update

.github/workflows/benchmark.yml

@@ -7,7 +7,6 @@ on:
 
 env:
   DIFFUSERS_IS_CI: yes
-  HF_HUB_ENABLE_HF_TRANSFER: 1
   HF_HOME: /mnt/cache
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
@@ -51,7 +50,7 @@ jobs:
 
       - name: Test suite reports artifacts
         if: ${{ always() }}
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v2
         with:
           name: benchmark_test_reports
           path: benchmarks/benchmark_outputs

.github/workflows/mirror_community_pipeline.yml

@@ -25,7 +25,7 @@ jobs:
     env:
       SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_COMMUNITY_MIRROR }}
 
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
     steps:
       # Checkout to correct ref
       #   If workflow dispatch

.github/workflows/nightly_tests.yml

@@ -43,7 +43,7 @@ jobs:
 
       - name: Pipeline Tests Artifacts
         if: ${{ always() }}
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v2
         with:
           name: test-pipelines.json
           path: reports
@@ -72,14 +72,14 @@ jobs:
         run: |
           python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
           python -m uv pip install -e [quality,test]
-          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+          python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git
           python -m uv pip install pytest-reportlog
       - name: Environment
         run: |
           python utils/print_env.py
       - name: Pipeline CUDA Test
         env:
-          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
           # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
           CUBLAS_WORKSPACE_CONFIG: :16:8
         run: |
@@ -95,7 +95,7 @@ jobs:
           cat reports/tests_pipeline_${{ matrix.module }}_cuda_failures_short.txt
       - name: Test suite reports artifacts
         if: ${{ always() }}
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v2
         with:
           name: pipeline_${{ matrix.module }}_test_reports
           path: reports
@@ -130,8 +130,8 @@ jobs:
       run: |
         python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python -m uv pip install -e [quality,test]
+        python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git
         python -m uv pip install peft@git+https://github.com/huggingface/peft.git
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
         python -m uv pip install pytest-reportlog
     - name: Environment
       run: python utils/print_env.py
@@ -139,7 +139,7 @@ jobs:
     - name: Run nightly PyTorch CUDA tests for non-pipeline modules
       if: ${{ matrix.module != 'examples'}}
       env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
         # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
         CUBLAS_WORKSPACE_CONFIG: :16:8
       run: |
@@ -152,7 +152,7 @@ jobs:
     - name: Run nightly example tests with Torch
       if: ${{ matrix.module == 'examples' }}
       env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
         # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
         CUBLAS_WORKSPACE_CONFIG: :16:8
       run: |
@@ -169,7 +169,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v2
       with:
         name: torch_${{ matrix.module }}_cuda_test_reports
         path: reports
@@ -201,7 +201,7 @@ jobs:
       run: |
         python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python -m uv pip install -e [quality,test]
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+        python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git
         python -m uv pip install pytest-reportlog
 
     - name: Environment
@@ -209,7 +209,7 @@ jobs:
 
     - name: Run nightly Flax TPU tests
       env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
       run: |
         python -m pytest -n 0 \
           -s -v -k "Flax" \
@@ -225,7 +225,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v2
       with:
         name: flax_tpu_test_reports
         path: reports
@@ -257,14 +257,14 @@ jobs:
       run: |
         python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python -m uv pip install -e [quality,test]
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+        python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git
         python -m uv pip install pytest-reportlog
     - name: Environment
       run: python utils/print_env.py
 
     - name: Run Nightly ONNXRuntime CUDA tests
       env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
       run: |
         python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
           -s -v -k "Onnx" \
@@ -280,9 +280,9 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v2
       with:
-        name: tests_onnx_cuda_reports
+        name: ${{ matrix.config.report }}_test_reports
         path: reports
 
     - name: Generate Report and Notify Channel
@@ -340,7 +340,7 @@ jobs:
 #
 #      - name: Test suite reports artifacts
 #        if: ${{ always() }}
-#        uses: actions/upload-artifact@v4
+#        uses: actions/upload-artifact@v2
 #        with:
 #          name: torch_mps_test_reports
 #          path: reports
@@ -396,7 +396,7 @@ jobs:
 #
 #      - name: Test suite reports artifacts
 #        if: ${{ always() }}
-#        uses: actions/upload-artifact@v4
+#        uses: actions/upload-artifact@v2
 #        with:
 #          name: torch_mps_test_reports
 #          path: reports

.github/workflows/notify_slack_about_release.yml

@@ -7,7 +7,7 @@ on:
 
 jobs:
   build:
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
 
     steps:
     - uses: actions/checkout@v3

.github/workflows/pr_dependency_test.yml

@@ -16,7 +16,7 @@ concurrency:
 
 jobs:
   check_dependencies:
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
       - name: Set up Python

.github/workflows/pr_flax_dependency_test.yml

@@ -16,7 +16,7 @@ concurrency:
 
 jobs:
   check_flax_dependencies:
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
       - name: Set up Python

.github/workflows/pr_test_fetcher.yml

@@ -171,7 +171,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v2
       with:
         name: pr_${{ matrix.config.report }}_test_reports
         path: reports

.github/workflows/pr_test_peft_backend.yml

@@ -20,7 +20,7 @@ env:
 
 jobs:
   check_code_quality:
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
       - name: Set up Python
@@ -40,7 +40,7 @@ jobs:
 
   check_repository_consistency:
     needs: check_code_quality
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
       - name: Set up Python
@@ -92,14 +92,12 @@ jobs:
       run: |
         python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python -m uv pip install -e [quality,test]
-        # TODO (sayakpaul, DN6): revisit `--no-deps`
         if [ "${{ matrix.lib-versions }}" == "main" ]; then
-            python -m pip install -U peft@git+https://github.com/huggingface/peft.git --no-deps
-            python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
-            pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
+            python -m pip install -U peft@git+https://github.com/huggingface/peft.git
+            python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git
+            python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
         else
-            python -m uv pip install -U peft --no-deps
-            python -m uv pip install -U transformers accelerate --no-deps
+            python -m uv pip install -U peft transformers accelerate
         fi
 
     - name: Environment
@@ -112,23 +110,23 @@ jobs:
         python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
           -s -v \
-          --make-reports=tests_${{ matrix.lib-versions }} \
+          --make-reports=tests_${{ matrix.config.report }} \
           tests/lora/
         python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
           -s -v \
-          --make-reports=tests_models_lora_${{ matrix.lib-versions }} \
+          --make-reports=tests_models_lora_${{ matrix.config.report }} \
           tests/models/ -k "lora"
 
 
     - name: Failure short reports
       if: ${{ failure() }}
       run: |
-        cat reports/tests_${{ matrix.lib-versions }}_failures_short.txt
-        cat reports/tests_models_lora_${{ matrix.lib-versions }}_failures_short.txt
+        cat reports/tests_${{ matrix.config.report }}_failures_short.txt
+        cat reports/tests_models_lora_${{ matrix.config.report }}_failures_short.txt
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v2
       with:
-        name: pr_${{ matrix.lib-versions }}_test_reports
+        name: pr_${{ matrix.config.report }}_test_reports
         path: reports

.github/workflows/pr_tests.yml

@@ -22,14 +22,13 @@ concurrency:
 
 env:
   DIFFUSERS_IS_CI: yes
-  HF_HUB_ENABLE_HF_TRANSFER: 1
   OMP_NUM_THREADS: 4
   MKL_NUM_THREADS: 4
   PYTEST_TIMEOUT: 60
 
 jobs:
   check_code_quality:
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
       - name: Set up Python
@@ -49,7 +48,7 @@ jobs:
 
   check_repository_consistency:
     needs: check_code_quality
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
       - name: Set up Python
@@ -169,9 +168,9 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v2
       with:
-        name: pr_${{ matrix.config.framework }}_${{ matrix.config.report }}_test_reports
+        name: pr_${{ matrix.config.report }}_test_reports
         path: reports
 
   run_staging_tests:
@@ -230,7 +229,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v2
       with:
         name: pr_${{ matrix.config.report }}_test_reports
         path: reports

.github/workflows/pr_torch_dependency_test.yml

@@ -16,7 +16,7 @@ concurrency:
 
 jobs:
   check_torch_dependencies:
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
       - name: Set up Python

.github/workflows/push_tests.yml

@@ -1,10 +1,11 @@
 name: Fast GPU Tests on main
 
 on:
-  workflow_dispatch:
   push:
     branches:
       - main
+      - "v*.*.*-release"
+      - "v*.*.*-patch"
     paths:
       - "src/diffusers/**.py"
       - "examples/**.py"
@@ -14,7 +15,6 @@ env:
   DIFFUSERS_IS_CI: yes
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
-  HF_HUB_ENABLE_HF_TRANSFER: 1
   PYTEST_TIMEOUT: 600
   PIPELINE_USAGE_CUTOFF: 50000
 
@@ -47,7 +47,7 @@ jobs:
           echo "pipeline_test_matrix=$matrix" >> $GITHUB_OUTPUT
       - name: Pipeline Tests Artifacts
         if: ${{ always() }}
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v2
         with:
           name: test-pipelines.json
           path: reports
@@ -77,7 +77,7 @@ jobs:
         run: |
           python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
           python -m uv pip install -e [quality,test]
-          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+          python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git
       - name: Environment
         run: |
           python utils/print_env.py
@@ -98,7 +98,7 @@ jobs:
           cat reports/tests_pipeline_${{ matrix.module }}_cuda_failures_short.txt
       - name: Test suite reports artifacts
         if: ${{ always() }}
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v2
         with:
           name: pipeline_${{ matrix.module }}_test_reports
           path: reports
@@ -128,8 +128,8 @@ jobs:
       run: |
         python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python -m uv pip install -e [quality,test]
+        python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git
         python -m uv pip install peft@git+https://github.com/huggingface/peft.git
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
 
     - name: Environment
       run: |
@@ -143,20 +143,20 @@ jobs:
       run: |
         python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
           -s -v -k "not Flax and not Onnx" \
-          --make-reports=tests_torch_cuda_${{ matrix.module }} \
+          --make-reports=tests_torch_cuda \
           tests/${{ matrix.module }}
 
     - name: Failure short reports
       if: ${{ failure() }}
       run: |
-        cat reports/tests_torch_cuda_${{ matrix.module }}_stats.txt
-        cat reports/tests_torch_cuda_${{ matrix.module }}_failures_short.txt
+        cat reports/tests_torch_cuda_stats.txt
+        cat reports/tests_torch_cuda_failures_short.txt
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v2
       with:
-        name: torch_cuda_test_reports_${{ matrix.module }}
+        name: torch_cuda_test_reports
         path: reports
 
   flax_tpu_tests:
@@ -178,7 +178,7 @@ jobs:
       run: |
         python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python -m uv pip install -e [quality,test]
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+        python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git
 
     - name: Environment
       run: |
@@ -201,7 +201,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v2
       with:
         name: flax_tpu_test_reports
         path: reports
@@ -226,7 +226,7 @@ jobs:
       run: |
         python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python -m uv pip install -e [quality,test]
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+        python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git
 
     - name: Environment
       run: |
@@ -249,7 +249,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v2
       with:
         name: onnx_cuda_test_reports
         path: reports
@@ -292,7 +292,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v2
       with:
         name: torch_compile_test_reports
         path: reports
@@ -334,7 +334,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v2
       with:
         name: torch_xformers_test_reports
         path: reports
@@ -385,7 +385,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v2
       with:
         name: examples_test_reports
         path: reports

.github/workflows/push_tests_fast.yml

@@ -18,7 +18,6 @@ env:
   HF_HOME: /mnt/cache
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
-  HF_HUB_ENABLE_HF_TRANSFER: 1
   PYTEST_TIMEOUT: 600
   RUN_SLOW: no
 
@@ -120,7 +119,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v2
       with:
         name: pr_${{ matrix.config.report }}_test_reports
         path: reports

.github/workflows/push_tests_mps.yml

@@ -13,7 +13,6 @@ env:
   HF_HOME: /mnt/cache
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
-  HF_HUB_ENABLE_HF_TRANSFER: 1
   PYTEST_TIMEOUT: 600
   RUN_SLOW: no
 
@@ -70,7 +69,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v2
       with:
         name: pr_torch_mps_test_reports
         path: reports

.github/workflows/pypi_publish.yaml

@@ -10,7 +10,7 @@ on:
 
 jobs:
   find-and-checkout-latest-branch:
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
     outputs:
       latest_branch: ${{ steps.set_latest_branch.outputs.latest_branch }}
     steps:
@@ -36,7 +36,7 @@ jobs:
 
   release:
     needs: find-and-checkout-latest-branch
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
 
     steps:
       - name: Checkout Repo

.github/workflows/release_tests_fast.yml

@@ -1,389 +0,0 @@
-# Duplicate workflow to push_tests.yml that is meant to run on release/patch branches as a final check
-# Creating a duplicate workflow here is simpler than adding complex path/branch parsing logic to push_tests.yml
-# Needs to be updated if push_tests.yml updated
-name: (Release) Fast GPU Tests on main
-
-on:
-  push:
-    branches:
-      - "v*.*.*-release"
-      - "v*.*.*-patch"
-
-env:
-  DIFFUSERS_IS_CI: yes
-  OMP_NUM_THREADS: 8
-  MKL_NUM_THREADS: 8
-  PYTEST_TIMEOUT: 600
-  PIPELINE_USAGE_CUTOFF: 50000
-
-jobs:
-  setup_torch_cuda_pipeline_matrix:
-    name: Setup Torch Pipelines CUDA Slow Tests Matrix
-    runs-on:
-      group: aws-general-8-plus
-    container:
-      image: diffusers/diffusers-pytorch-cpu
-    outputs:
-      pipeline_test_matrix: ${{ steps.fetch_pipeline_matrix.outputs.pipeline_test_matrix }}
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-      - name: Install dependencies
-        run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m uv pip install -e [quality,test]
-      - name: Environment
-        run: |
-          python utils/print_env.py
-      - name: Fetch Pipeline Matrix
-        id: fetch_pipeline_matrix
-        run: |
-          matrix=$(python utils/fetch_torch_cuda_pipeline_test_matrix.py)
-          echo $matrix
-          echo "pipeline_test_matrix=$matrix" >> $GITHUB_OUTPUT
-      - name: Pipeline Tests Artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: test-pipelines.json
-          path: reports
-
-  torch_pipelines_cuda_tests:
-    name: Torch Pipelines CUDA Tests
-    needs: setup_torch_cuda_pipeline_matrix
-    strategy:
-      fail-fast: false
-      max-parallel: 8
-      matrix:
-        module: ${{ fromJson(needs.setup_torch_cuda_pipeline_matrix.outputs.pipeline_test_matrix) }}
-    runs-on:
-      group: aws-g4dn-2xlarge
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus 0
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-      - name: NVIDIA-SMI
-        run: |
-          nvidia-smi
-      - name: Install dependencies
-        run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m uv pip install -e [quality,test]
-          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-      - name: Environment
-        run: |
-          python utils/print_env.py
-      - name: Slow PyTorch CUDA checkpoint tests on Ubuntu
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
-          # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-          CUBLAS_WORKSPACE_CONFIG: :16:8
-        run: |
-          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -s -v -k "not Flax and not Onnx" \
-            --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
-            tests/pipelines/${{ matrix.module }}
-      - name: Failure short reports
-        if: ${{ failure() }}
-        run: |
-          cat reports/tests_pipeline_${{ matrix.module }}_cuda_stats.txt
-          cat reports/tests_pipeline_${{ matrix.module }}_cuda_failures_short.txt
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: pipeline_${{ matrix.module }}_test_reports
-          path: reports
-
-  torch_cuda_tests:
-    name: Torch CUDA Tests
-    runs-on:
-      group: aws-g4dn-2xlarge
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus 0
-    defaults:
-      run:
-        shell: bash
-    strategy:
-      fail-fast: false
-      max-parallel: 2
-      matrix:
-        module: [models, schedulers, lora, others, single_file]
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test]
-        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-
-    - name: Environment
-      run: |
-        python utils/print_env.py
-
-    - name: Run PyTorch CUDA tests
-      env:
-        HF_TOKEN: ${{ secrets.HF_TOKEN }}
-        # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-        CUBLAS_WORKSPACE_CONFIG: :16:8
-      run: |
-        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -s -v -k "not Flax and not Onnx" \
-          --make-reports=tests_torch_${{ matrix.module }}_cuda \
-          tests/${{ matrix.module }}
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: |
-        cat reports/tests_torch_${{ matrix.module }}_cuda_stats.txt
-        cat reports/tests_torch_${{ matrix.module }}_cuda_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: torch_cuda_${{ matrix.module }}_test_reports
-        path: reports
-
-  flax_tpu_tests:
-    name: Flax TPU Tests
-    runs-on: docker-tpu
-    container:
-      image: diffusers/diffusers-flax-tpu
-      options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ --privileged
-    defaults:
-      run:
-        shell: bash
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test]
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-
-    - name: Environment
-      run: |
-        python utils/print_env.py
-
-    - name: Run slow Flax TPU tests
-      env:
-        HF_TOKEN: ${{ secrets.HF_TOKEN }}
-      run: |
-        python -m pytest -n 0 \
-          -s -v -k "Flax" \
-          --make-reports=tests_flax_tpu \
-          tests/
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: |
-        cat reports/tests_flax_tpu_stats.txt
-        cat reports/tests_flax_tpu_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: flax_tpu_test_reports
-        path: reports
-
-  onnx_cuda_tests:
-    name: ONNX CUDA Tests
-    runs-on:
-      group: aws-g4dn-2xlarge
-    container:
-      image: diffusers/diffusers-onnxruntime-cuda
-      options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ --gpus 0
-    defaults:
-      run:
-        shell: bash
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test]
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-
-    - name: Environment
-      run: |
-        python utils/print_env.py
-
-    - name: Run slow ONNXRuntime CUDA tests
-      env:
-        HF_TOKEN: ${{ secrets.HF_TOKEN }}
-      run: |
-        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -s -v -k "Onnx" \
-          --make-reports=tests_onnx_cuda \
-          tests/
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: |
-        cat reports/tests_onnx_cuda_stats.txt
-        cat reports/tests_onnx_cuda_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: onnx_cuda_test_reports
-        path: reports
-
-  run_torch_compile_tests:
-    name: PyTorch Compile CUDA tests
-
-    runs-on:
-      group: aws-g4dn-2xlarge
-
-    container:
-      image: diffusers/diffusers-pytorch-compile-cuda
-      options: --gpus 0 --shm-size "16gb" --ipc host
-
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: NVIDIA-SMI
-      run: |
-        nvidia-smi
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test,training]
-    - name: Environment
-      run: |
-        python utils/print_env.py
-    - name: Run example tests on GPU
-      env:
-        HF_TOKEN: ${{ secrets.HF_TOKEN }}
-        RUN_COMPILE: yes
-      run: |
-        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: cat reports/tests_torch_compile_cuda_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: torch_compile_test_reports
-        path: reports
-
-  run_xformers_tests:
-    name: PyTorch xformers CUDA tests
-
-    runs-on:
-      group: aws-g4dn-2xlarge
-
-    container:
-      image: diffusers/diffusers-pytorch-xformers-cuda
-      options: --gpus 0 --shm-size "16gb" --ipc host
-
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: NVIDIA-SMI
-      run: |
-        nvidia-smi
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test,training]
-    - name: Environment
-      run: |
-        python utils/print_env.py
-    - name: Run example tests on GPU
-      env:
-        HF_TOKEN: ${{ secrets.HF_TOKEN }}
-      run: |
-        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "xformers" --make-reports=tests_torch_xformers_cuda tests/
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: cat reports/tests_torch_xformers_cuda_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: torch_xformers_test_reports
-        path: reports
-
-  run_examples_tests:
-    name: Examples PyTorch CUDA tests on Ubuntu
-
-    runs-on:
-      group: aws-g4dn-2xlarge
-
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --gpus 0 --shm-size "16gb" --ipc host
-
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: NVIDIA-SMI
-      run: |
-        nvidia-smi
-
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test,training]
-
-    - name: Environment
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python utils/print_env.py
-
-    - name: Run example tests on GPU
-      env:
-        HF_TOKEN: ${{ secrets.HF_TOKEN }}
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install timm
-        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_cuda examples/
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: |
-        cat reports/examples_torch_cuda_stats.txt
-        cat reports/examples_torch_cuda_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: examples_test_reports
-        path: reports

.github/workflows/ssh-runner.yml

@@ -4,12 +4,8 @@ on:
   workflow_dispatch:
     inputs:
       runner_type:
-        description: 'Type of runner to test (aws-g6-4xlarge-plus: a10 or aws-g4dn-2xlarge: t4)'
-        type: choice
+        description: 'Type of runner to test (a10 or t4)'
         required: true
-        options:
-          - aws-g6-4xlarge-plus
-          - aws-g4dn-2xlarge
       docker_image:
         description: 'Name of the Docker image'
         required: true

.github/workflows/stale.yml

@@ -8,10 +8,7 @@ jobs:
   close_stale_issues:
     name: Close Stale Issues
     if: github.repository == 'huggingface/diffusers'
-    runs-on: ubuntu-22.04
-    permissions:
-      issues: write
-      pull-requests: write
+    runs-on: ubuntu-latest
     env:
       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
     steps:

.github/workflows/trufflehog.yml

@@ -5,7 +5,7 @@ name: Secret Leaks
 
 jobs:
   trufflehog:
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
     steps:
     - name: Checkout code
       uses: actions/checkout@v4

.github/workflows/typos.yml

@@ -5,7 +5,7 @@ on:
 
 jobs:
   build:
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
 
     steps:
       - uses: actions/checkout@v3

PHILOSOPHY.md

@@ -65,7 +65,7 @@ Pipelines are designed to be easy to use (therefore do not follow [*Simple over
 The following design principles are followed:
 - Pipelines follow the single-file policy. All pipelines can be found in individual directories under src/diffusers/pipelines. One pipeline folder corresponds to one diffusion paper/project/release. Multiple pipeline files can be gathered in one pipeline folder, as it’s done for [`src/diffusers/pipelines/stable-diffusion`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/stable_diffusion). If pipelines share similar functionality, one can make use of the [# Copied from mechanism](https://github.com/huggingface/diffusers/blob/125d783076e5bd9785beb05367a2d2566843a271/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py#L251).
 - Pipelines all inherit from [`DiffusionPipeline`].
-- Every pipeline consists of different model and scheduler components, that are documented in the [`model_index.json` file](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/model_index.json), are accessible under the same name as attributes of the pipeline and can be shared between pipelines with [`DiffusionPipeline.components`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.components) function.
+- Every pipeline consists of different model and scheduler components, that are documented in the [`model_index.json` file](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/model_index.json), are accessible under the same name as attributes of the pipeline and can be shared between pipelines with [`DiffusionPipeline.components`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.components) function.
 - Every pipeline should be loadable via the [`DiffusionPipeline.from_pretrained`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained) function.
 - Pipelines should be used **only** for inference.
 - Pipelines should be very readable, self-explanatory, and easy to tweak.

README.md

@@ -73,7 +73,7 @@ Generating outputs is super easy with 🤗 Diffusers. To generate an image from
 from diffusers import DiffusionPipeline
 import torch
 
-pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16)
+pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
 pipeline.to("cuda")
 pipeline("An image of a squirrel in Picasso style").images[0]
 ```
@@ -144,7 +144,7 @@ Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz9
   <tr style="border-top: 2px solid black">
     <td>Text-to-Image</td>
     <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/text2img">Stable Diffusion Text-to-Image</a></td>
-      <td><a href="https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5"> stable-diffusion-v1-5/stable-diffusion-v1-5 </a></td>
+      <td><a href="https://huggingface.co/runwayml/stable-diffusion-v1-5"> runwayml/stable-diffusion-v1-5 </a></td>
   </tr>
   <tr>
     <td>Text-to-Image</td>
@@ -174,7 +174,7 @@ Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz9
   <tr>
     <td>Text-guided Image-to-Image</td>
     <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/img2img">Stable Diffusion Image-to-Image</a></td>
-      <td><a href="https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5"> stable-diffusion-v1-5/stable-diffusion-v1-5 </a></td>
+      <td><a href="https://huggingface.co/runwayml/stable-diffusion-v1-5"> runwayml/stable-diffusion-v1-5 </a></td>
   </tr>
   <tr style="border-top: 2px solid black">
     <td>Text-guided Image Inpainting</td>

benchmarks/base_classes.py

@@ -34,7 +34,7 @@ from utils import (  # noqa: E402
 
 
 RESOLUTION_MAPPING = {
-    "Lykon/DreamShaper": (512, 512),
+    "runwayml/stable-diffusion-v1-5": (512, 512),
     "lllyasviel/sd-controlnet-canny": (512, 512),
     "diffusers/controlnet-canny-sdxl-1.0": (1024, 1024),
     "TencentARC/t2iadapter_canny_sd14v1": (512, 512),
@@ -268,7 +268,7 @@ class IPAdapterTextToImageBenchmark(TextToImageBenchmark):
 class ControlNetBenchmark(TextToImageBenchmark):
     pipeline_class = StableDiffusionControlNetPipeline
     aux_network_class = ControlNetModel
-    root_ckpt = "Lykon/DreamShaper"
+    root_ckpt = "runwayml/stable-diffusion-v1-5"
 
     url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/benchmarking/canny_image_condition.png"
     image = load_image(url).convert("RGB")
@@ -311,7 +311,7 @@ class ControlNetSDXLBenchmark(ControlNetBenchmark):
 class T2IAdapterBenchmark(ControlNetBenchmark):
     pipeline_class = StableDiffusionAdapterPipeline
     aux_network_class = T2IAdapter
-    root_ckpt = "Lykon/DreamShaper"
+    root_ckpt = "CompVis/stable-diffusion-v1-4"
 
     url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/benchmarking/canny_for_adapter.png"
     image = load_image(url).convert("L")

benchmarks/benchmark_ip_adapters.py

@@ -7,8 +7,7 @@ from base_classes import IPAdapterTextToImageBenchmark  # noqa: E402
 
 
 IP_ADAPTER_CKPTS = {
-    # because original SD v1.5 has been taken down.
-    "Lykon/DreamShaper": ("h94/IP-Adapter", "ip-adapter_sd15.bin"),
+    "runwayml/stable-diffusion-v1-5": ("h94/IP-Adapter", "ip-adapter_sd15.bin"),
     "stabilityai/stable-diffusion-xl-base-1.0": ("h94/IP-Adapter", "ip-adapter_sdxl.bin"),
 }
 
@@ -18,7 +17,7 @@ if __name__ == "__main__":
     parser.add_argument(
         "--ckpt",
         type=str,
-        default="rstabilityai/stable-diffusion-xl-base-1.0",
+        default="runwayml/stable-diffusion-v1-5",
         choices=list(IP_ADAPTER_CKPTS.keys()),
     )
     parser.add_argument("--batch_size", type=int, default=1)

benchmarks/benchmark_sd_img.py

@@ -11,9 +11,9 @@ if __name__ == "__main__":
     parser.add_argument(
         "--ckpt",
         type=str,
-        default="Lykon/DreamShaper",
+        default="runwayml/stable-diffusion-v1-5",
         choices=[
-            "Lykon/DreamShaper",
+            "runwayml/stable-diffusion-v1-5",
             "stabilityai/stable-diffusion-2-1",
             "stabilityai/stable-diffusion-xl-refiner-1.0",
             "stabilityai/sdxl-turbo",

benchmarks/benchmark_sd_inpainting.py

@@ -11,9 +11,9 @@ if __name__ == "__main__":
     parser.add_argument(
         "--ckpt",
         type=str,
-        default="Lykon/DreamShaper",
+        default="runwayml/stable-diffusion-v1-5",
         choices=[
-            "Lykon/DreamShaper",
+            "runwayml/stable-diffusion-v1-5",
             "stabilityai/stable-diffusion-2-1",
             "stabilityai/stable-diffusion-xl-base-1.0",
         ],

benchmarks/benchmark_text_to_image.py

@@ -7,7 +7,7 @@ from base_classes import TextToImageBenchmark, TurboTextToImageBenchmark  # noqa
 
 
 ALL_T2I_CKPTS = [
-    "Lykon/DreamShaper",
+    "runwayml/stable-diffusion-v1-5",
     "segmind/SSD-1B",
     "stabilityai/stable-diffusion-xl-base-1.0",
     "kandinsky-community/kandinsky-2-2-decoder",
@@ -21,7 +21,7 @@ if __name__ == "__main__":
     parser.add_argument(
         "--ckpt",
         type=str,
-        default="Lykon/DreamShaper",
+        default="runwayml/stable-diffusion-v1-5",
         choices=ALL_T2I_CKPTS,
     )
     parser.add_argument("--batch_size", type=int, default=1)

benchmarks/push_results.py

@@ -3,7 +3,7 @@ import sys
 
 import pandas as pd
 from huggingface_hub import hf_hub_download, upload_file
-from huggingface_hub.utils import EntryNotFoundError
+from huggingface_hub.utils._errors import EntryNotFoundError
 
 
 sys.path.append(".")

docker/diffusers-flax-cpu/Dockerfile

@@ -43,7 +43,6 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
         numpy==1.26.4 \
         scipy \
         tensorboard \
-        transformers \
-        hf_transfer
+        transformers
 
 CMD ["/bin/bash"]

docker/diffusers-flax-tpu/Dockerfile

@@ -45,7 +45,6 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
         numpy==1.26.4 \
         scipy \
         tensorboard \
-        transformers \
-        hf_transfer
+        transformers
 
 CMD ["/bin/bash"]

docker/diffusers-onnxruntime-cpu/Dockerfile

@@ -43,7 +43,6 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
         numpy==1.26.4 \
         scipy \
         tensorboard \
-        transformers \
-        hf_transfer
+        transformers
 
 CMD ["/bin/bash"]

docker/diffusers-onnxruntime-cuda/Dockerfile

@@ -28,7 +28,7 @@ ENV PATH="/opt/venv/bin:$PATH"
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
 RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
     python3.10 -m uv pip install --no-cache-dir \
-        "torch<2.5.0" \
+        torch \
         torchvision \
         torchaudio \
         "onnxruntime-gpu>=1.13.1" \
@@ -44,7 +44,6 @@ RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
         numpy==1.26.4 \
         scipy \
         tensorboard \
-        transformers \
-        hf_transfer
+        transformers
 
 CMD ["/bin/bash"]

docker/diffusers-pytorch-compile-cuda/Dockerfile

@@ -29,7 +29,7 @@ ENV PATH="/opt/venv/bin:$PATH"
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
 RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
     python3.10 -m uv pip install --no-cache-dir \
-    "torch<2.5.0" \
+    torch \
     torchvision \
     torchaudio \
     invisible_watermark && \
@@ -44,7 +44,6 @@ RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
     numpy==1.26.4 \
     scipy \
     tensorboard \
-    transformers \
-    hf_transfer
+    transformers
 
 CMD ["/bin/bash"]

docker/diffusers-pytorch-cpu/Dockerfile

@@ -29,7 +29,7 @@ ENV PATH="/opt/venv/bin:$PATH"
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
 RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
     python3.10 -m uv pip install --no-cache-dir \
-        "torch<2.5.0" \
+        torch \
         torchvision \
         torchaudio \
         invisible_watermark \
@@ -44,7 +44,6 @@ RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
         numpy==1.26.4 \
         scipy \
         tensorboard \
-        transformers matplotlib  \
-        hf_transfer
+        transformers matplotlib
 
 CMD ["/bin/bash"]

docker/diffusers-pytorch-cuda/Dockerfile

@@ -29,7 +29,7 @@ ENV PATH="/opt/venv/bin:$PATH"
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
 RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
     python3.10 -m uv pip install --no-cache-dir \
-    "torch<2.5.0" \
+    torch \
     torchvision \
     torchaudio \
     invisible_watermark && \
@@ -45,7 +45,6 @@ RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
     scipy \
     tensorboard \
     transformers \
-    pytorch-lightning  \
-    hf_transfer
+    pytorch-lightning
 
 CMD ["/bin/bash"]

docker/diffusers-pytorch-xformers-cuda/Dockerfile

@@ -29,7 +29,7 @@ ENV PATH="/opt/venv/bin:$PATH"
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
 RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
     python3.10 -m pip install --no-cache-dir \
-        "torch<2.5.0" \
+        torch \
         torchvision \
         torchaudio \
         invisible_watermark && \
@@ -45,7 +45,6 @@ RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
         scipy \
         tensorboard \
         transformers \
-        xformers  \
-        hf_transfer
+        xformers
 
 CMD ["/bin/bash"]

docs/source/en/_toctree.yml

@@ -56,7 +56,7 @@
   - local: using-diffusers/overview_techniques
     title: Overview
   - local: training/distributed_inference
-    title: Distributed inference
+    title: Distributed inference with multiple GPUs
   - local: using-diffusers/merge_loras
     title: Merge LoRAs
   - local: using-diffusers/scheduler_features
@@ -75,8 +75,6 @@
     title: Outpainting
   title: Advanced inference
 - sections:
-  - local: using-diffusers/cogvideox
-    title: CogVideoX
   - local: using-diffusers/sdxl
     title: Stable Diffusion XL
   - local: using-diffusers/sdxl_turbo
@@ -131,8 +129,6 @@
       title: T2I-Adapters
     - local: training/instructpix2pix
       title: InstructPix2Pix
-    - local: training/cogvideox
-      title: CogVideoX
     title: Models
   - isExpanded: false
     sections:
@@ -150,12 +146,6 @@
       title: Reinforcement learning training with DDPO
     title: Methods
   title: Training
-- sections:
-  - local: quantization/overview
-    title: Getting Started
-  - local: quantization/bitsandbytes
-    title: bitsandbytes
-  title: Quantization Methods
 - sections:
   - local: optimization/fp16
     title: Speed up inference
@@ -171,8 +161,6 @@
     title: DeepCache
   - local: optimization/tgate
     title: TGATE
-  - local: optimization/xdit
-    title: xDiT
   - sections:
     - local: using-diffusers/stable_diffusion_jax_how_to
       title: JAX/Flax
@@ -215,8 +203,6 @@
       title: Logging
     - local: api/outputs
       title: Outputs
-    - local: api/quantization
-      title: Quantization
     title: Main Classes
   - isExpanded: false
     sections:
@@ -254,8 +240,6 @@
         title: AuraFlowTransformer2DModel
       - local: api/models/cogvideox_transformer3d
         title: CogVideoXTransformer3DModel
-      - local: api/models/cogview3plus_transformer2d
-        title: CogView3PlusTransformer2DModel
       - local: api/models/dit_transformer2d
         title: DiTTransformer2DModel
       - local: api/models/flux_transformer
@@ -334,8 +318,6 @@
       title: BLIP-Diffusion
     - local: api/pipelines/cogvideox
       title: CogVideoX
-    - local: api/pipelines/cogview3
-      title: CogView3
     - local: api/pipelines/consistency_models
       title: Consistency Models
     - local: api/pipelines/controlnet

docs/source/en/api/loaders/single_file.md

@@ -22,6 +22,7 @@ The [`~loaders.FromSingleFileMixin.from_single_file`] method allows you to load:
 
 ## Supported pipelines
 
+- [`CogVideoXPipeline`]
 - [`StableDiffusionPipeline`]
 - [`StableDiffusionImg2ImgPipeline`]
 - [`StableDiffusionInpaintPipeline`]
@@ -49,6 +50,7 @@ The [`~loaders.FromSingleFileMixin.from_single_file`] method allows you to load:
 - [`UNet2DConditionModel`]
 - [`StableCascadeUNet`]
 - [`AutoencoderKL`]
+- [`AutoencoderKLCogVideoX`]
 - [`ControlNetModel`]
 - [`SD3Transformer2DModel`]
 - [`FluxTransformer2DModel`]

docs/source/en/api/models/cogview3plus_transformer2d.md

@@ -1,30 +0,0 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# CogView3PlusTransformer2DModel
-
-A Diffusion Transformer model for 2D data from [CogView3Plus](https://github.com/THUDM/CogView3) was introduced in [CogView3: Finer and Faster Text-to-Image Generation via Relay Diffusion](https://huggingface.co/papers/2403.05121) by Tsinghua University & ZhipuAI.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import CogView3PlusTransformer2DModel
-
-vae = CogView3PlusTransformer2DModel.from_pretrained("THUDM/CogView3Plus-3b", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
-```
-
-## CogView3PlusTransformer2DModel
-
-[[autodoc]] CogView3PlusTransformer2DModel
-
-## Transformer2DModelOutput
-
-[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

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

@@ -29,7 +29,7 @@ from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
 url = "https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth"  # can also be a local path
 controlnet = ControlNetModel.from_single_file(url)
 
-url = "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/v1-5-pruned.safetensors"  # can also be a local path
+url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned.safetensors"  # can also be a local path
 pipe = StableDiffusionControlNetPipeline.from_single_file(url, controlnet=controlnet)
 ```
 

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

@@ -29,7 +29,6 @@ The abstract of the paper is the following:
 | [AnimateDiffSparseControlNetPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py) | *Controlled Video-to-Video Generation with AnimateDiff using SparseCtrl* |
 | [AnimateDiffSDXLPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py) | *Video-to-Video Generation with AnimateDiff* |
 | [AnimateDiffVideoToVideoPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py) | *Video-to-Video Generation with AnimateDiff* |
-| [AnimateDiffVideoToVideoControlNetPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video_controlnet.py) | *Video-to-Video Generation with AnimateDiff using ControlNet* |
 
 ## Available checkpoints
 
@@ -519,97 +518,6 @@ Here are some sample outputs:
     </tr>
 </table>
 
-
-
-### AnimateDiffVideoToVideoControlNetPipeline
-
-AnimateDiff can be used together with ControlNets to enhance video-to-video generation by allowing for precise control over the output. ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala, and allows you to condition Stable Diffusion with an additional control image to ensure that the spatial information is preserved throughout the video. 
-
-This pipeline allows you to condition your generation both on the original video and on a sequence of control images.
-
-```python
-import torch
-from PIL import Image
-from tqdm.auto import tqdm
-
-from controlnet_aux.processor import OpenposeDetector
-from diffusers import AnimateDiffVideoToVideoControlNetPipeline
-from diffusers.utils import export_to_gif, load_video
-from diffusers import AutoencoderKL, ControlNetModel, MotionAdapter, LCMScheduler
-
-# Load the ControlNet
-controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16)
-# Load the motion adapter
-motion_adapter = MotionAdapter.from_pretrained("wangfuyun/AnimateLCM")
-# Load SD 1.5 based finetuned model
-vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16)
-pipe = AnimateDiffVideoToVideoControlNetPipeline.from_pretrained(
-    "SG161222/Realistic_Vision_V5.1_noVAE",
-    motion_adapter=motion_adapter,
-    controlnet=controlnet,
-    vae=vae,
-).to(device="cuda", dtype=torch.float16)
-
-# Enable LCM to speed up inference
-pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, beta_schedule="linear")
-pipe.load_lora_weights("wangfuyun/AnimateLCM", weight_name="AnimateLCM_sd15_t2v_lora.safetensors", adapter_name="lcm-lora")
-pipe.set_adapters(["lcm-lora"], [0.8])
-
-video = load_video("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/dance.gif")
-video = [frame.convert("RGB") for frame in video]
-
-prompt = "astronaut in space, dancing"
-negative_prompt = "bad quality, worst quality, jpeg artifacts, ugly"
-
-# Create controlnet preprocessor
-open_pose = OpenposeDetector.from_pretrained("lllyasviel/Annotators").to("cuda")
-
-# Preprocess controlnet images
-conditioning_frames = []
-for frame in tqdm(video):
-    conditioning_frames.append(open_pose(frame))
-
-strength = 0.8
-with torch.inference_mode():
-    video = pipe(
-        video=video,
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        num_inference_steps=10,
-        guidance_scale=2.0,
-        controlnet_conditioning_scale=0.75,
-        conditioning_frames=conditioning_frames,
-        strength=strength,
-        generator=torch.Generator().manual_seed(42),
-    ).frames[0]
-
-video = [frame.resize(conditioning_frames[0].size) for frame in video]
-export_to_gif(video, f"animatediff_vid2vid_controlnet.gif", fps=8)
-```
-
-Here are some sample outputs:
-
-<table align="center">
-    <tr>
-      <th align="center">Source Video</th>
-      <th align="center">Output Video</th>
-    </tr>
-    <tr>
-        <td align="center">
-          anime girl, dancing
-          <br />
-          <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/dance.gif" alt="anime girl, dancing" />
-        </td>
-        <td align="center">
-          astronaut in space, dancing
-          <br/>
-          <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff_vid2vid_controlnet.gif" alt="astronaut in space, dancing" />
-        </td>
-    </tr>
-</table>
-
-**The lights and composition were transferred from the Source Video.**
-
 ## Using Motion LoRAs
 
 Motion LoRAs are a collection of LoRAs that work with the `guoyww/animatediff-motion-adapter-v1-5-2` checkpoint. These LoRAs are responsible for adding specific types of motion to the animations.
@@ -914,89 +822,6 @@ export_to_gif(frames, "animatelcm-motion-lora.gif")
     </tr>
 </table>
 
-## Using FreeNoise
-
-[FreeNoise: Tuning-Free Longer Video Diffusion via Noise Rescheduling](https://arxiv.org/abs/2310.15169) by Haonan Qiu, Menghan Xia, Yong Zhang, Yingqing He, Xintao Wang, Ying Shan, Ziwei Liu.
-
-FreeNoise is a sampling mechanism that can generate longer videos with short-video generation models by employing noise-rescheduling, temporal attention over sliding windows, and weighted averaging of latent frames. It also can be used with multiple prompts to allow for interpolated video generations. More details are available in the paper.
-
-The currently supported AnimateDiff pipelines that can be used with FreeNoise are:
-- [`AnimateDiffPipeline`]
-- [`AnimateDiffControlNetPipeline`]
-- [`AnimateDiffVideoToVideoPipeline`]
-- [`AnimateDiffVideoToVideoControlNetPipeline`]
-
-In order to use FreeNoise, a single line needs to be added to the inference code after loading your pipelines.
-
-```diff
-+ pipe.enable_free_noise()
-```
-
-After this, either a single prompt could be used, or multiple prompts can be passed as a dictionary of integer-string pairs. The integer keys of the dictionary correspond to the frame index at which the influence of that prompt would be maximum. Each frame index should map to a single string prompt. The prompts for intermediate frame indices, that are not passed in the dictionary, are created by interpolating between the frame prompts that are passed. By default, simple linear interpolation is used. However, you can customize this behaviour with a callback to the `prompt_interpolation_callback` parameter when enabling FreeNoise.
-
-Full example:
-
-```python
-import torch
-from diffusers import AutoencoderKL, AnimateDiffPipeline, LCMScheduler, MotionAdapter
-from diffusers.utils import export_to_video, load_image
-
-# Load pipeline
-dtype = torch.float16
-motion_adapter = MotionAdapter.from_pretrained("wangfuyun/AnimateLCM", torch_dtype=dtype)
-vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=dtype)
-
-pipe = AnimateDiffPipeline.from_pretrained("emilianJR/epiCRealism", motion_adapter=motion_adapter, vae=vae, torch_dtype=dtype)
-pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, beta_schedule="linear")
-
-pipe.load_lora_weights(
-    "wangfuyun/AnimateLCM", weight_name="AnimateLCM_sd15_t2v_lora.safetensors", adapter_name="lcm_lora"
-)
-pipe.set_adapters(["lcm_lora"], [0.8])
-
-# Enable FreeNoise for long prompt generation
-pipe.enable_free_noise(context_length=16, context_stride=4)
-pipe.to("cuda")
-
-# Can be a single prompt, or a dictionary with frame timesteps
-prompt = {
-    0: "A caterpillar on a leaf, high quality, photorealistic",
-    40: "A caterpillar transforming into a cocoon, on a leaf, near flowers, photorealistic",
-    80: "A cocoon on a leaf, flowers in the backgrond, photorealistic",
-    120: "A cocoon maturing and a butterfly being born, flowers and leaves visible in the background, photorealistic",
-    160: "A beautiful butterfly, vibrant colors, sitting on a leaf, flowers in the background, photorealistic",
-    200: "A beautiful butterfly, flying away in a forest, photorealistic",
-    240: "A cyberpunk butterfly, neon lights, glowing",
-}
-negative_prompt = "bad quality, worst quality, jpeg artifacts"
-
-# Run inference
-output = pipe(
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    num_frames=256,
-    guidance_scale=2.5,
-    num_inference_steps=10,
-    generator=torch.Generator("cpu").manual_seed(0),
-)
-
-# Save video
-frames = output.frames[0]
-export_to_video(frames, "output.mp4", fps=16)
-```
-
-### FreeNoise memory savings
-
-Since FreeNoise processes multiple frames together, there are parts in the modeling where the memory required exceeds that available on normal consumer GPUs. The main memory bottlenecks that we identified are spatial and temporal attention blocks, upsampling and downsampling blocks, resnet blocks and feed-forward layers. Since most of these blocks operate effectively only on the channel/embedding dimension, one can perform chunked inference across the batch dimensions. The batch dimension in AnimateDiff are either spatial (`[B x F, H x W, C]`) or temporal (`B x H x W, F, C`) in nature (note that it may seem counter-intuitive, but the batch dimension here are correct, because spatial blocks process across the `B x F` dimension while the temporal blocks process across the `B x H x W` dimension). We introduce a `SplitInferenceModule` that makes it easier to chunk across any dimension and perform inference. This saves a lot of memory but comes at the cost of requiring more time for inference.
-
-```diff
-# Load pipeline and adapters
-# ...
-+ pipe.enable_free_noise_split_inference()
-+ pipe.unet.enable_forward_chunking(16)
-```
-
-The call to `pipe.enable_free_noise_split_inference` method accepts two parameters: `spatial_split_size` (defaults to `256`) and `temporal_split_size` (defaults to `16`). These can be configured based on how much VRAM you have available. A lower split size results in lower memory usage but slower inference, whereas a larger split size results in faster inference at the cost of more memory.
 
 ## Using `from_single_file` with the MotionAdapter
 
@@ -1041,12 +866,6 @@ pipe = AnimateDiffPipeline.from_pretrained("emilianJR/epiCRealism", motion_adapt
   - all
   - __call__
 
-## AnimateDiffVideoToVideoControlNetPipeline
-
-[[autodoc]] AnimateDiffVideoToVideoControlNetPipeline
-  - all
-  - __call__
-
 ## AnimateDiffPipelineOutput
 
 [[autodoc]] pipelines.animatediff.AnimateDiffPipelineOutput

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

@@ -29,16 +29,9 @@ Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers.m
 
 This pipeline was contributed by [zRzRzRzRzRzRzR](https://github.com/zRzRzRzRzRzRzR). The original codebase can be found [here](https://huggingface.co/THUDM). The original weights can be found under [hf.co/THUDM](https://huggingface.co/THUDM).
 
-There are two models available that can be used with the text-to-video and video-to-video CogVideoX pipelines:
-- [`THUDM/CogVideoX-2b`](https://huggingface.co/THUDM/CogVideoX-2b): The recommended dtype for running this model is `fp16`.
-- [`THUDM/CogVideoX-5b`](https://huggingface.co/THUDM/CogVideoX-5b): The recommended dtype for running this model is `bf16`.
-
-There is one model available that can be used with the image-to-video CogVideoX pipeline:
-- [`THUDM/CogVideoX-5b-I2V`](https://huggingface.co/THUDM/CogVideoX-5b-I2V): The recommended dtype for running this model is `bf16`.
-
-There are two models that support pose controllable generation (by the [Alibaba-PAI](https://huggingface.co/alibaba-pai) team):
-- [`alibaba-pai/CogVideoX-Fun-V1.1-2b-Pose`](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-2b-Pose): The recommended dtype for running this model is `bf16`.
-- [`alibaba-pai/CogVideoX-Fun-V1.1-5b-Pose`](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-5b-Pose): The recommended dtype for running this model is `bf16`.
+There are two models available that can be used with the CogVideoX pipeline:
+- [`THUDM/CogVideoX-2b`](https://huggingface.co/THUDM/CogVideoX-2b)
+- [`THUDM/CogVideoX-5b`](https://huggingface.co/THUDM/CogVideoX-5b)
 
 ## Inference
 
@@ -48,15 +41,10 @@ First, load the pipeline:
 
 ```python
 import torch
-from diffusers import CogVideoXPipeline, CogVideoXImageToVideoPipeline
-from diffusers.utils import export_to_video,load_image
-pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b").to("cuda") # or "THUDM/CogVideoX-2b" 
-```
+from diffusers import CogVideoXPipeline
+from diffusers.utils import export_to_video
 
-If you are using the image-to-video pipeline, load it as follows:
-
-```python
-pipe = CogVideoXImageToVideoPipeline.from_pretrained("THUDM/CogVideoX-5b-I2V").to("cuda")
+pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-2b").to("cuda")
 ```
 
 Then change the memory layout of the pipelines `transformer` component to `torch.channels_last`:
@@ -65,7 +53,7 @@ Then change the memory layout of the pipelines `transformer` component to `torch
 pipe.transformer.to(memory_format=torch.channels_last)
 ```
 
-Compile the components and run inference:
+Finally, compile the components and run inference:
 
 ```python
 pipe.transformer = torch.compile(pipeline.transformer, mode="max-autotune", fullgraph=True)
@@ -75,7 +63,7 @@ prompt = "A panda, dressed in a small, red jacket and a tiny hat, sits on a wood
 video = pipe(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0]
 ```
 
-The [T2V benchmark](https://gist.github.com/a-r-r-o-w/5183d75e452a368fd17448fcc810bd3f) results on an 80GB A100 machine are:
+The [benchmark](https://gist.github.com/a-r-r-o-w/5183d75e452a368fd17448fcc810bd3f) results on an 80GB A100 machine are:
 
 ```
 Without torch.compile(): Average inference time: 96.89 seconds.
@@ -89,45 +77,16 @@ CogVideoX-2b requires about 19 GB of GPU memory to decode 49 frames (6 seconds o
 - `pipe.enable_model_cpu_offload()`:
   - Without enabling cpu offloading, memory usage is `33 GB`
   - With enabling cpu offloading, memory usage is `19 GB`
-- `pipe.enable_sequential_cpu_offload()`:
-  - Similar to `enable_model_cpu_offload` but can significantly reduce memory usage at the cost of slow inference
-  - When enabled, memory usage is under `4 GB`
 - `pipe.vae.enable_tiling()`:
   - With enabling cpu offloading and tiling, memory usage is `11 GB`
 - `pipe.vae.enable_slicing()`
 
-### Quantized inference
-
-[torchao](https://github.com/pytorch/ao) and [optimum-quanto](https://github.com/huggingface/optimum-quanto/) can be used to quantize the text encoder, transformer and VAE modules to lower the memory requirements. This makes it possible to run the model on a free-tier T4 Colab or lower VRAM GPUs!
-
-It is also worth noting that torchao quantization is fully compatible with [torch.compile](/optimization/torch2.0#torchcompile), which allows for much faster inference speed. Additionally, models can be serialized and stored in a quantized datatype to save disk space with torchao. Find examples and benchmarks in the gists below.
-- [torchao](https://gist.github.com/a-r-r-o-w/4d9732d17412888c885480c6521a9897)
-- [quanto](https://gist.github.com/a-r-r-o-w/31be62828b00a9292821b85c1017effa)
-
 ## CogVideoXPipeline
 
 [[autodoc]] CogVideoXPipeline
   - all
   - __call__
 
-## CogVideoXImageToVideoPipeline
-
-[[autodoc]] CogVideoXImageToVideoPipeline
-  - all
-  - __call__
-
-## CogVideoXVideoToVideoPipeline
-
-[[autodoc]] CogVideoXVideoToVideoPipeline
-  - all
-  - __call__
-
-## CogVideoXFunControlPipeline
-
-[[autodoc]] CogVideoXFunControlPipeline
-  - all
-  - __call__
-
 ## CogVideoXPipelineOutput
 
-[[autodoc]] pipelines.cogvideo.pipeline_output.CogVideoXPipelineOutput
+[[autodoc]] pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput

docs/source/en/api/pipelines/cogview3.md

@@ -1,40 +0,0 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
--->
-
-# CogView3Plus
-
-[CogView3: Finer and Faster Text-to-Image Generation via Relay Diffusion](https://huggingface.co/papers/2403.05121) from Tsinghua University & ZhipuAI, by Wendi Zheng, Jiayan Teng, Zhuoyi Yang, Weihan Wang, Jidong Chen, Xiaotao Gu, Yuxiao Dong, Ming Ding, Jie Tang.
-
-The abstract from the paper is:
-
-*Recent advancements in text-to-image generative systems have been largely driven by diffusion models. However, single-stage text-to-image diffusion models still face challenges, in terms of computational efficiency and the refinement of image details. To tackle the issue, we propose CogView3, an innovative cascaded framework that enhances the performance of text-to-image diffusion. CogView3 is the first model implementing relay diffusion in the realm of text-to-image generation, executing the task by first creating low-resolution images and subsequently applying relay-based super-resolution. This methodology not only results in competitive text-to-image outputs but also greatly reduces both training and inference costs. Our experimental results demonstrate that CogView3 outperforms SDXL, the current state-of-the-art open-source text-to-image diffusion model, by 77.0% in human evaluations, all while requiring only about 1/2 of the inference time. The distilled variant of CogView3 achieves comparable performance while only utilizing 1/10 of the inference time by SDXL.*
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers.md) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading.md#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
-
-This pipeline was contributed by [zRzRzRzRzRzRzR](https://github.com/zRzRzRzRzRzRzR). The original codebase can be found [here](https://huggingface.co/THUDM). The original weights can be found under [hf.co/THUDM](https://huggingface.co/THUDM).
-
-## CogView3PlusPipeline
-
-[[autodoc]] CogView3PlusPipeline
-  - all
-  - __call__
-
-## CogView3PipelineOutput
-
-[[autodoc]] pipelines.cogview3.pipeline_output.CogView3PipelineOutput

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

@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team, The InstantX Team, and the XLabs Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team and The InstantX Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -31,14 +31,6 @@ This controlnet code is implemented by [The InstantX Team](https://huggingface.c
 | Depth | [The InstantX Team](https://huggingface.co/InstantX) | [Link](https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Depth) |
 | Union | [The InstantX Team](https://huggingface.co/InstantX) | [Link](https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union) |
 
-XLabs ControlNets are also supported, which was contributed by the [XLabs team](https://huggingface.co/XLabs-AI).
-
-| ControlNet type | Developer | Link |
-| -------- | ---------- | ---- |
-| Canny | [The XLabs Team](https://huggingface.co/XLabs-AI) | [Link](https://huggingface.co/XLabs-AI/flux-controlnet-canny-diffusers) |
-| Depth | [The XLabs Team](https://huggingface.co/XLabs-AI) | [Link](https://huggingface.co/XLabs-AI/flux-controlnet-depth-diffusers) |
-| HED | [The XLabs Team](https://huggingface.co/XLabs-AI) | [Link](https://huggingface.co/XLabs-AI/flux-controlnet-hed-diffusers) |
-
 
 <Tip>
 

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

@@ -163,28 +163,3 @@ image.save("flux-fp8-dev.png")
 [[autodoc]] FluxPipeline
 	- all
 	- __call__
-
-## FluxImg2ImgPipeline
-
-[[autodoc]] FluxImg2ImgPipeline
-	- all
-	- __call__
-
-## FluxInpaintPipeline
-
-[[autodoc]] FluxInpaintPipeline
-	- all
-	- __call__
-
-
-## FluxControlNetInpaintPipeline
-
-[[autodoc]] FluxControlNetInpaintPipeline
-	- all
-	- __call__
-
-## FluxControlNetImg2ImgPipeline
-
-[[autodoc]] FluxControlNetImg2ImgPipeline
-	- all
-	- __call__

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

@@ -105,11 +105,3 @@ image.save("kolors_ipa_sample.png")
 
 - all
 - __call__
-
-## KolorsImg2ImgPipeline
-
-[[autodoc]] KolorsImg2ImgPipeline
-
-- all
-- __call__
-

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

@@ -53,16 +53,8 @@ Since RegEx is supported as a way for matching layer identifiers, it is crucial
 	- all
 	- __call__
 
-## StableDiffusionPAGImg2ImgPipeline
-[[autodoc]] StableDiffusionPAGImg2ImgPipeline
-	- all
-	- __call__
-
 ## StableDiffusionControlNetPAGPipeline
 [[autodoc]] StableDiffusionControlNetPAGPipeline
-
-## StableDiffusionControlNetPAGInpaintPipeline
-[[autodoc]] StableDiffusionControlNetPAGInpaintPipeline
 	- all
 	- __call__
 

docs/source/en/api/pipelines/stable_diffusion/inpaint.md

@@ -19,7 +19,7 @@ The Stable Diffusion model can also be applied to inpainting which lets you edit
 It is recommended to use this pipeline with checkpoints that have been specifically fine-tuned for inpainting, such
 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.
+[runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) are also compatible but they might be less performant.
 
 <Tip>
 

docs/source/en/api/pipelines/stable_diffusion/overview.md

@@ -203,7 +203,7 @@ from diffusers import StableDiffusionImg2ImgPipeline
 import gradio as gr
 
 
-pipe = StableDiffusionImg2ImgPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
 
 gr.Interface.from_pipeline(pipe).launch()
 ```

docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_3.md

@@ -54,11 +54,6 @@ image = pipe(
 image.save("sd3_hello_world.png")
 ```
 
-**Note:** Stable Diffusion 3.5 can also be run using the SD3 pipeline, and all mentioned optimizations and techniques apply to it as well. In total there are three official models in the SD3 family:
-- [`stabilityai/stable-diffusion-3-medium-diffusers`](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers)
-- [`stabilityai/stable-diffusion-3.5-large`](https://huggingface.co/stabilityai/stable-diffusion-3-5-large)
-- [`stabilityai/stable-diffusion-3.5-large-turbo`](https://huggingface.co/stabilityai/stable-diffusion-3-5-large-turbo)
-
 ## Memory Optimisations for SD3
 
 SD3 uses three text encoders, one if which is the very large T5-XXL model. This makes it challenging to run the model on GPUs with less than 24GB of VRAM, even when using `fp16` precision. The following section outlines a few memory optimizations in Diffusers that make it easier to run SD3 on low resource hardware.
@@ -313,26 +308,6 @@ image = pipe("a picture of a cat holding a sign that says hello world").images[0
 image.save('sd3-single-file-t5-fp8.png')
 ```
 
-### Loading the single file checkpoint for the Stable Diffusion 3.5 Transformer Model
-
-```python
-import torch
-from diffusers import SD3Transformer2DModel, StableDiffusion3Pipeline
-
-transformer = SD3Transformer2DModel.from_single_file(
-    "https://huggingface.co/stabilityai/stable-diffusion-3.5-large-turbo/blob/main/sd3.5_large.safetensors",
-    torch_dtype=torch.bfloat16,
-)
-pipe = StableDiffusion3Pipeline.from_pretrained(
-    "stabilityai/stable-diffusion-3.5-large",
-    transformer=transformer,
-    torch_dtype=torch.bfloat16,
-)
-pipe.enable_model_cpu_offload()
-image = pipe("a cat holding a sign that says hello world").images[0]
-image.save("sd35.png")
-```
-
 ## StableDiffusion3Pipeline
 
 [[autodoc]] StableDiffusion3Pipeline

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

@@ -40,9 +40,8 @@ To generate a video from prompt, run the following Python code:
 ```python
 import torch
 from diffusers import TextToVideoZeroPipeline
-import imageio
 
-model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
+model_id = "runwayml/stable-diffusion-v1-5"
 pipe = TextToVideoZeroPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
 
 prompt = "A panda is playing guitar on times square"
@@ -64,7 +63,7 @@ import torch
 from diffusers import TextToVideoZeroPipeline
 import numpy as np
 
-model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
+model_id = "runwayml/stable-diffusion-v1-5"
 pipe = TextToVideoZeroPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
 seed = 0
 video_length = 24  #24 ÷ 4fps = 6 seconds
@@ -138,7 +137,7 @@ To generate a video from prompt with additional pose control
     from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
     from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor
 
-    model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
+    model_id = "runwayml/stable-diffusion-v1-5"
     controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16)
     pipe = StableDiffusionControlNetPipeline.from_pretrained(
         model_id, controlnet=controlnet, torch_dtype=torch.float16

docs/source/en/api/quantization.md

@@ -1,33 +0,0 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
-
--->
-
-# Quantization
-
-Quantization techniques reduce memory and computational costs by representing weights and activations with lower-precision data types like 8-bit integers (int8). This enables loading larger models you normally wouldn't be able to fit into memory, and speeding up inference. Diffusers supports 8-bit and 4-bit quantization with [bitsandbytes](https://huggingface.co/docs/bitsandbytes/en/index).
-
-Quantization techniques that aren't supported in Transformers can be added with the [`DiffusersQuantizer`] class.
-
-<Tip>
-
-Learn how to quantize models in the [Quantization](../quantization/overview) guide.
-
-</Tip>
-
-
-## BitsAndBytesConfig
-
-[[autodoc]] BitsAndBytesConfig
-
-## DiffusersQuantizer
-
-[[autodoc]] quantizers.base.DiffusersQuantizer

docs/source/en/api/schedulers/overview.md

@@ -45,15 +45,6 @@ Many schedulers are implemented from the [k-diffusion](https://github.com/crowso
 | N/A                 | [`DEISMultistepScheduler`]          |                                                                                                               |
 | N/A                 | [`UniPCMultistepScheduler`]         |                                                                                                               |
 
-## Noise schedules and schedule types
-| A1111/k-diffusion        | 🤗 Diffusers                                                               |
-|--------------------------|----------------------------------------------------------------------------|
-| Karras                   | init with `use_karras_sigmas=True`                                         |
-| sgm_uniform              | init with `timestep_spacing="trailing"`                                    |
-| simple                   | init with `timestep_spacing="trailing"`                                    |
-| exponential              | init with `timestep_spacing="linspace"`, `use_exponential_sigmas=True`     |
-| beta                     | init with `timestep_spacing="linspace"`, `use_beta_sigmas=True`            |
-
 All schedulers are built from the base [`SchedulerMixin`] class which implements low level utilities shared by all schedulers.
 
 ## SchedulerMixin

docs/source/en/community_projects.md

@@ -75,8 +75,4 @@ Happy exploring, and thank you for being part of the Diffusers community!
     <td><a href="https://github.com/cumulo-autumn/StreamDiffusion"> StreamDiffusion </a></td>
     <td>A Pipeline-Level Solution for Real-Time Interactive Generation</td>
   </tr>
-  <tr style="border-top: 2px solid black">
-    <td><a href="https://github.com/Netwrck/stable-diffusion-server"> Stable Diffusion Server </a></td>
-    <td>A server configured for Inpainting/Generation/img2img with one stable diffusion model</td>
-  </tr>
 </table>

docs/source/en/conceptual/evaluation.md

@@ -92,7 +92,7 @@ images = sd_pipeline(sample_prompts, num_images_per_prompt=1, generator=generato
 
 ![parti-prompts-14](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/parti-prompts-14.png)
 
-We can also set `num_images_per_prompt` accordingly to compare different images for the same prompt. Running the same pipeline but with a different checkpoint ([v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5)), yields:
+We can also set `num_images_per_prompt` accordingly to compare different images for the same prompt. Running the same pipeline but with a different checkpoint ([v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)), yields:
 
 ![parti-prompts-15](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/parti-prompts-15.png)
 
@@ -177,10 +177,10 @@ generator = torch.manual_seed(seed)
 images = sd_pipeline(prompts, num_images_per_prompt=1, generator=generator, output_type="np").images
 ```
 
-Then we load the [v1-5 checkpoint](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) to generate images:
+Then we load the [v1-5 checkpoint](https://huggingface.co/runwayml/stable-diffusion-v1-5) to generate images:
 
 ```python
-model_ckpt_1_5 = "stable-diffusion-v1-5/stable-diffusion-v1-5"
+model_ckpt_1_5 = "runwayml/stable-diffusion-v1-5"
 sd_pipeline_1_5 = StableDiffusionPipeline.from_pretrained(model_ckpt_1_5, torch_dtype=weight_dtype).to(device)
 
 images_1_5 = sd_pipeline_1_5(prompts, num_images_per_prompt=1, generator=generator, output_type="np").images
@@ -198,7 +198,7 @@ print(f"CLIP Score with v-1-5: {sd_clip_score_1_5}")
 # CLIP Score with v-1-5: 36.2137
 ```
 
-It seems like the [v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) checkpoint performs better than its predecessor. Note, however, that the number of prompts we used to compute the CLIP scores is quite low. For a more practical evaluation, this number should be way higher, and the prompts should be diverse.
+It seems like the [v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) checkpoint performs better than its predecessor. Note, however, that the number of prompts we used to compute the CLIP scores is quite low. For a more practical evaluation, this number should be way higher, and the prompts should be diverse.
 
 <Tip warning={true}>
 

docs/source/en/conceptual/philosophy.md

@@ -65,7 +65,7 @@ Pipelines are designed to be easy to use (therefore do not follow [*Simple over
 The following design principles are followed:
 - Pipelines follow the single-file policy. All pipelines can be found in individual directories under src/diffusers/pipelines. One pipeline folder corresponds to one diffusion paper/project/release. Multiple pipeline files can be gathered in one pipeline folder, as it’s done for [`src/diffusers/pipelines/stable-diffusion`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/stable_diffusion). If pipelines share similar functionality, one can make use of the [# Copied from mechanism](https://github.com/huggingface/diffusers/blob/125d783076e5bd9785beb05367a2d2566843a271/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py#L251).
 - Pipelines all inherit from [`DiffusionPipeline`].
-- Every pipeline consists of different model and scheduler components, that are documented in the [`model_index.json` file](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/model_index.json), are accessible under the same name as attributes of the pipeline and can be shared between pipelines with [`DiffusionPipeline.components`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.components) function.
+- Every pipeline consists of different model and scheduler components, that are documented in the [`model_index.json` file](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/model_index.json), are accessible under the same name as attributes of the pipeline and can be shared between pipelines with [`DiffusionPipeline.components`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.components) function.
 - Every pipeline should be loadable via the [`DiffusionPipeline.from_pretrained`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained) function.
 - Pipelines should be used **only** for inference.
 - Pipelines should be very readable, self-explanatory, and easy to tweak.

docs/source/en/optimization/coreml.md

@@ -95,17 +95,17 @@ print(f"Model downloaded at {model_path}")
 Once you have downloaded a snapshot of the model, you can test it using Apple's Python script.
 
 ```shell
-python -m python_coreml_stable_diffusion.pipeline --prompt "a photo of an astronaut riding a horse on mars" -i ./models/coreml-stable-diffusion-v1-4_original_packages/original/packages -o </path/to/output/image> --compute-unit CPU_AND_GPU --seed 93
+python -m python_coreml_stable_diffusion.pipeline --prompt "a photo of an astronaut riding a horse on mars" -i models/coreml-stable-diffusion-v1-4_original_packages -o </path/to/output/image> --compute-unit CPU_AND_GPU --seed 93
 ```
 
 Pass the path of the downloaded checkpoint with `-i` flag to the script. `--compute-unit` indicates the hardware you want to allow for inference. It must be one of the following options: `ALL`, `CPU_AND_GPU`, `CPU_ONLY`, `CPU_AND_NE`. You may also provide an optional output path, and a seed for reproducibility.
 
 The inference script assumes you're using the original version of the Stable Diffusion model, `CompVis/stable-diffusion-v1-4`. If you use another model, you *have* to specify its Hub id in the inference command line, using the `--model-version` option. This works for models already supported and custom models you trained or fine-tuned yourself.
 
-For example, if you want to use [`stable-diffusion-v1-5/stable-diffusion-v1-5`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5):
+For example, if you want to use [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5):
 
 ```shell
-python -m python_coreml_stable_diffusion.pipeline --prompt "a photo of an astronaut riding a horse on mars" --compute-unit ALL -o output --seed 93 -i models/coreml-stable-diffusion-v1-5_original_packages --model-version stable-diffusion-v1-5/stable-diffusion-v1-5
+python -m python_coreml_stable_diffusion.pipeline --prompt "a photo of an astronaut riding a horse on mars" --compute-unit ALL -o output --seed 93 -i models/coreml-stable-diffusion-v1-5_original_packages --model-version runwayml/stable-diffusion-v1-5
 ```
 
 ## Core ML inference in Swift

docs/source/en/optimization/deepcache.md

@@ -23,7 +23,7 @@ Then load and enable the [`DeepCacheSDHelper`](https://github.com/horseee/DeepCa
 ```diff
   import torch
   from diffusers import StableDiffusionPipeline
-  pipe = StableDiffusionPipeline.from_pretrained('stable-diffusion-v1-5/stable-diffusion-v1-5', torch_dtype=torch.float16).to("cuda")
+  pipe = StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5', torch_dtype=torch.float16).to("cuda")
 
 + from DeepCache import DeepCacheSDHelper
 + helper = DeepCacheSDHelper(pipe=pipe)

docs/source/en/optimization/fp16.md

@@ -47,7 +47,7 @@ import torch
 from diffusers import DiffusionPipeline
 
 pipe = DiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    "runwayml/stable-diffusion-v1-5",
     torch_dtype=torch.float16,
     use_safetensors=True,
 )

docs/source/en/optimization/habana.md

@@ -61,7 +61,7 @@ For more information, check out 🤗 Optimum Habana's [documentation](https://hu
 
 We benchmarked Habana's first-generation Gaudi and Gaudi2 with the [Habana/stable-diffusion](https://huggingface.co/Habana/stable-diffusion) and [Habana/stable-diffusion-2](https://huggingface.co/Habana/stable-diffusion-2) Gaudi configurations (mixed precision bf16/fp32) to demonstrate their performance.
 
-For [Stable Diffusion v1.5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) on 512x512 images:
+For [Stable Diffusion v1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) on 512x512 images:
 
 |                        | Latency (batch size = 1) | Throughput  |
 | ---------------------- |:------------------------:|:---------------------------:|

docs/source/en/optimization/memory.md

@@ -41,7 +41,7 @@ import torch
 from diffusers import StableDiffusionPipeline
 
 pipe = StableDiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    "runwayml/stable-diffusion-v1-5",
     torch_dtype=torch.float16,
     use_safetensors=True,
 )
@@ -66,7 +66,7 @@ import torch
 from diffusers import StableDiffusionPipeline, UniPCMultistepScheduler
 
 pipe = StableDiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    "runwayml/stable-diffusion-v1-5",
     torch_dtype=torch.float16,
     use_safetensors=True,
 )
@@ -92,7 +92,7 @@ import torch
 from diffusers import StableDiffusionPipeline
 
 pipe = StableDiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    "runwayml/stable-diffusion-v1-5",
     torch_dtype=torch.float16,
     use_safetensors=True,
 )
@@ -140,7 +140,7 @@ import torch
 from diffusers import StableDiffusionPipeline
 
 pipe = StableDiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    "runwayml/stable-diffusion-v1-5",
     torch_dtype=torch.float16,
     use_safetensors=True,
 )
@@ -201,7 +201,7 @@ def generate_inputs():
 
 
 pipe = StableDiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    "runwayml/stable-diffusion-v1-5",
     torch_dtype=torch.float16,
     use_safetensors=True,
 ).to("cuda")
@@ -265,7 +265,7 @@ class UNet2DConditionOutput:
 
 
 pipe = StableDiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    "runwayml/stable-diffusion-v1-5",
     torch_dtype=torch.float16,
     use_safetensors=True,
 ).to("cuda")
@@ -315,7 +315,7 @@ from diffusers import DiffusionPipeline
 import torch
 
 pipe = DiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    "runwayml/stable-diffusion-v1-5",
     torch_dtype=torch.float16,
     use_safetensors=True,
 ).to("cuda")

docs/source/en/optimization/mps.md

@@ -24,7 +24,7 @@ The `mps` backend uses PyTorch's `.to()` interface to move the Stable Diffusion
 ```python
 from diffusers import DiffusionPipeline
 
-pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
+pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
 pipe = pipe.to("mps")
 
 # Recommended if your computer has < 64 GB of RAM
@@ -46,7 +46,7 @@ If you're using **PyTorch 1.13**, you need to "prime" the pipeline with an addit
 ```diff
   from diffusers import DiffusionPipeline
 
-  pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5").to("mps")
+  pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5").to("mps")
   pipe.enable_attention_slicing()
 
   prompt = "a photo of an astronaut riding a horse on mars"
@@ -67,7 +67,7 @@ To prevent this from happening, we recommend *attention slicing* to reduce memor
 from diffusers import DiffusionPipeline
 import torch
 
-pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True).to("mps")
+pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True).to("mps")
 pipeline.enable_attention_slicing()
 ```
 

docs/source/en/optimization/onnx.md

@@ -27,7 +27,7 @@ To load and run inference, use the [`~optimum.onnxruntime.ORTStableDiffusionPipe
 ```python
 from optimum.onnxruntime import ORTStableDiffusionPipeline
 
-model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
+model_id = "runwayml/stable-diffusion-v1-5"
 pipeline = ORTStableDiffusionPipeline.from_pretrained(model_id, export=True)
 prompt = "sailing ship in storm by Leonardo da Vinci"
 image = pipeline(prompt).images[0]
@@ -44,7 +44,7 @@ To export the pipeline in the ONNX format offline and use it later for inference
 use the [`optimum-cli export`](https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli) command:
 
 ```bash
-optimum-cli export onnx --model stable-diffusion-v1-5/stable-diffusion-v1-5 sd_v15_onnx/
+optimum-cli export onnx --model runwayml/stable-diffusion-v1-5 sd_v15_onnx/
 ```
 
 Then to perform inference (you don't have to specify `export=True` again):

docs/source/en/optimization/open_vino.md

@@ -29,7 +29,7 @@ To load and run inference, use the [`~optimum.intel.OVStableDiffusionPipeline`].
 ```python
 from optimum.intel import OVStableDiffusionPipeline
 
-model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
+model_id = "runwayml/stable-diffusion-v1-5"
 pipeline = OVStableDiffusionPipeline.from_pretrained(model_id, export=True)
 prompt = "sailing ship in storm by Rembrandt"
 image = pipeline(prompt).images[0]

docs/source/en/optimization/tome.md

@@ -28,7 +28,7 @@ You can use ToMe from the [`tomesd`](https://github.com/dbolya/tomesd) library w
   import tomesd
 
   pipeline = StableDiffusionPipeline.from_pretrained(
-        "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True,
+        "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True,
   ).to("cuda")
 + tomesd.apply_patch(pipeline, ratio=0.5)
 

docs/source/en/optimization/torch2.0.md

@@ -34,7 +34,7 @@ However, if you want to explicitly enable it, you can set a [`DiffusionPipeline`
   from diffusers import DiffusionPipeline
 + from diffusers.models.attention_processor import AttnProcessor2_0
 
-  pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
+  pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
 + pipe.unet.set_attn_processor(AttnProcessor2_0())
 
   prompt = "a photo of an astronaut riding a horse on mars"
@@ -49,7 +49,7 @@ In some cases - such as making the pipeline more deterministic or converting it
   import torch
   from diffusers import DiffusionPipeline
 
-  pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
+  pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
 + pipe.unet.set_default_attn_processor()
 
   prompt = "a photo of an astronaut riding a horse on mars"
@@ -64,7 +64,7 @@ The `torch.compile` function can often provide an additional speed-up to your Py
 from diffusers import DiffusionPipeline
 import torch
 
-pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
+pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
 pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
 images = pipe(prompt, num_inference_steps=steps, num_images_per_prompt=batch_size).images[0]
 ```
@@ -92,7 +92,7 @@ Expand the dropdown below to find the code used to benchmark each pipeline:
 from diffusers import DiffusionPipeline
 import torch
 
-path = "stable-diffusion-v1-5/stable-diffusion-v1-5"
+path = "runwayml/stable-diffusion-v1-5"
 
 run_compile = True  # Set True / False
 
@@ -122,7 +122,7 @@ url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/st
 init_image = load_image(url)
 init_image = init_image.resize((512, 512))
 
-path = "stable-diffusion-v1-5/stable-diffusion-v1-5"
+path = "runwayml/stable-diffusion-v1-5"
 
 run_compile = True  # Set True / False
 
@@ -183,7 +183,7 @@ url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/st
 init_image = load_image(url)
 init_image = init_image.resize((512, 512))
 
-path = "stable-diffusion-v1-5/stable-diffusion-v1-5"
+path = "runwayml/stable-diffusion-v1-5"
 
 run_compile = True  # Set True / False
 controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16, use_safetensors=True)

docs/source/en/optimization/xdit.md

@@ -1,121 +0,0 @@
-# xDiT
-
-[xDiT](https://github.com/xdit-project/xDiT) is an inference engine designed for the large scale parallel deployment of Diffusion Transformers (DiTs). xDiT provides a suite of efficient parallel approaches for Diffusion Models, as well as GPU kernel accelerations.
-
-There are four parallel methods supported in xDiT, including [Unified Sequence Parallelism](https://arxiv.org/abs/2405.07719), [PipeFusion](https://arxiv.org/abs/2405.14430), CFG parallelism and data parallelism. The four parallel methods in xDiT can be configured in a hybrid manner, optimizing communication patterns to best suit the underlying network hardware.
-
-Optimization orthogonal to parallelization focuses on accelerating single GPU performance. In addition to utilizing well-known Attention optimization libraries, we leverage compilation acceleration technologies such as torch.compile and onediff.
-
-The overview of xDiT is shown as follows.
-
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/xDiT/documentation-images/resolve/main/methods/xdit_overview.png">
-</div>
-You can install xDiT using the following command:
-
-
-```bash
-pip install xfuser
-```
-
-Here's an example of using xDiT to accelerate inference of a Diffusers model.
-
-```diff
- import torch
- from diffusers import StableDiffusion3Pipeline
-
- from xfuser import xFuserArgs, xDiTParallel
- from xfuser.config import FlexibleArgumentParser
- from xfuser.core.distributed import get_world_group
-
- def main():
-+    parser = FlexibleArgumentParser(description="xFuser Arguments")
-+    args = xFuserArgs.add_cli_args(parser).parse_args()
-+    engine_args = xFuserArgs.from_cli_args(args)
-+    engine_config, input_config = engine_args.create_config()
-
-     local_rank = get_world_group().local_rank
-     pipe = StableDiffusion3Pipeline.from_pretrained(
-         pretrained_model_name_or_path=engine_config.model_config.model,
-         torch_dtype=torch.float16,
-     ).to(f"cuda:{local_rank}")
-    
-# do anything you want with pipeline here
-
-+    pipe = xDiTParallel(pipe, engine_config, input_config)
-
-     pipe(
-         height=input_config.height,
-         width=input_config.height,
-         prompt=input_config.prompt,
-         num_inference_steps=input_config.num_inference_steps,
-         output_type=input_config.output_type,
-         generator=torch.Generator(device="cuda").manual_seed(input_config.seed),
-     )
-
-+    if input_config.output_type == "pil":
-+        pipe.save("results", "stable_diffusion_3")
-
-if __name__ == "__main__":
-    main()
-
-```
-
-As you can see, we only need to use xFuserArgs from xDiT to get configuration parameters, and pass these parameters along with the pipeline object from the Diffusers library into xDiTParallel to complete the parallelization of a specific pipeline in Diffusers.
-
-xDiT runtime parameters can be viewed in the command line using `-h`, and you can refer to this [usage](https://github.com/xdit-project/xDiT?tab=readme-ov-file#2-usage) example for more details.
-
-xDiT needs to be launched using torchrun to support its multi-node, multi-GPU parallel capabilities. For example, the following command can be used for 8-GPU parallel inference:
-
-```bash
-torchrun --nproc_per_node=8 ./inference.py --model models/FLUX.1-dev --data_parallel_degree 2 --ulysses_degree 2 --ring_degree 2 --prompt "A snowy mountain" "A small dog" --num_inference_steps 50
-```
-
-## Supported models
-
-A subset of Diffusers models are supported in xDiT, such as Flux.1, Stable Diffusion 3, etc. The latest supported models can be found [here](https://github.com/xdit-project/xDiT?tab=readme-ov-file#-supported-dits).
-
-## Benchmark
-We tested different models on various machines, and here is some of the benchmark data.
-
-### Flux.1-schnell
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/xDiT/documentation-images/resolve/main/performance/flux/Flux-2k-L40.png">
-</div>
-
-
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/xDiT/documentation-images/resolve/main/performance/flux/Flux-2K-A100.png">
-</div>
-
-### Stable Diffusion 3
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/xDiT/documentation-images/resolve/main/performance/sd3/L40-SD3.png">
-</div>
-
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/xDiT/documentation-images/resolve/main/performance/sd3/A100-SD3.png">
-</div>
-
-### HunyuanDiT
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/xDiT/documentation-images/resolve/main/performance/hunuyuandit/L40-HunyuanDiT.png">
-</div>
-
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/xDiT/documentation-images/resolve/main/performance/hunuyuandit/V100-HunyuanDiT.png">
-</div>
-
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/xDiT/documentation-images/resolve/main/performance/hunuyuandit/T4-HunyuanDiT.png">
-</div>
-
-More detailed performance metric can be found on our [github page](https://github.com/xdit-project/xDiT?tab=readme-ov-file#perf).
-
-## Reference
-
-[xDiT-project](https://github.com/xdit-project/xDiT)
-
-[USP: A Unified Sequence Parallelism Approach for Long Context Generative AI](https://arxiv.org/abs/2405.07719)
-
-[PipeFusion: Displaced Patch Pipeline Parallelism for Inference of Diffusion Transformer Models](https://arxiv.org/abs/2405.14430)

docs/source/en/quantization/bitsandbytes.md

@@ -1,260 +0,0 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
-
--->
-
-# bitsandbytes
-
-[bitsandbytes](https://huggingface.co/docs/bitsandbytes/index) is the easiest option for quantizing a model to 8 and 4-bit. 8-bit quantization multiplies outliers in fp16 with non-outliers in int8, converts the non-outlier values back to fp16, and then adds them together to return the weights in fp16. This reduces the degradative effect outlier values have on a model's performance.
-
-4-bit quantization compresses a model even further, and it is commonly used with [QLoRA](https://hf.co/papers/2305.14314) to finetune quantized LLMs.
-
-
-To use bitsandbytes, make sure you have the following libraries installed:
-
-```bash
-pip install diffusers transformers accelerate bitsandbytes -U
-```
-
-Now you can quantize a model by passing a [`BitsAndBytesConfig`] to [`~ModelMixin.from_pretrained`]. This works for any model in any modality, as long as it supports loading with [Accelerate](https://hf.co/docs/accelerate/index) and contains `torch.nn.Linear` layers.
-
-<hfoptions id="bnb">
-<hfoption id="8-bit">
-
-Quantizing a model in 8-bit halves the memory-usage:
-
-```py
-from diffusers import FluxTransformer2DModel, BitsAndBytesConfig
-
-quantization_config = BitsAndBytesConfig(load_in_8bit=True)
-
-model_8bit = FluxTransformer2DModel.from_pretrained(
-    "black-forest-labs/FLUX.1-dev", 
-    subfolder="transformer",
-    quantization_config=quantization_config
-)
-```
-
-By default, all the other modules such as `torch.nn.LayerNorm` are converted to `torch.float16`. You can change the data type of these modules with the `torch_dtype` parameter if you want:
-
-```py
-from diffusers import FluxTransformer2DModel, BitsAndBytesConfig
-
-quantization_config = BitsAndBytesConfig(load_in_8bit=True)
-
-model_8bit = FluxTransformer2DModel.from_pretrained(
-    "black-forest-labs/FLUX.1-dev", 
-    subfolder="transformer",
-    quantization_config=quantization_config,
-    torch_dtype=torch.float32
-)
-model_8bit.transformer_blocks.layers[-1].norm2.weight.dtype
-```
-
-Once a model is quantized, you can push the model to the Hub with the [`~ModelMixin.push_to_hub`] method. The quantization `config.json` file is pushed first, followed by the quantized model weights. You can also save the serialized 4-bit models locally with [`~ModelMixin.save_pretrained`].
-
-</hfoption>
-<hfoption id="4-bit">
-
-Quantizing a model in 4-bit reduces your memory-usage by 4x:
-
-```py
-from diffusers import FluxTransformer2DModel, BitsAndBytesConfig
-
-quantization_config = BitsAndBytesConfig(load_in_4bit=True)
-
-model_4bit = FluxTransformer2DModel.from_pretrained(
-    "black-forest-labs/FLUX.1-dev", 
-    subfolder="transformer",
-    quantization_config=quantization_config
-)
-```
-
-By default, all the other modules such as `torch.nn.LayerNorm` are converted to `torch.float16`. You can change the data type of these modules with the `torch_dtype` parameter if you want:
-
-```py
-from diffusers import FluxTransformer2DModel, BitsAndBytesConfig
-
-quantization_config = BitsAndBytesConfig(load_in_4bit=True)
-
-model_4bit = FluxTransformer2DModel.from_pretrained(
-    "black-forest-labs/FLUX.1-dev", 
-    subfolder="transformer",
-    quantization_config=quantization_config,
-    torch_dtype=torch.float32
-)
-model_4bit.transformer_blocks.layers[-1].norm2.weight.dtype
-```
-
-Call [`~ModelMixin.push_to_hub`] after loading it in 4-bit precision. You can also save the serialized 4-bit models locally with [`~ModelMixin.save_pretrained`].  
-
-</hfoption>
-</hfoptions>
-
-<Tip warning={true}>
-
-Training with 8-bit and 4-bit weights are only supported for training *extra* parameters.
-
-</Tip>
-
-Check your memory footprint with the `get_memory_footprint` method:
-
-```py
-print(model.get_memory_footprint())
-```
-
-Quantized models can be loaded from the [`~ModelMixin.from_pretrained`] method without needing to specify the `quantization_config` parameters:
-
-```py
-from diffusers import FluxTransformer2DModel, BitsAndBytesConfig
-
-quantization_config = BitsAndBytesConfig(load_in_4bit=True)
-
-model_4bit = FluxTransformer2DModel.from_pretrained(
-    "hf-internal-testing/flux.1-dev-nf4-pkg", subfolder="transformer"
-)
-```
-
-## 8-bit (LLM.int8() algorithm)
-
-<Tip>
-
-Learn more about the details of 8-bit quantization in this [blog post](https://huggingface.co/blog/hf-bitsandbytes-integration)!
-
-</Tip>
-
-This section explores some of the specific features of 8-bit models, such as outlier thresholds and skipping module conversion.
-
-### Outlier threshold
-
-An "outlier" is a hidden state value greater than a certain threshold, and these values are computed in fp16. While the values are usually normally distributed ([-3.5, 3.5]), this distribution can be very different for large models ([-60, 6] or [6, 60]). 8-bit quantization works well for values ~5, but beyond that, there is a significant performance penalty. A good default threshold value is 6, but a lower threshold may be needed for more unstable models (small models or finetuning).
-
-To find the best threshold for your model, we recommend experimenting with the `llm_int8_threshold` parameter in [`BitsAndBytesConfig`]:
-
-```py
-from diffusers import FluxTransformer2DModel, BitsAndBytesConfig
-
-quantization_config = BitsAndBytesConfig(
-    load_in_8bit=True, llm_int8_threshold=10,
-)
-
-model_8bit = FluxTransformer2DModel.from_pretrained(
-    "black-forest-labs/FLUX.1-dev",
-    subfolder="transformer",
-    quantization_config=quantization_config,
-)
-```
-
-### Skip module conversion
-
-For some models, you don't need to quantize every module to 8-bit which can actually cause instability. For example, for diffusion models like [Stable Diffusion 3](../api/pipelines/stable_diffusion/stable_diffusion_3), the `proj_out` module can be skipped using the `llm_int8_skip_modules` parameter in [`BitsAndBytesConfig`]:
-
-```py
-from diffusers import SD3Transformer2DModel, BitsAndBytesConfig
-
-quantization_config = BitsAndBytesConfig(
-    load_in_8bit=True, llm_int8_skip_modules=["proj_out"],
-)
-
-model_8bit = SD3Transformer2DModel.from_pretrained(
-    "stabilityai/stable-diffusion-3-medium-diffusers",
-    subfolder="transformer",
-    quantization_config=quantization_config,
-)
-```
-
-
-## 4-bit (QLoRA algorithm)
-
-<Tip>
-
-Learn more about its details in this [blog post](https://huggingface.co/blog/4bit-transformers-bitsandbytes).
-
-</Tip>
-
-This section explores some of the specific features of 4-bit models, such as changing the compute data type, using the Normal Float 4 (NF4) data type, and using nested quantization.
-
-
-### Compute data type
-
-To speedup computation, you can change the data type from float32 (the default value) to bf16 using the `bnb_4bit_compute_dtype` parameter in [`BitsAndBytesConfig`]:
-
-```py
-import torch
-from diffusers import BitsAndBytesConfig
-
-quantization_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16)
-```
-
-### Normal Float 4 (NF4)
-
-NF4 is a 4-bit data type from the [QLoRA](https://hf.co/papers/2305.14314) paper, adapted for weights initialized from a normal distribution. You should use NF4 for training 4-bit base models. This can be configured with the `bnb_4bit_quant_type` parameter in the [`BitsAndBytesConfig`]:
-
-```py
-from diffusers import BitsAndBytesConfig
-
-nf4_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_quant_type="nf4",
-)
-
-model_nf4 = SD3Transformer2DModel.from_pretrained(
-    "stabilityai/stable-diffusion-3-medium-diffusers",
-    subfolder="transformer",
-    quantization_config=nf4_config,
-)
-```
-
-For inference, the `bnb_4bit_quant_type` does not have a huge impact on performance. However, to remain consistent with the model weights, you should use the `bnb_4bit_compute_dtype` and `torch_dtype` values.
-
-### Nested quantization
-
-Nested quantization is a technique that can save additional memory at no additional performance cost. This feature performs a second quantization of the already quantized weights to save an additional 0.4 bits/parameter. 
-
-```py
-from diffusers import BitsAndBytesConfig
-
-double_quant_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_use_double_quant=True,
-)
-
-double_quant_model = SD3Transformer2DModel.from_pretrained(
-    "stabilityai/stable-diffusion-3-medium-diffusers",
-    subfolder="transformer",
-    quantization_config=double_quant_config,
-)
-```
-
-## Dequantizing `bitsandbytes` models
-
-Once quantized, you can dequantize the model to the original precision but this might result in a small quality loss of the model. Make sure you have enough GPU RAM to fit the dequantized model. 
-
-```python
-from diffusers import BitsAndBytesConfig
-
-double_quant_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_use_double_quant=True,
-)
-
-double_quant_model = SD3Transformer2DModel.from_pretrained(
-    "stabilityai/stable-diffusion-3-medium-diffusers",
-    subfolder="transformer",
-    quantization_config=double_quant_config,
-)
-model.dequantize()
-```
-
-## Resources
-
-* [End-to-end notebook showing Flux.1 Dev inference in a free-tier Colab](https://gist.github.com/sayakpaul/c76bd845b48759e11687ac550b99d8b4)
-* [Training](https://gist.github.com/sayakpaul/05afd428bc089b47af7c016e42004527)

docs/source/en/quantization/overview.md

@@ -1,35 +0,0 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
-
--->
-
-# Quantization
-
-Quantization techniques focus on representing data with less information while also trying to not lose too much accuracy. This often means converting a data type to represent the same information with fewer bits. For example, if your model weights are stored as 32-bit floating points and they're quantized to 16-bit floating points, this halves the model size which makes it easier to store and reduces memory-usage. Lower precision can also speedup inference because it takes less time to perform calculations with fewer bits.
-
-<Tip>
-
-Interested in adding a new quantization method to Transformers? Refer to the [Contribute new quantization method guide](https://huggingface.co/docs/transformers/main/en/quantization/contribute) to learn more about adding a new quantization method.
-
-</Tip>
-
-<Tip>
-
-If you are new to the quantization field, we recommend you to check out these beginner-friendly courses about quantization in collaboration with DeepLearning.AI:
-
-* [Quantization Fundamentals with Hugging Face](https://www.deeplearning.ai/short-courses/quantization-fundamentals-with-hugging-face/)
-* [Quantization in Depth](https://www.deeplearning.ai/short-courses/quantization-in-depth/)
-
-</Tip>
-
-## When to use what?
-
-This section will be expanded once Diffusers has multiple quantization backends. Currently, we only support `bitsandbytes`. [This resource](https://huggingface.co/docs/transformers/main/en/quantization/overview#when-to-use-what) provides a good overview of the pros and cons of different quantization techniques. 

docs/source/en/quicktour.md

@@ -54,7 +54,7 @@ The [`DiffusionPipeline`] is the easiest way to use a pretrained diffusion syste
 
 Start by creating an instance of a [`DiffusionPipeline`] and specify which pipeline checkpoint you would like to download.
 You can use the [`DiffusionPipeline`] for any [checkpoint](https://huggingface.co/models?library=diffusers&sort=downloads) stored on the Hugging Face Hub.
-In this quicktour, you'll load the [`stable-diffusion-v1-5`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) checkpoint for text-to-image generation.
+In this quicktour, you'll load the [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) checkpoint for text-to-image generation.
 
 <Tip warning={true}>
 
@@ -67,7 +67,7 @@ Load the model with the [`~DiffusionPipeline.from_pretrained`] method:
 ```python
 >>> from diffusers import DiffusionPipeline
 
->>> pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", use_safetensors=True)
+>>> pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", use_safetensors=True)
 ```
 
 The [`DiffusionPipeline`] downloads and caches all modeling, tokenization, and scheduling components. You'll see that the Stable Diffusion pipeline is composed of the [`UNet2DConditionModel`] and [`PNDMScheduler`] among other things:
@@ -124,7 +124,7 @@ You can also use the pipeline locally. The only difference is you need to downlo
 
 ```bash
 !git lfs install
-!git clone https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5
+!git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
 ```
 
 Then load the saved weights into the pipeline:
@@ -142,7 +142,7 @@ Different schedulers come with different denoising speeds and quality trade-offs
 ```py
 >>> from diffusers import EulerDiscreteScheduler
 
->>> pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", use_safetensors=True)
+>>> pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", use_safetensors=True)
 >>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
 ```
 

docs/source/en/stable_diffusion.md

@@ -20,12 +20,12 @@ This is why it's important to get the most *computational* (speed) and *memory*
 
 This tutorial walks you through how to generate faster and better with the [`DiffusionPipeline`].
 
-Begin by loading the [`stable-diffusion-v1-5/stable-diffusion-v1-5`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) model:
+Begin by loading the [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) model:
 
 ```python
 from diffusers import DiffusionPipeline
 
-model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
+model_id = "runwayml/stable-diffusion-v1-5"
 pipeline = DiffusionPipeline.from_pretrained(model_id, use_safetensors=True)
 ```
 

docs/source/en/training/adapt_a_model.md

@@ -6,12 +6,12 @@ This guide will show you how to adapt a pretrained text-to-image model for inpai
 
 ## Configure UNet2DConditionModel parameters
 
-A [`UNet2DConditionModel`] by default accepts 4 channels in the [input sample](https://huggingface.co/docs/diffusers/v0.16.0/en/api/models#diffusers.UNet2DConditionModel.in_channels). For example, load a pretrained text-to-image model like [`stable-diffusion-v1-5/stable-diffusion-v1-5`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) and take a look at the number of `in_channels`:
+A [`UNet2DConditionModel`] by default accepts 4 channels in the [input sample](https://huggingface.co/docs/diffusers/v0.16.0/en/api/models#diffusers.UNet2DConditionModel.in_channels). For example, load a pretrained text-to-image model like [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) and take a look at the number of `in_channels`:
 
 ```py
 from diffusers import StableDiffusionPipeline
 
-pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", use_safetensors=True)
+pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", use_safetensors=True)
 pipeline.unet.config["in_channels"]
 4
 ```
@@ -33,7 +33,7 @@ Initialize a [`UNet2DConditionModel`] with the pretrained text-to-image model we
 ```py
 from diffusers import UNet2DConditionModel
 
-model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
+model_id = "runwayml/stable-diffusion-v1-5"
 unet = UNet2DConditionModel.from_pretrained(
     model_id,
     subfolder="unet",

docs/source/en/training/cogvideox.md

@@ -1,291 +0,0 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-# CogVideoX
-
-CogVideoX is a text-to-video generation model focused on creating more coherent videos aligned with a prompt. It achieves this using several methods.
-
-- a 3D variational autoencoder that compresses videos spatially and temporally, improving compression rate and video accuracy.
-
-- an expert transformer block to help align text and video, and a 3D full attention module for capturing and creating spatially and temporally accurate videos.
-
-The actual test of the video instruction dimension found that CogVideoX has good effects on consistent theme, dynamic information, consistent background, object information, smooth motion, color, scene, appearance style, and temporal style but cannot achieve good results with human action, spatial relationship, and multiple objects.
-
-Finetuning with Diffusers can help make up for these poor results. 
-
-## Data Preparation
-
-The training scripts accepts data in two formats.  
-
-The first format is suited for small-scale training, and the second format uses a CSV format, which is more appropriate for streaming data for large-scale training. In the future, Diffusers will support the `<Video>` tag.
-
-### Small format
-
-Two files where one file contains line-separated prompts and another file contains line-separated paths to video data (the path to video files must be relative to the path you pass when specifying `--instance_data_root`). Let's take a look at an example to understand this better!
-
-Assume you've specified `--instance_data_root` as `/dataset`, and that this directory contains the files: `prompts.txt` and `videos.txt`.
-
-The `prompts.txt` file should contain line-separated prompts:
-
-```
-A black and white animated sequence featuring a rabbit, named Rabbity Ribfried, and an anthropomorphic goat in a musical, playful environment, showcasing their evolving interaction.
-A black and white animated sequence on a ship's deck features a bulldog character, named Bully Bulldoger, showcasing exaggerated facial expressions and body language. The character progresses from confident to focused, then to strained and distressed, displaying a range of emotions as it navigates challenges. The ship's interior remains static in the background, with minimalistic details such as a bell and open door. The character's dynamic movements and changing expressions drive the narrative, with no camera movement to distract from its evolving reactions and physical gestures.
-...
-```
-
-The `videos.txt` file should contain line-separate paths to video files. Note that the path should be _relative_ to the `--instance_data_root` directory.
-
-```
-videos/00000.mp4
-videos/00001.mp4
-...
-```
-
-Overall, this is how your dataset would look like if you ran the `tree` command on the dataset root directory:
-
-```
-/dataset
-├── prompts.txt
-├── videos.txt
-├── videos
-    ├── videos/00000.mp4
-    ├── videos/00001.mp4
-    ├── ...
-```
-
-When using this format, the `--caption_column` must be `prompts.txt` and `--video_column` must be `videos.txt`.
-
-### Stream format
-
-You could use a single CSV file. For the sake of this example, assume you have a `metadata.csv` file. The expected format is:
-
-```
-<CAPTION_COLUMN>,<PATH_TO_VIDEO_COLUMN>
-"""A black and white animated sequence featuring a rabbit, named Rabbity Ribfried, and an anthropomorphic goat in a musical, playful environment, showcasing their evolving interaction.""","""00000.mp4"""
-"""A black and white animated sequence on a ship's deck features a bulldog character, named Bully Bulldoger, showcasing exaggerated facial expressions and body language. The character progresses from confident to focused, then to strained and distressed, displaying a range of emotions as it navigates challenges. The ship's interior remains static in the background, with minimalistic details such as a bell and open door. The character's dynamic movements and changing expressions drive the narrative, with no camera movement to distract from its evolving reactions and physical gestures.""","""00001.mp4"""
-...
-```
-
-In this case, the `--instance_data_root` should be the location where the videos are stored and `--dataset_name` should be either a path to local folder or a [`~datasets.load_dataset`] compatible dataset hosted on the Hub. Assuming you have videos of Minecraft gameplay at `https://huggingface.co/datasets/my-awesome-username/minecraft-videos`, you would have to specify `my-awesome-username/minecraft-videos`.
-
-When using this format, the `--caption_column` must be `<CAPTION_COLUMN>` and `--video_column` must be `<PATH_TO_VIDEO_COLUMN>`.
-
-You are not strictly restricted to the CSV format. Any format works as long as the `load_dataset` method supports the file format to load a basic `<PATH_TO_VIDEO_COLUMN>` and `<CAPTION_COLUMN>`. The reason for going through these dataset organization gymnastics for loading video data is because `load_dataset` does not fully support all kinds of video formats.
-
-> [!NOTE]
-> CogVideoX works best with long and descriptive LLM-augmented prompts for video generation. We recommend pre-processing your videos by first generating a summary using a VLM and then augmenting the prompts with an LLM. To generate the above captions, we use [MiniCPM-V-26](https://huggingface.co/openbmb/MiniCPM-V-2_6) and [Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct). A very barebones and no-frills example for this is available [here](https://gist.github.com/a-r-r-o-w/4dee20250e82f4e44690a02351324a4a). The official recommendation for augmenting prompts is [ChatGLM](https://huggingface.co/THUDM?search_models=chatglm) and a length of 50-100 words is considered good.
-
->![NOTE]
-> It is expected that your dataset is already pre-processed. If not, some basic pre-processing can be done by playing with the following parameters:
-> `--height`, `--width`, `--fps`, `--max_num_frames`, `--skip_frames_start` and `--skip_frames_end`.
-> Presently, all videos in your dataset should contain the same number of video frames when using a training batch size > 1.
-
-<!-- TODO: Implement frame packing in future to address above issue. -->
-
-## Training
-
-You need to setup your development environment by installing the necessary requirements. The following packages are required:
-- Torch 2.0 or above based on the training features you are utilizing (might require latest or nightly versions for quantized/deepspeed training)
-- `pip install diffusers transformers accelerate peft huggingface_hub` for all things modeling and training related
-- `pip install datasets decord` for loading video training data
-- `pip install bitsandbytes` for using 8-bit Adam or AdamW optimizers for memory-optimized training
-- `pip install wandb` optionally for monitoring training logs
-- `pip install deepspeed` optionally for [DeepSpeed](https://github.com/microsoft/DeepSpeed) training
-- `pip install prodigyopt` optionally if you would like to use the Prodigy optimizer for training
-
-To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
-
-Before running the script, make sure you install the library from source:
-```bash
-git clone https://github.com/huggingface/diffusers
-cd diffusers
-pip install -e .
-```
-
- 
-
-Then navigate to the example folder containing the training script and install the required dependencies for the script you're using:
-
-- PyTorch
-
-```bash
-cd examples/cogvideo
-pip install -r requirements.txt
-```
-
-And initialize an [🤗 Accelerate](https://github.com/huggingface/accelerate/) environment with:
-
-```bash
-accelerate config
-```
-
-Or for a default accelerate configuration without answering questions about your environment
-
-```bash
-accelerate config default
-```
-
-Or if your environment doesn't support an interactive shell (e.g., a notebook)
-
-```python
-from accelerate.utils import write_basic_config
-write_basic_config()
-```
-
-When running `accelerate config`, if you use torch.compile, there can be dramatic speedups. The PEFT library is used as a backend for LoRA training, so make sure to have `peft>=0.6.0` installed in your environment.
-
-If you would like to push your model to the Hub after training is completed with a neat model card, make sure you're logged in:
-
-```bash
-huggingface-cli login
-
-# Alternatively, you could upload your model manually using:
-# huggingface-cli upload my-cool-account-name/my-cool-lora-name /path/to/awesome/lora
-```
-
-Make sure your data is prepared as described in [Data Preparation](#data-preparation). When ready, you can begin training!
-
-Assuming you are training on 50 videos of a similar concept, we have found 1500-2000 steps to work well. The official recommendation, however, is 100 videos with a total of 4000 steps. Assuming you are training on a single GPU with a `--train_batch_size` of `1`:
-- 1500 steps on 50 videos would correspond to `30` training epochs
-- 4000 steps on 100 videos would correspond to `40` training epochs
-
-```bash
-#!/bin/bash
-
-GPU_IDS="0"
-
-accelerate launch --gpu_ids $GPU_IDS examples/cogvideo/train_cogvideox_lora.py \
-  --pretrained_model_name_or_path THUDM/CogVideoX-2b \
-  --cache_dir <CACHE_DIR> \
-  --instance_data_root <PATH_TO_WHERE_VIDEO_FILES_ARE_STORED> \
-  --dataset_name my-awesome-name/my-awesome-dataset \
-  --caption_column <CAPTION_COLUMN> \
-  --video_column <PATH_TO_VIDEO_COLUMN> \
-  --id_token <ID_TOKEN> \
-  --validation_prompt "<ID_TOKEN> Spiderman swinging over buildings:::A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance" \
-  --validation_prompt_separator ::: \
-  --num_validation_videos 1 \
-  --validation_epochs 10 \
-  --seed 42 \
-  --rank 64 \
-  --lora_alpha 64 \
-  --mixed_precision fp16 \
-  --output_dir /raid/aryan/cogvideox-lora \
-  --height 480 --width 720 --fps 8 --max_num_frames 49 --skip_frames_start 0 --skip_frames_end 0 \
-  --train_batch_size 1 \
-  --num_train_epochs 30 \
-  --checkpointing_steps 1000 \
-  --gradient_accumulation_steps 1 \
-  --learning_rate 1e-3 \
-  --lr_scheduler cosine_with_restarts \
-  --lr_warmup_steps 200 \
-  --lr_num_cycles 1 \
-  --enable_slicing \
-  --enable_tiling \
-  --optimizer Adam \
-  --adam_beta1 0.9 \
-  --adam_beta2 0.95 \
-  --max_grad_norm 1.0 \
-  --report_to wandb
-```
-
-To better track our training experiments, we're using the following flags in the command above:
-* `--report_to wandb` will ensure the training runs are tracked on Weights and Biases. To use it, be sure to install `wandb` with `pip install wandb`.
-* `validation_prompt` and `validation_epochs` to allow the script to do a few validation inference runs. This allows us to qualitatively check if the training is progressing as expected.
-
-Setting the `<ID_TOKEN>` is not necessary. From some limited experimentation, we found it works better (as it resembles [Dreambooth](https://huggingface.co/docs/diffusers/en/training/dreambooth) training) than without. When provided, the `<ID_TOKEN>` is appended to the beginning of each prompt. So, if your `<ID_TOKEN>` was `"DISNEY"` and your prompt was `"Spiderman swinging over buildings"`, the effective prompt used in training would be `"DISNEY Spiderman swinging over buildings"`. When not provided, you would either be training without any additional token or could augment your dataset to apply the token where you wish before starting the training.
-
-> [!NOTE]
-> You can pass `--use_8bit_adam` to reduce the memory requirements of training.
-
-> [!IMPORTANT]
-> The following settings have been tested at the time of adding CogVideoX LoRA training support:
-> - Our testing was primarily done on CogVideoX-2b. We will work on CogVideoX-5b and CogVideoX-5b-I2V soon
-> - One dataset comprised of 70 training videos of resolutions `200 x 480 x 720` (F x H x W). From this, by using frame skipping in data preprocessing, we created two smaller 49-frame and 16-frame datasets for faster experimentation and because the maximum limit recommended by the CogVideoX team is 49 frames. Out of the 70 videos, we created three groups of 10, 25 and 50 videos. All videos were similar in nature of the concept being trained.
-> - 25+ videos worked best for training new concepts and styles.
-> - We found that it is better to train with an identifier token that can be specified as `--id_token`. This is similar to Dreambooth-like training but normal finetuning without such a token works too.
-> - Trained concept seemed to work decently well when combined with completely unrelated prompts. We expect even better results if CogVideoX-5B is finetuned.
-> - The original repository uses a `lora_alpha` of `1`. We found this not suitable in many runs, possibly due to difference in modeling backends and training settings. Our recommendation is to set to the `lora_alpha` to either `rank` or `rank // 2`.
-> - If you're training on data whose captions generate bad results with the original model, a `rank` of 64 and above is good and also the recommendation by the team behind CogVideoX. If the generations are already moderately good on your training captions, a `rank` of 16/32 should work. We found that setting the rank too low, say `4`, is not ideal and doesn't produce promising results.
-> - The authors of CogVideoX recommend 4000 training steps and 100 training videos overall to achieve the best result. While that might yield the best results, we found from our limited experimentation that 2000 steps and 25 videos could also be sufficient.
-> - When using the Prodigy opitimizer for training, one can follow the recommendations from [this](https://huggingface.co/blog/sdxl_lora_advanced_script) blog. Prodigy tends to overfit quickly. From my very limited testing, I found a learning rate of `0.5` to be suitable in addition to `--prodigy_use_bias_correction`, `prodigy_safeguard_warmup` and `--prodigy_decouple`.
-> - The recommended learning rate by the CogVideoX authors and from our experimentation with Adam/AdamW is between `1e-3` and `1e-4` for a dataset of 25+ videos.
->
-> Note that our testing is not exhaustive due to limited time for exploration. Our recommendation would be to play around with the different knobs and dials to find the best settings for your data.
-
-<!-- TODO: Test finetuning with CogVideoX-5b and CogVideoX-5b-I2V and update scripts accordingly -->
-
-## Inference
-
-Once you have trained a lora model, the inference can be done simply loading the lora weights into the `CogVideoXPipeline`.
-
-```python
-import torch
-from diffusers import CogVideoXPipeline
-from diffusers.utils import export_to_video
-
-pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-2b", torch_dtype=torch.float16)
-# pipe.load_lora_weights("/path/to/lora/weights", adapter_name="cogvideox-lora") # Or,
-pipe.load_lora_weights("my-awesome-hf-username/my-awesome-lora-name", adapter_name="cogvideox-lora") # If loading from the HF Hub
-pipe.to("cuda")
-
-# Assuming lora_alpha=32 and rank=64 for training. If different, set accordingly
-pipe.set_adapters(["cogvideox-lora"], [32 / 64])
-
-prompt = "A vast, shimmering ocean flows gracefully under a twilight sky, its waves undulating in a mesmerizing dance of blues and greens. The surface glints with the last rays of the setting sun, casting golden highlights that ripple across the water. Seagulls soar above, their cries blending with the gentle roar of the waves. The horizon stretches infinitely, where the ocean meets the sky in a seamless blend of hues. Close-ups reveal the intricate patterns of the waves, capturing the fluidity and dynamic beauty of the sea in motion."
-frames = pipe(prompt, guidance_scale=6, use_dynamic_cfg=True).frames[0]
-export_to_video(frames, "output.mp4", fps=8)
-```
-
-
-## Reduce memory usage
-
-While testing using the diffusers library, all optimizations included in the diffusers library were enabled. This
-scheme has not been tested for actual memory usage on devices outside of **NVIDIA A100 / H100** architectures.
-Generally, this scheme can be adapted to all **NVIDIA Ampere architecture** and above devices. If optimizations are
-disabled, memory consumption will multiply, with peak memory usage being about 3 times the value in the table.
-However, speed will increase by about 3-4 times. You can selectively disable some optimizations, including:
-
-```
-pipe.enable_sequential_cpu_offload()
-pipe.vae.enable_slicing()
-pipe.vae.enable_tiling()
-```
-
-+ For multi-GPU inference, the `enable_sequential_cpu_offload()` optimization needs to be disabled.
-+ Using INT8 models will slow down inference, which is done to accommodate lower-memory GPUs while maintaining minimal
-  video quality loss, though inference speed will significantly decrease.
-+ The CogVideoX-2B model was trained in `FP16` precision, and all CogVideoX-5B models were trained in `BF16` precision.
-  We recommend using the precision in which the model was trained for inference.
-+ [PytorchAO](https://github.com/pytorch/ao) and [Optimum-quanto](https://github.com/huggingface/optimum-quanto/) can be
-  used to quantize the text encoder, transformer, and VAE modules to reduce the memory requirements of CogVideoX. This
-  allows the model to run on free T4 Colabs or GPUs with smaller memory! Also, note that TorchAO quantization is fully
-  compatible with `torch.compile`, which can significantly improve inference speed. FP8 precision must be used on
-  devices with NVIDIA H100 and above, requiring source installation of `torch`, `torchao`, `diffusers`, and `accelerate`
-  Python packages. CUDA 12.4 is recommended.
-+ The inference speed tests also used the above memory optimization scheme. Without memory optimization, inference speed
-  increases by about 10%. Only the `diffusers` version of the model supports quantization.
-+ The model only supports English input; other languages can be translated into English for use via large model
-  refinement.
-+ The memory usage of model fine-tuning is tested in an `8 * H100` environment, and the program automatically
-  uses `Zero 2` optimization. If a specific number of GPUs is marked in the table, that number or more GPUs must be used
-  for fine-tuning.
-
-
- | **Attribute**                        | **CogVideoX-2B**                                                       | **CogVideoX-5B**                                                       |
-| ------------------------------------ | ---------------------------------------------------------------------- | ---------------------------------------------------------------------- |
-| **Model Name**                       | CogVideoX-2B                                                           | CogVideoX-5B                                                           |
-| **Inference Precision**              | FP16* (Recommended), BF16, FP32, FP8*, INT8, Not supported INT4         | BF16 (Recommended), FP16, FP32, FP8*, INT8, Not supported INT4         |
-| **Single GPU Inference VRAM**        | FP16: Using diffusers 12.5GB* INT8: Using diffusers with torchao 7.8GB* | BF16: Using diffusers 20.7GB* INT8: Using diffusers with torchao 11.4GB* |
-| **Multi GPU Inference VRAM**         | FP16: Using diffusers 10GB*                                             | BF16: Using diffusers 15GB*                                             |
-| **Inference Speed**                  | Single A100: ~90 seconds, Single H100: ~45 seconds                      | Single A100: ~180 seconds, Single H100: ~90 seconds                     |
-| **Fine-tuning Precision**            | FP16                                                                   | BF16                                                                   |
-| **Fine-tuning VRAM Consumption**     | 47 GB (bs=1, LORA) 61 GB (bs=2, LORA) 62GB (bs=1, SFT)                 | 63 GB (bs=1, LORA) 80 GB (bs=2, LORA) 75GB (bs=1, SFT)                 |

docs/source/en/training/controlnet.md

@@ -276,7 +276,7 @@ That's it! You don't need to add any additional parameters to your training comm
 <hfoption id="PyTorch">
 
 ```bash
-export MODEL_DIR="stable-diffusion-v1-5/stable-diffusion-v1-5"
+export MODEL_DIR="runwayml/stable-diffusion-v1-5"
 export OUTPUT_DIR="path/to/save/model"
 
 accelerate launch train_controlnet.py \

docs/source/en/training/create_dataset.md

@@ -78,7 +78,7 @@ Now the dataset is available for training by passing the dataset name to the `--
 
 ```bash
 accelerate launch --mixed_precision="fp16"  train_text_to_image.py \
-  --pretrained_model_name_or_path="stable-diffusion-v1-5/stable-diffusion-v1-5" \
+  --pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5" \
   --dataset_name="name_of_your_dataset" \
   <other-arguments>
 ```

docs/source/en/training/distributed_inference.md

@@ -10,7 +10,7 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# Distributed inference
+# Distributed inference with multiple GPUs
 
 On distributed setups, you can run inference across multiple GPUs with 🤗 [Accelerate](https://huggingface.co/docs/accelerate/index) or [PyTorch Distributed](https://pytorch.org/tutorials/beginner/dist_overview.html), which is useful for generating with multiple prompts in parallel.
 
@@ -30,7 +30,7 @@ from accelerate import PartialState
 from diffusers import DiffusionPipeline
 
 pipeline = DiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
+    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
 )
 distributed_state = PartialState()
 pipeline.to(distributed_state.device)
@@ -66,7 +66,7 @@ import torch.multiprocessing as mp
 from diffusers import DiffusionPipeline
 
 sd = DiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
+    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
 )
 ```
 
@@ -109,131 +109,3 @@ torchrun run_distributed.py --nproc_per_node=2
 
 > [!TIP]
 > You can use `device_map` within a [`DiffusionPipeline`] to distribute its model-level components on multiple devices. Refer to the [Device placement](../tutorials/inference_with_big_models#device-placement) guide to learn more.
-
-## Model sharding
-
-Modern diffusion systems such as [Flux](../api/pipelines/flux) are very large and have multiple models. For example, [Flux.1-Dev](https://hf.co/black-forest-labs/FLUX.1-dev) is made up of two text encoders - [T5-XXL](https://hf.co/google/t5-v1_1-xxl) and [CLIP-L](https://hf.co/openai/clip-vit-large-patch14) - a [diffusion transformer](../api/models/flux_transformer), and a [VAE](../api/models/autoencoderkl). With a model this size, it can be challenging to run inference on consumer GPUs.
-
-Model sharding is a technique that distributes models across GPUs when the models don't fit on a single GPU. The example below assumes two 16GB GPUs are available for inference.
-
-Start by computing the text embeddings with the text encoders. Keep the text encoders on two GPUs by setting `device_map="balanced"`. The `balanced` strategy evenly distributes the model on all available GPUs. Use the `max_memory` parameter to allocate the maximum amount of memory for each text encoder on each GPU.
-
-> [!TIP]
-> **Only** load the text encoders for this step! The diffusion transformer and VAE are loaded in a later step to preserve memory.
-
-```py
-from diffusers import FluxPipeline
-import torch
-
-prompt = "a photo of a dog with cat-like look"
-
-pipeline = FluxPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-dev",
-    transformer=None,
-    vae=None,
-    device_map="balanced",
-    max_memory={0: "16GB", 1: "16GB"},
-    torch_dtype=torch.bfloat16
-)
-with torch.no_grad():
-    print("Encoding prompts.")
-    prompt_embeds, pooled_prompt_embeds, text_ids = pipeline.encode_prompt(
-        prompt=prompt, prompt_2=None, max_sequence_length=512
-    )
-```
-
-Once the text embeddings are computed, remove them from the GPU to make space for the diffusion transformer.
-
-```py
-import gc 
-
-def flush():
-    gc.collect()
-    torch.cuda.empty_cache()
-    torch.cuda.reset_max_memory_allocated()
-    torch.cuda.reset_peak_memory_stats()
-
-del pipeline.text_encoder
-del pipeline.text_encoder_2
-del pipeline.tokenizer
-del pipeline.tokenizer_2
-del pipeline
-
-flush()
-```
-
-Load the diffusion transformer next which has 12.5B parameters. This time, set `device_map="auto"` to automatically distribute the model across two 16GB GPUs. The `auto` strategy is backed by [Accelerate](https://hf.co/docs/accelerate/index) and available as a part of the [Big Model Inference](https://hf.co/docs/accelerate/concept_guides/big_model_inference) feature. It starts by distributing a model across the fastest device first (GPU) before moving to slower devices like the CPU and hard drive if needed. The trade-off of storing model parameters on slower devices is slower inference latency.
-
-```py
-from diffusers import FluxTransformer2DModel
-import torch 
-
-transformer = FluxTransformer2DModel.from_pretrained(
-    "black-forest-labs/FLUX.1-dev", 
-    subfolder="transformer",
-    device_map="auto",
-    torch_dtype=torch.bfloat16
-)
-```
-
-> [!TIP]
-> At any point, you can try `print(pipeline.hf_device_map)` to see how the various models are distributed across devices. This is useful for tracking the device placement of the models. You can also try `print(transformer.hf_device_map)` to see how the transformer model is sharded across devices.
-
-Add the transformer model to the pipeline for denoising, but set the other model-level components like the text encoders and VAE to `None` because you don't need them yet.
-
-```py
-pipeline = FluxPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-dev", ,
-    text_encoder=None,
-    text_encoder_2=None,
-    tokenizer=None,
-    tokenizer_2=None,
-    vae=None,
-    transformer=transformer,
-    torch_dtype=torch.bfloat16
-)
-
-print("Running denoising.")
-height, width = 768, 1360
-latents = pipeline(
-    prompt_embeds=prompt_embeds,
-    pooled_prompt_embeds=pooled_prompt_embeds,
-    num_inference_steps=50,
-    guidance_scale=3.5,
-    height=height,
-    width=width,
-    output_type="latent",
-).images
-```
-
-Remove the pipeline and transformer from memory as they're no longer needed.
-
-```py
-del pipeline.transformer
-del pipeline
-
-flush()
-```
-
-Finally, decode the latents with the VAE into an image. The VAE is typically small enough to be loaded on a single GPU.
-
-```py
-from diffusers import AutoencoderKL
-from diffusers.image_processor import VaeImageProcessor
-import torch 
-
-vae = AutoencoderKL.from_pretrained(ckpt_id, subfolder="vae", torch_dtype=torch.bfloat16).to("cuda")
-vae_scale_factor = 2 ** (len(vae.config.block_out_channels))
-image_processor = VaeImageProcessor(vae_scale_factor=vae_scale_factor)
-
-with torch.no_grad():
-    print("Running decoding.")
-    latents = FluxPipeline._unpack_latents(latents, height, width, vae_scale_factor)
-    latents = (latents / vae.config.scaling_factor) + vae.config.shift_factor
-
-    image = vae.decode(latents, return_dict=False)[0]
-    image = image_processor.postprocess(image, output_type="pil")
-    image[0].save("split_transformer.png")
-```
-
-By selectively loading and unloading the models you need at a given stage and sharding the largest models across multiple GPUs, it is possible to run inference with large models on consumer GPUs.

docs/source/en/training/dreambooth.md

@@ -315,7 +315,7 @@ That's it! You don't need to add any additional parameters to your training comm
 <hfoption id="PyTorch">
 
 ```bash
-export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-v1-5"
+export MODEL_NAME="runwayml/stable-diffusion-v1-5"
 export INSTANCE_DIR="./dog"
 export OUTPUT_DIR="path_to_saved_model"
 
@@ -374,7 +374,7 @@ unet = UNet2DConditionModel.from_pretrained("path/to/model/checkpoint-100/unet")
 text_encoder = CLIPTextModel.from_pretrained("path/to/model/checkpoint-100/checkpoint-100/text_encoder")
 
 pipeline = DiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", unet=unet, text_encoder=text_encoder, dtype=torch.float16,
+    "runwayml/stable-diffusion-v1-5", unet=unet, text_encoder=text_encoder, dtype=torch.float16,
 ).to("cuda")
 
 image = pipeline("A photo of sks dog in a bucket", num_inference_steps=50, guidance_scale=7.5).images[0]

docs/source/en/training/lcm_distill.md

@@ -193,7 +193,7 @@ Now you're ready to launch the training script and start distilling!
 For this guide, you'll use the `--train_shards_path_or_url` to specify the path to the [Conceptual Captions 12M](https://github.com/google-research-datasets/conceptual-12m) dataset stored on the Hub [here](https://huggingface.co/datasets/laion/conceptual-captions-12m-webdataset). Set the `MODEL_DIR` environment variable to the name of the teacher model and `OUTPUT_DIR` to where you want to save the model.
 
 ```bash
-export MODEL_DIR="stable-diffusion-v1-5/stable-diffusion-v1-5"
+export MODEL_DIR="runwayml/stable-diffusion-v1-5"
 export OUTPUT_DIR="path/to/saved/model"
 
 accelerate launch train_lcm_distill_sd_wds.py \
@@ -225,7 +225,7 @@ from diffusers import UNet2DConditionModel, DiffusionPipeline, LCMScheduler
 import torch
 
 unet = UNet2DConditionModel.from_pretrained("your-username/your-model", torch_dtype=torch.float16, variant="fp16")
-pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", unet=unet, torch_dtype=torch.float16, variant="fp16")
+pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", unet=unet, torch_dtype=torch.float16, variant="fp16")
 
 pipeline.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
 pipeline.to("cuda")

docs/source/en/training/lora.md

@@ -184,7 +184,7 @@ A full training run takes ~5 hours on a 2080 Ti GPU with 11GB of VRAM.
 </Tip>
 
 ```bash
-export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-v1-5"
+export MODEL_NAME="runwayml/stable-diffusion-v1-5"
 export OUTPUT_DIR="/sddata/finetune/lora/naruto"
 export HUB_MODEL_ID="naruto-lora"
 export DATASET_NAME="lambdalabs/naruto-blip-captions"
@@ -218,7 +218,7 @@ Once training has been completed, you can use your model for inference:
 from diffusers import AutoPipelineForText2Image
 import torch
 
-pipeline = AutoPipelineForText2Image.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
+pipeline = AutoPipelineForText2Image.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
 pipeline.load_lora_weights("path/to/lora/model", weight_name="pytorch_lora_weights.safetensors")
 image = pipeline("A naruto with blue eyes").images[0]
 ```

docs/source/en/training/text2image.md

@@ -167,7 +167,7 @@ To train on a local dataset, set the `TRAIN_DIR` and `OUTPUT_DIR` environment va
 </Tip>
 
 ```bash
-export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-v1-5"
+export MODEL_NAME="runwayml/stable-diffusion-v1-5"
 export dataset_name="lambdalabs/naruto-blip-captions"
 
 accelerate launch --mixed_precision="fp16"  train_text_to_image.py \
@@ -201,7 +201,7 @@ To train on a local dataset, set the `TRAIN_DIR` and `OUTPUT_DIR` environment va
 </Tip>
 
 ```bash
-export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-v1-5"
+export MODEL_NAME="runwayml/stable-diffusion-v1-5"
 export dataset_name="lambdalabs/naruto-blip-captions"
 
 python train_text_to_image_flax.py \

docs/source/en/training/text_inversion.md

@@ -193,7 +193,7 @@ One more thing before you launch the script. If you're interested in following a
 <hfoption id="PyTorch">
 
 ```bash
-export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-v1-5"
+export MODEL_NAME="runwayml/stable-diffusion-v1-5"
 export DATA_DIR="./cat"
 
 accelerate launch textual_inversion.py \
@@ -248,7 +248,7 @@ After training is complete, you can use your newly trained model for inference l
 from diffusers import StableDiffusionPipeline
 import torch
 
-pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
+pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
 pipeline.load_textual_inversion("sd-concepts-library/cat-toy")
 image = pipeline("A <cat-toy> train", num_inference_steps=50).images[0]
 image.save("cat-train.png")

docs/source/en/tutorials/inference_with_big_models.md

@@ -90,8 +90,8 @@ from diffusers import DiffusionPipeline
 import torch
 
 pipeline = DiffusionPipeline.from_pretrained(
--    "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True,
-+    "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True, device_map="balanced"
+-    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True,
++    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True, device_map="balanced"
 )
 image = pipeline("a dog").images[0]
 image
@@ -105,7 +105,7 @@ import torch
 
 max_memory = {0:"1GB", 1:"1GB"}
 pipeline = DiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    "runwayml/stable-diffusion-v1-5",
     torch_dtype=torch.float16,
     use_safetensors=True,
     device_map="balanced",

docs/source/en/tutorials/using_peft_for_inference.md

@@ -75,12 +75,6 @@ image
 
 ![pixel-art](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/peft_integration/diffusers_peft_lora_inference_12_1.png)
 
-<Tip>
-
-By default, if the most up-to-date versions of PEFT and Transformers are detected, `low_cpu_mem_usage` is set to `True` to speed up the loading time of LoRA checkpoints. 
-
-</Tip>
-
 ## Merge adapters
 
 You can also merge different adapter checkpoints for inference to blend their styles together.

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

@@ -109,7 +109,7 @@ Now, you can pass the callback function to the `callback_on_step_end` parameter
 import torch
 from diffusers import StableDiffusionPipeline
 
-pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16)
+pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
 pipeline = pipeline.to("cuda")
 
 prompt = "a photo of an astronaut riding a horse on mars"
@@ -139,7 +139,7 @@ In this example, the diffusion process is stopped after 10 steps even though `nu
 ```python
 from diffusers import StableDiffusionPipeline
 
-pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
+pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
 pipeline.enable_model_cpu_offload()
 num_inference_steps = 50
 
@@ -171,13 +171,14 @@ def latents_to_rgb(latents):
     weights = (
         (60, -60, 25, -70),
         (60,  -5, 15, -50),
-        (60,  10, -5, -35),
+        (60,  10, -5, -35)
     )
 
     weights_tensor = torch.t(torch.tensor(weights, dtype=latents.dtype).to(latents.device))
     biases_tensor = torch.tensor((150, 140, 130), dtype=latents.dtype).to(latents.device)
     rgb_tensor = torch.einsum("...lxy,lr -> ...rxy", latents, weights_tensor) + biases_tensor.unsqueeze(-1).unsqueeze(-1)
-    image_array = rgb_tensor.clamp(0, 255).byte().cpu().numpy().transpose(1, 2, 0)
+    image_array = rgb_tensor.clamp(0, 255)[0].byte().cpu().numpy()
+    image_array = image_array.transpose(1, 2, 0)
 
     return Image.fromarray(image_array)
 ```
@@ -188,7 +189,7 @@ def latents_to_rgb(latents):
 def decode_tensors(pipe, step, timestep, callback_kwargs):
     latents = callback_kwargs["latents"]
 
-    image = latents_to_rgb(latents[0])
+    image = latents_to_rgb(latents)
     image.save(f"{step}.png")
 
     return callback_kwargs

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

@@ -1,120 +0,0 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-# CogVideoX
-
-CogVideoX is a text-to-video generation model focused on creating more coherent videos aligned with a prompt. It achieves this using several methods.
-
-- a 3D variational autoencoder that compresses videos spatially and temporally, improving compression rate and video accuracy.
-
-- an expert transformer block to help align text and video, and a 3D full attention module for capturing and creating spatially and temporally accurate videos.
- 
-
-
-## Load model checkpoints
-Model weights may be stored in separate subfolders on the Hub or locally, in which case, you should use the [`~DiffusionPipeline.from_pretrained`] method.
-
-
-```py
-from diffusers import CogVideoXPipeline, CogVideoXImageToVideoPipeline
-pipe = CogVideoXPipeline.from_pretrained(
-    "THUDM/CogVideoX-2b",
-    torch_dtype=torch.float16
-)
-
-pipe = CogVideoXImageToVideoPipeline.from_pretrained(
-    "THUDM/CogVideoX-5b-I2V",
-    torch_dtype=torch.bfloat16
-)
-
-```
-
-## Text-to-Video
-For text-to-video, pass a text prompt. By default, CogVideoX generates a 720x480 video for the best results.
-
-```py
-import torch
-from diffusers import CogVideoXPipeline
-from diffusers.utils import export_to_video
-
-prompt = "An elderly gentleman, with a serene expression, sits at the water's edge, a steaming cup of tea by his side. He is engrossed in his artwork, brush in hand, as he renders an oil painting on a canvas that's propped up against a small, weathered table. The sea breeze whispers through his silver hair, gently billowing his loose-fitting white shirt, while the salty air adds an intangible element to his masterpiece in progress. The scene is one of tranquility and inspiration, with the artist's canvas capturing the vibrant hues of the setting sun reflecting off the tranquil sea."
-
-pipe = CogVideoXPipeline.from_pretrained(
-    "THUDM/CogVideoX-5b",
-    torch_dtype=torch.bfloat16
-)
-
-pipe.enable_model_cpu_offload()
-pipe.vae.enable_tiling()
-
-video = pipe(
-    prompt=prompt,
-    num_videos_per_prompt=1,
-    num_inference_steps=50,
-    num_frames=49,
-    guidance_scale=6,
-    generator=torch.Generator(device="cuda").manual_seed(42),
-).frames[0]
-
-export_to_video(video, "output.mp4", fps=8)
-
-```
-
-
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cogvideox/cogvideox_out.gif" alt="generated image of an astronaut in a jungle"/>
-</div>
-
-
-## Image-to-Video
-
-
-You'll use the [THUDM/CogVideoX-5b-I2V](https://huggingface.co/THUDM/CogVideoX-5b-I2V)  checkpoint for this guide.
-
-```py
-import torch
-from diffusers import CogVideoXImageToVideoPipeline
-from diffusers.utils import export_to_video, load_image
-
-prompt = "A vast, shimmering ocean flows gracefully under a twilight sky, its waves undulating in a mesmerizing dance of blues and greens. The surface glints with the last rays of the setting sun, casting golden highlights that ripple across the water. Seagulls soar above, their cries blending with the gentle roar of the waves. The horizon stretches infinitely, where the ocean meets the sky in a seamless blend of hues. Close-ups reveal the intricate patterns of the waves, capturing the fluidity and dynamic beauty of the sea in motion."
-image = load_image(image="cogvideox_rocket.png")
-pipe = CogVideoXImageToVideoPipeline.from_pretrained(
-    "THUDM/CogVideoX-5b-I2V",
-    torch_dtype=torch.bfloat16
-)
- 
-pipe.vae.enable_tiling()
-pipe.vae.enable_slicing()
-
-video = pipe(
-    prompt=prompt,
-    image=image,
-    num_videos_per_prompt=1,
-    num_inference_steps=50,
-    num_frames=49,
-    guidance_scale=6,
-    generator=torch.Generator(device="cuda").manual_seed(42),
-).frames[0]
-
-export_to_video(video, "output.mp4", fps=8)
-```
-
-<div class="flex gap-4">
-  <div>
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cogvideox/cogvideox_rocket.png"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">initial image</figcaption>
-  </div>
-  <div>
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cogvideox/cogvideox_outrocket.gif"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">generated video</figcaption>
-  </div>
-</div>
-

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

@@ -33,7 +33,7 @@ from diffusers import AutoPipelineForText2Image
 import torch
 
 pipeline = AutoPipelineForText2Image.from_pretrained(
-	"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16"
+	"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16"
 ).to("cuda")
 ```
 
@@ -52,18 +52,18 @@ image
 
 ## Popular models
 
-The most common text-to-image models are [Stable Diffusion v1.5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5), [Stable Diffusion XL (SDXL)](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0), and [Kandinsky 2.2](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder). There are also ControlNet models or adapters that can be used with text-to-image models for more direct control in generating images. The results from each model are slightly different because of their architecture and training process, but no matter which model you choose, their usage is more or less the same. Let's use the same prompt for each model and compare their results.
+The most common text-to-image models are [Stable Diffusion v1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5), [Stable Diffusion XL (SDXL)](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0), and [Kandinsky 2.2](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder). There are also ControlNet models or adapters that can be used with text-to-image models for more direct control in generating images. The results from each model are slightly different because of their architecture and training process, but no matter which model you choose, their usage is more or less the same. Let's use the same prompt for each model and compare their results.
 
 ### Stable Diffusion v1.5
 
-[Stable Diffusion v1.5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) is a latent diffusion model initialized from [Stable Diffusion v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4), and finetuned for 595K steps on 512x512 images from the LAION-Aesthetics V2 dataset. You can use this model like:
+[Stable Diffusion v1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) is a latent diffusion model initialized from [Stable Diffusion v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4), and finetuned for 595K steps on 512x512 images from the LAION-Aesthetics V2 dataset. You can use this model like:
 
 ```py
 from diffusers import AutoPipelineForText2Image
 import torch
 
 pipeline = AutoPipelineForText2Image.from_pretrained(
-	"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16"
+	"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16"
 ).to("cuda")
 generator = torch.Generator("cuda").manual_seed(31)
 image = pipeline("Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", generator=generator).images[0]
@@ -106,7 +106,7 @@ image
 
 ### ControlNet
 
-ControlNet models are auxiliary models or adapters that are finetuned on top of text-to-image models, such as [Stable Diffusion v1.5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5). Using ControlNet models in combination with text-to-image models offers diverse options for more explicit control over how to generate an image. With ControlNet, you add an additional conditioning input image to the model. For example, if you provide an image of a human pose (usually represented as multiple keypoints that are connected into a skeleton) as a conditioning input, the model generates an image that follows the pose of the image. Check out the more in-depth [ControlNet](controlnet) guide to learn more about other conditioning inputs and how to use them.
+ControlNet models are auxiliary models or adapters that are finetuned on top of text-to-image models, such as [Stable Diffusion v1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5). Using ControlNet models in combination with text-to-image models offers diverse options for more explicit control over how to generate an image. With ControlNet, you add an additional conditioning input image to the model. For example, if you provide an image of a human pose (usually represented as multiple keypoints that are connected into a skeleton) as a conditioning input, the model generates an image that follows the pose of the image. Check out the more in-depth [ControlNet](controlnet) guide to learn more about other conditioning inputs and how to use them.
 
 In this example, let's condition the ControlNet with a human pose estimation image. Load the ControlNet model pretrained on human pose estimations:
 
@@ -125,7 +125,7 @@ Pass the `controlnet` to the [`AutoPipelineForText2Image`], and provide the prom
 
 ```py
 pipeline = AutoPipelineForText2Image.from_pretrained(
-	"stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, variant="fp16"
+	"runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, variant="fp16"
 ).to("cuda")
 generator = torch.Generator("cuda").manual_seed(31)
 image = pipeline("Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", image=pose_image, generator=generator).images[0]
@@ -164,7 +164,7 @@ from diffusers import AutoPipelineForText2Image
 import torch
 
 pipeline = AutoPipelineForText2Image.from_pretrained(
-	"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16"
+	"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16"
 ).to("cuda")
 image = pipeline(
 	"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", height=768, width=512
@@ -191,7 +191,7 @@ from diffusers import AutoPipelineForText2Image
 import torch
 
 pipeline = AutoPipelineForText2Image.from_pretrained(
-	"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16
+	"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
 ).to("cuda")
 image = pipeline(
 	"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", guidance_scale=3.5
@@ -223,7 +223,7 @@ from diffusers import AutoPipelineForText2Image
 import torch
 
 pipeline = AutoPipelineForText2Image.from_pretrained(
-	"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16
+	"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
 ).to("cuda")
 image = pipeline(
 	prompt="Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
@@ -254,7 +254,7 @@ from diffusers import AutoPipelineForText2Image
 import torch
 
 pipeline = AutoPipelineForText2Image.from_pretrained(
-	"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16
+	"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
 ).to("cuda")
 generator = torch.Generator(device="cuda").manual_seed(30)
 image = pipeline(
@@ -285,7 +285,7 @@ from diffusers import AutoPipelineForText2Image
 import torch
 
 pipeline = AutoPipelineForText2Image.from_pretrained(
-	"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16
+	"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
 ).to("cuda")
 image = pipeline(
 	prompt_embeds=prompt_embeds, # generated from Compel
@@ -309,7 +309,7 @@ PyTorch 2.0 also supports a more memory-efficient attention mechanism called [*s
 from diffusers import AutoPipelineForText2Image
 import torch
 
-pipeline = AutoPipelineForText2Image.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16").to("cuda")
+pipeline = AutoPipelineForText2Image.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16").to("cuda")
 pipeline.unet = torch.compile(pipeline.unet, mode="reduce-overhead", fullgraph=True)
 ```
 

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

@@ -84,7 +84,7 @@ import torch
 
 controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16, use_safetensors=True)
 pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
+    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
 )
 
 pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
@@ -144,7 +144,7 @@ import torch
 
 controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11f1p_sd15_depth", torch_dtype=torch.float16, use_safetensors=True)
 pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
+    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
 )
 
 pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
@@ -229,7 +229,7 @@ from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel,
 
 controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16, use_safetensors=True)
 pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
+    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
 )
 
 pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
@@ -277,7 +277,7 @@ from PIL import Image
 import cv2
 
 controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", use_safetensors=True)
-pipe = StableDiffusionControlNetPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, use_safetensors=True).to("cuda")
+pipe = StableDiffusionControlNetPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", controlnet=controlnet, use_safetensors=True).to("cuda")
 
 original_image = load_image("https://huggingface.co/takuma104/controlnet_dev/resolve/main/bird_512x512.png")
 
@@ -454,7 +454,7 @@ image = base(
 
 <Tip>
 
-Replace the SDXL model with a model like [stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) to use multiple conditioning inputs with Stable Diffusion models.
+Replace the SDXL model with a model like [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) to use multiple conditioning inputs with Stable Diffusion models.
 
 </Tip>
 

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

@@ -61,7 +61,7 @@ feature_extractor = CLIPImageProcessor.from_pretrained(clip_model_id)
 clip_model = CLIPModel.from_pretrained(clip_model_id)
 
 pipeline = DiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    "runwayml/stable-diffusion-v1-5",
     custom_pipeline="clip_guided_stable_diffusion",
     clip_model=clip_model,
     feature_extractor=feature_extractor,
@@ -78,7 +78,7 @@ Community pipelines can also be loaded from a local file if you pass a file path
 
 ```py
 pipeline = DiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    "runwayml/stable-diffusion-v1-5",
     custom_pipeline="./path/to/pipeline_directory/",
     clip_model=clip_model,
     feature_extractor=feature_extractor,
@@ -97,7 +97,7 @@ For example, to load from the main branch:
 
 ```py
 pipeline = DiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    "runwayml/stable-diffusion-v1-5",
     custom_pipeline="clip_guided_stable_diffusion",
     custom_revision="main",
     clip_model=clip_model,
@@ -113,7 +113,7 @@ For example, to load from a previous version of Diffusers like v0.25.0:
 
 ```py
 pipeline = DiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    "runwayml/stable-diffusion-v1-5",
     custom_pipeline="clip_guided_stable_diffusion",
     custom_revision="v0.25.0",
     clip_model=clip_model,
@@ -235,7 +235,7 @@ from diffusers import DiffusionPipeline, DDIMScheduler
 from diffusers.utils import load_image
 
 pipeline = DiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5-inpainting",
+    "Lykon/dreamshaper-8-inpainting",
     custom_pipeline="hd_painter"
 )
 pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)

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

@@ -30,7 +30,7 @@ import torch
 from diffusers import DiffusionPipeline
 
 pipeline = DiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, safety_checker=None
+    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, safety_checker=None
 ).to("cuda")
 pipeline.enable_freeu(s1=0.9, s2=0.2, b1=1.5, b2=1.6)
 generator = torch.Generator(device="cpu").manual_seed(33)

docs/source/en/using-diffusers/img2img.md

@@ -66,7 +66,7 @@ make_image_grid([init_image, image], rows=1, cols=2)
 
 ## Popular models
 
-The most popular image-to-image models are [Stable Diffusion v1.5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5), [Stable Diffusion XL (SDXL)](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0), and [Kandinsky 2.2](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder). The results from the Stable Diffusion and Kandinsky models vary due to their architecture differences and training process; you can generally expect SDXL to produce higher quality images than Stable Diffusion v1.5. Let's take a quick look at how to use each of these models and compare their results.
+The most popular image-to-image models are [Stable Diffusion v1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5), [Stable Diffusion XL (SDXL)](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0), and [Kandinsky 2.2](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder). The results from the Stable Diffusion and Kandinsky models vary due to their architecture differences and training process; you can generally expect SDXL to produce higher quality images than Stable Diffusion v1.5. Let's take a quick look at how to use each of these models and compare their results.
 
 ### Stable Diffusion v1.5
 
@@ -78,7 +78,7 @@ from diffusers import AutoPipelineForImage2Image
 from diffusers.utils import make_image_grid, load_image
 
 pipeline = AutoPipelineForImage2Image.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
@@ -203,7 +203,7 @@ from diffusers import AutoPipelineForImage2Image
 from diffusers.utils import make_image_grid, load_image
 
 pipeline = AutoPipelineForImage2Image.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
@@ -247,7 +247,7 @@ from diffusers import AutoPipelineForImage2Image
 from diffusers.utils import make_image_grid, load_image
 
 pipeline = AutoPipelineForImage2Image.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
@@ -334,7 +334,7 @@ import torch
 from diffusers.utils import make_image_grid
 
 pipeline = AutoPipelineForText2Image.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
@@ -370,7 +370,7 @@ from diffusers import AutoPipelineForImage2Image
 from diffusers.utils import make_image_grid, load_image
 
 pipeline = AutoPipelineForImage2Image.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
@@ -433,7 +433,7 @@ from diffusers import AutoPipelineForImage2Image
 from diffusers.utils import make_image_grid, load_image
 
 pipeline = AutoPipelineForImage2Image.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
@@ -499,7 +499,7 @@ from diffusers import AutoPipelineForImage2Image
 import torch
 
 pipeline = AutoPipelineForImage2Image.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
@@ -536,7 +536,7 @@ import torch
 
 controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11f1p_sd15_depth", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
 pipeline = AutoPipelineForImage2Image.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, variant="fp16", use_safetensors=True
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed

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

@@ -419,7 +419,7 @@ canny_image = Image.fromarray(image)
 
 controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
 pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    "runwayml/stable-diffusion-v1-5",
     controlnet=controlnet,
     torch_dtype=torch.float16,
     safety_checker=None,

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

@@ -35,7 +35,7 @@ This guide will show you how to perform inference with TCD-LoRAs for a variety o
 
 | Base model                                                                                      | TCD-LoRA checkpoint                                            |
 |-------------------------------------------------------------------------------------------------|----------------------------------------------------------------|
-| [stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5)                  | [TCD-SD15](https://huggingface.co/h1t/TCD-SD15-LoRA)           |
+| [stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)                  | [TCD-SD15](https://huggingface.co/h1t/TCD-SD15-LoRA)           |
 | [stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base)       | [TCD-SD21-base](https://huggingface.co/h1t/TCD-SD21-base-LoRA) |
 | [stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) | [TCD-SDXL](https://huggingface.co/h1t/TCD-SDXL-LoRA)           |
 

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

@@ -95,7 +95,7 @@ from diffusers import AutoPipelineForInpainting
 from diffusers.utils import load_image
 from PIL import Image
 
-pipeline = AutoPipelineForInpainting.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16).to('cuda')
+pipeline = AutoPipelineForInpainting.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to('cuda')
 
 mask = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/seashore_mask.png")
 blurred_mask = pipeline.mask_processor.blur(mask, blur_factor=33)
@@ -216,13 +216,12 @@ make_image_grid([init_image, mask_image, image], rows=1, cols=3)
 
 ## Non-inpaint specific checkpoints
 
-
-So far, this guide has used inpaint specific checkpoints such as [stable-diffusion-v1-5/stable-diffusion-inpainting](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting). But you can also use regular checkpoints like [stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5). Let's compare the results of the two checkpoints.
+So far, this guide has used inpaint specific checkpoints such as [runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting). But you can also use regular checkpoints like [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5). Let's compare the results of the two checkpoints.
 
 The image on the left is generated from a regular checkpoint, and the image on the right is from an inpaint checkpoint. You'll immediately notice the image on the left is not as clean, and you can still see the outline of the area the model is supposed to inpaint. The image on the right is much cleaner and the inpainted area appears more natural.
 
 <hfoptions id="regular-specific">
-<hfoption id="stable-diffusion-v1-5/stable-diffusion-v1-5">
+<hfoption id="runwayml/stable-diffusion-v1-5">
 
 ```py
 import torch
@@ -230,7 +229,7 @@ from diffusers import AutoPipelineForInpainting
 from diffusers.utils import load_image, make_image_grid
 
 pipeline = AutoPipelineForInpainting.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16"
+    "runwayml/stable-diffusion-v1-5", 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
@@ -277,7 +276,7 @@ make_image_grid([init_image, image], rows=1, cols=2)
 <div class="flex gap-4">
   <div>
     <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/non-inpaint-specific.png"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">stable-diffusion-v1-5/stable-diffusion-v1-5</figcaption>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">runwayml/stable-diffusion-v1-5</figcaption>
   </div>
   <div>
     <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint-specific.png"/>
@@ -288,7 +287,7 @@ make_image_grid([init_image, image], rows=1, cols=2)
 However, for more basic tasks like erasing an object from an image (like the rocks in the road for example), a regular checkpoint yields pretty good results. There isn't as noticeable of difference between the regular and inpaint checkpoint.
 
 <hfoptions id="inpaint">
-<hfoption id="stable-diffusion-v1-5/stable-diffusion-v1-5">
+<hfoption id="runwayml/stable-diffusion-v1-5">
 
 ```py
 import torch
@@ -296,7 +295,7 @@ from diffusers import AutoPipelineForInpainting
 from diffusers.utils import load_image, make_image_grid
 
 pipeline = AutoPipelineForInpainting.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16"
+    "runwayml/stable-diffusion-v1-5", 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
@@ -339,7 +338,7 @@ make_image_grid([init_image, image], rows=1, cols=2)
 <div class="flex gap-4">
   <div>
     <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/regular-inpaint-basic.png"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">stable-diffusion-v1-5/stable-diffusion-v1-5</figcaption>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">runwayml/stable-diffusion-v1-5</figcaption>
   </div>
   <div>
     <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/specific-inpaint-basic.png"/>
@@ -519,7 +518,7 @@ from diffusers.utils import load_image
 from PIL import Image
 
 generator = torch.Generator(device='cuda').manual_seed(0)
-pipeline = AutoPipelineForInpainting.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16).to('cuda')
+pipeline = AutoPipelineForInpainting.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to('cuda')
 
 base = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/seashore.png")
 mask = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/seashore_mask.png")
@@ -555,7 +554,7 @@ from diffusers import AutoPipelineForText2Image, AutoPipelineForInpainting
 from diffusers.utils import load_image, make_image_grid
 
 pipeline = AutoPipelineForText2Image.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed

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

@@ -380,7 +380,7 @@ from diffusers import StableDiffusionPipeline, DDIMScheduler
 from diffusers.utils import load_image
 
 pipeline = StableDiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    "runwayml/stable-diffusion-v1-5",
     torch_dtype=torch.float16,
 ).to("cuda")
 pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
@@ -421,7 +421,7 @@ from diffusers.utils import load_image
 from insightface.app import FaceAnalysis
 
 pipeline = StableDiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    "runwayml/stable-diffusion-v1-5",
     torch_dtype=torch.float16,
 ).to("cuda")
 pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
@@ -617,7 +617,7 @@ controlnet_model_path = "lllyasviel/control_v11f1p_sd15_depth"
 controlnet = ControlNetModel.from_pretrained(controlnet_model_path, torch_dtype=torch.float16)
 
 pipeline = StableDiffusionControlNetPipeline.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)
 pipeline.to("cuda")
 pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
 ```

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

@@ -41,7 +41,7 @@ The [`DiffusionPipeline`] class is a simple and generic way to load the latest t
 ```python
 from diffusers import DiffusionPipeline
 
-pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", use_safetensors=True)
+pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", use_safetensors=True)
 ```
 
 This same checkpoint can also be used for an image-to-image task. The [`DiffusionPipeline`] class can handle any task as long as you provide the appropriate inputs. For example, for an image-to-image task, you need to pass an initial image to the pipeline.
@@ -49,7 +49,7 @@ This same checkpoint can also be used for an image-to-image task. The [`Diffusio
 ```py
 from diffusers import DiffusionPipeline
 
-pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", use_safetensors=True)
+pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", use_safetensors=True)
 
 init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png")
 prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
@@ -64,7 +64,7 @@ Checkpoints can be loaded by their specific pipeline class if you already know i
 ```python
 from diffusers import StableDiffusionPipeline
 
-pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", use_safetensors=True)
+pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", use_safetensors=True)
 ```
 
 This same checkpoint may also be used for another task like image-to-image. To differentiate what task you want to use the checkpoint for, you have to use the corresponding task-specific pipeline class. For example, to use the same checkpoint for image-to-image, use the [`StableDiffusionImg2ImgPipeline`] class.
@@ -72,7 +72,7 @@ This same checkpoint may also be used for another task like image-to-image. To d
 ```py
 from diffusers import StableDiffusionImg2ImgPipeline
 
-pipeline = StableDiffusionImg2ImgPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", use_safetensors=True)
+pipeline = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", use_safetensors=True)
 ```
 
 </hfoption>
@@ -101,7 +101,7 @@ To load a pipeline locally, use [git-lfs](https://git-lfs.github.com/) to manual
 
 ```bash
 git-lfs install
-git clone https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5
+git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
 ```
 
 This creates a local folder, ./stable-diffusion-v1-5, on your disk and you should pass its path to [`~DiffusionPipeline.from_pretrained`].
@@ -311,7 +311,7 @@ Diffusers implements a [safety checker](https://github.com/huggingface/diffusers
 ```python
 from diffusers import DiffusionPipeline
 
-pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", safety_checker=None, use_safetensors=True)
+pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None, use_safetensors=True)
 """
 You have disabled the safety checker for <class 'diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline'> by passing `safety_checker=None`. Ensure that you abide by the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend keeping the safety filter enabled in all public-facing circumstances, disabling it only for use cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .
 """
@@ -341,7 +341,7 @@ There are two important arguments for loading variants:
 
   If you only set `torch_dtype=torch.float16`, the default fp32 weights are downloaded first and then converted to fp16.
 
-- `variant` specifies which files should be loaded from the repository. For example, if you want to load a non-EMA variant of a UNet from [stable-diffusion-v1-5/stable-diffusion-v1-5](https://hf.co/stable-diffusion-v1-5/stable-diffusion-v1-5/tree/main/unet), set `variant="non_ema"` to download the `non_ema` file.
+- `variant` specifies which files should be loaded from the repository. For example, if you want to load a non-EMA variant of a UNet from [runwayml/stable-diffusion-v1-5](https://hf.co/runwayml/stable-diffusion-v1-5/tree/main/unet), set `variant="non_ema"` to download the `non_ema` file.
 
 <hfoptions id="variants">
 <hfoption id="fp16">
@@ -351,7 +351,7 @@ from diffusers import DiffusionPipeline
 import torch
 
 pipeline = DiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", variant="fp16", torch_dtype=torch.float16, use_safetensors=True
+    "runwayml/stable-diffusion-v1-5", variant="fp16", torch_dtype=torch.float16, use_safetensors=True
 )
 ```
 
@@ -360,7 +360,7 @@ pipeline = DiffusionPipeline.from_pretrained(
 
 ```py
 pipeline = DiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", variant="non_ema", use_safetensors=True
+    "runwayml/stable-diffusion-v1-5", variant="non_ema", use_safetensors=True
 )
 ```
 
@@ -375,14 +375,14 @@ Use the `variant` parameter in the [`DiffusionPipeline.save_pretrained`] method
 ```python
 from diffusers import DiffusionPipeline
 
-pipeline.save_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", variant="fp16")
+pipeline.save_pretrained("runwayml/stable-diffusion-v1-5", variant="fp16")
 ```
 
 </hfoption>
 <hfoption id="non_ema">
 
 ```py
-pipeline.save_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", variant="non_ema")
+pipeline.save_pretrained("runwayml/stable-diffusion-v1-5", variant="non_ema")
 ```
 
 </hfoption>
@@ -408,12 +408,12 @@ As a class method, [`DiffusionPipeline.from_pretrained`] is responsible for two
 - Download the latest version of the folder structure required for inference and cache it. If the latest folder structure is available in the local cache, [`DiffusionPipeline.from_pretrained`] reuses the cache and won't redownload the files.
 - Load the cached weights into the correct pipeline [class](../api/pipelines/overview#diffusers-summary) - retrieved from the `model_index.json` file - and return an instance of it.
 
-The pipelines' underlying folder structure corresponds directly with their class instances. For example, the [`StableDiffusionPipeline`] corresponds to the folder structure in [`stable-diffusion-v1-5/stable-diffusion-v1-5`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5).
+The pipelines' underlying folder structure corresponds directly with their class instances. For example, the [`StableDiffusionPipeline`] corresponds to the folder structure in [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5).
 
 ```python
 from diffusers import DiffusionPipeline
 
-repo_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
+repo_id = "runwayml/stable-diffusion-v1-5"
 pipeline = DiffusionPipeline.from_pretrained(repo_id, use_safetensors=True)
 print(pipeline)
 ```
@@ -461,7 +461,7 @@ StableDiffusionPipeline {
 }
 ```
 
-Compare the components of the pipeline instance to the [`stable-diffusion-v1-5/stable-diffusion-v1-5`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/tree/main) folder structure, and you'll see there is a separate folder for each of the components in the repository:
+Compare the components of the pipeline instance to the [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5/tree/main) folder structure, and you'll see there is a separate folder for each of the components in the repository:
 
 ```
 .
@@ -524,7 +524,7 @@ CLIPTokenizer(
 )
 ```
 
-Every pipeline expects a [`model_index.json`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/model_index.json) file that tells the [`DiffusionPipeline`]:
+Every pipeline expects a [`model_index.json`](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/model_index.json) file that tells the [`DiffusionPipeline`]:
 
 - which pipeline class to load from `_class_name`
 - which version of 🧨 Diffusers was used to create the model in `_diffusers_version`

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

@@ -54,7 +54,7 @@ Because textual inversion creates embeddings, it cannot be used on its own like
 from diffusers import AutoPipelineForText2Image
 import torch
 
-pipeline = AutoPipelineForText2Image.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
+pipeline = AutoPipelineForText2Image.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
 ```
 
 Now you can load the textual inversion embeddings with the [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] method and generate some images. Let's load the [sd-concepts-library/gta5-artwork](https://huggingface.co/sd-concepts-library/gta5-artwork) embeddings and you'll need to include the special word `<gta5-artwork>` in your prompt to trigger it:
@@ -269,7 +269,7 @@ from diffusers import AutoPipelineForText2Image
 import torch
 from diffusers.utils import load_image
 
-pipeline = AutoPipelineForText2Image.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
+pipeline = AutoPipelineForText2Image.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
 ```
 
 Then load the IP-Adapter weights and add it to the pipeline with the [`~loaders.IPAdapterMixin.load_ip_adapter`] method.
@@ -350,7 +350,7 @@ image_encoder = CLIPVisionModelWithProjection.from_pretrained(
 )
 
 pipeline = AutoPipelineForText2Image.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    "runwayml/stable-diffusion-v1-5",
     image_encoder=image_encoder,
     torch_dtype=torch.float16
 ).to("cuda")

docs/source/en/using-diffusers/other-formats.md

@@ -37,7 +37,7 @@ Make sure you have the [Safetensors](https://hf.co/docs/safetensors) library ins
 
 Safetensors stores weights in a safetensors file. Diffusers loads safetensors files by default if they're available and the Safetensors library is installed. There are two ways safetensors files can be organized:
 
-1. Diffusers-multifolder layout: there may be several separate safetensors files, one for each pipeline component (text encoder, UNet, VAE), organized in subfolders (check out the [stable-diffusion-v1-5/stable-diffusion-v1-5](https://hf.co/stable-diffusion-v1-5/stable-diffusion-v1-5/tree/main) repository as an example)
+1. Diffusers-multifolder layout: there may be several separate safetensors files, one for each pipeline component (text encoder, UNet, VAE), organized in subfolders (check out the [runwayml/stable-diffusion-v1-5](https://hf.co/runwayml/stable-diffusion-v1-5/tree/main) repository as an example)
 2. single-file layout: all the model weights may be saved in a single file (check out the [WarriorMama777/OrangeMixs](https://hf.co/WarriorMama777/OrangeMixs/tree/main/Models/AbyssOrangeMix) repository as an example)
 
 <hfoptions id="safetensors">
@@ -49,7 +49,7 @@ Use the [`~DiffusionPipeline.from_pretrained`] method to load a model with safet
 from diffusers import DiffusionPipeline
 
 pipeline = DiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    "runwayml/stable-diffusion-v1-5",
     use_safetensors=True
 )
 ```
@@ -118,7 +118,7 @@ Use the [`~loaders.FromSingleFileMixin.from_single_file`] method to directly loa
 from diffusers import StableDiffusionPipeline
 
 pipeline = StableDiffusionPipeline.from_single_file(
-    "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/v1-5-pruned.ckpt"
+    "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned.ckpt"
 )
 ```
 
@@ -314,7 +314,7 @@ Or you could use a ControlNet model in the pipeline.
 ```py
 from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
 
-ckpt_path = "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors"
+ckpt_path = "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors"
 controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny")
 pipeline = StableDiffusionControlNetPipeline.from_single_file(ckpt_path, controlnet=controlnet)
 ```

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

@@ -121,7 +121,7 @@ Now when you run the same pipeline twice, you'll get identical results.
 import torch
 from diffusers import DDIMScheduler, StableDiffusionPipeline
 
-pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", use_safetensors=True).to("cuda")
+pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", use_safetensors=True).to("cuda")
 pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
 g = torch.Generator(device="cuda")
 
@@ -141,7 +141,7 @@ print("L_inf dist =", abs(result1 - result2).max())
 
 A practical application of creating reproducible pipelines is *deterministic batch generation*. You generate a batch of images and select one image to improve with a more detailed prompt. The main idea is to pass a list of [Generator's](https://pytorch.org/docs/stable/generated/torch.Generator.html) to the pipeline and tie each `Generator` to a seed so you can reuse it.
 
-Let's use the [stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) checkpoint and generate a batch of images.
+Let's use the [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) checkpoint and generate a batch of images.
 
 ```py
 import torch
@@ -149,7 +149,7 @@ from diffusers import DiffusionPipeline
 from diffusers.utils import make_image_grid
 
 pipeline = DiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
+    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
 )
 pipeline = pipeline.to("cuda")
 ```

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

@@ -16,14 +16,14 @@ specific language governing permissions and limitations under the License.
 
 Diffusion pipelines are a collection of interchangeable schedulers and models that can be mixed and matched to tailor a pipeline to a specific use case. The scheduler encapsulates the entire denoising process such as the number of denoising steps and the algorithm for finding the denoised sample. A scheduler is not parameterized or trained so they don't take very much memory. The model is usually only concerned with the forward pass of going from a noisy input to a less noisy sample.
 
-This guide will show you how to load schedulers and models to customize a pipeline. You'll use the [stable-diffusion-v1-5/stable-diffusion-v1-5](https://hf.co/stable-diffusion-v1-5/stable-diffusion-v1-5) checkpoint throughout this guide, so let's load it first.
+This guide will show you how to load schedulers and models to customize a pipeline. You'll use the [runwayml/stable-diffusion-v1-5](https://hf.co/runwayml/stable-diffusion-v1-5) checkpoint throughout this guide, so let's load it first.
 
 ```py
 import torch
 from diffusers import DiffusionPipeline
 
 pipeline = DiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
+    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
 ).to("cuda")
 ```
 
@@ -56,14 +56,14 @@ For example, to load the [`DDIMScheduler`]:
 ```py
 from diffusers import DDIMScheduler, DiffusionPipeline
 
-ddim = DDIMScheduler.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="scheduler")
+ddim = DDIMScheduler.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="scheduler")
 ```
 
 Then you can pass the newly loaded scheduler to the pipeline.
 
 ```python
 pipeline = DiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", scheduler=ddim, torch_dtype=torch.float16, use_safetensors=True
+    "runwayml/stable-diffusion-v1-5", scheduler=ddim, torch_dtype=torch.float16, use_safetensors=True
 ).to("cuda")
 ```
 
@@ -78,7 +78,7 @@ import torch
 from diffusers import DiffusionPipeline
 
 pipeline = DiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
+    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
 ).to("cuda")
 
 prompt = "A photograph of an astronaut riding a horse on Mars, high resolution, high definition."
@@ -180,11 +180,11 @@ from flax.training.common_utils import shard
 from diffusers import FlaxStableDiffusionPipeline, FlaxDPMSolverMultistepScheduler
 
 scheduler, scheduler_state = FlaxDPMSolverMultistepScheduler.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    "runwayml/stable-diffusion-v1-5",
     subfolder="scheduler"
 )
 pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    "runwayml/stable-diffusion-v1-5",
     scheduler=scheduler,
     variant="bf16",
     dtype=jax.numpy.bfloat16,
@@ -216,12 +216,12 @@ images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images
 
 Models are loaded from the [`ModelMixin.from_pretrained`] method, which downloads and caches the latest version of the model weights and configurations. If the latest files are available in the local cache, [`~ModelMixin.from_pretrained`] reuses files in the cache instead of re-downloading them.
 
-Models can be loaded from a subfolder with the `subfolder` argument. For example, the model weights for [stable-diffusion-v1-5/stable-diffusion-v1-5](https://hf.co/stable-diffusion-v1-5/stable-diffusion-v1-5) are stored in the [unet](https://hf.co/stable-diffusion-v1-5/stable-diffusion-v1-5/tree/main/unet) subfolder.
+Models can be loaded from a subfolder with the `subfolder` argument. For example, the model weights for [runwayml/stable-diffusion-v1-5](https://hf.co/runwayml/stable-diffusion-v1-5) are stored in the [unet](https://hf.co/runwayml/stable-diffusion-v1-5/tree/main/unet) subfolder.
 
 ```python
 from diffusers import UNet2DConditionModel
 
-unet = UNet2DConditionModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="unet", use_safetensors=True)
+unet = UNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet", use_safetensors=True)
 ```
 
 They can also be directly loaded from a [repository](https://huggingface.co/google/ddpm-cifar10-32/tree/main).
@@ -238,7 +238,7 @@ To load and save model variants, specify the `variant` argument in [`ModelMixin.
 from diffusers import UNet2DConditionModel
 
 unet = UNet2DConditionModel.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="unet", variant="non_ema", use_safetensors=True
+    "runwayml/stable-diffusion-v1-5", subfolder="unet", variant="non_ema", use_safetensors=True
 )
 unet.save_pretrained("./local-unet", variant="non_ema")
 ```

docs/source/en/using-diffusers/t2i_adapter.md

@@ -73,7 +73,7 @@ from diffusers import StableDiffusionAdapterPipeline, T2IAdapter
 
 adapter = T2IAdapter.from_pretrained("TencentARC/t2iadapter_canny_sd15v2", torch_dtype=torch.float16)
 pipeline = StableDiffusionAdapterPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    "runwayml/stable-diffusion-v1-5",
     adapter=adapter,
     torch_dtype=torch.float16,
 )

docs/source/en/using-diffusers/text-img2vid.md

@@ -23,59 +23,6 @@ This guide will show you how to generate videos, how to configure video model pa
 
 [Stable Video Diffusions (SVD)](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid), [I2VGen-XL](https://huggingface.co/ali-vilab/i2vgen-xl/), [AnimateDiff](https://huggingface.co/guoyww/animatediff), and [ModelScopeT2V](https://huggingface.co/ali-vilab/text-to-video-ms-1.7b) are popular models used for video diffusion. Each model is distinct. For example, AnimateDiff inserts a motion modeling module into a frozen text-to-image model to generate personalized animated images, whereas SVD is entirely pretrained from scratch with a three-stage training process to generate short high-quality videos.
 
-[CogVideoX](https://huggingface.co/collections/THUDM/cogvideo-66c08e62f1685a3ade464cce) is another popular video generation model. The model is a multidimensional transformer that integrates text, time, and space. It employs full attention in the attention module and includes an expert block at the layer level to spatially align text and video.
-
-### CogVideoX
- 
-[CogVideoX](../api/pipelines/cogvideox) uses a 3D Variational Autoencoder (VAE) to compress videos along the spatial and temporal dimensions.
-
-Begin by loading the [`CogVideoXPipeline`] and passing an initial text or image to generate a video.
-<Tip>
-
-CogVideoX is available for image-to-video and text-to-video. [THUDM/CogVideoX-5b-I2V](https://huggingface.co/THUDM/CogVideoX-5b-I2V) uses the [`CogVideoXImageToVideoPipeline`] for image-to-video. [THUDM/CogVideoX-5b](https://huggingface.co/THUDM/CogVideoX-5b) and [THUDM/CogVideoX-2b](https://huggingface.co/THUDM/CogVideoX-2b) are available for text-to-video with the [`CogVideoXPipeline`].
- 
-</Tip>
-
-```py
-import torch
-from diffusers import CogVideoXImageToVideoPipeline
-from diffusers.utils import export_to_video, load_image
-
-prompt = "A vast, shimmering ocean flows gracefully under a twilight sky, its waves undulating in a mesmerizing dance of blues and greens. The surface glints with the last rays of the setting sun, casting golden highlights that ripple across the water. Seagulls soar above, their cries blending with the gentle roar of the waves. The horizon stretches infinitely, where the ocean meets the sky in a seamless blend of hues. Close-ups reveal the intricate patterns of the waves, capturing the fluidity and dynamic beauty of the sea in motion."
-image = load_image(image="cogvideox_rocket.png")
-pipe = CogVideoXImageToVideoPipeline.from_pretrained(
-    "THUDM/CogVideoX-5b-I2V",
-    torch_dtype=torch.bfloat16
-)
- 
-pipe.vae.enable_tiling()
-pipe.vae.enable_slicing()
-
-video = pipe(
-    prompt=prompt,
-    image=image,
-    num_videos_per_prompt=1,
-    num_inference_steps=50,
-    num_frames=49,
-    guidance_scale=6,
-    generator=torch.Generator(device="cuda").manual_seed(42),
-).frames[0]
-
-export_to_video(video, "output.mp4", fps=8)
-```
-
-<div class="flex gap-4">
-  <div>
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cogvideox/cogvideox_rocket.png"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">initial image</figcaption>
-  </div>
-  <div>
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cogvideox/cogvideox_outrocket.gif"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">generated video</figcaption>
-  </div>
-</div>
-
- 
 ### Stable Video Diffusion
 
 [SVD](../api/pipelines/svd) is based on the Stable Diffusion 2.1 model and it is trained on images, then low-resolution videos, and finally a smaller dataset of high-resolution videos. This model generates a short 2-4 second video from an initial image. You can learn more details about model, like micro-conditioning, in the [Stable Video Diffusion](../using-diffusers/svd) guide.
@@ -418,7 +365,7 @@ import torch
 from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
 from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor
 
-model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
+model_id = "runwayml/stable-diffusion-v1-5"
 controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16)
 pipeline = StableDiffusionControlNetPipeline.from_pretrained(
     model_id, controlnet=controlnet, torch_dtype=torch.float16
@@ -464,7 +411,7 @@ import torch
 from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
 from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor
 
-model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
+model_id = "runwayml/stable-diffusion-v1-5"
 controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
 pipeline = StableDiffusionControlNetPipeline.from_pretrained(
     model_id, controlnet=controlnet, torch_dtype=torch.float16