text stringlengths 3 14.4k | source stringclasses 273
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Use [`torch.compile`](https://huggingface.co/docs/diffusers/main/en/tutorials/fast_diffusion#torchcompile) to reduce the inference latency.
First, load the pipeline:
```python
from diffusers import HunyuanDiTPipeline
import torch
pipeline = HunyuanDiTPipeline.from_pretrained(
"Tencent-Hunyuan/HunyuanDiT-Diffusers"... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuandit/#inference | #inference | .md | 143_3 |
By loading the T5 text encoder in 8 bits, you can run the pipeline in just under 6 GBs of GPU VRAM. Refer to [this script](https://gist.github.com/sayakpaul/3154605f6af05b98a41081aaba5ca43e) for details.
Furthermore, you can use the [`~HunyuanDiT2DModel.enable_forward_chunking`] method to reduce memory usage. Feed-fo... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuandit/#memory-optimization | #memory-optimization | .md | 143_4 |
HunyuanDiTPipeline
Pipeline for English/Chinese-to-image generation using HunyuanDiT.
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.)
Hunyua... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuandit/#hunyuanditpipeline | #hunyuanditpipeline | .md | 143_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/pixart_sigma.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart_sigma/ | .md | 144_0 | |
[PixArt-Σ: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation](https://huggingface.co/papers/2403.04692) is Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enz... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart_sigma.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart_sigma/#pixart-σ | #pixart-σ | .md | 144_1 |
Run the [`PixArtSigmaPipeline`] with under 8GB GPU VRAM by loading the text encoder in 8-bit precision. Let's walk through a full-fledged example.
First, install the [bitsandbytes](https://github.com/TimDettmers/bitsandbytes) library:
```bash
pip install -U bitsandbytes
```
Then load the text encoder in 8-bit:
... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart_sigma.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart_sigma/#inference-with-under-8gb-gpu-vram | #inference-with-under-8gb-gpu-vram | .md | 144_2 |
PixArtSigmaPipeline
Pipeline for text-to-image generation using PixArt-Sigma.
- all
- __call__ | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart_sigma.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart_sigma/#pixartsigmapipeline | #pixartsigmapipeline | .md | 144_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/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/ | .md | 145_0 | |
[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 introdu... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#perturbed-attention-guidance | #perturbed-attention-guidance | .md | 145_1 |
AnimateDiffPAGPipeline
Pipeline for text-to-video generation using
[AnimateDiff](https://huggingface.co/docs/diffusers/en/api/pipelines/animatediff) and [Perturbed Attention
Guidance](https://huggingface.co/docs/diffusers/en/using-diffusers/pag).
This model inherits from [`DiffusionPipeline`]. Check the superclass ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#animatediffpagpipeline | #animatediffpagpipeline | .md | 145_2 |
HunyuanDiTPAGPipeline
Pipeline for English/Chinese-to-image generation using HunyuanDiT and [Perturbed Attention
Guidance](https://huggingface.co/docs/diffusers/en/using-diffusers/pag).
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#hunyuanditpagpipeline | #hunyuanditpagpipeline | .md | 145_3 |
KolorsPAGPipeline
- all
- __call__ | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#kolorspagpipeline | #kolorspagpipeline | .md | 145_4 |
StableDiffusionPAGInpaintPipeline
Pipeline for text-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 pipeline also in... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionpaginpaintpipeline | #stablediffusionpaginpaintpipeline | .md | 145_5 |
StableDiffusionPAGPipeline
Pipeline for text-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 pipeline also inherits ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionpagpipeline | #stablediffusionpagpipeline | .md | 145_6 |
StableDiffusionPAGImg2ImgPipeline
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 pip... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionpagimg2imgpipeline | #stablediffusionpagimg2imgpipeline | .md | 145_7 |
StableDiffusionControlNetPAGPipeline
Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, e... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusioncontrolnetpagpipeline | #stablediffusioncontrolnetpagpipeline | .md | 145_8 |
StableDiffusionControlNetPAGInpaintPipeline
Pipeline for image inpainting using Stable Diffusion with ControlNet guidance.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, et... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusioncontrolnetpaginpaintpipeline | #stablediffusioncontrolnetpaginpaintpipeline | .md | 145_9 |
StableDiffusionXLPAGPipeline
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.) ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlpagpipeline | #stablediffusionxlpagpipeline | .md | 145_10 |
StableDiffusionXLPAGImg2ImgPipeline
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,... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlpagimg2imgpipeline | #stablediffusionxlpagimg2imgpipeline | .md | 145_11 |
StableDiffusionXLPAGInpaintPipeline
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,... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlpaginpaintpipeline | #stablediffusionxlpaginpaintpipeline | .md | 145_12 |
StableDiffusionXLControlNetPAGPipeline
Pipeline for text-to-image generation using Stable Diffusion XL with ControlNet guidance.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular devi... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlcontrolnetpagpipeline | #stablediffusionxlcontrolnetpagpipeline | .md | 145_13 |
StableDiffusionXLControlNetPAGImg2ImgPipeline
Pipeline for image-to-image generation using Stable Diffusion XL with ControlNet guidance.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or sav... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlcontrolnetpagimg2imgpipeline | #stablediffusionxlcontrolnetpagimg2imgpipeline | .md | 145_14 |
StableDiffusion3PAGPipeline
[PAG pipeline](https://huggingface.co/docs/diffusers/main/en/using-diffusers/pag) for text-to-image generation
using Stable Diffusion 3.
Args:
transformer ([`SD3Transformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([`FlowMatc... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusion3pagpipeline | #stablediffusion3pagpipeline | .md | 145_15 |
StableDiffusion3PAGImg2ImgPipeline
[PAG pipeline](https://huggingface.co/docs/diffusers/main/en/using-diffusers/pag) for image-to-image generation
using Stable Diffusion 3.
Args:
transformer ([`SD3Transformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([`... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusion3pagimg2imgpipeline | #stablediffusion3pagimg2imgpipeline | .md | 145_16 |
PixArtSigmaPAGPipeline
[PAG pipeline](https://huggingface.co/docs/diffusers/main/en/using-diffusers/pag) for text-to-image generation
using PixArt-Sigma.
- all
- __call__ | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#pixartsigmapagpipeline | #pixartsigmapagpipeline | .md | 145_17 |
<!--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/self_attention_guidance.md | https://huggingface.co/docs/diffusers/en/api/pipelines/self_attention_guidance/ | .md | 146_0 | |
[Improving Sample Quality of Diffusion Models Using Self-Attention Guidance](https://huggingface.co/papers/2210.00939) is by Susung Hong et al.
The abstract from the paper is:
*Denoising diffusion models (DDMs) have attracted attention for their exceptional generation quality and diversity. This success is largely ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/self_attention_guidance.md | https://huggingface.co/docs/diffusers/en/api/pipelines/self_attention_guidance/#self-attention-guidance | #self-attention-guidance | .md | 146_1 |
StableDiffusionSAGPipeline
Pipeline for text-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 pipeline also inherits ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/self_attention_guidance.md | https://huggingface.co/docs/diffusers/en/api/pipelines/self_attention_guidance/#stablediffusionsagpipeline | #stablediffusionsagpipeline | .md | 146_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/self_attention_guidance.md | https://huggingface.co/docs/diffusers/en/api/pipelines/self_attention_guidance/#stablediffusionoutput | #stablediffusionoutput | .md | 146_3 |
<!--Copyright 2024 Marigold 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 by ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/ | .md | 147_0 | |
Marigold was proposed in [Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation](https://huggingface.co/papers/2312.02145), a CVPR 2024 Oral paper by [Bingxin Ke](http://www.kebingxin.com/), [Anton Obukhov](htt... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigold-pipelines-for-computer-vision-tasks | #marigold-pipelines-for-computer-vision-tasks | .md | 147_1 |
Each pipeline supports one Computer Vision task, which takes an input RGB image as input and produces a *prediction* of the modality of interest, such as a depth map of the input image.
Currently, the following tasks are implemented:
| Pipeline ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#available-pipelines | #available-pipelines | .md | 147_2 |
The original checkpoints can be found under the [PRS-ETH](https://huggingface.co/prs-eth/) Hugging Face organization.
<Tip>
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 p... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#available-checkpoints | #available-checkpoints | .md | 147_3 |
MarigoldDepthPipeline
Pipeline for monocular depth estimation using the Marigold method: https://marigoldmonodepth.github.io.
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, runnin... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigolddepthpipeline | #marigolddepthpipeline | .md | 147_4 |
MarigoldNormalsPipeline
Pipeline for monocular normals estimation using the Marigold method: https://marigoldmonodepth.github.io.
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, ru... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigoldnormalspipeline | #marigoldnormalspipeline | .md | 147_5 |
MarigoldDepthOutput
Output class for Marigold monocular depth prediction pipeline.
Args:
prediction (`np.ndarray`, `torch.Tensor`):
Predicted depth maps with values in the range [0, 1]. The shape is always $numimages imes 1 imes height
imes width$, regardless of whether the images were passed as a 4D array or a lis... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigolddepthoutput | #marigolddepthoutput | .md | 147_6 |
MarigoldNormalsOutput
Output class for Marigold monocular normals prediction pipeline.
Args:
prediction (`np.ndarray`, `torch.Tensor`):
Predicted normals with values in the range [-1, 1]. The shape is always $numimages imes 3 imes height
imes width$, regardless of whether the images were passed as a 4D array or a l... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigoldnormalsoutput | #marigoldnormalsoutput | .md | 147_7 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/ | .md | 148_0 | |
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#limitations-under-the-license | #limitations-under-the-license | .md | 148_1 |
[CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://arxiv.org/abs/2408.06072) from Tsinghua University & ZhipuAI, by Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, Da Yin, Xiaotao Gu, Yuxuan Zhang, Weihan Wang... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#cogvideox | #cogvideox | .md | 148_2 |
Use [`torch.compile`](https://huggingface.co/docs/diffusers/main/en/tutorials/fast_diffusion#torchcompile) to reduce the inference latency.
First, load the pipeline:
```python
import torch
from diffusers import CogVideoXPipeline, CogVideoXImageToVideoPipeline
from diffusers.utils import export_to_video,load_image
p... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#inference | #inference | .md | 148_3 |
CogVideoX-2b requires about 19 GB of GPU memory to decode 49 frames (6 seconds of video at 8 FPS) with output resolution 720x480 (W x H), which makes it not possible to run on consumer GPUs or free-tier T4 Colab. The following memory optimizations could be used to reduce the memory footprint. For replication, you can r... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#memory-optimization | #memory-optimization | .md | 148_4 |
Quantization helps reduce the memory requirements of very large models by storing model weights in a lower precision data type. However, quantization may have varying impact on video quality depending on the video model.
Refer to the [Quantization](../../quantization/overview) overview to learn more about supported q... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#quantization | #quantization | .md | 148_5 |
CogVideoXPipeline
Pipeline for text-to-video generation using CogVideoX.
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.)
Args:
vae ([`Autoen... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#cogvideoxpipeline | #cogvideoxpipeline | .md | 148_6 |
CogVideoXImageToVideoPipeline
Pipeline for image-to-video generation using CogVideoX.
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.)
Args:
... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#cogvideoximagetovideopipeline | #cogvideoximagetovideopipeline | .md | 148_7 |
CogVideoXVideoToVideoPipeline
Pipeline for video-to-video generation using CogVideoX.
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.)
Args:
... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#cogvideoxvideotovideopipeline | #cogvideoxvideotovideopipeline | .md | 148_8 |
CogVideoXFunControlPipeline
Pipeline for controlled text-to-video generation using CogVideoX Fun.
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/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#cogvideoxfuncontrolpipeline | #cogvideoxfuncontrolpipeline | .md | 148_9 |
CogVideoXPipelineOutput
Output class for CogVideo pipelines.
Args:
frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
denoised PIL image sequences of length `num_frames.` It can also be a NumPy ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#cogvideoxpipelineoutput | #cogvideoxpipelineoutput | .md | 148_10 |
<!--Copyright 2024 The HuggingFace Team, The Black Forest 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/control_flux_inpaint.md | https://huggingface.co/docs/diffusers/en/api/pipelines/control_flux_inpaint/ | .md | 149_0 | |
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 r... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/control_flux_inpaint.md | https://huggingface.co/docs/diffusers/en/api/pipelines/control_flux_inpaint/#fluxcontrolinpaint | #fluxcontrolinpaint | .md | 149_1 |
FluxControlInpaintPipeline
The Flux pipeline for image inpainting using Flux-dev-Depth/Canny.
Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
Args:
transformer ([`FluxTransformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([`FlowMatc... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/control_flux_inpaint.md | https://huggingface.co/docs/diffusers/en/api/pipelines/control_flux_inpaint/#fluxcontrolinpaintpipeline | #fluxcontrolinpaintpipeline | .md | 149_2 |
FluxPipelineOutput
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)`. PIL images or numpy array present the denoised images of the diffusion pipeli... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/control_flux_inpaint.md | https://huggingface.co/docs/diffusers/en/api/pipelines/control_flux_inpaint/#fluxpipelineoutput | #fluxpipelineoutput | .md | 149_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/pipelines/amused.md | https://huggingface.co/docs/diffusers/en/api/pipelines/amused/ | .md | 150_0 | |
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 usefu... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/amused.md | https://huggingface.co/docs/diffusers/en/api/pipelines/amused/#amused | #amused | .md | 150_1 |
AmusedPipeline
- __call__
- all
- enable_xformers_memory_efficient_attention
- disable_xformers_memory_efficient_attention
AmusedImg2ImgPipeline
- __call__
- all
- enable_xformers_memory_efficient_attention
- disable_xformers_memory_efficient_attention
AmusedInpaintPipeline
- __call__
- all
- enable_xformers_memory... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/amused.md | https://huggingface.co/docs/diffusers/en/api/pipelines/amused/#amusedpipeline | #amusedpipeline | .md | 150_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 applicable law or ag... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/allegro.md | https://huggingface.co/docs/diffusers/en/api/pipelines/allegro/ | .md | 151_0 | |
[Allegro: Open the Black Box of Commercial-Level Video Generation Model](https://huggingface.co/papers/2410.15458) from RhymesAI, by Yuan Zhou, Qiuyue Wang, Yuxuan Cai, Huan Yang.
The abstract from the paper is:
*Significant advancements have been made in the field of video generation, with the open-source communit... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/allegro.md | https://huggingface.co/docs/diffusers/en/api/pipelines/allegro/#allegro | #allegro | .md | 151_1 |
Quantization helps reduce the memory requirements of very large models by storing model weights in a lower precision data type. However, quantization may have varying impact on video quality depending on the video model.
Refer to the [Quantization](../../quantization/overview) overview to learn more about supported q... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/allegro.md | https://huggingface.co/docs/diffusers/en/api/pipelines/allegro/#quantization | #quantization | .md | 151_2 |
AllegroPipeline
Pipeline for text-to-video generation using Allegro.
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.)
Args:
vae ([`AllegroAut... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/allegro.md | https://huggingface.co/docs/diffusers/en/api/pipelines/allegro/#allegropipeline | #allegropipeline | .md | 151_3 |
AllegroPipelineOutput
Output class for Allegro pipelines.
Args:
frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
denoised PIL image sequences of length `num_frames.` It can also be a NumPy arr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/allegro.md | https://huggingface.co/docs/diffusers/en/api/pipelines/allegro/#allegropipelineoutput | #allegropipelineoutput | .md | 151_4 |
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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
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/value_guided_sampling.md | https://huggingface.co/docs/diffusers/en/api/pipelines/value_guided_sampling/ | .md | 152_0 | |
<Tip warning={true}>
🧪 This is an experimental pipeline for reinforcement learning!
</Tip>
This pipeline is based on the [Planning with Diffusion for Flexible Behavior Synthesis](https://huggingface.co/papers/2205.09991) paper by Michael Janner, Yilun Du, Joshua B. Tenenbaum, Sergey Levine.
The abstract from t... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/value_guided_sampling.md | https://huggingface.co/docs/diffusers/en/api/pipelines/value_guided_sampling/#value-guided-planning | #value-guided-planning | .md | 152_1 |
[[autodoc]] ValueGuidedRLPipeline: No module named 'diffusers.diffusers' | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/value_guided_sampling.md | https://huggingface.co/docs/diffusers/en/api/pipelines/value_guided_sampling/#valueguidedrlpipeline | #valueguidedrlpipeline | .md | 152_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 applicable law or agreed to... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/unclip.md | https://huggingface.co/docs/diffusers/en/api/pipelines/unclip/ | .md | 153_0 | |
[Hierarchical Text-Conditional Image Generation with CLIP Latents](https://huggingface.co/papers/2204.06125) is by Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen. The unCLIP model in 🤗 Diffusers comes from kakaobrain's [karlo](https://github.com/kakaobrain/karlo).
The abstract from the paper is ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/unclip.md | https://huggingface.co/docs/diffusers/en/api/pipelines/unclip/#unclip | #unclip | .md | 153_1 |
UnCLIPPipeline
Pipeline for text-to-image generation using unCLIP.
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:
text_encoder ([`~transformers.CLIPTextModel... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/unclip.md | https://huggingface.co/docs/diffusers/en/api/pipelines/unclip/#unclippipeline | #unclippipeline | .md | 153_2 |
UnCLIPImageVariationPipeline
Pipeline to generate image variations from an input image using UnCLIP.
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:
text_enco... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/unclip.md | https://huggingface.co/docs/diffusers/en/api/pipelines/unclip/#unclipimagevariationpipeline | #unclipimagevariationpipeline | .md | 153_3 |
ImagePipelineOutput
Output class for image 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)`. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/unclip.md | https://huggingface.co/docs/diffusers/en/api/pipelines/unclip/#imagepipelineoutput | #imagepipelineoutput | .md | 153_4 |
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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
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pia.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pia/ | .md | 154_0 | |
[PIA: Your Personalized Image Animator via Plug-and-Play Modules in Text-to-Image Models](https://arxiv.org/abs/2312.13964) by Yiming Zhang, Zhening Xing, Yanhong Zeng, Youqing Fang, Kai Chen
Recent advancements in personalized text-to-image (T2I) models have revolutionized content creation, empowering non-experts to... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pia.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pia/#overview | #overview | .md | 154_1 |
| Pipeline | Tasks | Demo
|---|---|:---:|
| [PIAPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pia/pipeline_pia.py) | *Image-to-Video Generation with PIA* | | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pia.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pia/#available-pipelines | #available-pipelines | .md | 154_2 |
Motion Adapter checkpoints for PIA can be found under the [OpenMMLab org](https://huggingface.co/openmmlab/PIA-condition-adapter). These checkpoints are meant to work with any model based on Stable Diffusion 1.5 | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pia.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pia/#available-checkpoints | #available-checkpoints | .md | 154_3 |
PIA works with a MotionAdapter checkpoint and a Stable Diffusion 1.5 model checkpoint. The MotionAdapter is a collection of Motion Modules that are responsible for adding coherent motion across image frames. These modules are applied after the Resnet and Attention blocks in the Stable Diffusion UNet. In addition to the... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pia.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pia/#usage-example | #usage-example | .md | 154_4 |
[FreeInit: Bridging Initialization Gap in Video Diffusion Models](https://arxiv.org/abs/2312.07537) by Tianxing Wu, Chenyang Si, Yuming Jiang, Ziqi Huang, Ziwei Liu.
FreeInit is an effective method that improves temporal consistency and overall quality of videos generated using video-diffusion-models without any addi... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pia.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pia/#using-freeinit | #using-freeinit | .md | 154_5 |
PIAPipeline
Pipeline for text-to-video generation.
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 inherits the following loading methods:
- [`~lo... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pia.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pia/#piapipeline | #piapipeline | .md | 154_6 |
PIAPipelineOutput
Output class for PIAPipeline.
Args:
frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
Nested list of length `batch_size` with denoised PIL image sequences of length `num_frames`, NumPy array of
shape `(batch_size, num_frames, channels, height, width, Torch tensor of shape `(ba... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pia.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pia/#piapipelineoutput | #piapipelineoutput | .md | 154_7 |
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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
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/dance_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/dance_diffusion/ | .md | 155_0 | |
[Dance Diffusion](https://github.com/Harmonai-org/sample-generator) is by Zach Evans.
Dance Diffusion is the first in a suite of generative audio tools for producers and musicians released by [Harmonai](https://github.com/Harmonai-org).
<Tip>
Make sure to check out the Schedulers [guide](../../using-diffusers/sch... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/dance_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/dance_diffusion/#dance-diffusion | #dance-diffusion | .md | 155_1 |
DanceDiffusionPipeline
Pipeline for audio generation.
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.).
Parameters:
unet ([`UNet1DModel`]):
A `UNet1DModel` to denoise... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/dance_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/dance_diffusion/#dancediffusionpipeline | #dancediffusionpipeline | .md | 155_2 |
AudioPipelineOutput
Output class for audio pipelines.
Args:
audios (`np.ndarray`)
List of denoised audio samples of a NumPy array of shape `(batch_size, num_channels, sample_rate)`. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/dance_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/dance_diffusion/#audiopipelineoutput | #audiopipelineoutput | .md | 155_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/pipelines/blip_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/blip_diffusion/ | .md | 156_0 | |
BLIP-Diffusion was proposed in [BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing](https://arxiv.org/abs/2305.14720). It enables zero-shot subject-driven generation and control-guided zero-shot generation.
The abstract from the paper is:
*Subject-driven text-to... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/blip_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/blip_diffusion/#blip-diffusion | #blip-diffusion | .md | 156_1 |
BlipDiffusionPipeline
Pipeline for Zero-Shot Subject Driven Generation using Blip Diffusion.
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.) ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/blip_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/blip_diffusion/#blipdiffusionpipeline | #blipdiffusionpipeline | .md | 156_2 |
BlipDiffusionControlNetPipeline
Pipeline for Canny Edge based Controlled subject-driven generation using Blip Diffusion.
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 ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/blip_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/blip_diffusion/#blipdiffusioncontrolnetpipeline | #blipdiffusioncontrolnetpipeline | .md | 156_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/aura_flow.md | https://huggingface.co/docs/diffusers/en/api/pipelines/aura_flow/ | .md | 157_0 | |
AuraFlow is inspired by [Stable Diffusion 3](../pipelines/stable_diffusion/stable_diffusion_3) and is by far the largest text-to-image generation model that comes with an Apache 2.0 license. This model achieves state-of-the-art results on the [GenEval](https://github.com/djghosh13/geneval) benchmark.
It was developed... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/aura_flow.md | https://huggingface.co/docs/diffusers/en/api/pipelines/aura_flow/#auraflow | #auraflow | .md | 157_1 |
Quantization helps reduce the memory requirements of very large models by storing model weights in a lower precision data type. However, quantization may have varying impact on video quality depending on the video model.
Refer to the [Quantization](../../quantization/overview) overview to learn more about supported q... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/aura_flow.md | https://huggingface.co/docs/diffusers/en/api/pipelines/aura_flow/#quantization | #quantization | .md | 157_2 |
AuraFlowPipeline
Args:
tokenizer (`T5TokenizerFast`):
Tokenizer of class
[T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
text_encoder ([`T5EncoderModel`]):
Frozen text-encoder. AuraFlow uses
[T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Encode... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/aura_flow.md | https://huggingface.co/docs/diffusers/en/api/pipelines/aura_flow/#auraflowpipeline | #auraflowpipeline | .md | 157_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
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/animatediff.md | https://huggingface.co/docs/diffusers/en/api/pipelines/animatediff/ | .md | 158_0 | |
[AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://arxiv.org/abs/2307.04725) by Yuwei Guo, Ceyuan Yang, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, Bo Dai.
The abstract of the paper is the following:
*With the advance of text-to-image models (e.g., Stable Diffusio... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/animatediff.md | https://huggingface.co/docs/diffusers/en/api/pipelines/animatediff/#overview | #overview | .md | 158_1 |
| Pipeline | Tasks | Demo
|---|---|:---:|
| [AnimateDiffPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/animatediff/pipeline_animatediff.py) | *Text-to-Video Generation with AnimateDiff* |
| [AnimateDiffControlNetPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffu... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/animatediff.md | https://huggingface.co/docs/diffusers/en/api/pipelines/animatediff/#available-pipelines | #available-pipelines | .md | 158_2 |
Motion Adapter checkpoints can be found under [guoyww](https://huggingface.co/guoyww/). These checkpoints are meant to work with any model based on Stable Diffusion 1.4/1.5. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/animatediff.md | https://huggingface.co/docs/diffusers/en/api/pipelines/animatediff/#available-checkpoints | #available-checkpoints | .md | 158_3 |
AnimateDiff works with a MotionAdapter checkpoint and a Stable Diffusion model checkpoint. The MotionAdapter is a collection of Motion Modules that are responsible for adding coherent motion across image frames. These modules are applied after the Resnet and Attention blocks in Stable Diffusion UNet.
The following ex... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/animatediff.md | https://huggingface.co/docs/diffusers/en/api/pipelines/animatediff/#animatediffpipeline | #animatediffpipeline | .md | 158_4 |
AnimateDiff can also be used with ControlNets 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 contro... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/animatediff.md | https://huggingface.co/docs/diffusers/en/api/pipelines/animatediff/#animatediffcontrolnetpipeline | #animatediffcontrolnetpipeline | .md | 158_5 |
[SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion Models](https://arxiv.org/abs/2311.16933) for achieving controlled generation in text-to-video diffusion models by Yuwei Guo, Ceyuan Yang, Anyi Rao, Maneesh Agrawala, Dahua Lin, and Bo Dai.
The abstract from the paper is:
*The development of text-to-vid... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/animatediff.md | https://huggingface.co/docs/diffusers/en/api/pipelines/animatediff/#animatediffsparsecontrolnetpipeline | #animatediffsparsecontrolnetpipeline | .md | 158_6 |
```python
import torch
from diffusers import AnimateDiffSparseControlNetPipeline
from diffusers.models import AutoencoderKL, MotionAdapter, SparseControlNetModel
from diffusers.schedulers import DPMSolverMultistepScheduler
from diffusers.utils import export_to_gif, load_image
model_id = "SG161222/Realistic_Vision_V5... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/animatediff.md | https://huggingface.co/docs/diffusers/en/api/pipelines/animatediff/#using-sparsectrl-scribble | #using-sparsectrl-scribble | .md | 158_7 |
```python
import torch
from diffusers import AnimateDiffSparseControlNetPipeline
from diffusers.models import AutoencoderKL, MotionAdapter, SparseControlNetModel
from diffusers.schedulers import DPMSolverMultistepScheduler
from diffusers.utils import export_to_gif, load_image
model_id = "SG161222/Realistic_Vision_V5... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/animatediff.md | https://huggingface.co/docs/diffusers/en/api/pipelines/animatediff/#using-sparsectrl-rgb | #using-sparsectrl-rgb | .md | 158_8 |
AnimateDiff can also be used with SDXL models. This is currently an experimental feature as only a beta release of the motion adapter checkpoint is available.
```python
import torch
from diffusers.models import MotionAdapter
from diffusers import AnimateDiffSDXLPipeline, DDIMScheduler
from diffusers.utils import expo... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/animatediff.md | https://huggingface.co/docs/diffusers/en/api/pipelines/animatediff/#animatediffsdxlpipeline | #animatediffsdxlpipeline | .md | 158_9 |
AnimateDiff can also be used to generate visually similar videos or enable style/character/background or other edits starting from an initial video, allowing you to seamlessly explore creative possibilities.
```python
import imageio
import requests
import torch
from diffusers import AnimateDiffVideoToVideoPipeline, D... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/animatediff.md | https://huggingface.co/docs/diffusers/en/api/pipelines/animatediff/#animatediffvideotovideopipeline | #animatediffvideotovideopipeline | .md | 158_10 |
AnimateDiff can be used together with ControlNets to enhance video-to-video generation by allowing for precise control over the output. 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, an... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/animatediff.md | https://huggingface.co/docs/diffusers/en/api/pipelines/animatediff/#animatediffvideotovideocontrolnetpipeline | #animatediffvideotovideocontrolnetpipeline | .md | 158_11 |
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