update

[LoRA] refactor lora loading at the model-level (#11719 )
* factor out stuff from load_lora_adapter(). * simplifying text encoder lora loading. * fix peft.py * fix logging locations. * formatting * fix * update * update * update
2025-12-07 04:54:47 +08:00 · 2025-06-19 11:31:49 +02:00 · 2025-06-19 13:06:25 +05:30 · 2025-06-19 11:09:33 +05:30 · 2025-06-19 08:07:47 +05:30 · 2025-06-19 07:46:01 +05:30
1137 changed files with 15312 additions and 3962 deletions
--- a/.github/workflows/build_docker_images.yml
+++ b/.github/workflows/build_docker_images.yml
@@ -38,15 +38,16 @@ jobs:
          token: ${{ secrets.GITHUB_TOKEN }}

      - name: Build Changed Docker Images
+        env: 
+          CHANGED_FILES: ${{ steps.file_changes.outputs.all }}
        run: |
-          CHANGED_FILES="${{ steps.file_changes.outputs.all }}"
-          for FILE in $CHANGED_FILES; do
-            # skip anything that isn’t still on disk
+          echo "$CHANGED_FILES"
+          for FILE in $CHANGED_FILES; do 
+            # skip anything that isn't still on disk
            if [[ ! -f "$FILE" ]]; then
              echo "Skipping removed file $FILE"
              continue
-            fi
-            
+            fi           
            if [[ "$FILE" == docker/*Dockerfile ]]; then
              DOCKER_PATH="${FILE%/Dockerfile}"
              DOCKER_TAG=$(basename "$DOCKER_PATH")
--- a/.github/workflows/nightly_tests.yml
+++ b/.github/workflows/nightly_tests.yml
@@ -13,8 +13,9 @@ env:
  PYTEST_TIMEOUT: 600
  RUN_SLOW: yes
  RUN_NIGHTLY: yes
-  PIPELINE_USAGE_CUTOFF: 5000
+  PIPELINE_USAGE_CUTOFF: 0
  SLACK_API_TOKEN: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
+  CONSOLIDATED_REPORT_PATH: consolidated_test_report.md

 jobs:
  setup_torch_cuda_pipeline_matrix:
@@ -99,11 +100,6 @@ jobs:
        with:
          name: pipeline_${{ matrix.module }}_test_reports
          path: reports
-      - name: Generate Report and Notify Channel
-        if: always()
-        run: |
-          pip install slack_sdk tabulate
-          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY

  run_nightly_tests_for_other_torch_modules:
    name: Nightly Torch CUDA Tests
@@ -142,7 +138,6 @@ jobs:
        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
        # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
        CUBLAS_WORKSPACE_CONFIG: :16:8
-        RUN_COMPILE: yes
      run: |
        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
          -s -v -k "not Flax and not Onnx" \
@@ -175,12 +170,6 @@ jobs:
        name: torch_${{ matrix.module }}_cuda_test_reports
        path: reports

-    - name: Generate Report and Notify Channel
-      if: always()
-      run: |
-        pip install slack_sdk tabulate
-        python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
-
  run_torch_compile_tests:
    name: PyTorch Compile CUDA tests

@@ -224,12 +213,6 @@ jobs:
        name: torch_compile_test_reports
        path: reports

-    - name: Generate Report and Notify Channel
-      if: always()
-      run: |
-        pip install slack_sdk tabulate
-        python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
-  
  run_big_gpu_torch_tests:
    name: Torch tests on big GPU
    strategy:
@@ -280,12 +263,7 @@ jobs:
        with:
          name: torch_cuda_big_gpu_test_reports
          path: reports
-      - name: Generate Report and Notify Channel
-        if: always()
-        run: |
-          pip install slack_sdk tabulate
-          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
-          
+
  torch_minimum_version_cuda_tests:
    name: Torch Minimum Version CUDA Tests
    runs-on:
@@ -342,63 +320,6 @@ jobs:
        with:
          name: torch_minimum_version_cuda_test_reports
          path: reports
- 
-  run_flax_tpu_tests:
-    name: Nightly Flax TPU Tests
-    runs-on:
-      group: gcp-ct5lp-hightpu-8t
-    if: github.event_name == 'schedule'
-
-    container:
-      image: diffusers/diffusers-flax-tpu
-      options: --shm-size "16gb" --ipc host --privileged ${{ vars.V5_LITEPOD_8_ENV}} -v /mnt/hf_cache:/mnt/hf_cache
-    defaults:
-      run:
-        shell: bash
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test]
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-        python -m uv pip install pytest-reportlog
-
-    - name: Environment
-      run: python utils/print_env.py
-
-    - name: Run nightly Flax TPU tests
-      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-      run: |
-        python -m pytest -n 0 \
-          -s -v -k "Flax" \
-          --make-reports=tests_flax_tpu \
-          --report-log=tests_flax_tpu.log \
-          tests/
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: |
-        cat reports/tests_flax_tpu_stats.txt
-        cat reports/tests_flax_tpu_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: flax_tpu_test_reports
-        path: reports
-
-    - name: Generate Report and Notify Channel
-      if: always()
-      run: |
-        pip install slack_sdk tabulate
-        python utils/log_reports.py >> $GITHUB_STEP_SUMMARY

  run_nightly_onnx_tests:
    name: Nightly ONNXRuntime CUDA tests on Ubuntu
@@ -449,18 +370,12 @@ jobs:
        name: tests_onnx_cuda_reports
        path: reports

-    - name: Generate Report and Notify Channel
-      if: always()
-      run: |
-        pip install slack_sdk tabulate
-        python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
-
  run_nightly_quantization_tests:
    name: Torch quantization nightly tests
    strategy:
      fail-fast: false
      max-parallel: 2
-      matrix: 
+      matrix:
        config:
          - backend: "bitsandbytes"
            test_location: "bnb"
@@ -520,12 +435,7 @@ jobs:
        with:
          name: torch_cuda_${{ matrix.config.backend }}_reports
          path: reports
-      - name: Generate Report and Notify Channel
-        if: always()
-        run: |
-          pip install slack_sdk tabulate
-          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
-
+          
  run_nightly_pipeline_level_quantization_tests:
    name: Torch quantization nightly tests
    strategy:
@@ -574,12 +484,117 @@ jobs:
        with:
          name: torch_cuda_pipeline_level_quant_reports
          path: reports
-      - name: Generate Report and Notify Channel
-        if: always()
+
+  run_flax_tpu_tests:
+    name: Nightly Flax TPU Tests
+    runs-on:
+      group: gcp-ct5lp-hightpu-8t
+    if: github.event_name == 'schedule'
+
+    container:
+      image: diffusers/diffusers-flax-tpu
+      options: --shm-size "16gb" --ipc host --privileged ${{ vars.V5_LITEPOD_8_ENV}} -v /mnt/hf_cache:/mnt/hf_cache
+    defaults:
+      run:
+        shell: bash
+    steps:
+    - name: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
+
+    - name: Install dependencies
+      run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
+        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+        python -m uv pip install pytest-reportlog
+
+    - name: Environment
+      run: python utils/print_env.py
+
+    - name: Run nightly Flax TPU tests
+      env:
+        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+      run: |
+        python -m pytest -n 0 \
+          -s -v -k "Flax" \
+          --make-reports=tests_flax_tpu \
+          --report-log=tests_flax_tpu.log \
+          tests/
+
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: |
+        cat reports/tests_flax_tpu_stats.txt
+        cat reports/tests_flax_tpu_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v4
+      with:
+        name: flax_tpu_test_reports
+        path: reports
+
+  generate_consolidated_report:
+    name: Generate Consolidated Test Report
+    needs: [
+      run_nightly_tests_for_torch_pipelines,
+      run_nightly_tests_for_other_torch_modules,
+      run_torch_compile_tests,
+      run_big_gpu_torch_tests,
+      run_nightly_quantization_tests,
+      run_nightly_pipeline_level_quantization_tests,
+      run_nightly_onnx_tests,
+      torch_minimum_version_cuda_tests,
+      run_flax_tpu_tests
+    ]
+    if: always()
+    runs-on:
+      group: aws-general-8-plus
+    container:
+      image: diffusers/diffusers-pytorch-cpu
+    steps:
+      - name: Checkout diffusers
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 2
+
+      - name: Create reports directory
+        run: mkdir -p combined_reports
+
+      - name: Download all test reports
+        uses: actions/download-artifact@v4
+        with:
+          path: artifacts
+
+      - name: Prepare reports
        run: |
+          # Move all report files to a single directory for processing
+          find artifacts -name "*.txt" -exec cp {} combined_reports/ \;
+
+      - name: Install dependencies
+        run: |
+          pip install -e .[test]
          pip install slack_sdk tabulate
-          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
-  
+
+      - name: Generate consolidated report
+        run: |
+          python utils/consolidated_test_report.py \
+            --reports_dir combined_reports \
+            --output_file $CONSOLIDATED_REPORT_PATH \
+            --slack_channel_name diffusers-ci-nightly
+
+      - name: Show consolidated report
+        run: |
+          cat $CONSOLIDATED_REPORT_PATH >> $GITHUB_STEP_SUMMARY
+
+      - name: Upload consolidated report
+        uses: actions/upload-artifact@v4
+        with:
+          name: consolidated_test_report
+          path: ${{ env.CONSOLIDATED_REPORT_PATH }}
+
 # M1 runner currently not well supported
 # TODO: (Dhruv) add these back when we setup better testing for Apple Silicon
 #  run_nightly_tests_apple_m1:
--- a/.github/workflows/pr_style_bot.yml
+++ b/.github/workflows/pr_style_bot.yml
@@ -14,4 +14,4 @@ jobs:
    with:
      python_quality_dependencies: "[quality]"
    secrets:
-      bot_token: ${{ secrets.GITHUB_TOKEN }}
+      bot_token: ${{ secrets.HF_STYLE_BOT_ACTION }}
--- a/.github/workflows/pr_tests.yml
+++ b/.github/workflows/pr_tests.yml
@@ -291,8 +291,8 @@ jobs:
    - name: Failure short reports
      if: ${{ failure() }}
      run: |
-        cat reports/tests_lora_failures_short.txt
-        cat reports/tests_models_lora_failures_short.txt
+        cat reports/tests_peft_main_failures_short.txt
+        cat reports/tests_models_lora_peft_main_failures_short.txt

    - name: Test suite reports artifacts
      if: ${{ always() }}
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/PHILOSOPHY.md
+++ b/PHILOSOPHY.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/TRANSLATING.md
+++ b/docs/TRANSLATING.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/_toctree.yml
+++ b/docs/source/en/_toctree.yml
@@ -92,8 +92,6 @@
    title: API Reference
  title: Hybrid Inference
 - sections:
-  - local: using-diffusers/cogvideox
-    title: CogVideoX
  - local: using-diffusers/consisid
    title: ConsisID
  - local: using-diffusers/sdxl
@@ -178,10 +176,12 @@
 - sections:
  - local: optimization/fp16
    title: Accelerate inference
+  - local: optimization/cache
+    title: Caching
  - local: optimization/memory
    title: Reduce memory usage
-  - local: optimization/torch2.0
-    title: PyTorch 2.0
+  - local: optimization/pruna
+    title: Pruna
  - local: optimization/xformers
    title: xFormers
  - local: optimization/tome
@@ -285,6 +285,8 @@
        title: AllegroTransformer3DModel
      - local: api/models/aura_flow_transformer2d
        title: AuraFlowTransformer2DModel
+      - local: api/models/chroma_transformer
+        title: ChromaTransformer2DModel
      - local: api/models/cogvideox_transformer3d
        title: CogVideoXTransformer3DModel
      - local: api/models/cogview3plus_transformer2d
@@ -407,6 +409,8 @@
      title: AutoPipeline
    - local: api/pipelines/blip_diffusion
      title: BLIP-Diffusion
+    - local: api/pipelines/chroma
+      title: Chroma
    - local: api/pipelines/cogvideox
      title: CogVideoX
    - local: api/pipelines/cogview3
--- a/docs/source/en/advanced_inference/outpaint.md
+++ b/docs/source/en/advanced_inference/outpaint.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/activations.md
+++ b/docs/source/en/api/activations.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/attnprocessor.md
+++ b/docs/source/en/api/attnprocessor.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/cache.md
+++ b/docs/source/en/api/cache.md
@@ -1,4 +1,4 @@
-<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+<!-- 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
@@ -11,71 +11,19 @@ specific language governing permissions and limitations under the License. -->

 # Caching methods

-## Pyramid Attention Broadcast
+Cache methods speedup diffusion transformers by storing and reusing intermediate outputs of specific layers, such as attention and feedforward layers, instead of recalculating them at each inference step.

-[Pyramid Attention Broadcast](https://huggingface.co/papers/2408.12588) from Xuanlei Zhao, Xiaolong Jin, Kai Wang, Yang You.
-
-Pyramid Attention Broadcast (PAB) is a method that speeds up inference in diffusion models by systematically skipping attention computations between successive inference steps and reusing cached attention states. The attention states are not very different between successive inference steps. The most prominent difference is in the spatial attention blocks, not as much in the temporal attention blocks, and finally the least in the cross attention blocks. Therefore, many cross attention computation blocks can be skipped, followed by the temporal and spatial attention blocks. By combining other techniques like sequence parallelism and classifier-free guidance parallelism, PAB achieves near real-time video generation.
-
-Enable PAB with [`~PyramidAttentionBroadcastConfig`] on any pipeline. For some benchmarks, refer to [this](https://github.com/huggingface/diffusers/pull/9562) pull request.
-
-```python
-import torch
-from diffusers import CogVideoXPipeline, PyramidAttentionBroadcastConfig
-
-pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16)
-pipe.to("cuda")
-
-# Increasing the value of `spatial_attention_timestep_skip_range[0]` or decreasing the value of
-# `spatial_attention_timestep_skip_range[1]` will decrease the interval in which pyramid attention
-# broadcast is active, leader to slower inference speeds. However, large intervals can lead to
-# poorer quality of generated videos.
-config = PyramidAttentionBroadcastConfig(
-    spatial_attention_block_skip_range=2,
-    spatial_attention_timestep_skip_range=(100, 800),
-    current_timestep_callback=lambda: pipe.current_timestep,
-)
-pipe.transformer.enable_cache(config)
-```
-
-## Faster Cache
-
-[FasterCache](https://huggingface.co/papers/2410.19355) from Zhengyao Lv, Chenyang Si, Junhao Song, Zhenyu Yang, Yu Qiao, Ziwei Liu, Kwan-Yee K. Wong.
-
-FasterCache is a method that speeds up inference in diffusion transformers by:
- Reusing attention states between successive inference steps, due to high similarity between them
- Skipping unconditional branch prediction used in classifier-free guidance by revealing redundancies between unconditional and conditional branch outputs for the same timestep, and therefore approximating the unconditional branch output using the conditional branch output
-
-```python
-import torch
-from diffusers import CogVideoXPipeline, FasterCacheConfig
-
-pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16)
-pipe.to("cuda")
-
-config = FasterCacheConfig(
-    spatial_attention_block_skip_range=2,
-    spatial_attention_timestep_skip_range=(-1, 681),
-    current_timestep_callback=lambda: pipe.current_timestep,
-    attention_weight_callback=lambda _: 0.3,
-    unconditional_batch_skip_range=5,
-    unconditional_batch_timestep_skip_range=(-1, 781),
-    tensor_format="BFCHW",
-)
-pipe.transformer.enable_cache(config)
-```
-
-### CacheMixin
+## CacheMixin

 [[autodoc]] CacheMixin

-### PyramidAttentionBroadcastConfig
+## PyramidAttentionBroadcastConfig

 [[autodoc]] PyramidAttentionBroadcastConfig

 [[autodoc]] apply_pyramid_attention_broadcast

-### FasterCacheConfig
+## FasterCacheConfig

 [[autodoc]] FasterCacheConfig

--- a/docs/source/en/api/configuration.md
+++ b/docs/source/en/api/configuration.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/image_processor.md
+++ b/docs/source/en/api/image_processor.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/internal_classes_overview.md
+++ b/docs/source/en/api/internal_classes_overview.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/loaders/ip_adapter.md
+++ b/docs/source/en/api/loaders/ip_adapter.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/loaders/lora.md
+++ b/docs/source/en/api/loaders/lora.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
@@ -98,4 +98,8 @@ To learn more about how to load LoRA weights, see the [LoRA](../../using-diffuse

 ## LoraBaseMixin

-[[autodoc]] loaders.lora_base.LoraBaseMixin
+[[autodoc]] loaders.lora_base.LoraBaseMixin
+
+## WanLoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.WanLoraLoaderMixin
--- a/docs/source/en/api/loaders/peft.md
+++ b/docs/source/en/api/loaders/peft.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/loaders/single_file.md
+++ b/docs/source/en/api/loaders/single_file.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/loaders/textual_inversion.md
+++ b/docs/source/en/api/loaders/textual_inversion.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/loaders/transformer_sd3.md
+++ b/docs/source/en/api/loaders/transformer_sd3.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/loaders/unet.md
+++ b/docs/source/en/api/loaders/unet.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/logging.md
+++ b/docs/source/en/api/logging.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/allegro_transformer3d.md
+++ b/docs/source/en/api/models/allegro_transformer3d.md
@@ -1,4 +1,4 @@
-<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+<!-- 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
--- a/docs/source/en/api/models/asymmetricautoencoderkl.md
+++ b/docs/source/en/api/models/asymmetricautoencoderkl.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/aura_flow_transformer2d.md
+++ b/docs/source/en/api/models/aura_flow_transformer2d.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/auto_model.md
+++ b/docs/source/en/api/models/auto_model.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/autoencoder_dc.md
+++ b/docs/source/en/api/models/autoencoder_dc.md
@@ -1,4 +1,4 @@
-<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+<!-- 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
--- a/docs/source/en/api/models/autoencoder_kl_hunyuan_video.md
+++ b/docs/source/en/api/models/autoencoder_kl_hunyuan_video.md
@@ -1,4 +1,4 @@
-<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+<!-- 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
--- a/docs/source/en/api/models/autoencoder_kl_wan.md
+++ b/docs/source/en/api/models/autoencoder_kl_wan.md
@@ -1,4 +1,4 @@
-<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+<!-- 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
--- a/docs/source/en/api/models/autoencoder_oobleck.md
+++ b/docs/source/en/api/models/autoencoder_oobleck.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/autoencoder_tiny.md
+++ b/docs/source/en/api/models/autoencoder_tiny.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/autoencoderkl.md
+++ b/docs/source/en/api/models/autoencoderkl.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/autoencoderkl_allegro.md
+++ b/docs/source/en/api/models/autoencoderkl_allegro.md
@@ -1,4 +1,4 @@
-<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+<!-- 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
--- a/docs/source/en/api/models/autoencoderkl_cogvideox.md
+++ b/docs/source/en/api/models/autoencoderkl_cogvideox.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/autoencoderkl_cosmos.md
+++ b/docs/source/en/api/models/autoencoderkl_cosmos.md
@@ -1,4 +1,4 @@
-<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+<!-- 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
--- a/docs/source/en/api/models/autoencoderkl_ltx_video.md
+++ b/docs/source/en/api/models/autoencoderkl_ltx_video.md
@@ -1,4 +1,4 @@
-<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+<!-- 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
--- a/docs/source/en/api/models/autoencoderkl_mochi.md
+++ b/docs/source/en/api/models/autoencoderkl_mochi.md
@@ -1,4 +1,4 @@
-<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+<!-- 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
--- a/docs/source/en/api/models/chroma_transformer.md
+++ b/docs/source/en/api/models/chroma_transformer.md
@@ -0,0 +1,19 @@
+<!--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.
+-->
+
+# ChromaTransformer2DModel
+
+A modified flux Transformer model from [Chroma](https://huggingface.co/lodestones/Chroma)
+
+## ChromaTransformer2DModel
+
+[[autodoc]] ChromaTransformer2DModel
--- a/docs/source/en/api/models/cogvideox_transformer3d.md
+++ b/docs/source/en/api/models/cogvideox_transformer3d.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/cogview3plus_transformer2d.md
+++ b/docs/source/en/api/models/cogview3plus_transformer2d.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/cogview4_transformer2d.md
+++ b/docs/source/en/api/models/cogview4_transformer2d.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/consisid_transformer3d.md
+++ b/docs/source/en/api/models/consisid_transformer3d.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/consistency_decoder_vae.md
+++ b/docs/source/en/api/models/consistency_decoder_vae.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/controlnet.md
+++ b/docs/source/en/api/models/controlnet.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/controlnet_flux.md
+++ b/docs/source/en/api/models/controlnet_flux.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team and The InstantX Team. All rights reserved.
+<!--Copyright 2025 The HuggingFace Team and The InstantX 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
--- a/docs/source/en/api/models/controlnet_hunyuandit.md
+++ b/docs/source/en/api/models/controlnet_hunyuandit.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team and Tencent Hunyuan Team. All rights reserved.
+<!--Copyright 2025 The HuggingFace Team and Tencent Hunyuan 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
--- a/docs/source/en/api/models/controlnet_sana.md
+++ b/docs/source/en/api/models/controlnet_sana.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/controlnet_sd3.md
+++ b/docs/source/en/api/models/controlnet_sd3.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team and The InstantX Team. All rights reserved.
+<!--Copyright 2025 The HuggingFace Team and The InstantX 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
--- a/docs/source/en/api/models/controlnet_sparsectrl.md
+++ b/docs/source/en/api/models/controlnet_sparsectrl.md
@@ -1,4 +1,4 @@
-<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+<!-- 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
--- a/docs/source/en/api/models/controlnet_union.md
+++ b/docs/source/en/api/models/controlnet_union.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team and The InstantX Team. All rights reserved.
+<!--Copyright 2025 The HuggingFace Team and The InstantX 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
--- a/docs/source/en/api/models/cosmos_transformer3d.md
+++ b/docs/source/en/api/models/cosmos_transformer3d.md
@@ -1,4 +1,4 @@
-<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+<!-- 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
--- a/docs/source/en/api/models/dit_transformer2d.md
+++ b/docs/source/en/api/models/dit_transformer2d.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/flux_transformer.md
+++ b/docs/source/en/api/models/flux_transformer.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/hidream_image_transformer.md
+++ b/docs/source/en/api/models/hidream_image_transformer.md
@@ -1,4 +1,4 @@
-<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+<!-- 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
--- a/docs/source/en/api/models/hunyuan_transformer2d.md
+++ b/docs/source/en/api/models/hunyuan_transformer2d.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/hunyuan_video_transformer_3d.md
+++ b/docs/source/en/api/models/hunyuan_video_transformer_3d.md
@@ -1,4 +1,4 @@
-<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+<!-- 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
--- a/docs/source/en/api/models/latte_transformer3d.md
+++ b/docs/source/en/api/models/latte_transformer3d.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/ltx_video_transformer3d.md
+++ b/docs/source/en/api/models/ltx_video_transformer3d.md
@@ -1,4 +1,4 @@
-<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+<!-- 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
--- a/docs/source/en/api/models/lumina2_transformer2d.md
+++ b/docs/source/en/api/models/lumina2_transformer2d.md
@@ -1,4 +1,4 @@
-<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+<!-- 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
--- a/docs/source/en/api/models/lumina_nextdit2d.md
+++ b/docs/source/en/api/models/lumina_nextdit2d.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/mochi_transformer3d.md
+++ b/docs/source/en/api/models/mochi_transformer3d.md
@@ -1,4 +1,4 @@
-<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+<!-- 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
--- a/docs/source/en/api/models/omnigen_transformer.md
+++ b/docs/source/en/api/models/omnigen_transformer.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/overview.md
+++ b/docs/source/en/api/models/overview.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/pixart_transformer2d.md
+++ b/docs/source/en/api/models/pixart_transformer2d.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/prior_transformer.md
+++ b/docs/source/en/api/models/prior_transformer.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/sana_transformer2d.md
+++ b/docs/source/en/api/models/sana_transformer2d.md
@@ -1,4 +1,4 @@
-<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+<!-- 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
--- a/docs/source/en/api/models/sd3_transformer2d.md
+++ b/docs/source/en/api/models/sd3_transformer2d.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/stable_audio_transformer.md
+++ b/docs/source/en/api/models/stable_audio_transformer.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/stable_cascade_unet.md
+++ b/docs/source/en/api/models/stable_cascade_unet.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/transformer2d.md
+++ b/docs/source/en/api/models/transformer2d.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/transformer_temporal.md
+++ b/docs/source/en/api/models/transformer_temporal.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/unet-motion.md
+++ b/docs/source/en/api/models/unet-motion.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/unet.md
+++ b/docs/source/en/api/models/unet.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/unet2d-cond.md
+++ b/docs/source/en/api/models/unet2d-cond.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/unet2d.md
+++ b/docs/source/en/api/models/unet2d.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/unet3d-cond.md
+++ b/docs/source/en/api/models/unet3d-cond.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/uvit2d.md
+++ b/docs/source/en/api/models/uvit2d.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/vq.md
+++ b/docs/source/en/api/models/vq.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/models/wan_transformer_3d.md
+++ b/docs/source/en/api/models/wan_transformer_3d.md
@@ -1,4 +1,4 @@
-<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+<!-- 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
--- a/docs/source/en/api/normalization.md
+++ b/docs/source/en/api/normalization.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/outputs.md
+++ b/docs/source/en/api/outputs.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/pipelines/allegro.md
+++ b/docs/source/en/api/pipelines/allegro.md
@@ -1,4 +1,4 @@
-<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+<!-- 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
--- a/docs/source/en/api/pipelines/amused.md
+++ b/docs/source/en/api/pipelines/amused.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/pipelines/animatediff.md
+++ b/docs/source/en/api/pipelines/animatediff.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/pipelines/attend_and_excite.md
+++ b/docs/source/en/api/pipelines/attend_and_excite.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/pipelines/audioldm.md
+++ b/docs/source/en/api/pipelines/audioldm.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/pipelines/audioldm2.md
+++ b/docs/source/en/api/pipelines/audioldm2.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/pipelines/aura_flow.md
+++ b/docs/source/en/api/pipelines/aura_flow.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/pipelines/auto_pipeline.md
+++ b/docs/source/en/api/pipelines/auto_pipeline.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/pipelines/blip_diffusion.md
+++ b/docs/source/en/api/pipelines/blip_diffusion.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/pipelines/chroma.md
+++ b/docs/source/en/api/pipelines/chroma.md
@@ -0,0 +1,71 @@
+<!--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.
+-->
+
+# Chroma
+
+<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>
+
+Chroma is a text to image generation model based on Flux.
+
+Original model checkpoints for Chroma can be found [here](https://huggingface.co/lodestones/Chroma).
+
+<Tip>
+
+Chroma can use all the same optimizations as Flux.
+
+</Tip>
+
+## Inference (Single File)
+
+The `ChromaTransformer2DModel` supports loading checkpoints in the original format. This is also useful when trying to load finetunes or quantized versions of the models that have been published by the community.
+
+The following example demonstrates how to run Chroma from a single file.
+
+Then run the following example
+
+```python
+import torch
+from diffusers import ChromaTransformer2DModel, ChromaPipeline
+from transformers import T5EncoderModel
+
+bfl_repo = "black-forest-labs/FLUX.1-dev"
+dtype = torch.bfloat16
+
+transformer = ChromaTransformer2DModel.from_single_file("https://huggingface.co/lodestones/Chroma/blob/main/chroma-unlocked-v35.safetensors", torch_dtype=dtype)
+
+text_encoder = T5EncoderModel.from_pretrained(bfl_repo, subfolder="text_encoder_2", torch_dtype=dtype)
+tokenizer = T5Tokenizer.from_pretrained(bfl_repo, subfolder="tokenizer_2", torch_dtype=dtype)
+
+pipe = ChromaPipeline.from_pretrained(bfl_repo, transformer=transformer, text_encoder=text_encoder, tokenizer=tokenizer, torch_dtype=dtype)
+
+pipe.enable_model_cpu_offload()
+
+prompt = "A cat holding a sign that says hello world"
+image = pipe(
+    prompt,
+    guidance_scale=4.0,
+    output_type="pil",
+    num_inference_steps=26,
+    generator=torch.Generator("cpu").manual_seed(0)
+).images[0]
+
+image.save("image.png")
+```
+
+## ChromaPipeline
+
+[[autodoc]] ChromaPipeline
+	- all
+	- __call__
--- a/docs/source/en/api/pipelines/cogvideox.md
+++ b/docs/source/en/api/pipelines/cogvideox.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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.
@@ -13,150 +13,181 @@
 # limitations under the License.
 -->

-# CogVideoX
-
-<div class="flex flex-wrap space-x-1">
-  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
+<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>

-[CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://huggingface.co/papers/2408.06072) from Tsinghua University & ZhipuAI, by Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, Da Yin, Xiaotao Gu, Yuxuan Zhang, Weihan Wang, Yean Cheng, Ting Liu, Bin Xu, Yuxiao Dong, Jie Tang.
+# CogVideoX

-The abstract from the paper is:
+[CogVideoX](https://huggingface.co/papers/2408.06072) is a large diffusion transformer model - available in 2B and 5B parameters - designed to generate longer and more consistent videos from text. This model uses a 3D causal variational autoencoder to more efficiently process video data by reducing sequence length (and associated training compute) and preventing flickering in generated videos. An "expert" transformer with adaptive LayerNorm improves alignment between text and video, and 3D full attention helps accurately capture motion and time in generated videos.

-*We introduce CogVideoX, a large-scale diffusion transformer model designed for generating videos based on text prompts. To efficently model video data, we propose to levearge a 3D Variational Autoencoder (VAE) to compresses videos along both spatial and temporal dimensions. To improve the text-video alignment, we propose an expert transformer with the expert adaptive LayerNorm to facilitate the deep fusion between the two modalities. By employing a progressive training technique, CogVideoX is adept at producing coherent, long-duration videos characterized by significant motion. In addition, we develop an effectively text-video data processing pipeline that includes various data preprocessing strategies and a video captioning method. It significantly helps enhance the performance of CogVideoX, improving both generation quality and semantic alignment. Results show that CogVideoX demonstrates state-of-the-art performance across both multiple machine metrics and human evaluations. The model weight of CogVideoX-2B is publicly available at https://github.com/THUDM/CogVideo.*
+You can find all the original CogVideoX checkpoints under the [CogVideoX](https://huggingface.co/collections/THUDM/cogvideo-66c08e62f1685a3ade464cce) collection.

-<Tip>
+> [!TIP]
+> Click on the CogVideoX models in the right sidebar for more examples of other video generation tasks.

-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.
+The example below demonstrates how to generate a video optimized for memory or inference speed.

-</Tip>
+<hfoptions id="usage">
+<hfoption id="memory">

-This pipeline was contributed by [zRzRzRzRzRzRzR](https://github.com/zRzRzRzRzRzRzR). The original codebase can be found [here](https://huggingface.co/THUDM). The original weights can be found under [hf.co/THUDM](https://huggingface.co/THUDM).
+Refer to the [Reduce memory usage](../../optimization/memory) guide for more details about the various memory saving techniques.

-There are three official CogVideoX checkpoints for text-to-video and video-to-video.
-
-| checkpoints | recommended inference dtype |
-|:---:|:---:|
-| [`THUDM/CogVideoX-2b`](https://huggingface.co/THUDM/CogVideoX-2b) | torch.float16 |
-| [`THUDM/CogVideoX-5b`](https://huggingface.co/THUDM/CogVideoX-5b) | torch.bfloat16 |
-| [`THUDM/CogVideoX1.5-5b`](https://huggingface.co/THUDM/CogVideoX1.5-5b) | torch.bfloat16 |
-
-There are two official CogVideoX checkpoints available for image-to-video.
-
-| checkpoints | recommended inference dtype |
-|:---:|:---:|
-| [`THUDM/CogVideoX-5b-I2V`](https://huggingface.co/THUDM/CogVideoX-5b-I2V) | torch.bfloat16 |
-| [`THUDM/CogVideoX-1.5-5b-I2V`](https://huggingface.co/THUDM/CogVideoX-1.5-5b-I2V) | torch.bfloat16 |
-
-For the CogVideoX 1.5 series:
- Text-to-video (T2V) works best at a resolution of 1360x768 because it was trained with that specific resolution.
- Image-to-video (I2V) works for multiple resolutions. The width can vary from 768 to 1360, but the height must be 768. The height/width must be divisible by 16.
- Both T2V and I2V models support generation with 81 and 161 frames and work best at this value. Exporting videos at 16 FPS is recommended.
-
-There are two official CogVideoX checkpoints that support pose controllable generation (by the [Alibaba-PAI](https://huggingface.co/alibaba-pai) team).
-
-| checkpoints | recommended inference dtype |
-|:---:|:---:|
-| [`alibaba-pai/CogVideoX-Fun-V1.1-2b-Pose`](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-2b-Pose) | torch.bfloat16 |
-| [`alibaba-pai/CogVideoX-Fun-V1.1-5b-Pose`](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-5b-Pose) | torch.bfloat16 |
-
-## Inference
-
-Use [`torch.compile`](https://huggingface.co/docs/diffusers/main/en/tutorials/fast_diffusion#torchcompile) to reduce the inference latency.
-
-First, load the pipeline:
-
-```python
-import torch
-from diffusers import CogVideoXPipeline, CogVideoXImageToVideoPipeline
-from diffusers.utils import export_to_video,load_image
-pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b").to("cuda") # or "THUDM/CogVideoX-2b" 
-```
-
-If you are using the image-to-video pipeline, load it as follows:
-
-```python
-pipe = CogVideoXImageToVideoPipeline.from_pretrained("THUDM/CogVideoX-5b-I2V").to("cuda")
-```
-
-Then change the memory layout of the pipelines `transformer` component to `torch.channels_last`:
-
-```python
-pipe.transformer.to(memory_format=torch.channels_last)
-```
-
-Compile the components and run inference:
-
-```python
-pipe.transformer = torch.compile(pipeline.transformer, mode="max-autotune", fullgraph=True)
-
-# CogVideoX works well with long and well-described prompts
-prompt = "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance."
-video = pipe(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0]
-```
-
-The [T2V benchmark](https://gist.github.com/a-r-r-o-w/5183d75e452a368fd17448fcc810bd3f) results on an 80GB A100 machine are:
-
-```
-Without torch.compile(): Average inference time: 96.89 seconds.
-With torch.compile(): Average inference time: 76.27 seconds.
-```
-
-### Memory optimization
-
-CogVideoX-2b requires about 19 GB of GPU memory to decode 49 frames (6 seconds of video at 8 FPS) with output resolution 720x480 (W x H), which makes it not possible to run on consumer GPUs or free-tier T4 Colab. The following memory optimizations could be used to reduce the memory footprint. For replication, you can refer to [this](https://gist.github.com/a-r-r-o-w/3959a03f15be5c9bd1fe545b09dfcc93) script.
-
- `pipe.enable_model_cpu_offload()`:
-  - Without enabling cpu offloading, memory usage is `33 GB`
-  - With enabling cpu offloading, memory usage is `19 GB`
- `pipe.enable_sequential_cpu_offload()`:
-  - Similar to `enable_model_cpu_offload` but can significantly reduce memory usage at the cost of slow inference
-  - When enabled, memory usage is under `4 GB`
- `pipe.vae.enable_tiling()`:
-  - With enabling cpu offloading and tiling, memory usage is `11 GB`
- `pipe.vae.enable_slicing()`
-
-## 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 [`CogVideoXPipeline`] for inference with bitsandbytes.
+The quantized CogVideoX 5B model below requires ~16GB of VRAM.

 ```py
 import torch
-from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, CogVideoXTransformer3DModel, CogVideoXPipeline
+from diffusers import CogVideoXPipeline, AutoModel
+from diffusers.quantizers import PipelineQuantizationConfig
+from diffusers.hooks import apply_group_offloading
 from diffusers.utils import export_to_video
-from transformers import BitsAndBytesConfig as BitsAndBytesConfig, T5EncoderModel

-quant_config = BitsAndBytesConfig(load_in_8bit=True)
-text_encoder_8bit = T5EncoderModel.from_pretrained(
-    "THUDM/CogVideoX-2b",
-    subfolder="text_encoder",
-    quantization_config=quant_config,
-    torch_dtype=torch.float16,
+# quantize weights to int8 with torchao
+pipeline_quant_config = PipelineQuantizationConfig(
+  quant_backend="torchao",
+  quant_kwargs={"quant_type": "int8wo"},
+  components_to_quantize=["transformer"]
 )

-quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
-transformer_8bit = CogVideoXTransformer3DModel.from_pretrained(
-    "THUDM/CogVideoX-2b",
+# fp8 layerwise weight-casting
+transformer = AutoModel.from_pretrained(
+    "THUDM/CogVideoX-5b",
    subfolder="transformer",
-    quantization_config=quant_config,
-    torch_dtype=torch.float16,
+    torch_dtype=torch.bfloat16
+)
+transformer.enable_layerwise_casting(
+    storage_dtype=torch.float8_e4m3fn, compute_dtype=torch.bfloat16
 )

 pipeline = CogVideoXPipeline.from_pretrained(
-    "THUDM/CogVideoX-2b",
-    text_encoder=text_encoder_8bit,
-    transformer=transformer_8bit,
-    torch_dtype=torch.float16,
-    device_map="balanced",
+    "THUDM/CogVideoX-5b",
+    transformer=transformer,
+    quantization_config=pipeline_quant_config,
+    torch_dtype=torch.bfloat16
 )
+pipeline.to("cuda")

-prompt = "A detailed wooden toy ship with intricately carved masts and sails is seen gliding smoothly over a plush, blue carpet that mimics the waves of the sea. The ship's hull is painted a rich brown, with tiny windows. The carpet, soft and textured, provides a perfect backdrop, resembling an oceanic expanse. Surrounding the ship are various other toys and children's items, hinting at a playful environment. The scene captures the innocence and imagination of childhood, with the toy ship's journey symbolizing endless adventures in a whimsical, indoor setting."
-video = pipeline(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0]
-export_to_video(video, "ship.mp4", fps=8)
+# model-offloading
+pipeline.enable_model_cpu_offload()
+
+prompt = """
+A detailed wooden toy ship with intricately carved masts and sails is seen gliding smoothly over a plush, blue carpet that mimics the waves of the sea. 
+The ship's hull is painted a rich brown, with tiny windows. The carpet, soft and textured, provides a perfect backdrop, resembling an oceanic expanse. 
+Surrounding the ship are various other toys and children's items, hinting at a playful environment. The scene captures the innocence and imagination of childhood, 
+with the toy ship's journey symbolizing endless adventures in a whimsical, indoor setting.
+"""
+
+video = pipeline(
+    prompt=prompt,
+    guidance_scale=6,
+    num_inference_steps=50
+).frames[0]
+export_to_video(video, "output.mp4", fps=8)
 ```

+</hfoption>
+<hfoption id="inference speed">
+
+[Compilation](../../optimization/fp16#torchcompile) is slow the first time but subsequent calls to the pipeline are faster.
+
+The average inference time with torch.compile on a 80GB A100 is 76.27 seconds compared to 96.89 seconds for an uncompiled model.
+
+```py
+import torch
+from diffusers import CogVideoXPipeline
+from diffusers.utils import export_to_video
+
+pipeline = CogVideoXPipeline.from_pretrained(
+    "THUDM/CogVideoX-2b",
+    torch_dtype=torch.float16
+).to("cuda")
+
+# torch.compile
+pipeline.transformer.to(memory_format=torch.channels_last)
+pipeline.transformer = torch.compile(
+    pipeline.transformer, mode="max-autotune", fullgraph=True
+)
+
+prompt = """
+A detailed wooden toy ship with intricately carved masts and sails is seen gliding smoothly over a plush, blue carpet that mimics the waves of the sea. 
+The ship's hull is painted a rich brown, with tiny windows. The carpet, soft and textured, provides a perfect backdrop, resembling an oceanic expanse. 
+Surrounding the ship are various other toys and children's items, hinting at a playful environment. The scene captures the innocence and imagination of childhood, 
+with the toy ship's journey symbolizing endless adventures in a whimsical, indoor setting.
+"""
+
+video = pipeline(
+    prompt=prompt,
+    guidance_scale=6,
+    num_inference_steps=50
+).frames[0]
+export_to_video(video, "output.mp4", fps=8)
+```
+
+</hfoption>
+</hfoptions>
+
+## Notes
+
+- CogVideoX supports LoRAs with [`~loaders.CogVideoXLoraLoaderMixin.load_lora_weights`].
+
+  <details>
+  <summary>Show example code</summary>
+
+  ```py
+  import torch
+  from diffusers import CogVideoXPipeline
+  from diffusers.hooks import apply_group_offloading
+  from diffusers.utils import export_to_video
+
+  pipeline = CogVideoXPipeline.from_pretrained(
+      "THUDM/CogVideoX-5b",
+      torch_dtype=torch.bfloat16
+  )
+  pipeline.to("cuda")
+
+  # load LoRA weights
+  pipeline.load_lora_weights("finetrainers/CogVideoX-1.5-crush-smol-v0", adapter_name="crush-lora")
+  pipeline.set_adapters("crush-lora", 0.9)
+
+  # model-offloading
+  pipeline.enable_model_cpu_offload()
+
+  prompt = """
+  PIKA_CRUSH A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press.
+  """
+  negative_prompt = "inconsistent motion, blurry motion, worse quality, degenerate outputs, deformed outputs"
+
+  video = pipeline(
+      prompt=prompt, 
+      negative_prompt=negative_prompt, 
+      num_frames=81, 
+      height=480,
+      width=768,
+      num_inference_steps=50
+  ).frames[0]
+  export_to_video(video, "output.mp4", fps=16)
+  ```
+
+  </details>
+
+- The text-to-video (T2V) checkpoints work best with a resolution of 1360x768 because that was the resolution it was pretrained on.
+
+- The image-to-video (I2V) checkpoints work with multiple resolutions. The width can vary from 768 to 1360, but the height must be 758. Both height and width must be divisible by 16.
+
+- Both T2V and I2V checkpoints work best with 81 and 161 frames. It is recommended to export the generated video at 16fps.
+
+- Refer to the table below to view memory usage when various memory-saving techniques are enabled.
+
+  | method | memory usage (enabled) | memory usage (disabled) |
+  |---|---|---|
+  | enable_model_cpu_offload | 19GB | 33GB |
+  | enable_sequential_cpu_offload | <4GB | ~33GB (very slow inference speed) |
+  | enable_tiling | 11GB (with enable_model_cpu_offload) | --- |
+ 
 ## CogVideoXPipeline

 [[autodoc]] CogVideoXPipeline
--- a/docs/source/en/api/pipelines/cogview3.md
+++ b/docs/source/en/api/pipelines/cogview3.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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.
--- a/docs/source/en/api/pipelines/cogview4.md
+++ b/docs/source/en/api/pipelines/cogview4.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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.
--- a/docs/source/en/api/pipelines/consisid.md
+++ b/docs/source/en/api/pipelines/consisid.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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.
--- a/docs/source/en/api/pipelines/consistency_models.md
+++ b/docs/source/en/api/pipelines/consistency_models.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/pipelines/control_flux_inpaint.md
+++ b/docs/source/en/api/pipelines/control_flux_inpaint.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team, The Black Forest Team. All rights reserved.
+<!--Copyright 2025 The HuggingFace Team, The Black Forest 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
--- a/docs/source/en/api/pipelines/controlnet.md
+++ b/docs/source/en/api/pipelines/controlnet.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--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
--- a/docs/source/en/api/pipelines/controlnet_flux.md
+++ b/docs/source/en/api/pipelines/controlnet_flux.md
@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team, The InstantX Team, and the XLabs Team. All rights reserved.
+<!--Copyright 2025 The HuggingFace Team, The InstantX Team, and the XLabs 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
--- a/Show More
+++ b/Show More