up

Merge branch 'main' into vae-tests-mixin
up
2025-10-17 11:11:01 +05:30 · 2025-10-17 10:44:09 +05:30 · 2025-10-17 10:43:52 +05:30 · 2025-10-17 08:00:37 +05:30 · 2025-09-24 10:44:15 +05:30 · 2025-09-24 09:43:58 +05:30
241 changed files with 1245 additions and 14753 deletions
@@ -7,7 +7,7 @@ on:
 env:
  DIFFUSERS_IS_CI: yes
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
  HF_HOME: /mnt/cache
  OMP_NUM_THREADS: 8
  MKL_NUM_THREADS: 8
@@ -42,39 +42,18 @@ jobs:
          CHANGED_FILES: ${{ steps.file_changes.outputs.all }}
        run: |
          echo "$CHANGED_FILES"
          ALLOWED_IMAGES=(
            diffusers-pytorch-cpu
            diffusers-pytorch-cuda
            diffusers-pytorch-xformers-cuda
            diffusers-pytorch-minimum-cuda
            diffusers-doc-builder
          )
          declare -A IMAGES_TO_BUILD=()
          for FILE in $CHANGED_FILES; do 
            # skip anything that isn't still on disk
-            if [[ ! -e "$FILE" ]]; then
+            if [[ ! -f "$FILE" ]]; then
              echo "Skipping removed file $FILE"
              continue
            fi           
-
+            if [[ "$FILE" == docker/*Dockerfile ]]; then
-            for IMAGE in "${ALLOWED_IMAGES[@]}"; do
+              DOCKER_PATH="${FILE%/Dockerfile}"
-              if [[ "$FILE" == docker/${IMAGE}/* ]]; then
+              DOCKER_TAG=$(basename "$DOCKER_PATH")
-                IMAGES_TO_BUILD["$IMAGE"]=1
+              echo "Building Docker image for $DOCKER_TAG"
-              fi
+              docker build -t "$DOCKER_TAG" "$DOCKER_PATH"
-            done
+            fi
          done
          if [[ ${#IMAGES_TO_BUILD[@]} -eq 0 ]]; then
            echo "No relevant Docker changes detected."
            exit 0
          fi
          for IMAGE in "${!IMAGES_TO_BUILD[@]}"; do
            DOCKER_PATH="docker/${IMAGE}"
            echo "Building Docker image for $IMAGE"
            docker build -t "$IMAGE" "$DOCKER_PATH"
          done
        if: steps.file_changes.outputs.all != ''
@@ -7,7 +7,7 @@ on:
 env:
  DIFFUSERS_IS_CI: yes
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
  OMP_NUM_THREADS: 8
  MKL_NUM_THREADS: 8
  PYTEST_TIMEOUT: 600
@@ -26,7 +26,7 @@ concurrency:
 env:
  DIFFUSERS_IS_CI: yes
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
  OMP_NUM_THREADS: 4
  MKL_NUM_THREADS: 4
  PYTEST_TIMEOUT: 60
@@ -22,7 +22,7 @@ concurrency:
 env:
  DIFFUSERS_IS_CI: yes
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
  OMP_NUM_THREADS: 4
  MKL_NUM_THREADS: 4
  PYTEST_TIMEOUT: 60
@@ -24,7 +24,7 @@ env:
  DIFFUSERS_IS_CI: yes
  OMP_NUM_THREADS: 8
  MKL_NUM_THREADS: 8
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
  PYTEST_TIMEOUT: 600
  PIPELINE_USAGE_CUTOFF: 1000000000 # set high cutoff so that only always-test pipelines run
@@ -14,7 +14,7 @@ env:
  DIFFUSERS_IS_CI: yes
  OMP_NUM_THREADS: 8
  MKL_NUM_THREADS: 8
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
  PYTEST_TIMEOUT: 600
  PIPELINE_USAGE_CUTOFF: 50000
@@ -18,7 +18,7 @@ env:
  HF_HOME: /mnt/cache
  OMP_NUM_THREADS: 8
  MKL_NUM_THREADS: 8
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
  PYTEST_TIMEOUT: 600
  RUN_SLOW: no
@@ -8,7 +8,7 @@ env:
  HF_HOME: /mnt/cache
  OMP_NUM_THREADS: 8
  MKL_NUM_THREADS: 8
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
  PYTEST_TIMEOUT: 600
  RUN_SLOW: no
@@ -171,7 +171,7 @@ Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz9
  <tr style="border-top: 2px solid black">
    <td>Text-guided Image Inpainting</td>
    <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/inpaint">Stable Diffusion Inpainting</a></td>
-      <td><a href="https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting"> stable-diffusion-v1-5/stable-diffusion-inpainting </a></td>
+      <td><a href="https://huggingface.co/runwayml/stable-diffusion-inpainting"> runwayml/stable-diffusion-inpainting </a></td>
  </tr>
  <tr style="border-top: 2px solid black">
    <td>Image Variation</td>
@@ -33,7 +33,7 @@ RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.
 RUN uv pip install --no-cache-dir \
    accelerate \
    numpy==1.26.4 \
-    hf_xet \
+    hf_transfer \
    setuptools==69.5.1 \
    bitsandbytes \
    torchao \
@@ -44,6 +44,6 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
        scipy \
        tensorboard \
        transformers \
-        hf_xet
+        hf_transfer
 CMD ["/bin/bash"]
@@ -38,12 +38,13 @@ RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
        datasets \
        hf-doc-builder \
        huggingface-hub \
-        hf_xet \
+        hf_transfer \
        Jinja2 \
        librosa \
        numpy==1.26.4 \
        scipy \
        tensorboard \
-        transformers
+        transformers \
        hf_transfer
 CMD ["/bin/bash"]
@@ -31,7 +31,7 @@ RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.
 RUN uv pip install --no-cache-dir \
    accelerate \
    numpy==1.26.4 \
-    hf_xet
+    hf_transfer
 RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean
@@ -44,6 +44,6 @@ RUN uv pip install --no-cache-dir \
    accelerate \
    numpy==1.26.4 \
    pytorch-lightning \
-    hf_xet
+    hf_transfer
 CMD ["/bin/bash"]
@@ -47,6 +47,6 @@ RUN uv pip install --no-cache-dir \
    accelerate \
    numpy==1.26.4 \
    pytorch-lightning \
-    hf_xet
+    hf_transfer
 CMD ["/bin/bash"]
@@ -44,7 +44,7 @@ RUN uv pip install --no-cache-dir \
    accelerate \
    numpy==1.26.4 \
    pytorch-lightning \
-    hf_xet \
+    hf_transfer \
    xformers
 CMD ["/bin/bash"]
@@ -1,4 +1,5 @@
- sections:
+- title: Get started
  sections:
  - local: index
    title: Diffusers
  - local: installation
@@ -7,8 +8,9 @@
    title: Quickstart
  - local: stable_diffusion
    title: Basic performance
-  title: Get started
+
- isExpanded: false
+- title: Pipelines
  isExpanded: false
  sections:
  - local: using-diffusers/loading
    title: DiffusionPipeline
@@ -26,8 +28,9 @@
    title: Model formats
  - local: using-diffusers/push_to_hub
    title: Sharing pipelines and models
-  title: Pipelines
+
- isExpanded: false
+- title: Adapters
  isExpanded: false
  sections:
  - local: tutorials/using_peft_for_inference
    title: LoRA
@@ -41,8 +44,9 @@
    title: DreamBooth
  - local: using-diffusers/textual_inversion_inference
    title: Textual inversion
-  title: Adapters
+
- isExpanded: false
+- title: Inference
  isExpanded: false
  sections:
  - local: using-diffusers/weighted_prompts
    title: Prompting
@@ -52,8 +56,9 @@
    title: Batch inference
  - local: training/distributed_inference
    title: Distributed inference
-  title: Inference
+
- isExpanded: false
+- title: Inference optimization
  isExpanded: false
  sections:
  - local: optimization/fp16
    title: Accelerate inference
@@ -65,7 +70,8 @@
    title: Reduce memory usage
  - local: optimization/speed-memory-optims
    title: Compiling and offloading quantized models
-  - sections:
+  - title: Community optimizations
    sections:
    - local: optimization/pruna
      title: Pruna
    - local: optimization/xformers
@@ -84,9 +90,9 @@
      title: ParaAttention
    - local: using-diffusers/image_quality
      title: FreeU
-    title: Community optimizations
+
-  title: Inference optimization
+- title: Hybrid Inference
- isExpanded: false
+  isExpanded: false
  sections:
  - local: hybrid_inference/overview
    title: Overview
@@ -96,8 +102,9 @@
    title: VAE Encode
  - local: hybrid_inference/api_reference
    title: API Reference
-  title: Hybrid Inference
+
- isExpanded: false
+- title: Modular Diffusers
  isExpanded: false
  sections:
  - local: modular_diffusers/overview
    title: Overview
@@ -119,8 +126,9 @@
    title: ComponentsManager
  - local: modular_diffusers/guiders
    title: Guiders
-  title: Modular Diffusers
+
- isExpanded: false
+- title: Training
  isExpanded: false
  sections:
  - local: training/overview
    title: Overview
@@ -130,7 +138,8 @@
    title: Adapt a model to a new task
  - local: tutorials/basic_training
    title: Train a diffusion model
-  - sections:
+  - title: Models
    sections:
    - local: training/unconditional_training
      title: Unconditional image generation
    - local: training/text2image
@@ -149,8 +158,8 @@
      title: InstructPix2Pix
    - local: training/cogvideox
      title: CogVideoX
-    title: Models
+  - title: Methods
-  - sections:
+    sections:
    - local: training/text_inversion
      title: Textual Inversion
    - local: training/dreambooth
@@ -163,9 +172,9 @@
      title: Latent Consistency Distillation
    - local: training/ddpo
      title: Reinforcement learning training with DDPO
-    title: Methods
+
-  title: Training
+- title: Quantization
- isExpanded: false
+  isExpanded: false
  sections:
  - local: quantization/overview
    title: Getting started
@@ -179,8 +188,9 @@
    title: quanto
  - local: quantization/modelopt
    title: NVIDIA ModelOpt
-  title: Quantization
+
- isExpanded: false
+- title: Model accelerators and hardware
  isExpanded: false
  sections:
  - local: optimization/onnx
    title: ONNX
@@ -194,8 +204,9 @@
    title: Intel Gaudi
  - local: optimization/neuron
    title: AWS Neuron
-  title: Model accelerators and hardware
+
- isExpanded: false
+- title: Specific pipeline examples
  isExpanded: false
  sections:
  - local: using-diffusers/consisid
    title: ConsisID
@@ -221,10 +232,12 @@
    title: Stable Video Diffusion
  - local: using-diffusers/marigold_usage
    title: Marigold Computer Vision
-  title: Specific pipeline examples
+
- isExpanded: false
+- title: Resources
  isExpanded: false
  sections:
-  - sections:
+  - title: Task recipes
    sections:
    - local: using-diffusers/unconditional_image_generation
      title: Unconditional image generation
    - local: using-diffusers/conditional_image_generation
@@ -239,7 +252,6 @@
      title: Video generation
    - local: using-diffusers/depth2img
      title: Depth-to-image
    title: Task recipes
  - local: using-diffusers/write_own_pipeline
    title: Understanding pipelines, models and schedulers
  - local: community_projects
@@ -254,10 +266,12 @@
    title: Diffusers' Ethical Guidelines
  - local: conceptual/evaluation
    title: Evaluating Diffusion Models
-  title: Resources
+
- isExpanded: false
+- title: API
  isExpanded: false
  sections:
-  - sections:
+  - title: Main Classes
    sections:
    - local: api/configuration
      title: Configuration
    - local: api/logging
@@ -268,8 +282,8 @@
      title: Quantization
    - local: api/parallel
      title: Parallel inference
-    title: Main Classes
+  - title: Modular
-  - sections:
+    sections:
    - local: api/modular_diffusers/pipeline
      title: Pipeline
    - local: api/modular_diffusers/pipeline_blocks
@@ -280,8 +294,8 @@
      title: Components and configs
    - local: api/modular_diffusers/guiders
      title: Guiders
-    title: Modular
+  - title: Loaders
-  - sections:
+    sections:
    - local: api/loaders/ip_adapter
      title: IP-Adapter
    - local: api/loaders/lora
@@ -296,13 +310,14 @@
      title: SD3Transformer2D
    - local: api/loaders/peft
      title: PEFT
-    title: Loaders
+  - title: Models
-  - sections:
+    sections:
    - local: api/models/overview
      title: Overview
    - local: api/models/auto_model
      title: AutoModel
-    - sections:
+    - title: ControlNets
      sections:
      - local: api/models/controlnet
        title: ControlNetModel
      - local: api/models/controlnet_union
@@ -317,14 +332,12 @@
        title: SD3ControlNetModel
      - local: api/models/controlnet_sparsectrl
        title: SparseControlNetModel
-      title: ControlNets
+    - title: Transformers
-    - sections:
+      sections:
      - local: api/models/allegro_transformer3d
        title: AllegroTransformer3DModel
      - local: api/models/aura_flow_transformer2d
        title: AuraFlowTransformer2DModel
      - local: api/models/transformer_bria_fibo
        title: BriaFiboTransformer2DModel
      - local: api/models/bria_transformer
        title: BriaTransformer2DModel
      - local: api/models/chroma_transformer
@@ -349,8 +362,6 @@
        title: HiDreamImageTransformer2DModel
      - local: api/models/hunyuan_transformer2d
        title: HunyuanDiT2DModel
      - local: api/models/hunyuanimage_transformer_2d
        title: HunyuanImageTransformer2DModel
      - local: api/models/hunyuan_video_transformer_3d
        title: HunyuanVideoTransformer3DModel
      - local: api/models/latte_transformer3d
@@ -385,8 +396,8 @@
        title: TransformerTemporalModel
      - local: api/models/wan_transformer_3d
        title: WanTransformer3DModel
-      title: Transformers
+    - title: UNets
-    - sections:
+      sections:
      - local: api/models/stable_cascade_unet
        title: StableCascadeUNet
      - local: api/models/unet
@@ -401,8 +412,8 @@
        title: UNetMotionModel
      - local: api/models/uvit2d
        title: UViT2DModel
-      title: UNets
+    - title: VAEs
-    - sections:
+      sections:
      - local: api/models/asymmetricautoencoderkl
        title: AsymmetricAutoencoderKL
      - local: api/models/autoencoder_dc
@@ -415,10 +426,6 @@
        title: AutoencoderKLCogVideoX
      - local: api/models/autoencoderkl_cosmos
        title: AutoencoderKLCosmos
      - local: api/models/autoencoder_kl_hunyuanimage
        title: AutoencoderKLHunyuanImage
      - local: api/models/autoencoder_kl_hunyuanimage_refiner
        title: AutoencoderKLHunyuanImageRefiner
      - local: api/models/autoencoder_kl_hunyuan_video
        title: AutoencoderKLHunyuanVideo
      - local: api/models/autoencoderkl_ltx_video
@@ -439,226 +446,210 @@
        title: Tiny AutoEncoder
      - local: api/models/vq
        title: VQModel
-      title: VAEs
+  - title: Pipelines
-    title: Models
+    sections:
  - sections:
    - local: api/pipelines/overview
      title: Overview
-    - sections:
+    - local: api/pipelines/allegro
-      - local: api/pipelines/audioldm
+      title: Allegro
-        title: AudioLDM
+    - local: api/pipelines/amused
-      - local: api/pipelines/audioldm2
+      title: aMUSEd
-        title: AudioLDM 2
+    - local: api/pipelines/animatediff
-      - local: api/pipelines/dance_diffusion
+      title: AnimateDiff
-        title: Dance Diffusion
+    - local: api/pipelines/attend_and_excite
-      - local: api/pipelines/musicldm
+      title: Attend-and-Excite
-        title: MusicLDM
+    - local: api/pipelines/audioldm
-      - local: api/pipelines/stable_audio
+      title: AudioLDM
-        title: Stable Audio
+    - local: api/pipelines/audioldm2
-      title: Audio
+      title: AudioLDM 2
    - local: api/pipelines/aura_flow
      title: AuraFlow
    - local: api/pipelines/auto_pipeline
      title: AutoPipeline
-    - sections:
+    - local: api/pipelines/blip_diffusion
-      - local: api/pipelines/amused
+      title: BLIP-Diffusion
-        title: aMUSEd
+    - local: api/pipelines/bria_3_2
-      - local: api/pipelines/animatediff
+      title: Bria 3.2
-        title: AnimateDiff
+    - local: api/pipelines/chroma
-      - local: api/pipelines/attend_and_excite
+      title: Chroma
-        title: Attend-and-Excite
+    - local: api/pipelines/cogvideox
-      - local: api/pipelines/aura_flow
+      title: CogVideoX
-        title: AuraFlow
+    - local: api/pipelines/cogview3
-      - local: api/pipelines/blip_diffusion
+      title: CogView3
-        title: BLIP-Diffusion
+    - local: api/pipelines/cogview4
-      - local: api/pipelines/bria_3_2
+      title: CogView4
-        title: Bria 3.2
+    - local: api/pipelines/consisid
-      - local: api/pipelines/bria_fibo
+      title: ConsisID
-        title: Bria Fibo
+    - local: api/pipelines/consistency_models
-      - local: api/pipelines/chroma
+      title: Consistency Models
-        title: Chroma
+    - local: api/pipelines/controlnet
-      - local: api/pipelines/cogview3
+      title: ControlNet
-        title: CogView3
+    - local: api/pipelines/controlnet_flux
-      - local: api/pipelines/cogview4
+      title: ControlNet with Flux.1
-        title: CogView4
+    - local: api/pipelines/controlnet_hunyuandit
-      - local: api/pipelines/consistency_models
+      title: ControlNet with Hunyuan-DiT
-        title: Consistency Models
+    - local: api/pipelines/controlnet_sd3
-      - local: api/pipelines/controlnet
+      title: ControlNet with Stable Diffusion 3
-        title: ControlNet
+    - local: api/pipelines/controlnet_sdxl
-      - local: api/pipelines/controlnet_flux
+      title: ControlNet with Stable Diffusion XL
-        title: ControlNet with Flux.1
+    - local: api/pipelines/controlnet_sana
-      - local: api/pipelines/controlnet_hunyuandit
+      title: ControlNet-Sana
-        title: ControlNet with Hunyuan-DiT
+    - local: api/pipelines/controlnetxs
-      - local: api/pipelines/controlnet_sd3
+      title: ControlNet-XS
-        title: ControlNet with Stable Diffusion 3
+    - local: api/pipelines/controlnetxs_sdxl
-      - local: api/pipelines/controlnet_sdxl
+      title: ControlNet-XS with Stable Diffusion XL
-        title: ControlNet with Stable Diffusion XL
+    - local: api/pipelines/controlnet_union
-      - local: api/pipelines/controlnet_sana
+      title: ControlNetUnion
-        title: ControlNet-Sana
+    - local: api/pipelines/cosmos
-      - local: api/pipelines/controlnetxs
+      title: Cosmos
-        title: ControlNet-XS
+    - local: api/pipelines/dance_diffusion
-      - local: api/pipelines/controlnetxs_sdxl
+      title: Dance Diffusion
-        title: ControlNet-XS with Stable Diffusion XL
+    - local: api/pipelines/ddim
-      - local: api/pipelines/controlnet_union
+      title: DDIM
-        title: ControlNetUnion
+    - local: api/pipelines/ddpm
-      - local: api/pipelines/cosmos
+      title: DDPM
-        title: Cosmos
+    - local: api/pipelines/deepfloyd_if
-      - local: api/pipelines/ddim
+      title: DeepFloyd IF
-        title: DDIM
+    - local: api/pipelines/diffedit
-      - local: api/pipelines/ddpm
+      title: DiffEdit
-        title: DDPM
+    - local: api/pipelines/dit
-      - local: api/pipelines/deepfloyd_if
+      title: DiT
-        title: DeepFloyd IF
+    - local: api/pipelines/easyanimate
-      - local: api/pipelines/diffedit
+      title: EasyAnimate
-        title: DiffEdit
+    - local: api/pipelines/flux
-      - local: api/pipelines/dit
+      title: Flux
-        title: DiT
+    - local: api/pipelines/control_flux_inpaint
-      - local: api/pipelines/easyanimate
+      title: FluxControlInpaint
-        title: EasyAnimate
+    - local: api/pipelines/framepack
-      - local: api/pipelines/flux
+      title: Framepack
-        title: Flux
+    - local: api/pipelines/hidream
-      - local: api/pipelines/control_flux_inpaint
+      title: HiDream-I1
-        title: FluxControlInpaint
+    - local: api/pipelines/hunyuandit
-      - local: api/pipelines/hidream
+      title: Hunyuan-DiT
-        title: HiDream-I1
+    - local: api/pipelines/hunyuan_video
-      - local: api/pipelines/hunyuandit
+      title: HunyuanVideo
-        title: Hunyuan-DiT
+    - local: api/pipelines/i2vgenxl
-      - local: api/pipelines/pix2pix
+      title: I2VGen-XL
-        title: InstructPix2Pix
+    - local: api/pipelines/pix2pix
-      - local: api/pipelines/kandinsky
+      title: InstructPix2Pix
-        title: Kandinsky 2.1
+    - local: api/pipelines/kandinsky
-      - local: api/pipelines/kandinsky_v22
+      title: Kandinsky 2.1
-        title: Kandinsky 2.2
+    - local: api/pipelines/kandinsky_v22
-      - local: api/pipelines/kandinsky3
+      title: Kandinsky 2.2
-        title: Kandinsky 3
+    - local: api/pipelines/kandinsky3
-      - local: api/pipelines/kandinsky5
+      title: Kandinsky 3
-        title: Kandinsky 5
+    - local: api/pipelines/kolors
-      - local: api/pipelines/kolors
+      title: Kolors
-        title: Kolors
+    - local: api/pipelines/latent_consistency_models
-      - local: api/pipelines/latent_consistency_models
+      title: Latent Consistency Models
-        title: Latent Consistency Models
+    - local: api/pipelines/latent_diffusion
-      - local: api/pipelines/latent_diffusion
+      title: Latent Diffusion
-        title: Latent Diffusion
+    - local: api/pipelines/latte
-      - local: api/pipelines/ledits_pp
+      title: Latte
-        title: LEDITS++
+    - local: api/pipelines/ledits_pp
-      - local: api/pipelines/lumina2
+      title: LEDITS++
-        title: Lumina 2.0
+    - local: api/pipelines/ltx_video
-      - local: api/pipelines/lumina
+      title: LTXVideo
-        title: Lumina-T2X
+    - local: api/pipelines/lumina2
-      - local: api/pipelines/marigold
+      title: Lumina 2.0
-        title: Marigold
+    - local: api/pipelines/lumina
-      - local: api/pipelines/panorama
+      title: Lumina-T2X
-        title: MultiDiffusion
+    - local: api/pipelines/marigold
-      - local: api/pipelines/omnigen
+      title: Marigold
-        title: OmniGen
+    - local: api/pipelines/mochi
-      - local: api/pipelines/pag
+      title: Mochi
-        title: PAG
+    - local: api/pipelines/panorama
-      - local: api/pipelines/paint_by_example
+      title: MultiDiffusion
-        title: Paint by Example
+    - local: api/pipelines/musicldm
-      - local: api/pipelines/pixart
+      title: MusicLDM
-        title: PixArt-α
+    - local: api/pipelines/omnigen
-      - local: api/pipelines/pixart_sigma
+      title: OmniGen
-        title: PixArt-Σ
+    - local: api/pipelines/pag
-      - local: api/pipelines/prx
+      title: PAG
-        title: PRX
+    - local: api/pipelines/paint_by_example
-      - local: api/pipelines/qwenimage
+      title: Paint by Example
-        title: QwenImage
+    - local: api/pipelines/pia
-      - local: api/pipelines/sana
+      title: Personalized Image Animator (PIA)
-        title: Sana
+    - local: api/pipelines/pixart
-      - local: api/pipelines/sana_sprint
+      title: PixArt-α
-        title: Sana Sprint
+    - local: api/pipelines/pixart_sigma
-      - local: api/pipelines/self_attention_guidance
+      title: PixArt-Σ
-        title: Self-Attention Guidance
+    - local: api/pipelines/qwenimage
-      - local: api/pipelines/semantic_stable_diffusion
+      title: QwenImage
-        title: Semantic Guidance
+    - local: api/pipelines/sana
-      - local: api/pipelines/shap_e
+      title: Sana
-        title: Shap-E
+    - local: api/pipelines/sana_sprint
-      - local: api/pipelines/stable_cascade
+      title: Sana Sprint
-        title: Stable Cascade
+    - local: api/pipelines/self_attention_guidance
-      - sections:
+      title: Self-Attention Guidance
-        - local: api/pipelines/stable_diffusion/overview
+    - local: api/pipelines/semantic_stable_diffusion
-          title: Overview
+      title: Semantic Guidance
-        - local: api/pipelines/stable_diffusion/depth2img
+    - local: api/pipelines/shap_e
-          title: Depth-to-image
+      title: Shap-E
-        - local: api/pipelines/stable_diffusion/gligen
+    - local: api/pipelines/skyreels_v2
-          title: GLIGEN (Grounded Language-to-Image Generation)
+      title: SkyReels-V2
-        - local: api/pipelines/stable_diffusion/image_variation
+    - local: api/pipelines/stable_audio
-          title: Image variation
+      title: Stable Audio
-        - local: api/pipelines/stable_diffusion/img2img
+    - local: api/pipelines/stable_cascade
-          title: Image-to-image
+      title: Stable Cascade
-        - local: api/pipelines/stable_diffusion/inpaint
+    - title: Stable Diffusion
-          title: Inpainting
+      sections:
-        - local: api/pipelines/stable_diffusion/k_diffusion
+      - local: api/pipelines/stable_diffusion/overview
-          title: K-Diffusion
+        title: Overview
-        - local: api/pipelines/stable_diffusion/latent_upscale
+      - local: api/pipelines/stable_diffusion/depth2img
-          title: Latent upscaler
+        title: Depth-to-image
-        - local: api/pipelines/stable_diffusion/ldm3d_diffusion
+      - local: api/pipelines/stable_diffusion/gligen
-          title: LDM3D Text-to-(RGB, Depth), Text-to-(RGB-pano, Depth-pano), LDM3D
+        title: GLIGEN (Grounded Language-to-Image Generation)
-            Upscaler
+      - local: api/pipelines/stable_diffusion/image_variation
-        - local: api/pipelines/stable_diffusion/stable_diffusion_safe
+        title: Image variation
-          title: Safe Stable Diffusion
+      - local: api/pipelines/stable_diffusion/img2img
-        - local: api/pipelines/stable_diffusion/sdxl_turbo
+        title: Image-to-image
          title: SDXL Turbo
        - local: api/pipelines/stable_diffusion/stable_diffusion_2
          title: Stable Diffusion 2
        - local: api/pipelines/stable_diffusion/stable_diffusion_3
          title: Stable Diffusion 3
        - local: api/pipelines/stable_diffusion/stable_diffusion_xl
          title: Stable Diffusion XL
        - local: api/pipelines/stable_diffusion/upscale
          title: Super-resolution
        - local: api/pipelines/stable_diffusion/adapter
          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/unclip
        title: unCLIP
      - local: api/pipelines/unidiffuser
        title: UniDiffuser
      - local: api/pipelines/value_guided_sampling
        title: Value-guided sampling
      - local: api/pipelines/visualcloze
        title: VisualCloze
      - local: api/pipelines/wuerstchen
        title: Wuerstchen
      title: Image
    - sections:
      - local: api/pipelines/allegro
        title: Allegro
      - local: api/pipelines/cogvideox
        title: CogVideoX
      - local: api/pipelines/consisid
        title: ConsisID
      - local: api/pipelines/framepack
        title: Framepack
      - local: api/pipelines/hunyuanimage21
        title: HunyuanImage2.1
      - local: api/pipelines/hunyuan_video
        title: HunyuanVideo
      - local: api/pipelines/i2vgenxl
        title: I2VGen-XL
      - local: api/pipelines/latte
        title: Latte
      - local: api/pipelines/ltx_video
        title: LTXVideo
      - local: api/pipelines/mochi
        title: Mochi
      - local: api/pipelines/pia
        title: Personalized Image Animator (PIA)
      - local: api/pipelines/skyreels_v2
        title: SkyReels-V2
      - local: api/pipelines/stable_diffusion/svd
-        title: Stable Video Diffusion
+        title: Image-to-video
-      - local: api/pipelines/text_to_video
+      - local: api/pipelines/stable_diffusion/inpaint
-        title: Text-to-video
+        title: Inpainting
-      - local: api/pipelines/text_to_video_zero
+      - local: api/pipelines/stable_diffusion/k_diffusion
-        title: Text2Video-Zero
+        title: K-Diffusion
-      - local: api/pipelines/wan
+      - local: api/pipelines/stable_diffusion/latent_upscale
-        title: Wan
+        title: Latent upscaler
-      title: Video
+      - local: api/pipelines/stable_diffusion/ldm3d_diffusion
-    title: Pipelines
+        title: LDM3D Text-to-(RGB, Depth), Text-to-(RGB-pano, Depth-pano), LDM3D Upscaler
-  - sections:
+      - local: api/pipelines/stable_diffusion/stable_diffusion_safe
        title: Safe Stable Diffusion
      - local: api/pipelines/stable_diffusion/sdxl_turbo
        title: SDXL Turbo
      - local: api/pipelines/stable_diffusion/stable_diffusion_2
        title: Stable Diffusion 2
      - local: api/pipelines/stable_diffusion/stable_diffusion_3
        title: Stable Diffusion 3
      - local: api/pipelines/stable_diffusion/stable_diffusion_xl
        title: Stable Diffusion XL
      - local: api/pipelines/stable_diffusion/upscale
        title: Super-resolution
      - local: api/pipelines/stable_diffusion/adapter
        title: T2I-Adapter
      - local: api/pipelines/stable_diffusion/text2img
        title: Text-to-image
    - local: api/pipelines/stable_unclip
      title: Stable unCLIP
    - local: api/pipelines/text_to_video
      title: Text-to-video
    - local: api/pipelines/text_to_video_zero
      title: Text2Video-Zero
    - local: api/pipelines/unclip
      title: unCLIP
    - local: api/pipelines/unidiffuser
      title: UniDiffuser
    - local: api/pipelines/value_guided_sampling
      title: Value-guided sampling
    - local: api/pipelines/visualcloze
      title: VisualCloze
    - local: api/pipelines/wan
      title: Wan
    - local: api/pipelines/wuerstchen
      title: Wuerstchen
  - title: Schedulers
    sections:
    - local: api/schedulers/overview
      title: Overview
    - local: api/schedulers/cm_stochastic_iterative
@@ -727,8 +718,8 @@
      title: UniPCMultistepScheduler
    - local: api/schedulers/vq_diffusion
      title: VQDiffusionScheduler
-    title: Schedulers
+  - title: Internal classes
-  - sections:
+    sections:
    - local: api/internal_classes_overview
      title: Overview
    - local: api/attnprocessor
@@ -745,5 +736,3 @@
      title: VAE Image Processor
    - local: api/video_processor
      title: Video Processor
    title: Internal classes
  title: API
@@ -107,9 +107,6 @@ LoRA is a fast and lightweight training method that inserts and trains a signifi
 [[autodoc]] loaders.lora_pipeline.QwenImageLoraLoaderMixin
 ## KandinskyLoraLoaderMixin
 [[autodoc]] loaders.lora_pipeline.KandinskyLoraLoaderMixin
 ## LoraBaseMixin
 [[autodoc]] loaders.lora_base.LoraBaseMixin
@@ -39,7 +39,7 @@ mask_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images
 original_image = load_image(img_url).resize((512, 512))
 mask_image = load_image(mask_url).resize((512, 512))
-pipe = StableDiffusionInpaintPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-inpainting")
+pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
 pipe.vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5")
 pipe.to("cuda")
@@ -1,32 +0,0 @@
 <!-- Copyright 2025 The HuggingFace Team. All rights reserved.
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
 http://www.apache.org/licenses/LICENSE-2.0
 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
 an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
 specific language governing permissions and limitations under the License. -->
 # AutoencoderKLHunyuanImage
 The 2D variational autoencoder (VAE) model with KL loss used in [HunyuanImage2.1].
 The model can be loaded with the following code snippet.
 ```python
 from diffusers import AutoencoderKLHunyuanImage
 vae = AutoencoderKLHunyuanImage.from_pretrained("hunyuanvideo-community/HunyuanImage-2.1-Diffusers", subfolder="vae", torch_dtype=torch.bfloat16)
 ```
 ## AutoencoderKLHunyuanImage
 [[autodoc]] AutoencoderKLHunyuanImage
  - decode
  - all
 ## DecoderOutput
 [[autodoc]] models.autoencoders.vae.DecoderOutput
@@ -1,32 +0,0 @@
 <!-- Copyright 2025 The HuggingFace Team. All rights reserved.
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
 http://www.apache.org/licenses/LICENSE-2.0
 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
 an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
 specific language governing permissions and limitations under the License. -->
 # AutoencoderKLHunyuanImageRefiner
 The 3D variational autoencoder (VAE) model with KL loss used in [HunyuanImage2.1](https://github.com/Tencent-Hunyuan/HunyuanImage-2.1) for its refiner pipeline.
 The model can be loaded with the following code snippet.
 ```python
 from diffusers import AutoencoderKLHunyuanImageRefiner
 vae = AutoencoderKLHunyuanImageRefiner.from_pretrained("hunyuanvideo-community/HunyuanImage-2.1-Refiner-Diffusers", subfolder="vae", torch_dtype=torch.bfloat16)
 ```
 ## AutoencoderKLHunyuanImageRefiner
 [[autodoc]] AutoencoderKLHunyuanImageRefiner
  - decode
  - all
 ## DecoderOutput
 [[autodoc]] models.autoencoders.vae.DecoderOutput
@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
 # ChromaTransformer2DModel
-A modified flux Transformer model from [Chroma](https://huggingface.co/lodestones/Chroma1-HD)
+A modified flux Transformer model from [Chroma](https://huggingface.co/lodestones/Chroma)
 ## ChromaTransformer2DModel
@@ -1,30 +0,0 @@
 <!-- Copyright 2025 The HuggingFace Team. All rights reserved.
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
 http://www.apache.org/licenses/LICENSE-2.0
 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
 an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
 specific language governing permissions and limitations under the License. -->
 # HunyuanImageTransformer2DModel
 A Diffusion Transformer model for [HunyuanImage2.1](https://github.com/Tencent-Hunyuan/HunyuanImage-2.1).
 The model can be loaded with the following code snippet.
 ```python
 from diffusers import HunyuanImageTransformer2DModel
 transformer = HunyuanImageTransformer2DModel.from_pretrained("hunyuanvideo-community/HunyuanImage-2.1-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
 ```
 ## HunyuanImageTransformer2DModel
 [[autodoc]] HunyuanImageTransformer2DModel
 ## Transformer2DModelOutput
 [[autodoc]] models.modeling_outputs.Transformer2DModelOutput
@@ -1,19 +0,0 @@
 <!--Copyright 2025 The HuggingFace Team. All rights reserved.
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
 http://www.apache.org/licenses/LICENSE-2.0
 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
 an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
 specific language governing permissions and limitations under the License.
 -->
 # BriaFiboTransformer2DModel
 A modified flux Transformer model from [Bria](https://huggingface.co/briaai/FIBO)
 ## BriaFiboTransformer2DModel
 [[autodoc]] BriaFiboTransformer2DModel
@@ -1,45 +0,0 @@
 <!--Copyright 2025 The HuggingFace Team. All rights reserved.
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
 http://www.apache.org/licenses/LICENSE-2.0
 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
 an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
 specific language governing permissions and limitations under the License.
 -->
 # Bria Fibo
 Text-to-image models have mastered imagination - but not control. FIBO changes that.
 FIBO is trained on structured JSON captions up to 1,000+ words and designed to understand and control different visual parameters such as lighting, composition, color, and camera settings, enabling precise and reproducible outputs.
 With only 8 billion parameters, FIBO provides a new level of image quality, prompt adherence and proffesional control.
 FIBO is trained exclusively on a structured prompt and will not work with freeform text prompts.
 you can use the [FIBO-VLM-prompt-to-JSON](https://huggingface.co/briaai/FIBO-VLM-prompt-to-JSON) model or the [FIBO-gemini-prompt-to-JSON](https://huggingface.co/briaai/FIBO-gemini-prompt-to-JSON)  to convert your freeform text prompt to a structured JSON prompt.
 its not recommended to use freeform text prompts directly with FIBO, as it will not produce the best results.
 you can learn more about FIBO in  [Bria Fibo Hugging Face page](https://huggingface.co/briaai/FIBO).
 ## Usage
 _As the model is gated, before using it with diffusers you first need to go to the [Bria Fibo Hugging Face page](https://huggingface.co/briaai/FIBO), fill in the form and accept the gate. Once you are in, you need to login so that your system knows you’ve accepted the gate._
 Use the command below to log in:
 ```bash
 hf auth login
 ```
 ## BriaPipeline
 [[autodoc]] BriaPipeline
 	- all
 	- __call__
@@ -19,21 +19,20 @@ specific language governing permissions and limitations under the License.
 Chroma is a text to image generation model based on Flux.
-Original model checkpoints for Chroma can be found here:
+Original model checkpoints for Chroma can be found [here](https://huggingface.co/lodestones/Chroma).
 * High-resolution finetune: [lodestones/Chroma1-HD](https://huggingface.co/lodestones/Chroma1-HD)
 * Base model: [lodestones/Chroma1-Base](https://huggingface.co/lodestones/Chroma1-Base)
 * Original repo with progress checkpoints: [lodestones/Chroma](https://huggingface.co/lodestones/Chroma) (loading this repo with `from_pretrained` will load a Diffusers-compatible version of the `unlocked-v37` checkpoint)
 > [!TIP]
 > Chroma can use all the same optimizations as Flux.
 ## Inference
 The Diffusers version of Chroma is based on the [`unlocked-v37`](https://huggingface.co/lodestones/Chroma/blob/main/chroma-unlocked-v37.safetensors) version of the original model, which is available in the [Chroma repository](https://huggingface.co/lodestones/Chroma).
 ```python
 import torch
 from diffusers import ChromaPipeline
-pipe = ChromaPipeline.from_pretrained("lodestones/Chroma1-HD", torch_dtype=torch.bfloat16)
+pipe = ChromaPipeline.from_pretrained("lodestones/Chroma", torch_dtype=torch.bfloat16)
 pipe.enable_model_cpu_offload()
 prompt = [
@@ -64,10 +63,10 @@ Then run the following example
 import torch
 from diffusers import ChromaTransformer2DModel, ChromaPipeline
-model_id = "lodestones/Chroma1-HD"
+model_id = "lodestones/Chroma"
 dtype = torch.bfloat16
-transformer = ChromaTransformer2DModel.from_single_file("https://huggingface.co/lodestones/Chroma1-HD/blob/main/Chroma1-HD.safetensors", torch_dtype=dtype)
+transformer = ChromaTransformer2DModel.from_single_file("https://huggingface.co/lodestones/Chroma/blob/main/chroma-unlocked-v37.safetensors", torch_dtype=dtype)
 pipe = ChromaPipeline.from_pretrained(model_id, transformer=transformer, torch_dtype=dtype)
 pipe.enable_model_cpu_offload()
@@ -1,152 +0,0 @@
 <!-- Copyright 2025 The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License. -->
 # HunyuanImage2.1
 HunyuanImage-2.1 is a 17B text-to-image model that is capable of generating 2K (2048 x 2048) resolution images
 HunyuanImage-2.1 comes in the following variants:
 | model type | model id |
 |:----------:|:--------:|
 | HunyuanImage-2.1 | [hunyuanvideo-community/HunyuanImage-2.1-Diffusers](https://huggingface.co/hunyuanvideo-community/HunyuanImage-2.1-Diffusers) |
 | HunyuanImage-2.1-Distilled | [hunyuanvideo-community/HunyuanImage-2.1-Distilled-Diffusers](https://huggingface.co/hunyuanvideo-community/HunyuanImage-2.1-Distilled-Diffusers) |
 | HunyuanImage-2.1-Refiner | [hunyuanvideo-community/HunyuanImage-2.1-Refiner-Diffusers](https://huggingface.co/hunyuanvideo-community/HunyuanImage-2.1-Refiner-Diffusers) |
 > [!TIP]
 > [Caching](../../optimization/cache) may also speed up inference by storing and reusing intermediate outputs.
 ## HunyuanImage-2.1
 HunyuanImage-2.1 applies [Adaptive Projected Guidance (APG)](https://huggingface.co/papers/2410.02416) combined with Classifier-Free Guidance (CFG) in the denoising loop. `HunyuanImagePipeline` has a `guider` component (read more about [Guider](../modular_diffusers/guiders.md)) and does not take a `guidance_scale` parameter at runtime. To change guider-related parameters, e.g., `guidance_scale`, you can update the `guider` configuration instead.
 ```python
 import torch
 from diffusers import HunyuanImagePipeline
 pipe = HunyuanImagePipeline.from_pretrained(
    "hunyuanvideo-community/HunyuanImage-2.1-Diffusers", 
    torch_dtype=torch.bfloat16
 )
 pipe = pipe.to("cuda")
 ``` 
 You can inspect the `guider` object:
 ```py
 >>> pipe.guider
 AdaptiveProjectedMixGuidance {
  "_class_name": "AdaptiveProjectedMixGuidance",
  "_diffusers_version": "0.36.0.dev0",
  "adaptive_projected_guidance_momentum": -0.5,
  "adaptive_projected_guidance_rescale": 10.0,
  "adaptive_projected_guidance_scale": 10.0,
  "adaptive_projected_guidance_start_step": 5,
  "enabled": true,
  "eta": 0.0,
  "guidance_rescale": 0.0,
  "guidance_scale": 3.5,
  "start": 0.0,
  "stop": 1.0,
  "use_original_formulation": false
 }
 State:
  step: None
  num_inference_steps: None
  timestep: None
  count_prepared: 0
  enabled: True
  num_conditions: 2
  momentum_buffer: None
  is_apg_enabled: False
  is_cfg_enabled: True
 ```
 To update the guider with a different configuration, use the `new()` method. For example, to generate an image with `guidance_scale=5.0` while keeping all other default guidance parameters:
 ```py
 import torch
 from diffusers import HunyuanImagePipeline
 pipe = HunyuanImagePipeline.from_pretrained(
    "hunyuanvideo-community/HunyuanImage-2.1-Diffusers", 
    torch_dtype=torch.bfloat16
 )
 pipe = pipe.to("cuda")
 # Update the guider configuration
 pipe.guider = pipe.guider.new(guidance_scale=5.0)
 prompt = (
    "A cute, cartoon-style anthropomorphic penguin plush toy with fluffy fur, standing in a painting studio, "
    "wearing a red knitted scarf and a red beret with the word 'Tencent' on it, holding a paintbrush with a "
    "focused expression as it paints an oil painting of the Mona Lisa, rendered in a photorealistic photographic style."
 )
 image = pipe(
    prompt=prompt, 
    num_inference_steps=50, 
    height=2048, 
    width=2048,
 ).images[0]
 image.save("image.png")
 ```
 ## HunyuanImage-2.1-Distilled
 use `distilled_guidance_scale` with the guidance-distilled checkpoint, 
 ```py
 import torch
 from diffusers import HunyuanImagePipeline
 pipe = HunyuanImagePipeline.from_pretrained("hunyuanvideo-community/HunyuanImage-2.1-Distilled-Diffusers", torch_dtype=torch.bfloat16)
 pipe = pipe.to("cuda")
 prompt = (
    "A cute, cartoon-style anthropomorphic penguin plush toy with fluffy fur, standing in a painting studio, "
    "wearing a red knitted scarf and a red beret with the word 'Tencent' on it, holding a paintbrush with a "
    "focused expression as it paints an oil painting of the Mona Lisa, rendered in a photorealistic photographic style."
 )
 out = pipe(
    prompt,
    num_inference_steps=8,
    distilled_guidance_scale=3.25,
    height=2048,
    width=2048,
    generator=generator,
 ).images[0]
 ```
 ## HunyuanImagePipeline
 [[autodoc]] HunyuanImagePipeline
  - all
  - __call__
 ## HunyuanImageRefinerPipeline
 [[autodoc]] HunyuanImageRefinerPipeline
  - all
  - __call__
 ## HunyuanImagePipelineOutput
 [[autodoc]] pipelines.hunyuan_image.pipeline_output.HunyuanImagePipelineOutput
@@ -1,149 +0,0 @@
 <!--Copyright 2025 The HuggingFace Team. All rights reserved.
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
 http://www.apache.org/licenses/LICENSE-2.0
 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
 an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
 specific language governing permissions and limitations under the License.
 -->
 # Kandinsky 5.0
 Kandinsky 5.0 is created by the Kandinsky team: Alexey Letunovskiy, Maria Kovaleva, Ivan Kirillov, Lev Novitskiy, Denis Koposov, Dmitrii Mikhailov, Anna Averchenkova, Andrey Shutkin, Julia Agafonova, Olga Kim, Anastasiia Kargapoltseva, Nikita Kiselev, Anna Dmitrienko,  Anastasia Maltseva, Kirill Chernyshev, Ilia Vasiliev, Viacheslav Vasilev, Vladimir Polovnikov, Yury Kolabushin, Alexander Belykh, Mikhail Mamaev, Anastasia Aliaskina, Tatiana Nikulina, Polina Gavrilova, Vladimir Arkhipkin, Vladimir Korviakov, Nikolai Gerasimenko, Denis Parkhomenko, Denis Dimitrov
 Kandinsky 5.0 is a family of diffusion models for Video & Image generation. Kandinsky 5.0 T2V Lite is a lightweight video generation model (2B parameters) that ranks #1 among open-source models in its class. It outperforms larger models and offers the best understanding of Russian concepts in the open-source ecosystem.
 The model introduces several key innovations:
 - **Latent diffusion pipeline** with **Flow Matching** for improved training stability
 - **Diffusion Transformer (DiT)** as the main generative backbone with cross-attention to text embeddings
 - Dual text encoding using **Qwen2.5-VL** and **CLIP** for comprehensive text understanding
 - **HunyuanVideo 3D VAE** for efficient video encoding and decoding
 - **Sparse attention mechanisms** (NABLA) for efficient long-sequence processing
 The original codebase can be found at [ai-forever/Kandinsky-5](https://github.com/ai-forever/Kandinsky-5).
 > [!TIP]
 > Check out the [AI Forever](https://huggingface.co/ai-forever) organization on the Hub for the official model checkpoints for text-to-video generation, including pretrained, SFT, no-CFG, and distilled variants.
 ## Available Models
 Kandinsky 5.0 T2V Lite comes in several variants optimized for different use cases:
 | model_id | Description | Use Cases |
 |------------|-------------|-----------|
 | **ai-forever/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers** | 5 second Supervised Fine-Tuned model | Highest generation quality |
 | **ai-forever/Kandinsky-5.0-T2V-Lite-sft-10s-Diffusers** | 10 second Supervised Fine-Tuned model | Highest generation quality |
 | **ai-forever/Kandinsky-5.0-T2V-Lite-nocfg-5s-Diffusers** | 5 second Classifier-Free Guidance distilled | 2× faster inference |
 | **ai-forever/Kandinsky-5.0-T2V-Lite-nocfg-10s-Diffusers** | 10 second Classifier-Free Guidance distilled | 2× faster inference |
 | **ai-forever/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers** | 5 second Diffusion distilled to 16 steps | 6× faster inference, minimal quality loss |
 | **ai-forever/Kandinsky-5.0-T2V-Lite-distilled16steps-10s-Diffusers** | 10 second Diffusion distilled to 16 steps | 6× faster inference, minimal quality loss |
 | **ai-forever/Kandinsky-5.0-T2V-Lite-pretrain-5s-Diffusers** | 5 second Base pretrained model | Research and fine-tuning |
 | **ai-forever/Kandinsky-5.0-T2V-Lite-pretrain-10s-Diffusers** | 10 second Base pretrained model | Research and fine-tuning |
 All models are available in 5-second and 10-second video generation versions.
 ## Kandinsky5T2VPipeline
 [[autodoc]] Kandinsky5T2VPipeline
    - all
    - __call__
 ## Usage Examples
 ### Basic Text-to-Video Generation
 ```python
 import torch
 from diffusers import Kandinsky5T2VPipeline
 from diffusers.utils import export_to_video
 # Load the pipeline
 model_id = "ai-forever/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers"
 pipe = Kandinsky5T2VPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
 pipe = pipe.to("cuda")
 # Generate video
 prompt = "A cat and a dog baking a cake together in a kitchen."
 negative_prompt = "Static, 2D cartoon, cartoon, 2d animation, paintings, images, worst quality, low quality, ugly, deformed, walking backwards"
 output = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    height=512,
    width=768,
    num_frames=121,  # ~5 seconds at 24fps
    num_inference_steps=50,
    guidance_scale=5.0,
 ).frames[0]
 export_to_video(output, "output.mp4", fps=24, quality=9)
 ```
 ### 10 second Models
 **⚠️ Warning!** all 10 second models should be used with Flex attention and max-autotune-no-cudagraphs compilation:
 ```python
 pipe = Kandinsky5T2VPipeline.from_pretrained(
    "ai-forever/Kandinsky-5.0-T2V-Lite-sft-10s-Diffusers", 
    torch_dtype=torch.bfloat16
 )
 pipe = pipe.to("cuda")
 pipe.transformer.set_attention_backend(
    "flex"
 )                                       # <--- Set attention backend to Flex
 pipe.transformer.compile(
    mode="max-autotune-no-cudagraphs", 
    dynamic=True
 )                                       # <--- Compile with max-autotune-no-cudagraphs
 prompt = "A cat and a dog baking a cake together in a kitchen."
 negative_prompt = "Static, 2D cartoon, cartoon, 2d animation, paintings, images, worst quality, low quality, ugly, deformed, walking backwards"
 output = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    height=512,
    width=768,
    num_frames=241,
    num_inference_steps=50,
    guidance_scale=5.0,
 ).frames[0]
 export_to_video(output, "output.mp4", fps=24, quality=9)
 ```
 ### Diffusion Distilled model
 **⚠️ Warning!** all nocfg and diffusion distilled models should be inferred without CFG (```guidance_scale=1.0```):
 ```python
 model_id = "ai-forever/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers"
 pipe = Kandinsky5T2VPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
 pipe = pipe.to("cuda")
 output = pipe(
    prompt="A beautiful sunset over mountains",
    num_inference_steps=16,  # <--- Model is distilled in 16 steps
    guidance_scale=1.0,      # <--- no CFG
 ).frames[0]
 export_to_video(output, "output.mp4", fps=24, quality=9)
 ```
 ## Citation
 ```bibtex
@misc{kandinsky2025,
    author = {Alexey Letunovskiy and Maria Kovaleva and Ivan Kirillov and Lev Novitskiy and Denis Koposov and
              Dmitrii Mikhailov and Anna Averchenkova and Andrey Shutkin and Julia Agafonova and Olga Kim and
              Anastasiia Kargapoltseva and Nikita Kiselev and Vladimir Arkhipkin and Vladimir Korviakov and
              Nikolai Gerasimenko and Denis Parkhomenko and Anna Dmitrienko and Anastasia Maltseva and
              Kirill Chernyshev and Ilia Vasiliev and Viacheslav Vasilev and Vladimir Polovnikov and
              Yury Kolabushin and Alexander Belykh and Mikhail Mamaev and Anastasia Aliaskina and
              Tatiana Nikulina and Polina Gavrilova and Denis Dimitrov},
    title = {Kandinsky 5.0: A family of diffusion models for Video & Image generation},
    howpublished = {\url{https://github.com/ai-forever/Kandinsky-5}},
    year = 2025
 }
 ```
@@ -1,131 +0,0 @@
 <!-- Copyright 2025 The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License. -->
 # PRX
 PRX generates high-quality images from text using a simplified MMDIT architecture where text tokens don't update through transformer blocks. It employs flow matching with discrete scheduling for efficient sampling and uses Google's T5Gemma-2B-2B-UL2 model for multi-language text encoding. The ~1.3B parameter transformer delivers fast inference without sacrificing quality. You can choose between Flux VAE (8x compression, 16 latent channels) for balanced quality and speed or DC-AE (32x compression, 32 latent channels) for latent compression and faster processing.
 ## Available models
 PRX offers multiple variants with different VAE configurations, each optimized for specific resolutions. Base models excel with detailed prompts, capturing complex compositions and subtle details. Fine-tuned models trained on the [Alchemist dataset](https://huggingface.co/datasets/yandex/alchemist) improve aesthetic quality, especially with simpler prompts.
 | Model | Resolution | Fine-tuned | Distilled | Description | Suggested prompts | Suggested parameters | Recommended dtype |
 |:-----:|:-----------------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|
 | [`Photoroom/prx-256-t2i`](https://huggingface.co/Photoroom/prx-256-t2i)| 256 | No | No | Base model pre-trained at 256 with Flux VAE|Works best with detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` |
 | [`Photoroom/prx-256-t2i-sft`](https://huggingface.co/Photoroom/prx-256-t2i-sft)| 512 | Yes | No | Fine-tuned on the [Alchemist dataset](https://huggingface.co/datasets/yandex/alchemist) dataset with Flux VAE | Can handle less detailed prompts|28 steps, cfg=5.0| `torch.bfloat16` |
 | [`Photoroom/prx-512-t2i`](https://huggingface.co/Photoroom/prx-512-t2i)| 512 | No | No | Base model pre-trained at 512 with Flux VAE |Works best with detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` |
 | [`Photoroom/prx-512-t2i-sft`](https://huggingface.co/Photoroom/prx-512-t2i-sft)| 512 | Yes | No | Fine-tuned on the [Alchemist dataset](https://huggingface.co/datasets/yandex/alchemist) dataset with Flux VAE | Can handle less detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` |
 | [`Photoroom/prx-512-t2i-sft-distilled`](https://huggingface.co/Photoroom/prx-512-t2i-sft-distilled)| 512 | Yes | Yes | 8-step distilled model from [`Photoroom/prx-512-t2i-sft`](https://huggingface.co/Photoroom/prx-512-t2i-sft) | Can handle less detailed prompts in natural language|8 steps, cfg=1.0| `torch.bfloat16` |
 | [`Photoroom/prx-512-t2i-dc-ae`](https://huggingface.co/Photoroom/prx-512-t2i-dc-ae)| 512 | No | No | Base model pre-trained at 512 with [Deep Compression Autoencoder (DC-AE)](https://hanlab.mit.edu/projects/dc-ae)|Works best with detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` |
 | [`Photoroom/prx-512-t2i-dc-ae-sft`](https://huggingface.co/Photoroom/prx-512-t2i-dc-ae-sft)| 512 | Yes | No | Fine-tuned on the [Alchemist dataset](https://huggingface.co/datasets/yandex/alchemist) dataset with [Deep Compression Autoencoder (DC-AE)](https://hanlab.mit.edu/projects/dc-ae) | Can handle less detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` |
 | [`Photoroom/prx-512-t2i-dc-ae-sft-distilled`](https://huggingface.co/Photoroom/prx-512-t2i-dc-ae-sft-distilled)| 512 | Yes | Yes | 8-step distilled model from [`Photoroom/prx-512-t2i-dc-ae-sft-distilled`](https://huggingface.co/Photoroom/prx-512-t2i-dc-ae-sft-distilled) | Can handle less detailed prompts in natural language|8 steps, cfg=1.0| `torch.bfloat16` |s
 Refer to [this](https://huggingface.co/collections/Photoroom/prx-models-68e66254c202ebfab99ad38e) collection for more information.
 ## Loading the pipeline
 Load the pipeline with [`~DiffusionPipeline.from_pretrained`].
 ```py
 from diffusers.pipelines.prx import PRXPipeline
 # Load pipeline - VAE and text encoder will be loaded from HuggingFace
 pipe = PRXPipeline.from_pretrained("Photoroom/prx-512-t2i-sft", torch_dtype=torch.bfloat16)
 pipe.to("cuda")
 prompt = "A front-facing portrait of a lion the golden savanna at sunset."
 image = pipe(prompt, num_inference_steps=28, guidance_scale=5.0).images[0]
 image.save("prx_output.png")
 ```
 ### Manual Component Loading
 Load components individually to customize the pipeline for instance to use quantized models.
 ```py
 import torch
 from diffusers.pipelines.prx import PRXPipeline
 from diffusers.models import AutoencoderKL, AutoencoderDC
 from diffusers.models.transformers.transformer_prx import PRXTransformer2DModel
 from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
 from transformers import T5GemmaModel, GemmaTokenizerFast
 from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig
 from transformers import BitsAndBytesConfig as BitsAndBytesConfig
 quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
 # Load transformer
 transformer = PRXTransformer2DModel.from_pretrained(
    "checkpoints/prx-512-t2i-sft",
    subfolder="transformer",
    quantization_config=quant_config,
    torch_dtype=torch.bfloat16,
 )
 # Load scheduler
 scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
    "checkpoints/prx-512-t2i-sft", subfolder="scheduler"
 )
 # Load T5Gemma text encoder
 t5gemma_model = T5GemmaModel.from_pretrained("google/t5gemma-2b-2b-ul2",
                                            quantization_config=quant_config,
                                            torch_dtype=torch.bfloat16)
 text_encoder = t5gemma_model.encoder.to(dtype=torch.bfloat16)
 tokenizer = GemmaTokenizerFast.from_pretrained("google/t5gemma-2b-2b-ul2")
 tokenizer.model_max_length = 256
 # Load VAE - choose either Flux VAE or DC-AE
 # Flux VAE
 vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev",
                                    subfolder="vae",
                                    quantization_config=quant_config,
                                    torch_dtype=torch.bfloat16)
 pipe = PRXPipeline(
    transformer=transformer,
    scheduler=scheduler,
    text_encoder=text_encoder,
    tokenizer=tokenizer,
    vae=vae
 )
 pipe.to("cuda")
 ```
 ## Memory Optimization
 For memory-constrained environments:
 ```py
 import torch
 from diffusers.pipelines.prx import PRXPipeline
 pipe = PRXPipeline.from_pretrained("Photoroom/prx-512-t2i-sft", torch_dtype=torch.bfloat16)
 pipe.enable_model_cpu_offload()  # Offload components to CPU when not in use
 # Or use sequential CPU offload for even lower memory
 pipe.enable_sequential_cpu_offload()
 ```
 ## PRXPipeline
 [[autodoc]] PRXPipeline
  - all
  - __call__
 ## PRXPipelineOutput
 [[autodoc]] pipelines.prx.pipeline_output.PRXPipelineOutput
@@ -21,7 +21,7 @@ The Stable Diffusion model can also infer depth based on an image using [MiDaS](
 > [!TIP]
 > Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
 >
-> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
+> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
 ## StableDiffusionDepth2ImgPipeline
@@ -21,14 +21,14 @@ The Stable Diffusion model can also be applied to inpainting which lets you edit
 ## Tips
 It is recommended to use this pipeline with checkpoints that have been specifically fine-tuned for inpainting, such
-as [stable-diffusion-v1-5/stable-diffusion-inpainting](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting). Default
+as [runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting). Default
 text-to-image Stable Diffusion checkpoints, such as
 [stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) are also compatible but they might be less performant.
 > [!TIP]
 > Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
 >
-> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
+> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
 ## StableDiffusionInpaintPipeline
@@ -17,7 +17,7 @@ The Stable Diffusion latent upscaler model was created by [Katherine Crowson](ht
 > [!TIP]
 > Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
 >
-> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
+> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
 ## StableDiffusionLatentUpscalePipeline
@@ -22,7 +22,7 @@ Stable Diffusion is trained on 512x512 images from a subset of the LAION-5B data
 For more details about how Stable Diffusion works and how it differs from the base latent diffusion model, take a look at the Stability AI [announcement](https://stability.ai/blog/stable-diffusion-announcement) and our own [blog post](https://huggingface.co/blog/stable_diffusion#how-does-stable-diffusion-work) for more technical details.
-You can find the original codebase for Stable Diffusion v1.0 at [CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion) and Stable Diffusion v2.0 at [Stability-AI/stablediffusion](https://github.com/Stability-AI/stablediffusion) as well as their original scripts for various tasks. Additional official checkpoints for the different Stable Diffusion versions and tasks can be found on the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations. Explore these organizations to find the best checkpoint for your use-case!
+You can find the original codebase for Stable Diffusion v1.0 at [CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion) and Stable Diffusion v2.0 at [Stability-AI/stablediffusion](https://github.com/Stability-AI/stablediffusion) as well as their original scripts for various tasks. Additional official checkpoints for the different Stable Diffusion versions and tasks can be found on the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations. Explore these organizations to find the best checkpoint for your use-case!
 The table below summarizes the available Stable Diffusion pipelines, their supported tasks, and an interactive demo:
@@ -64,7 +64,7 @@ The table below summarizes the available Stable Diffusion pipelines, their suppo
            <a href="./inpaint">StableDiffusionInpaint</a>
            </td>
            <td class="px-4 py-2 text-gray-700">inpainting</td>
-            <td class="px-4 py-2"><a href="https://huggingface.co/spaces/stable-diffusion-v1-5/stable-diffusion-inpainting"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"/></a>
+            <td class="px-4 py-2"><a href="https://huggingface.co/spaces/runwayml/stable-diffusion-inpainting"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"/></a>
            </td>
        </tr>
        <tr>
@@ -36,7 +36,7 @@ Here are some examples for how to use Stable Diffusion 2 for each task:
 > [!TIP]
 > Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
 >
-> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
+> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
 ## Text-to-image
@@ -25,7 +25,7 @@ The abstract from the paper is:
 > [!TIP]
 > Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
 >
-> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
+> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
 ## StableDiffusionPipeline
@@ -21,7 +21,7 @@ The Stable Diffusion upscaler diffusion model was created by the researchers and
 > [!TIP]
 > Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
 >
-> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
+> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
 ## StableDiffusionUpscalePipeline
@@ -21,7 +21,6 @@ Refer to the table below for an overview of the available attention families and
 | attention family | main feature |
 |---|---|
 | FlashAttention | minimizes memory reads/writes through tiling and recomputation |
 | AI Tensor Engine for ROCm | FlashAttention implementation optimized for AMD ROCm accelerators |
 | SageAttention | quantizes attention to int8 |
 | PyTorch native | built-in PyTorch implementation using [scaled_dot_product_attention](./fp16#scaled-dot-product-attention) |
 | xFormers | memory-efficient attention with support for various attention kernels |
@@ -140,7 +139,6 @@ Refer to the table below for a complete list of available attention backends and
 | `_native_xla` | [PyTorch native](https://docs.pytorch.org/docs/stable/generated/torch.nn.attention.SDPBackend.html#torch.nn.attention.SDPBackend) | XLA-optimized attention |
 | `flash` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | FlashAttention-2 |
 | `flash_varlen` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | Variable length FlashAttention |
 | `aiter` | [AI Tensor Engine for ROCm](https://github.com/ROCm/aiter) | FlashAttention for AMD ROCm |
 | `_flash_3` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | FlashAttention-3 |
 | `_flash_varlen_3` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | Variable length FlashAttention-3 |
 | `_flash_3_hub` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | FlashAttention-3 from kernels |
@@ -16,12 +16,12 @@ pipeline.unet.config["in_channels"]
 4
 ```
-Inpainting requires 9 channels in the input sample. You can check this value in a pretrained inpainting model like [`stable-diffusion-v1-5/stable-diffusion-inpainting`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting):
+Inpainting requires 9 channels in the input sample. You can check this value in a pretrained inpainting model like [`runwayml/stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting):
 ```py
 from diffusers import StableDiffusionPipeline
-pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-inpainting", use_safetensors=True)
+pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", use_safetensors=True)
 pipeline.unet.config["in_channels"]
 9
 ```
@@ -215,7 +215,7 @@ from diffusers import AutoPipelineForInpainting, LCMScheduler
 from diffusers.utils import load_image, make_image_grid
 pipe = AutoPipelineForInpainting.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-inpainting",
+    "runwayml/stable-diffusion-inpainting",
    torch_dtype=torch.float16,
    variant="fp16",
 ).to("cuda")
@@ -112,7 +112,7 @@ blurred_mask
 ## Popular models
-[Stable Diffusion Inpainting](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting), [Stable Diffusion XL (SDXL) Inpainting](https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1), and [Kandinsky 2.2 Inpainting](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder-inpaint) are among the most popular models for inpainting. SDXL typically produces higher resolution images than Stable Diffusion v1.5, and Kandinsky 2.2 is also capable of generating high-quality images.
+[Stable Diffusion Inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting), [Stable Diffusion XL (SDXL) Inpainting](https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1), and [Kandinsky 2.2 Inpainting](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder-inpaint) are among the most popular models for inpainting. SDXL typically produces higher resolution images than Stable Diffusion v1.5, and Kandinsky 2.2 is also capable of generating high-quality images.
 ### Stable Diffusion Inpainting
@@ -124,7 +124,7 @@ from diffusers import AutoPipelineForInpainting
 from diffusers.utils import load_image, make_image_grid
 pipeline = AutoPipelineForInpainting.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
+    "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
@@ -244,7 +244,7 @@ make_image_grid([init_image, image], rows=1, cols=2)
 ```
 </hfoption>
-<hfoption id="stable-diffusion-v1-5/stable-diffusion-inpainting">
+<hfoption id="runwayml/stable-diffusion-inpainting">
 ```py
 import torch
@@ -252,7 +252,7 @@ from diffusers import AutoPipelineForInpainting
 from diffusers.utils import load_image, make_image_grid
 pipeline = AutoPipelineForInpainting.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
+    "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
@@ -278,7 +278,7 @@ make_image_grid([init_image, image], rows=1, cols=2)
  </div>
  <div>
    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint-specific.png"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">stable-diffusion-v1-5/stable-diffusion-inpainting</figcaption>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">runwayml/stable-diffusion-inpainting</figcaption>
  </div>
 </div>
@@ -308,7 +308,7 @@ make_image_grid([init_image, image], rows=1, cols=2)
 ```
 </hfoption>
-<hfoption id="stable-diffusion-v1-5/stable-diffusion-inpaint">
+<hfoption id="runwayml/stable-diffusion-inpaint">
 ```py
 import torch
@@ -316,7 +316,7 @@ from diffusers import AutoPipelineForInpainting
 from diffusers.utils import load_image, make_image_grid
 pipeline = AutoPipelineForInpainting.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
+    "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
@@ -340,7 +340,7 @@ make_image_grid([init_image, image], rows=1, cols=2)
  </div>
  <div>
    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/specific-inpaint-basic.png"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">stable-diffusion-v1-5/stable-diffusion-inpainting</figcaption>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">runwayml/stable-diffusion-inpainting</figcaption>
  </div>
 </div>
@@ -358,7 +358,7 @@ from diffusers.utils import load_image, make_image_grid
 device = "cuda"
 pipeline = AutoPipelineForInpainting.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-inpainting",
+    "runwayml/stable-diffusion-inpainting",
    torch_dtype=torch.float16,
    variant="fp16"
 )
@@ -396,7 +396,7 @@ from diffusers import AutoPipelineForInpainting
 from diffusers.utils import load_image, make_image_grid
 pipeline = AutoPipelineForInpainting.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
+    "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
@@ -441,7 +441,7 @@ from diffusers import AutoPipelineForInpainting
 from diffusers.utils import load_image, make_image_grid
 pipeline = AutoPipelineForInpainting.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
+    "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
@@ -481,7 +481,7 @@ from diffusers import AutoPipelineForInpainting
 from diffusers.utils import load_image, make_image_grid
 pipeline = AutoPipelineForInpainting.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
+    "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
@@ -606,7 +606,7 @@ from diffusers import AutoPipelineForInpainting, AutoPipelineForImage2Image
 from diffusers.utils import load_image, make_image_grid
 pipeline = AutoPipelineForInpainting.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
+    "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
@@ -683,7 +683,7 @@ from diffusers import AutoPipelineForInpainting
 from diffusers.utils import make_image_grid
 pipeline = AutoPipelineForInpainting.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16,
+    "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16,
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
@@ -714,7 +714,7 @@ controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpai
 # pass ControlNet to the pipeline
 pipeline = StableDiffusionControlNetInpaintPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-inpainting", controlnet=controlnet, torch_dtype=torch.float16, variant="fp16"
+    "runwayml/stable-diffusion-inpainting", controlnet=controlnet, torch_dtype=torch.float16, variant="fp16"
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
@@ -173,7 +173,7 @@ mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data
 init_image = download_image(img_url).resize((512, 512))
 mask_image = download_image(mask_url).resize((512, 512))
-path = "stable-diffusion-v1-5/stable-diffusion-inpainting"
+path = "runwayml/stable-diffusion-inpainting"
 run_compile = True  # Set True / False
@@ -28,12 +28,12 @@ pipeline.unet.config["in_channels"]
 4
 ```
-인페인팅은 입력 샘플에 9개의 채널이 필요합니다. [`stable-diffusion-v1-5/stable-diffusion-inpainting`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting)와 같은 사전학습된 인페인팅 모델에서 이 값을 확인할 수 있습니다:
+인페인팅은 입력 샘플에 9개의 채널이 필요합니다. [`runwayml/stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting)와 같은 사전학습된 인페인팅 모델에서 이 값을 확인할 수 있습니다:
 ```py
 from diffusers import StableDiffusionPipeline
-pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-inpainting")
+pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
 pipeline.unet.config["in_channels"]
 9
 ```
@@ -14,7 +14,7 @@ specific language governing permissions and limitations under the License.
 [[open-in-colab]]
-[`StableDiffusionInpaintPipeline`]은 마스크와 텍스트 프롬프트를 제공하여 이미지의 특정 부분을 편집할 수 있도록 합니다. 이 기능은 인페인팅 작업을 위해 특별히 훈련된 [`stable-diffusion-v1-5/stable-diffusion-inpainting`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting)과 같은 Stable Diffusion 버전을 사용합니다.
+[`StableDiffusionInpaintPipeline`]은 마스크와 텍스트 프롬프트를 제공하여 이미지의 특정 부분을 편집할 수 있도록 합니다. 이 기능은 인페인팅 작업을 위해 특별히 훈련된 [`runwayml/stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting)과 같은 Stable Diffusion 버전을 사용합니다.
 먼저 [`StableDiffusionInpaintPipeline`] 인스턴스를 불러옵니다:
@@ -27,7 +27,7 @@ from io import BytesIO
 from diffusers import StableDiffusionInpaintPipeline
 pipeline = StableDiffusionInpaintPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-inpainting",
+    "runwayml/stable-diffusion-inpainting",
    torch_dtype=torch.float16,
 )
 pipeline = pipeline.to("cuda")
@@ -61,3 +61,12 @@ image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]
 > [!WARNING]
 > 이전의 실험적인 인페인팅 구현에서는 품질이 낮은 다른 프로세스를 사용했습니다. 이전 버전과의 호환성을 보장하기 위해 새 모델이 포함되지 않은 사전학습된 파이프라인을 불러오면 이전 인페인팅 방법이 계속 적용됩니다.
 아래 Space에서 이미지 인페인팅을 직접 해보세요!
 <iframe
  src="https://runwayml-stable-diffusion-inpainting.hf.space"
  frameborder="0"
  width="850"
  height="500"
 ></iframe>
@@ -16,12 +16,12 @@ pipeline.unet.config["in_channels"]
 4
 ```
-而图像修复任务需要输入样本具有9个通道。您可以在 [`stable-diffusion-v1-5/stable-diffusion-inpainting`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting) 这样的预训练修复模型中验证此参数：
+而图像修复任务需要输入样本具有9个通道。您可以在 [`runwayml/stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting) 这样的预训练修复模型中验证此参数：
 ```python
 from diffusers import StableDiffusionPipeline
-pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-inpainting", use_safetensors=True)
+pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", use_safetensors=True)
 pipeline.unet.config["in_channels"]
 9
 ```
@@ -1328,7 +1328,7 @@ model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined"
 # Load Stable Diffusion Inpainting Pipeline with custom pipeline
 pipe = DiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-inpainting",
+    "runwayml/stable-diffusion-inpainting",
    custom_pipeline="text_inpainting",
    segmentation_model=model,
    segmentation_processor=processor
@@ -126,7 +126,7 @@ EXAMPLE_DOC_STRING = """
        ...     "lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16
        ... )
        >>> pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
-        ...     "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
+        ...     "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
        ... )
        >>> pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
@@ -347,7 +347,7 @@ class AdaptiveMaskInpaintPipeline(
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
            about a model's potential harms.
        feature_extractor ([`~transformers.CLIPImageProcessor`]):
            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -429,8 +429,8 @@ class AdaptiveMaskInpaintPipeline(
                "The configuration file of the unet has set the default `sample_size` to smaller than"
                " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                " in the config might lead to incorrect results in future versions. If you have downloaded this"
                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
@@ -970,7 +970,7 @@ class AdaptiveMaskInpaintPipeline(
        >>> default_mask_image = download_image(mask_url).resize((512, 512))
        >>> pipe = AdaptiveMaskInpaintPipeline.from_pretrained(
-        ...     "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16
+        ...     "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")
@@ -1095,7 +1095,7 @@ class AdaptiveMaskInpaintPipeline(
        # 8. Check that sizes of mask, masked image and latents match
        if num_channels_unet == 9:
-            # default case for stable-diffusion-v1-5/stable-diffusion-inpainting
+            # default case for runwayml/stable-diffusion-inpainting
            num_channels_mask = mask.shape[1]
            num_channels_masked_image = masked_image_latents.shape[1]
            if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
@@ -62,7 +62,7 @@ class ComposableStableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin)
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
        feature_extractor ([`CLIPImageProcessor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """
@@ -145,8 +145,8 @@ class ComposableStableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin)
                "The configuration file of the unet has set the default `sample_size` to smaller than"
                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                " in the config might lead to incorrect results in future versions. If you have downloaded this"
                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
@@ -1276,7 +1276,7 @@ class FrescoV2VPipeline(StableDiffusionControlNetImg2ImgPipeline):
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
            about a model's potential harms.
        feature_extractor ([`~transformers.CLIPImageProcessor`]):
            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -678,7 +678,7 @@ class StableDiffusionHDPainterPipeline(StableDiffusionInpaintPipeline):
        # 8. Check that sizes of mask, masked image and latents match
        if num_channels_unet == 9:
-            # default case for stable-diffusion-v1-5/stable-diffusion-inpainting
+            # default case for runwayml/stable-diffusion-inpainting
            num_channels_mask = mask.shape[1]
            num_channels_masked_image = masked_image_latents.shape[1]
            if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
@@ -78,7 +78,7 @@ class ImageToImageInpaintingPipeline(DiffusionPipeline):
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
        feature_extractor ([`CLIPImageProcessor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """
@@ -86,7 +86,7 @@ class InstaFlowPipeline(
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
            about a model's potential harms.
        feature_extractor ([`~transformers.CLIPImageProcessor`]):
            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -165,8 +165,8 @@ class InstaFlowPipeline(
                "The configuration file of the unet has set the default `sample_size` to smaller than"
                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                " in the config might lead to incorrect results in future versions. If you have downloaded this"
                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
@@ -166,7 +166,7 @@ class IPAdapterFaceIDStableDiffusionPipeline(
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
            about a model's potential harms.
        feature_extractor ([`~transformers.CLIPImageProcessor`]):
            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -247,8 +247,8 @@ class IPAdapterFaceIDStableDiffusionPipeline(
                "The configuration file of the unet has set the default `sample_size` to smaller than"
                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                " in the config might lead to incorrect results in future versions. If you have downloaded this"
                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
@@ -414,7 +414,7 @@ class StableDiffusionHighResFixPipeline(StableDiffusionPipeline):
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
            about a model's potential harms.
        feature_extractor ([`~transformers.CLIPImageProcessor`]):
            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -222,7 +222,7 @@ class LatentConsistencyModelWalkPipeline(
            supports [`LCMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
            about a model's potential harms.
        feature_extractor ([`~transformers.CLIPImageProcessor`]):
            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -302,7 +302,7 @@ class LLMGroundedDiffusionPipeline(
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
            about a model's potential harms.
        feature_extractor ([`~transformers.CLIPImageProcessor`]):
            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -392,8 +392,8 @@ class LLMGroundedDiffusionPipeline(
                "The configuration file of the unet has set the default `sample_size` to smaller than"
                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                " in the config might lead to incorrect results in future versions. If you have downloaded this"
                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
@@ -552,8 +552,8 @@ class StableDiffusionLongPromptWeightingPipeline(
                "The configuration file of the unet has set the default `sample_size` to smaller than"
                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                " in the config might lead to incorrect results in future versions. If you have downloaded this"
                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
@@ -1765,7 +1765,7 @@ class SDXLLongPromptWeightingPipeline(
            # Check that sizes of mask, masked image and latents match
            if num_channels_unet == 9:
-                # default case for stable-diffusion-v1-5/stable-diffusion-inpainting
+                # default case for runwayml/stable-diffusion-inpainting
                num_channels_mask = mask.shape[1]
                num_channels_masked_image = masked_image_latents.shape[1]
                if num_channels_latents + num_channels_mask + num_channels_masked_image != num_channels_unet:
@@ -3729,8 +3729,8 @@ class MatryoshkaPipeline(
                "The configuration file of the unet has set the default `sample_size` to smaller than"
                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                " in the config might lead to incorrect results in future versions. If you have downloaded this"
                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
@@ -78,7 +78,7 @@ class MultilingualStableDiffusion(DiffusionPipeline, StableDiffusionMixin):
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
        feature_extractor ([`CLIPImageProcessor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """
@@ -1607,7 +1607,7 @@ class KolorsControlNetInpaintPipeline(
        # 9. Check that sizes of mask, masked image and latents match
        if num_channels_unet == 9:
-            # default case for stable-diffusion-v1-5/stable-diffusion-inpainting
+            # default case for runwayml/stable-diffusion-inpainting
            num_channels_mask = mask.shape[1]
            num_channels_masked_image = masked_image_latents.shape[1]
            if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
@@ -135,7 +135,7 @@ class FabricPipeline(DiffusionPipeline):
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
            about a model's potential harms.
    """
@@ -163,8 +163,8 @@ class FabricPipeline(DiffusionPipeline):
                "The configuration file of the unet has set the default `sample_size` to smaller than"
                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                " in the config might lead to incorrect results in future versions. If you have downloaded this"
                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
@@ -1487,7 +1487,7 @@ class KolorsInpaintPipeline(
        # 8. Check that sizes of mask, masked image and latents match
        if num_channels_unet == 9:
-            # default case for stable-diffusion-v1-5/stable-diffusion-inpainting
+            # default case for runwayml/stable-diffusion-inpainting
            num_channels_mask = mask.shape[1]
            num_channels_masked_image = masked_image_latents.shape[1]
            if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
@@ -106,7 +106,7 @@ class Prompt2PromptPipeline(
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
            about a model's potential harms.
        feature_extractor ([`~transformers.CLIPImageProcessor`]):
            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -187,8 +187,8 @@ class Prompt2PromptPipeline(
                "The configuration file of the unet has set the default `sample_size` to smaller than"
                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                " in the config might lead to incorrect results in future versions. If you have downloaded this"
                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
@@ -1730,7 +1730,7 @@ class StyleAlignedSDXLPipeline(
            # Check that sizes of mask, masked image and latents match
            if num_channels_unet == 9:
-                # default case for stable-diffusion-v1-5/stable-diffusion-inpainting
+                # default case for runwayml/stable-diffusion-inpainting
                num_channels_mask = mask.shape[1]
                num_channels_masked_image = masked_image_latents.shape[1]
                if num_channels_latents + num_channels_mask + num_channels_masked_image != num_channels_unet:
@@ -59,7 +59,7 @@ EXAMPLE_DOC_STRING = """
        >>> import torch
        >>> from diffusers import StableDiffusionPipeline
-        >>> pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16)
+        >>> pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
        >>> pipe = pipe.to("cuda")
        >>> prompt = "a photo of an astronaut riding a horse on mars"
@@ -392,7 +392,7 @@ class StableDiffusionBoxDiffPipeline(
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
            about a model's potential harms.
        feature_extractor ([`~transformers.CLIPImageProcessor`]):
            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -473,8 +473,8 @@ class StableDiffusionBoxDiffPipeline(
                "The configuration file of the unet has set the default `sample_size` to smaller than"
                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                " in the config might lead to incorrect results in future versions. If you have downloaded this"
                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
@@ -42,7 +42,7 @@ EXAMPLE_DOC_STRING = """
        ```py
        >>> import torch
        >>> from diffusers import StableDiffusionPipeline
-        >>> pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16)
+        >>> pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
        >>> pipe = pipe.to("cuda")
        >>> prompt = "a photo of an astronaut riding a horse on mars"
        >>> image = pipe(prompt).images[0]
@@ -359,7 +359,7 @@ class StableDiffusionPAGPipeline(
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
            about a model's potential harms.
        feature_extractor ([`~transformers.CLIPImageProcessor`]):
            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -440,8 +440,8 @@ class StableDiffusionPAGPipeline(
                "The configuration file of the unet has set the default `sample_size` to smaller than"
                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                " in the config might lead to incorrect results in future versions. If you have downloaded this"
                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
@@ -100,7 +100,7 @@ class StableDiffusionUpscaleLDM3DPipeline(
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
            about a model's potential harms.
        feature_extractor ([`~transformers.CLIPImageProcessor`]):
            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -2042,7 +2042,7 @@ class StableDiffusionXL_AE_Pipeline(
        # 8. Check that sizes of mask, masked image and latents match
        if num_channels_unet == 9:
-            # default case for stable-diffusion-v1-5/stable-diffusion-inpainting
+            # default case for runwayml/stable-diffusion-inpainting
            num_channels_mask = mask.shape[1]
            num_channels_masked_image = masked_image_latents.shape[1]
            if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
@@ -188,7 +188,7 @@ class StableDiffusionXLControlNetAdapterPipeline(
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
        feature_extractor ([`CLIPImageProcessor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """
@@ -330,7 +330,7 @@ class StableDiffusionXLControlNetAdapterInpaintPipeline(
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
        feature_extractor ([`CLIPImageProcessor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
        requires_aesthetics_score (`bool`, *optional*, defaults to `"False"`):
@@ -1569,7 +1569,7 @@ class StableDiffusionXLControlNetAdapterInpaintPipeline(
        # 8. Check that sizes of mask, masked image and latents match
        if num_channels_unet == 9:
-            # default case for stable-diffusion-v1-5/stable-diffusion-inpainting
+            # default case for runwayml/stable-diffusion-inpainting
            num_channels_mask = mask.shape[1]
            num_channels_masked_image = masked_image_latents.shape[1]
            if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
@@ -46,7 +46,7 @@ EXAMPLE_DOC_STRING = """
        >>> import torch
        >>> from diffusers import StableDiffusionPipeline
-        >>> pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16)
+        >>> pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
        >>> pipe = pipe.to("cuda")
        >>> prompt = "a photo of an astronaut riding a horse on mars"
@@ -86,7 +86,7 @@ class Zero1to3StableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin):
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
        feature_extractor ([`CLIPImageProcessor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
        cc_projection ([`CCProjection`]):
@@ -164,8 +164,8 @@ class Zero1to3StableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin):
                "The configuration file of the unet has set the default `sample_size` to smaller than"
                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                " in the config might lead to incorrect results in future versions. If you have downloaded this"
                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
@@ -288,7 +288,7 @@ class RerenderAVideoPipeline(StableDiffusionControlNetImg2ImgPipeline):
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
        feature_extractor ([`CLIPImageProcessor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """
@@ -54,7 +54,7 @@ EXAMPLE_DOC_STRING = """
        >>> # load control net and stable diffusion v1-5
        >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
        >>> pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
-        ...     "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
+        ...     "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
        ... )
        >>> # speed up diffusion process with faster scheduler and memory optimization
@@ -158,7 +158,7 @@ EXAMPLE_DOC_STRING = """
        >>> # load control net and stable diffusion v1-5
        >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
        >>> pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
-        ...     "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
+        ...     "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
        ... )
        >>> # speed up diffusion process with faster scheduler and memory optimization
@@ -64,7 +64,7 @@ class StableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin):
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
        feature_extractor ([`CLIPImageProcessor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """
@@ -114,7 +114,7 @@ class SdeDragPipeline(DiffusionPipeline):
        >>> from diffusers import DDIMScheduler, DiffusionPipeline
        >>> # Load the pipeline
-        >>> model_path = "stable-diffusion-v1-5/stable-diffusion-v1-5"
+        >>> model_path = "runwayml/stable-diffusion-v1-5"
        >>> scheduler = DDIMScheduler.from_pretrained(model_path, subfolder="scheduler")
        >>> pipe = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler, custom_pipeline="sde_drag")
        >>> pipe.to('cuda')
@@ -46,7 +46,7 @@ class StableDiffusionComparisonPipeline(DiffusionPipeline, StableDiffusionMixin)
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionMegaSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
        feature_extractor ([`CLIPImageProcessor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """
@@ -36,7 +36,7 @@ EXAMPLE_DOC_STRING = """
        >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
        >>> pipe_controlnet = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
-                "stable-diffusion-v1-5/stable-diffusion-v1-5",
+                "runwayml/stable-diffusion-v1-5",
                controlnet=controlnet,
                safety_checker=None,
                torch_dtype=torch.float16
@@ -81,7 +81,7 @@ EXAMPLE_DOC_STRING = """
        >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg", torch_dtype=torch.float16)
        >>> pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
-                "stable-diffusion-v1-5/stable-diffusion-inpainting", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
+                "runwayml/stable-diffusion-inpainting", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
            )
        >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
@@ -80,7 +80,7 @@ EXAMPLE_DOC_STRING = """
        >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg", torch_dtype=torch.float16)
        >>> pipe = StableDiffusionControlNetInpaintImg2ImgPipeline.from_pretrained(
-                "stable-diffusion-v1-5/stable-diffusion-inpainting", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
+                "runwayml/stable-diffusion-inpainting", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
            )
        >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
@@ -37,7 +37,7 @@ EXAMPLE_DOC_STRING = """
        >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
        >>> pipe = StableDiffusionControlNetReferencePipeline.from_pretrained(
-                "stable-diffusion-v1-5/stable-diffusion-v1-5",
+                "runwayml/stable-diffusion-v1-5",
                controlnet=controlnet,
                safety_checker=None,
                torch_dtype=torch.float16
@@ -43,7 +43,7 @@ EXAMPLE_DOC_STRING = """
        >>> import torch
        >>> from diffusers import StableDiffusionPipeline
-        >>> pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", custom_pipeline="stable_diffusion_ipex")
+        >>> pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", custom_pipeline="stable_diffusion_ipex")
        >>> # For Float32
        >>> pipe.prepare_for_ipex(prompt, dtype=torch.float32, height=512, width=512) #value of image height/width should be consistent with the pipeline inference
@@ -85,7 +85,7 @@ class StableDiffusionIPEXPipeline(
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
        feature_extractor ([`CLIPImageProcessor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """
@@ -161,8 +161,8 @@ class StableDiffusionIPEXPipeline(
                "The configuration file of the unet has set the default `sample_size` to smaller than"
                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                " in the config might lead to incorrect results in future versions. If you have downloaded this"
                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
@@ -47,7 +47,7 @@ class StableDiffusionMegaPipeline(DiffusionPipeline, StableDiffusionMixin):
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionMegaSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
        feature_extractor ([`CLIPImageProcessor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """
@@ -46,7 +46,7 @@ EXAMPLE_DOC_STRING = """
        >>> input_image = load_image("https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png")
        >>> pipe = StableDiffusionReferencePipeline.from_pretrained(
-                "stable-diffusion-v1-5/stable-diffusion-v1-5",
+                "runwayml/stable-diffusion-v1-5",
                safety_checker=None,
                torch_dtype=torch.float16
                ).to('cuda:0')
@@ -112,7 +112,7 @@ class StableDiffusionReferencePipeline(
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
        feature_extractor ([`CLIPImageProcessor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """
@@ -194,8 +194,8 @@ class StableDiffusionReferencePipeline(
                "The configuration file of the unet has set the default `sample_size` to smaller than"
                " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                " in the config might lead to incorrect results in future versions. If you have downloaded this"
                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
@@ -167,7 +167,7 @@ class StableDiffusionRepaintPipeline(
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
        feature_extractor ([`CLIPImageProcessor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """
@@ -249,8 +249,8 @@ class StableDiffusionRepaintPipeline(
                "The configuration file of the unet has set the default `sample_size` to smaller than"
                " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                " in the config might lead to incorrect results in future versions. If you have downloaded this"
                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
@@ -678,7 +678,7 @@ class TensorRTStableDiffusionImg2ImgPipeline(DiffusionPipeline):
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
        feature_extractor ([`CLIPImageProcessor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """
@@ -766,8 +766,8 @@ class TensorRTStableDiffusionImg2ImgPipeline(DiffusionPipeline):
                "The configuration file of the unet has set the default `sample_size` to smaller than"
                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                " in the config might lead to incorrect results in future versions. If you have downloaded this"
                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
@@ -682,7 +682,7 @@ class TensorRTStableDiffusionInpaintPipeline(DiffusionPipeline):
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
        feature_extractor ([`CLIPImageProcessor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """
@@ -770,8 +770,8 @@ class TensorRTStableDiffusionInpaintPipeline(DiffusionPipeline):
                "The configuration file of the unet has set the default `sample_size` to smaller than"
                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                " in the config might lead to incorrect results in future versions. If you have downloaded this"
                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
@@ -594,7 +594,7 @@ class TensorRTStableDiffusionPipeline(DiffusionPipeline):
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
        feature_extractor ([`CLIPImageProcessor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """
@@ -682,8 +682,8 @@ class TensorRTStableDiffusionPipeline(DiffusionPipeline):
                "The configuration file of the unet has set the default `sample_size` to smaller than"
                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                " in the config might lead to incorrect results in future versions. If you have downloaded this"
                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
@@ -52,7 +52,7 @@ class TextInpainting(DiffusionPipeline, StableDiffusionMixin):
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
        feature_extractor ([`CLIPImageProcessor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """
@@ -1223,7 +1223,7 @@ class AnyTextPipeline(
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
            about a model's potential harms.
        feature_extractor ([`~transformers.CLIPImageProcessor`]):
            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -5,7 +5,7 @@ This script was added by @thedarkzeno .
 Please note that this script is not actively maintained, you can open an issue and tag @thedarkzeno or @patil-suraj though.
 ```bash
-export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-inpainting"
+export MODEL_NAME="runwayml/stable-diffusion-inpainting"
 export INSTANCE_DIR="path-to-instance-images"
 export OUTPUT_DIR="path-to-save-model"
@@ -29,7 +29,7 @@ Prior-preservation is used to avoid overfitting and language-drift. Refer to the
 According to the paper, it's recommended to generate `num_epochs * num_samples` images for prior-preservation. 200-300 works well for most cases.
 ```bash
-export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-inpainting"
+export MODEL_NAME="runwayml/stable-diffusion-inpainting"
 export INSTANCE_DIR="path-to-instance-images"
 export CLASS_DIR="path-to-class-images"
 export OUTPUT_DIR="path-to-save-model"
@@ -60,7 +60,7 @@ With the help of gradient checkpointing and the 8-bit optimizer from bitsandbyte
 To install `bitandbytes` please refer to this [readme](https://github.com/TimDettmers/bitsandbytes#requirements--installation).
 ```bash
-export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-inpainting"
+export MODEL_NAME="runwayml/stable-diffusion-inpainting"
 export INSTANCE_DIR="path-to-instance-images"
 export CLASS_DIR="path-to-class-images"
 export OUTPUT_DIR="path-to-save-model"
@@ -92,7 +92,7 @@ Pass the `--train_text_encoder` argument to the script to enable training `text_
 ___Note: Training text encoder requires more memory, with this option the training won't fit on 16GB GPU. It needs at least 24GB VRAM.___
 ```bash
-export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-inpainting"
+export MODEL_NAME="runwayml/stable-diffusion-inpainting"
 export INSTANCE_DIR="path-to-instance-images"
 export CLASS_DIR="path-to-class-images"
 export OUTPUT_DIR="path-to-save-model"
@@ -55,7 +55,7 @@ The Accelerate launch command is used to train a model using multiple GPUs and m
 ```
 accelerate launch --mixed_precision "fp16" \
 tutorial_train_ip-adapter.py \
--pretrained_model_name_or_path="stable-diffusion-v1-5/stable-diffusion-v1-5/" \
+--pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5/" \
 --image_encoder_path="{image_encoder_path}" \
 --data_json_file="{data.json}" \
 --data_root_path="{image_path}" \
@@ -73,7 +73,7 @@ tutorial_train_ip-adapter.py \
 ```
 accelerate launch --num_processes 8 --multi_gpu --mixed_precision "fp16" \
  tutorial_train_ip-adapter.py \
-  --pretrained_model_name_or_path="stable-diffusion-v1-5/stable-diffusion-v1-5/" \
+  --pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5/" \
  --image_encoder_path="{image_encoder_path}" \
  --data_json_file="{data.json}" \
  --data_root_path="{image_path}" \
@@ -27,7 +27,7 @@ You can build multiple datasets for every subject and upload them to the 🤗 hu
 Before launching the training script, make sure to select the inpainting the target model, the output directory and the 🤗 datasets.
 ```bash
-export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-inpainting"
+export MODEL_NAME="runwayml/stable-diffusion-inpainting"
 export OUTPUT_DIR="path-to-save-model"
 export DATASET_1="gzguevara/mr_potato_head_masked"
@@ -177,7 +177,7 @@ class PromptDiffusionPipeline(
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
            about a model's potential harms.
        feature_extractor ([`~transformers.CLIPImageProcessor`]):
            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -238,7 +238,7 @@ def parse_args() -> argparse.Namespace:
 # EXAMPLE USAGE:
 #
-# python vae_roundtrip.py --use_cuda --pretrained_model_name_or_path "stable-diffusion-v1-5/stable-diffusion-v1-5" --subfolder "vae" --input_image "foo.png"
+# python vae_roundtrip.py --use_cuda --pretrained_model_name_or_path "runwayml/stable-diffusion-v1-5" --subfolder "vae" --input_image "foo.png"
 #
 # python vae_roundtrip.py --use_cuda --pretrained_model_name_or_path "madebyollin/taesd" --use_tiny_nn --input_image "foo.png"
 #
@@ -24,8 +24,7 @@ args = args.parse_args()
 def _extract_into_tensor(arr, timesteps, broadcast_shape):
-    # from: https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/guided_diffusion/gaussian_diffusion.py#L895
+    # from: https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/guided_diffusion/gaussian_diffusion.py#L895    """
    #  """
    res = arr[timesteps].float()
    dims_to_append = len(broadcast_shape) - len(res.shape)
    return res[(...,) + (None,) * dims_to_append]
@@ -508,9 +507,7 @@ def rename_state_dict(sd, embedding):
 # encode with stable diffusion vae
-pipe = StableDiffusionPipeline.from_pretrained(
+pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
    "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16
 )
 pipe.vae.cuda()
 # construct original decoder with jitted model
@@ -1093,7 +1090,7 @@ def new_constructor(self, **kwargs):
 Encoder.__init__ = new_constructor
-vae = AutoencoderKL.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="vae")
+vae = AutoencoderKL.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="vae")
 consistency_vae = ConsistencyDecoderVAE(
    encoder_args=vae.encoder.constructor_arguments,
    decoder_args=unet.config,
@@ -1120,7 +1117,7 @@ print((sample_consistency_orig - sample_consistency_new_3).abs().sum())
 print("running with diffusers pipeline")
 pipe = DiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", vae=consistency_vae, torch_dtype=torch.float16
+    "runwayml/stable-diffusion-v1-5", vae=consistency_vae, torch_dtype=torch.float16
 )
 pipe.to("cuda")
@@ -1,345 +0,0 @@
 #!/usr/bin/env python3
 """
 Script to convert PRX checkpoint from original codebase to diffusers format.
 """
 import argparse
 import json
 import os
 import sys
 from dataclasses import asdict, dataclass
 from typing import Dict, Tuple
 import torch
 from safetensors.torch import save_file
 from diffusers.models.transformers.transformer_prx import PRXTransformer2DModel
 from diffusers.pipelines.prx import PRXPipeline
 DEFAULT_RESOLUTION = 512
@dataclass(frozen=True)
 class PRXBase:
    context_in_dim: int = 2304
    hidden_size: int = 1792
    mlp_ratio: float = 3.5
    num_heads: int = 28
    depth: int = 16
    axes_dim: Tuple[int, int] = (32, 32)
    theta: int = 10_000
    time_factor: float = 1000.0
    time_max_period: int = 10_000
@dataclass(frozen=True)
 class PRXFlux(PRXBase):
    in_channels: int = 16
    patch_size: int = 2
@dataclass(frozen=True)
 class PRXDCAE(PRXBase):
    in_channels: int = 32
    patch_size: int = 1
 def build_config(vae_type: str) -> Tuple[dict, int]:
    if vae_type == "flux":
        cfg = PRXFlux()
    elif vae_type == "dc-ae":
        cfg = PRXDCAE()
    else:
        raise ValueError(f"Unsupported VAE type: {vae_type}. Use 'flux' or 'dc-ae'")
    config_dict = asdict(cfg)
    config_dict["axes_dim"] = list(config_dict["axes_dim"])  # type: ignore[index]
    return config_dict
 def create_parameter_mapping(depth: int) -> dict:
    """Create mapping from old parameter names to new diffusers names."""
    # Key mappings for structural changes
    mapping = {}
    # Map old structure (layers in PRXBlock) to new structure (layers in PRXAttention)
    for i in range(depth):
        # QKV projections moved to attention module
        mapping[f"blocks.{i}.img_qkv_proj.weight"] = f"blocks.{i}.attention.img_qkv_proj.weight"
        mapping[f"blocks.{i}.txt_kv_proj.weight"] = f"blocks.{i}.attention.txt_kv_proj.weight"
        # QK norm moved to attention module and renamed to match Attention's qk_norm structure
        mapping[f"blocks.{i}.qk_norm.query_norm.scale"] = f"blocks.{i}.attention.norm_q.weight"
        mapping[f"blocks.{i}.qk_norm.key_norm.scale"] = f"blocks.{i}.attention.norm_k.weight"
        mapping[f"blocks.{i}.qk_norm.query_norm.weight"] = f"blocks.{i}.attention.norm_q.weight"
        mapping[f"blocks.{i}.qk_norm.key_norm.weight"] = f"blocks.{i}.attention.norm_k.weight"
        # K norm for text tokens moved to attention module
        mapping[f"blocks.{i}.k_norm.scale"] = f"blocks.{i}.attention.norm_added_k.weight"
        mapping[f"blocks.{i}.k_norm.weight"] = f"blocks.{i}.attention.norm_added_k.weight"
        # Attention output projection
        mapping[f"blocks.{i}.attn_out.weight"] = f"blocks.{i}.attention.to_out.0.weight"
    return mapping
 def convert_checkpoint_parameters(old_state_dict: Dict[str, torch.Tensor], depth: int) -> Dict[str, torch.Tensor]:
    """Convert old checkpoint parameters to new diffusers format."""
    print("Converting checkpoint parameters...")
    mapping = create_parameter_mapping(depth)
    converted_state_dict = {}
    for key, value in old_state_dict.items():
        new_key = key
        # Apply specific mappings if needed
        if key in mapping:
            new_key = mapping[key]
            print(f"  Mapped: {key} -> {new_key}")
        converted_state_dict[new_key] = value
    print(f"✓ Converted {len(converted_state_dict)} parameters")
    return converted_state_dict
 def create_transformer_from_checkpoint(checkpoint_path: str, config: dict) -> PRXTransformer2DModel:
    """Create and load PRXTransformer2DModel from old checkpoint."""
    print(f"Loading checkpoint from: {checkpoint_path}")
    # Load old checkpoint
    if not os.path.exists(checkpoint_path):
        raise FileNotFoundError(f"Checkpoint not found: {checkpoint_path}")
    old_checkpoint = torch.load(checkpoint_path, map_location="cpu")
    # Handle different checkpoint formats
    if isinstance(old_checkpoint, dict):
        if "model" in old_checkpoint:
            state_dict = old_checkpoint["model"]
        elif "state_dict" in old_checkpoint:
            state_dict = old_checkpoint["state_dict"]
        else:
            state_dict = old_checkpoint
    else:
        state_dict = old_checkpoint
    print(f"✓ Loaded checkpoint with {len(state_dict)} parameters")
    # Convert parameter names if needed
    model_depth = int(config.get("depth", 16))
    converted_state_dict = convert_checkpoint_parameters(state_dict, depth=model_depth)
    # Create transformer with config
    print("Creating PRXTransformer2DModel...")
    transformer = PRXTransformer2DModel(**config)
    # Load state dict
    print("Loading converted parameters...")
    missing_keys, unexpected_keys = transformer.load_state_dict(converted_state_dict, strict=False)
    if missing_keys:
        print(f"⚠ Missing keys: {missing_keys}")
    if unexpected_keys:
        print(f"⚠ Unexpected keys: {unexpected_keys}")
    if not missing_keys and not unexpected_keys:
        print("✓ All parameters loaded successfully!")
    return transformer
 def create_scheduler_config(output_path: str, shift: float):
    """Create FlowMatchEulerDiscreteScheduler config."""
    scheduler_config = {"_class_name": "FlowMatchEulerDiscreteScheduler", "num_train_timesteps": 1000, "shift": shift}
    scheduler_path = os.path.join(output_path, "scheduler")
    os.makedirs(scheduler_path, exist_ok=True)
    with open(os.path.join(scheduler_path, "scheduler_config.json"), "w") as f:
        json.dump(scheduler_config, f, indent=2)
    print("✓ Created scheduler config")
 def download_and_save_vae(vae_type: str, output_path: str):
    """Download and save VAE to local directory."""
    from diffusers import AutoencoderDC, AutoencoderKL
    vae_path = os.path.join(output_path, "vae")
    os.makedirs(vae_path, exist_ok=True)
    if vae_type == "flux":
        print("Downloading FLUX VAE from black-forest-labs/FLUX.1-dev...")
        vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae")
    else:  # dc-ae
        print("Downloading DC-AE VAE from mit-han-lab/dc-ae-f32c32-sana-1.1-diffusers...")
        vae = AutoencoderDC.from_pretrained("mit-han-lab/dc-ae-f32c32-sana-1.1-diffusers")
    vae.save_pretrained(vae_path)
    print(f"✓ Saved VAE to {vae_path}")
 def download_and_save_text_encoder(output_path: str):
    """Download and save T5Gemma text encoder and tokenizer."""
    from transformers import GemmaTokenizerFast
    from transformers.models.t5gemma.modeling_t5gemma import T5GemmaModel
    text_encoder_path = os.path.join(output_path, "text_encoder")
    tokenizer_path = os.path.join(output_path, "tokenizer")
    os.makedirs(text_encoder_path, exist_ok=True)
    os.makedirs(tokenizer_path, exist_ok=True)
    print("Downloading T5Gemma model from google/t5gemma-2b-2b-ul2...")
    t5gemma_model = T5GemmaModel.from_pretrained("google/t5gemma-2b-2b-ul2")
    # Extract and save only the encoder
    t5gemma_encoder = t5gemma_model.encoder
    t5gemma_encoder.save_pretrained(text_encoder_path)
    print(f"✓ Saved T5GemmaEncoder to {text_encoder_path}")
    print("Downloading tokenizer from google/t5gemma-2b-2b-ul2...")
    tokenizer = GemmaTokenizerFast.from_pretrained("google/t5gemma-2b-2b-ul2")
    tokenizer.model_max_length = 256
    tokenizer.save_pretrained(tokenizer_path)
    print(f"✓ Saved tokenizer to {tokenizer_path}")
 def create_model_index(vae_type: str, default_image_size: int, output_path: str):
    """Create model_index.json for the pipeline."""
    if vae_type == "flux":
        vae_class = "AutoencoderKL"
    else:  # dc-ae
        vae_class = "AutoencoderDC"
    model_index = {
        "_class_name": "PRXPipeline",
        "_diffusers_version": "0.31.0.dev0",
        "_name_or_path": os.path.basename(output_path),
        "default_sample_size": default_image_size,
        "scheduler": ["diffusers", "FlowMatchEulerDiscreteScheduler"],
        "text_encoder": ["prx", "T5GemmaEncoder"],
        "tokenizer": ["transformers", "GemmaTokenizerFast"],
        "transformer": ["diffusers", "PRXTransformer2DModel"],
        "vae": ["diffusers", vae_class],
    }
    model_index_path = os.path.join(output_path, "model_index.json")
    with open(model_index_path, "w") as f:
        json.dump(model_index, f, indent=2)
 def main(args):
    # Validate inputs
    if not os.path.exists(args.checkpoint_path):
        raise FileNotFoundError(f"Checkpoint not found: {args.checkpoint_path}")
    config = build_config(args.vae_type)
    # Create output directory
    os.makedirs(args.output_path, exist_ok=True)
    print(f"✓ Output directory: {args.output_path}")
    # Create transformer from checkpoint
    transformer = create_transformer_from_checkpoint(args.checkpoint_path, config)
    # Save transformer
    transformer_path = os.path.join(args.output_path, "transformer")
    os.makedirs(transformer_path, exist_ok=True)
    # Save config
    with open(os.path.join(transformer_path, "config.json"), "w") as f:
        json.dump(config, f, indent=2)
    # Save model weights as safetensors
    state_dict = transformer.state_dict()
    save_file(state_dict, os.path.join(transformer_path, "diffusion_pytorch_model.safetensors"))
    print(f"✓ Saved transformer to {transformer_path}")
    # Create scheduler config
    create_scheduler_config(args.output_path, args.shift)
    download_and_save_vae(args.vae_type, args.output_path)
    download_and_save_text_encoder(args.output_path)
    # Create model_index.json
    create_model_index(args.vae_type, args.resolution, args.output_path)
    # Verify the pipeline can be loaded
    try:
        pipeline = PRXPipeline.from_pretrained(args.output_path)
        print("Pipeline loaded successfully!")
        print(f"Transformer: {type(pipeline.transformer).__name__}")
        print(f"VAE: {type(pipeline.vae).__name__}")
        print(f"Text Encoder: {type(pipeline.text_encoder).__name__}")
        print(f"Scheduler: {type(pipeline.scheduler).__name__}")
        # Display model info
        num_params = sum(p.numel() for p in pipeline.transformer.parameters())
        print(f"✓ Transformer parameters: {num_params:,}")
    except Exception as e:
        print(f"Pipeline verification failed: {e}")
        return False
    print("Conversion completed successfully!")
    print(f"Converted pipeline saved to: {args.output_path}")
    print(f"VAE type: {args.vae_type}")
    return True
 if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Convert PRX checkpoint to diffusers format")
    parser.add_argument(
        "--checkpoint_path", type=str, required=True, help="Path to the original PRX checkpoint (.pth file )"
    )
    parser.add_argument(
        "--output_path", type=str, required=True, help="Output directory for the converted diffusers pipeline"
    )
    parser.add_argument(
        "--vae_type",
        type=str,
        choices=["flux", "dc-ae"],
        required=True,
        help="VAE type to use: 'flux' for AutoencoderKL (16 channels) or 'dc-ae' for AutoencoderDC (32 channels)",
    )
    parser.add_argument(
        "--resolution",
        type=int,
        choices=[256, 512, 1024],
        default=DEFAULT_RESOLUTION,
        help="Target resolution for the model (256, 512, or 1024). Affects the transformer's sample_size.",
    )
    parser.add_argument(
        "--shift",
        type=float,
        default=3.0,
        help="Shift for the scheduler",
    )
    args = parser.parse_args()
    try:
        success = main(args)
        if not success:
            sys.exit(1)
    except Exception as e:
        print(f"Conversion failed: {e}")
        import traceback
        traceback.print_exc()
        sys.exit(1)
@@ -149,9 +149,7 @@ else:
    _import_structure["guiders"].extend(
        [
            "AdaptiveProjectedGuidance",
            "AdaptiveProjectedMixGuidance",
            "AutoGuidance",
            "BaseGuidance",
            "ClassifierFreeGuidance",
            "ClassifierFreeZeroStarGuidance",
            "FrequencyDecoupledGuidance",
@@ -186,8 +184,6 @@ else:
            "AutoencoderKLAllegro",
            "AutoencoderKLCogVideoX",
            "AutoencoderKLCosmos",
            "AutoencoderKLHunyuanImage",
            "AutoencoderKLHunyuanImageRefiner",
            "AutoencoderKLHunyuanVideo",
            "AutoencoderKLLTXVideo",
            "AutoencoderKLMagvit",
@@ -198,7 +194,6 @@ else:
            "AutoencoderOobleck",
            "AutoencoderTiny",
            "AutoModel",
            "BriaFiboTransformer2DModel",
            "BriaTransformer2DModel",
            "CacheMixin",
            "ChromaTransformer2DModel",
@@ -221,12 +216,10 @@ else:
            "HunyuanDiT2DControlNetModel",
            "HunyuanDiT2DModel",
            "HunyuanDiT2DMultiControlNetModel",
            "HunyuanImageTransformer2DModel",
            "HunyuanVideoFramepackTransformer3DModel",
            "HunyuanVideoTransformer3DModel",
            "I2VGenXLUNet",
            "Kandinsky3UNet",
            "Kandinsky5Transformer3DModel",
            "LatteTransformer3DModel",
            "LTXVideoTransformer3DModel",
            "Lumina2Transformer2DModel",
@@ -240,7 +233,6 @@ else:
            "ParallelConfig",
            "PixArtTransformer2DModel",
            "PriorTransformer",
            "PRXTransformer2DModel",
            "QwenImageControlNetModel",
            "QwenImageMultiControlNetModel",
            "QwenImageTransformer2DModel",
@@ -431,7 +423,6 @@ else:
            "AuraFlowPipeline",
            "BlipDiffusionControlNetPipeline",
            "BlipDiffusionPipeline",
            "BriaFiboPipeline",
            "BriaPipeline",
            "ChromaImg2ImgPipeline",
            "ChromaPipeline",
@@ -469,8 +460,6 @@ else:
            "HunyuanDiTControlNetPipeline",
            "HunyuanDiTPAGPipeline",
            "HunyuanDiTPipeline",
            "HunyuanImagePipeline",
            "HunyuanImageRefinerPipeline",
            "HunyuanSkyreelsImageToVideoPipeline",
            "HunyuanVideoFramepackPipeline",
            "HunyuanVideoImageToVideoPipeline",
@@ -485,7 +474,6 @@ else:
            "ImageTextPipelineOutput",
            "Kandinsky3Img2ImgPipeline",
            "Kandinsky3Pipeline",
            "Kandinsky5T2VPipeline",
            "KandinskyCombinedPipeline",
            "KandinskyImg2ImgCombinedPipeline",
            "KandinskyImg2ImgPipeline",
@@ -529,7 +517,6 @@ else:
            "PixArtAlphaPipeline",
            "PixArtSigmaPAGPipeline",
            "PixArtSigmaPipeline",
            "PRXPipeline",
            "QwenImageControlNetInpaintPipeline",
            "QwenImageControlNetPipeline",
            "QwenImageEditInpaintPipeline",
@@ -858,9 +845,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
    else:
        from .guiders import (
            AdaptiveProjectedGuidance,
            AdaptiveProjectedMixGuidance,
            AutoGuidance,
            BaseGuidance,
            ClassifierFreeGuidance,
            ClassifierFreeZeroStarGuidance,
            FrequencyDecoupledGuidance,
@@ -891,8 +876,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
            AutoencoderKLAllegro,
            AutoencoderKLCogVideoX,
            AutoencoderKLCosmos,
            AutoencoderKLHunyuanImage,
            AutoencoderKLHunyuanImageRefiner,
            AutoencoderKLHunyuanVideo,
            AutoencoderKLLTXVideo,
            AutoencoderKLMagvit,
@@ -903,7 +886,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
            AutoencoderOobleck,
            AutoencoderTiny,
            AutoModel,
            BriaFiboTransformer2DModel,
            BriaTransformer2DModel,
            CacheMixin,
            ChromaTransformer2DModel,
@@ -926,12 +908,10 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
            HunyuanDiT2DControlNetModel,
            HunyuanDiT2DModel,
            HunyuanDiT2DMultiControlNetModel,
            HunyuanImageTransformer2DModel,
            HunyuanVideoFramepackTransformer3DModel,
            HunyuanVideoTransformer3DModel,
            I2VGenXLUNet,
            Kandinsky3UNet,
            Kandinsky5Transformer3DModel,
            LatteTransformer3DModel,
            LTXVideoTransformer3DModel,
            Lumina2Transformer2DModel,
@@ -945,7 +925,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
            ParallelConfig,
            PixArtTransformer2DModel,
            PriorTransformer,
            PRXTransformer2DModel,
            QwenImageControlNetModel,
            QwenImageMultiControlNetModel,
            QwenImageTransformer2DModel,
@@ -1106,7 +1085,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
            AudioLDM2UNet2DConditionModel,
            AudioLDMPipeline,
            AuraFlowPipeline,
            BriaFiboPipeline,
            BriaPipeline,
            ChromaImg2ImgPipeline,
            ChromaPipeline,
@@ -1144,8 +1122,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
            HunyuanDiTControlNetPipeline,
            HunyuanDiTPAGPipeline,
            HunyuanDiTPipeline,
            HunyuanImagePipeline,
            HunyuanImageRefinerPipeline,
            HunyuanSkyreelsImageToVideoPipeline,
            HunyuanVideoFramepackPipeline,
            HunyuanVideoImageToVideoPipeline,
@@ -1160,7 +1136,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
            ImageTextPipelineOutput,
            Kandinsky3Img2ImgPipeline,
            Kandinsky3Pipeline,
            Kandinsky5T2VPipeline,
            KandinskyCombinedPipeline,
            KandinskyImg2ImgCombinedPipeline,
            KandinskyImg2ImgPipeline,
@@ -1204,7 +1179,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
            PixArtAlphaPipeline,
            PixArtSigmaPAGPipeline,
            PixArtSigmaPipeline,
            PRXPipeline,
            QwenImageControlNetInpaintPipeline,
            QwenImageControlNetPipeline,
            QwenImageEditInpaintPipeline,
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
sayakpaul	137bf5af89	up	2025-10-17 11:11:01 +05:30
Sayak Paul	7ba8d9a238	Merge branch 'main' into vae-tests-mixin	2025-10-17 10:44:09 +05:30
sayakpaul	74160ed00f	up	2025-10-17 10:43:52 +05:30
Sayak Paul	1ac55e7a7e	Merge branch 'main' into vae-tests-mixin	2025-10-17 08:00:37 +05:30
sayakpaul	7b8817ec04	up	2025-09-24 10:44:15 +05:30
Sayak Paul	9a6ecfbcc4	Merge branch 'main' into vae-tests-mixin	2025-09-24 09:43:58 +05:30
sayakpaul	6a01c4681c	u[	2025-09-24 09:30:34 +05:30
sayakpaul	3a106f05ee	up	2025-09-24 09:20:42 +05:30
Sayak Paul	378090705f	Merge branch 'main' into vae-tests-mixin	2025-09-24 09:17:19 +05:30
sayakpaul	769b7452ed	up	2025-09-24 09:15:50 +05:30
sayakpaul	01aa188d8d	up	2025-09-23 14:05:51 +05:30
sayakpaul	490c4761b4	up	2025-09-23 13:45:27 +05:30
sayakpaul	cfe1e2e3fa	up	2025-09-23 13:32:05 +05:30