text stringlengths 3 14.4k | source stringclasses 273
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It is recommended to use this pipeline with checkpoints that have been specifically fine-tuned for inpainting, such
as [runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting). Default
text-to-image Stable Diffusion checkpoints, such as
[stable-diffusion-v1-5/stable-diffusion-... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/inpaint.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/inpaint/#tips | #tips | .md | 173_2 |
StableDiffusionInpaintPipeline
Pipeline for text-guided image inpainting using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipeline also i... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/inpaint.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/inpaint/#stablediffusioninpaintpipeline | #stablediffusioninpaintpipeline | .md | 173_3 |
StableDiffusionPipelineOutput
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
nsfw_content_detected (`List[bool]`)
List indicating whether the c... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/inpaint.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/inpaint/#stablediffusionpipelineoutput | #stablediffusionpipelineoutput | .md | 173_4 |
FlaxStableDiffusionInpaintPipeline
- all
- __call__ | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/inpaint.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/inpaint/#flaxstablediffusioninpaintpipeline | #flaxstablediffusioninpaintpipeline | .md | 173_5 |
[[autodoc]] FlaxStableDiffusionPipelineOutput: module diffusers.pipelines.stable_diffusion has no attribute FlaxStableDiffusionPipelineOutput | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/inpaint.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/inpaint/#flaxstablediffusionpipelineoutput | #flaxstablediffusionpipelineoutput | .md | 173_6 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_xl/ | .md | 174_0 | |
Stable Diffusion XL (SDXL) was proposed in [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://huggingface.co/papers/2307.01952) by Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach.
The abstract from the paper is:
... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_xl/#stable-diffusion-xl | #stable-diffusion-xl | .md | 174_1 |
- Using SDXL with a DPM++ scheduler for less than 50 steps is known to produce [visual artifacts](https://github.com/huggingface/diffusers/issues/5433) because the solver becomes numerically unstable. To fix this issue, take a look at this [PR](https://github.com/huggingface/diffusers/pull/5541) which recommends for OD... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_xl/#tips | #tips | .md | 174_2 |
StableDiffusionXLPipeline
Pipeline for text-to-image generation using Stable Diffusion XL.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
T... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_xl/#stablediffusionxlpipeline | #stablediffusionxlpipeline | .md | 174_3 |
StableDiffusionXLImg2ImgPipeline
Pipeline for text-to-image generation using Stable Diffusion XL.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, et... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_xl/#stablediffusionxlimg2imgpipeline | #stablediffusionxlimg2imgpipeline | .md | 174_4 |
StableDiffusionXLInpaintPipeline
Pipeline for text-to-image generation using Stable Diffusion XL.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, et... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_xl/#stablediffusionxlinpaintpipeline | #stablediffusionxlinpaintpipeline | .md | 174_5 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/depth2img.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/depth2img/ | .md | 175_0 | |
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>
Make sure to check out the Stable Di... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/depth2img.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/depth2img/#depth-to-image | #depth-to-image | .md | 175_1 |
StableDiffusionDepth2ImgPipeline
Pipeline for text-guided depth-based image-to-image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.)... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/depth2img.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/depth2img/#stablediffusiondepth2imgpipeline | #stablediffusiondepth2imgpipeline | .md | 175_2 |
StableDiffusionPipelineOutput
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
nsfw_content_detected (`List[bool]`)
List indicating whether the c... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/depth2img.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/depth2img/#stablediffusionpipelineoutput | #stablediffusionpipelineoutput | .md | 175_3 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/upscale.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/upscale/ | .md | 176_0 | |
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>
Make sure to check out the Stable... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/upscale.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/upscale/#super-resolution | #super-resolution | .md | 176_1 |
StableDiffusionUpscalePipeline
Pipeline for text-guided image super-resolution using Stable Diffusion 2.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipelin... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/upscale.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/upscale/#stablediffusionupscalepipeline | #stablediffusionupscalepipeline | .md | 176_2 |
StableDiffusionPipelineOutput
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
nsfw_content_detected (`List[bool]`)
List indicating whether the c... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/upscale.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/upscale/#stablediffusionpipelineoutput | #stablediffusionpipelineoutput | .md | 176_3 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/svd.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/svd/ | .md | 177_0 | |
Stable Video Diffusion was proposed in [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://hf.co/papers/2311.15127) by Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, Varun Jampani... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/svd.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/svd/#stable-video-diffusion | #stable-video-diffusion | .md | 177_1 |
Video generation is memory-intensive and one way to reduce your memory usage is to set `enable_forward_chunking` on the pipeline's UNet so you don't run the entire feedforward layer at once. Breaking it up into chunks in a loop is more efficient.
Check out the [Text or image-to-video](text-img2vid) guide for more det... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/svd.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/svd/#tips | #tips | .md | 177_2 |
StableVideoDiffusionPipeline
Pipeline to generate video from an input image using Stable Video Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
vae ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/svd.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/svd/#stablevideodiffusionpipeline | #stablevideodiffusionpipeline | .md | 177_3 |
StableVideoDiffusionPipelineOutput
Output class for Stable Video Diffusion pipeline.
Args:
frames (`[List[List[PIL.Image.Image]]`, `np.ndarray`, `torch.Tensor`]):
List of denoised PIL images of length `batch_size` or numpy array or torch tensor of shape `(batch_size,
num_frames, height, width, num_channels)`. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/svd.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/svd/#stablevideodiffusionpipelineoutput | #stablevideodiffusionpipelineoutput | .md | 177_4 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/img2img.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/img2img/ | .md | 178_0 | |
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 Dif... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/img2img.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/img2img/#image-to-image | #image-to-image | .md | 178_1 |
StableDiffusionImg2ImgPipeline
Pipeline for text-guided image-to-image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipeli... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/img2img.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/img2img/#stablediffusionimg2imgpipeline | #stablediffusionimg2imgpipeline | .md | 178_2 |
StableDiffusionPipelineOutput
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
nsfw_content_detected (`List[bool]`)
List indicating whether the c... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/img2img.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/img2img/#stablediffusionpipelineoutput | #stablediffusionpipelineoutput | .md | 178_3 |
FlaxStableDiffusionImg2ImgPipeline
- all
- __call__ | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/img2img.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/img2img/#flaxstablediffusionimg2imgpipeline | #flaxstablediffusionimg2imgpipeline | .md | 178_4 |
[[autodoc]] FlaxStableDiffusionPipelineOutput: module diffusers.pipelines.stable_diffusion has no attribute FlaxStableDiffusionPipelineOutput | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/img2img.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/img2img/#flaxstablediffusionpipelineoutput | #flaxstablediffusionpipelineoutput | .md | 178_5 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/adapter.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/adapter/ | .md | 179_0 | |
[T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.08453) by Chong Mou, Xintao Wang, Liangbin Xie, Jian Zhang, Zhongang Qi, Ying Shan, Xiaohu Qie.
Using the pretrained models we can provide control images (for example, a depth map) to co... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/adapter.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/adapter/#t2i-adapter | #t2i-adapter | .md | 179_1 |
StableDiffusionAdapterPipeline
Pipeline for text-to-image generation using Stable Diffusion augmented with T2I-Adapter
https://arxiv.org/abs/2302.08453
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as dow... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/adapter.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/adapter/#stablediffusionadapterpipeline | #stablediffusionadapterpipeline | .md | 179_2 |
StableDiffusionXLAdapterPipeline
Pipeline for text-to-image generation using Stable Diffusion augmented with T2I-Adapter
https://arxiv.org/abs/2302.08453
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as d... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/adapter.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/adapter/#stablediffusionxladapterpipeline | #stablediffusionxladapterpipeline | .md | 179_3 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_safe.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_safe/ | .md | 180_0 | |
Safe Stable Diffusion was proposed in [Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models](https://huggingface.co/papers/2211.05105) and mitigates inappropriate degeneration from Stable Diffusion models because they're trained on unfiltered web-crawled datasets. For instance Stable Diffusi... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_safe.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_safe/#safe-stable-diffusion | #safe-stable-diffusion | .md | 180_1 |
Use the `safety_concept` property of [`StableDiffusionPipelineSafe`] to check and edit the current safety concept:
```python
>>> from diffusers import StableDiffusionPipelineSafe
>>> pipeline = StableDiffusionPipelineSafe.from_pretrained("AIML-TUDA/stable-diffusion-safe")
>>> pipeline.safety_concept
'an image showin... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_safe.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_safe/#tips | #tips | .md | 180_2 |
StableDiffusionPipelineSafe
Pipeline based on the [`StableDiffusionPipeline`] for text-to-image generation using Safe Latent Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particul... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_safe.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_safe/#stablediffusionpipelinesafe | #stablediffusionpipelinesafe | .md | 180_3 |
StableDiffusionSafePipelineOutput
Output class for Safe Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. PIL images or numpy array present the denoised images of ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_safe.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_safe/#stablediffusionsafepipelineoutput | #stablediffusionsafepipelineoutput | .md | 180_4 |
<!--Copyright 2024 The GLIGEN Authors and The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required b... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/gligen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/gligen/ | .md | 181_0 | |
The GLIGEN model was created by researchers and engineers from [University of Wisconsin-Madison, Columbia University, and Microsoft](https://github.com/gligen/GLIGEN). The [`StableDiffusionGLIGENPipeline`] and [`StableDiffusionGLIGENTextImagePipeline`] can generate photorealistic images conditioned on grounding inputs.... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/gligen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/gligen/#gligen-grounded-language-to-image-generation | #gligen-grounded-language-to-image-generation | .md | 181_1 |
StableDiffusionGLIGENPipeline
Pipeline for text-to-image generation using Stable Diffusion with Grounded-Language-to-Image Generation (GLIGEN).
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/gligen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/gligen/#stablediffusiongligenpipeline | #stablediffusiongligenpipeline | .md | 181_2 |
StableDiffusionGLIGENTextImagePipeline
Pipeline for text-to-image generation using Stable Diffusion with Grounded-Language-to-Image Generation (GLIGEN).
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as do... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/gligen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/gligen/#stablediffusiongligentextimagepipeline | #stablediffusiongligentextimagepipeline | .md | 181_3 |
StableDiffusionPipelineOutput
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
nsfw_content_detected (`List[bool]`)
List indicating whether the c... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/gligen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/gligen/#stablediffusionpipelineoutput | #stablediffusionpipelineoutput | .md | 181_4 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/asymmetricautoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/asymmetricautoencoderkl/ | .md | 182_0 | |
Improved larger variational autoencoder (VAE) model with KL loss for inpainting task: [Designing a Better Asymmetric VQGAN for StableDiffusion](https://arxiv.org/abs/2306.04632) by Zixin Zhu, Xuelu Feng, Dongdong Chen, Jianmin Bao, Le Wang, Yinpeng Chen, Lu Yuan, Gang Hua.
The abstract from the paper is:
*StableDif... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/asymmetricautoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/asymmetricautoencoderkl/#asymmetricautoencoderkl | #asymmetricautoencoderkl | .md | 182_1 |
* [https://huggingface.co/cross-attention/asymmetric-autoencoder-kl-x-1-5](https://huggingface.co/cross-attention/asymmetric-autoencoder-kl-x-1-5)
* [https://huggingface.co/cross-attention/asymmetric-autoencoder-kl-x-2](https://huggingface.co/cross-attention/asymmetric-autoencoder-kl-x-2) | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/asymmetricautoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/asymmetricautoencoderkl/#available-checkpoints | #available-checkpoints | .md | 182_2 |
```python
from diffusers import AsymmetricAutoencoderKL, StableDiffusionInpaintPipeline
from diffusers.utils import load_image, make_image_grid
prompt = "a photo of a person with beard"
img_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/celeba_hq_256.png"
mask_url = "... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/asymmetricautoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/asymmetricautoencoderkl/#example-usage | #example-usage | .md | 182_3 |
AsymmetricAutoencoderKL
Designing a Better Asymmetric VQGAN for StableDiffusion https://arxiv.org/abs/2306.04632 . A VAE model with KL loss
for encoding images into latents and decoding latent representations into images.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic m... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/asymmetricautoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/asymmetricautoencoderkl/#asymmetricautoencoderkl | #asymmetricautoencoderkl | .md | 182_4 |
AutoencoderKLOutput
Output of AutoencoderKL encoding method.
Args:
latent_dist (`DiagonalGaussianDistribution`):
Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`.
`DiagonalGaussianDistribution` allows for sampling latents from the distribution. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/asymmetricautoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/asymmetricautoencoderkl/#autoencoderkloutput | #autoencoderkloutput | .md | 182_5 |
DecoderOutput
Output of decoding method.
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):
The decoded output sample from the last layer of the model. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/asymmetricautoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/asymmetricautoencoderkl/#decoderoutput | #decoderoutput | .md | 182_6 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/dit_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/dit_transformer2d/ | .md | 183_0 | |
A Transformer model for image-like data from [DiT](https://huggingface.co/papers/2212.09748). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/dit_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/dit_transformer2d/#dittransformer2dmodel | #dittransformer2dmodel | .md | 183_1 |
DiTTransformer2DModel
A 2D Transformer model as introduced in DiT (https://arxiv.org/abs/2212.09748).
Parameters:
num_attention_heads (int, optional, defaults to 16): The number of heads to use for multi-head attention.
attention_head_dim (int, optional, defaults to 72): The number of channels in each head.
in_chan... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/dit_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/dit_transformer2d/#dittransformer2dmodel | #dittransformer2dmodel | .md | 183_2 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_union.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_union/ | .md | 184_0 | |
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.
*We design a new architecture that can support 10+ ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_union.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_union/#controlnetunionmodel | #controlnetunionmodel | .md | 184_1 |
By default the [`ControlNetUnionModel`] should be loaded with [`~ModelMixin.from_pretrained`].
```py
from diffusers import StableDiffusionXLControlNetUnionPipeline, ControlNetUnionModel
controlnet = ControlNetUnionModel.from_pretrained("xinsir/controlnet-union-sdxl-1.0")
pipe = StableDiffusionXLControlNetUnionPipeli... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_union.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_union/#loading | #loading | .md | 184_2 |
ControlNetUnionModel
A ControlNetUnion model.
Args:
in_channels (`int`, defaults to 4):
The number of channels in the input sample.
flip_sin_to_cos (`bool`, defaults to `True`):
Whether to flip the sin to cos in the time embedding.
freq_shift (`int`, defaults to 0):
The frequency shift to apply to the time embeddin... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_union.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_union/#controlnetunionmodel | #controlnetunionmodel | .md | 184_3 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl/ | .md | 185_0 | |
The variational autoencoder (VAE) model with KL loss was introduced in [Auto-Encoding Variational Bayes](https://arxiv.org/abs/1312.6114v11) by Diederik P. Kingma and Max Welling. The model is used in 🤗 Diffusers to encode images into latents and to decode latent representations into images.
The abstract from the pa... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl/#autoencoderkl | #autoencoderkl | .md | 185_1 |
By default the [`AutoencoderKL`] should be loaded with [`~ModelMixin.from_pretrained`], but it can also be loaded
from the original format using [`FromOriginalModelMixin.from_single_file`] as follows:
```py
from diffusers import AutoencoderKL
url = "https://huggingface.co/stabilityai/sd-vae-ft-mse-original/blob/main... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl/#loading-from-the-original-format | #loading-from-the-original-format | .md | 185_2 |
AutoencoderKL
A VAE model with KL loss for encoding images into latents and decoding latent representations into images.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
Parameters:
in_channels (int, *o... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl/#autoencoderkl | #autoencoderkl | .md | 185_3 |
AutoencoderKLOutput
Output of AutoencoderKL encoding method.
Args:
latent_dist (`DiagonalGaussianDistribution`):
Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`.
`DiagonalGaussianDistribution` allows for sampling latents from the distribution. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl/#autoencoderkloutput | #autoencoderkloutput | .md | 185_4 |
DecoderOutput
Output of decoding method.
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):
The decoded output sample from the last layer of the model. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl/#decoderoutput | #decoderoutput | .md | 185_5 |
FlaxAutoencoderKL | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl/#flaxautoencoderkl | #flaxautoencoderkl | .md | 185_6 |
[[autodoc]] FlaxAutoencoderKLOutput: No module named 'flax' | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl/#flaxautoencoderkloutput | #flaxautoencoderkloutput | .md | 185_7 |
[[autodoc]] FlaxDecoderOutput: No module named 'flax' | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl/#flaxdecoderoutput | #flaxdecoderoutput | .md | 185_8 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/overview.md | https://huggingface.co/docs/diffusers/en/api/models/overview/ | .md | 186_0 | |
🤗 Diffusers provides pretrained models for popular algorithms and modules to create custom diffusion systems. The primary function of models is to denoise an input sample as modeled by the distribution \\(p_{\theta}(x_{t-1}|x_{t})\\).
All models are built from the base [`ModelMixin`] class which is a [`torch.nn.Mod... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/overview.md | https://huggingface.co/docs/diffusers/en/api/models/overview/#models | #models | .md | 186_1 |
ModelMixin
Base class for all models.
[`ModelMixin`] takes care of storing the model configuration and provides methods for loading, downloading and
saving models.
- **config_name** ([`str`]) -- Filename to save a model to when calling [`~models.ModelMixin.save_pretrained`]. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/overview.md | https://huggingface.co/docs/diffusers/en/api/models/overview/#modelmixin | #modelmixin | .md | 186_2 |
FlaxModelMixin | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/overview.md | https://huggingface.co/docs/diffusers/en/api/models/overview/#flaxmodelmixin | #flaxmodelmixin | .md | 186_3 |
PushToHubMixin
A Mixin to push a model, scheduler, or pipeline to the Hugging Face Hub. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/overview.md | https://huggingface.co/docs/diffusers/en/api/models/overview/#pushtohubmixin | #pushtohubmixin | .md | 186_4 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/flux_transformer.md | https://huggingface.co/docs/diffusers/en/api/models/flux_transformer/ | .md | 187_0 | |
A Transformer model for image-like data from [Flux](https://blackforestlabs.ai/announcing-black-forest-labs/). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/flux_transformer.md | https://huggingface.co/docs/diffusers/en/api/models/flux_transformer/#fluxtransformer2dmodel | #fluxtransformer2dmodel | .md | 187_1 |
FluxTransformer2DModel
The Transformer model introduced in Flux.
Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
Args:
patch_size (`int`, defaults to `1`):
Patch size to turn the input data into small patches.
in_channels (`int`, defaults to `64`):
The number of channels in the input.
out_chan... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/flux_transformer.md | https://huggingface.co/docs/diffusers/en/api/models/flux_transformer/#fluxtransformer2dmodel | #fluxtransformer2dmodel | .md | 187_2 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/consisid_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/consisid_transformer3d/ | .md | 188_0 | |
A Diffusion Transformer model for 3D data from [ConsisID](https://github.com/PKU-YuanGroup/ConsisID) was introduced in [Identity-Preserving Text-to-Video Generation by Frequency Decomposition](https://arxiv.org/pdf/2411.17440) by Peking University & University of Rochester & etc.
The model can be loaded with the foll... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/consisid_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/consisid_transformer3d/#consisidtransformer3dmodel | #consisidtransformer3dmodel | .md | 188_1 |
[[autodoc]] ConsisIDTransformer3DModel | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/consisid_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/consisid_transformer3d/#consisidtransformer3dmodel | #consisidtransformer3dmodel | .md | 188_2 |
[[autodoc]] models.modeling_outputs.Transformer2DModelOutput | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/consisid_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/consisid_transformer3d/#transformer2dmodeloutput | #transformer2dmodeloutput | .md | 188_3 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoder_oobleck.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoder_oobleck/ | .md | 189_0 | |
The Oobleck variational autoencoder (VAE) model with KL loss was introduced in [Stability-AI/stable-audio-tools](https://github.com/Stability-AI/stable-audio-tools) and [Stable Audio Open](https://huggingface.co/papers/2407.14358) by Stability AI. The model is used in 🤗 Diffusers to encode audio waveforms into latents... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoder_oobleck.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoder_oobleck/#autoencoderoobleck | #autoencoderoobleck | .md | 189_1 |
AutoencoderOobleck
An autoencoder for encoding waveforms into latents and decoding latent representations into waveforms. First
introduced in Stable Audio.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoder_oobleck.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoder_oobleck/#autoencoderoobleck | #autoencoderoobleck | .md | 189_2 |
OobleckDecoderOutput
Output of decoding method.
Args:
sample (`torch.Tensor` of shape `(batch_size, audio_channels, sequence_length)`):
The decoded output sample from the last layer of the model.
OobleckDecoderOutput
Output of decoding method.
Args:
sample (`torch.Tensor` of shape `(batch_size, audio_channels... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoder_oobleck.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoder_oobleck/#oobleckdecoderoutput | #oobleckdecoderoutput | .md | 189_3 |
AutoencoderOobleckOutput
Output of AutoencoderOobleck encoding method.
Args:
latent_dist (`OobleckDiagonalGaussianDistribution`):
Encoded outputs of `Encoder` represented as the mean and standard deviation of
`OobleckDiagonalGaussianDistribution`. `OobleckDiagonalGaussianDistribution` allows for sampling latents
fr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoder_oobleck.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoder_oobleck/#autoencoderoobleckoutput | #autoencoderoobleckoutput | .md | 189_4 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/transformer2d/ | .md | 190_0 | |
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.... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/transformer2d/#transformer2dmodel | #transformer2dmodel | .md | 190_1 |
Transformer2DModel
A 2D Transformer model for image-like data.
Parameters:
num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
in_channels (`int`, *optional*):
The n... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/transformer2d/#transformer2dmodel | #transformer2dmodel | .md | 190_2 |
Transformer2DModelOutput
The output of [`Transformer2DModel`].
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
The hidden states output conditioned on the `encoder_hidden_states` inp... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/transformer2d/#transformer2dmodeloutput | #transformer2dmodeloutput | .md | 190_3 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl_cogvideox.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl_cogvideox/ | .md | 191_0 | |
The 3D variational autoencoder (VAE) model with KL loss used in [CogVideoX](https://github.com/THUDM/CogVideo) was introduced in [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://github.com/THUDM/CogVideo/blob/main/resources/CogVideoX.pdf) by Tsinghua University & ZhipuAI.
The model can b... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl_cogvideox.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl_cogvideox/#autoencoderklcogvideox | #autoencoderklcogvideox | .md | 191_1 |
AutoencoderKLCogVideoX
A VAE model with KL loss for encoding images into latents and decoding latent representations into images. Used in
[CogVideoX](https://github.com/THUDM/CogVideo).
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (s... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl_cogvideox.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl_cogvideox/#autoencoderklcogvideox | #autoencoderklcogvideox | .md | 191_2 |
AutoencoderKLOutput
Output of AutoencoderKL encoding method.
Args:
latent_dist (`DiagonalGaussianDistribution`):
Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`.
`DiagonalGaussianDistribution` allows for sampling latents from the distribution. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl_cogvideox.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl_cogvideox/#autoencoderkloutput | #autoencoderkloutput | .md | 191_3 |
DecoderOutput
Output of decoding method.
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):
The decoded output sample from the last layer of the model. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl_cogvideox.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl_cogvideox/#decoderoutput | #decoderoutput | .md | 191_4 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/aura_flow_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/aura_flow_transformer2d/ | .md | 192_0 | |
A Transformer model for image-like data from [AuraFlow](https://blog.fal.ai/auraflow/). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/aura_flow_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/aura_flow_transformer2d/#auraflowtransformer2dmodel | #auraflowtransformer2dmodel | .md | 192_1 |
AuraFlowTransformer2DModel
A 2D Transformer model as introduced in AuraFlow (https://blog.fal.ai/auraflow/).
Parameters:
sample_size (`int`): The width of the latent images. This is fixed during training since
it is used to learn a number of position embeddings.
patch_size (`int`): Patch size to turn the input data... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/aura_flow_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/aura_flow_transformer2d/#auraflowtransformer2dmodel | #auraflowtransformer2dmodel | .md | 192_2 |
<!--Copyright 2024 The HuggingFace Team and The InstantX Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_flux.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_flux/ | .md | 193_0 | |
FluxControlNetModel is an implementation of ControlNet for Flux.1.
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 gene... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_flux.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_flux/#fluxcontrolnetmodel | #fluxcontrolnetmodel | .md | 193_1 |
By default the [`FluxControlNetModel`] should be loaded with [`~ModelMixin.from_pretrained`].
```py
from diffusers import FluxControlNetPipeline
from diffusers.models import FluxControlNetModel, FluxMultiControlNetModel
controlnet = FluxControlNetModel.from_pretrained("InstantX/FLUX.1-dev-Controlnet-Canny")
pipe = F... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_flux.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_flux/#loading-from-the-original-format | #loading-from-the-original-format | .md | 193_2 |
FluxControlNetModel | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_flux.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_flux/#fluxcontrolnetmodel | #fluxcontrolnetmodel | .md | 193_3 |
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