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name: "\U0001F31F Remote VAE"
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description: Feedback for remote VAE pilot
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labels: [ "Remote VAE" ]
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body:
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- type: textarea
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id: positive
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validations:
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required: true
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attributes:
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label: Did you like the remote VAE solution?
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description: |
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If you liked it, we would appreciate it if you could elaborate what you liked.
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- type: textarea
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id: feedback
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validations:
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required: true
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attributes:
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label: What can be improved about the current solution?
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description: |
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Let us know the things you would like to see improved. Note that we will work optimizing the solution once the pilot is over and we have usage.
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- type: textarea
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id: others
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validations:
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required: true
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attributes:
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label: What other VAEs you would like to see if the pilot goes well?
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description: |
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Provide a list of the VAEs you would like to see in the future if the pilot goes well.
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- type: textarea
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id: additional-info
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attributes:
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label: Notify the members of the team
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description: |
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Tag the following folks when submitting this feedback: @hlky @sayakpaul

.github/workflows/pr_tests.yml

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run: |
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python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
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python -m uv pip install -e [quality,test]
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python -m uv pip install accelerate
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pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
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pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
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- name: Environment
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run: |

.github/workflows/push_tests.yml

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name: Fast GPU Tests on main
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on:
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pull_request:
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branches: main
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paths:
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- "src/diffusers/models/modeling_utils.py"
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- "src/diffusers/models/model_loading_utils.py"
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- "src/diffusers/pipelines/pipeline_utils.py"
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- "src/diffusers/pipeline_loading_utils.py"
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workflow_dispatch:
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push:
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branches:
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path: reports
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flax_tpu_tests:
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if: ${{ github.event_name != 'pull_request' }}
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name: Flax TPU Tests
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runs-on:
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group: gcp-ct5lp-hightpu-8t
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path: reports
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onnx_cuda_tests:
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if: ${{ github.event_name != 'pull_request' }}
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name: ONNX CUDA Tests
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runs-on:
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group: aws-g4dn-2xlarge
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path: reports
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run_torch_compile_tests:
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if: ${{ github.event_name != 'pull_request' }}
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name: PyTorch Compile CUDA tests
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runs-on:
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path: reports
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run_xformers_tests:
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if: ${{ github.event_name != 'pull_request' }}
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name: PyTorch xformers CUDA tests
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runs-on:
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container:
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image: diffusers/diffusers-pytorch-cuda
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options: --gpus 0 --shm-size "16gb" --ipc host
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steps:
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- name: Checkout diffusers
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uses: actions/checkout@v3
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- name: NVIDIA-SMI
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run: |
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nvidia-smi
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- name: Install dependencies
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python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"

.github/workflows/run_tests_from_a_pr.yml

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default: 'diffusers/diffusers-pytorch-cuda'
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description: 'Name of the Docker image'
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required: true
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branch:
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description: 'PR Branch to test on'
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pr_number:
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description: 'PR number to test on'
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required: true
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test:
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description: 'Tests to run (e.g.: `tests/models`).'
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exit 1
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fi
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if [[ ! "$PY_TEST" =~ ^tests/(models|pipelines) ]]; then
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echo "Error: The input string must contain either 'models' or 'pipelines' after 'tests/'."
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if [[ ! "$PY_TEST" =~ ^tests/(models|pipelines|lora) ]]; then
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echo "Error: The input string must contain either 'models', 'pipelines', or 'lora' after 'tests/'."
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exit 1
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fi
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exit 1
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fi
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echo "$PY_TEST"
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shell: bash -e {0}
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- name: Checkout PR branch
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uses: actions/checkout@v4
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with:
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ref: ${{ github.event.inputs.branch }}
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repository: ${{ github.event.pull_request.head.repo.full_name }}
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ref: refs/pull/${{ inputs.pr_number }}/head
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- name: Install pytest
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run: |

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

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- For smaller resolution videos, try lower values of `shift` (between `2.0` to `5.0`) in the [Scheduler](https://huggingface.co/docs/diffusers/main/en/api/schedulers/flow_match_euler_discrete#diffusers.FlowMatchEulerDiscreteScheduler.shift). For larger resolution images, try higher values (between `7.0` and `12.0`). The default value is `7.0` for HunyuanVideo.
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- For more information about supported resolutions and other details, please refer to the original repository [here](https://github.com/Tencent/HunyuanVideo/).
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## Available models
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The following models are available for the [`HunyuanVideoPipeline`](text-to-video) pipeline:
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| Model name | Description |
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|:---|:---|
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| [`hunyuanvideo-community/HunyuanVideo`](https://huggingface.co/hunyuanvideo-community/HunyuanVideo) | Official HunyuanVideo (guidance-distilled). Performs best at multiple resolutions and frames. Performs best with `guidance_scale=6.0`, `true_cfg_scale=1.0` and without a negative prompt. |
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| [`https://huggingface.co/Skywork/SkyReels-V1-Hunyuan-T2V`](https://huggingface.co/Skywork/SkyReels-V1-Hunyuan-T2V) | Skywork's custom finetune of HunyuanVideo (de-distilled). Performs best with `97x544x960` resolution, `guidance_scale=1.0`, `true_cfg_scale=6.0` and a negative prompt. |
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The following models are available for the image-to-video pipeline:
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| Model name | Description |
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|:---|:---|
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| [`https://huggingface.co/Skywork/SkyReels-V1-Hunyuan-I2V`](https://huggingface.co/Skywork/SkyReels-V1-Hunyuan-I2V) | Skywork's custom finetune of HunyuanVideo (de-distilled). Performs best with `97x544x960` resolution. Performs best at `97x544x960` resolution, `guidance_scale=1.0`, `true_cfg_scale=6.0` and a negative prompt. |
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## Quantization
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Quantization helps reduce the memory requirements of very large models by storing model weights in a lower precision data type. However, quantization may have varying impact on video quality depending on the video model.

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

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| [DiT](dit) | text2image |
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| [Flux](flux) | text2image |
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| [Hunyuan-DiT](hunyuandit) | text2image |
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| [I2VGen-XL](i2vgenxl) | text2video |
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| [I2VGen-XL](i2vgenxl) | image2video |
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| [InstructPix2Pix](pix2pix) | image editing |
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| [Kandinsky 2.1](kandinsky) | text2image, image2image, inpainting, interpolation |
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| [Kandinsky 2.2](kandinsky_v22) | text2image, image2image, inpainting |

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

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from transformers import SiglipVisionModel, SiglipImageProcessor
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image_encoder_id = "google/siglip-so400m-patch14-384"
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ip_adapter_id = "guiyrt/InstantX-SD3.5-Large-IP-Adapter-diffusers"
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ip_adapter_id = "InstantX/SD3.5-Large-IP-Adapter"
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feature_extractor = SiglipImageProcessor.from_pretrained(
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examples/README.md

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| [**Text-to-Image fine-tuning**](./text_to_image) |||
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| [**Textual Inversion**](./textual_inversion) | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb)
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| [**Dreambooth**](./dreambooth) | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb)
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| [**ControlNet**](./controlnet) | ✅ | ✅ | -
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| [**InstructPix2Pix**](./instruct_pix2pix) | ✅ | ✅ | -
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| [**Reinforcement Learning for Control**](./reinforcement_learning) | - | - | coming soon.
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| [**ControlNet**](./controlnet) | ✅ | ✅ | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/controlnet.ipynb)
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| [**InstructPix2Pix**](./instruct_pix2pix) | ✅ | ✅ | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/InstructPix2Pix_using_diffusers.ipynb)
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| [**Reinforcement Learning for Control**](./reinforcement_learning) | - | - | [Notebook1](https://github.com/huggingface/notebooks/blob/main/diffusers/reinforcement_learning_for_control.ipynb), [Notebook2](https://github.com/huggingface/notebooks/blob/main/diffusers/reinforcement_learning_with_diffusers.ipynb)
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## Community
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