update

stevhliu · stevhliu · commit c39b998e4577 · 2025-02-21T10:58:31.000-08:00
diff --git a/docs/source/en/api/pipelines/amused.md b/docs/source/en/api/pipelines/amused.md
@@ -12,10 +12,6 @@ specific language governing permissions and limitations under the License.
 
 # aMUSEd
 
-<div class="flex flex-wrap space-x-1">
-  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
-</div>
-
 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.
 
 Amused is a lightweight text to image model based off of the [MUSE](https://arxiv.org/abs/2301.00704) architecture. Amused is particularly useful in applications that require a lightweight and fast model such as generating many images quickly at once.
diff --git a/docs/source/en/api/pipelines/consisid.md b/docs/source/en/api/pipelines/consisid.md
@@ -15,6 +15,10 @@
 
 # ConsisID
 
+<div class="flex flex-wrap space-x-1">
+  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
+</div>
+
 [Identity-Preserving Text-to-Video Generation by Frequency Decomposition](https://arxiv.org/abs/2411.17440) from Peking University & University of Rochester & etc, by Shenghai Yuan, Jinfa Huang, Xianyi He, Yunyang Ge, Yujun Shi, Liuhan Chen, Jiebo Luo, Li Yuan.
 
 The abstract from the paper is:
diff --git a/docs/source/en/api/pipelines/control_flux_inpaint.md b/docs/source/en/api/pipelines/control_flux_inpaint.md
@@ -12,6 +12,10 @@ specific language governing permissions and limitations under the License.
 
 # FluxControlInpaint
 
+<div class="flex flex-wrap space-x-1">
+  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
+</div>
+
 FluxControlInpaintPipeline is an implementation of Inpainting for Flux.1 Depth/Canny models. It is a pipeline that allows you to inpaint images using the Flux.1 Depth/Canny models. The pipeline takes an image and a mask as input and returns the inpainted image.
 
 FLUX.1 Depth and Canny [dev] is a 12 billion parameter rectified flow transformer capable of generating an image based on a text description while following the structure of a given input image. **This is not a ControlNet model**.
diff --git a/docs/source/en/api/pipelines/controlnet.md b/docs/source/en/api/pipelines/controlnet.md
@@ -12,6 +12,10 @@ specific language governing permissions and limitations under the License.
 
 # ControlNet
 
+<div class="flex flex-wrap space-x-1">
+  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
+</div>
+
 ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala.
 
 With a ControlNet model, you can provide an additional control image to condition and control Stable Diffusion generation. For example, if you provide a depth map, the ControlNet model generates an image that'll preserve the spatial information from the depth map. It is a more flexible and accurate way to control the image generation process.
diff --git a/docs/source/en/api/pipelines/controlnet_flux.md b/docs/source/en/api/pipelines/controlnet_flux.md
@@ -12,6 +12,10 @@ specific language governing permissions and limitations under the License.
 
 # ControlNet with Flux.1
 
+<div class="flex flex-wrap space-x-1">
+  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
+</div>
+
 FluxControlNetPipeline is an implementation of ControlNet for Flux.1.
 
 ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala.
diff --git a/docs/source/en/api/pipelines/controlnet_sd3.md b/docs/source/en/api/pipelines/controlnet_sd3.md
@@ -12,6 +12,10 @@ specific language governing permissions and limitations under the License.
 
 # ControlNet with Stable Diffusion 3
 
+<div class="flex flex-wrap space-x-1">
+  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
+</div>
+
 StableDiffusion3ControlNetPipeline is an implementation of ControlNet for Stable Diffusion 3.
 
 ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala.
diff --git a/docs/source/en/api/pipelines/controlnet_sdxl.md b/docs/source/en/api/pipelines/controlnet_sdxl.md
@@ -12,6 +12,10 @@ specific language governing permissions and limitations under the License.
 
 # ControlNet with Stable Diffusion XL
 
+<div class="flex flex-wrap space-x-1">
+  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
+</div>
+
 ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala.
 
 With a ControlNet model, you can provide an additional control image to condition and control Stable Diffusion generation. For example, if you provide a depth map, the ControlNet model generates an image that'll preserve the spatial information from the depth map. It is a more flexible and accurate way to control the image generation process.
diff --git a/docs/source/en/api/pipelines/controlnet_union.md b/docs/source/en/api/pipelines/controlnet_union.md
@@ -12,6 +12,10 @@ specific language governing permissions and limitations under the License.
 
 # ControlNetUnion
 
+<div class="flex flex-wrap space-x-1">
+  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
+</div>
+
 ControlNetUnionModel is an implementation of ControlNet for Stable Diffusion XL.
 
 The ControlNet model was introduced in [ControlNetPlus](https://github.com/xinsir6/ControlNetPlus) by xinsir6. It supports multiple conditioning inputs without increasing computation.
diff --git a/docs/source/en/api/pipelines/controlnetxs.md b/docs/source/en/api/pipelines/controlnetxs.md
@@ -12,6 +12,10 @@ specific language governing permissions and limitations under the License.
 
 # ControlNet-XS
 
+<div class="flex flex-wrap space-x-1">
+  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
+</div>
+
 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.
 
 Like the original ControlNet model, you can provide an additional control image to condition and control Stable Diffusion generation. For example, if you provide a depth map, the ControlNet model generates an image that'll preserve the spatial information from the depth map. It is a more flexible and accurate way to control the image generation process.
diff --git a/docs/source/en/api/pipelines/pag.md b/docs/source/en/api/pipelines/pag.md
@@ -12,6 +12,10 @@ specific language governing permissions and limitations under the License.
 
 # Perturbed-Attention Guidance
 
+<div class="flex flex-wrap space-x-1">
+  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
+</div>
+
 [Perturbed-Attention Guidance (PAG)](https://ku-cvlab.github.io/Perturbed-Attention-Guidance/) is a new diffusion sampling guidance that improves sample quality across both unconditional and conditional settings, achieving this without requiring further training or the integration of external modules.
 
 PAG was introduced in [Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance](https://huggingface.co/papers/2403.17377) by Donghoon Ahn, Hyoungwon Cho, Jaewon Min, Wooseok Jang, Jungwoo Kim, SeonHwa Kim, Hyun Hee Park, Kyong Hwan Jin and Seungryong Kim.
diff --git a/docs/source/en/api/pipelines/panorama.md b/docs/source/en/api/pipelines/panorama.md
@@ -12,6 +12,10 @@ specific language governing permissions and limitations under the License.
 
 # MultiDiffusion
 
+<div class="flex flex-wrap space-x-1">
+  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
+</div>
+
 [MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation](https://huggingface.co/papers/2302.08113) is by Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel.
 
 The abstract from the paper is:
diff --git a/docs/source/en/api/pipelines/stable_diffusion/depth2img.md b/docs/source/en/api/pipelines/stable_diffusion/depth2img.md
@@ -12,6 +12,10 @@ specific language governing permissions and limitations under the License.
 
 # Depth-to-image
 
+<div class="flex flex-wrap space-x-1">
+  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
+</div>
+
 The Stable Diffusion model can also infer depth based on an image using [MiDaS](https://github.com/isl-org/MiDaS). This allows you to pass a text prompt and an initial image to condition the generation of new images as well as a `depth_map` to preserve the image structure.
 
 <Tip>
diff --git a/docs/source/en/api/pipelines/stable_diffusion/img2img.md b/docs/source/en/api/pipelines/stable_diffusion/img2img.md
@@ -12,6 +12,10 @@ specific language governing permissions and limitations under the License.
 
 # Image-to-image
 
+<div class="flex flex-wrap space-x-1">
+  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
+</div>
+
 The Stable Diffusion model can also be applied to image-to-image generation by passing a text prompt and an initial image to condition the generation of new images.
 
 The [`StableDiffusionImg2ImgPipeline`] uses the diffusion-denoising mechanism proposed in [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://huggingface.co/papers/2108.01073) by Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon.
diff --git a/docs/source/en/api/pipelines/stable_diffusion/inpaint.md b/docs/source/en/api/pipelines/stable_diffusion/inpaint.md
@@ -12,6 +12,10 @@ specific language governing permissions and limitations under the License.
 
 # Inpainting
 
+<div class="flex flex-wrap space-x-1">
+  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
+</div>
+
 The Stable Diffusion model can also be applied to inpainting which lets you edit specific parts of an image by providing a mask and a text prompt using Stable Diffusion.
 
 ## Tips
diff --git a/docs/source/en/api/pipelines/stable_diffusion/ldm3d_diffusion.md b/docs/source/en/api/pipelines/stable_diffusion/ldm3d_diffusion.md
@@ -12,6 +12,10 @@ specific language governing permissions and limitations under the License.
 
 # Text-to-(RGB, depth)
 
+<div class="flex flex-wrap space-x-1">
+  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
+</div>
+
 LDM3D was proposed in [LDM3D: Latent Diffusion Model for 3D](https://huggingface.co/papers/2305.10853) by Gabriela Ben Melech Stan, Diana Wofk, Scottie Fox, Alex Redden, Will Saxton, Jean Yu, Estelle Aflalo, Shao-Yen Tseng, Fabio Nonato, Matthias Muller, and Vasudev Lal. LDM3D generates an image and a depth map from a given text prompt unlike the existing text-to-image diffusion models such as [Stable Diffusion](./overview) which only generates an image. With almost the same number of parameters, LDM3D achieves to create a latent space that can compress both the RGB images and the depth maps.
 
 Two checkpoints are available for use:
diff --git a/docs/source/en/api/pipelines/stable_diffusion/text2img.md b/docs/source/en/api/pipelines/stable_diffusion/text2img.md
@@ -12,6 +12,10 @@ specific language governing permissions and limitations under the License.
 
 # Text-to-image
 
+<div class="flex flex-wrap space-x-1">
+  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
+</div>
+
 The Stable Diffusion model was created by researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/), [Runway](https://github.com/runwayml), and [LAION](https://laion.ai/). The [`StableDiffusionPipeline`] is capable of generating photorealistic images given any text input. It's trained on 512x512 images from a subset of the LAION-5B dataset. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and can run on consumer GPUs. Latent diffusion is the research on top of which Stable Diffusion was built. It was proposed in [High-Resolution Image Synthesis with Latent Diffusion Models](https://huggingface.co/papers/2112.10752) by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer.
 
 The abstract from the paper is:
diff --git a/docs/source/en/api/pipelines/stable_diffusion/upscale.md b/docs/source/en/api/pipelines/stable_diffusion/upscale.md
@@ -12,6 +12,10 @@ specific language governing permissions and limitations under the License.
 
 # Super-resolution
 
+<div class="flex flex-wrap space-x-1">
+  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
+</div>
+
 The Stable Diffusion upscaler diffusion model was created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/), and [LAION](https://laion.ai/). It is used to enhance the resolution of input images by a factor of 4.
 
 <Tip>
diff --git a/docs/source/en/api/pipelines/stable_unclip.md b/docs/source/en/api/pipelines/stable_unclip.md
@@ -12,6 +12,10 @@ specific language governing permissions and limitations under the License.
 
 # Stable unCLIP
 
+<div class="flex flex-wrap space-x-1">
+  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
+</div>
+
 Stable unCLIP checkpoints are finetuned from [Stable Diffusion 2.1](./stable_diffusion/stable_diffusion_2) checkpoints to condition on CLIP image embeddings.
 Stable unCLIP still conditions on text embeddings. Given the two separate conditionings, stable unCLIP can be used
 for text guided image variation. When combined with an unCLIP prior, it can also be used for full text to image generation.
diff --git a/docs/source/en/api/pipelines/text_to_video_zero.md b/docs/source/en/api/pipelines/text_to_video_zero.md
@@ -12,6 +12,10 @@ specific language governing permissions and limitations under the License.
 
 # Text2Video-Zero
 
+<div class="flex flex-wrap space-x-1">
+  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
+</div>
+
 [Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators](https://huggingface.co/papers/2303.13439) is by Levon Khachatryan, Andranik Movsisyan, Vahram Tadevosyan, Roberto Henschel, [Zhangyang Wang](https://www.ece.utexas.edu/people/faculty/atlas-wang), Shant Navasardyan, [Humphrey Shi](https://www.humphreyshi.com).
 
 Text2Video-Zero enables zero-shot video generation using either:
