mirror of
https://github.com/huggingface/diffusers.git
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title: Lumina-T2X
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- local: api/pipelines/marigold
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title: Marigold
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- local: api/pipelines/nucleusmoe_image
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title: NucleusMoE-Image
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- local: api/pipelines/omnigen
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title: OmniGen
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- local: api/pipelines/ovis_image
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30
docs/source/en/api/pipelines/nucleusmoe_image.md
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30
docs/source/en/api/pipelines/nucleusmoe_image.md
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@@ -0,0 +1,30 @@
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<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License. -->
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# NucleusMoE-Image
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[NucleusMoE-Image](https://huggingface.co/NucleusAI/NucleusMoE-Image) is a text-to-image model that pairs a single-stream DiT with Mixture-of-Experts feed-forward layers, cross-attention to a Qwen3-VL text encoder, and a flow-matching Euler discrete scheduler.
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> [!TIP]
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> 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-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
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## NucleusMoEImagePipeline
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[[autodoc]] NucleusMoEImagePipeline
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- all
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- __call__
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## NucleusMoEImagePipelineOutput
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[[autodoc]] pipelines.nucleusmoe_image.pipeline_output.NucleusMoEImagePipelineOutput
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