update

2025-07-24 22:18:15 +05:30 · 2025-07-21 09:03:32 +05:30 · 2025-07-21 08:56:47 +05:30 · 2025-07-17 13:05:43 +05:30 · 2025-07-17 07:14:35 +05:30 · 2025-07-16 12:03:09 +02:00
271 changed files with 2909 additions and 15274 deletions
@@ -79,14 +79,14 @@ jobs:
      # Check secret is set
      - name: whoami
-        run: hf auth whoami
+        run: huggingface-cli whoami
        env:
            HF_TOKEN: ${{ secrets.HF_TOKEN_MIRROR_COMMUNITY_PIPELINES }}
      # Push to HF! (under subfolder based on checkout ref)
      # https://huggingface.co/datasets/diffusers/community-pipelines-mirror
      - name: Mirror community pipeline to HF
-        run: hf upload diffusers/community-pipelines-mirror ./examples/community ${PATH_IN_REPO} --repo-type dataset
+        run: huggingface-cli upload diffusers/community-pipelines-mirror ./examples/community ${PATH_IN_REPO} --repo-type dataset
        env:
            PATH_IN_REPO: ${{ env.PATH_IN_REPO }}
            HF_TOKEN: ${{ secrets.HF_TOKEN_MIRROR_COMMUNITY_PIPELINES }}
@@ -0,0 +1,141 @@
 name: Fast PR tests for Modular
 on:
  pull_request:
    branches: [main]
    paths:
      - "src/diffusers/modular_pipelines/**.py"
      - "src/diffusers/models/modeling_utils.py"
      - "src/diffusers/models/model_loading_utils.py"
      - "src/diffusers/pipelines/pipeline_utils.py"
      - "src/diffusers/pipeline_loading_utils.py"
      - "src/diffusers/loaders/lora_base.py"
      - "src/diffusers/loaders/lora_pipeline.py"
      - "src/diffusers/loaders/peft.py"
      - "tests/modular_pipelines/**.py"
      - ".github/**.yml"
      - "utils/**.py"
      - "setup.py"
  push:
    branches:
      - ci-*
 concurrency:
  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
  cancel-in-progress: true
 env:
  DIFFUSERS_IS_CI: yes
  HF_HUB_ENABLE_HF_TRANSFER: 1
  OMP_NUM_THREADS: 4
  MKL_NUM_THREADS: 4
  PYTEST_TIMEOUT: 60
 jobs:
  check_code_quality:
    runs-on: ubuntu-22.04
    steps:
      - uses: actions/checkout@v3
      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: "3.10"
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install .[quality]
      - name: Check quality
        run: make quality
      - name: Check if failure
        if: ${{ failure() }}
        run: |
          echo "Quality check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make style && make quality'" >> $GITHUB_STEP_SUMMARY
  check_repository_consistency:
    needs: check_code_quality
    runs-on: ubuntu-22.04
    steps:
      - uses: actions/checkout@v3
      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: "3.10"
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install .[quality]
      - name: Check repo consistency
        run: |
          python utils/check_copies.py
          python utils/check_dummies.py
          python utils/check_support_list.py
          make deps_table_check_updated
      - name: Check if failure
        if: ${{ failure() }}
        run: |
          echo "Repo consistency check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make fix-copies'" >> $GITHUB_STEP_SUMMARY
  run_fast_tests:
    needs: [check_code_quality, check_repository_consistency]
    strategy:
      fail-fast: false
      matrix:
        config:
          - name: Fast PyTorch Modular Pipeline CPU tests
            framework: pytorch_pipelines
            runner: aws-highmemory-32-plus
            image: diffusers/diffusers-pytorch-cpu
            report: torch_cpu_modular_pipelines
    name: ${{ matrix.config.name }}
    runs-on:
      group: ${{ matrix.config.runner }}
    container:
      image: ${{ matrix.config.image }}
      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
    defaults:
      run:
        shell: bash
    steps:
    - name: Checkout diffusers
      uses: actions/checkout@v3
      with:
        fetch-depth: 2
    - name: Install dependencies
      run: |
        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
        python -m uv pip install -e [quality,test]
        pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
    - name: Environment
      run: |
        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
        python utils/print_env.py
    - name: Run fast PyTorch Pipeline CPU tests
      if: ${{ matrix.config.framework == 'pytorch_pipelines' }}
      run: |
        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
        python -m pytest -n 8 --max-worker-restart=0 --dist=loadfile \
          -s -v -k "not Flax and not Onnx" \
          --make-reports=tests_${{ matrix.config.report }} \
          tests/modular_pipelines
    - name: Failure short reports
      if: ${{ failure() }}
      run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt
    - name: Test suite reports artifacts
      if: ${{ always() }}
      uses: actions/upload-artifact@v4
      with:
        name: pr_${{ matrix.config.framework }}_${{ matrix.config.report }}_test_reports
        path: reports
@@ -13,7 +13,6 @@ on:
      - "src/diffusers/loaders/peft.py"
      - "tests/pipelines/test_pipelines_common.py"
      - "tests/models/test_modeling_common.py"
      - "examples/**/*.py"
  workflow_dispatch:
 concurrency:
@@ -31,7 +31,7 @@ pip install -r requirements.txt
 We need to be authenticated to access some of the checkpoints used during benchmarking:
 ```sh
-hf auth login
+huggingface-cli login
 ```
 We use an L40 GPU with 128GB RAM to run the benchmark CI. As such, the benchmarks are configured to run on NVIDIA GPUs. So, make sure you have access to a similar machine (or modify the benchmarking scripts accordingly).
@@ -1,39 +1,36 @@
- title: Get started
+- sections:
  sections:
  - local: index
-    title: Diffusers
+    title: 🧨 Diffusers
  - local: installation
    title: Installation
  - local: quicktour
    title: Quicktour
  - local: stable_diffusion
    title: Effective and efficient diffusion
-
+  - local: installation
- title: DiffusionPipeline
+    title: Installation
-  isExpanded: false
+  title: Get started
-  sections:
+- sections:
-  - local: using-diffusers/loading
+  - local: tutorials/tutorial_overview
-    title: Load pipelines
+    title: Overview
  - local: using-diffusers/write_own_pipeline
    title: Understanding pipelines, models and schedulers
  - local: tutorials/autopipeline
    title: AutoPipeline
  - local: tutorials/basic_training
    title: Train a diffusion model
  title: Tutorials
 - sections:
  - local: using-diffusers/loading
    title: Load pipelines
  - local: using-diffusers/custom_pipeline_overview
    title: Load community pipelines and components
  - local: using-diffusers/callback
    title: Pipeline callbacks
  - local: using-diffusers/reusing_seeds
    title: Reproducible pipelines
  - local: using-diffusers/schedulers
    title: Load schedulers and models
  - local: using-diffusers/scheduler_features
    title: Scheduler features
  - local: using-diffusers/other-formats
    title: Model files and layouts
  - local: using-diffusers/push_to_hub
    title: Push files to the Hub
-
+  title: Load pipelines and adapters
- title: Adapters
+- sections:
  isExpanded: false
  sections:
  - local: tutorials/using_peft_for_inference
    title: LoRA
  - local: using-diffusers/ip_adapter
@@ -46,12 +43,25 @@
    title: DreamBooth
  - local: using-diffusers/textual_inversion_inference
    title: Textual inversion
-
+  title: Adapters
 - title: Inference
  isExpanded: false
-  sections:
+- sections:
-  - local: using-diffusers/weighted_prompts
+  - local: using-diffusers/unconditional_image_generation
-    title: Prompt techniques
+    title: Unconditional image generation
  - local: using-diffusers/conditional_image_generation
    title: Text-to-image
  - local: using-diffusers/img2img
    title: Image-to-image
  - local: using-diffusers/inpaint
    title: Inpainting
  - local: using-diffusers/text-img2vid
    title: Video generation
  - local: using-diffusers/depth2img
    title: Depth-to-image
  title: Generative tasks
 - sections:
  - local: using-diffusers/overview_techniques
    title: Overview
  - local: using-diffusers/create_a_server
    title: Create a server
  - local: using-diffusers/batched_inference
@@ -66,38 +76,14 @@
    title: Reproducible pipelines
  - local: using-diffusers/image_quality
    title: Controlling image quality
-
+  - local: using-diffusers/weighted_prompts
- title: Inference optimization
+    title: Prompt techniques
-  isExpanded: false
+  title: Inference techniques
-  sections:
+- sections:
-  - local: optimization/fp16
+  - local: advanced_inference/outpaint
-    title: Accelerate inference
+    title: Outpainting
-  - local: optimization/cache
+  title: Advanced inference
-    title: Caching
+- sections:
  - local: optimization/memory
    title: Reduce memory usage
  - local: optimization/speed-memory-optims
    title: Compile and offloading quantized models
  - title: Community optimizations
    sections:
    - local: optimization/pruna
      title: Pruna
    - local: optimization/xformers
      title: xFormers
    - local: optimization/tome
      title: Token merging
    - local: optimization/deepcache
      title: DeepCache
    - local: optimization/tgate
      title: TGATE
    - local: optimization/xdit
      title: xDiT
    - local: optimization/para_attn
      title: ParaAttention
 - title: Hybrid Inference
  isExpanded: false
  sections:
  - local: hybrid_inference/overview
    title: Overview
  - local: hybrid_inference/vae_decode
@@ -106,10 +92,8 @@
    title: VAE Encode
  - local: hybrid_inference/api_reference
    title: API Reference
-
+  title: Hybrid Inference
- title: Modular Diffusers
+- sections:
  isExpanded: false
  sections:
  - local: modular_diffusers/overview
    title: Overview
  - local: modular_diffusers/modular_pipeline
@@ -128,88 +112,8 @@
    title: Auto Pipeline Blocks
  - local: modular_diffusers/end_to_end_guide
    title: End-to-End Example
-
+  title: Modular Diffusers
- title: Training
+- sections:
  isExpanded: false
  sections:
  - local: training/overview
    title: Overview
  - local: training/create_dataset
    title: Create a dataset for training
  - local: training/adapt_a_model
    title: Adapt a model to a new task
  - local: tutorials/basic_training
    title: Train a diffusion model
  - title: Models
    sections:
    - local: training/unconditional_training
      title: Unconditional image generation
    - local: training/text2image
      title: Text-to-image
    - local: training/sdxl
      title: Stable Diffusion XL
    - local: training/kandinsky
      title: Kandinsky 2.2
    - local: training/wuerstchen
      title: Wuerstchen
    - local: training/controlnet
      title: ControlNet
    - local: training/t2i_adapters
      title: T2I-Adapters
    - local: training/instructpix2pix
      title: InstructPix2Pix
    - local: training/cogvideox
      title: CogVideoX
  - title: Methods
    sections:
    - local: training/text_inversion
      title: Textual Inversion
    - local: training/dreambooth
      title: DreamBooth
    - local: training/lora
      title: LoRA
    - local: training/custom_diffusion
      title: Custom Diffusion
    - local: training/lcm_distill
      title: Latent Consistency Distillation
    - local: training/ddpo
      title: Reinforcement learning training with DDPO
 - title: Quantization
  isExpanded: false
  sections:
  - local: quantization/overview
    title: Getting started
  - local: quantization/bitsandbytes
    title: bitsandbytes
  - local: quantization/gguf
    title: gguf
  - local: quantization/torchao
    title: torchao
  - local: quantization/quanto
    title: quanto
 - title: Model accelerators and hardware
  isExpanded: false
  sections:
  - local: using-diffusers/stable_diffusion_jax_how_to
    title: JAX/Flax
  - local: optimization/onnx
    title: ONNX
  - local: optimization/open_vino
    title: OpenVINO
  - local: optimization/coreml
    title: Core ML
  - local: optimization/mps
    title: Metal Performance Shaders (MPS)
  - local: optimization/habana
    title: Intel Gaudi
  - local: optimization/neuron
    title: AWS Neuron
 - title: Specific pipeline examples
  isExpanded: false
  sections:
  - local: using-diffusers/consisid
    title: ConsisID
  - local: using-diffusers/sdxl
@@ -234,30 +138,106 @@
    title: Stable Video Diffusion
  - local: using-diffusers/marigold_usage
    title: Marigold Computer Vision
-
+  title: Specific pipeline examples
- title: Resources
+- sections:
-  isExpanded: false
+  - local: training/overview
-  sections:
+    title: Overview
-  - title: Task recipes
+  - local: training/create_dataset
    title: Create a dataset for training
  - local: training/adapt_a_model
    title: Adapt a model to a new task
  - isExpanded: false
    sections:
-    - local: using-diffusers/unconditional_image_generation
+    - local: training/unconditional_training
      title: Unconditional image generation
-    - local: using-diffusers/conditional_image_generation
+    - local: training/text2image
      title: Text-to-image
-    - local: using-diffusers/img2img
+    - local: training/sdxl
-      title: Image-to-image
+      title: Stable Diffusion XL
-    - local: using-diffusers/inpaint
+    - local: training/kandinsky
-      title: Inpainting
+      title: Kandinsky 2.2
-    - local: advanced_inference/outpaint
+    - local: training/wuerstchen
-      title: Outpainting
+      title: Wuerstchen
-    - local: using-diffusers/text-img2vid
+    - local: training/controlnet
-      title: Video generation
+      title: ControlNet
-    - local: using-diffusers/depth2img
+    - local: training/t2i_adapters
-      title: Depth-to-image
+      title: T2I-Adapters
-  - local: using-diffusers/write_own_pipeline
+    - local: training/instructpix2pix
-    title: Understanding pipelines, models and schedulers
+      title: InstructPix2Pix
-  - local: community_projects
+    - local: training/cogvideox
-    title: Projects built with Diffusers
+      title: CogVideoX
    title: Models
  - isExpanded: false
    sections:
    - local: training/text_inversion
      title: Textual Inversion
    - local: training/dreambooth
      title: DreamBooth
    - local: training/lora
      title: LoRA
    - local: training/custom_diffusion
      title: Custom Diffusion
    - local: training/lcm_distill
      title: Latent Consistency Distillation
    - local: training/ddpo
      title: Reinforcement learning training with DDPO
    title: Methods
  title: Training
 - sections:
  - local: quantization/overview
    title: Getting Started
  - local: quantization/bitsandbytes
    title: bitsandbytes
  - local: quantization/gguf
    title: gguf
  - local: quantization/torchao
    title: torchao
  - local: quantization/quanto
    title: quanto
  title: Quantization Methods
 - sections:
  - local: optimization/fp16
    title: Accelerate inference
  - local: optimization/cache
    title: Caching
  - local: optimization/memory
    title: Reduce memory usage
  - local: optimization/speed-memory-optims
    title: Compile and offloading quantized models
  - local: optimization/pruna
    title: Pruna
  - local: optimization/xformers
    title: xFormers
  - local: optimization/tome
    title: Token merging
  - local: optimization/deepcache
    title: DeepCache
  - local: optimization/tgate
    title: TGATE
  - local: optimization/xdit
    title: xDiT
  - local: optimization/para_attn
    title: ParaAttention
  - sections:
    - local: using-diffusers/stable_diffusion_jax_how_to
      title: JAX/Flax
    - local: optimization/onnx
      title: ONNX
    - local: optimization/open_vino
      title: OpenVINO
    - local: optimization/coreml
      title: Core ML
    title: Optimized model formats
  - sections:
    - local: optimization/mps
      title: Metal Performance Shaders (MPS)
    - local: optimization/habana
      title: Intel Gaudi
    - local: optimization/neuron
      title: AWS Neuron
    title: Optimized hardware
  title: Accelerate inference and reduce memory
 - sections:
  - local: conceptual/philosophy
    title: Philosophy
  - local: using-diffusers/controlling_generation
@@ -268,11 +248,13 @@
    title: Diffusers' Ethical Guidelines
  - local: conceptual/evaluation
    title: Evaluating Diffusion Models
-
+  title: Conceptual Guides
- title: API
+- sections:
-  isExpanded: false
+  - local: community_projects
-  sections:
+    title: Projects built with Diffusers
-  - title: Main Classes
+  title: Community Projects
 - sections:
  - isExpanded: false
    sections:
    - local: api/configuration
      title: Configuration
@@ -282,7 +264,8 @@
      title: Outputs
    - local: api/quantization
      title: Quantization
-  - title: Loaders
+    title: Main Classes
  - isExpanded: false
    sections:
    - local: api/loaders/ip_adapter
      title: IP-Adapter
@@ -298,14 +281,14 @@
      title: SD3Transformer2D
    - local: api/loaders/peft
      title: PEFT
-  - title: Models
+    title: Loaders
  - isExpanded: false
    sections:
    - local: api/models/overview
      title: Overview
    - local: api/models/auto_model
      title: AutoModel
-    - title: ControlNets
+    - sections:
      sections:
      - local: api/models/controlnet
        title: ControlNetModel
      - local: api/models/controlnet_union
@@ -320,8 +303,8 @@
        title: SD3ControlNetModel
      - local: api/models/controlnet_sparsectrl
        title: SparseControlNetModel
-    - title: Transformers
+      title: ControlNets
-      sections:
+    - sections:
      - local: api/models/allegro_transformer3d
        title: AllegroTransformer3DModel
      - local: api/models/aura_flow_transformer2d
@@ -370,8 +353,6 @@
        title: SanaTransformer2DModel
      - local: api/models/sd3_transformer2d
        title: SD3Transformer2DModel
      - local: api/models/skyreels_v2_transformer_3d
        title: SkyReelsV2Transformer3DModel
      - local: api/models/stable_audio_transformer
        title: StableAudioDiTModel
      - local: api/models/transformer2d
@@ -380,8 +361,8 @@
        title: TransformerTemporalModel
      - local: api/models/wan_transformer_3d
        title: WanTransformer3DModel
-    - title: UNets
+      title: Transformers
-      sections:
+    - sections:
      - local: api/models/stable_cascade_unet
        title: StableCascadeUNet
      - local: api/models/unet
@@ -396,8 +377,8 @@
        title: UNetMotionModel
      - local: api/models/uvit2d
        title: UViT2DModel
-    - title: VAEs
+      title: UNets
-      sections:
+    - sections:
      - local: api/models/asymmetricautoencoderkl
        title: AsymmetricAutoencoderKL
      - local: api/models/autoencoder_dc
@@ -428,7 +409,9 @@
        title: Tiny AutoEncoder
      - local: api/models/vq
        title: VQModel
-  - title: Pipelines
+      title: VAEs
    title: Models
  - isExpanded: false
    sections:
    - local: api/pipelines/overview
      title: Overview
@@ -564,14 +547,11 @@
      title: Semantic Guidance
    - local: api/pipelines/shap_e
      title: Shap-E
    - local: api/pipelines/skyreels_v2
      title: SkyReels-V2
    - local: api/pipelines/stable_audio
      title: Stable Audio
    - local: api/pipelines/stable_cascade
      title: Stable Cascade
-    - title: Stable Diffusion
+    - sections:
      sections:
      - local: api/pipelines/stable_diffusion/overview
        title: Overview
      - local: api/pipelines/stable_diffusion/depth2img
@@ -608,6 +588,7 @@
        title: T2I-Adapter
      - local: api/pipelines/stable_diffusion/text2img
        title: Text-to-image
      title: Stable Diffusion
    - local: api/pipelines/stable_unclip
      title: Stable unCLIP
    - local: api/pipelines/text_to_video
@@ -626,7 +607,8 @@
      title: Wan
    - local: api/pipelines/wuerstchen
      title: Wuerstchen
-  - title: Schedulers
+    title: Pipelines
  - isExpanded: false
    sections:
    - local: api/schedulers/overview
      title: Overview
@@ -696,7 +678,8 @@
      title: UniPCMultistepScheduler
    - local: api/schedulers/vq_diffusion
      title: VQDiffusionScheduler
-  - title: Internal classes
+    title: Schedulers
  - isExpanded: false
    sections:
    - local: api/internal_classes_overview
      title: Overview
@@ -714,3 +697,5 @@
      title: VAE Image Processor
    - local: api/video_processor
      title: Video Processor
    title: Internal classes
  title: API
@@ -16,7 +16,7 @@ Schedulers from [`~schedulers.scheduling_utils.SchedulerMixin`] and models from
 <Tip>
-To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `hf auth login`.
+To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `huggingface-cli login`.
 </Tip>
@@ -26,7 +26,6 @@ LoRA is a fast and lightweight training method that inserts and trains a signifi
 - [`HunyuanVideoLoraLoaderMixin`] provides similar functions for [HunyuanVideo](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hunyuan_video).
 - [`Lumina2LoraLoaderMixin`] provides similar functions for [Lumina2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/lumina2).
 - [`WanLoraLoaderMixin`] provides similar functions for [Wan](https://huggingface.co/docs/diffusers/main/en/api/pipelines/wan).
 - [`SkyReelsV2LoraLoaderMixin`] provides similar functions for [SkyReels-V2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/skyreels_v2).
 - [`CogView4LoraLoaderMixin`] provides similar functions for [CogView4](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogview4).
 - [`AmusedLoraLoaderMixin`] is for the [`AmusedPipeline`].
 - [`HiDreamImageLoraLoaderMixin`] provides similar functions for [HiDream Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hidream)
@@ -93,10 +92,6 @@ To learn more about how to load LoRA weights, see the [LoRA](../../using-diffuse
 [[autodoc]] loaders.lora_pipeline.WanLoraLoaderMixin
 ## SkyReelsV2LoraLoaderMixin
 [[autodoc]] loaders.lora_pipeline.SkyReelsV2LoraLoaderMixin
 ## AmusedLoraLoaderMixin
 [[autodoc]] loaders.lora_pipeline.AmusedLoraLoaderMixin
@@ -105,6 +100,6 @@ To learn more about how to load LoRA weights, see the [LoRA](../../using-diffuse
 [[autodoc]] loaders.lora_pipeline.HiDreamImageLoraLoaderMixin
-## LoraBaseMixin
+## WanLoraLoaderMixin
-[[autodoc]] loaders.lora_base.LoraBaseMixin
+[[autodoc]] loaders.lora_pipeline.WanLoraLoaderMixin
@@ -1,30 +0,0 @@
 <!-- Copyright 2024 The HuggingFace Team. All rights reserved.
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
 http://www.apache.org/licenses/LICENSE-2.0
 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
 an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
 specific language governing permissions and limitations under the License. -->
 # SkyReelsV2Transformer3DModel
 A Diffusion Transformer model for 3D video-like data was introduced in [SkyReels-V2](https://github.com/SkyworkAI/SkyReels-V2) by the Skywork AI.
 The model can be loaded with the following code snippet.
 ```python
 from diffusers import SkyReelsV2Transformer3DModel
 transformer = SkyReelsV2Transformer3DModel.from_pretrained("Skywork/SkyReels-V2-DF-1.3B-540P-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
 ```
 ## SkyReelsV2Transformer3DModel
 [[autodoc]] SkyReelsV2Transformer3DModel
 ## Transformer2DModelOutput
 [[autodoc]] models.modeling_outputs.Transformer2DModelOutput
@@ -1,367 +0,0 @@
 <!-- Copyright 2024 The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License. -->
 <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>
 # SkyReels-V2: Infinite-length Film Generative model
 [SkyReels-V2](https://huggingface.co/papers/2504.13074) by the SkyReels Team.
 *Recent advances in video generation have been driven by diffusion models and autoregressive frameworks, yet critical challenges persist in harmonizing prompt adherence, visual quality, motion dynamics, and duration: compromises in motion dynamics to enhance temporal visual quality, constrained video duration (5-10 seconds) to prioritize resolution, and inadequate shot-aware generation stemming from general-purpose MLLMs' inability to interpret cinematic grammar, such as shot composition, actor expressions, and camera motions. These intertwined limitations hinder realistic long-form synthesis and professional film-style generation. To address these limitations, we propose SkyReels-V2, an Infinite-length Film Generative Model, that synergizes Multi-modal Large Language Model (MLLM), Multi-stage Pretraining, Reinforcement Learning, and Diffusion Forcing Framework. Firstly, we design a comprehensive structural representation of video that combines the general descriptions by the Multi-modal LLM and the detailed shot language by sub-expert models. Aided with human annotation, we then train a unified Video Captioner, named SkyCaptioner-V1, to efficiently label the video data. Secondly, we establish progressive-resolution pretraining for the fundamental video generation, followed by a four-stage post-training enhancement: Initial concept-balanced Supervised Fine-Tuning (SFT) improves baseline quality; Motion-specific Reinforcement Learning (RL) training with human-annotated and synthetic distortion data addresses dynamic artifacts; Our diffusion forcing framework with non-decreasing noise schedules enables long-video synthesis in an efficient search space; Final high-quality SFT refines visual fidelity. All the code and models are available at [this https URL](https://github.com/SkyworkAI/SkyReels-V2).*
 You can find all the original SkyReels-V2 checkpoints under the [Skywork](https://huggingface.co/collections/Skywork/skyreels-v2-6801b1b93df627d441d0d0d9) organization.
 The following SkyReels-V2 models are supported in Diffusers:
 - [SkyReels-V2 DF 1.3B - 540P](https://huggingface.co/Skywork/SkyReels-V2-DF-1.3B-540P-Diffusers)
 - [SkyReels-V2 DF 14B - 540P](https://huggingface.co/Skywork/SkyReels-V2-DF-14B-540P-Diffusers)
 - [SkyReels-V2 DF 14B - 720P](https://huggingface.co/Skywork/SkyReels-V2-DF-14B-720P-Diffusers)
 - [SkyReels-V2 T2V 14B - 540P](https://huggingface.co/Skywork/SkyReels-V2-T2V-14B-540P-Diffusers)
 - [SkyReels-V2 T2V 14B - 720P](https://huggingface.co/Skywork/SkyReels-V2-T2V-14B-720P-Diffusers)
 - [SkyReels-V2 I2V 1.3B - 540P](https://huggingface.co/Skywork/SkyReels-V2-I2V-1.3B-540P-Diffusers)
 - [SkyReels-V2 I2V 14B - 540P](https://huggingface.co/Skywork/SkyReels-V2-I2V-14B-540P-Diffusers)
 - [SkyReels-V2 I2V 14B - 720P](https://huggingface.co/Skywork/SkyReels-V2-I2V-14B-720P-Diffusers)
 - [SkyReels-V2 FLF2V 1.3B - 540P](https://huggingface.co/Skywork/SkyReels-V2-FLF2V-1.3B-540P-Diffusers)
 > [!TIP]
 > Click on the SkyReels-V2 models in the right sidebar for more examples of video generation.
 ### A _Visual_ Demonstration
        An example with these parameters:
        base_num_frames=97, num_frames=97, num_inference_steps=30, ar_step=5, causal_block_size=5
        vae_scale_factor_temporal -> 4
        num_latent_frames: (97-1)//vae_scale_factor_temporal+1 = 25 frames -> 5 blocks of 5 frames each
        base_num_latent_frames = (97-1)//vae_scale_factor_temporal+1 = 25 → blocks = 25//5 = 5 blocks
        This 5 blocks means the maximum context length of the model is 25 frames in the latent space.
        Asynchronous Processing Timeline:
        ┌─────────────────────────────────────────────────────────────────┐
        │ Steps:    1    6   11   16   21   26   31   36   41   46   50   │
        │ Block 1: [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■]                       │
        │ Block 2:      [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■]                  │
        │ Block 3:           [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■]             │
        │ Block 4:                [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■]        │
        │ Block 5:                     [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■]   │
        └─────────────────────────────────────────────────────────────────┘
        For Long Videos (num_frames > base_num_frames):
        base_num_frames acts as the "sliding window size" for processing long videos.
        Example: 257-frame video with base_num_frames=97, overlap_history=17
        ┌──── Iteration 1 (frames 1-97) ────┐
        │ Processing window: 97 frames      │ → 5 blocks, async processing
        │ Generates: frames 1-97            │
        └───────────────────────────────────┘
                    ┌────── Iteration 2 (frames 81-177) ──────┐
                    │ Processing window: 97 frames            │
                    │ Overlap: 17 frames (81-97) from prev    │ → 5 blocks, async processing
                    │ Generates: frames 98-177                │
                    └─────────────────────────────────────────┘
                                ┌────── Iteration 3 (frames 161-257) ──────┐
                                │ Processing window: 97 frames             │
                                │ Overlap: 17 frames (161-177) from prev   │ → 5 blocks, async processing
                                │ Generates: frames 178-257                │
                                └──────────────────────────────────────────┘
        Each iteration independently runs the asynchronous processing with its own 5 blocks.
        base_num_frames controls:
        1. Memory usage (larger window = more VRAM)
        2. Model context length (must match training constraints)
        3. Number of blocks per iteration (base_num_latent_frames // causal_block_size)
        Each block takes 30 steps to complete denoising.
        Block N starts at step: 1 + (N-1) x ar_step
        Total steps: 30 + (5-1) x 5 = 50 steps
        Synchronous mode (ar_step=0) would process all blocks/frames simultaneously:
        ┌──────────────────────────────────────────────┐
        │ Steps:       1            ...            30  │
        │ All blocks: [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■] │
        └──────────────────────────────────────────────┘
        Total steps: 30 steps
        An example on how the step matrix is constructed for asynchronous processing:
        Given the parameters: (num_inference_steps=30, flow_shift=8, num_frames=97, ar_step=5, causal_block_size=5)
        - num_latent_frames = (97 frames - 1) // (4 temporal downsampling) + 1 = 25
        - step_template = [999, 995, 991, 986, 980, 975, 969, 963, 956, 948,
                           941, 932, 922, 912, 901, 888, 874, 859, 841, 822,
                           799, 773, 743, 708, 666, 615, 551, 470, 363, 216]
        The algorithm creates a 50x25 step_matrix where:
        - Row 1:  [999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999]
        - Row 2:  [995, 995, 995, 995, 995, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999]
        - Row 3:  [991, 991, 991, 991, 991, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999]
        - ...
        - Row 7:  [969, 969, 969, 969, 969, 995, 995, 995, 995, 995, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999]
        - ...
        - Row 21: [799, 799, 799, 799, 799, 888, 888, 888, 888, 888, 941, 941, 941, 941, 941, 975, 975, 975, 975, 975, 999, 999, 999, 999, 999]
        - ...
        - Row 35: [  0,   0,   0,   0,   0, 216, 216, 216, 216, 216, 666, 666, 666, 666, 666, 822, 822, 822, 822, 822, 901, 901, 901, 901, 901]
        - ...
        - Row 42: [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 551, 551, 551, 551, 551, 773, 773, 773, 773, 773]
        - ...
        - Row 50: [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 216, 216, 216, 216, 216]
        Detailed Row 6 Analysis:
        - step_matrix[5]:       [ 975, 975, 975, 975, 975, 999, 999, 999, 999, 999, 999,  ...,  999]
        - step_index[5]:        [   6,   6,   6,   6,   6,   1,   1,   1,   1,   1,   0,  ...,    0]
        - step_update_mask[5]:  [True,True,True,True,True,True,True,True,True,True,False, ...,False]
        - valid_interval[5]:    (0, 25)
        Key Pattern: Block i lags behind Block i-1 by exactly ar_step=5 timesteps, creating the
        staggered "diffusion forcing" effect where later blocks condition on cleaner earlier blocks.
 ### Text-to-Video Generation
 The example below demonstrates how to generate a video from text.
 <hfoptions id="T2V usage">
 <hfoption id="T2V memory">
 Refer to the [Reduce memory usage](../../optimization/memory) guide for more details about the various memory saving techniques.
 From the original repo:
 >You can use --ar_step 5 to enable asynchronous inference. When asynchronous inference, --causal_block_size 5 is recommended while it is not supposed to be set for synchronous generation... Asynchronous inference will take more steps to diffuse the whole sequence which means it will be SLOWER than synchronous mode. In our experiments, asynchronous inference may improve the instruction following and visual consistent performance.
 ```py
 # pip install ftfy
 import torch
 from diffusers import AutoModel, SkyReelsV2DiffusionForcingPipeline, UniPCMultistepScheduler
 from diffusers.utils import export_to_video
 vae = AutoModel.from_pretrained("Skywork/SkyReels-V2-DF-14B-540P-Diffusers", subfolder="vae", torch_dtype=torch.float32)
 transformer = AutoModel.from_pretrained("Skywork/SkyReels-V2-DF-14B-540P-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
 pipeline = SkyReelsV2DiffusionForcingPipeline.from_pretrained(
    "Skywork/SkyReels-V2-DF-14B-540P-Diffusers",
    vae=vae,
    transformer=transformer,
    torch_dtype=torch.bfloat16
 )
 flow_shift = 8.0  # 8.0 for T2V, 5.0 for I2V
 pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config, flow_shift=flow_shift)
 pipeline = pipeline.to("cuda")
 prompt = "A cat and a dog baking a cake together in a kitchen. The cat is carefully measuring flour, while the dog is stirring the batter with a wooden spoon. The kitchen is cozy, with sunlight streaming through the window."
 output = pipeline(
    prompt=prompt,
    num_inference_steps=30,
    height=544,  # 720 for 720P
    width=960,   # 1280 for 720P
    num_frames=97,
    base_num_frames=97,  # 121 for 720P
    ar_step=5,  # Controls asynchronous inference (0 for synchronous mode)
    causal_block_size=5,  # Number of frames in each block for asynchronous processing
    overlap_history=None,  # Number of frames to overlap for smooth transitions in long videos; 17 for long video generations
    addnoise_condition=20,  # Improves consistency in long video generation
 ).frames[0]
 export_to_video(output, "T2V.mp4", fps=24, quality=8)
 ```
 </hfoption>
 </hfoptions>
 ### First-Last-Frame-to-Video Generation
 The example below demonstrates how to use the image-to-video pipeline to generate a video using a text description, a starting frame, and an ending frame.
 <hfoptions id="FLF2V usage">
 <hfoption id="usage">
 ```python
 import numpy as np
 import torch
 import torchvision.transforms.functional as TF
 from diffusers import AutoencoderKLWan, SkyReelsV2DiffusionForcingImageToVideoPipeline, UniPCMultistepScheduler
 from diffusers.utils import export_to_video, load_image
 model_id = "Skywork/SkyReels-V2-DF-14B-720P-Diffusers"
 vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
 pipeline = SkyReelsV2DiffusionForcingImageToVideoPipeline.from_pretrained(
    model_id, vae=vae, torch_dtype=torch.bfloat16
 )
 flow_shift = 5.0  # 8.0 for T2V, 5.0 for I2V
 pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config, flow_shift=flow_shift)
 pipeline.to("cuda")
 first_frame = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_first_frame.png")
 last_frame = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_last_frame.png")
 def aspect_ratio_resize(image, pipeline, max_area=720 * 1280):
    aspect_ratio = image.height / image.width
    mod_value = pipeline.vae_scale_factor_spatial * pipeline.transformer.config.patch_size[1]
    height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
    width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
    image = image.resize((width, height))
    return image, height, width
 def center_crop_resize(image, height, width):
    # Calculate resize ratio to match first frame dimensions
    resize_ratio = max(width / image.width, height / image.height)
    # Resize the image
    width = round(image.width * resize_ratio)
    height = round(image.height * resize_ratio)
    size = [width, height]
    image = TF.center_crop(image, size)
    return image, height, width
 first_frame, height, width = aspect_ratio_resize(first_frame, pipeline)
 if last_frame.size != first_frame.size:
    last_frame, _, _ = center_crop_resize(last_frame, height, width)
 prompt = "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird's feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective."
 output = pipeline(
    image=first_frame, last_image=last_frame, prompt=prompt, height=height, width=width, guidance_scale=5.0
 ).frames[0]
 export_to_video(output, "output.mp4", fps=24, quality=8)
 ```
 </hfoption>
 </hfoptions>
 ### Video-to-Video Generation
 <hfoptions id="V2V usage">
 <hfoption id="usage">
 `SkyReelsV2DiffusionForcingVideoToVideoPipeline` extends a given video.
 ```python
 import numpy as np
 import torch
 import torchvision.transforms.functional as TF
 from diffusers import AutoencoderKLWan, SkyReelsV2DiffusionForcingVideoToVideoPipeline, UniPCMultistepScheduler
 from diffusers.utils import export_to_video, load_video
 model_id = "Skywork/SkyReels-V2-DF-14B-540P-Diffusers"
 vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
 pipeline = SkyReelsV2DiffusionForcingVideoToVideoPipeline.from_pretrained(
    model_id, vae=vae, torch_dtype=torch.bfloat16
 )
 flow_shift = 5.0  # 8.0 for T2V, 5.0 for I2V
 pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config, flow_shift=flow_shift)
 pipeline.to("cuda")
 video = load_video("input_video.mp4")
 prompt = "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird's feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective."
 output = pipeline(
    video=video, prompt=prompt, height=544, width=960, guidance_scale=5.0,
    num_inference_steps=30, num_frames=257, base_num_frames=97#, ar_step=5, causal_block_size=5,
 ).frames[0]
 export_to_video(output, "output.mp4", fps=24, quality=8)
 # Total frames will be the number of frames of given video + 257
 ```
 </hfoption>
 </hfoptions>
 ## Notes
 - SkyReels-V2 supports LoRAs with [`~loaders.SkyReelsV2LoraLoaderMixin.load_lora_weights`].
  <details>
  <summary>Show example code</summary>
  ```py
  # pip install ftfy
  import torch
  from diffusers import AutoModel, SkyReelsV2DiffusionForcingPipeline
  from diffusers.utils import export_to_video
  vae = AutoModel.from_pretrained(
      "Skywork/SkyReels-V2-DF-1.3B-540P-Diffusers", subfolder="vae", torch_dtype=torch.float32
  )
  pipeline = SkyReelsV2DiffusionForcingPipeline.from_pretrained(
      "Skywork/SkyReels-V2-DF-1.3B-540P-Diffusers", vae=vae, torch_dtype=torch.bfloat16
  )
  pipeline.to("cuda")
  pipeline.load_lora_weights("benjamin-paine/steamboat-willie-1.3b", adapter_name="steamboat-willie")
  pipeline.set_adapters("steamboat-willie")
  pipeline.enable_model_cpu_offload()
  # 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
  shadows and warm highlights. Medium composition, front view, low angle, with depth of field.
  """
  output = pipeline(
      prompt=prompt,
      num_frames=97,
      guidance_scale=6.0,
  ).frames[0]
  export_to_video(output, "output.mp4", fps=24)
  ```
  </details>
 ## SkyReelsV2DiffusionForcingPipeline
 [[autodoc]] SkyReelsV2DiffusionForcingPipeline
  - all
  - __call__
 ## SkyReelsV2DiffusionForcingImageToVideoPipeline
 [[autodoc]] SkyReelsV2DiffusionForcingImageToVideoPipeline
  - all
  - __call__
 ## SkyReelsV2DiffusionForcingVideoToVideoPipeline
 [[autodoc]] SkyReelsV2DiffusionForcingVideoToVideoPipeline
  - all
  - __call__
 ## SkyReelsV2Pipeline
 [[autodoc]] SkyReelsV2Pipeline
  - all
  - __call__
 ## SkyReelsV2ImageToVideoPipeline
 [[autodoc]] SkyReelsV2ImageToVideoPipeline
  - all
  - __call__
 ## SkyReelsV2PipelineOutput
 [[autodoc]] pipelines.skyreels_v2.pipeline_output.SkyReelsV2PipelineOutput
@@ -31,7 +31,7 @@ _As the model is gated, before using it with diffusers you first need to go to t
 Use the command below to log in:
 ```bash
-hf auth login
+huggingface-cli login
 ```
 <Tip>
@@ -27,19 +27,19 @@ Learn how to quantize models in the [Quantization](../quantization/overview) gui
 ## BitsAndBytesConfig
-[[autodoc]] quantizers.quantization_config.BitsAndBytesConfig
+[[autodoc]] BitsAndBytesConfig
 ## GGUFQuantizationConfig
-[[autodoc]] quantizers.quantization_config.GGUFQuantizationConfig
+[[autodoc]] GGUFQuantizationConfig
 ## QuantoConfig
-[[autodoc]] quantizers.quantization_config.QuantoConfig
+[[autodoc]] QuantoConfig
 ## TorchAoConfig
-[[autodoc]] quantizers.quantization_config.TorchAoConfig
+[[autodoc]] TorchAoConfig
 ## DiffusersQuantizer
@@ -12,24 +12,37 @@ specific language governing permissions and limitations under the License.
 <p align="center">
    <br>
-    <img src="https://raw.githubusercontent.com/huggingface/diffusers/77aadfee6a891ab9fcfb780f87c693f7a5beeb8e/docs/source/imgs/diffusers_library.jpg" width="400" style="border: none;"/>
+    <img src="https://raw.githubusercontent.com/huggingface/diffusers/77aadfee6a891ab9fcfb780f87c693f7a5beeb8e/docs/source/imgs/diffusers_library.jpg" width="400"/>
    <br>
 </p>
 # Diffusers
-Diffusers is a library of state-of-the-art pretrained diffusion models for generating videos, images, and audio.
+🤗 Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or want to train your own diffusion model, 🤗 Diffusers is a modular toolbox that supports both. Our library is designed with a focus on [usability over performance](conceptual/philosophy#usability-over-performance), [simple over easy](conceptual/philosophy#simple-over-easy), and [customizability over abstractions](conceptual/philosophy#tweakable-contributorfriendly-over-abstraction).
-The library revolves around the [`DiffusionPipeline`], an API designed for:
+The library has three main components:
- easy inference with only a few lines of code
+- State-of-the-art diffusion pipelines for inference with just a few lines of code. There are many pipelines in 🤗 Diffusers, check out the table in the pipeline [overview](api/pipelines/overview) for a complete list of available pipelines and the task they solve.
- flexibility to mix-and-match pipeline components (models, schedulers)
+- Interchangeable [noise schedulers](api/schedulers/overview) for balancing trade-offs between generation speed and quality.
- loading and using adapters like LoRA
+- Pretrained [models](api/models) that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems.
-Diffusers also comes with optimizations - such as offloading and quantization - to ensure even the largest models are accessible on memory-constrained devices. If memory is not an issue, Diffusers supports torch.compile to boost inference speed.
+<div class="mt-10">
-
+  <div class="w-full flex flex-col space-y-4 md:space-y-0 md:grid md:grid-cols-2 md:gap-y-4 md:gap-x-5">
-Get started right away with a Diffusers model on the [Hub](https://huggingface.co/models?library=diffusers&sort=trending) today!
+    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./tutorials/tutorial_overview"
-
+      ><div class="w-full text-center bg-gradient-to-br from-blue-400 to-blue-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Tutorials</div>
-## Learn
+      <p class="text-gray-700">Learn the fundamental skills you need to start generating outputs, build your own diffusion system, and train a diffusion model. We recommend starting here if you're using 🤗 Diffusers for the first time!</p>
-
+    </a>
-If you're a beginner, we recommend starting with the [Hugging Face Diffusion Models Course](https://huggingface.co/learn/diffusion-course/unit0/1). You'll learn the theory behind diffusion models, and learn how to use the Diffusers library to generate images, fine-tune your own models, and more.
+    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./using-diffusers/loading_overview"
      ><div class="w-full text-center bg-gradient-to-br from-indigo-400 to-indigo-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">How-to guides</div>
      <p class="text-gray-700">Practical guides for helping you load pipelines, models, and schedulers. You'll also learn how to use pipelines for specific tasks, control how outputs are generated, optimize for inference speed, and different training techniques.</p>
    </a>
    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./conceptual/philosophy"
      ><div class="w-full text-center bg-gradient-to-br from-pink-400 to-pink-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Conceptual guides</div>
      <p class="text-gray-700">Understand why the library was designed the way it was, and learn more about the ethical guidelines and safety implementations for using the library.</p>
   </a>
    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./api/models/overview"
      ><div class="w-full text-center bg-gradient-to-br from-purple-400 to-purple-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Reference</div>
      <p class="text-gray-700">Technical descriptions of how 🤗 Diffusers classes and methods work.</p>
    </a>
  </div>
 </div>
@@ -239,12 +239,6 @@ The `step()` function is [called](https://github.com/huggingface/diffusers/blob/
 In general, the `sigmas` should [stay on the CPU](https://github.com/huggingface/diffusers/blob/35a969d297cba69110d175ee79c59312b9f49e1e/src/diffusers/schedulers/scheduling_euler_discrete.py#L240) to avoid the communication sync and latency.
 <Tip>
 Refer to the [torch.compile and Diffusers: A Hands-On Guide to Peak Performance](https://pytorch.org/blog/torch-compile-and-diffusers-a-hands-on-guide-to-peak-performance/) blog post for maximizing performance with `torch.compile` for diffusion models.
 </Tip>
 ### Benchmarks
 Refer to the [diffusers/benchmarks](https://huggingface.co/datasets/diffusers/benchmarks) dataset to see inference latency and memory usage data for compiled pipelines.
@@ -305,5 +299,3 @@ pipeline.fuse_qkv_projections()
 - Read the [Presenting Flux Fast: Making Flux go brrr on H100s](https://pytorch.org/blog/presenting-flux-fast-making-flux-go-brrr-on-h100s/) blog post to learn more about how you can combine all of these optimizations with [TorchInductor](https://docs.pytorch.org/docs/stable/torch.compiler.html) and [AOTInductor](https://docs.pytorch.org/docs/stable/torch.compiler_aot_inductor.html) for a ~2.5x speedup using recipes from [flux-fast](https://github.com/huggingface/flux-fast).
    These recipes support AMD hardware and [Flux.1 Kontext Dev](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev).
 - Read the [torch.compile and Diffusers: A Hands-On Guide to Peak Performance](https://pytorch.org/blog/torch-compile-and-diffusers-a-hands-on-guide-to-peak-performance/) blog post
 to maximize performance when using `torch.compile`.
@@ -11,7 +11,7 @@ specific language governing permissions and limitations under the License.
 -->
-# Getting started
+# Quantization
 Quantization focuses on representing data with fewer bits while also trying to preserve the precision of the original data. This often means converting a data type to represent the same information with fewer bits. For example, if your model weights are stored as 32-bit floating points and they're quantized to 16-bit floating points, this halves the model size which makes it easier to store and reduces memory usage. Lower precision can also speedup inference because it takes less time to perform calculations with fewer bits.
@@ -19,25 +19,19 @@ Diffusers supports multiple quantization backends to make large diffusion models
 ## Pipeline-level quantization
-There are two ways to use [`~quantizers.PipelineQuantizationConfig`] depending on how much customization you want to apply to the quantization configuration. 
+There are two ways you can use [`~quantizers.PipelineQuantizationConfig`] depending on the level of control you want over the quantization specifications of each model in the pipeline.
- for basic use cases, define the `quant_backend`, `quant_kwargs`, and `components_to_quantize` arguments
+- for more basic and simple use cases, you only need to define the `quant_backend`, `quant_kwargs`, and `components_to_quantize`
- for granular quantization control, define a `quant_mapping` that provides the quantization configuration for individual model components
+- for more granular quantization control, provide a `quant_mapping` that provides the quantization specifications for the individual model components
-### Basic quantization
+### Simple quantization
 Initialize [`~quantizers.PipelineQuantizationConfig`] with the following parameters.
 - `quant_backend` specifies which quantization backend to use. Currently supported backends include: `bitsandbytes_4bit`, `bitsandbytes_8bit`, `gguf`, `quanto`, and `torchao`.
- `quant_kwargs` specifies the quantization arguments to use.
+- `quant_kwargs` contains the specific quantization arguments to use.
 > [!TIP]
 > These `quant_kwargs` arguments are different for each backend. Refer to the [Quantization API](../api/quantization) docs to view the arguments for each backend.
 - `components_to_quantize` specifies which components of the pipeline to quantize. Typically, you should quantize the most compute intensive components like the transformer. The text encoder is another component to consider quantizing if a pipeline has more than one such as [`FluxPipeline`]. The example below quantizes the T5 text encoder in [`FluxPipeline`] while keeping the CLIP model intact.
 The example below loads the bitsandbytes backend with the following arguments from [`~quantizers.quantization_config.BitsAndBytesConfig`], `load_in_4bit`, `bnb_4bit_quant_type`, and `bnb_4bit_compute_dtype`.
 ```py
 import torch
 from diffusers import DiffusionPipeline
@@ -62,13 +56,13 @@ pipe = DiffusionPipeline.from_pretrained(
 image = pipe("photo of a cute dog").images[0]
 ```
-### Advanced quantization
+### quant_mapping
-The `quant_mapping` argument provides more options for how to quantize each individual component in a pipeline, like combining different quantization backends.
+The `quant_mapping` argument provides more flexible options for how to quantize each individual component in a pipeline, like combining different quantization backends.
 Initialize [`~quantizers.PipelineQuantizationConfig`] and pass a `quant_mapping` to it. The `quant_mapping` allows you to specify the quantization options for each component in the pipeline such as the transformer and text encoder.
-The example below uses two quantization backends, [`~quantizers.quantization_config.QuantoConfig`] and [`transformers.BitsAndBytesConfig`], for the transformer and text encoder.
+The example below uses two quantization backends, [`~quantizers.QuantoConfig`] and [`transformers.BitsAndBytesConfig`], for the transformer and text encoder.
 ```py
 import torch
@@ -91,7 +85,7 @@ pipeline_quant_config = PipelineQuantizationConfig(
 There is a separate bitsandbytes backend in [Transformers](https://huggingface.co/docs/transformers/main_classes/quantization#transformers.BitsAndBytesConfig). You need to import and use [`transformers.BitsAndBytesConfig`] for components that come from Transformers. For example, `text_encoder_2` in [`FluxPipeline`] is a [`~transformers.T5EncoderModel`] from Transformers so you need to use [`transformers.BitsAndBytesConfig`] instead of [`diffusers.BitsAndBytesConfig`].
 > [!TIP]
-> Use the [basic quantization](#basic-quantization) method above if you don't want to manage these distinct imports or aren't sure where each pipeline component comes from.
+> Use the [simple quantization](#simple-quantization) method above if you don't want to manage these distinct imports or aren't sure where each pipeline component comes from.
 ```py
 import torch
@@ -145,10 +145,10 @@ When running `accelerate config`, if you use torch.compile, there can be dramati
 If you would like to push your model to the Hub after training is completed with a neat model card, make sure you're logged in:
 ```bash
-hf auth login
+huggingface-cli login
 # Alternatively, you could upload your model manually using:
-# hf upload my-cool-account-name/my-cool-lora-name /path/to/awesome/lora
+# huggingface-cli upload my-cool-account-name/my-cool-lora-name /path/to/awesome/lora
 ```
 Make sure your data is prepared as described in [Data Preparation](#data-preparation). When ready, you can begin training!
@@ -67,7 +67,7 @@ dataset = load_dataset(
 Then use the [`~datasets.Dataset.push_to_hub`] method to upload the dataset to the Hub:
 ```python
-# assuming you have ran the hf auth login command in a terminal
+# assuming you have ran the huggingface-cli login command in a terminal
 dataset.push_to_hub("name_of_your_dataset")
 # if you want to push to a private repo, simply pass private=True:
@@ -42,7 +42,7 @@ We encourage you to share your model with the community, and in order to do that
 Or login in from the terminal:
 ```bash
-hf auth login
+huggingface-cli login
 ```
 Since the model checkpoints are quite large, install [Git-LFS](https://git-lfs.com/) to version these large files:
@@ -0,0 +1,23 @@
 <!--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.
 -->
 # Overview
 Welcome to 🧨 Diffusers! If you're new to diffusion models and generative AI, and want to learn more, then you've come to the right place. These beginner-friendly tutorials are designed to provide a gentle introduction to diffusion models and help you understand the library fundamentals - the core components and how 🧨 Diffusers is meant to be used.
 You'll learn how to use a pipeline for inference to rapidly generate things, and then deconstruct that pipeline to really understand how to use the library as a modular toolbox for building your own diffusion systems. In the next lesson, you'll learn how to train your own diffusion model to generate what you want.
 After completing the tutorials, you'll have gained the necessary skills to start exploring the library on your own and see how to use it for your own projects and applications.
 Feel free to join our community on [Discord](https://discord.com/invite/JfAtkvEtRb) or the [forums](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) to connect and collaborate with other users and developers!
 Let's start diffusing! 🧨
@@ -319,19 +319,6 @@ If you expect to varied resolutions during inference with this feature, then mak
 There are still scenarios where recompulation is unavoidable, such as when the hotswapped LoRA targets more layers than the initial adapter. Try to load the LoRA that targets the most layers *first*. For more details about this limitation, refer to the PEFT [hotswapping](https://huggingface.co/docs/peft/main/en/package_reference/hotswap#peft.utils.hotswap.hotswap_adapter) docs.
 <details>
 <summary>Technical details of hotswapping</summary>
 The [`~loaders.lora_base.LoraBaseMixin.enable_lora_hotswap`] method converts the LoRA scaling factor from floats to torch.tensors and pads the shape of the weights to the largest required shape to avoid reassigning the whole attribute when the data in the weights are replaced.
 This is why the `max_rank` argument is important. The results are unchanged even when the values are padded with zeros. Computation may be slower though depending on the padding size.
 Since no new LoRA attributes are added, each subsequent LoRA is only allowed to target the same layers, or subset of layers, the first LoRA targets. Choosing the LoRA loading order is important because if the LoRAs target disjoint layers, you may end up creating a dummy LoRA that targets the union of all target layers.
 For more implementation details, take a look at the [`hotswap.py`](https://github.com/huggingface/peft/blob/92d65cafa51c829484ad3d95cf71d09de57ff066/src/peft/utils/hotswap.py) file.
 </details>
 ## Merge
 The weights from each LoRA can be merged together to produce a blend of multiple existing styles. There are several methods for merging LoRAs, each of which differ in *how* the weights are merged (may affect generation quality).
@@ -687,5 +674,3 @@ Browse the [LoRA Studio](https://lorastudio.co/models) for different LoRAs to us
 ></iframe>
 You can find additional LoRAs in the [FLUX LoRA the Explorer](https://huggingface.co/spaces/multimodalart/flux-lora-the-explorer) and [LoRA the Explorer](https://huggingface.co/spaces/multimodalart/LoraTheExplorer) Spaces.
 Check out the [Fast LoRA inference for Flux with Diffusers and PEFT](https://huggingface.co/blog/lora-fast) blog post to learn how to optimize LoRA inference with methods like FlashAttention-3 and fp8 quantization.
@@ -0,0 +1,18 @@
 <!--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.
 -->
 # Overview
 The inference pipeline supports and enables a wide range of techniques that are divided into two categories:
 * Pipeline functionality: these techniques modify the pipeline or extend it for other applications. For example, pipeline callbacks add new features to a pipeline and a pipeline can also be extended for distributed inference.
 * Improve inference quality: these techniques increase the visual quality of the generated images. For example, you can enhance your prompts with GPT2 to create better images with lower effort.
@@ -37,7 +37,7 @@ Diffusers는 Stable Diffusion 추론을 위해 PyTorch `mps`를 사용해 Apple
 ```python
-# `hf auth login`에 로그인되어 있음을 확인
+# `huggingface-cli login`에 로그인되어 있음을 확인
 from diffusers import DiffusionPipeline
 pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
@@ -75,7 +75,7 @@ dataset = load_dataset(
 [push_to_hub(https://huggingface.co/docs/datasets/v2.13.1/en/package_reference/main_classes#datasets.Dataset.push_to_hub) 을 사용해서 Hub에 데이터셋을 업로드 합니다:
 ```python
-# 터미널에서 hf auth login 커맨드를 이미 실행했다고 가정합니다
+# 터미널에서 huggingface-cli login 커맨드를 이미 실행했다고 가정합니다
 dataset.push_to_hub("name_of_your_dataset")
 # 개인 repo로 push 하고 싶다면, `private=True` 을 추가하세요:
@@ -39,7 +39,7 @@ specific language governing permissions and limitations under the License.
 모델을 저장하거나 커뮤니티와 공유하려면 Hugging Face 계정에 로그인하세요(아직 계정이 없는 경우 [생성](https://huggingface.co/join)하세요):
 ```bash
-hf auth login
+huggingface-cli login
 ```
 ## Text-to-image
@@ -42,7 +42,7 @@ Unconditional 이미지 생성은 학습에 사용된 데이터셋과 유사한
 또는 터미널로 로그인할 수 있습니다:
 ```bash
-hf auth login
+huggingface-cli login
 ```
 모델 체크포인트가 상당히 크기 때문에 [Git-LFS](https://git-lfs.com/)에서 대용량 파일의 버전 관리를 할 수 있습니다.
@@ -42,7 +42,7 @@ Stable Diffusion 모델들은 학습 및 저장된 프레임워크와 다운로
 시작하기 전에 스크립트를 실행할 🤗 Diffusers의 로컬 클론(clone)이 있는지 확인하고 Hugging Face 계정에 로그인하여 pull request를 열고 변환된 모델을 허브에 푸시할 수 있도록 하세요.
 ```bash
-hf auth login
+huggingface-cli login
 ```
 스크립트를 사용하려면:
@@ -69,7 +69,7 @@ Note also that we use PEFT library as backend for LoRA training, make sure to ha
 Lastly, we recommend logging into your HF account so that your trained LoRA is automatically uploaded to the hub:
 ```bash
-hf auth login
+huggingface-cli login
 ```
 This command will prompt you for a token. Copy-paste yours from your [settings/tokens](https://huggingface.co/settings/tokens),and press Enter.
@@ -67,7 +67,7 @@ Note also that we use PEFT library as backend for LoRA training, make sure to ha
 Lastly, we recommend logging into your HF account so that your trained LoRA is automatically uploaded to the hub:
 ```bash
-hf auth login
+huggingface-cli login
 ```
 This command will prompt you for a token. Copy-paste yours from your [settings/tokens](https://huggingface.co/settings/tokens),and press Enter.
@@ -1,24 +1,3 @@
 # /// script
 # requires-python = ">=3.10"
 # dependencies = [
 #   "torch",
 #   "torchvision",
 #   "diffusers @ git+https://github.com/huggingface/diffusers.git@main",
 #   "transformers",
 #   "accelerate",
 #   "peft",
 #   "safetensors",
 #   "huggingface_hub",
 #   "datasets",
 #   "Pillow",
 #   "tqdm",
 #   "bitsandbytes",
 #   "sentencepiece",
 #   "protobuf",
 #   "prodigyopt",
 # ]
 # ///
 #!/usr/bin/env python
 # coding=utf-8
 # Copyright 2025 The HuggingFace Inc. team. All rights reserved.
@@ -34,20 +13,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # /// script
 # dependencies = [
 #     "diffusers @ git+https://github.com/huggingface/diffusers.git",
 #     "torch>=2.0.0",
 #     "accelerate>=0.31.0",
 #     "transformers>=4.41.2",
 #     "ftfy",
 #     "tensorboard",
 #     "Jinja2",
 #     "peft>=0.11.1",
 #     "sentencepiece",
 # ]
 # ///
 import argparse
 import copy
 import itertools
@@ -1006,7 +971,6 @@ class DreamBoothDataset(Dataset):
    def __init__(
        self,
        args,
        instance_data_root,
        instance_prompt,
        class_prompt,
@@ -1016,8 +980,10 @@ class DreamBoothDataset(Dataset):
        class_num=None,
        size=1024,
        repeats=1,
        center_crop=False,
    ):
        self.size = size
        self.center_crop = center_crop
        self.instance_prompt = instance_prompt
        self.custom_instance_prompts = None
@@ -1092,7 +1058,7 @@ class DreamBoothDataset(Dataset):
        if interpolation is None:
            raise ValueError(f"Unsupported interpolation mode {interpolation=}.")
        train_resize = transforms.Resize(size, interpolation=interpolation)
-        train_crop = transforms.CenterCrop(size) if args.center_crop else transforms.RandomCrop(size)
+        train_crop = transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size)
        train_flip = transforms.RandomHorizontalFlip(p=1.0)
        train_transforms = transforms.Compose(
            [
@@ -1109,11 +1075,11 @@ class DreamBoothDataset(Dataset):
                # flip
                image = train_flip(image)
            if args.center_crop:
-                y1 = max(0, int(round((image.height - self.size) / 2.0)))
+                y1 = max(0, int(round((image.height - args.resolution) / 2.0)))
-                x1 = max(0, int(round((image.width - self.size) / 2.0)))
+                x1 = max(0, int(round((image.width - args.resolution) / 2.0)))
                image = train_crop(image)
            else:
-                y1, x1, h, w = train_crop.get_params(image, (self.size, self.size))
+                y1, x1, h, w = train_crop.get_params(image, (args.resolution, args.resolution))
                image = crop(image, y1, x1, h, w)
            image = train_transforms(image)
            self.pixel_values.append(image)
@@ -1136,7 +1102,7 @@ class DreamBoothDataset(Dataset):
        self.image_transforms = transforms.Compose(
            [
                transforms.Resize(size, interpolation=interpolation),
-                transforms.CenterCrop(size) if args.center_crop else transforms.RandomCrop(size),
+                transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
                transforms.ToTensor(),
                transforms.Normalize([0.5], [0.5]),
            ]
@@ -1356,7 +1322,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
@@ -1861,7 +1827,6 @@ def main(args):
    # Dataset and DataLoaders creation:
    train_dataset = DreamBoothDataset(
        args=args,
        instance_data_root=args.instance_data_dir,
        instance_prompt=args.instance_prompt,
        train_text_encoder_ti=args.train_text_encoder_ti,
@@ -1871,6 +1836,7 @@ def main(args):
        class_num=args.num_class_images,
        size=args.resolution,
        repeats=args.repeats,
        center_crop=args.center_crop,
    )
    train_dataloader = torch.utils.data.DataLoader(
@@ -13,20 +13,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # /// script
 # dependencies = [
 #     "diffusers @ git+https://github.com/huggingface/diffusers.git",
 #     "torch>=2.0.0",
 #     "accelerate>=0.31.0",
 #     "transformers>=4.41.2",
 #     "ftfy",
 #     "tensorboard",
 #     "Jinja2",
 #     "peft>=0.11.1",
 #     "sentencepiece",
 # ]
 # ///
 import argparse
 import gc
 import hashlib
@@ -1064,7 +1050,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_dir = Path(args.output_dir, args.logging_dir)
@@ -13,20 +13,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # /// script
 # dependencies = [
 #     "diffusers @ git+https://github.com/huggingface/diffusers.git",
 #     "torch>=2.0.0",
 #     "accelerate>=0.31.0",
 #     "transformers>=4.41.2",
 #     "ftfy",
 #     "tensorboard",
 #     "Jinja2",
 #     "peft>=0.11.1",
 #     "sentencepiece",
 # ]
 # ///
 import argparse
 import gc
 import itertools
@@ -1306,7 +1292,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    if args.do_edm_style_training and args.snr_gamma is not None:
@@ -125,10 +125,10 @@ When running `accelerate config`, if we specify torch compile mode to True there
 If you would like to push your model to the HF Hub after training is completed with a neat model card, make sure you're logged in:
 ```
-hf auth login
+huggingface-cli login
 # Alternatively, you could upload your model manually using:
-# hf upload my-cool-account-name/my-cool-lora-name /path/to/awesome/lora
+# huggingface-cli upload my-cool-account-name/my-cool-lora-name /path/to/awesome/lora
 ```
 Make sure your data is prepared as described in [Data Preparation](#data-preparation). When ready, you can begin training!
@@ -962,7 +962,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
@@ -984,7 +984,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
@@ -10,7 +10,7 @@ To incorporate additional condition latents, we expand the input features of Cog
 > As the model is gated, before using it with diffusers you first need to go to the [CogView4 Hugging Face page](https://huggingface.co/THUDM/CogView4-6B), fill in the form and accept the gate. Once you are in, you need to log in so that your system knows you’ve accepted the gate. Use the command below to log in:
 ```bash
-hf auth login
+huggingface-cli login
 ```
 The example command below shows how to launch fine-tuning for pose conditions. The dataset ([`raulc0399/open_pose_controlnet`](https://huggingface.co/datasets/raulc0399/open_pose_controlnet)) being used here already has the pose conditions of the original images, so we don't have to compute them.
@@ -705,7 +705,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_out_dir = Path(args.output_dir, args.logging_dir)
@@ -3129,7 +3129,7 @@ from io import BytesIO
 from diffusers import DiffusionPipeline
 # load the pipeline
-# make sure you're logged in with `hf auth login`
+# make sure you're logged in with `huggingface-cli login`
 model_id_or_path = "stable-diffusion-v1-5/stable-diffusion-v1-5"
 # can also be used with dreamlike-art/dreamlike-photoreal-2.0
 pipe = DiffusionPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16, custom_pipeline="pipeline_fabric").to("cuda")
@@ -877,7 +877,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_dir = Path(args.output_dir, args.logging_dir)
@@ -709,7 +709,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_dir = Path(args.output_dir, args.logging_dir)
@@ -872,7 +872,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_dir = Path(args.output_dir, args.logging_dir)
@@ -842,7 +842,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_dir = Path(args.output_dir, args.logging_dir)
@@ -882,7 +882,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_dir = Path(args.output_dir, args.logging_dir)
@@ -359,7 +359,7 @@ wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/ma
 We encourage you to store or share your model with the community. To use huggingface hub, please login to your Hugging Face account, or ([create one](https://huggingface.co/docs/diffusers/main/en/training/hf.co/join) if you don’t have one already):
 ```sh
-hf auth login
+huggingface-cli login
 ```
 Make sure you have the `MODEL_DIR`,`OUTPUT_DIR` and `HUB_MODEL_ID` environment variables set. The `OUTPUT_DIR` and `HUB_MODEL_ID` variables specify where to save the model to on the Hub:
@@ -22,7 +22,7 @@ Here is a gpu memory consumption for reference, tested on a single A100 with 80G
 > **Gated access**
 >
-> As the model is gated, before using it with diffusers you first need to go to the [FLUX.1 [dev] Hugging Face page](https://huggingface.co/black-forest-labs/FLUX.1-dev), fill in the form and accept the gate. Once you are in, you need to log in so that your system knows you’ve accepted the gate. Use the command below to log in: `hf auth login`
+> As the model is gated, before using it with diffusers you first need to go to the [FLUX.1 [dev] Hugging Face page](https://huggingface.co/black-forest-labs/FLUX.1-dev), fill in the form and accept the gate. Once you are in, you need to log in so that your system knows you’ve accepted the gate. Use the command below to log in: `huggingface-cli login`
 ## Running locally with PyTorch
@@ -88,7 +88,7 @@ wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/ma
 wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png
 ```
-Then run `hf auth login` to log into your Hugging Face account. This is needed to be able to push the trained ControlNet parameters to Hugging Face Hub.
+Then run `huggingface-cli login` to log into your Hugging Face account. This is needed to be able to push the trained ControlNet parameters to Hugging Face Hub.
 we can define the num_layers, num_single_layers, which determines the size of the control(default values are num_layers=4, num_single_layers=10)
@@ -56,7 +56,7 @@ First download the SD3 model from [Hugging Face Hub](https://huggingface.co/stab
 > As the model is gated, before using it with diffusers you first need to go to the [Stable Diffusion 3 Medium Hugging Face page](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers) or [Stable Diffusion 3.5 Large Hugging Face page](https://huggingface.co/stabilityai/stable-diffusion-3.5-medium), fill in the form and accept the gate. Once you are in, you need to log in so that your system knows you’ve accepted the gate. Use the command below to log in:
 ```bash
-hf auth login
+huggingface-cli login
 ```
 This will also allow us to push the trained model parameters to the Hugging Face Hub platform.
@@ -58,7 +58,7 @@ wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/ma
 wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png
 ```
-Then run `hf auth login` to log into your Hugging Face account. This is needed to be able to push the trained ControlNet parameters to Hugging Face Hub.
+Then run `huggingface-cli login` to log into your Hugging Face account. This is needed to be able to push the trained ControlNet parameters to Hugging Face Hub.
 ```bash
 export MODEL_DIR="stabilityai/stable-diffusion-xl-base-1.0"
@@ -734,7 +734,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_dir = Path(args.output_dir, args.logging_dir)
@@ -665,7 +665,7 @@ def main():
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging.basicConfig(
@@ -814,7 +814,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_out_dir = Path(args.output_dir, args.logging_dir)
@@ -928,7 +928,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
@@ -829,7 +829,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_dir = Path(args.output_dir, args.logging_dir)
@@ -663,7 +663,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_dir = Path(args.output_dir, args.logging_dir)
@@ -330,7 +330,7 @@ For this example we want to directly store the trained LoRA embeddings on the Hu
 we need to be logged in and add the `--push_to_hub` flag.
 ```bash
-hf auth login
+huggingface-cli login
 ```
 Now we can start training!
@@ -19,7 +19,7 @@ The `train_dreambooth_flux.py` script shows how to implement the training proced
 > As the model is gated, before using it with diffusers you first need to go to the [FLUX.1 [dev] Hugging Face page](https://huggingface.co/black-forest-labs/FLUX.1-dev), fill in the form and accept the gate. Once you are in, you need to log in so that your system knows you’ve accepted the gate. Use the command below to log in:
 ```bash
-hf auth login
+huggingface-cli login
 ```
 This will also allow us to push the trained model parameters to the Hugging Face Hub platform.
@@ -95,7 +95,7 @@ accelerate launch train_dreambooth_lora_hidream.py \
 For using `push_to_hub`, make you're logged into your Hugging Face account:
 ```bash
-hf auth login
+huggingface-cli login
 ```
 To better track our training experiments, we're using the following flags in the command above:
@@ -101,7 +101,7 @@ accelerate launch train_dreambooth_lora_lumina2.py \
 For using `push_to_hub`, make you're logged into your Hugging Face account:
 ```bash
-hf auth login
+huggingface-cli login
 ```
 To better track our training experiments, we're using the following flags in the command above:
@@ -101,7 +101,7 @@ accelerate launch train_dreambooth_lora_sana.py \
 For using `push_to_hub`, make you're logged into your Hugging Face account:
 ```bash
-hf auth login
+huggingface-cli login
 ```
 To better track our training experiments, we're using the following flags in the command above:
@@ -8,7 +8,7 @@ The `train_dreambooth_sd3.py` script shows how to implement the training procedu
 > As the model is gated, before using it with diffusers you first need to go to the [Stable Diffusion 3 Medium Hugging Face page](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers), fill in the form and accept the gate. Once you are in, you need to log in so that your system knows you’ve accepted the gate. Use the command below to log in:
 ```bash
-hf auth login
+huggingface-cli login
 ```
 This will also allow us to push the trained model parameters to the Hugging Face Hub platform.
@@ -807,7 +807,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_dir = Path(args.output_dir, args.logging_dir)
@@ -13,20 +13,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # /// script
 # dependencies = [
 #     "diffusers @ git+https://github.com/huggingface/diffusers.git",
 #     "torch>=2.0.0",
 #     "accelerate>=0.31.0",
 #     "transformers>=4.41.2",
 #     "ftfy",
 #     "tensorboard",
 #     "Jinja2",
 #     "peft>=0.11.1",
 #     "sentencepiece",
 # ]
 # ///
 import argparse
 import copy
 import gc
@@ -1027,7 +1013,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
@@ -756,7 +756,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_dir = Path(args.output_dir, args.logging_dir)
@@ -13,20 +13,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # /// script
 # dependencies = [
 #     "diffusers @ git+https://github.com/huggingface/diffusers.git",
 #     "torch>=2.0.0",
 #     "accelerate>=0.31.0",
 #     "transformers>=4.41.2",
 #     "ftfy",
 #     "tensorboard",
 #     "Jinja2",
 #     "peft>=0.11.1",
 #     "sentencepiece",
 # ]
 # ///
 import argparse
 import copy
 import itertools
@@ -1065,7 +1051,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
@@ -1199,7 +1199,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
@@ -1614,7 +1614,7 @@ def main(args):
    )
    if args.cond_image_column is not None:
        logger.info("I2I fine-tuning enabled.")
-    batch_sampler = BucketBatchSampler(train_dataset, batch_size=args.train_batch_size, drop_last=True)
+    batch_sampler = BucketBatchSampler(train_dataset, batch_size=args.train_batch_size, drop_last=False)
    train_dataloader = torch.utils.data.DataLoader(
        train_dataset,
        batch_sampler=batch_sampler,
@@ -58,7 +58,6 @@ from diffusers.training_utils import (
    compute_density_for_timestep_sampling,
    compute_loss_weighting_for_sd3,
    free_memory,
    offload_models,
 )
 from diffusers.utils import (
    check_min_version,
@@ -936,7 +935,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
@@ -1365,34 +1364,43 @@ def main(args):
    # provided (i.e. the --instance_prompt is used for all images), we encode the instance prompt once to avoid
    # the redundant encoding.
    if not train_dataset.custom_instance_prompts:
-        with offload_models(text_encoding_pipeline, device=accelerator.device, offload=args.offload):
+        if args.offload:
-            (
+            text_encoding_pipeline = text_encoding_pipeline.to(accelerator.device)
-                instance_prompt_hidden_states_t5,
+        (
-                instance_prompt_hidden_states_llama3,
+            instance_prompt_hidden_states_t5,
-                instance_pooled_prompt_embeds,
+            instance_prompt_hidden_states_llama3,
-                _,
+            instance_pooled_prompt_embeds,
-                _,
+            _,
-                _,
+            _,
-            ) = compute_text_embeddings(args.instance_prompt, text_encoding_pipeline)
+            _,
        ) = compute_text_embeddings(args.instance_prompt, text_encoding_pipeline)
        if args.offload:
            text_encoding_pipeline = text_encoding_pipeline.to("cpu")
    # Handle class prompt for prior-preservation.
    if args.with_prior_preservation:
-        with offload_models(text_encoding_pipeline, device=accelerator.device, offload=args.offload):
+        if args.offload:
-            (class_prompt_hidden_states_t5, class_prompt_hidden_states_llama3, class_pooled_prompt_embeds, _, _, _) = (
+            text_encoding_pipeline = text_encoding_pipeline.to(accelerator.device)
-                compute_text_embeddings(args.class_prompt, text_encoding_pipeline)
+        (class_prompt_hidden_states_t5, class_prompt_hidden_states_llama3, class_pooled_prompt_embeds, _, _, _) = (
-            )
+            compute_text_embeddings(args.class_prompt, text_encoding_pipeline)
        )
        if args.offload:
            text_encoding_pipeline = text_encoding_pipeline.to("cpu")
    validation_embeddings = {}
    if args.validation_prompt is not None:
-        with offload_models(text_encoding_pipeline, device=accelerator.device, offload=args.offload):
+        if args.offload:
-            (
+            text_encoding_pipeline = text_encoding_pipeline.to(accelerator.device)
-                validation_embeddings["prompt_embeds_t5"],
+        (
-                validation_embeddings["prompt_embeds_llama3"],
+            validation_embeddings["prompt_embeds_t5"],
-                validation_embeddings["pooled_prompt_embeds"],
+            validation_embeddings["prompt_embeds_llama3"],
-                validation_embeddings["negative_prompt_embeds_t5"],
+            validation_embeddings["pooled_prompt_embeds"],
-                validation_embeddings["negative_prompt_embeds_llama3"],
+            validation_embeddings["negative_prompt_embeds_t5"],
-                validation_embeddings["negative_pooled_prompt_embeds"],
+            validation_embeddings["negative_prompt_embeds_llama3"],
-            ) = compute_text_embeddings(args.validation_prompt, text_encoding_pipeline)
+            validation_embeddings["negative_pooled_prompt_embeds"],
        ) = compute_text_embeddings(args.validation_prompt, text_encoding_pipeline)
        if args.offload:
            text_encoding_pipeline = text_encoding_pipeline.to("cpu")
    # If custom instance prompts are NOT provided (i.e. the instance prompt is used for all images),
    # pack the statically computed variables appropriately here. This is so that we don't
@@ -1573,10 +1581,12 @@ def main(args):
                if args.cache_latents:
                    model_input = latents_cache[step].sample()
                else:
-                    with offload_models(vae, device=accelerator.device, offload=args.offload):
+                    if args.offload:
-                        pixel_values = batch["pixel_values"].to(dtype=vae.dtype)
+                        vae = vae.to(accelerator.device)
                    pixel_values = batch["pixel_values"].to(dtype=vae.dtype)
                    model_input = vae.encode(pixel_values).latent_dist.sample()
-
+                    if args.offload:
                        vae = vae.to("cpu")
                model_input = (model_input - vae_config_shift_factor) * vae_config_scaling_factor
                model_input = model_input.to(dtype=weight_dtype)
@@ -859,7 +859,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
@@ -13,20 +13,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # /// script
 # dependencies = [
 #     "diffusers @ git+https://github.com/huggingface/diffusers.git",
 #     "torch>=2.0.0",
 #     "accelerate>=1.0.0",
 #     "transformers>=4.47.0",
 #     "ftfy",
 #     "tensorboard",
 #     "Jinja2",
 #     "peft>=0.14.0",
 #     "sentencepiece",
 # ]
 # ///
 import argparse
 import copy
 import itertools
@@ -866,7 +852,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
@@ -1063,7 +1063,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
@@ -983,7 +983,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    if args.do_edm_style_training and args.snr_gamma is not None:
@@ -988,7 +988,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
@@ -13,7 +13,7 @@ To incorporate additional condition latents, we expand the input features of Flu
 > As the model is gated, before using it with diffusers you first need to go to the [FLUX.1 [dev] Hugging Face page](https://huggingface.co/black-forest-labs/FLUX.1-dev), fill in the form and accept the gate. Once you are in, you need to log in so that your system knows you’ve accepted the gate. Use the command below to log in:
 ```bash
-hf auth login
+huggingface-cli login
 ```
 The example command below shows how to launch fine-tuning for pose conditions. The dataset ([`raulc0399/open_pose_controlnet`](https://huggingface.co/datasets/raulc0399/open_pose_controlnet)) being used here already has the pose conditions of the original images, so we don't have to compute them.
@@ -697,7 +697,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_out_dir = Path(args.output_dir, args.logging_dir)
@@ -725,7 +725,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    if args.use_lora_bias and args.gaussian_init_lora:
        raise ValueError("`gaussian` LoRA init scheme isn't supported when `use_lora_bias` is True.")
@@ -430,7 +430,7 @@ def main():
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    if args.non_ema_revision is not None:
@@ -483,7 +483,7 @@ def main():
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    if args.non_ema_revision is not None:
@@ -41,7 +41,7 @@ For all our examples, we will directly store the trained weights on the Hub, so
 Run the following command to authenticate your token
 ```bash
-hf auth login
+huggingface-cli login
 ```
 We also use [Weights and Biases](https://docs.wandb.ai/quickstart) logging by default, because it is really useful to monitor the training progress by regularly generating sample images during training. To install wandb, run
@@ -444,7 +444,7 @@ def main():
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_dir = os.path.join(args.output_dir, args.logging_dir)
@@ -330,7 +330,7 @@ def main():
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_dir = Path(args.output_dir, args.logging_dir)
@@ -342,7 +342,7 @@ def main():
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_dir = Path(args.output_dir, args.logging_dir)
@@ -445,7 +445,7 @@ def main():
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_dir = os.path.join(args.output_dir, args.logging_dir)
@@ -1249,7 +1249,7 @@ class EasyPipelineForText2Image(AutoPipelineForText2Image):
        <Tip>
        To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
-        `hf auth login`.
+        `huggingface-cli login`.
        </Tip>
@@ -1358,7 +1358,7 @@ class EasyPipelineForText2Image(AutoPipelineForText2Image):
        <Tip>
        To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
-        `hf auth login`.
+        `huggingface-cli login`.
        </Tip>
@@ -1507,7 +1507,7 @@ class EasyPipelineForImage2Image(AutoPipelineForImage2Image):
        <Tip>
        To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
-        `hf auth login`.
+        `huggingface-cli login`.
        </Tip>
@@ -1617,7 +1617,7 @@ class EasyPipelineForImage2Image(AutoPipelineForImage2Image):
        <Tip>
        To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
-        `hf auth login`.
+        `huggingface-cli login`.
        </Tip>
@@ -1766,7 +1766,7 @@ class EasyPipelineForInpainting(AutoPipelineForInpainting):
        <Tip>
        To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
-        `hf auth login
+        `huggingface-cli login`.
        </Tip>
@@ -1875,7 +1875,7 @@ class EasyPipelineForInpainting(AutoPipelineForInpainting):
        <Tip>
        To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
-        `hf auth login
+        `huggingface-cli login`.
        </Tip>
@@ -568,7 +568,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_dir = Path(args.output_dir, args.logging_dir)
@@ -789,7 +789,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
@@ -899,7 +899,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_dir = Path(args.output_dir, args.logging_dir)
@@ -470,7 +470,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_dir = Path(args.output_dir, args.logging_dir)
@@ -512,7 +512,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_dir = Path(args.output_dir, args.logging_dir)
@@ -502,7 +502,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_dir = Path(args.output_dir, args.logging_dir)
@@ -609,7 +609,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_dir = Path(args.output_dir, args.logging_dir)
@@ -39,7 +39,7 @@ python compute_embeddings.py
 It should create a file named `embeddings.parquet`. We're then ready to launch training. First, authenticate so that you can access the Flux.1 Dev model: 
 ```bash
-hf auth login
+huggingface-cli
 ```
 Then launch:
@@ -587,7 +587,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
@@ -47,11 +47,11 @@ pip install git+https://github.com/xinyu1205/recognize-anything.git --no-deps
 Download the pre-trained model:
 ```bash
-hf download --resume-download xinyu1205/recognize_anything_model ram_swin_large_14m.pth
+huggingface-cli download --resume-download xinyu1205/recognize_anything_model ram_swin_large_14m.pth
-hf download --resume-download IDEA-Research/grounding-dino-base
+huggingface-cli download --resume-download IDEA-Research/grounding-dino-base
-hf download --resume-download Salesforce/blip2-flan-t5-xxl
+huggingface-cli download --resume-download Salesforce/blip2-flan-t5-xxl
-hf download --resume-download clip-vit-large-patch14
+huggingface-cli download --resume-download clip-vit-large-patch14
-hf download --resume-download masterful/gligen-1-4-generation-text-box
+huggingface-cli download --resume-download masterful/gligen-1-4-generation-text-box
 ```
 Make the training data on 8 GPUs:
@@ -66,7 +66,7 @@ torchrun --master_port 17673 --nproc_per_node=8 make_datasets.py \
 You can download the COCO training data from
 ```bash
-hf download --resume-download Hzzone/GLIGEN_COCO coco_train2017.pth
+huggingface-cli download --resume-download Hzzone/GLIGEN_COCO coco_train2017.pth
 ```
 It's in the format of
@@ -125,7 +125,7 @@ Note that although the pre-trained GLIGEN model has been loaded, the parameters
 The trained model can be downloaded from
 ```bash
-hf download --resume-download Hzzone/GLIGEN_COCO config.json diffusion_pytorch_model.safetensors
+huggingface-cli download --resume-download Hzzone/GLIGEN_COCO config.json diffusion_pytorch_model.safetensors
 ```
 You can run `demo.ipynb` to visualize the generated images.
@@ -488,7 +488,7 @@ def main():
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    if args.non_ema_revision is not None:
@@ -366,7 +366,7 @@ def main():
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_dir = os.path.join(args.output_dir, args.logging_dir)
@@ -34,7 +34,7 @@ For this example we want to directly store the trained LoRA embeddings on the Hu
 we need to be logged in and add the `--push_to_hub` flag.
 ```bash
-hf auth login
+huggingface-cli login
 ```
 Now we can start training!
@@ -396,7 +396,7 @@ def main():
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_dir = os.path.join(args.output_dir, args.logging_dir)
@@ -684,7 +684,7 @@ def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_dir = Path(args.output_dir, args.logging_dir)
@@ -60,7 +60,7 @@ You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need
 Run the following command to authenticate your token
 ```bash
-hf auth login
+huggingface-cli login
 ```
 If you have already cloned the repo, then you won't need to go through these steps.
@@ -551,7 +551,7 @@ def main():
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    logging_dir = os.path.join(args.output_dir, args.logging_dir)
@@ -153,7 +153,7 @@ def parse_args():
        "--use_auth_token",
        action="store_true",
        help=(
-            "Will use the token generated when running `hf auth login` (necessary to use this script with"
+            "Will use the token generated when running `huggingface-cli login` (necessary to use this script with"
            " private models)."
        ),
    )
@@ -41,7 +41,7 @@ You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need
 Run the following command to authenticate your token
 ```bash
-hf auth login
+huggingface-cli login
 ```
 If you have already cloned the repo, then you won't need to go through these steps.
@@ -415,7 +415,7 @@ def main():
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
-            " Please use `hf auth login` to authenticate with the Hub."
+            " Please use `huggingface-cli login` to authenticate with the Hub."
        )
    if args.non_ema_revision is not None:
@@ -46,7 +46,7 @@ You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need
 Run the following command to authenticate your token
 ```bash
-hf auth login
+huggingface-cli login
 ```
 If you have already cloned the repo, then you won't need to go through these steps.
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
DN6	3aabef5de4	update	2025-07-24 22:18:15 +05:30
DN6	39be374591	update	2025-07-21 09:03:32 +05:30
DN6	54e17f3084	update	2025-07-21 08:56:47 +05:30
DN6	80702d222d	update	2025-07-17 13:05:43 +05:30
DN6	625cc8ede8	update	2025-07-17 07:14:35 +05:30
yiyixuxu	a2a9e4eadb	Merge branch 'modular-test' of github.com:huggingface/diffusers into modular-test	2025-07-16 12:03:09 +02:00
yiyixuxu	0998bd75ad	up	2025-07-16 12:02:58 +02:00
yiyixuxu	5f560d05a2	up	2025-07-16 11:58:23 +02:00
yiyixuxu	4b7a9e9fa9	prepare_latents_inpaint always return noise and image_latents	2025-07-16 11:57:29 +02:00
yiyixuxu	d8fa2de36f	remove more unused func	2025-07-16 04:29:27 +02:00
YiYi Xu	4df2739a5e	Merge branch 'main' into modular-test	2025-07-15 16:27:33 -10:00
yiyixuxu	d92855ddf0	style	2025-07-16 04:26:27 +02:00
yiyixuxu	0a5c90ed47	add names property to pipeline blocks	2025-07-16 04:25:26 +02:00
yiyixuxu	0fa58127f8	make style	2025-07-15 03:05:36 +02:00
yiyixuxu	b165cf3742	rearrage the params to groups: default params /image params /batch params / callback params	2025-07-15 03:03:29 +02:00