update

.github/workflows/benchmark.yml

@@ -13,13 +13,13 @@ env:
 
 jobs:
   torch_pipelines_cuda_benchmark_tests:
-    env:
+    env: 
       SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_BENCHMARK }}
     name: Torch Core Pipelines CUDA Benchmarking Tests
     strategy:
       fail-fast: false
       max-parallel: 1
-    runs-on:
+    runs-on: 
       group: aws-g6-4xlarge-plus
     container:
       image: diffusers/diffusers-pytorch-compile-cuda
@@ -50,7 +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
@@ -59,7 +59,7 @@ jobs:
         if: ${{ success() }}
         run: |
           pip install requests && python utils/notify_benchmarking_status.py --status=success
-
+        
       - name: Report failure status
         if: ${{ failure() }}
         run: |

.github/workflows/mirror_community_pipeline.yml

@@ -24,7 +24,7 @@ jobs:
   mirror_community_pipeline:
     env:
       SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_COMMUNITY_MIRROR }}
-
+    
     runs-on: ubuntu-latest
     steps:
       # Checkout to correct ref
@@ -95,7 +95,7 @@ jobs:
         if: ${{ success() }}
         run: |
           pip install requests && python utils/notify_community_pipelines_mirror.py --status=success
-
+      
       - name: Report failure status
         if: ${{ failure() }}
         run: |

.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
@@ -116,7 +116,6 @@ jobs:
       run:
         shell: bash
     strategy:
-      fail-fast: false
       max-parallel: 2
       matrix:
         module: [models, schedulers, lora, others, single_file, examples]
@@ -130,8 +129,8 @@ 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 +138,7 @@ 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 +151,7 @@ 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 +168,7 @@ 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 +200,7 @@ 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 +208,7 @@ 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 +224,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v2
       with:
         name: flax_tpu_test_reports
         path: reports
@@ -257,14 +256,14 @@ jobs:
       run: |
         python -m venv /opt/venv && export PATH="/opt/venv/bin:$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 +279,9 @@ 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
@@ -291,118 +290,64 @@ jobs:
         pip install slack_sdk tabulate
         python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
 
-# M1 runner currently not well supported
-# TODO: (Dhruv) add these back when we setup better testing for Apple Silicon
-#  run_nightly_tests_apple_m1:
-#    name: Nightly PyTorch MPS tests on MacOS
-#    runs-on: [ self-hosted, apple-m1 ]
-#    if: github.event_name == 'schedule'
-#
-#    steps:
-#      - name: Checkout diffusers
-#        uses: actions/checkout@v3
-#        with:
-#          fetch-depth: 2
-#
-#      - name: Clean checkout
-#        shell: arch -arch arm64 bash {0}
-#        run: |
-#          git clean -fxd
-#      - name: Setup miniconda
-#        uses: ./.github/actions/setup-miniconda
-#        with:
-#          python-version: 3.9
-#
-#      - name: Install dependencies
-#        shell: arch -arch arm64 bash {0}
-#        run: |
-#          ${CONDA_RUN} python -m pip install --upgrade pip uv
-#          ${CONDA_RUN} python -m uv pip install -e [quality,test]
-#          ${CONDA_RUN} python -m uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
-#          ${CONDA_RUN} python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate
-#          ${CONDA_RUN} python -m uv pip install pytest-reportlog
-#      - name: Environment
-#        shell: arch -arch arm64 bash {0}
-#        run: |
-#          ${CONDA_RUN} python utils/print_env.py
-#      - name: Run nightly PyTorch tests on M1 (MPS)
-#        shell: arch -arch arm64 bash {0}
-#        env:
-#          HF_HOME: /System/Volumes/Data/mnt/cache
-#          HF_TOKEN: ${{ secrets.HF_TOKEN }}
-#        run: |
-#          ${CONDA_RUN} python -m pytest -n 1 -s -v --make-reports=tests_torch_mps \
-#            --report-log=tests_torch_mps.log \
-#            tests/
-#      - name: Failure short reports
-#        if: ${{ failure() }}
-#        run: cat reports/tests_torch_mps_failures_short.txt
-#
-#      - name: Test suite reports artifacts
-#        if: ${{ always() }}
-#        uses: actions/upload-artifact@v4
-#        with:
-#          name: torch_mps_test_reports
-#          path: reports
-#
-#      - name: Generate Report and Notify Channel
-#        if: always()
-#        run: |
-#          pip install slack_sdk tabulate
-#          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY  run_nightly_tests_apple_m1:
-#    name: Nightly PyTorch MPS tests on MacOS
-#    runs-on: [ self-hosted, apple-m1 ]
-#    if: github.event_name == 'schedule'
-#
-#    steps:
-#      - name: Checkout diffusers
-#        uses: actions/checkout@v3
-#        with:
-#          fetch-depth: 2
-#
-#      - name: Clean checkout
-#        shell: arch -arch arm64 bash {0}
-#        run: |
-#          git clean -fxd
-#      - name: Setup miniconda
-#        uses: ./.github/actions/setup-miniconda
-#        with:
-#          python-version: 3.9
-#
-#      - name: Install dependencies
-#        shell: arch -arch arm64 bash {0}
-#        run: |
-#          ${CONDA_RUN} python -m pip install --upgrade pip uv
-#          ${CONDA_RUN} python -m uv pip install -e [quality,test]
-#          ${CONDA_RUN} python -m uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
-#          ${CONDA_RUN} python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate
-#          ${CONDA_RUN} python -m uv pip install pytest-reportlog
-#      - name: Environment
-#        shell: arch -arch arm64 bash {0}
-#        run: |
-#          ${CONDA_RUN} python utils/print_env.py
-#      - name: Run nightly PyTorch tests on M1 (MPS)
-#        shell: arch -arch arm64 bash {0}
-#        env:
-#          HF_HOME: /System/Volumes/Data/mnt/cache
-#          HF_TOKEN: ${{ secrets.HF_TOKEN }}
-#        run: |
-#          ${CONDA_RUN} python -m pytest -n 1 -s -v --make-reports=tests_torch_mps \
-#            --report-log=tests_torch_mps.log \
-#            tests/
-#      - name: Failure short reports
-#        if: ${{ failure() }}
-#        run: cat reports/tests_torch_mps_failures_short.txt
-#
-#      - name: Test suite reports artifacts
-#        if: ${{ always() }}
-#        uses: actions/upload-artifact@v4
-#        with:
-#          name: torch_mps_test_reports
-#          path: reports
-#
-#      - name: Generate Report and Notify Channel
-#        if: always()
-#        run: |
-#          pip install slack_sdk tabulate
-#          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
+  run_nightly_tests_apple_m1:
+    name: Nightly PyTorch MPS tests on MacOS
+    runs-on: [ self-hosted, apple-m1 ]
+    if: github.event_name == 'schedule'
+
+    steps:
+      - name: Checkout diffusers
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 2
+
+      - name: Clean checkout
+        shell: arch -arch arm64 bash {0}
+        run: |
+          git clean -fxd
+
+      - name: Setup miniconda
+        uses: ./.github/actions/setup-miniconda
+        with:
+          python-version: 3.9
+
+      - name: Install dependencies
+        shell: arch -arch arm64 bash {0}
+        run: |
+          ${CONDA_RUN} python -m pip install --upgrade pip uv
+          ${CONDA_RUN} python -m uv pip install -e [quality,test]
+          ${CONDA_RUN} python -m uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
+          ${CONDA_RUN} python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate
+          ${CONDA_RUN} python -m uv pip install pytest-reportlog
+
+      - name: Environment
+        shell: arch -arch arm64 bash {0}
+        run: |
+          ${CONDA_RUN} python utils/print_env.py
+
+      - name: Run nightly PyTorch tests on M1 (MPS)
+        shell: arch -arch arm64 bash {0}
+        env:
+          HF_HOME: /System/Volumes/Data/mnt/cache
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: |
+          ${CONDA_RUN} python -m pytest -n 1 -s -v --make-reports=tests_torch_mps \
+            --report-log=tests_torch_mps.log \
+            tests/
+
+      - name: Failure short reports
+        if: ${{ failure() }}
+        run: cat reports/tests_torch_mps_failures_short.txt
+
+      - name: Test suite reports artifacts
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v2
+        with:
+          name: torch_mps_test_reports
+          path: reports
+
+      - name: Generate Report and Notify Channel
+        if: always()
+        run: |
+          pip install slack_sdk tabulate
+          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY

.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

@@ -95,7 +95,7 @@ jobs:
         if [ "${{ matrix.lib-versions }}" == "main" ]; then
             python -m pip install -U peft@git+https://github.com/huggingface/peft.git
             python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git
-            pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+            python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
         else
             python -m uv pip install -U peft transformers accelerate
         fi
@@ -110,23 +110,23 @@ jobs:
         python -m venv /opt/venv && export PATH="/opt/venv/bin:$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

@@ -168,9 +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:
@@ -229,7 +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/push_tests.yml

@@ -1,7 +1,6 @@
-name: Fast GPU Tests on main
+name: Slow Tests on main
 
 on:
-  workflow_dispatch:
   push:
     branches:
       - main
@@ -46,7 +45,7 @@ jobs:
           echo "pipeline_test_matrix=$matrix" >> $GITHUB_OUTPUT
       - name: Pipeline Tests Artifacts
         if: ${{ always() }}
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v2
         with:
           name: test-pipelines.json
           path: reports
@@ -76,7 +75,7 @@ jobs:
         run: |
           python -m venv /opt/venv && export PATH="/opt/venv/bin:$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
@@ -97,7 +96,7 @@ 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
@@ -113,8 +112,6 @@ jobs:
       run:
         shell: bash
     strategy:
-      fail-fast: false
-      max-parallel: 2
       matrix:
         module: [models, schedulers, lora, others, single_file]
     steps:
@@ -127,8 +124,8 @@ 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: |
@@ -142,20 +139,20 @@ 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:
@@ -177,7 +174,7 @@ 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: |
@@ -200,7 +197,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v2
       with:
         name: flax_tpu_test_reports
         path: reports
@@ -225,7 +222,7 @@ jobs:
       run: |
         python -m venv /opt/venv && export PATH="/opt/venv/bin:$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: |
@@ -248,7 +245,7 @@ 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
@@ -291,7 +288,7 @@ 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
@@ -333,7 +330,7 @@ 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
@@ -384,7 +381,7 @@ 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

@@ -119,7 +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

@@ -69,7 +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/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

@@ -9,9 +9,6 @@ jobs:
     name: Close Stale Issues
     if: github.repository == 'huggingface/diffusers'
     runs-on: ubuntu-latest
-    permissions:
-      issues: write
-      pull-requests: write
     env:
       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
     steps:

CONTRIBUTING.md

@@ -57,13 +57,13 @@ Any question or comment related to the Diffusers library can be asked on the [di
 - ...
 
 Every question that is asked on the forum or on Discord actively encourages the community to publicly
-share knowledge and might very well help a beginner in the future who has the same question you're
+share knowledge and might very well help a beginner in the future that has the same question you're
 having. Please do pose any questions you might have.
 In the same spirit, you are of immense help to the community by answering such questions because this way you are publicly documenting knowledge for everybody to learn from.
 
 **Please** keep in mind that the more effort you put into asking or answering a question, the higher
 the quality of the publicly documented knowledge. In the same way, well-posed and well-answered questions create a high-quality knowledge database accessible to everybody, while badly posed questions or answers reduce the overall quality of the public knowledge database.
-In short, a high quality question or answer is *precise*, *concise*, *relevant*, *easy-to-understand*, *accessible*, and *well-formatted/well-posed*. For more information, please have a look through the [How to write a good issue](#how-to-write-a-good-issue) section.
+In short, a high quality question or answer is *precise*, *concise*, *relevant*, *easy-to-understand*, *accessible*, and *well-formated/well-posed*. For more information, please have a look through the [How to write a good issue](#how-to-write-a-good-issue) section.
 
 **NOTE about channels**:
 [*The forum*](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) is much better indexed by search engines, such as Google. Posts are ranked by popularity rather than chronologically. Hence, it's easier to look up questions and answers that we posted some time ago.
@@ -503,4 +503,4 @@ $ git push --set-upstream origin your-branch-for-syncing
 
 ### Style guide
 
-For documentation strings, 🧨 Diffusers follows the [Google style](https://google.github.io/styleguide/pyguide.html).
+For documentation strings, 🧨 Diffusers follows the [Google style](https://google.github.io/styleguide/pyguide.html).

PHILOSOPHY.md

@@ -15,7 +15,7 @@ specific language governing permissions and limitations under the License.
 🧨 Diffusers provides **state-of-the-art** pretrained diffusion models across multiple modalities.
 Its purpose is to serve as a **modular toolbox** for both inference and training.
 
-We aim to build a library that stands the test of time and therefore take API design very seriously.
+We aim at building a library that stands the test of time and therefore take API design very seriously.
 
 In a nutshell, Diffusers is built to be a natural extension of PyTorch. Therefore, most of our design choices are based on [PyTorch's Design Principles](https://pytorch.org/docs/stable/community/design.html#pytorch-design-philosophy). Let's go over the most important ones:
 
@@ -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.
@@ -107,4 +107,4 @@ The following design principles are followed:
 - Every scheduler exposes the timesteps to be "looped over" via a `timesteps` attribute, which is an array of timesteps the model will be called upon.
 - The `step(...)` function takes a predicted model output and the "current" sample (x_t) and returns the "previous", slightly more denoised sample (x_t-1).
 - Given the complexity of diffusion schedulers, the `step` function does not expose all the complexity and can be a bit of a "black box".
-- In almost all cases, novel schedulers shall be implemented in a new scheduling file.
+- In almost all cases, novel schedulers shall be implemented in a new scheduling file.

README.md

@@ -67,13 +67,13 @@ Please refer to the [How to use Stable Diffusion in Apple Silicon](https://huggi
 
 ## Quickstart
 
-Generating outputs is super easy with 🤗 Diffusers. To generate an image from text, use the `from_pretrained` method to load any pretrained diffusion model (browse the [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) for 30,000+ checkpoints):
+Generating outputs is super easy with 🤗 Diffusers. To generate an image from text, use the `from_pretrained` method to load any pretrained diffusion model (browse the [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) for 27.000+ checkpoints):
 
 ```python
 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>
@@ -202,7 +202,6 @@ Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz9
 
 - https://github.com/microsoft/TaskMatrix
 - https://github.com/invoke-ai/InvokeAI
-- https://github.com/InstantID/InstantID
 - https://github.com/apple/ml-stable-diffusion
 - https://github.com/Sanster/lama-cleaner
 - https://github.com/IDEA-Research/Grounded-Segment-Anything
@@ -210,7 +209,7 @@ Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz9
 - https://github.com/deep-floyd/IF
 - https://github.com/bentoml/BentoML
 - https://github.com/bmaltais/kohya_ss
-- +14,000 other amazing GitHub repositories 💪
+- +12.000 other amazing GitHub repositories 💪
 
 Thank you for using us ❤️.
 

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)

docs/source/en/_toctree.yml

@@ -161,8 +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
@@ -192,10 +190,6 @@
   - local: conceptual/evaluation
     title: Evaluating Diffusion Models
   title: Conceptual Guides
-- sections:
-  - local: community_projects
-    title: Projects built with Diffusers
-  title: Community Projects
 - sections:
   - isExpanded: false
     sections:
@@ -225,78 +219,60 @@
     sections:
     - local: api/models/overview
       title: Overview
-    - sections:
-      - local: api/models/controlnet
-        title: ControlNetModel
-      - local: api/models/controlnet_flux
-        title: FluxControlNetModel
-      - local: api/models/controlnet_hunyuandit
-        title: HunyuanDiT2DControlNetModel
-      - local: api/models/controlnet_sd3
-        title: SD3ControlNetModel
-      - local: api/models/controlnet_sparsectrl
-        title: SparseControlNetModel
-      title: ControlNets
-    - sections:
-      - local: api/models/aura_flow_transformer2d
-        title: AuraFlowTransformer2DModel
-      - local: api/models/cogvideox_transformer3d
-        title: CogVideoXTransformer3DModel
-      - local: api/models/dit_transformer2d
-        title: DiTTransformer2DModel
-      - local: api/models/flux_transformer
-        title: FluxTransformer2DModel
-      - local: api/models/hunyuan_transformer2d
-        title: HunyuanDiT2DModel
-      - local: api/models/latte_transformer3d
-        title: LatteTransformer3DModel
-      - local: api/models/lumina_nextdit2d
-        title: LuminaNextDiT2DModel
-      - local: api/models/pixart_transformer2d
-        title: PixArtTransformer2DModel
-      - local: api/models/prior_transformer
-        title: PriorTransformer
-      - local: api/models/sd3_transformer2d
-        title: SD3Transformer2DModel
-      - local: api/models/stable_audio_transformer
-        title: StableAudioDiTModel
-      - local: api/models/transformer2d
-        title: Transformer2DModel
-      - local: api/models/transformer_temporal
-        title: TransformerTemporalModel
-      title: Transformers
-    - sections:
-      - local: api/models/stable_cascade_unet
-        title: StableCascadeUNet
-      - local: api/models/unet
-        title: UNet1DModel
-      - local: api/models/unet2d
-        title: UNet2DModel
-      - local: api/models/unet2d-cond
-        title: UNet2DConditionModel
-      - local: api/models/unet3d-cond
-        title: UNet3DConditionModel
-      - local: api/models/unet-motion
-        title: UNetMotionModel
-      - local: api/models/uvit2d
-        title: UViT2DModel
-      title: UNets
-    - sections:
-      - local: api/models/autoencoderkl
-        title: AutoencoderKL
-      - local: api/models/autoencoderkl_cogvideox
-        title: AutoencoderKLCogVideoX
-      - local: api/models/asymmetricautoencoderkl
-        title: AsymmetricAutoencoderKL
-      - local: api/models/consistency_decoder_vae
-        title: ConsistencyDecoderVAE
-      - local: api/models/autoencoder_oobleck
-        title: Oobleck AutoEncoder
-      - local: api/models/autoencoder_tiny
-        title: Tiny AutoEncoder
-      - local: api/models/vq
-        title: VQModel
-      title: VAEs
+    - local: api/models/unet
+      title: UNet1DModel
+    - local: api/models/unet2d
+      title: UNet2DModel
+    - local: api/models/unet2d-cond
+      title: UNet2DConditionModel
+    - local: api/models/unet3d-cond
+      title: UNet3DConditionModel
+    - local: api/models/unet-motion
+      title: UNetMotionModel
+    - local: api/models/uvit2d
+      title: UViT2DModel
+    - local: api/models/vq
+      title: VQModel
+    - local: api/models/autoencoderkl
+      title: AutoencoderKL
+    - local: api/models/asymmetricautoencoderkl
+      title: AsymmetricAutoencoderKL
+    - local: api/models/autoencoder_tiny
+      title: Tiny AutoEncoder
+    - local: api/models/autoencoder_oobleck
+      title: Oobleck AutoEncoder
+    - local: api/models/consistency_decoder_vae
+      title: ConsistencyDecoderVAE
+    - local: api/models/transformer2d
+      title: Transformer2DModel
+    - local: api/models/pixart_transformer2d
+      title: PixArtTransformer2DModel
+    - local: api/models/dit_transformer2d
+      title: DiTTransformer2DModel
+    - local: api/models/hunyuan_transformer2d
+      title: HunyuanDiT2DModel
+    - local: api/models/aura_flow_transformer2d
+      title: AuraFlowTransformer2DModel
+    - local: api/models/latte_transformer3d
+      title: LatteTransformer3DModel
+    - local: api/models/lumina_nextdit2d
+      title: LuminaNextDiT2DModel
+    - local: api/models/transformer_temporal
+      title: TransformerTemporalModel
+    - local: api/models/sd3_transformer2d
+      title: SD3Transformer2DModel
+    - local: api/models/stable_audio_transformer
+      title: StableAudioDiTModel
+    - local: api/models/prior_transformer
+      title: PriorTransformer
+    - local: api/models/controlnet
+      title: ControlNetModel
+    - local: api/models/controlnet_hunyuandit
+      title: HunyuanDiT2DControlNetModel
+    - local: api/models/controlnet_sd3
+      title: SD3ControlNetModel
+    - local: api/models/controlnet_sparsectrl
+      title: SparseControlNetModel
     title: Models
   - isExpanded: false
     sections:
@@ -318,14 +294,10 @@
       title: AutoPipeline
     - local: api/pipelines/blip_diffusion
       title: BLIP-Diffusion
-    - local: api/pipelines/cogvideox
-      title: CogVideoX
     - local: api/pipelines/consistency_models
       title: Consistency Models
     - local: api/pipelines/controlnet
       title: ControlNet
-    - local: api/pipelines/controlnet_flux
-      title: ControlNet with Flux.1
     - local: api/pipelines/controlnet_hunyuandit
       title: ControlNet with Hunyuan-DiT
     - local: api/pipelines/controlnet_sd3
@@ -348,8 +320,6 @@
       title: DiffEdit
     - local: api/pipelines/dit
       title: DiT
-    - local: api/pipelines/flux
-      title: Flux
     - local: api/pipelines/hunyuandit
       title: Hunyuan-DiT
     - local: api/pipelines/i2vgenxl

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

@@ -51,7 +51,6 @@ The [`~loaders.FromSingleFileMixin.from_single_file`] method allows you to load:
 - [`AutoencoderKL`]
 - [`ControlNetModel`]
 - [`SD3Transformer2DModel`]
-- [`FluxTransformer2DModel`]
 
 ## FromSingleFileMixin
 

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

@@ -1,37 +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. -->
-
-# AutoencoderKLCogVideoX
-
-The 3D variational autoencoder (VAE) model with KL loss used in [CogVideoX](https://github.com/THUDM/CogVideo) was introduced in [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://github.com/THUDM/CogVideo/blob/main/resources/CogVideoX.pdf) by Tsinghua University & ZhipuAI.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import AutoencoderKLCogVideoX
-
-vae = AutoencoderKLCogVideoX.from_pretrained("THUDM/CogVideoX-2b", subfolder="vae", torch_dtype=torch.float16).to("cuda")
-```
-
-## AutoencoderKLCogVideoX
-
-[[autodoc]] AutoencoderKLCogVideoX
-    - decode
-    - encode
-    - all
-
-## AutoencoderKLOutput
-
-[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
-
-## DecoderOutput
-
-[[autodoc]] models.autoencoders.vae.DecoderOutput

docs/source/en/api/models/cogvideox_transformer3d.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. -->
-
-# CogVideoXTransformer3DModel
-
-A Diffusion Transformer model for 3D data from [CogVideoX](https://github.com/THUDM/CogVideo) was introduced in [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://github.com/THUDM/CogVideo/blob/main/resources/CogVideoX.pdf) by Tsinghua University & ZhipuAI.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import CogVideoXTransformer3DModel
-
-vae = CogVideoXTransformer3DModel.from_pretrained("THUDM/CogVideoX-2b", subfolder="transformer", torch_dtype=torch.float16).to("cuda")
-```
-
-## CogVideoXTransformer3DModel
-
-[[autodoc]] CogVideoXTransformer3DModel
-
-## 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/models/controlnet_flux.md

@@ -1,45 +0,0 @@
-<!--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
-
-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.
--->
-
-# FluxControlNetModel
-
-FluxControlNetModel is an implementation of ControlNet for Flux.1.
-
-The ControlNet model was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, Maneesh Agrawala. It provides a greater degree of control over text-to-image generation by conditioning the model on additional inputs such as edge maps, depth maps, segmentation maps, and keypoints for pose detection.
-
-The abstract from the paper is:
-
-*We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with "zero convolutions" (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.*
-
-## Loading from the original format
-
-By default the [`FluxControlNetModel`] should be loaded with [`~ModelMixin.from_pretrained`].
-
-```py
-from diffusers import FluxControlNetPipeline
-from diffusers.models import FluxControlNetModel, FluxMultiControlNetModel
-
-controlnet = FluxControlNetModel.from_pretrained("InstantX/FLUX.1-dev-Controlnet-Canny")
-pipe = FluxControlNetPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", controlnet=controlnet)
-
-controlnet = FluxControlNetModel.from_pretrained("InstantX/FLUX.1-dev-Controlnet-Canny")
-controlnet = FluxMultiControlNetModel([controlnet])
-pipe = FluxControlNetPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", controlnet=controlnet)
-```
-
-## FluxControlNetModel
-
-[[autodoc]] FluxControlNetModel
-
-## FluxControlNetOutput
-
-[[autodoc]] models.controlnet_flux.FluxControlNetOutput

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

@@ -1,19 +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.
--->
-
-# FluxTransformer2DModel
-
-A Transformer model for image-like data from [Flux](https://blackforestlabs.ai/announcing-black-forest-labs/).
-
-## FluxTransformer2DModel
-
-[[autodoc]] FluxTransformer2DModel

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

@@ -1,19 +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.
--->
-
-# StableCascadeUNet
-
-A UNet model from the [Stable Cascade pipeline](../pipelines/stable_cascade.md).
-
-## StableCascadeUNet
-
-[[autodoc]] models.unets.unet_stable_cascade.StableCascadeUNet

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/aura_flow.md

@@ -18,7 +18,7 @@ It was developed by the Fal team and more details about it can be found in [this
 
 <Tip>
 
-AuraFlow can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details.
+AuraFlow can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details. 
 
 </Tip>
 

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

@@ -1,123 +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: Text-to-Video Diffusion Models with An Expert Transformer](https://arxiv.org/abs/2408.06072) from Tsinghua University & ZhipuAI, by Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, Da Yin, Xiaotao Gu, Yuxuan Zhang, Weihan Wang, Yean Cheng, Ting Liu, Bin Xu, Yuxiao Dong, Jie Tang.
-
-The abstract from the paper is:
-
-*We introduce CogVideoX, a large-scale diffusion transformer model designed for generating videos based on text prompts. To efficently model video data, we propose to levearge a 3D Variational Autoencoder (VAE) to compresses videos along both spatial and temporal dimensions. To improve the text-video alignment, we propose an expert transformer with the expert adaptive LayerNorm to facilitate the deep fusion between the two modalities. By employing a progressive training technique, CogVideoX is adept at producing coherent, long-duration videos characterized by significant motion. In addition, we develop an effectively text-video data processing pipeline that includes various data preprocessing strategies and a video captioning method. It significantly helps enhance the performance of CogVideoX, improving both generation quality and semantic alignment. Results show that CogVideoX demonstrates state-of-the-art performance across both multiple machine metrics and human evaluations. The model weight of CogVideoX-2B is publicly available at https://github.com/THUDM/CogVideo.*
-
-<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).
-
-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`.
-
-## Inference
-
-Use [`torch.compile`](https://huggingface.co/docs/diffusers/main/en/tutorials/fast_diffusion#torchcompile) to reduce the inference latency.
-
-First, load the pipeline:
-
-```python
-import torch
-from diffusers import CogVideoXPipeline, CogVideoXImageToVideoPipeline
-from diffusers.utils import export_to_video,load_image
-pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b").to("cuda") # or "THUDM/CogVideoX-2b" 
-```
-
-If you are using the image-to-video pipeline, load it as follows:
-
-```python
-pipe = CogVideoXImageToVideoPipeline.from_pretrained("THUDM/CogVideoX-5b-I2V").to("cuda")
-```
-
-Then change the memory layout of the pipelines `transformer` component to `torch.channels_last`:
-
-```python
-pipe.transformer.to(memory_format=torch.channels_last)
-```
-
-Compile the components and run inference:
-
-```python
-pipe.transformer = torch.compile(pipeline.transformer, mode="max-autotune", fullgraph=True)
-
-# CogVideoX works well with long and well-described prompts
-prompt = "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance."
-video = pipe(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0]
-```
-
-The [T2V benchmark](https://gist.github.com/a-r-r-o-w/5183d75e452a368fd17448fcc810bd3f) results on an 80GB A100 machine are:
-
-```
-Without torch.compile(): Average inference time: 96.89 seconds.
-With torch.compile(): Average inference time: 76.27 seconds.
-```
-
-### Memory optimization
-
-CogVideoX-2b requires about 19 GB of GPU memory to decode 49 frames (6 seconds of video at 8 FPS) with output resolution 720x480 (W x H), which makes it not possible to run on consumer GPUs or free-tier T4 Colab. The following memory optimizations could be used to reduce the memory footprint. For replication, you can refer to [this](https://gist.github.com/a-r-r-o-w/3959a03f15be5c9bd1fe545b09dfcc93) script.
-
-- `pipe.enable_model_cpu_offload()`:
-  - Without enabling cpu offloading, memory usage is `33 GB`
-  - With enabling cpu offloading, memory usage is `19 GB`
-- `pipe.enable_sequential_cpu_offload()`:
-  - Similar to `enable_model_cpu_offload` but can significantly reduce memory usage at the cost of slow inference
-  - When enabled, memory usage is under `4 GB`
-- `pipe.vae.enable_tiling()`:
-  - With enabling cpu offloading and tiling, memory usage is `11 GB`
-- `pipe.vae.enable_slicing()`
-
-### 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__
-
-## CogVideoXPipelineOutput
-
-[[autodoc]] pipelines.cogvideo.pipeline_output.CogVideoXPipelineOutput

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

@@ -1,48 +0,0 @@
-<!--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
-
-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.
--->
-
-# ControlNet with Flux.1
-
-FluxControlNetPipeline is an implementation of ControlNet for Flux.1.
-
-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.
-
-With a ControlNet model, you can provide an additional control image to condition and control Stable Diffusion generation. For example, if you provide a depth map, the ControlNet model generates an image that'll preserve the spatial information from the depth map. It is a more flexible and accurate way to control the image generation process.
-
-The abstract from the paper is:
-
-*We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with "zero convolutions" (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.*
-
-This controlnet code is implemented by [The InstantX Team](https://huggingface.co/InstantX). You can find pre-trained checkpoints for Flux-ControlNet in the table below:
-
-
-| ControlNet type | Developer | Link |
-| -------- | ---------- | ---- |
-| Canny | [The InstantX Team](https://huggingface.co/InstantX) | [Link](https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Canny) |
-| 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) |
-
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
-
-## FluxControlNetPipeline
-[[autodoc]] FluxControlNetPipeline
-	- all
-	- __call__
-
-
-## FluxPipelineOutput
-[[autodoc]] pipelines.flux.pipeline_output.FluxPipelineOutput

docs/source/en/api/pipelines/controlnet_sd3.md

@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--Copyright 2023 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
@@ -22,16 +22,7 @@ The abstract from the paper is:
 
 *We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with "zero convolutions" (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.*
 
-This controlnet code is mainly implemented by [The InstantX Team](https://huggingface.co/InstantX). The inpainting-related code was developed by [The Alimama Creative Team](https://huggingface.co/alimama-creative). You can find pre-trained checkpoints for SD3-ControlNet in the table below:
-
-
-| ControlNet type | Developer | Link |
-| -------- | ---------- | ---- |
-| Canny | [The InstantX Team](https://huggingface.co/InstantX) | [Link](https://huggingface.co/InstantX/SD3-Controlnet-Canny) |
-| Pose | [The InstantX Team](https://huggingface.co/InstantX) | [Link](https://huggingface.co/InstantX/SD3-Controlnet-Pose) |
-| Tile | [The InstantX Team](https://huggingface.co/InstantX) | [Link](https://huggingface.co/InstantX/SD3-Controlnet-Tile) |
-| Inpainting | [The AlimamaCreative Team](https://huggingface.co/alimama-creative) | [link](https://huggingface.co/alimama-creative/SD3-Controlnet-Inpainting) |
-
+This code is implemented by [The InstantX Team](https://huggingface.co/InstantX). You can find pre-trained checkpoints for SD3-ControlNet on [The InstantX Team](https://huggingface.co/InstantX) Hub profile.
 
 <Tip>
 
@@ -44,10 +35,5 @@ Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers)
 	- all
 	- __call__
 
-## StableDiffusion3ControlNetInpaintingPipeline
-[[autodoc]] pipelines.controlnet_sd3.pipeline_stable_diffusion_3_controlnet_inpainting.StableDiffusion3ControlNetInpaintingPipeline
-	- all
-	- __call__
-
 ## StableDiffusion3PipelineOutput
 [[autodoc]] pipelines.stable_diffusion_3.pipeline_output.StableDiffusion3PipelineOutput

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

@@ -1,190 +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.
--->
-
-# Flux
-
-Flux is a series of text-to-image generation models based on diffusion transformers. To know more about Flux, check out the original [blog post](https://blackforestlabs.ai/announcing-black-forest-labs/) by the creators of Flux, Black Forest Labs.
-
-Original model checkpoints for Flux can be found [here](https://huggingface.co/black-forest-labs). Original inference code can be found [here](https://github.com/black-forest-labs/flux).
-
-<Tip>
-
-Flux can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details. Additionally, Flux can benefit from quantization for memory efficiency with a trade-off in inference latency. Refer to [this blog post](https://huggingface.co/blog/quanto-diffusers) to learn more.  For an exhaustive list of resources, check out [this gist](https://gist.github.com/sayakpaul/b664605caf0aa3bf8585ab109dd5ac9c).
-
-</Tip>
-
-Flux comes in two variants:
-
-* Timestep-distilled (`black-forest-labs/FLUX.1-schnell`)
-* Guidance-distilled (`black-forest-labs/FLUX.1-dev`)
-
-Both checkpoints have slightly difference usage which we detail below.
-
-### Timestep-distilled
-
-* `max_sequence_length` cannot be more than 256.
-* `guidance_scale` needs to be 0.
-* As this is a timestep-distilled model, it benefits from fewer sampling steps.
-
-```python
-import torch
-from diffusers import FluxPipeline
-
-pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
-pipe.enable_model_cpu_offload()
-
-prompt = "A cat holding a sign that says hello world"
-out = pipe(
-    prompt=prompt,
-    guidance_scale=0.,
-    height=768,
-    width=1360,
-    num_inference_steps=4,
-    max_sequence_length=256,
-).images[0]
-out.save("image.png")
-```
-
-### Guidance-distilled
-
-* The guidance-distilled variant takes about 50 sampling steps for good-quality generation.
-* It doesn't have any limitations around the `max_sequence_length`.
-
-```python
-import torch
-from diffusers import FluxPipeline
-
-pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
-pipe.enable_model_cpu_offload()
-
-prompt = "a tiny astronaut hatching from an egg on the moon"
-out = pipe(
-    prompt=prompt,
-    guidance_scale=3.5,
-    height=768,
-    width=1360,
-    num_inference_steps=50,
-).images[0]
-out.save("image.png")
-```
-
-## Running FP16 inference
-Flux can generate high-quality images with FP16 (i.e. to accelerate inference on Turing/Volta GPUs) but produces different outputs compared to FP32/BF16. The issue is that some activations in the text encoders have to be clipped when running in FP16, which affects the overall image. Forcing text encoders to run with FP32 inference thus removes this output difference. See [here](https://github.com/huggingface/diffusers/pull/9097#issuecomment-2272292516) for details.
-
-FP16 inference code:
-```python
-import torch
-from diffusers import FluxPipeline
-
-pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16) # can replace schnell with dev
-# to run on low vram GPUs (i.e. between 4 and 32 GB VRAM)
-pipe.enable_sequential_cpu_offload()
-pipe.vae.enable_slicing()
-pipe.vae.enable_tiling()
-
-pipe.to(torch.float16) # casting here instead of in the pipeline constructor because doing so in the constructor loads all models into CPU memory at once
-
-prompt = "A cat holding a sign that says hello world"
-out = pipe(
-    prompt=prompt,
-    guidance_scale=0.,
-    height=768,
-    width=1360,
-    num_inference_steps=4,
-    max_sequence_length=256,
-).images[0]
-out.save("image.png")
-```
-
-## Single File Loading for the `FluxTransformer2DModel`
-
-The `FluxTransformer2DModel` supports loading checkpoints in the original format shipped by Black Forest Labs. This is also useful when trying to load finetunes or quantized versions of the models that have been published by the community.
-
-<Tip>
-`FP8` inference can be brittle depending on the GPU type, CUDA version, and `torch` version that you are using. It is recommended that you use the `optimum-quanto` library in order to run FP8 inference on your machine.
-</Tip>
-
-The following example demonstrates how to run Flux with less than 16GB of VRAM.
-
-First install `optimum-quanto`
-
-```shell
-pip install optimum-quanto
-```
-
-Then run the following example
-
-```python
-import torch
-from diffusers import FluxTransformer2DModel, FluxPipeline
-from transformers import T5EncoderModel, CLIPTextModel
-from optimum.quanto import freeze, qfloat8, quantize
-
-bfl_repo = "black-forest-labs/FLUX.1-dev"
-dtype = torch.bfloat16
-
-transformer = FluxTransformer2DModel.from_single_file("https://huggingface.co/Kijai/flux-fp8/blob/main/flux1-dev-fp8.safetensors", torch_dtype=dtype)
-quantize(transformer, weights=qfloat8)
-freeze(transformer)
-
-text_encoder_2 = T5EncoderModel.from_pretrained(bfl_repo, subfolder="text_encoder_2", torch_dtype=dtype)
-quantize(text_encoder_2, weights=qfloat8)
-freeze(text_encoder_2)
-
-pipe = FluxPipeline.from_pretrained(bfl_repo, transformer=None, text_encoder_2=None, torch_dtype=dtype)
-pipe.transformer = transformer
-pipe.text_encoder_2 = text_encoder_2
-
-pipe.enable_model_cpu_offload()
-
-prompt = "A cat holding a sign that says hello world"
-image = pipe(
-    prompt,
-    guidance_scale=3.5,
-    output_type="pil",
-    num_inference_steps=20,
-    generator=torch.Generator("cpu").manual_seed(0)
-).images[0]
-
-image.save("flux-fp8-dev.png")
-```
-
-## FluxPipeline
-
-[[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

@@ -14,7 +14,7 @@ specific language governing permissions and limitations under the License.
 
 ![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/kolors/kolors_header_collage.png)
 
-Kolors is a large-scale text-to-image generation model based on latent diffusion, developed by [the Kuaishou Kolors team](https://github.com/Kwai-Kolors/Kolors). Trained on billions of text-image pairs, Kolors exhibits significant advantages over both open-source and closed-source models in visual quality, complex semantic accuracy, and text rendering for both Chinese and English characters. Furthermore, Kolors supports both Chinese and English inputs, demonstrating strong performance in understanding and generating Chinese-specific content. For more details, please refer to this [technical report](https://github.com/Kwai-Kolors/Kolors/blob/master/imgs/Kolors_paper.pdf).
+Kolors is a large-scale text-to-image generation model based on latent diffusion, developed by [the Kuaishou Kolors team](kwai-kolors@kuaishou.com). Trained on billions of text-image pairs, Kolors exhibits significant advantages over both open-source and closed-source models in visual quality, complex semantic accuracy, and text rendering for both Chinese and English characters. Furthermore, Kolors supports both Chinese and English inputs, demonstrating strong performance in understanding and generating Chinese-specific content. For more details, please refer to this [technical report](https://github.com/Kwai-Kolors/Kolors/blob/master/imgs/Kolors_paper.pdf).
 
 The abstract from the technical report is:
 
@@ -74,7 +74,7 @@ image_encoder = CLIPVisionModelWithProjection.from_pretrained(
 
 pipe = KolorsPipeline.from_pretrained(
     "Kwai-Kolors/Kolors-diffusers", image_encoder=image_encoder, torch_dtype=torch.float16, variant="fp16"
-)
+).to("cuda")
 pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True)
 
 pipe.load_ip_adapter(
@@ -105,11 +105,3 @@ image.save("kolors_ipa_sample.png")
 
 - all
 - __call__
-
-## KolorsImg2ImgPipeline
-
-[[autodoc]] KolorsImg2ImgPipeline
-
-- all
-- __call__
-

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

@@ -59,7 +59,7 @@ First, load the pipeline:
 
 ```python
 from diffusers import LuminaText2ImgPipeline
-import torch
+import torch 
 
 pipeline = LuminaText2ImgPipeline.from_pretrained(
 	"Alpha-VLLM/Lumina-Next-SFT-diffusers", torch_dtype=torch.bfloat16
@@ -87,4 +87,4 @@ image = pipeline(prompt="Upper body of a young woman in a Victorian-era outfit w
 [[autodoc]] LuminaText2ImgPipeline
 	- all
 	- __call__
-
+	

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

@@ -30,64 +30,63 @@ The table below lists all the pipelines currently available in 🤗 Diffusers an
 
 | Pipeline | Tasks |
 |---|---|
-| [aMUSEd](amused) | text2image |
+| [AltDiffusion](alt_diffusion) | image2image |
 | [AnimateDiff](animatediff) | text2video |
 | [Attend-and-Excite](attend_and_excite) | text2image |
+| [Audio Diffusion](audio_diffusion) | image2audio |
 | [AudioLDM](audioldm) | text2audio |
 | [AudioLDM2](audioldm2) | text2audio |
-| [AuraFlow](auraflow) | text2image |
 | [BLIP Diffusion](blip_diffusion) | text2image |
-| [CogVideoX](cogvideox) | text2video |
 | [Consistency Models](consistency_models) | unconditional image generation |
 | [ControlNet](controlnet) | text2image, image2image, inpainting |
-| [ControlNet with Flux.1](controlnet_flux) | text2image |
-| [ControlNet with Hunyuan-DiT](controlnet_hunyuandit) | text2image |
-| [ControlNet with Stable Diffusion 3](controlnet_sd3) | text2image |
 | [ControlNet with Stable Diffusion XL](controlnet_sdxl) | text2image |
 | [ControlNet-XS](controlnetxs) | text2image |
 | [ControlNet-XS with Stable Diffusion XL](controlnetxs_sdxl) | text2image |
+| [Cycle Diffusion](cycle_diffusion) | image2image |
 | [Dance Diffusion](dance_diffusion) | unconditional audio generation |
 | [DDIM](ddim) | unconditional image generation |
 | [DDPM](ddpm) | unconditional image generation |
 | [DeepFloyd IF](deepfloyd_if) | text2image, image2image, inpainting, super-resolution |
 | [DiffEdit](diffedit) | inpainting |
 | [DiT](dit) | text2image |
-| [Flux](flux) | text2image |
-| [Hunyuan-DiT](hunyuandit) | text2image |
-| [I2VGen-XL](i2vgenxl) | text2video |
+| [GLIGEN](stable_diffusion/gligen) | text2image |
 | [InstructPix2Pix](pix2pix) | image editing |
 | [Kandinsky 2.1](kandinsky) | text2image, image2image, inpainting, interpolation |
 | [Kandinsky 2.2](kandinsky_v22) | text2image, image2image, inpainting |
 | [Kandinsky 3](kandinsky3) | text2image, image2image |
-| [Kolors](kolors) | text2image |
 | [Latent Consistency Models](latent_consistency_models) | text2image |
 | [Latent Diffusion](latent_diffusion) | text2image, super-resolution |
-| [Latte](latte) | text2image |
+| [LDM3D](stable_diffusion/ldm3d_diffusion) | text2image, text-to-3D, text-to-pano, upscaling |
 | [LEDITS++](ledits_pp) | image editing |
-| [Lumina-T2X](lumina) | text2image |
-| [Marigold](marigold) | depth |
 | [MultiDiffusion](panorama) | text2image |
 | [MusicLDM](musicldm) | text2audio |
-| [PAG](pag) | text2image |
 | [Paint by Example](paint_by_example) | inpainting |
-| [PIA](pia) | image2video |
+| [ParaDiGMS](paradigms) | text2image |
+| [Pix2Pix Zero](pix2pix_zero) | image editing |
 | [PixArt-α](pixart) | text2image |
-| [PixArt-Σ](pixart_sigma) | text2image |
+| [PNDM](pndm) | unconditional image generation |
+| [RePaint](repaint) | inpainting |
+| [Score SDE VE](score_sde_ve) | unconditional image generation |
 | [Self-Attention Guidance](self_attention_guidance) | text2image |
 | [Semantic Guidance](semantic_stable_diffusion) | text2image |
 | [Shap-E](shap_e) | text-to-3D, image-to-3D |
+| [Spectrogram Diffusion](spectrogram_diffusion) |  |
 | [Stable Audio](stable_audio) | text2audio |
-| [Stable Cascade](stable_cascade) | text2image |
 | [Stable Diffusion](stable_diffusion/overview) | text2image, image2image, depth2image, inpainting, image variation, latent upscaler, super-resolution |
+| [Stable Diffusion Model Editing](model_editing) | model editing |
 | [Stable Diffusion XL](stable_diffusion/stable_diffusion_xl) | text2image, image2image, inpainting |
 | [Stable Diffusion XL Turbo](stable_diffusion/sdxl_turbo) | text2image, image2image, inpainting |
 | [Stable unCLIP](stable_unclip) | text2image, image variation |
+| [Stochastic Karras VE](stochastic_karras_ve) | unconditional image generation |
 | [T2I-Adapter](stable_diffusion/adapter) | text2image |
 | [Text2Video](text_to_video) | text2video, video2video |
 | [Text2Video-Zero](text_to_video_zero) | text2video |
 | [unCLIP](unclip) | text2image, image variation |
+| [Unconditional Latent Diffusion](latent_diffusion_uncond) | unconditional image generation |
 | [UniDiffuser](unidiffuser) | text2image, image2text, image variation, text variation, unconditional image generation, unconditional audio generation |
 | [Value-guided planning](value_guided_sampling) | value guided sampling |
+| [Versatile Diffusion](versatile_diffusion) | text2image, image variation |
+| [VQ Diffusion](vq_diffusion) | text2image |
 | [Wuerstchen](wuerstchen) | text2image |
 
 ## DiffusionPipeline

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

@@ -20,34 +20,11 @@ The abstract from the paper is:
 
 *Recent studies have demonstrated that diffusion models are capable of generating high-quality samples, but their quality heavily depends on sampling guidance techniques, such as classifier guidance (CG) and classifier-free guidance (CFG). These techniques are often not applicable in unconditional generation or in various downstream tasks such as image restoration. In this paper, we propose a novel sampling guidance, called Perturbed-Attention Guidance (PAG), which improves diffusion sample quality across both unconditional and conditional settings, achieving this without requiring additional training or the integration of external modules. PAG is designed to progressively enhance the structure of samples throughout the denoising process. It involves generating intermediate samples with degraded structure by substituting selected self-attention maps in diffusion U-Net with an identity matrix, by considering the self-attention mechanisms' ability to capture structural information, and guiding the denoising process away from these degraded samples. In both ADM and Stable Diffusion, PAG surprisingly improves sample quality in conditional and even unconditional scenarios. Moreover, PAG significantly improves the baseline performance in various downstream tasks where existing guidances such as CG or CFG cannot be fully utilized, including ControlNet with empty prompts and image restoration such as inpainting and deblurring.*
 
-PAG can be used by specifying the `pag_applied_layers` as a parameter when instantiating a PAG pipeline. It can be a single string or a list of strings. Each string can be a unique layer identifier or a regular expression to identify one or more layers.
-
-- Full identifier as a normal string: `down_blocks.2.attentions.0.transformer_blocks.0.attn1.processor`
-- Full identifier as a RegEx: `down_blocks.2.(attentions|motion_modules).0.transformer_blocks.0.attn1.processor`
-- Partial identifier as a RegEx: `down_blocks.2`, or `attn1`
-- List of identifiers (can be combo of strings and ReGex): `["blocks.1", "blocks.(14|20)", r"down_blocks\.(2,3)"]`
-
-<Tip warning={true}>
-
-Since RegEx is supported as a way for matching layer identifiers, it is crucial to use it correctly otherwise there might be unexpected behaviour. The recommended way to use PAG is by specifying layers as `blocks.{layer_index}` and `blocks.({layer_index_1|layer_index_2|...})`. Using it in any other way, while doable, may bypass our basic validation checks and give you unexpected results.
-
-</Tip>
-
 ## AnimateDiffPAGPipeline
 [[autodoc]] AnimateDiffPAGPipeline
   - all
   - __call__
 
-## HunyuanDiTPAGPipeline
-[[autodoc]] HunyuanDiTPAGPipeline
-  - all
-  - __call__
-
-## KolorsPAGPipeline
-[[autodoc]] KolorsPAGPipeline
-  - all
-  - __call__
-
 ## StableDiffusionPAGPipeline
 [[autodoc]] StableDiffusionPAGPipeline
 	- all
@@ -77,19 +54,3 @@ Since RegEx is supported as a way for matching layer identifiers, it is crucial
 [[autodoc]] StableDiffusionXLControlNetPAGPipeline
 	- all
 	- __call__
-
-## StableDiffusionXLControlNetPAGImg2ImgPipeline
-[[autodoc]] StableDiffusionXLControlNetPAGImg2ImgPipeline
-	- all
-	- __call__
-
-## StableDiffusion3PAGPipeline
-[[autodoc]] StableDiffusion3PAGPipeline
-	- all
-	- __call__
-
-
-## PixArtSigmaPAGPipeline
-[[autodoc]] PixArtSigmaPAGPipeline
-	- all
-	- __call__

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

@@ -16,12 +16,12 @@ Stable Audio was proposed in [Stable Audio Open](https://arxiv.org/abs/2407.1435
 
 Stable Audio Open generates variable-length (up to 47s) stereo audio at 44.1kHz from text prompts. It comprises three components: an autoencoder that compresses waveforms into a manageable sequence length, a T5-based text embedding for text conditioning, and a transformer-based diffusion (DiT) model that operates in the latent space of the autoencoder.
 
-Stable Audio is trained on a corpus of around 48k audio recordings, where around 47k are from Freesound and the rest are from the Free Music Archive (FMA). All audio files are licensed under CC0, CC BY, or CC Sampling+. This data is used to train the autoencoder and the DiT.
+Stable Audio is trained on a corpus of around 48k audio recordings, where around 47k are from Freesound and the rest are from the Free Music Archive (FMA). All audio files are licensed under CC0, CC BY, or CC Sampling+. This data is used to train the autoencoder and the DiT. 
 
 The abstract of the paper is the following:
 *Open generative models are vitally important for the community, allowing for fine-tunes and serving as baselines when presenting new models. However, most current text-to-audio models are private and not accessible for artists and researchers to build upon. Here we describe the architecture and training process of a new open-weights text-to-audio model trained with Creative Commons data. Our evaluation shows that the model's performance is competitive with the state-of-the-art across various metrics. Notably, the reported FDopenl3 results (measuring the realism of the generations) showcase its potential for high-quality stereo sound synthesis at 44.1kHz.*
 
-This pipeline was contributed by [Yoach Lacombe](https://huggingface.co/ylacombe). The original codebase can be found at [Stability-AI/stable-audio-tools](https://github.com/Stability-AI/stable-audio-tools).
+This pipeline was contributed by [Yoach Lacombe](https://huggingface.co/ylacombe). The original codebase can be found at [Stability-AI/stable-audio-tool](https://github.com/Stability-AI/stable-audio-tool).
 
 ## Tips
 

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/text_to_video_zero.md

@@ -41,7 +41,7 @@ To generate a video from prompt, run the following Python code:
 import torch
 from diffusers import TextToVideoZeroPipeline
 
-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"
@@ -63,7 +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
@@ -137,7 +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/community_projects.md

@@ -1,78 +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.
--->
-
-# Community Projects
-
-Welcome to Community Projects. This space is dedicated to showcasing the incredible work and innovative applications created by our vibrant community using the `diffusers` library.
-
-This section aims to:
-
-- Highlight diverse and inspiring projects built with `diffusers`
-- Foster knowledge sharing within our community
-- Provide real-world examples of how `diffusers` can be leveraged
-
-Happy exploring, and thank you for being part of the Diffusers community!
-
-<table>
-    <tr>
-        <th>Project Name</th>
-        <th>Description</th>
-    </tr>
-  <tr style="border-top: 2px solid black">
-    <td><a href="https://github.com/carson-katri/dream-textures"> dream-textures </a></td>
-    <td>Stable Diffusion built-in to Blender</td>
-  </tr>
-  <tr style="border-top: 2px solid black">
-    <td><a href="https://github.com/megvii-research/HiDiffusion"> HiDiffusion </a></td>
-    <td>Increases the resolution and speed of your diffusion model by only adding a single line of code</td>
-  </tr>
-  <tr style="border-top: 2px solid black">
-    <td><a href="https://github.com/lllyasviel/IC-Light"> IC-Light </a></td>
-    <td>IC-Light is a project to manipulate the illumination of images</td>
-  </tr>
-  <tr style="border-top: 2px solid black">
-    <td><a href="https://github.com/InstantID/InstantID"> InstantID </a></td>
-    <td>InstantID : Zero-shot Identity-Preserving Generation in Seconds</td>
-  </tr>
-  <tr style="border-top: 2px solid black">
-    <td><a href="https://github.com/Sanster/IOPaint"> IOPaint </a></td>
-    <td>Image inpainting tool powered by SOTA AI Model. Remove any unwanted object, defect, people from your pictures or erase and replace(powered by stable diffusion) any thing on your pictures.</td>
-  </tr>
-  <tr style="border-top: 2px solid black">
-    <td><a href="https://github.com/bmaltais/kohya_ss"> Kohya </a></td>
-    <td>Gradio GUI for Kohya's Stable Diffusion trainers</td>
-  </tr>
-  <tr style="border-top: 2px solid black">
-    <td><a href="https://github.com/magic-research/magic-animate"> MagicAnimate </a></td>
-    <td>MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model</td>
-  </tr>
-  <tr style="border-top: 2px solid black">
-    <td><a href="https://github.com/levihsu/OOTDiffusion"> OOTDiffusion </a></td>
-    <td>Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on</td>
-  </tr>
-  <tr style="border-top: 2px solid black">
-    <td><a href="https://github.com/vladmandic/automatic"> SD.Next </a></td>
-    <td>SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models</td>
-  </tr>
-  <tr style="border-top: 2px solid black">
-    <td><a href="https://github.com/ashawkey/stable-dreamfusion"> stable-dreamfusion </a></td>
-    <td>Text-to-3D & Image-to-3D & Mesh Exportation with NeRF + Diffusion</td>
-  </tr>
-  <tr style="border-top: 2px solid black">
-    <td><a href="https://github.com/HVision-NKU/StoryDiffusion"> StoryDiffusion </a></td>
-    <td>StoryDiffusion can create a magic story by generating consistent images and videos.</td>
-  </tr>
-  <tr style="border-top: 2px solid black">
-    <td><a href="https://github.com/cumulo-autumn/StreamDiffusion"> StreamDiffusion </a></td>
-    <td>A Pipeline-Level Solution for Real-Time Interactive Generation</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

@@ -102,10 +102,10 @@ Pass the path of the downloaded checkpoint with `-i` flag to the script. `--comp
 
 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,
 )
@@ -125,5 +125,3 @@ image
     <figcaption class="mt-2 text-center text-sm text-gray-500">distilled Stable Diffusion + Tiny AutoEncoder</figcaption>
   </div>
 </div>
-
-More tiny autoencoder models for other Stable Diffusion models, like Stable Diffusion 3, are available from [madebyollin](https://huggingface.co/madebyollin).

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

@@ -1,122 +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://github.com/xdit-project/xDiT/raw/main/assets/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://github.com/xdit-project/xDiT/raw/main/assets/performance/flux/Flux-2k-L40.png">
-</div>
-
-
-<div class="flex justify-center">
-    <img src="https://github.com/xdit-project/xDiT/raw/main/assets/performance/flux/Flux-2K-A100.png">
-</div>
-
-### Stable Diffusion 3
-<div class="flex justify-center">
-    <img src="https://github.com/xdit-project/xDiT/raw/main/assets/performance/sd3/L40-SD3.png">
-</div>
-
-<div class="flex justify-center">
-    <img src="https://github.com/xdit-project/xDiT/raw/main/assets/performance/sd3/A100-SD3.png">
-</div>
-
-### HunyuanDiT
-<div class="flex justify-center">
-    <img src="https://github.com/xdit-project/xDiT/raw/main/assets/performance/hunuyuandit/L40-HunyuanDiT.png">
-</div>
-
-<div class="flex justify-center">
-    <img src="https://github.com/xdit-project/xDiT/raw/main/assets/performance/hunuyuandit/A100-HunyuanDiT.png">
-</div>
-
-<div class="flex justify-center">
-    <img src="https://github.com/xdit-project/xDiT/raw/main/assets/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/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)
 ```
 
@@ -238,7 +238,7 @@ Pretty impressive! Let's tweak the second image - corresponding to the `Generato
 ```python
 prompts = [
     "portrait photo of the oldest warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3  --beta --upbeta",
-    "portrait photo of an old warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3  --beta --upbeta",
+    "portrait photo of a old warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3  --beta --upbeta",
     "portrait photo of a warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3  --beta --upbeta",
     "portrait photo of a young warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3  --beta --upbeta",
 ]

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/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

@@ -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)
@@ -48,7 +48,7 @@ accelerate launch run_distributed.py --num_processes=2
 
 <Tip>
 
-Refer to this minimal example [script](https://gist.github.com/sayakpaul/cfaebd221820d7b43fae638b4dfa01ba) for running inference across multiple GPUs. To learn more, take a look at the [Distributed Inference with 🤗 Accelerate](https://huggingface.co/docs/accelerate/en/usage_guides/distributed_inference#distributed-inference-with-accelerate) guide.
+To learn more, take a look at the [Distributed Inference with 🤗 Accelerate](https://huggingface.co/docs/accelerate/en/usage_guides/distributed_inference#distributed-inference-with-accelerate) guide.
 
 </Tip>
 
@@ -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
 )
 ```
 
@@ -108,4 +108,4 @@ 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.
+> 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.

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/instructpix2pix.md

@@ -14,7 +14,7 @@ specific language governing permissions and limitations under the License.
 
 [InstructPix2Pix](https://hf.co/papers/2211.09800) is a Stable Diffusion model trained to edit images from human-provided instructions. For example, your prompt can be "turn the clouds rainy" and the model will edit the input image accordingly. This model is conditioned on the text prompt (or editing instruction) and the input image.
 
-This guide will explore the [train_instruct_pix2pix.py](https://github.com/huggingface/diffusers/blob/main/examples/instruct_pix2pix/train_instruct_pix2pix.py) training script to help you become familiar with it, and how you can adapt it for your own use case.
+This guide will explore the [train_instruct_pix2pix.py](https://github.com/huggingface/diffusers/blob/main/examples/instruct_pix2pix/train_instruct_pix2pix.py) training script to help you become familiar with it, and how you can adapt it for your own use-case.
 
 Before running the script, make sure you install the library from source:
 
@@ -117,7 +117,7 @@ optimizer = optimizer_cls(
 )
 ```
 
-Next, the edited images and edit instructions are [preprocessed](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L624) and [tokenized](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L610C24-L610C24). It is important the same image transformations are applied to the original and edited images.
+Next, the edited images and and edit instructions are [preprocessed](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L624) and [tokenized](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L610C24-L610C24). It is important the same image transformations are applied to the original and edited images.
 
 ```py
 def preprocess_train(examples):
@@ -249,4 +249,4 @@ The SDXL training script is discussed in more detail in the [SDXL training](sdxl
 
 Congratulations on training your own InstructPix2Pix model! 🥳 To learn more about the model, it may be helpful to:
 
-- Read the [Instruction-tuning Stable Diffusion with InstructPix2Pix](https://huggingface.co/blog/instruction-tuning-sd) blog post to learn more about some experiments we've done with InstructPix2Pix, dataset preparation, and results for different instructions.
+- Read the [Instruction-tuning Stable Diffusion with InstructPix2Pix](https://huggingface.co/blog/instruction-tuning-sd) blog post to learn more about some experiments we've done with InstructPix2Pix, dataset preparation, and results for different instructions.

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/fast_diffusion.md

@@ -35,7 +35,7 @@ pip3 install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu
 ```
 
 > [!TIP]
-> The results reported below are from a 80GB 400W A100 with its clock rate set to the maximum.
+> The results reported below are from a 80GB 400W A100 with its clock rate set to the maximum. 
 > If you're interested in the full benchmarking code, take a look at [huggingface/diffusion-fast](https://github.com/huggingface/diffusion-fast).
 
 
@@ -168,7 +168,7 @@ Using SDPA attention and compiling both the UNet and VAE cuts the latency from 3
 </div>
 
 > [!TIP]
-> From PyTorch 2.3.1, you can control the caching behavior of `torch.compile()`. This is particularly beneficial for compilation modes like `"max-autotune"` which performs a grid-search over several compilation flags to find the optimal configuration. Learn more in the [Compile Time Caching in torch.compile](https://pytorch.org/tutorials/recipes/torch_compile_caching_tutorial.html) tutorial.
+> From PyTorch 2.3.1, you can control the caching behavior of `torch.compile()`. This is particularly beneficial for compilation modes like `"max-autotune"` which performs a grid-search over several compilation flags to find the optimal configuration. Learn more in the [Compile Time Caching in torch.compile](https://pytorch.org/tutorials/recipes/torch_compile_caching_tutorial.html) tutorial. 
 
 ### Prevent graph breaks
 

docs/source/en/tutorials/inference_with_big_models.md

@@ -18,13 +18,13 @@ A modern diffusion model, like [Stable Diffusion XL (SDXL)](../using-diffusers/s
 * Two text encoders
 * A UNet for denoising
 
-Usually, the text encoders and the denoiser are much larger compared to the VAE.
+Usually, the text encoders and the denoiser are much larger compared to the VAE. 
 
 As models get bigger and better, it’s possible your model is so big that even a single copy won’t fit in memory. But that doesn’t mean it can’t be loaded. If you have more than one GPU, there is more memory available to store your model. In this case, it’s better to split your model checkpoint into several smaller *checkpoint shards*.
 
 When a text encoder checkpoint has multiple shards, like [T5-xxl for SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers/tree/main/text_encoder_3), it is automatically handled by the [Transformers](https://huggingface.co/docs/transformers/index) library as it is a required dependency of Diffusers when using the [`StableDiffusion3Pipeline`]. More specifically, Transformers will automatically handle the loading of multiple shards within the requested model class and get it ready so that inference can be performed.
 
-The denoiser checkpoint can also have multiple shards and supports inference thanks to the [Accelerate](https://huggingface.co/docs/accelerate/index) library.
+The denoiser checkpoint can also have multiple shards and supports inference thanks to the [Accelerate](https://huggingface.co/docs/accelerate/index) library. 
 
 > [!TIP]
 > Refer to the [Handling big models for inference](https://huggingface.co/docs/accelerate/main/en/concept_guides/big_model_inference) guide for general guidance when working with big models that are hard to fit into memory.
@@ -43,7 +43,7 @@ unet.save_pretrained("sdxl-unet-sharded", max_shard_size="5GB")
 The size of the fp32 variant of the SDXL UNet checkpoint is ~10.4GB. Set the `max_shard_size` parameter to 5GB to create 3 shards. After saving, you can load them in [`StableDiffusionXLPipeline`]:
 
 ```python
-from diffusers import UNet2DConditionModel, StableDiffusionXLPipeline
+from diffusers import UNet2DConditionModel, StableDiffusionXLPipeline 
 import torch
 
 unet = UNet2DConditionModel.from_pretrained(
@@ -57,14 +57,14 @@ image = pipeline("a cute dog running on the grass", num_inference_steps=30).imag
 image.save("dog.png")
 ```
 
-If placing all the model-level components on the GPU at once is not feasible, use [`~DiffusionPipeline.enable_model_cpu_offload`] to help you:
+If placing all the model-level components on the GPU at once is not feasible, use [`~DiffusionPipeline.enable_model_cpu_offload`] to help you: 
 
 ```diff
 - pipeline.to("cuda")
 + pipeline.enable_model_cpu_offload()
 ```
 
-In general, we recommend sharding when a checkpoint is more than 5GB (in fp32).
+In general, we recommend sharding when a checkpoint is more than 5GB (in fp32). 
 
 ## Device placement
 
@@ -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

@@ -34,7 +34,7 @@ pipe_id = "stabilityai/stable-diffusion-xl-base-1.0"
 pipe = DiffusionPipeline.from_pretrained(pipe_id, torch_dtype=torch.float16).to("cuda")
 ```
 
-Next, load a [CiroN2022/toy-face](https://huggingface.co/CiroN2022/toy-face) adapter with the [`~diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] method. With the 🤗 PEFT integration, you can assign a specific `adapter_name` to the checkpoint, which lets you easily switch between different LoRA checkpoints. Let's call this adapter `"toy"`.
+Next, load a [CiroN2022/toy-face](https://huggingface.co/CiroN2022/toy-face) adapter with the [`~diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] method. With the 🤗 PEFT integration, you can assign a specific `adapter_name` to the checkpoint, which let's you easily switch between different LoRA checkpoints. Let's call this adapter `"toy"`.
 
 ```python
 pipe.load_lora_weights("CiroN2022/toy-face", weight_name="toy_face_sdxl.safetensors", adapter_name="toy")

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

@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
 
 # Pipeline callbacks
 
-The denoising loop of a pipeline can be modified with custom defined functions using the `callback_on_step_end` parameter. The callback function is executed at the end of each step, and modifies the pipeline attributes and variables for the next step. This is really useful for *dynamically* adjusting certain pipeline attributes or modifying tensor variables. This versatility allows for interesting use cases such as changing the prompt embeddings at each timestep, assigning different weights to the prompt embeddings, and editing the guidance scale. With callbacks, you can implement new features without modifying the underlying code!
+The denoising loop of a pipeline can be modified with custom defined functions using the `callback_on_step_end` parameter. The callback function is executed at the end of each step, and modifies the pipeline attributes and variables for the next step. This is really useful for *dynamically* adjusting certain pipeline attributes or modifying tensor variables. This versatility allows for interesting use-cases such as changing the prompt embeddings at each timestep, assigning different weights to the prompt embeddings, and editing the guidance scale. With callbacks, you can implement new features without modifying the underlying code!
 
 > [!TIP]
 > 🤗 Diffusers currently only supports `callback_on_step_end`, but feel free to open a [feature request](https://github.com/huggingface/diffusers/issues/new/choose) if you have a cool use-case and require a callback function with a different execution point!
@@ -75,7 +75,7 @@ out.images[0].save("official_callback.png")
     <figcaption class="mt-2 text-center text-sm text-gray-500">without SDXLCFGCutoffCallback</figcaption>
   </div>
   <div>
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/with_cfg_callback.png" alt="generated image of a sports car at the road with cfg callback" />
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/with_cfg_callback.png" alt="generated image of a a sports car at the road with cfg callback" />
     <figcaption class="mt-2 text-center text-sm text-gray-500">with SDXLCFGCutoffCallback</figcaption>
   </div>
 </div>
@@ -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
 

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)
@@ -256,7 +256,7 @@ make_image_grid([init_image, mask_image, output], rows=1, cols=3)
 
 ## Guess mode
 
-[Guess mode](https://github.com/lllyasviel/ControlNet/discussions/188) does not require supplying a prompt to a ControlNet at all! This forces the ControlNet encoder to do its best to "guess" the contents of the input control map (depth map, pose estimation, canny edge, etc.).
+[Guess mode](https://github.com/lllyasviel/ControlNet/discussions/188) does not require supplying a prompt to a ControlNet at all! This forces the ControlNet encoder to do it's best to "guess" the contents of the input control map (depth map, pose estimation, canny edge, etc.).
 
 Guess mode adjusts the scale of the output residuals from a ControlNet by a fixed ratio depending on the block depth. The shallowest `DownBlock` corresponds to 0.1, and as the blocks get deeper, the scale increases exponentially such that the scale of the `MidBlock` output becomes 1.0.
 
@@ -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)
@@ -289,9 +289,9 @@ scheduler = DPMSolverMultistepScheduler.from_pretrained(pipe_id, subfolder="sche
 3. Load an image processor:
 
 ```python
-from transformers import CLIPImageProcessor
+from transformers import CLIPFeatureExtractor
 
-feature_extractor = CLIPImageProcessor.from_pretrained(pipe_id, subfolder="feature_extractor")
+feature_extractor = CLIPFeatureExtractor.from_pretrained(pipe_id, subfolder="feature_extractor")
 ```
 
 <Tip warning={true}>

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)           |
 
@@ -212,14 +212,14 @@ TCD-LoRA is very versatile, and it can be combined with other adapter types like
 import torch
 import numpy as np
 from PIL import Image
-from transformers import DPTImageProcessor, DPTForDepthEstimation
+from transformers import DPTFeatureExtractor, DPTForDepthEstimation
 from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline
 from diffusers.utils import load_image, make_image_grid
 from scheduling_tcd import TCDScheduler
 
 device = "cuda"
 depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to(device)
-feature_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")
+feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-hybrid-midas")
 
 def get_depth_map(image):
     image = feature_extractor(images=image, return_tensors="pt").pixel_values.to(device)

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/merge_loras.md

@@ -14,9 +14,9 @@ specific language governing permissions and limitations under the License.
 
 It can be fun and creative to use multiple [LoRAs]((https://huggingface.co/docs/peft/conceptual_guides/adapter#low-rank-adaptation-lora)) together to generate something entirely new and unique. This works by merging multiple LoRA weights together to produce images that are a blend of different styles. Diffusers provides a few methods to merge LoRAs depending on *how* you want to merge their weights, which can affect image quality.
 
-This guide will show you how to merge LoRAs using the [`~loaders.PeftAdapterMixin.set_adapters`] and [add_weighted_adapter](https://huggingface.co/docs/peft/package_reference/lora#peft.LoraModel.add_weighted_adapter) methods. To improve inference speed and reduce memory-usage of merged LoRAs, you'll also see how to use the [`~loaders.StableDiffusionLoraLoaderMixin.fuse_lora`] method to fuse the LoRA weights with the original weights of the underlying model.
+This guide will show you how to merge LoRAs using the [`~loaders.UNet2DConditionLoadersMixin.set_adapters`] and [`~peft.LoraModel.add_weighted_adapter`] methods. To improve inference speed and reduce memory-usage of merged LoRAs, you'll also see how to use the [`~loaders.StableDiffusionLoraLoaderMixin.fuse_lora`] method to fuse the LoRA weights with the original weights of the underlying model.
 
-For this guide, load a Stable Diffusion XL (SDXL) checkpoint and the [KappaNeuro/studio-ghibli-style](https://huggingface.co/KappaNeuro/studio-ghibli-style) and [Norod78/sdxl-chalkboarddrawing-lora](https://huggingface.co/Norod78/sdxl-chalkboarddrawing-lora) LoRAs with the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] method. You'll need to assign each LoRA an `adapter_name` to combine them later.
+For this guide, load a Stable Diffusion XL (SDXL) checkpoint and the [KappaNeuro/studio-ghibli-style]() and [Norod78/sdxl-chalkboarddrawing-lora]() LoRAs with the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] method. You'll need to assign each LoRA an `adapter_name` to combine them later.
 
 ```py
 from diffusers import DiffusionPipeline
@@ -29,7 +29,7 @@ pipeline.load_lora_weights("lordjia/by-feng-zikai", weight_name="fengzikai_v1.0_
 
 ## set_adapters
 
-The [`~loaders.PeftAdapterMixin.set_adapters`] method merges LoRA adapters by concatenating their weighted matrices. Use the adapter name to specify which LoRAs to merge, and the `adapter_weights` parameter to control the scaling for each LoRA. For example, if `adapter_weights=[0.5, 0.5]`, then the merged LoRA output is an average of both LoRAs. Try adjusting the adapter weights to see how it affects the generated image!
+The [`~loaders.UNet2DConditionLoadersMixin.set_adapters`] method merges LoRA adapters by concatenating their weighted matrices. Use the adapter name to specify which LoRAs to merge, and the `adapter_weights` parameter to control the scaling for each LoRA. For example, if `adapter_weights=[0.5, 0.5]`, then the merged LoRA output is an average of both LoRAs. Try adjusting the adapter weights to see how it affects the generated image!
 
 ```py
 pipeline.set_adapters(["ikea", "feng"], adapter_weights=[0.7, 0.8])
@@ -47,19 +47,19 @@ image
 ## add_weighted_adapter
 
 > [!WARNING]
-> This is an experimental method that adds PEFTs [add_weighted_adapter](https://huggingface.co/docs/peft/package_reference/lora#peft.LoraModel.add_weighted_adapter) method to Diffusers to enable more efficient merging methods. Check out this [issue](https://github.com/huggingface/diffusers/issues/6892) if you're interested in learning more about the motivation and design behind this integration.
+> This is an experimental method that adds PEFTs [`~peft.LoraModel.add_weighted_adapter`] method to Diffusers to enable more efficient merging methods. Check out this [issue](https://github.com/huggingface/diffusers/issues/6892) if you're interested in learning more about the motivation and design behind this integration.
 
-The [add_weighted_adapter](https://huggingface.co/docs/peft/package_reference/lora#peft.LoraModel.add_weighted_adapter) method provides access to more efficient merging method such as [TIES and DARE](https://huggingface.co/docs/peft/developer_guides/model_merging). To use these merging methods, make sure you have the latest stable version of Diffusers and PEFT installed.
+The [`~peft.LoraModel.add_weighted_adapter`] method provides access to more efficient merging method such as [TIES and DARE](https://huggingface.co/docs/peft/developer_guides/model_merging). To use these merging methods, make sure you have the latest stable version of Diffusers and PEFT installed.
 
 ```bash
 pip install -U diffusers peft
 ```
 
-There are three steps to merge LoRAs with the [add_weighted_adapter](https://huggingface.co/docs/peft/package_reference/lora#peft.LoraModel.add_weighted_adapter) method:
+There are three steps to merge LoRAs with the [`~peft.LoraModel.add_weighted_adapter`] method:
 
-1. Create a [PeftModel](https://huggingface.co/docs/peft/package_reference/peft_model#peft.PeftModel) from the underlying model and LoRA checkpoint.
+1. Create a [`~peft.PeftModel`] from the underlying model and LoRA checkpoint.
 2. Load a base UNet model and the LoRA adapters.
-3. Merge the adapters using the [add_weighted_adapter](https://huggingface.co/docs/peft/package_reference/lora#peft.LoraModel.add_weighted_adapter) method and the merging method of your choice.
+3. Merge the adapters using the [`~peft.LoraModel.add_weighted_adapter`] method and the merging method of your choice.
 
 Let's dive deeper into what these steps entail.
 
@@ -92,7 +92,7 @@ pipeline = DiffusionPipeline.from_pretrained(
 pipeline.load_lora_weights("ostris/ikea-instructions-lora-sdxl", weight_name="ikea_instructions_xl_v1_5.safetensors", adapter_name="ikea")
 ```
 
-Now you'll create a [PeftModel](https://huggingface.co/docs/peft/package_reference/peft_model#peft.PeftModel) from the loaded LoRA checkpoint by combining the SDXL UNet and the LoRA UNet from the pipeline.
+Now you'll create a [`~peft.PeftModel`] from the loaded LoRA checkpoint by combining the SDXL UNet and the LoRA UNet from the pipeline.
 
 ```python
 from peft import get_peft_model, LoraConfig
@@ -112,7 +112,7 @@ ikea_peft_model.load_state_dict(original_state_dict, strict=True)
 > [!TIP]
 > You can optionally push the ikea_peft_model to the Hub by calling `ikea_peft_model.push_to_hub("ikea_peft_model", token=TOKEN)`.
 
-Repeat this process to create a [PeftModel](https://huggingface.co/docs/peft/package_reference/peft_model#peft.PeftModel) from the [lordjia/by-feng-zikai](https://huggingface.co/lordjia/by-feng-zikai) LoRA.
+Repeat this process to create a [`~peft.PeftModel`] from the [lordjia/by-feng-zikai](https://huggingface.co/lordjia/by-feng-zikai) LoRA.
 
 ```python
 pipeline.delete_adapters("ikea")
@@ -148,7 +148,7 @@ model = PeftModel.from_pretrained(base_unet, "stevhliu/ikea_peft_model", use_saf
 model.load_adapter("stevhliu/feng_peft_model", use_safetensors=True, subfolder="feng", adapter_name="feng")
 ```
 
-3. Merge the adapters using the [add_weighted_adapter](https://huggingface.co/docs/peft/package_reference/lora#peft.LoraModel.add_weighted_adapter) method and the merging method of your choice (learn more about other merging methods in this [blog post](https://huggingface.co/blog/peft_merging)). For this example, let's use the `"dare_linear"` method to merge the LoRAs.
+3. Merge the adapters using the [`~peft.LoraModel.add_weighted_adapter`] method and the merging method of your choice (learn more about other merging methods in this [blog post](https://huggingface.co/blog/peft_merging)). For this example, let's use the `"dare_linear"` method to merge the LoRAs.
 
 > [!WARNING]
 > Keep in mind the LoRAs need to have the same rank to be merged!
@@ -182,9 +182,9 @@ image
 
 ## fuse_lora
 
-Both the [`~loaders.PeftAdapterMixin.set_adapters`] and [add_weighted_adapter](https://huggingface.co/docs/peft/package_reference/lora#peft.LoraModel.add_weighted_adapter) methods require loading the base model and the LoRA adapters separately which incurs some overhead. The [`~loaders.lora_base.LoraBaseMixin.fuse_lora`] method allows you to fuse the LoRA weights directly with the original weights of the underlying model. This way, you're only loading the model once which can increase inference and lower memory-usage.
+Both the [`~loaders.UNet2DConditionLoadersMixin.set_adapters`] and [`~peft.LoraModel.add_weighted_adapter`] methods require loading the base model and the LoRA adapters separately which incurs some overhead. The [`~loaders.StableDiffusionLoraLoaderMixin.fuse_lora`] method allows you to fuse the LoRA weights directly with the original weights of the underlying model. This way, you're only loading the model once which can increase inference and lower memory-usage.
 
-You can use PEFT to easily fuse/unfuse multiple adapters directly into the model weights (both UNet and text encoder) using the [`~loaders.lora_base.LoraBaseMixin.fuse_lora`] method, which can lead to a speed-up in inference and lower VRAM usage.
+You can use PEFT to easily fuse/unfuse multiple adapters directly into the model weights (both UNet and text encoder) using the [`~loaders.StableDiffusionLoraLoaderMixin.fuse_lora`] method, which can lead to a speed-up in inference and lower VRAM usage.
 
 For example, if you have a base model and adapters loaded and set as active with the following adapter weights:
 
@@ -199,7 +199,7 @@ pipeline.load_lora_weights("lordjia/by-feng-zikai", weight_name="fengzikai_v1.0_
 pipeline.set_adapters(["ikea", "feng"], adapter_weights=[0.7, 0.8])
 ```
 
-Fuse these LoRAs into the UNet with the [`~loaders.lora_base.LoraBaseMixin.fuse_lora`] method. The `lora_scale` parameter controls how much to scale the output by with the LoRA weights. It is important to make the `lora_scale` adjustments in the [`~loaders.lora_base.LoraBaseMixin.fuse_lora`] method because it won’t work if you try to pass `scale` to the `cross_attention_kwargs` in the pipeline.
+Fuse these LoRAs into the UNet with the [`~loaders.StableDiffusionLoraLoaderMixin.fuse_lora`] method. The `lora_scale` parameter controls how much to scale the output by with the LoRA weights. It is important to make the `lora_scale` adjustments in the [`~loaders.StableDiffusionLoraLoaderMixin.fuse_lora`] method because it won’t work if you try to pass `scale` to the `cross_attention_kwargs` in the pipeline.
 
 ```py
 pipeline.fuse_lora(adapter_names=["ikea", "feng"], lora_scale=1.0)
@@ -226,7 +226,7 @@ image = pipeline("A bowl of ramen shaped like a cute kawaii bear, by Feng Zikai"
 image
 ```
 
-You can call [`~~loaders.lora_base.LoraBaseMixin.unfuse_lora`] to restore the original model's weights (for example, if you want to use a different `lora_scale` value). However, this only works if you've only fused one LoRA adapter to the original model. If you've fused multiple LoRAs, you'll need to reload the model.
+You can call [`~loaders.StableDiffusionLoraLoaderMixin.unfuse_lora`] to restore the original model's weights (for example, if you want to use a different `lora_scale` value). However, this only works if you've only fused one LoRA adapter to the original model. If you've fused multiple LoRAs, you'll need to reload the model.
 
 ```py
 pipeline.unfuse_lora()

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/pag.md

@@ -22,7 +22,7 @@ This guide will show you how to use PAG for various tasks and use cases.
 You can apply PAG to the [`StableDiffusionXLPipeline`] for tasks such as text-to-image, image-to-image, and inpainting. To enable PAG for a specific task, load the pipeline using the [AutoPipeline](../api/pipelines/auto_pipeline) API with the `enable_pag=True` flag and the `pag_applied_layers` argument.
 
 > [!TIP]
-> 🤗 Diffusers currently only supports using PAG with selected SDXL pipelines and [`PixArtSigmaPAGPipeline`]. But feel free to open a [feature request](https://github.com/huggingface/diffusers/issues/new/choose) if you want to add PAG support to a new pipeline!
+> 🤗 Diffusers currently only supports using PAG with selected SDXL pipelines, but feel free to open a [feature request](https://github.com/huggingface/diffusers/issues/new/choose) if you want to add PAG support to a new pipeline!
 
 <hfoptions id="tasks">
 <hfoption id="Text-to-image">
@@ -130,10 +130,10 @@ prompt = "a dog catching a frisbee in the jungle"
 
 generator = torch.Generator(device="cpu").manual_seed(0)
 image = pipeline(
-    prompt,
-    image=init_image,
-    strength=0.8,
-    guidance_scale=guidance_scale,
+    prompt, 
+    image=init_image, 
+    strength=0.8, 
+    guidance_scale=guidance_scale, 
     pag_scale=pag_scale,
     generator=generator).images[0]
 ```
@@ -161,14 +161,14 @@ pipeline_inpaint = AutoPipelineForInpaiting.from_pretrained("stabilityai/stable-
 pipeline = AutoPipelineForInpaiting.from_pipe(pipeline_inpaint, enable_pag=True)
 ```
 
-This still works when your pipeline has a different task:
+This still works when your pipeline has a different task: 
 
 ```py
 pipeline_t2i = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)
 pipeline = AutoPipelineForInpaiting.from_pipe(pipeline_t2i, enable_pag=True)
 ```
 
-Let's generate an image!
+Let's generate an image! 
 
 ```py
 img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
@@ -258,7 +258,7 @@ for pag_scale in [0.0, 3.0]:
   </div>
 </div>
 
-## PAG with IP-Adapter
+## PAG with IP-Adapter 
 
 [IP-Adapter](https://hf.co/papers/2308.06721) is a popular model that can be plugged into diffusion models to enable image prompting without any changes to the underlying model. You can enable PAG on a pipeline with IP-Adapter loaded.
 
@@ -317,7 +317,7 @@ PAG reduces artifacts and improves the overall compposition.
 </div>
 
 
-## Configure parameters
+## Configure parameters 
 
 ### pag_applied_layers
 

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

@@ -365,7 +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
@@ -411,7 +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

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

@@ -31,7 +31,7 @@ from diffusers.utils import make_image_grid
 Pick a Stable Diffusion checkpoint and a pre-learned concept from the [Stable Diffusion Conceptualizer](https://huggingface.co/spaces/sd-concepts-library/stable-diffusion-conceptualizer):
 
 ```py
-pretrained_model_name_or_path = "stable-diffusion-v1-5/stable-diffusion-v1-5"
+pretrained_model_name_or_path = "runwayml/stable-diffusion-v1-5"
 repo_id_embeds = "sd-concepts-library/cat-toy"
 ```
 

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

@@ -366,7 +366,7 @@ from diffusers import StableDiffusionPipeline
 from compel import Compel, DiffusersTextualInversionManager
 
 pipe = StableDiffusionPipeline.from_pretrained(
-  "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16,
+  "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16,
   use_safetensors=True, variant="fp16").to("cuda")
 pipe.load_textual_inversion("sd-concepts-library/midjourney-style")
 ```

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

@@ -116,7 +116,7 @@ As you can see, this is already more complex than the DDPM pipeline which only c
 
 </Tip>
 
-Now that you know what you need for the Stable Diffusion pipeline, load all these components with the [`~ModelMixin.from_pretrained`] method. You can find them in the pretrained [`stable-diffusion-v1-5/stable-diffusion-v1-5`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) checkpoint, and each component is stored in a separate subfolder:
+Now that you know what you need for the Stable Diffusion pipeline, load all these components with the [`~ModelMixin.from_pretrained`] method. You can find them in the pretrained [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) checkpoint, and each component is stored in a separate subfolder:
 
 ```py
 >>> from PIL import Image

docs/source/ja/quicktour.md

@@ -54,7 +54,7 @@ specific language governing permissions and limitations under the License.
 
 まず、[`DiffusionPipeline`]のインスタンスを作成し、ダウンロードしたいパイプラインのチェックポイントを指定します。
 この[`DiffusionPipeline`]はHugging Face Hubに保存されている任意の[チェックポイント](https://huggingface.co/models?library=diffusers&sort=downloads)を使用することができます。
-この案内では、[`stable-diffusion-v1-5`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5)チェックポイントでテキストから画像へ生成します。
+この案内では、[`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5)チェックポイントでテキストから画像へ生成します。
 
 <Tip warning={true}>
 
@@ -67,7 +67,7 @@ specific language governing permissions and limitations under the License.
 ```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)
 ```
 [`DiffusionPipeline`]は全てのモデリング、トークン化、スケジューリングコンポーネントをダウンロードしてキャッシュします。Stable Diffusionパイプラインは[`UNet2DConditionModel`]と[`PNDMScheduler`]などで構成されています：
 
@@ -123,7 +123,7 @@ PyTorchと同じように、ジェネレータオブジェクトをGPUに移す
 
 ```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
 ```
 
 保存したウェイトをパイプラインにロードします：
@@ -141,7 +141,7 @@ PyTorchと同じように、ジェネレータオブジェクトをGPUに移す
 ```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/ja/stable_diffusion.md

@@ -20,12 +20,12 @@ specific language governing permissions and limitations under the License.
 
 このチュートリアルでは、[`DiffusionPipeline`]を用いて、より速く、より良い計算を行う方法を説明します。
 
-まず、[`stable-diffusion-v1-5/stable-diffusion-v1-5`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5)モデルをロードします：
+まず、[`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5)モデルをロードします：
 
 ```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/ja/tutorials/autopipeline.md

@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
 
 # AutoPipeline
 
-Diffusersは様々なタスクをこなすことができ、テキストから画像、画像から画像、画像の修復など、複数のタスクに対して同じように事前学習された重みを再利用することができます。しかし、ライブラリや拡散モデルに慣れていない場合、どのタスクにどのパイプラインを使えばいいのかがわかりにくいかもしれません。例えば、 [stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) チェックポイントをテキストから画像に変換するために使用している場合、それぞれ[`StableDiffusionImg2ImgPipeline`]クラスと[`StableDiffusionInpaintPipeline`]クラスでチェックポイントをロードすることで、画像から画像や画像の修復にも使えることを知らない可能性もあります。
+Diffusersは様々なタスクをこなすことができ、テキストから画像、画像から画像、画像の修復など、複数のタスクに対して同じように事前学習された重みを再利用することができます。しかし、ライブラリや拡散モデルに慣れていない場合、どのタスクにどのパイプラインを使えばいいのかがわかりにくいかもしれません。例えば、 [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) チェックポイントをテキストから画像に変換するために使用している場合、それぞれ[`StableDiffusionImg2ImgPipeline`]クラスと[`StableDiffusionInpaintPipeline`]クラスでチェックポイントをロードすることで、画像から画像や画像の修復にも使えることを知らない可能性もあります。
 
 `AutoPipeline` クラスは、🤗 Diffusers の様々なパイプラインをよりシンプルするために設計されています。この汎用的でタスク重視のパイプラインによってタスクそのものに集中することができます。`AutoPipeline` は、使用するべき正しいパイプラインクラスを自動的に検出するため、特定のパイプラインクラス名を知らなくても、タスクのチェックポイントを簡単にロードできます。
 
@@ -25,14 +25,14 @@ Diffusersは様々なタスクをこなすことができ、テキストから
 このチュートリアルでは、`AutoPipeline` を使用して、事前に学習された重みが与えられたときに、特定のタスクを読み込むためのパイプラインクラスを自動的に推測する方法を示します。
 
 ## タスクに合わせてAutoPipeline を選択する
-まずはチェックポイントを選ぶことから始めましょう。例えば、 [stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) チェックポイントでテキストから画像への変換したいなら、[`AutoPipelineForText2Image`]を使います:
+まずはチェックポイントを選ぶことから始めましょう。例えば、 [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) チェックポイントでテキストから画像への変換したいなら、[`AutoPipelineForText2Image`]を使います:
 
 ```py
 from diffusers import AutoPipelineForText2Image
 import torch
 
 pipeline = AutoPipelineForText2Image.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 = "peasant and dragon combat, wood cutting style, viking era, bevel with rune"
 
@@ -46,7 +46,7 @@ image
 
 [`AutoPipelineForText2Image`] を具体的に見ていきましょう:
 
-1. [`model_index.json`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/model_index.json) ファイルから `"stable-diffusion"` クラスを自動的に検出します。
+1. [`model_index.json`](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/model_index.json) ファイルから `"stable-diffusion"` クラスを自動的に検出します。
 2. `"stable-diffusion"` のクラス名に基づいて、テキストから画像へ変換する [`StableDiffusionPipeline`] を読み込みます。
 
 同様に、画像から画像へ変換する場合、[`AutoPipelineForImage2Image`] は `model_index.json` ファイルから `"stable-diffusion"` チェックポイントを検出し、対応する [`StableDiffusionImg2ImgPipeline`] を読み込みます。また、入力画像にノイズの量やバリエーションの追加を決めるための強さなど、パイプラインクラスに固有の追加引数を渡すこともできます:
@@ -59,7 +59,7 @@ from PIL import Image
 from io import BytesIO
 
 pipeline = AutoPipelineForImage2Image.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    "runwayml/stable-diffusion-v1-5",
     torch_dtype=torch.float16,
     use_safetensors=True,
 ).to("cuda")
@@ -128,7 +128,7 @@ from diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image
 import torch
 
 pipeline_text2img = AutoPipelineForText2Image.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
 )
 print(type(pipeline_text2img))
 "<class 'diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline'>"
@@ -148,7 +148,7 @@ from diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image
 import torch
 
 pipeline_text2img = AutoPipelineForText2Image.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    "runwayml/stable-diffusion-v1-5",
     torch_dtype=torch.float16,
     use_safetensors=True,
     requires_safety_checker=False,

docs/source/ko/conceptual/contribution.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.
 -->
 
-# Diffusers에 기여하는 방법 🧨 [[how-to-contribute-to-diffusers-]]
+# Diffusers에 기여하는 방법 🧨
 
 오픈 소스 커뮤니티에서의 기여를 환영합니다! 누구나 참여할 수 있으며, 코드뿐만 아니라 질문에 답변하거나 문서를 개선하는 등 모든 유형의 참여가 가치 있고 감사히 여겨집니다. 질문에 답변하고 다른 사람들을 도와주며 소통하고 문서를 개선하는 것은 모두 커뮤니티에게 큰 도움이 됩니다. 따라서 관심이 있다면 두려워하지 말고 참여해보세요!
 
@@ -18,9 +18,9 @@ specific language governing permissions and limitations under the License.
 
 어떤 방식으로든 기여하려는 경우, 우리는 개방적이고 환영하며 친근한 커뮤니티의 일부가 되기 위해 노력하고 있습니다. 우리의 [행동 강령](https://github.com/huggingface/diffusers/blob/main/CODE_OF_CONDUCT.md)을 읽고 상호 작용 중에 이를 존중하도록 주의해주시기 바랍니다. 또한 프로젝트를 안내하는 [윤리 지침](https://huggingface.co/docs/diffusers/conceptual/ethical_guidelines)에 익숙해지고 동일한 투명성과 책임성의 원칙을 준수해주시기를 부탁드립니다.
 
-우리는 커뮤니티로부터의 피드백을 매우 중요하게 생각하므로, 라이브러리를 개선하는 데 도움이 될 가치 있는 피드백이 있다고 생각되면 망설이지 말고 의견을 제시해주세요 - 모든 메시지, 댓글, 이슈, Pull Request(PR)는 읽히고 고려됩니다.
+우리는 커뮤니티로부터의 피드백을 매우 중요하게 생각하므로, 라이브러리를 개선하는 데 도움이 될 가치 있는 피드백이 있다고 생각되면 망설이지 말고 의견을 제시해주세요 - 모든 메시지, 댓글, 이슈, 풀 리퀘스트(PR)는 읽히고 고려됩니다.
 
-## 개요 [[overview]]
+## 개요
 
 이슈에 있는 질문에 답변하는 것에서부터 코어 라이브러리에 새로운 diffusion 모델을 추가하는 것까지 다양한 방법으로 기여를 할 수 있습니다.
 
@@ -38,9 +38,9 @@ specific language governing permissions and limitations under the License.
 
 앞서 말한 대로, **모든 기여는 커뮤니티에게 가치가 있습니다**. 이어지는 부분에서 각 기여에 대해 조금 더 자세히 설명하겠습니다.
 
-4부터 9까지의 모든 기여에는 Pull Request을 열어야 합니다. [Pull Request 열기](#how-to-open-a-pr)에서 자세히 설명되어 있습니다.
+4부터 9까지의 모든 기여에는 PR을 열어야 합니다. [PR을 열기](#how-to-open-a-pr)에서 자세히 설명되어 있습니다.
 
-### 1. Diffusers 토론 포럼이나 Diffusers Discord에서 질문하고 답변하기 [[1-asking-and-answering-questions-on-the-diffusers-discussion-forum-or-on-the-diffusers-discord]]
+### 1. Diffusers 토론 포럼이나 Diffusers Discord에서 질문하고 답변하기
 
 Diffusers 라이브러리와 관련된 모든 질문이나 의견은 [토론 포럼](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63)이나 [Discord](https://discord.gg/G7tWnz98XR)에서 할 수 있습니다. 이러한 질문과 의견에는 다음과 같은 내용이 포함됩니다(하지만 이에 국한되지는 않습니다):
 - 지식을 공유하기 위해서 훈련 또는 추론 실험에 대한 결과 보고
@@ -54,7 +54,7 @@ Diffusers 라이브러리와 관련된 모든 질문이나 의견은 [토론 포
 - Diffusion 모델에 대한 윤리적 질문
 - ...
 
-포럼이나 Discord에서 질문을 하면 커뮤니티가 지식을 공개적으로 공유하도록 장려되며, 향후 동일한 질문을 가진 초보자에게도 도움이 될 수 있습니다. 따라서 궁금한 질문은 언제든지 하시기 바랍니다.
+포럼이나 Discord에서 질문을 하면 커뮤니티가 지식을 공개적으로 공유하도록 장려되며, 미래에 동일한 질문을 가진 초보자에게도 도움이 될 수 있습니다. 따라서 궁금한 질문은 언제든지 하시기 바랍니다.
 또한, 이러한 질문에 답변하는 것은 커뮤니티에게 매우 큰 도움이 됩니다. 왜냐하면 이렇게 하면 모두가 학습할 수 있는 공개적인 지식을 문서화하기 때문입니다.
 
 **주의**하십시오. 질문이나 답변에 투자하는 노력이 많을수록 공개적으로 문서화된 지식의 품질이 높아집니다. 마찬가지로, 잘 정의되고 잘 답변된 질문은 모두에게 접근 가능한 고품질 지식 데이터베이스를 만들어줍니다. 반면에 잘못된 질문이나 답변은 공개 지식 데이터베이스의 전반적인 품질을 낮출 수 있습니다.
@@ -64,9 +64,9 @@ Diffusers 라이브러리와 관련된 모든 질문이나 의견은 [토론 포
 [*포럼*](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63)은 구글과 같은 검색 엔진에서 더 잘 색인화됩니다. 게시물은 인기에 따라 순위가 매겨지며, 시간순으로 정렬되지 않습니다. 따라서 이전에 게시한 질문과 답변을 쉽게 찾을 수 있습니다.
 또한, 포럼에 게시된 질문과 답변은 쉽게 링크할 수 있습니다.
 반면 *Discord*는 채팅 형식으로 되어 있어 빠른 대화를 유도합니다.
-질문에 대한 답변을 빠르게 받을 수는 있겠지만, 시간이 지나면 질문이 더 이상 보이지 않습니다. 또한, Discord에서 이전에 게시된 정보를 찾는 것은 훨씬 어렵습니다. 따라서 포럼을 사용하여 고품질의 질문과 답변을 하여 커뮤니티를 위한 오래 지속되는 지식을 만들기를 권장합니다. Discord에서의 토론이 매우 흥미로운 답변과 결론을 이끌어내는 경우, 해당 정보를 포럼에 게시하여 향후 독자들에게 더 쉽게 액세스할 수 있도록 권장합니다.
+질문에 대한 답변을 빠르게 받을 수는 있겠지만, 시간이 지나면 질문이 더 이상 보이지 않습니다. 또한, Discord에서 이전에 게시된 정보를 찾는 것은 훨씬 어렵습니다. 따라서 포럼을 사용하여 고품질의 질문과 답변을 하여 커뮤니티를 위한 오래 지속되는 지식을 만들기를 권장합니다. Discord에서의 토론이 매우 흥미로운 답변과 결론을 이끌어내는 경우, 해당 정보를 포럼에 게시하여 미래 독자들에게 더 쉽게 액세스할 수 있도록 권장합니다.
 
-### 2. GitHub 이슈 탭에서 새로운 이슈 열기 [[2-opening-new-issues-on-the-github-issues-tab]]
+### 2. GitHub 이슈 탭에서 새로운 이슈 열기
 
 🧨 Diffusers 라이브러리는 사용자들이 마주치는 문제를 알려주는 덕분에 견고하고 신뢰할 수 있습니다. 따라서 이슈를 보고해주셔서 감사합니다.
 
@@ -81,52 +81,53 @@ Diffusers 라이브러리와 관련된 모든 질문이나 의견은 [토론 포
 - 이슈가 최신 Diffusers 버전으로 업데이트하면 해결될 수 있는지 확인해주세요. 이슈를 게시하기 전에 `python -c "import diffusers; print(diffusers.__version__)"` 명령을 실행하여 현재 사용 중인 Diffusers 버전이 최신 버전과 일치하거나 더 높은지 확인해주세요.
 - 새로운 이슈를 열 때 투자하는 노력이 많을수록 답변의 품질이 높아지고 Diffusers 이슈 전체의 품질도 향상됩니다.
 
-#### 2.1 재현 가능한 최소한의 버그 리포트 [[21-reproducible-minimal-bug-reports]]
+#### 2.1 재현가능하고 최소한인 버그 리포트
 
+새로운 이슈는 일반적으로 다음과 같은 내용을 포함합니다.
 
-버그 리포트는 항상 재현 가능한 코드 조각을 포함하고 가능한 한 최소한이어야 하며 간결해야 합니다.
+버그 보고서는 항상 재현 가능한 코드 조각을 포함하고 가능한 한 최소한이어야 하며 간결해야 합니다.
 자세히 말하면:
 - 버그를 가능한 한 좁혀야 합니다. **전체 코드 파일을 그냥 던지지 마세요**.
 - 코드의 서식을 지정해야 합니다.
 - Diffusers가 의존하는 외부 라이브러리를 제외한 다른 외부 라이브러리는 포함하지 마십시오.
-- **항상** 사용자 환경에 대한 모든 필요한 정보를 제공하세요. 이를 위해 쉘에서 `diffusers-cli env`를 실행하고 표시된 정보를 이슈에 복사하여 붙여넣을 수 있습니다.
-- 이슈를 설명해야 합니다. 독자가 문제가 무엇인지, 왜 문제가 되는지 모른다면 이슈를 해결할 수 없습니다. 
-- **항상** 독자가 가능한 한 적은 노력으로 문제를 재현할 수 있어야 합니다. 코드 조각이 라이브러리가 없거나 정의되지 않은 변수 때문에 실행되지 않는 경우 독자가 도움을 줄 수 없습니다. 재현 가능한 코드 조각이 가능한 한 최소화되고 간단한 Python 셸에 복사하여 붙여넣을 수 있도록 해야 합니다.
+- **반드시** 환경에 대한 모든 필요한 정보를 제공해야 합니다. 이를 위해 쉘에서 `diffusers-cli env`를 실행하고 표시된 정보를 이슈에 복사하여 붙여넣을 수 있습니다.
+- 이슈를 설명해야 합니다. 독자가 문제가 무엇이며 왜 문제인지 모르면 해결할 수 없습니다.
+- **항상** 독자가 가능한 한 적은 노력으로 문제를 재현할 수 있도록 해야 합니다. 코드 조각이 라이브러리가 없거나 정의되지 않은 변수 때문에 실행되지 않는 경우 독자가 도움을 줄 수 없습니다. 재현 가능한 코드 조각이 가능한 한 최소화되고 간단한 Python 셸에 복사하여 붙여넣을 수 있도록 해야 합니다.
 - 문제를 재현하기 위해 모델과/또는 데이터셋이 필요한 경우 독자가 해당 모델이나 데이터셋에 접근할 수 있도록 해야 합니다. 모델이나 데이터셋을 [Hub](https://huggingface.co)에 업로드하여 쉽게 다운로드할 수 있도록 할 수 있습니다. 문제 재현을 가능한 한 쉽게하기 위해 모델과 데이터셋을 가능한 한 작게 유지하려고 노력하세요.
 
 자세한 내용은 [좋은 이슈 작성 방법](#how-to-write-a-good-issue) 섹션을 참조하세요.
 
-버그 리포트를 열려면 [여기](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=bug&projects=&template=bug-report.yml)를 클릭하세요.
+버그 보고서를 열려면 [여기](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=bug&projects=&template=bug-report.yml)를 클릭하세요.
 
 
-#### 2.2. 기능 요청 [[22-feature-requests]]
+#### 2.2. 기능 요청
 
 세계적인 기능 요청은 다음 사항을 다룹니다:
 
 1. 먼저 동기부여:
-* 라이브러리와 관련된 문제/불만이 있나요? 그렇다면 왜 그런지 설명해주세요. 문제를 보여주는 코드 조각을 제공하는 것이 가장 좋습니다.
+* 라이브러리와 관련된 문제/불만이 있는가요? 그렇다면 왜 그런지 설명해주세요. 문제를 보여주는 코드 조각을 제공하는 것이 가장 좋습니다.
 * 프로젝트에 필요한 기능인가요? 우리는 그에 대해 듣고 싶습니다!
 * 커뮤니티에 도움이 될 수 있는 것을 작업했고 그것에 대해 생각하고 있는가요? 멋지네요! 어떤 문제를 해결했는지 알려주세요.
 2. 기능을 *상세히 설명하는* 문단을 작성해주세요;
-3. 향후 사용을 보여주는 **코드 조각**을 제공해주세요;
-4. 논문과 관련된 내용인 경우 링크를 첨부해주세요;
-5. 도움이 될 수 있다고 생각되는 추가 정보(그림, 스크린샷 등)를 첨부해주세요.
+3. 미래 사용을 보여주는 **코드 조각**을 제공해주세요;
+4. 이것이 논문과 관련된 경우 링크를 첨부해주세요;
+5. 도움이 될 수 있는 추가 정보(그림, 스크린샷 등)를 첨부해주세요.
 
 기능 요청은 [여기](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feature_request.md&title=)에서 열 수 있습니다.
 
-#### 2.3 피드백 [[23-feedback]]
+#### 2.3 피드백
 
-라이브러리 디자인과 그것이 왜 좋은지 또는 나쁜지에 대한 이유에 대한 피드백은 핵심 메인테이너가 사용자 친화적인 라이브러리를 만드는 데 엄청난 도움이 됩니다. 현재 디자인 철학을 이해하려면 [여기](https://huggingface.co/docs/diffusers/conceptual/philosophy)를 참조해 주세요. 특정 디자인 선택이 현재 디자인 철학과 맞지 않는다고 생각되면, 그 이유와 어떻게 변경되어야 하는지 설명해 주세요. 반대로 특정 디자인 선택이 디자인 철학을 너무 따르기 때문에 사용 사례를 제한한다고 생각되면, 그 이유와 어떻게 변경되어야 하는지 설명해 주세요. 특정 디자인 선택이 매우 유용하다고 생각되면, 향후 디자인 결정에 큰 도움이 되므로 이에 대한 의견을 남겨 주세요.
+라이브러리 디자인과 그것이 왜 좋은지 또는 나쁜지에 대한 이유에 대한 피드백은 핵심 메인테이너가 사용자 친화적인 라이브러리를 만드는 데 엄청난 도움이 됩니다. 현재 디자인 철학을 이해하려면 [여기](https://huggingface.co/docs/diffusers/conceptual/philosophy)를 참조해 주세요. 특정 디자인 선택이 현재 디자인 철학과 맞지 않는다고 생각되면, 그 이유와 어떻게 변경되어야 하는지 설명해 주세요. 반대로 특정 디자인 선택이 디자인 철학을 너무 따르기 때문에 사용 사례를 제한한다고 생각되면, 그 이유와 어떻게 변경되어야 하는지 설명해 주세요. 특정 디자인 선택이 매우 유용하다고 생각되면, 미래의 디자인 결정에 큰 도움이 되므로 이에 대한 의견을 남겨 주세요.
 
 피드백에 관한 이슈는 [여기](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=)에서 열 수 있습니다.
 
-#### 2.4 기술적인 질문 [[24-technical-questions]]
+#### 2.4 기술적인 질문
 
 기술적인 질문은 주로 라이브러리의 특정 코드가 왜 특정 방식으로 작성되었는지 또는 코드의 특정 부분이 무엇을 하는지에 대한 질문입니다. 질문하신 코드 부분에 대한 링크를 제공하고 해당 코드 부분이 이해하기 어려운 이유에 대한 자세한 설명을 해주시기 바랍니다.
 
 기술적인 질문에 관한 이슈를 [여기](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=bug&template=bug-report.yml)에서 열 수 있습니다.
 
-#### 2.5 새로운 모델, 스케줄러 또는 파이프라인 추가 제안 [[25-proposal-to-add-a-new-model-scheduler-or-pipeline]]
+#### 2.5 새로운 모델, 스케줄러 또는 파이프라인 추가 제안
 
 만약 diffusion 모델 커뮤니티에서 Diffusers 라이브러리에 추가하고 싶은 새로운 모델, 파이프라인 또는 스케줄러가 있다면, 다음 정보를 제공해주세요:
 
@@ -134,34 +135,34 @@ Diffusers 라이브러리와 관련된 모든 질문이나 의견은 [토론 포
 * 해당 모델의 오픈 소스 구현에 대한 링크
 * 모델 가중치가 있는 경우, 가중치의 링크
 
-직접 모델에 기여하고 싶다면, 가장 잘 안내해드릴 수 있습니다. 또한, 가능하다면 구성 요소(모델, 스케줄러, 파이프라인 등)의 원저자를 GitHub 핸들로 태그하는 것을 잊지 마세요.
+모델에 직접 기여하고자 하는 경우, 최선의 안내를 위해 우리에게 알려주세요. 또한, 가능하다면 구성 요소(모델, 스케줄러, 파이프라인 등)의 원래 저자를 GitHub 핸들로 태그하는 것을 잊지 마세요.
 
 모델/파이프라인/스케줄러에 대한 요청을 [여기](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=New+model%2Fpipeline%2Fscheduler&template=new-model-addition.yml)에서 열 수 있습니다.
 
-### 3. GitHub 이슈 탭에서 문제에 대한 답변하기 [[3-answering-issues-on-the-github-issues-tab]]
+### 3. GitHub 이슈 탭에서 문제에 대한 답변하기
 
 GitHub에서 이슈에 대한 답변을 하기 위해서는 Diffusers에 대한 기술적인 지식이 필요할 수 있지만, 정확한 답변이 아니더라도 모두가 시도해기를 권장합니다. 이슈에 대한 고품질 답변을 제공하기 위한 몇 가지 팁:
 - 가능한 한 간결하고 최소한으로 유지합니다.
 - 주제에 집중합니다. 이슈에 대한 답변은 해당 이슈에 관련된 내용에만 집중해야 합니다.
-- 자신의 주장을 증명하거나 장려하는 코드, 논문 또는 기타 출처는 링크를 제공하세요.
+- 코드, 논문 또는 다른 소스를 제공하여 답변을 증명하거나 지지합니다.
 - 코드로 답변합니다. 간단한 코드 조각이 이슈에 대한 답변이거나 이슈를 해결하는 방법을 보여준다면, 완전히 재현 가능한 코드 조각을 제공해주세요.
 
 또한, 많은 이슈들은 단순히 주제와 무관하거나 다른 이슈의 중복이거나 관련이 없는 경우가 많습니다. 이러한 이슈들에 대한 답변을 제공하고, 이슈 작성자에게 더 정확한 정보를 제공하거나, 중복된 이슈에 대한 링크를 제공하거나, [포럼](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) 이나 [Discord](https://discord.gg/G7tWnz98XR)로 리디렉션하는 것은 메인테이너에게 큰 도움이 됩니다.
 
 이슈가 올바른 버그 보고서이고 소스 코드에서 수정이 필요하다고 확인한 경우, 다음 섹션을 살펴보세요.
 
-다음 모든 기여에 대해서는 PR을 열여야 합니다. [Pull Request 열기](#how-to-open-a-pr) 섹션에서 자세히 설명되어 있습니다.
+다음 모든 기여에 대해서는 PR을 열여야 합니다. [PR 열기](#how-to-open-a-pr) 섹션에서 자세히 설명되어 있습니다.
 
-### 4. "Good first issue" 고치기 [[4-fixing-a-good-first-issue]]
+### 4. "Good first issue" 고치기
 
 *Good first issues*는 [Good first issue](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) 라벨로 표시됩니다. 일반적으로, 이슈는 이미 잠재적인 해결책이 어떻게 보이는지 설명하고 있어서 수정하기 쉽습니다.
 만약 이슈가 아직 닫히지 않았고 이 문제를 해결해보고 싶다면, "이 이슈를 해결해보고 싶습니다."라는 메시지를 남기면 됩니다. 일반적으로 세 가지 시나리오가 있습니다:
-- a.) 이슈 설명에 이미 수정 사항을 제안하는 경우, 해결책이 이해되고 합리적으로 보인다면, PR 또는 드래프트 PR을 열어서 수정할 수 있습니다.
-- b.) 이슈 설명에 수정 사항이 제안되어 있지 않은 경우, 제안한 수정 사항이 가능할지 물어볼 수 있고, Diffusers 팀의 누군가가 곧 답변해줄 것입니다. 만약 어떻게 수정할지 좋은 아이디어가 있다면, 직접 PR을 열어도 됩니다.
+- a.) 이슈 설명이 이미 해결책을 제안합니다. 이 경우, 해결책이 이해되고 합리적으로 보인다면, PR 또는 드래프트 PR을 열어서 수정할 수 있습니다.
+- b.) 이슈 설명이 해결책을 제안하지 않습니다. 이 경우, 어떤 해결책이 가능할지 물어볼 수 있고, Diffusers 팀의 누군가가 곧 답변해줄 것입니다. 만약 어떻게 수정할지 좋은 아이디어가 있다면, 직접 PR을 열어도 됩니다.
 - c.) 이미 이 문제를 해결하기 위해 열린 PR이 있지만, 이슈가 아직 닫히지 않았습니다. PR이 더 이상 진행되지 않았다면, 새로운 PR을 열고 이전 PR에 링크를 걸면 됩니다. PR은 종종 원래 기여자가 갑자기 시간을 내지 못해 더 이상 진행하지 못하는 경우에 더 이상 진행되지 않게 됩니다. 이는 오픈 소스에서 자주 발생하는 일이며 매우 정상적인 상황입니다. 이 경우, 커뮤니티는 새로 시도하고 기존 PR의 지식을 활용해주면 매우 기쁠 것입니다. 이미 PR이 있고 활성화되어 있다면, 제안을 해주거나 PR을 검토하거나 PR에 기여할 수 있는지 물어보는 등 작성자를 도와줄 수 있습니다.
 
 
-### 5. 문서에 기여하기 [[5-contribute-to-the-documentation]]
+### 5. 문서에 기여하기
 
 좋은 라이브러리는 항상 좋은 문서를 갖고 있습니다! 공식 문서는 라이브러리를 처음 사용하는 사용자들에게 첫 번째 접점 중 하나이며, 따라서 문서에 기여하는 것은 매우 가치 있는 기여입니다.
 
@@ -179,7 +180,7 @@ GitHub에서 이슈에 대한 답변을 하기 위해서는 Diffusers에 대한
 문서에 대한 변경 사항을 로컬에서 확인하는 방법은 [이 페이지](https://github.com/huggingface/diffusers/tree/main/docs)를 참조해주세요.
 
 
-### 6. 커뮤니티 파이프라인에 기여하기 [[6-contribute-a-community-pipeline]]
+### 6. 커뮤니티 파이프라인에 기여하기
 
 > [!TIP]
 > 커뮤니티 파이프라인에 대해 자세히 알아보려면 [커뮤니티 파이프라인](../using-diffusers/custom_pipeline_overview#community-pipelines) 가이드를 읽어보세요. 커뮤니티 파이프라인이 왜 필요한지 궁금하다면 GitHub 이슈 [#841](https://github.com/huggingface/diffusers/issues/841)를 확인해보세요 (기본적으로, 우리는 diffusion 모델이 추론에 사용될 수 있는 모든 방법을 유지할 수 없지만 커뮤니티가 이를 구축하는 것을 방해하고 싶지 않습니다).
@@ -245,7 +246,7 @@ output = pipeline()
 <hfoptions id="pipeline type">
 <hfoption id="GitHub pipeline">
 
-GitHub 파이프라인을 공유하려면 Diffusers [저장소](https://github.com/huggingface/diffusers)에서 Pull Request를 열고 one_step_unet.py 파일을 [examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community) 하위 폴더에 추가하세요.
+GitHub 파이프라인을 공유하려면 Diffusers [저장소](https://github.com/huggingface/diffusers)에서 PR을 열고 one_step_unet.py 파일을 [examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community) 하위 폴더에 추가하세요.
 
 </hfoption>
 <hfoption id="Hub pipeline">
@@ -255,7 +256,7 @@ Hub 파이프라인을 공유하려면, 허브에 모델 저장소를 생성하
 </hfoption>
 </hfoptions>
 
-### 7. 훈련 예제에 기여하기 [[7-contribute-to-training-examples]]
+### 7. 훈련 예제에 기여하기
 
 Diffusers 예제는 [examples](https://github.com/huggingface/diffusers/tree/main/examples) 폴더에 있는 훈련 스크립트의 모음입니다.
 
@@ -267,7 +268,7 @@ Diffusers 예제는 [examples](https://github.com/huggingface/diffusers/tree/mai
 연구용 훈련 예제는 [examples/research_projects](https://github.com/huggingface/diffusers/tree/main/examples/research_projects)에 위치하며, 공식 훈련 예제는 `research_projects` 및 `community` 폴더를 제외한 [examples](https://github.com/huggingface/diffusers/tree/main/examples)의 모든 폴더를 포함합니다.
 공식 훈련 예제는 Diffusers의 핵심 메인테이너가 유지 관리하며, 연구용 훈련 예제는 커뮤니티가 유지 관리합니다.
 이는 공식 파이프라인 vs 커뮤니티 파이프라인에 대한 [6. 커뮤니티 파이프라인 기여하기](#6-contribute-a-community-pipeline)에서 제시한 이유와 동일합니다: 핵심 메인테이너가 diffusion 모델의 모든 가능한 훈련 방법을 유지 관리하는 것은 현실적으로 불가능합니다.
-Diffusers 핵심 메인테이너와 커뮤니티가 특정 훈련 패러다임을 너무 실험적이거나 충분히 대중적이지 않다고 판단한다면, 해당 훈련 코드는 `research_projects` 폴더에 넣고 작성자에 의해 관리되어야 합니다.
+Diffusers 핵심 메인테잉너와 커뮤니티가 특정 훈련 패러다임을 너무 실험적이거나 충분히 인기 없는 것으로 간주하는 경우, 해당 훈련 코드는 `research_projects` 폴더에 넣고 작성자가 유지 관리해야 합니다.
 
 공식 훈련 및 연구 예제는 하나 이상의 훈련 스크립트, requirements.txt 파일 및 README.md 파일을 포함하는 디렉토리로 구성됩니다. 사용자가 훈련 예제를 사용하려면 리포지토리를 복제해야 합니다:
 
@@ -297,14 +298,14 @@ Diffusers와 긴밀하게 통합되어 있기 때문에, 기여자들이 [Accele
 
 만약 공식 훈련 예제에 기여하는 경우, [examples/test_examples.py](https://github.com/huggingface/diffusers/blob/main/examples/test_examples.py)에 테스트를 추가하는 것도 확인해주세요. 비공식 훈련 예제에는 이 작업이 필요하지 않습니다.
 
-### 8. "Good second issue" 고치기 [[8-fixing-a-good-second-issue]]
+### 8. "Good second issue" 고치기
 
 "Good second issue"는 [Good second issue](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22Good+second+issue%22) 라벨로 표시됩니다. Good second issue는 [Good first issues](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)보다 해결하기가 더 복잡합니다.
 이슈 설명은 일반적으로 이슈를 해결하는 방법에 대해 덜 구체적이며, 관심 있는 기여자는 라이브러리에 대한 꽤 깊은 이해가 필요합니다.
 Good second issue를 해결하고자 하는 경우, 해당 이슈를 해결하기 위해 PR을 열고 PR을 이슈에 링크하세요. 이미 해당 이슈에 대한 PR이 열려있지만 병합되지 않은 경우, 왜 병합되지 않았는지 이해하기 위해 살펴보고 개선된 PR을 열어보세요.
 Good second issue는 일반적으로 Good first issue 이슈보다 병합하기가 더 어려우므로, 핵심 메인테이너에게 도움을 요청하는 것이 좋습니다. PR이 거의 완료된 경우, 핵심 메인테이너는 PR에 참여하여 커밋하고 병합을 진행할 수 있습니다.
 
-### 9. 파이프라인, 모델, 스케줄러 추가하기 [[9-adding-pipelines-models-schedulers]]
+### 9. 파이프라인, 모델, 스케줄러 추가하기
 
 파이프라인, 모델, 스케줄러는 Diffusers 라이브러리에서 가장 중요한 부분입니다.
 이들은 최첨단 diffusion 기술에 쉽게 접근하도록 하며, 따라서 커뮤니티가 강력한 생성형 AI 애플리케이션을 만들 수 있도록 합니다.
@@ -322,9 +323,9 @@ PR에 원본 코드베이스/논문 링크를 추가하고, 가능하면 PR에
 
 PR에서 막힌 경우나 도움이 필요한 경우, 첫 번째 리뷰나 도움을 요청하는 메시지를 남기는 것을 주저하지 마세요.
 
-#### Copied from mechanism [[copied-from-mechanism]]
+#### Copied from mechanism
 
-`# Copied from mechanism` 은 파이프라인, 모델 또는 스케줄러 코드를 추가할 때 이해해야 할 독특하고 중요한 기능입니다. 이것은 Diffusers 코드베이스 전반에서 볼 수 있으며, 이를 사용하는 이유는 코드베이스를 이해하고 유지 관리하기 쉽게 만들기 위해서입니다. `# Copied from mechanism` 으로 표시된 코드는 복사한 코드와 정확히 동일하도록 강제됩니다. 이렇게 하면 `make fix-copies`를 실행할 때마다 여러 파일에 걸쳐 변경 사항을 쉽게 업데이트하고 전파할 수 있습니다.
+`# Copied from mechanism` 은 파이프라인, 모델 또는 스케줄러 코드를 추가할 때 이해해야 할 독특하고 중요한 기능입니다. Diffusers 코드베이스 전체에서 이를 자주 볼 수 있는데, 이를 사용하는 이유는 코드베이스를 이해하기 쉽고 유지 관리하기 쉽게 유지하기 위함입니다. `# Copied from mechanism` 으로 표시된 코드는 복사한 코드와 정확히 동일하도록 강제됩니다. 이를 통해 `make fix-copies`를 실행할 때 많은 파일에 걸쳐 변경 사항을 쉽게 업데이트하고 전파할 수 있습니다.
 
 예를 들어, 아래 코드 예제에서 [`~diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput`]은 원래 코드이며, `AltDiffusionPipelineOutput`은 `# Copied from mechanism`을 사용하여 복사합니다. 유일한 차이점은 클래스 접두사를 `Stable`에서 `Alt`로 변경한 것입니다.
 
@@ -346,7 +347,7 @@ class AltDiffusionPipelineOutput(BaseOutput):
 
 더 자세히 알고 싶다면 [~Don't~ Repeat Yourself*](https://huggingface.co/blog/transformers-design-philosophy#4-machine-learning-models-are-static) 블로그 포스트의 이 섹션을 읽어보세요.
 
-## 좋은 이슈 작성 방법 [[how-to-write-a-good-issue]]
+## 좋은 이슈 작성 방법
 
 **이슈를 잘 작성할수록 빠르게 해결될 가능성이 높아집니다.**
 
@@ -355,16 +356,16 @@ class AltDiffusionPipelineOutput(BaseOutput):
 3. **재현 가능성**: 재현 가능한 코드 조각이 없으면 해결할 수 없습니다. 버그를 발견한 경우, 유지 관리자는 그 버그를 재현할 수 있어야 합니다. 이슈에 재현 가능한 코드 조각을 포함해야 합니다. 코드 조각은 Python 인터프리터에 복사하여 붙여넣을 수 있는 형태여야 합니다. 코드 조각이 작동해야 합니다. 즉, 누락된 import나 이미지에 대한 링크가 없어야 합니다. 이슈에는 오류 메시지와 정확히 동일한 오류 메시지를 재현하기 위해 수정하지 않고 복사하여 붙여넣을 수 있는 코드 조각이 포함되어야 합니다. 이슈에 사용자의 로컬 모델 가중치나 로컬 데이터를 사용하는 경우, 독자가 액세스할 수 없는 경우 이슈를 해결할 수 없습니다. 데이터나 모델을 공유할 수 없는 경우, 더미 모델이나 더미 데이터를 만들어 사용해보세요.
 4. **간결성**: 가능한 한 간결하게 유지하여 독자가 문제를 빠르게 이해할 수 있도록 도와주세요. 문제와 관련이 없는 코드나 정보는 모두 제거해주세요. 버그를 발견한 경우, 문제를 설명하는 가장 간단한 코드 예제를 만들어보세요. 버그를 발견한 후에는 작업 흐름 전체를 문제에 던지는 것이 아니라, 에러가 발생하는 훈련 코드의 어느 부분이 문제인지 먼저 이해하고 몇 줄로 재현해보세요. 전체 데이터셋 대신 더미 데이터를 사용해보세요.
 5. 링크 추가하기. 특정한 이름, 메서드, 또는 모델을 참조하는 경우, 독자가 더 잘 이해할 수 있도록 링크를 제공해주세요. 특정 PR이나 이슈를 참조하는 경우, 해당 이슈에 링크를 걸어주세요. 독자가 무엇을 말하는지 알고 있다고 가정하지 마세요. 이슈에 링크를 추가할수록 좋습니다.
-6. 포맷팅. 코드를 파이썬 코드 구문으로, 에러 메시지를 일반 코드 구문으로 형식화하여 이슈를 깔끔하게 작성하세요. 자세한 내용은 [GitHub 공식 포맷팅 문서](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax)를 참조하세요.
-7. 여러분의 이슈를 단순히 해결해야 할 티켓으로 생각하지 말고, 잘 작성된 백과사전 항목으로 생각해보세요. 추가된 모든 이슈는 공개적으로 이용 가능한 지식에 대한 기여입니다. 잘 작성된 이슈를 추가함으로써 메인테이너가 여러분의 이슈를 더 쉽게 해결할 수 있게 할 뿐만 아니라, 전체 커뮤니티가 라이브러리의 특정 측면을 더 잘 이해할 수 있도록 도움을 주게 됩니다.
+6. 포맷팅. 파이썬 코드 구문으로 코드를 포맷팅하고, 일반 코드 구문으로 에러 메시지를 포맷팅해주세요. 자세한 내용은 [공식 GitHub 포맷팅 문서](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax)를 참조하세요.
+7. 이슈를 해결해야 하는 티켓이 아니라, 잘 작성된 백과사전 항목으로 생각해보세요. 추가된 이슈는 공개적으로 사용 가능한 지식에 기여하는 것입니다. 잘 작성된 이슈를 추가함으로써 메인테이너가 문제를 해결하는 데 도움을 주는 것뿐만 아니라, 전체 커뮤니티가 라이브러리의 특정 측면을 더 잘 이해할 수 있도록 도움을 주는 것입니다.
 
-## 좋은 PR 작성 방법 [[how-to-write-a-good-pr]]
+## 좋은 PR 작성 방법
 
-1. 카멜레온이 되세요. 기존의 디자인 패턴과 구문을 이해하고, 여러분이 추가하는 코드가 기존 코드베이스와 자연스럽게 어우러지도록 해야 합니다. 기존 디자인 패턴이나 사용자 인터페이스와 크게 다른 Pull Request들은 병합되지 않습니다.
-2. 레이저처럼 집중하세요. Pull Request는 하나의 문제, 오직 하나의 문제만 해결해야 합니다. "이왕 추가하는 김에 다른 문제도 고치자"는 함정에 빠지지 않도록 주의하세요. 여러 개의 관련 없는 문제를 해결하는 한 번에 해결하는 Pull Request들은 검토하기가 훨씬 더 어렵습니다.
+1. 카멜레온이 되세요. 기존의 디자인 패턴과 구문을 이해하고, 코드 추가가 기존 코드베이스에 매끄럽게 흐르도록 해야 합니다. 기존 디자인 패턴이나 사용자 인터페이스와 크게 다른 PR은 병합되지 않습니다.
+2. 초점을 맞추세요. 하나의 문제만 해결하는 PR을 작성해야 합니다. "추가하면서 다른 문제도 해결하기"에 빠지지 않도록 주의하세요. 여러 개의 관련 없는 문제를 해결하는 PR을 작성하는 것은 리뷰하기가 훨씬 어렵습니다.
 3. 도움이 되는 경우, 추가한 내용이 어떻게 사용되는지 예제 코드 조각을 추가해보세요.
-4. Pull Request의 제목은 기여 내용을 요약해야 합니다.
-5. Pull Request가 이슈를 해결하는 경우, Pull Request의 설명에 이슈 번호를 언급하여 연결되도록 해주세요 (이슈를 참조하는 사람들이 작업 중임을 알 수 있도록).
+4. PR의 제목은 기여 내용을 요약해야 합니다.
+5. PR이 이슈를 해결하는 경우, PR 설명에 이슈 번호를 언급하여 연결되도록 해주세요 (이슈를 참조하는 사람들이 작업 중임을 알 수 있도록).
 6. 진행 중인 작업을 나타내려면 제목에 `[WIP]`를 접두사로 붙여주세요. 이는 중복 작업을 피하고, 병합 준비가 된 PR과 구분할 수 있도록 도움이 됩니다.
 7. [좋은 이슈를 작성하는 방법](#how-to-write-a-good-issue)에 설명된 대로 텍스트를 구성하고 형식을 지정해보세요.
 8. 기존 테스트가 통과하는지 확인하세요
@@ -373,10 +374,10 @@ class AltDiffusionPipelineOutput(BaseOutput):
 `RUN_SLOW=1 python -m pytest tests/test_my_new_model.py`.
 CircleCI는 느린 테스트를 실행하지 않지만, GitHub Actions는 매일 실행합니다!
 10. 모든 공개 메서드는 마크다운과 잘 작동하는 정보성 docstring을 가져야 합니다. 예시로 [`pipeline_latent_diffusion.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py)를 참조하세요.
-11. 리포지토리가 빠르게 성장하고 있기 때문에, 리포지토리에 큰 부담을 주는 파일이 추가되지 않도록 주의해야 합니다. 이미지, 비디오 및 기타 텍스트가 아닌 파일을 포함합니다. 이러한 파일을 배치하기 위해 hf.co 호스팅 `dataset`인 [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) 또는 [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images)를 활용하는 것이 우선입니다.
+11. 레포지토리가 빠르게 성장하고 있기 때문에, 레포지토리에 큰 부담을 주는 파일이 추가되지 않도록 주의해야 합니다. 이미지, 비디오 및 기타 텍스트가 아닌 파일을 포함합니다. 이러한 파일을 배치하기 위해 hf.co 호스팅 `dataset`인 [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) 또는 [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images)를 활용하는 것이 우선입니다.
 외부 기여인 경우, 이미지를 PR에 추가하고 Hugging Face 구성원에게 이미지를 이 데이터셋으로 이동하도록 요청하세요.
 
-## PR을 열기 위한 방법 [[how-to-open-a-pr]]
+## PR을 열기 위한 방법
 
 코드를 작성하기 전에, 이미 누군가가 같은 작업을 하고 있는지 확인하기 위해 기존의 PR이나 이슈를 검색하는 것이 좋습니다. 확실하지 않은 경우, 피드백을 받기 위해 이슈를 열어보는 것이 항상 좋은 아이디어입니다.
 
@@ -402,7 +403,7 @@ CircleCI는 느린 테스트를 실행하지 않지만, GitHub Actions는 매일
 
 `main` 브랜치 위에서 **절대** 작업하지 마세요.
 
-4. 가상 환경에서 다음 명령을 실행하여 개발 환경을 설정하세요:
+1. 가상 환경에서 다음 명령을 실행하여 개발 환경을 설정하세요:
 
  ```bash
  $ pip install -e ".[dev]"
@@ -466,7 +467,7 @@ CircleCI는 느린 테스트를 실행하지 않지만, GitHub Actions는 매일
 
 7. 메인테이너가 변경 사항을 요청하는 것은 괜찮습니다. 핵심 기여자들에게도 일어나는 일입니다! 따라서 변경 사항을 Pull request에서 볼 수 있도록 로컬 브랜치에서 작업하고 변경 사항을 포크에 푸시하면 자동으로 Pull request에 나타납니다.
 
-### 테스트 [[tests]]
+### 테스트
 
 라이브러리 동작과 여러 예제를 테스트하기 위해 포괄적인 테스트 묶음이 포함되어 있습니다. 라이브러리 테스트는 [tests 폴더](https://github.com/huggingface/diffusers/tree/main/tests)에서 찾을 수 있습니다.
 
@@ -493,7 +494,7 @@ $ python -m unittest discover -s tests -t . -v
 $ python -m unittest discover -s examples -t examples -v
 ```
 
-### upstream(HuggingFace) main과 forked main 동기화하기 [[syncing-forked-main-with-upstream-huggingface-main]]
+### upstream(main)과 forked main 동기화하기
 
 upstream 저장소에 불필요한 참조 노트를 추가하고 관련 개발자에게 알림을 보내는 것을 피하기 위해,
 forked 저장소의 main 브랜치를 동기화할 때 다음 단계를 따르세요:
@@ -506,6 +507,6 @@ $ git commit -m '<your message without GitHub references>'
 $ git push --set-upstream origin your-branch-for-syncing
 ```
 
-### 스타일 가이드 [[style-guide]]
+### 스타일 가이드
 
 Documentation string에 대해서는, 🧨 Diffusers는 [Google 스타일](https://google.github.io/styleguide/pyguide.html)을 따릅니다.

docs/source/ko/conceptual/evaluation.md

@@ -88,7 +88,7 @@ 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)
 
 
-`num_images_per_prompt`를 설정하여 동일한 프롬프트에 대해 다른 이미지를 비교할 수도 있습니다. 다른 체크포인트([v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5))로 동일한 파이프라인을 실행하면 다음과 같은 결과가 나옵니다:
+`num_images_per_prompt`를 설정하여 동일한 프롬프트에 대해 다른 이미지를 비교할 수도 있습니다. 다른 체크포인트([v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5))로 동일한 파이프라인을 실행하면 다음과 같은 결과가 나옵니다:
 
 ![parti-prompts-15](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/parti-prompts-15.png)
 
@@ -170,10 +170,10 @@ generator = torch.manual_seed(seed)
 images = sd_pipeline(prompts, num_images_per_prompt=1, generator=generator, output_type="np").images
 ```
 
-그런 다음 [v1-5 checkpoint](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5)를 로드하여 이미지를 생성합니다:
+그런 다음 [v1-5 checkpoint](https://huggingface.co/runwayml/stable-diffusion-v1-5)를 로드하여 이미지를 생성합니다:
 
 ```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
@@ -191,7 +191,7 @@ print(f"CLIP Score with v-1-5: {sd_clip_score_1_5}")
 # CLIP Score with v-1-5: 36.2137
 ```
 
-[v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) 체크포인트가 이전 버전보다 더 나은 성능을 보이는 것 같습니다. 그러나 CLIP 점수를 계산하기 위해 사용한 프롬프트의 수가 상당히 적습니다. 보다 실용적인 평가를 위해서는 이 수를 훨씬 높게 설정하고, 프롬프트를 다양하게 사용해야 합니다.
+[v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) 체크포인트가 이전 버전보다 더 나은 성능을 보이는 것 같습니다. 그러나 CLIP 점수를 계산하기 위해 사용한 프롬프트의 수가 상당히 적습니다. 보다 실용적인 평가를 위해서는 이 수를 훨씬 높게 설정하고, 프롬프트를 다양하게 사용해야 합니다.
 
 <Tip warning={true}>
 

docs/source/ko/conceptual/philosophy.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.
 -->
 
-# 철학 [[philosophy]]
+# 철학 [[philosophy]] 
 
 🧨 Diffusers는 다양한 모달리티에서 **최신의** 사전 훈련된 diffusion 모델을 제공합니다.
 그 목적은 추론과 훈련을 위한 **모듈식 툴박스**로 사용되는 것입니다.
@@ -59,7 +59,7 @@ Diffusers에서는 이러한 철학을 파이프라인과 스케줄러에 모두
 다음과 같은 설계 원칙을 따릅니다:
 - 파이프라인은 단일 파일 정책을 따릅니다. 모든 파이프라인은 src/diffusers/pipelines의 개별 디렉토리에 있습니다. 하나의 파이프라인 폴더는 하나의 diffusion 논문/프로젝트/릴리스에 해당합니다. 여러 파이프라인 파일은 하나의 파이프라인 폴더에 모을 수 있습니다. 예를 들어 [`src/diffusers/pipelines/stable-diffusion`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/stable_diffusion)에서 그렇게 하고 있습니다. 파이프라인이 유사한 기능을 공유하는 경우, [# Copied from mechanism](https://github.com/huggingface/diffusers/blob/125d783076e5bd9785beb05367a2d2566843a271/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py#L251)을 사용할 수 있습니다.
 - 파이프라인은 모두 [`DiffusionPipeline`]을 상속합니다.
-- 각 파이프라인은 서로 다른 모델 및 스케줄러 구성 요소로 구성되어 있으며, 이는 [`model_index.json` 파일](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/model_index.json)에 문서화되어 있으며, 파이프라인의 속성 이름과 동일한 이름으로 액세스할 수 있으며, [`DiffusionPipeline.components`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.components) 함수를 통해 파이프라인 간에 공유할 수 있습니다.
+- 각 파이프라인은 서로 다른 모델 및 스케줄러 구성 요소로 구성되어 있으며, 이는 [`model_index.json` 파일](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/model_index.json)에 문서화되어 있으며, 파이프라인의 속성 이름과 동일한 이름으로 액세스할 수 있으며, [`DiffusionPipeline.components`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.components) 함수를 통해 파이프라인 간에 공유할 수 있습니다.
 - 각 파이프라인은 [`DiffusionPipeline.from_pretrained`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained) 함수를 통해 로드할 수 있어야 합니다.
 - 파이프라인은 추론에**만** 사용되어야 합니다.
 - 파이프라인은 매우 가독성이 좋고, 이해하기 쉽고, 쉽게 조정할 수 있도록 설계되어야 합니다.

docs/source/ko/optimization/coreml.md

@@ -103,10 +103,10 @@ python -m python_coreml_stable_diffusion.pipeline --prompt "a photo of an astron
 
 추론 스크립트에서는 Stable Diffusion 모델의 원래 버전인 `CompVis/stable-diffusion-v1-4`를 사용한다고 가정합니다. 다른 모델을 사용하는 경우 추론 명령줄에서 `--model-version` 옵션을 사용하여 해당 허브 ID를 *지정*해야 합니다. 이는 이미 지원되는 모델과 사용자가 직접 학습하거나 파인튜닝한 사용자 지정 모델에 적용됩니다.
 
-예를 들어, [`stable-diffusion-v1-5/stable-diffusion-v1-5`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5)를 사용하려는 경우입니다:
+예를 들어, [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5)를 사용하려는 경우입니다:
 
 ```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
 ```