Merge branch 'main' of https://github.com/huggingface/diffusers into dreambooth-example

Author: patil-suraj

.github/workflows/pr_tests.yml

@@ -41,4 +41,15 @@ jobs:
 
     - name: Run all non-slow selected tests on CPU
       run: |
-        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile -s tests/
+        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=tests_torch_cpu tests/
+
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: cat reports/tests_torch_cpu_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v2
+      with:
+        name: pr_torch_test_reports
+        path: reports

.github/workflows/push_tests.yml

@@ -49,4 +49,66 @@ jobs:
       env:
         HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
       run: |
-        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s tests/
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=tests_torch_gpu tests/
+
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: cat reports/tests_torch_gpu_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v2
+      with:
+        name: torch_test_reports
+        path: reports
+
+
+
+  run_examples_single_gpu:
+    name: Examples tests
+    strategy:
+      fail-fast: false
+      matrix:
+        machine_type: [ single-gpu ]
+    runs-on: [ self-hosted, docker-gpu, '${{ matrix.machine_type }}' ]
+    container:
+      image: nvcr.io/nvidia/pytorch:22.07-py3
+      options: --gpus 0 --shm-size "16gb" --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: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip uninstall -y torch torchvision torchtext
+        python -m pip install torch --extra-index-url https://download.pytorch.org/whl/cu116
+        python -m pip install -e .[quality,test,training]
+
+    - name: Environment
+      run: |
+        python utils/print_env.py
+
+    - name: Run example tests on GPU
+      env:
+        HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
+      run: |
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_gpu examples/
+
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: cat reports/examples_torch_gpu_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v2
+      with:
+        name: examples_test_reports
+        path: reports

_typos.toml

@@ -4,8 +4,9 @@
 [default.extend-identifiers]
 
 [default.extend-words]
-NIN_="NIN" # NIN is used in scripts/convert_ncsnpp_original_checkpoint_to_diffusers.py
+NIN="NIN" # NIN is used in scripts/convert_ncsnpp_original_checkpoint_to_diffusers.py
 nd="np" # nd may be np (numpy)
+parms="parms" # parms is used in scripts/convert_original_stable_diffusion_to_diffusers.py
 
 
 [files]

docs/source/api/models.mdx

@@ -45,3 +45,21 @@ The models are built on the base class ['ModelMixin'] that is a `torch.nn.module
 
 ## AutoencoderKL
 [[autodoc]] AutoencoderKL
+
+## FlaxModelMixin
+[[autodoc]] FlaxModelMixin
+
+## FlaxUNet2DConditionOutput
+[[autodoc]] models.unet_2d_condition_flax.FlaxUNet2DConditionOutput
+
+## FlaxUNet2DConditionModel
+[[autodoc]] FlaxUNet2DConditionModel
+
+## FlaxDecoderOutput
+[[autodoc]] models.vae_flax.FlaxDecoderOutput
+
+## FlaxAutoencoderKLOutput
+[[autodoc]] models.vae_flax.FlaxAutoencoderKLOutput
+
+## FlaxAutoencoderKL
+[[autodoc]] FlaxAutoencoderKL

docs/source/api/pipelines/overview.mdx

@@ -53,7 +53,7 @@ available a colab notebook to directly try them out.
 | [stable_diffusion](./stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Text-to-Image Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
 | [stable_diffusion](./stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Image-to-Image Text-Guided Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb)
 | [stable_diffusion](./stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Text-Guided Image Inpainting | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb)
-| [stochatic_karras_ve](./stochatic_karras_ve) | [**Elucidating the Design Space of Diffusion-Based Generative Models**](https://arxiv.org/abs/2206.00364) | Unconditional Image Generation | 
+| [stochastic_karras_ve](./stochastic_karras_ve) | [**Elucidating the Design Space of Diffusion-Based Generative Models**](https://arxiv.org/abs/2206.00364) | Unconditional Image Generation | 
 
 **Note**: Pipelines are simple examples of how to play around with the diffusion systems as described in the corresponding papers. 
 
@@ -85,7 +85,7 @@ not be used for training. If you want to store the gradients during the forward
 We are more than happy about any contribution to the officially supported pipelines 🤗. We aspire
 all of our pipelines to be  **self-contained**, **easy-to-tweak**, **beginner-friendly** and for **one-purpose-only**.
 
-- **Self-contained**: A pipeline shall be as self-contained as possible. More specifically, this means that all functionality should be either directly defined in the pipeline file iteslf, should be inherited from (and only from) the [`DiffusionPipeline` class](.../diffusion_pipeline) or be directly attached to the model and scheduler components of the pipeline. 
+- **Self-contained**: A pipeline shall be as self-contained as possible. More specifically, this means that all functionality should be either directly defined in the pipeline file itself, should be inherited from (and only from) the [`DiffusionPipeline` class](.../diffusion_pipeline) or be directly attached to the model and scheduler components of the pipeline. 
 - **Easy-to-use**: Pipelines should be extremely easy to use - one should be able to load the pipeline and 
 use it for its designated task, *e.g.* text-to-image generation, in just a couple of lines of code. Most 
 logic including pre-processing, an unrolled diffusion loop, and post-processing should all happen inside the `__call__` method.

docs/source/index.mdx

@@ -44,6 +44,6 @@ available a colab notebook to directly try them out.
 | [stable_diffusion](./api/pipelines/stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Text-to-Image Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
 | [stable_diffusion](./api/pipelines/stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Image-to-Image Text-Guided Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb)
 | [stable_diffusion](./api/pipelines/stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Text-Guided Image Inpainting | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb)
-| [stochatic_karras_ve](./api/pipelines/stochatic_karras_ve) | [**Elucidating the Design Space of Diffusion-Based Generative Models**](https://arxiv.org/abs/2206.00364) | Unconditional Image Generation | 
+| [stochastic_karras_ve](./api/pipelines/stochastic_karras_ve) | [**Elucidating the Design Space of Diffusion-Based Generative Models**](https://arxiv.org/abs/2206.00364) | Unconditional Image Generation | 
 
 **Note**: Pipelines are simple examples of how to play around with the diffusion systems as described in the corresponding papers. 

docs/source/using-diffusers/configuration.mdx

@@ -27,6 +27,6 @@ pip install diffusers
 
 ### Schedulers
 
-### Pipeliens
+### Pipelines
 
 

docs/source/using-diffusers/unconditional_image_generation.mdx

@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
 
 
 
-# Unonditional Image Generation
+# Unconditional Image Generation
 
 The [`DiffusionPipeline`] is the easiest way to use a pre-trained diffusion system for inference
 

examples/community/README.md

@@ -2,5 +2,6 @@
 
 **Community** examples consist of both inference and training examples that have been added by the community.
 
-| Example |      Description      |      Author      |      |
-|:----------|:-------------|:-------------|------:|
+| Example   |      Description      |      Author      |    Colab  |
+|:----------|:----------------------|:-----------------|----------:|
+| CLIP Guided Stable Diffusion | Doing CLIP guidance for text to image generation with Stable Diffusion| [Suraj Patil](https://github.com/patil-suraj/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/CLIP_Guided_Stable_diffusion_with_diffusers.ipynb) |