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docs/source/en/_toctree.yml

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title: Overview
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- local: using-diffusers/create_a_server
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title: Create a server
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- local: using-diffusers/batched_inference
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title: Batch inference
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- local: training/distributed_inference
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title: Distributed inference
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- local: using-diffusers/scheduler_features

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

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specific language governing permissions and limitations under the License.
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> [!WARNING]
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> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
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# aMUSEd
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aMUSEd was introduced in [aMUSEd: An Open MUSE Reproduction](https://huggingface.co/papers/2401.01808) by Suraj Patil, William Berman, Robin Rombach, and Patrick von Platen.

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

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specific language governing permissions and limitations under the License.
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> [!WARNING]
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> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
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# Attend-and-Excite
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Attend-and-Excite for Stable Diffusion was proposed in [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://attendandexcite.github.io/Attend-and-Excite/) and provides textual attention control over image generation.

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

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> [!WARNING]
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> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
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# AudioLDM
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AudioLDM was proposed in [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://huggingface.co/papers/2301.12503) by Haohe Liu et al. Inspired by [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview), AudioLDM

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

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> [!WARNING]
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> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
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# BLIP-Diffusion
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BLIP-Diffusion was proposed in [BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing](https://huggingface.co/papers/2305.14720). It enables zero-shot subject-driven generation and control-guided zero-shot generation.

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

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> [!WARNING]
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> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
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# ControlNet-XS
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<div class="flex flex-wrap space-x-1">

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

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> [!WARNING]
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> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
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# ControlNet-XS with Stable Diffusion XL
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ControlNet-XS was introduced in [ControlNet-XS](https://vislearn.github.io/ControlNet-XS/) by Denis Zavadski and Carsten Rother. It is based on the observation that the control model in the [original ControlNet](https://huggingface.co/papers/2302.05543) can be made much smaller and still produce good results.

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

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</Tip>
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## Loading original format checkpoints
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Original format checkpoints that have not been converted to diffusers-expected format can be loaded using the `from_single_file` method.
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```python
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import torch
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from diffusers import Cosmos2TextToImagePipeline, CosmosTransformer3DModel
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model_id = "nvidia/Cosmos-Predict2-2B-Text2Image"
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transformer = CosmosTransformer3DModel.from_single_file(
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"https://huggingface.co/nvidia/Cosmos-Predict2-2B-Text2Image/blob/main/model.pt",
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torch_dtype=torch.bfloat16,
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).to("cuda")
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pipe = Cosmos2TextToImagePipeline.from_pretrained(model_id, transformer=transformer, torch_dtype=torch.bfloat16)
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pipe.to("cuda")
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prompt = "A close-up shot captures a vibrant yellow scrubber vigorously working on a grimy plate, its bristles moving in circular motions to lift stubborn grease and food residue. The dish, once covered in remnants of a hearty meal, gradually reveals its original glossy surface. Suds form and bubble around the scrubber, creating a satisfying visual of cleanliness in progress. The sound of scrubbing fills the air, accompanied by the gentle clinking of the dish against the sink. As the scrubber continues its task, the dish transforms, gleaming under the bright kitchen lights, symbolizing the triumph of cleanliness over mess."
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negative_prompt = "The video captures a series of frames showing ugly scenes, static with no motion, motion blur, over-saturation, shaky footage, low resolution, grainy texture, pixelated images, poorly lit areas, underexposed and overexposed scenes, poor color balance, washed out colors, choppy sequences, jerky movements, low frame rate, artifacting, color banding, unnatural transitions, outdated special effects, fake elements, unconvincing visuals, poorly edited content, jump cuts, visual noise, and flickering. Overall, the video is of poor quality."
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output = pipe(
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prompt=prompt, negative_prompt=negative_prompt, generator=torch.Generator().manual_seed(1)
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).images[0]
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output.save("output.png")
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```
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## CosmosTextToWorldPipeline
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[[autodoc]] CosmosTextToWorldPipeline

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

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> [!WARNING]
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> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
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# Dance Diffusion
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[Dance Diffusion](https://github.com/Harmonai-org/sample-generator) is by Zach Evans.

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

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> [!WARNING]
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> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
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# DiffEdit
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[DiffEdit: Diffusion-based semantic image editing with mask guidance](https://huggingface.co/papers/2210.11427) is by Guillaume Couairon, Jakob Verbeek, Holger Schwenk, and Matthieu Cord.

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