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Update transformer2d.md title #8375

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12 changes: 6 additions & 6 deletions docs/source/en/_toctree.yml
Original file line number Diff line number Diff line change
Expand Up @@ -237,19 +237,19 @@
- local: api/models/consistency_decoder_vae
title: ConsistencyDecoderVAE
- local: api/models/transformer2d
title: Transformer2D
title: Transformer2DModel
- local: api/models/pixart_transformer2d
title: PixArtTransformer2D
title: PixArtTransformer2DModel
- local: api/models/dit_transformer2d
title: DiTTransformer2D
title: DiTTransformer2DModel
- local: api/models/hunyuan_transformer_2d
title: HunyuanDiT2DModel
- local: api/models/transformer_temporal
title: Transformer Temporal
title: TransformerTemporalModel
- local: api/models/prior_transformer
title: Prior Transformer
title: PriorTransformer
- local: api/models/controlnet
title: ControlNet
title: ControlNetModel
title: Models
isExpanded: false
- sections:
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2 changes: 1 addition & 1 deletion docs/source/en/api/models/controlnet.md
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Expand Up @@ -10,7 +10,7 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
specific language governing permissions and limitations under the License.
-->

# ControlNet
# ControlNetModel

The ControlNet model was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, Maneesh Agrawala. It provides a greater degree of control over text-to-image generation by conditioning the model on additional inputs such as edge maps, depth maps, segmentation maps, and keypoints for pose detection.

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2 changes: 1 addition & 1 deletion docs/source/en/api/models/dit_transformer2d.md
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Expand Up @@ -10,7 +10,7 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
specific language governing permissions and limitations under the License.
-->

# DiTTransformer2D
# DiTTransformer2DModel

A Transformer model for image-like data from [DiT](https://huggingface.co/papers/2212.09748).

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2 changes: 1 addition & 1 deletion docs/source/en/api/models/pixart_transformer2d.md
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Expand Up @@ -10,7 +10,7 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
specific language governing permissions and limitations under the License.
-->

# PixArtTransformer2D
# PixArtTransformer2DModel

A Transformer model for image-like data from [PixArt-Alpha](https://huggingface.co/papers/2310.00426) and [PixArt-Sigma](https://huggingface.co/papers/2403.04692).

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2 changes: 1 addition & 1 deletion docs/source/en/api/models/prior_transformer.md
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Expand Up @@ -10,7 +10,7 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
specific language governing permissions and limitations under the License.
-->

# Prior Transformer
# PriorTransformer

The Prior Transformer was originally introduced in [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://huggingface.co/papers/2204.06125) by Ramesh et al. It is used to predict CLIP image embeddings from CLIP text embeddings; image embeddings are predicted through a denoising diffusion process.

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2 changes: 1 addition & 1 deletion docs/source/en/api/models/transformer2d.md
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Expand Up @@ -10,7 +10,7 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
specific language governing permissions and limitations under the License.
-->

# Transformer2D
# Transformer2DModel

A Transformer model for image-like data from [CompVis](https://huggingface.co/CompVis) that is based on the [Vision Transformer](https://huggingface.co/papers/2010.11929) introduced by Dosovitskiy et al. The [`Transformer2DModel`] accepts discrete (classes of vector embeddings) or continuous (actual embeddings) inputs.

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2 changes: 1 addition & 1 deletion docs/source/en/api/models/transformer_temporal.md
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Expand Up @@ -10,7 +10,7 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
specific language governing permissions and limitations under the License.
-->

# Transformer Temporal
# TransformerTemporalModel

A Transformer model for video-like data.

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