Merge branch 'main' into ays

.github/workflows/nightly_tests.yml

@@ -19,7 +19,7 @@ env:
 jobs:
   setup_torch_cuda_pipeline_matrix:
     name: Setup Torch Pipelines Matrix
-    runs-on: ubuntu-latest
+    runs-on: diffusers/diffusers-pytorch-cpu
     outputs:
       pipeline_test_matrix: ${{ steps.fetch_pipeline_matrix.outputs.pipeline_test_matrix }}
     steps:
@@ -67,19 +67,19 @@ jobs:
           fetch-depth: 2
       - name: NVIDIA-SMI
         run: nvidia-smi
-      
+
       - name: Install dependencies
         run: |
           python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
           python -m uv pip install -e [quality,test]
           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: Nightly PyTorch CUDA checkpoint (pipelines) tests 
+
+      - name: Nightly PyTorch CUDA checkpoint (pipelines) tests
         env:
           HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
           # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
@@ -88,9 +88,9 @@ jobs:
           python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
             -s -v -k "not Flax and not Onnx" \
             --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
-            --report-log=tests_pipeline_${{ matrix.module }}_cuda.log \ 
+            --report-log=tests_pipeline_${{ matrix.module }}_cuda.log \
             tests/pipelines/${{ matrix.module }}
-      
+
       - name: Failure short reports
         if: ${{ failure() }}
         run: |
@@ -103,7 +103,7 @@ jobs:
         with:
           name: pipeline_${{ matrix.module }}_test_reports
           path: reports
-      
+
       - name: Generate Report and Notify Channel
         if: always()
         run: |
@@ -139,7 +139,7 @@ jobs:
       run: python utils/print_env.py
 
     - name: Run nightly PyTorch CUDA tests for non-pipeline modules
-      if: ${{ matrix.module != 'examples'}} 
+      if: ${{ matrix.module != 'examples'}}
       env:
         HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
         # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
@@ -148,7 +148,7 @@ jobs:
         python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
           -s -v -k "not Flax and not Onnx" \
           --make-reports=tests_torch_${{ matrix.module }}_cuda \
-          --report-log=tests_torch_${{ matrix.module }}_cuda.log \ 
+          --report-log=tests_torch_${{ matrix.module }}_cuda.log \
           tests/${{ matrix.module }}
 
     - name: Run nightly example tests with Torch
@@ -161,13 +161,13 @@ jobs:
         python -m uv pip install peft@git+https://github.com/huggingface/peft.git
         python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
           -s -v --make-reports=examples_torch_cuda \
-          --report-log=examples_torch_cuda.log \ 
+          --report-log=examples_torch_cuda.log \
           examples/
 
     - name: Failure short reports
       if: ${{ failure() }}
       run: |
-        cat reports/tests_torch_${{ matrix.module }}_cuda_stats.txt 
+        cat reports/tests_torch_${{ matrix.module }}_cuda_stats.txt
         cat reports/tests_torch_${{ matrix.module }}_cuda_failures_short.txt
 
     - name: Test suite reports artifacts
@@ -218,13 +218,13 @@ jobs:
         python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
           -s -v -k "not Flax and not Onnx" \
           --make-reports=tests_torch_lora_cuda \
-          --report-log=tests_torch_lora_cuda.log \ 
+          --report-log=tests_torch_lora_cuda.log \
           tests/lora
-    
+
     - name: Failure short reports
       if: ${{ failure() }}
       run: |
-        cat reports/tests_torch_lora_cuda_stats.txt 
+        cat reports/tests_torch_lora_cuda_stats.txt
         cat reports/tests_torch_lora_cuda_failures_short.txt
 
     - name: Test suite reports artifacts
@@ -239,12 +239,12 @@ jobs:
       run: |
         pip install slack_sdk tabulate
         python scripts/log_reports.py >> $GITHUB_STEP_SUMMARY
-  
+
   run_flax_tpu_tests:
     name: Nightly Flax TPU Tests
     runs-on: docker-tpu
     if: github.event_name == 'schedule'
-    
+
     container:
       image: diffusers/diffusers-flax-tpu
       options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/ --privileged
@@ -274,7 +274,7 @@ jobs:
         python -m pytest -n 0 \
           -s -v -k "Flax" \
           --make-reports=tests_flax_tpu \
-          --report-log=tests_flax_tpu.log \ 
+          --report-log=tests_flax_tpu.log \
           tests/
 
     - name: Failure short reports
@@ -302,7 +302,7 @@ jobs:
     container:
       image: diffusers/diffusers-onnxruntime-cuda
       options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
-    
+
     steps:
     - name: Checkout diffusers
       uses: actions/checkout@v3
@@ -321,15 +321,15 @@ jobs:
 
     - name: Environment
       run: python utils/print_env.py
-    
+
     - name: Run nightly ONNXRuntime CUDA tests
       env:
         HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
       run: |
         python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
           -s -v -k "Onnx" \
           --make-reports=tests_onnx_cuda \
-          --report-log=tests_onnx_cuda.log \ 
+          --report-log=tests_onnx_cuda.log \
           tests/
 
     - name: Failure short reports
@@ -344,7 +344,7 @@ jobs:
       with:
         name: ${{ matrix.config.report }}_test_reports
         path: reports
-    
+
     - name: Generate Report and Notify Channel
       if: always()
       run: |

.github/workflows/push_tests.yml

@@ -21,7 +21,7 @@ env:
 jobs:
   setup_torch_cuda_pipeline_matrix:
     name: Setup Torch Pipelines CUDA Slow Tests Matrix
-    runs-on: ubuntu-latest
+    runs-on: diffusers/diffusers-pytorch-cpu
     outputs:
       pipeline_test_matrix: ${{ steps.fetch_pipeline_matrix.outputs.pipeline_test_matrix }}
     steps:

.github/workflows/ssh-runner.yml

@@ -0,0 +1,46 @@
+name: SSH into runners
+
+on:
+  workflow_dispatch:
+    inputs:
+      runner_type:
+        description: 'Type of runner to test (a10 or t4)'
+        required: true
+      docker_image:
+        description: 'Name of the Docker image'
+        required: true
+
+env:
+  IS_GITHUB_CI: "1"
+  HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
+  HF_HOME: /mnt/cache
+  DIFFUSERS_IS_CI: yes
+  OMP_NUM_THREADS: 8
+  MKL_NUM_THREADS: 8
+  RUN_SLOW: yes
+
+jobs:
+  ssh_runner:
+    name: "SSH"
+    runs-on: [single-gpu, nvidia-gpu, "${{ github.event.inputs.runner_type }}", ci]
+    container:
+      image: ${{ github.event.inputs.docker_image }}
+      options: --gpus all --privileged --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+
+    steps:
+      - name: Checkout diffusers
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 2
+
+      - name: NVIDIA-SMI
+        run: |
+          nvidia-smi
+
+      - name: Tailscale # In order to be able to SSH when a test fails
+        uses: huggingface/tailscale-action@v1
+        with:
+          authkey: ${{ secrets.TAILSCALE_SSH_AUTHKEY }}
+          slackChannel: ${{ secrets.SLACK_CIFEEDBACK_CHANNEL }}
+          slackToken: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
+          waitForSSH: true

docs/source/en/_toctree.yml

@@ -87,10 +87,6 @@
     title: Shap-E
   - local: using-diffusers/diffedit
     title: DiffEdit
-  - local: using-diffusers/custom_pipeline_examples
-    title: Community pipelines
-  - local: using-diffusers/contribute_pipeline
-    title: Contribute a community pipeline
   - local: using-diffusers/inference_with_lcm_lora
     title: Latent Consistency Model-LoRA
   - local: using-diffusers/inference_with_lcm

docs/source/en/conceptual/contribution.md

@@ -198,38 +198,81 @@ Anything displayed on [the official Diffusers doc page](https://huggingface.co/d
 
 Please have a look at [this page](https://github.com/huggingface/diffusers/tree/main/docs) on how to verify changes made to the documentation locally.
 
-
 ### 6. Contribute a community pipeline
 
-[Pipelines](https://huggingface.co/docs/diffusers/api/pipelines/overview) are usually the first point of contact between the Diffusers library and the user.
-Pipelines are examples of how to use Diffusers [models](https://huggingface.co/docs/diffusers/api/models/overview) and [schedulers](https://huggingface.co/docs/diffusers/api/schedulers/overview).
-We support two types of pipelines:
+> [!TIP]
+> Read the [Community pipelines](../using-diffusers/custom_pipeline_overview#community-pipelines) guide to learn more about the difference between a GitHub and Hugging Face Hub community pipeline. If you're interested in why we have community pipelines, take a look at GitHub Issue [#841](https://github.com/huggingface/diffusers/issues/841) (basically, we can't maintain all the possible ways diffusion models can be used for inference but we also don't want to prevent the community from building them).
+
+Contributing a community pipeline is a great way to share your creativity and work with the community. It lets you build on top of the [`DiffusionPipeline`] so that anyone can load and use it by setting the `custom_pipeline` parameter. This section will walk you through how to create a simple pipeline where the UNet only does a single forward pass and calls the scheduler once (a "one-step" pipeline).
+
+1. Create a one_step_unet.py file for your community pipeline. This file can contain whatever package you want to use as long as it's installed by the user. Make sure you only have one pipeline class that inherits from [`DiffusionPipeline`] to load model weights and the scheduler configuration from the Hub. Add a UNet and scheduler to the `__init__` function.
+
+    You should also add the `register_modules` function to ensure your pipeline and its components can be saved with [`~DiffusionPipeline.save_pretrained`].
+
+```py
+from diffusers import DiffusionPipeline
+import torch
+
+class UnetSchedulerOneForwardPipeline(DiffusionPipeline):
+    def __init__(self, unet, scheduler):
+        super().__init__()
+
+        self.register_modules(unet=unet, scheduler=scheduler)
+```
+
+1. In the forward pass (which we recommend defining as `__call__`), you can add any feature you'd like. For the "one-step" pipeline, create a random image and call the UNet and scheduler once by setting `timestep=1`.
+
+```py
+  from diffusers import DiffusionPipeline
+  import torch
+
+  class UnetSchedulerOneForwardPipeline(DiffusionPipeline):
+      def __init__(self, unet, scheduler):
+          super().__init__()
+
+          self.register_modules(unet=unet, scheduler=scheduler)
 
-- Official Pipelines
-- Community Pipelines
+      def __call__(self):
+          image = torch.randn(
+              (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),
+          )
+          timestep = 1
+
+          model_output = self.unet(image, timestep).sample
+          scheduler_output = self.scheduler.step(model_output, timestep, image).prev_sample
+
+          return scheduler_output
+```
 
-Both official and community pipelines follow the same design and consist of the same type of components.
+Now you can run the pipeline by passing a UNet and scheduler to it or load pretrained weights if the pipeline structure is identical.
+
+```py
+from diffusers import DDPMScheduler, UNet2DModel
+
+scheduler = DDPMScheduler()
+unet = UNet2DModel()
+
+pipeline = UnetSchedulerOneForwardPipeline(unet=unet, scheduler=scheduler)
+output = pipeline()
+# load pretrained weights
+pipeline = UnetSchedulerOneForwardPipeline.from_pretrained("google/ddpm-cifar10-32", use_safetensors=True)
+output = pipeline()
+```
 
-Official pipelines are tested and maintained by the core maintainers of Diffusers. Their code
-resides in [src/diffusers/pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines).
-In contrast, community pipelines are contributed and maintained purely by the **community** and are **not** tested.
-They reside in [examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community) and while they can be accessed via the [PyPI diffusers package](https://pypi.org/project/diffusers/), their code is not part of the PyPI distribution.
+You can either share your pipeline as a GitHub community pipeline or Hub community pipeline.
 
-The reason for the distinction is that the core maintainers of the Diffusers library cannot maintain and test all
-possible ways diffusion models can be used for inference, but some of them may be of interest to the community.
-Officially released diffusion pipelines,
-such as Stable Diffusion are added to the core src/diffusers/pipelines package which ensures
-high quality of maintenance, no backward-breaking code changes, and testing.
-More bleeding edge pipelines should be added as community pipelines. If usage for a community pipeline is high, the pipeline can be moved to the official pipelines upon request from the community. This is one of the ways we strive to be a community-driven library.
+<hfoptions id="pipeline type">
+<hfoption id="GitHub pipeline">
 
-To add a community pipeline, one should add a <name-of-the-community>.py file to [examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community) and adapt the [examples/community/README.md](https://github.com/huggingface/diffusers/tree/main/examples/community/README.md) to include an example of the new pipeline.
+Share your GitHub pipeline by opening a pull request on the Diffusers [repository](https://github.com/huggingface/diffusers) and add the one_step_unet.py file to the [examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community) subfolder.
 
-An example can be seen [here](https://github.com/huggingface/diffusers/pull/2400).
+</hfoption>
+<hfoption id="Hub pipeline">
 
-Community pipeline PRs are only checked at a superficial level and ideally they should be maintained by their original authors.
+Share your Hub pipeline by creating a model repository on the Hub and uploading the one_step_unet.py file to it.
 
-Contributing a community pipeline is a great way to understand how Diffusers models and schedulers work. Having contributed a community pipeline is usually the first stepping stone to contributing an official pipeline to the
-core package.
+</hfoption>
+</hfoptions>
 
 ### 7. Contribute to training examples