, 3, 3, 3),
-        decoder_layers_per_block: Tuple[int, ...] = (3, 3, 3, 3, 3, 3),
+        encoder_layers_per_block: Tuple[int] = (2, 2, 2, 3, 3, 3),
+        decoder_layers_per_block: Tuple[int] = (3, 3, 3, 3, 3, 3),
         encoder_qkv_multiscales: Tuple[Tuple[int, ...], ...] = ((), (), (), (5,), (5,), (5,)),
         decoder_qkv_multiscales: Tuple[Tuple[int, ...], ...] = ((), (), (), (5,), (5,), (5,)),
         upsample_block_type: str = "pixel_shuffle",
@@ -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.
 
@@ -78,9 +78,9 @@ class AutoencoderKL(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalModel
         self,
         in_channels: int = 3,
         out_channels: int = 3,
-        down_block_types: Tuple[str, ...] = ("DownEncoderBlock2D",),
-        up_block_types: Tuple[str, ...] = ("UpDecoderBlock2D",),
-        block_out_channels: Tuple[int, ...] = (64,),
+        down_block_types: Tuple[str] = ("DownEncoderBlock2D",),
+        up_block_types: Tuple[str] = ("UpDecoderBlock2D",),
+        block_out_channels: Tuple[int] = (64,),
         layers_per_block: int = 1,
         act_fn: str = "silu",
         latent_channels: int = 4,
@@ -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).
@@ -995,19 +995,19 @@ class AutoencoderKLCogVideoX(ModelMixin, AutoencoderMixin, ConfigMixin, FromOrig
         self,
         in_channels: int = 3,
         out_channels: int = 3,
-        down_block_types: Tuple[str, ...] = (
+        down_block_types: Tuple[str] = (
             "CogVideoXDownBlock3D",
             "CogVideoXDownBlock3D",
             "CogVideoXDownBlock3D",
             "CogVideoXDownBlock3D",
         ),
-        up_block_types: Tuple[str, ...] = (
+        up_block_types: Tuple[str] = (
             "CogVideoXUpBlock3D",
             "CogVideoXUpBlock3D",
             "CogVideoXUpBlock3D",
             "CogVideoXUpBlock3D",
         ),
-        block_out_channels: Tuple[int, ...] = (128, 256, 256, 512),
+        block_out_channels: Tuple[int] = (128, 256, 256, 512),
         latent_channels: int = 16,
         layers_per_block: int = 3,
         act_fn: str = "silu",
@@ -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).
@@ -653,7 +653,7 @@ class AutoencoderKLHunyuanVideo(ModelMixin, AutoencoderMixin, ConfigMixin):
             "HunyuanVideoUpBlock3D",
             "HunyuanVideoUpBlock3D",
         ),
-        block_out_channels: Tuple[int, ...] = (128, 256, 512, 512),
+        block_out_channels: Tuple[int] = (128, 256, 512, 512),
         layers_per_block: int = 2,
         act_fn: str = "silu",
         norm_num_groups: int = 32,
@@ -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).
@@ -688,8 +688,8 @@ class AutoencoderKLMochi(ModelMixin, AutoencoderMixin, ConfigMixin):
         self,
         in_channels: int = 15,
         out_channels: int = 3,
-        encoder_block_out_channels: Tuple[int, ...] = (64, 128, 256, 384),
-        decoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 768),
+        encoder_block_out_channels: Tuple[int] = (64, 128, 256, 384),
+        decoder_block_out_channels: Tuple[int] = (128, 256, 512, 768),
         latent_channels: int = 12,
         layers_per_block: Tuple[int, ...] = (3, 3, 4, 6, 3),
         act_fn: str = "silu",
@@ -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

@@ -16,7 +16,7 @@
 # QwenImageVAE is further fine-tuned from the Wan Video VAE to achieve improved performance.
 # For more information about the Wan VAE, please refer to:
 # - GitHub: https://github.com/Wan-Video/Wan2.1
-# - Paper: https://huggingface.co/papers/2503.20314
+# - arXiv: https://arxiv.org/abs/2503.20314
 
 from typing import List, Optional, Tuple, Union
 
@@ -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.
 
@@ -679,7 +679,7 @@ class AutoencoderKLQwenImage(ModelMixin, AutoencoderMixin, ConfigMixin, FromOrig
         self,
         base_dim: int = 96,
         z_dim: int = 16,
-        dim_mult: Tuple[int, ...] = (1, 2, 4, 4),
+        dim_mult: Tuple[int] = [1, 2, 4, 4],
         num_res_blocks: int = 2,
         attn_scales: List[float] = [],
         temperal_downsample: List[bool] = [False, True, True],
@@ -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):
@@ -31,7 +31,7 @@ class TemporalDecoder(nn.Module):
         self,
         in_channels: int = 4,
         out_channels: int = 3,
-        block_out_channels: Tuple[int, ...] = (128, 256, 512, 512),
+        block_out_channels: Tuple[int] = (128, 256, 512, 512),
         layers_per_block: int = 2,
     ):
         super().__init__()
@@ -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.
 
@@ -172,8 +172,8 @@ class AutoencoderKLTemporalDecoder(ModelMixin, AutoencoderMixin, ConfigMixin):
         self,
         in_channels: int = 3,
         out_channels: int = 3,
-        down_block_types: Tuple[str, ...] = ("DownEncoderBlock2D",),
-        block_out_channels: Tuple[int, ...] = (64,),
+        down_block_types: Tuple[str] = ("DownEncoderBlock2D",),
+        block_out_channels: Tuple[int] = (64,),
         layers_per_block: int = 1,
         latent_channels: int = 4,
         sample_size: int = 32,

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__(
@@ -971,7 +968,7 @@ class AutoencoderKLWan(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalMo
         base_dim: int = 96,
         decoder_base_dim: Optional[int] = None,
         z_dim: int = 16,
-        dim_mult: Tuple[int, ...] = (1, 2, 4, 4),
+        dim_mult: Tuple[int] = [1, 2, 4, 4],
         num_res_blocks: int = 2,
         attn_scales: List[float] = [],
         temperal_downsample: List[bool] = [False, True, True],
@@ -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