text stringlengths 3 14.4k | source stringclasses 273
values | url stringlengths 47 172 | source_section stringlengths 0 95 | file_type stringclasses 1
value | id stringlengths 3 6 |
|---|---|---|---|---|---|
Loading the original LTX Video checkpoints is also possible with [`~ModelMixin.from_single_file`]. We recommend using `from_single_file` for the Lightricks series of models, as they plan to release multiple models in the future in the single file format.
```python
import torch
from diffusers import AutoencoderKLLTXVi... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#loading-single-files | #loading-single-files | .md | 127_2 |
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/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#quantization | #quantization | .md | 127_3 |
LTXPipeline
Pipeline for text-to-video generation.
Reference: https://github.com/Lightricks/LTX-Video
Args:
transformer ([`LTXVideoTransformer3DModel`]):
Conditional Transformer architecture to denoise the encoded video latents.
scheduler ([`FlowMatchEulerDiscreteScheduler`]):
A scheduler to be used in combinatio... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#ltxpipeline | #ltxpipeline | .md | 127_4 |
LTXImageToVideoPipeline
Pipeline for image-to-video generation.
Reference: https://github.com/Lightricks/LTX-Video
Args:
transformer ([`LTXVideoTransformer3DModel`]):
Conditional Transformer architecture to denoise the encoded video latents.
scheduler ([`FlowMatchEulerDiscreteScheduler`]):
A scheduler to be used ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#ltximagetovideopipeline | #ltximagetovideopipeline | .md | 127_5 |
LTXPipelineOutput
Output class for LTX 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 array or To... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#ltxpipelineoutput | #ltxpipelineoutput | .md | 127_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/musicldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/musicldm/ | .md | 128_0 | |
MusicLDM was proposed in [MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategies](https://huggingface.co/papers/2308.01546) by Ke Chen, Yusong Wu, Haohe Liu, Marianna Nezhurina, Taylor Berg-Kirkpatrick, Shlomo Dubnov.
MusicLDM takes a text prompt as input and predicts the corres... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/musicldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/musicldm/#musicldm | #musicldm | .md | 128_1 |
When constructing a prompt, keep in mind:
* Descriptive prompt inputs work best; use adjectives to describe the sound (for example, "high quality" or "clear") and make the prompt context specific where possible (e.g. "melodic techno with a fast beat and synths" works better than "techno").
* Using a *negative prompt*... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/musicldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/musicldm/#tips | #tips | .md | 128_2 |
MusicLDMPipeline
Pipeline for text-to-audio generation using MusicLDM.
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 ([`AutoencoderKL`]):
Variational Au... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/musicldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/musicldm/#musicldmpipeline | #musicldmpipeline | .md | 128_3 |
<!-- # Copyright 2024 The HuggingFace Team. All rights reserved. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/ | .md | 129_0 | |
[Latte: Latent Diffusion Transformer for Video Generation](https://arxiv.org/abs/2401.03048) from Monash University, Shanghai AI Lab, Nanjing University, and Nanyang Technological Univers... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/#latte | #latte | .md | 129_1 |
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 LattePipeline
pipeline = LattePipeline.from_pretrained(
"maxin-cn/Latte-1", torch_dtype=torch.float16
).... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/#inference | #inference | .md | 129_2 |
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/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/#quantization | #quantization | .md | 129_3 |
LattePipeline
Pipeline for text-to-video generation using Latte.
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 ([`AutoencoderKL`... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/#lattepipeline | #lattepipeline | .md | 129_4 |
<!--Copyright 2023 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/controlnetxs_sdxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs_sdxl/ | .md | 130_0 | |
ControlNet-XS was introduced in [ControlNet-XS](https://vislearn.github.io/ControlNet-XS/) by Denis Zavadski and Carsten Rother. It is based on the observation that the control model in the [original ControlNet](https://huggingface.co/papers/2302.05543) can be made much smaller and still produce good results.
Like th... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs_sdxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs_sdxl/#controlnet-xs-with-stable-diffusion-xl | #controlnet-xs-with-stable-diffusion-xl | .md | 130_1 |
StableDiffusionXLControlNetXSPipeline
Pipeline for text-to-image generation using Stable Diffusion XL with ControlNet-XS guidance.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular de... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs_sdxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs_sdxl/#stablediffusionxlcontrolnetxspipeline | #stablediffusionxlcontrolnetxspipeline | .md | 130_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/controlnetxs_sdxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs_sdxl/#stablediffusionpipelineoutput | #stablediffusionpipelineoutput | .md | 130_3 |
<!-- Copyright 2024 The HuggingFace Team. All rights reserved. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuan_video/ | .md | 131_0 | |
[HunyuanVideo](https://www.arxiv.org/abs/2412.03603) by Tencent.
*Recent advancements in video generation have significantly impacted daily life for both individuals and industries. However, the leading video generation models remain closed-source, resulting in a notable performance gap between industry capabilities ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuan_video/#hunyuanvideo | #hunyuanvideo | .md | 131_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/hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuan_video/#quantization | #quantization | .md | 131_2 |
HunyuanVideoPipeline
Pipeline for text-to-video generation using HunyuanVideo.
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 ([`LlamaModel`]):
... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuan_video/#hunyuanvideopipeline | #hunyuanvideopipeline | .md | 131_3 |
HunyuanVideoPipelineOutput
Output class for HunyuanVideo 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... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuan_video/#hunyuanvideopipelineoutput | #hunyuanvideopipelineoutput | .md | 131_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/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/ | .md | 132_0 | |
[ModelScope Text-to-Video Technical Report](https://arxiv.org/abs/2308.06571) is by Jiuniu Wang, Hangjie Yuan, Dayou Chen, Yingya Zhang, Xiang Wang, Shiwei Zhang.
The abstract from the paper is:
*This paper introduces ModelScopeT2V, a text-to-video synthesis model that evolves from a text-to-image synthesis model (... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#text-to-video | #text-to-video | .md | 132_1 |
Let's start by generating a short video with the default length of 16 frames (2s at 8 fps):
```python
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import export_to_video
pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16"... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#text-to-video-ms-17b | #text-to-video-ms-17b | .md | 132_2 |
Zeroscope are watermark-free model and have been trained on specific sizes such as `576x320` and `1024x576`.
One should first generate a video using the lower resolution checkpoint [`cerspense/zeroscope_v2_576w`](https://huggingface.co/cerspense/zeroscope_v2_576w) with [`TextToVideoSDPipeline`],
which can then be upsca... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#cerspensezeroscopev2576w--cerspensezeroscopev2xl | #cerspensezeroscopev2576w--cerspensezeroscopev2xl | .md | 132_3 |
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/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#tips | #tips | .md | 132_4 |
TextToVideoSDPipeline
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 method... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#texttovideosdpipeline | #texttovideosdpipeline | .md | 132_5 |
VideoToVideoSDPipeline
Pipeline for text-guided video-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 ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#videotovideosdpipeline | #videotovideosdpipeline | .md | 132_6 |
TextToVideoSDPipelineOutput
Output class for text-to-video 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... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#texttovideosdpipelineoutput | #texttovideosdpipelineoutput | .md | 132_7 |
<!--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/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/ | .md | 133_0 | |
ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala.
With a ControlNet model, you can provide an additional control image to condition and control Stable Diffusion generation. For example, ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#controlnet | #controlnet | .md | 133_1 |
StableDiffusionControlNetPipeline
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, etc.... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusioncontrolnetpipeline | #stablediffusioncontrolnetpipeline | .md | 133_2 |
StableDiffusionControlNetImg2ImgPipeline
Pipeline for image-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 devi... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusioncontrolnetimg2imgpipeline | #stablediffusioncontrolnetimg2imgpipeline | .md | 133_3 |
StableDiffusionControlNetInpaintPipeline
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, etc.)... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusioncontrolnetinpaintpipeline | #stablediffusioncontrolnetinpaintpipeline | .md | 133_4 |
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/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusionpipelineoutput | #stablediffusionpipelineoutput | .md | 133_5 |
FlaxStableDiffusionControlNetPipeline
- all
- __call__ | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#flaxstablediffusioncontrolnetpipeline | #flaxstablediffusioncontrolnetpipeline | .md | 133_6 |
[[autodoc]] FlaxStableDiffusionPipelineOutput: module diffusers.pipelines.stable_diffusion has no attribute FlaxStableDiffusionPipelineOutput | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#flaxstablediffusioncontrolnetpipelineoutput | #flaxstablediffusioncontrolnetpipelineoutput | .md | 133_7 |
<!--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/diffedit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/diffedit/ | .md | 134_0 | |
[DiffEdit: Diffusion-based semantic image editing with mask guidance](https://huggingface.co/papers/2210.11427) is by Guillaume Couairon, Jakob Verbeek, Holger Schwenk, and Matthieu Cord.
The abstract from the paper is:
*Image generation has recently seen tremendous advances, with diffusion models allowing to synth... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/diffedit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/diffedit/#diffedit | #diffedit | .md | 134_1 |
* The pipeline can generate masks that can be fed into other inpainting pipelines.
* In order to generate an image using this pipeline, both an image mask (source and target prompts can be manually specified or generated, and passed to [`~StableDiffusionDiffEditPipeline.generate_mask`])
and a set of partially inverted ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/diffedit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/diffedit/#tips | #tips | .md | 134_2 |
StableDiffusionDiffEditPipeline
<Tip warning={true}>
This is an experimental feature!
</Tip>
Pipeline for text-guided image inpainting using Stable Diffusion and DiffEdit.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/diffedit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/diffedit/#stablediffusiondiffeditpipeline | #stablediffusiondiffeditpipeline | .md | 134_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/diffedit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/diffedit/#stablediffusionpipelineoutput | #stablediffusionpipelineoutput | .md | 134_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/attend_and_excite.md | https://huggingface.co/docs/diffusers/en/api/pipelines/attend_and_excite/ | .md | 135_0 | |
Attend-and-Excite for Stable Diffusion was proposed in [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://attendandexcite.github.io/Attend-and-Excite/) and provides textual attention control over image generation.
The abstract from the paper is:
*Recent text-to-image g... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/attend_and_excite.md | https://huggingface.co/docs/diffusers/en/api/pipelines/attend_and_excite/#attend-and-excite | #attend-and-excite | .md | 135_1 |
StableDiffusionAttendAndExcitePipeline
Pipeline for text-to-image generation using Stable Diffusion and Attend-and-Excite.
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/attend_and_excite.md | https://huggingface.co/docs/diffusers/en/api/pipelines/attend_and_excite/#stablediffusionattendandexcitepipeline | #stablediffusionattendandexcitepipeline | .md | 135_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/attend_and_excite.md | https://huggingface.co/docs/diffusers/en/api/pipelines/attend_and_excite/#stablediffusionpipelineoutput | #stablediffusionpipelineoutput | .md | 135_3 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/consisid.md | https://huggingface.co/docs/diffusers/en/api/pipelines/consisid/ | .md | 136_0 | |
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/consisid.md | https://huggingface.co/docs/diffusers/en/api/pipelines/consisid/#limitations-under-the-license | #limitations-under-the-license | .md | 136_1 |
[Identity-Preserving Text-to-Video Generation by Frequency Decomposition](https://arxiv.org/abs/2411.17440) from Peking University & University of Rochester & etc, by Shenghai Yuan, Jinfa Huang, Xianyi He, Yunyang Ge, Yujun Shi, Liuhan Chen, Jiebo Luo, Li Yuan.
The abstract from the paper is:
*Identity-preserving t... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/consisid.md | https://huggingface.co/docs/diffusers/en/api/pipelines/consisid/#consisid | #consisid | .md | 136_2 |
ConsisID requires about 44 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 refer... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/consisid.md | https://huggingface.co/docs/diffusers/en/api/pipelines/consisid/#memory-optimization | #memory-optimization | .md | 136_3 |
[[autodoc]] ConsisIDPipeline
- all
- __call__ | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/consisid.md | https://huggingface.co/docs/diffusers/en/api/pipelines/consisid/#consisidpipeline | #consisidpipeline | .md | 136_4 |
[[autodoc]] pipelines.consisid.pipeline_output.ConsisIDPipelineOutput | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/consisid.md | https://huggingface.co/docs/diffusers/en/api/pipelines/consisid/#consisidpipelineoutput | #consisidpipelineoutput | .md | 136_5 |
<!-- Copyright 2024 The HuggingFace Team. All rights reserved. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/mochi.md | https://huggingface.co/docs/diffusers/en/api/pipelines/mochi/ | .md | 137_0 | |
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/mochi.md | https://huggingface.co/docs/diffusers/en/api/pipelines/mochi/#limitations-under-the-license | #limitations-under-the-license | .md | 137_1 |
> [!TIP]
> Only a research preview of the model weights is available at the moment.
[Mochi 1](https://huggingface.co/genmo/mochi-1-preview) is a video generation model by Genmo with a strong focus on prompt adherence and motion quality. The model features a 10B parameter Asmmetric Diffusion Transformer (AsymmDiT) arc... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/mochi.md | https://huggingface.co/docs/diffusers/en/api/pipelines/mochi/#mochi-1-preview | #mochi-1-preview | .md | 137_2 |
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/mochi.md | https://huggingface.co/docs/diffusers/en/api/pipelines/mochi/#quantization | #quantization | .md | 137_3 |
The following example will download the full precision `mochi-1-preview` weights and produce the highest quality results but will require at least 42GB VRAM to run.
```python
import torch
from diffusers import MochiPipeline
from diffusers.utils import export_to_video
pipe = MochiPipeline.from_pretrained("genmo/mochi... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/mochi.md | https://huggingface.co/docs/diffusers/en/api/pipelines/mochi/#generating-videos-with-mochi-1-preview | #generating-videos-with-mochi-1-preview | .md | 137_4 |
The following example will use the `bfloat16` variant of the model and requires 22GB VRAM to run. There is a slight drop in the quality of the generated video as a result.
```python
import torch
from diffusers import MochiPipeline
from diffusers.utils import export_to_video
pipe = MochiPipeline.from_pretrained("genm... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/mochi.md | https://huggingface.co/docs/diffusers/en/api/pipelines/mochi/#using-a-lower-precision-variant-to-save-memory | #using-a-lower-precision-variant-to-save-memory | .md | 137_5 |
The [Genmo Mochi implementation](https://github.com/genmoai/mochi/tree/main) uses different precision values for each stage in the inference process. The text encoder and VAE use `torch.float32`, while the DiT uses `torch.bfloat16` with the [attention kernel](https://pytorch.org/docs/stable/generated/torch.nn.attention... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/mochi.md | https://huggingface.co/docs/diffusers/en/api/pipelines/mochi/#reproducing-the-results-from-the-genmo-mochi-repo | #reproducing-the-results-from-the-genmo-mochi-repo | .md | 137_6 |
It is possible to split the large Mochi transformer across multiple GPUs using the `device_map` and `max_memory` options in `from_pretrained`. In the following example we split the model across two GPUs, each with 24GB of VRAM.
```python
import torch
from diffusers import MochiPipeline, MochiTransformer3DModel
from d... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/mochi.md | https://huggingface.co/docs/diffusers/en/api/pipelines/mochi/#running-inference-with-multiple-gpus | #running-inference-with-multiple-gpus | .md | 137_7 |
You can use `from_single_file` to load the Mochi transformer in its original format.
<Tip>
Diffusers currently doesn't support using the FP8 scaled versions of the Mochi single file checkpoints.
</Tip>
```python
import torch
from diffusers import MochiPipeline, MochiTransformer3DModel
from diffusers.utils import ex... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/mochi.md | https://huggingface.co/docs/diffusers/en/api/pipelines/mochi/#using-single-file-loading-with-the-mochi-transformer | #using-single-file-loading-with-the-mochi-transformer | .md | 137_8 |
MochiPipeline
The mochi pipeline for text-to-video generation.
Reference: https://github.com/genmoai/models
Args:
transformer ([`MochiTransformer3DModel`]):
Conditional Transformer architecture to denoise the encoded video latents.
scheduler ([`FlowMatchEulerDiscreteScheduler`]):
A scheduler to be used in combina... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/mochi.md | https://huggingface.co/docs/diffusers/en/api/pipelines/mochi/#mochipipeline | #mochipipeline | .md | 137_9 |
MochiPipelineOutput
Output class for Mochi 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 array o... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/mochi.md | https://huggingface.co/docs/diffusers/en/api/pipelines/mochi/#mochipipelineoutput | #mochipipelineoutput | .md | 137_10 |
<!--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_audio.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_audio/ | .md | 138_0 | |
Stable Audio was proposed in [Stable Audio Open](https://arxiv.org/abs/2407.14358) by Zach Evans et al. . it takes a text prompt as input and predicts the corresponding sound or music sample.
Stable Audio Open generates variable-length (up to 47s) stereo audio at 44.1kHz from text prompts. It comprises three componen... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_audio.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_audio/#stable-audio | #stable-audio | .md | 138_1 |
When constructing a prompt, keep in mind:
* Descriptive prompt inputs work best; use adjectives to describe the sound (for example, "high quality" or "clear") and make the prompt context specific where possible (e.g. "melodic techno with a fast beat and synths" works better than "techno").
* Using a *negative prompt*... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_audio.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_audio/#tips | #tips | .md | 138_2 |
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/stable_audio.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_audio/#quantization | #quantization | .md | 138_3 |
StableAudioPipeline
Pipeline for text-to-audio generation using StableAudio.
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 ([`AutoencoderOobleck`]):
Var... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_audio.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_audio/#stableaudiopipeline | #stableaudiopipeline | .md | 138_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/ddim.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ddim/ | .md | 139_0 | |
[Denoising Diffusion Implicit Models](https://huggingface.co/papers/2010.02502) (DDIM) by Jiaming Song, Chenlin Meng and Stefano Ermon.
The abstract from the paper is:
*Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulat... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ddim.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ddim/#ddim | #ddim | .md | 139_1 |
DDIMPipeline
Pipeline for image 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 ([`UNet2DModel`]):
A `UNet2DModel` to denoise the encod... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ddim.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ddim/#ddimpipeline | #ddimpipeline | .md | 139_2 |
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/ddim.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ddim/#imagepipelineoutput | #imagepipelineoutput | .md | 139_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_cascade.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_cascade/ | .md | 140_0 | |
This model is built upon the [Würstchen](https://openreview.net/forum?id=gU58d5QeGv) architecture and its main
difference to other models like Stable Diffusion is that it is working at a much smaller latent space. Why is this
important? The smaller the latent space, the **faster** you can run inference and the **cheape... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_cascade.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_cascade/#stable-cascade | #stable-cascade | .md | 140_1 |
Stable Cascade consists of three models: Stage A, Stage B and Stage C, representing a cascade to generate images,
hence the name "Stable Cascade".
Stage A & B are used to compress images, similar to what the job of the VAE is in Stable Diffusion.
However, with this setup, a much higher compression of images can be ac... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_cascade.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_cascade/#model-overview | #model-overview | .md | 140_2 |
```python
import torch
from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline
prompt = "an image of a shiba inu, donning a spacesuit and helmet"
negative_prompt = ""
prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", variant="bf16", torch_dtype=torch.bfloat1... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_cascade.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_cascade/#usage-example | #usage-example | .md | 140_3 |
```python
import torch
from diffusers import (
StableCascadeDecoderPipeline,
StableCascadePriorPipeline,
StableCascadeUNet,
)
prompt = "an image of a shiba inu, donning a spacesuit and helmet"
negative_prompt = ""
prior_unet = StableCascadeUNet.from_pretrained("stabilityai/stable-cascade-prior", subfolder="prior_lite... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_cascade.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_cascade/#using-the-lite-versions-of-the-stage-b-and-stage-c-models | #using-the-lite-versions-of-the-stage-b-and-stage-c-models | .md | 140_4 |
Loading the original format checkpoints is supported via `from_single_file` method in the StableCascadeUNet.
```python
import torch
from diffusers import (
StableCascadeDecoderPipeline,
StableCascadePriorPipeline,
StableCascadeUNet,
)
prompt = "an image of a shiba inu, donning a spacesuit and helmet"
negative_prompt... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_cascade.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_cascade/#loading-original-checkpoints-with-fromsinglefile | #loading-original-checkpoints-with-fromsinglefile | .md | 140_5 |
The model is intended for research purposes for now. Possible research areas and tasks include
- Research on generative models.
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and us... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_cascade.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_cascade/#direct-use | #direct-use | .md | 140_6 |
The model was not trained to be factual or true representations of people or events,
and therefore using the model to generate such content is out-of-scope for the abilities of this model.
The model should not be used in any way that violates Stability AI's [Acceptable Use Policy](https://stability.ai/use-policy). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_cascade.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_cascade/#out-of-scope-use | #out-of-scope-use | .md | 140_7 |
- Faces and people in general may not be generated properly.
- The autoencoding part of the model is lossy. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_cascade.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_cascade/#limitations | #limitations | .md | 140_8 |
StableCascadeCombinedPipeline
Combined Pipeline for text-to-image generation using Stable Cascade.
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, e... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_cascade.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_cascade/#stablecascadecombinedpipeline | #stablecascadecombinedpipeline | .md | 140_9 |
StableCascadePriorPipeline
Pipeline for generating image prior for Stable Cascade.
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:
pri... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_cascade.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_cascade/#stablecascadepriorpipeline | #stablecascadepriorpipeline | .md | 140_10 |
StableCascadePriorPipelineOutput
Output class for WuerstchenPriorPipeline.
Args:
image_embeddings (`torch.Tensor` or `np.ndarray`)
Prior image embeddings for text prompt
prompt_embeds (`torch.Tensor`):
Text embeddings for the prompt.
negative_prompt_embeds (`torch.Tensor`):
Text embeddings for the negative prompt. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_cascade.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_cascade/#stablecascadepriorpipelineoutput | #stablecascadepriorpipelineoutput | .md | 140_11 |
StableCascadeDecoderPipeline
Pipeline for generating images from the Stable Cascade model.
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.)
A... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_cascade.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_cascade/#stablecascadedecoderpipeline | #stablecascadedecoderpipeline | .md | 140_12 |
<!--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/audioldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/audioldm/ | .md | 141_0 | |
AudioLDM was proposed in [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://huggingface.co/papers/2301.12503) by Haohe Liu et al. Inspired by [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview), AudioLDM
is a text-to-audio _latent diffusion model (LDM... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/audioldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/audioldm/#audioldm | #audioldm | .md | 141_1 |
When constructing a prompt, keep in mind:
* Descriptive prompt inputs work best; you can use adjectives to describe the sound (for example, "high quality" or "clear") and make the prompt context specific (for example, "water stream in a forest" instead of "stream").
* It's best to use general terms like "cat" or "dog... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/audioldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/audioldm/#tips | #tips | .md | 141_2 |
AudioLDMPipeline
Pipeline for text-to-audio generation using AudioLDM.
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 ([`AutoencoderKL`]):
Variational Au... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/audioldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/audioldm/#audioldmpipeline | #audioldmpipeline | .md | 141_3 |
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/audioldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/audioldm/#audiopipelineoutput | #audiopipelineoutput | .md | 141_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/consistency_models.md | https://huggingface.co/docs/diffusers/en/api/pipelines/consistency_models/ | .md | 142_0 | |
Consistency Models were proposed in [Consistency Models](https://huggingface.co/papers/2303.01469) by Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever.
The abstract from the paper is:
*Diffusion models have significantly advanced the fields of image, audio, and video generation, but they depend on an ite... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/consistency_models.md | https://huggingface.co/docs/diffusers/en/api/pipelines/consistency_models/#consistency-models | #consistency-models | .md | 142_1 |
For an additional speed-up, use `torch.compile` to generate multiple images in <1 second:
```diff
import torch
from diffusers import ConsistencyModelPipeline
device = "cuda"
# Load the cd_bedroom256_lpips checkpoint.
model_id_or_path = "openai/diffusers-cd_bedroom256_lpips"
pipe = ConsistencyModelPipeline.from_pretr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/consistency_models.md | https://huggingface.co/docs/diffusers/en/api/pipelines/consistency_models/#tips | #tips | .md | 142_2 |
ConsistencyModelPipeline
Pipeline for unconditional or class-conditional image 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.).
Args:
unet ([`UNet2DModel... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/consistency_models.md | https://huggingface.co/docs/diffusers/en/api/pipelines/consistency_models/#consistencymodelpipeline | #consistencymodelpipeline | .md | 142_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/consistency_models.md | https://huggingface.co/docs/diffusers/en/api/pipelines/consistency_models/#imagepipelineoutput | #imagepipelineoutput | .md | 142_4 |
<!--Copyright 2024 The HuggingFace Team and Tencent Hunyuan 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... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuandit/ | .md | 143_0 | |
[Hunyuan-DiT : A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding](https://arxiv.org/abs/2405.08748) from Tencent Hunyuan.
The abstract from the... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuandit/#hunyuan-dit | #hunyuan-dit | .md | 143_1 |
You can optimize the pipeline's runtime and memory consumption with torch.compile and feed-forward chunking. To learn about other optimization methods, check out the [Speed up inference](../../optimization/fp16) and [Reduce memory usage](../../optimization/memory) guides. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuandit/#optimization | #optimization | .md | 143_2 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.