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 |
|---|---|---|---|---|---|
Stable unCLIP can be leveraged for text-to-image generation by pipelining it with the prior model of KakaoBrain's open source DALL-E 2 replication [Karlo](https://huggingface.co/kakaobrain/karlo-v1-alpha):
```python
import torch
from diffusers import UnCLIPScheduler, DDPMScheduler, StableUnCLIPPipeline
from diffusers... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_unclip.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_unclip/#text-to-image-generation | #text-to-image-generation | .md | 98_3 |
```python
from diffusers import StableUnCLIPImg2ImgPipeline
from diffusers.utils import load_image
import torch
pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1-unclip", torch_dtype=torch.float16, variation="fp16"
)
pipe = pipe.to("cuda")
url = "https://huggingface.co/datasets/hf-... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_unclip.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_unclip/#text-guided-image-to-image-variation | #text-guided-image-to-image-variation | .md | 98_4 |
StableUnCLIPPipeline
Pipeline for text-to-image generation using stable 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.).
The pipeline also inherits the follo... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_unclip.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_unclip/#stableunclippipeline | #stableunclippipeline | .md | 98_5 |
StableUnCLIPImg2ImgPipeline
Pipeline for text-guided image-to-image generation using stable 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.).
The pipeline als... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_unclip.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_unclip/#stableunclipimg2imgpipeline | #stableunclipimg2imgpipeline | .md | 98_6 |
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/stable_unclip.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_unclip/#imagepipelineoutput | #imagepipelineoutput | .md | 98_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/unidiffuser.md | https://huggingface.co/docs/diffusers/en/api/pipelines/unidiffuser/ | .md | 99_0 | |
The UniDiffuser model was proposed in [One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale](https://huggingface.co/papers/2303.06555) by Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, Jun Zhu.
The abstract from the paper is:
*This paper proposes a u... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/unidiffuser.md | https://huggingface.co/docs/diffusers/en/api/pipelines/unidiffuser/#unidiffuser | #unidiffuser | .md | 99_1 |
Because the UniDiffuser model is trained to model the joint distribution of (image, text) pairs, it is capable of performing a diverse range of generation tasks: | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/unidiffuser.md | https://huggingface.co/docs/diffusers/en/api/pipelines/unidiffuser/#usage-examples | #usage-examples | .md | 99_2 |
Unconditional generation (where we start from only latents sampled from a standard Gaussian prior) from a [`UniDiffuserPipeline`] will produce a (image, text) pair:
```python
import torch
from diffusers import UniDiffuserPipeline
device = "cuda"
model_id_or_path = "thu-ml/unidiffuser-v1"
pipe = UniDiffuserPipeline.... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/unidiffuser.md | https://huggingface.co/docs/diffusers/en/api/pipelines/unidiffuser/#unconditional-image-and-text-generation | #unconditional-image-and-text-generation | .md | 99_3 |
UniDiffuser is also capable of sampling from conditional distributions; that is, the distribution of images conditioned on a text prompt or the distribution of texts conditioned on an image.
Here is an example of sampling from the conditional image distribution (text-to-image generation or text-conditioned image genera... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/unidiffuser.md | https://huggingface.co/docs/diffusers/en/api/pipelines/unidiffuser/#text-to-image-generation | #text-to-image-generation | .md | 99_4 |
Similarly, UniDiffuser can also produce text samples given an image (image-to-text or image-conditioned text generation):
```python
import torch
from diffusers import UniDiffuserPipeline
from diffusers.utils import load_image
device = "cuda"
model_id_or_path = "thu-ml/unidiffuser-v1"
pipe = UniDiffuserPipeline.from... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/unidiffuser.md | https://huggingface.co/docs/diffusers/en/api/pipelines/unidiffuser/#image-to-text-generation | #image-to-text-generation | .md | 99_5 |
The UniDiffuser authors suggest performing image variation through a "round-trip" generation method, where given an input image, we first perform an image-to-text generation, and then perform a text-to-image generation on the outputs of the first generation.
This produces a new image which is semantically similar to th... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/unidiffuser.md | https://huggingface.co/docs/diffusers/en/api/pipelines/unidiffuser/#image-variation | #image-variation | .md | 99_6 |
Similarly, text variation can be performed on an input prompt with a text-to-image generation followed by a image-to-text generation:
```python
import torch
from diffusers import UniDiffuserPipeline
device = "cuda"
model_id_or_path = "thu-ml/unidiffuser-v1"
pipe = UniDiffuserPipeline.from_pretrained(model_id_or_pat... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/unidiffuser.md | https://huggingface.co/docs/diffusers/en/api/pipelines/unidiffuser/#text-variation | #text-variation | .md | 99_7 |
UniDiffuserPipeline
Pipeline for a bimodal image-text model which supports unconditional text and image generation, text-conditioned
image generation, image-conditioned text generation, and joint image-text generation.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generi... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/unidiffuser.md | https://huggingface.co/docs/diffusers/en/api/pipelines/unidiffuser/#unidiffuserpipeline | #unidiffuserpipeline | .md | 99_8 |
ImageTextPipelineOutput
Output class for joint image-text 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)`.
text (`List[str]` or `List[List[str]]`)
List of generated text strings o... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/unidiffuser.md | https://huggingface.co/docs/diffusers/en/api/pipelines/unidiffuser/#imagetextpipelineoutput | #imagetextpipelineoutput | .md | 99_9 |
<!--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/semantic_stable_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/semantic_stable_diffusion/ | .md | 100_0 | |
Semantic Guidance for Diffusion Models was proposed in [SEGA: Instructing Text-to-Image Models using Semantic Guidance](https://huggingface.co/papers/2301.12247) and provides strong semantic control over image generation.
Small changes to the text prompt usually result in entirely different output images. However, with... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/semantic_stable_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/semantic_stable_diffusion/#semantic-guidance | #semantic-guidance | .md | 100_1 |
SemanticStableDiffusionPipeline
Pipeline for text-to-image generation using Stable Diffusion with latent editing.
This model inherits from [`DiffusionPipeline`] and builds on the [`StableDiffusionPipeline`]. Check the superclass
documentation for the generic methods implemented for all pipelines (downloading, savin... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/semantic_stable_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/semantic_stable_diffusion/#semanticstablediffusionpipeline | #semanticstablediffusionpipeline | .md | 100_2 |
SemanticStableDiffusionPipelineOutput
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 wheth... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/semantic_stable_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/semantic_stable_diffusion/#semanticstablediffusionpipelineoutput | #semanticstablediffusionpipelineoutput | .md | 100_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/controlnet_union.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_union/ | .md | 101_0 | |
ControlNetUnionModel is an implementation of ControlNet for Stable Diffusion XL.
The ControlNet model was introduced in [ControlNetPlus](https://github.com/xinsir6/ControlNetPlus) by xinsir6. It supports multiple conditioning inputs without increasing computation.
*We design a new architecture that can support 10+ ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_union.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_union/#controlnetunion | #controlnetunion | .md | 101_1 |
StableDiffusionXLControlNetUnionPipeline
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 de... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_union.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_union/#stablediffusionxlcontrolnetunionpipeline | #stablediffusionxlcontrolnetunionpipeline | .md | 101_2 |
StableDiffusionXLControlNetUnionImg2ImgPipeline
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 s... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_union.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_union/#stablediffusionxlcontrolnetunionimg2imgpipeline | #stablediffusionxlcontrolnetunionimg2imgpipeline | .md | 101_3 |
StableDiffusionXLControlNetUnionInpaintPipeline
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 partic... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_union.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_union/#stablediffusionxlcontrolnetunioninpaintpipeline | #stablediffusionxlcontrolnetunioninpaintpipeline | .md | 101_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/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/overview/ | .md | 102_0 | |
Pipelines provide a simple way to run state-of-the-art diffusion models in inference by bundling all of the necessary components (multiple independently-trained models, schedulers, and processors) into a single end-to-end class. Pipelines are flexible and they can be adapted to use different schedulers or even model co... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/overview/#pipelines | #pipelines | .md | 102_1 |
DiffusionPipeline
Base class for all pipelines.
[`DiffusionPipeline`] stores all components (models, schedulers, and processors) for diffusion pipelines and
provides methods for loading, downloading and saving models. It also includes methods to:
- move all PyTorch modules to the device of your choice
- enable/di... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/overview/#diffusionpipeline | #diffusionpipeline | .md | 102_2 |
[[autodoc]] FlaxDiffusionPipeline: No module named 'flax' | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/overview/#flaxdiffusionpipeline | #flaxdiffusionpipeline | .md | 102_3 |
PushToHubMixin
A Mixin to push a model, scheduler, or pipeline to the Hugging Face Hub. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/overview/#pushtohubmixin | #pushtohubmixin | .md | 102_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/latent_consistency_models.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latent_consistency_models/ | .md | 103_0 | |
Latent Consistency Models (LCMs) were proposed in [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://huggingface.co/papers/2310.04378) by Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao.
The abstract of the paper is as follows:
*Latent Diffusion models (LDMs... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latent_consistency_models.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latent_consistency_models/#latent-consistency-models | #latent-consistency-models | .md | 103_1 |
LatentConsistencyModelPipeline
Pipeline for text-to-image generation using a latent consistency model.
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 ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latent_consistency_models.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latent_consistency_models/#latentconsistencymodelpipeline | #latentconsistencymodelpipeline | .md | 103_2 |
LatentConsistencyModelImg2ImgPipeline
Pipeline for image-to-image generation using a latent consistency model.
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 p... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latent_consistency_models.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latent_consistency_models/#latentconsistencymodelimg2imgpipeline | #latentconsistencymodelimg2imgpipeline | .md | 103_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/latent_consistency_models.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latent_consistency_models/#stablediffusionpipelineoutput | #stablediffusionpipelineoutput | .md | 103_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/paint_by_example.md | https://huggingface.co/docs/diffusers/en/api/pipelines/paint_by_example/ | .md | 104_0 | |
[Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://huggingface.co/papers/2211.13227) is by Binxin Yang, Shuyang Gu, Bo Zhang, Ting Zhang, Xuejin Chen, Xiaoyan Sun, Dong Chen, Fang Wen.
The abstract from the paper is:
*Language-guided image editing has achieved great success recently. In ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/paint_by_example.md | https://huggingface.co/docs/diffusers/en/api/pipelines/paint_by_example/#paint-by-example | #paint-by-example | .md | 104_1 |
Paint by Example is supported by the official [Fantasy-Studio/Paint-by-Example](https://huggingface.co/Fantasy-Studio/Paint-by-Example) checkpoint. The checkpoint is warm-started from [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) to inpaint partly masked images conditioned on exa... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/paint_by_example.md | https://huggingface.co/docs/diffusers/en/api/pipelines/paint_by_example/#tips | #tips | .md | 104_2 |
PaintByExamplePipeline
<Tip warning={true}>
🧪 This is an experimental feature!
</Tip>
Pipeline for image-guided image inpainting using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, savin... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/paint_by_example.md | https://huggingface.co/docs/diffusers/en/api/pipelines/paint_by_example/#paintbyexamplepipeline | #paintbyexamplepipeline | .md | 104_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/paint_by_example.md | https://huggingface.co/docs/diffusers/en/api/pipelines/paint_by_example/#stablediffusionpipelineoutput | #stablediffusionpipelineoutput | .md | 104_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 agreed to... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/ | .md | 105_0 | |
Kandinsky 2.1 is created by [Arseniy Shakhmatov](https://github.com/cene555), [Anton Razzhigaev](https://github.com/razzant), [Aleksandr Nikolich](https://github.com/AlexWortega), [Vladimir Arkhipkin](https://github.com/oriBetelgeuse), [Igor Pavlov](https://github.com/boomb0om), [Andrey Kuznetsov](https://github.com/ku... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinsky-21 | #kandinsky-21 | .md | 105_1 |
KandinskyPriorPipeline
Pipeline for generating image prior for Kandinsky
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:
prior ([`Prio... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskypriorpipeline | #kandinskypriorpipeline | .md | 105_2 |
KandinskyPipeline
Pipeline for text-to-image generation using Kandinsky
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:
text_encoder (... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskypipeline | #kandinskypipeline | .md | 105_3 |
KandinskyCombinedPipeline
Combined Pipeline for text-to-image generation using Kandinsky
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.)
Arg... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskycombinedpipeline | #kandinskycombinedpipeline | .md | 105_4 |
KandinskyImg2ImgPipeline
Pipeline for image-to-image generation using Kandinsky
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:
text_e... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskyimg2imgpipeline | #kandinskyimg2imgpipeline | .md | 105_5 |
KandinskyImg2ImgCombinedPipeline
Combined Pipeline for image-to-image generation using Kandinsky
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/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskyimg2imgcombinedpipeline | #kandinskyimg2imgcombinedpipeline | .md | 105_6 |
KandinskyInpaintPipeline
Pipeline for text-guided image inpainting using Kandinsky2.1
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/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskyinpaintpipeline | #kandinskyinpaintpipeline | .md | 105_7 |
KandinskyInpaintCombinedPipeline
Combined Pipeline for generation using Kandinsky
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:
text... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskyinpaintcombinedpipeline | #kandinskyinpaintcombinedpipeline | .md | 105_8 |
<!--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.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs/ | .md | 106_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.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs/#controlnet-xs | #controlnet-xs | .md | 106_1 |
StableDiffusionControlNetXSPipeline
Pipeline for text-to-image generation using Stable Diffusion 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 device,... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs/#stablediffusioncontrolnetxspipeline | #stablediffusioncontrolnetxspipeline | .md | 106_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.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs/#stablediffusionpipelineoutput | #stablediffusionpipelineoutput | .md | 106_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/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/ | .md | 107_0 | |
<img src="https://github.com/dome272/Wuerstchen/assets/61938694/0617c863-165a-43ee-9303-2a17299a0cf9">
[Wuerstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models](https://huggingface.co/papers/2306.00637) is by Pablo Pernias, Dominic Rampas, Mats L. Richter and Christopher Pal and Marc Aubr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#würstchen | #würstchen | .md | 107_1 |
Würstchen is a diffusion model, whose text-conditional model works in a highly compressed latent space of images. Why is this important? Compressing data can reduce computational costs for both training and inference by magnitudes. Training on 1024x1024 images is way more expensive than training on 32x32. Usually, othe... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#würstchen-overview | #würstchen-overview | .md | 107_2 |
After the initial paper release, we have improved numerous things in the architecture, training and sampling, making Würstchen competitive to current state-of-the-art models in many ways. We are excited to release this new version together with Diffusers. Here is a list of the improvements.
- Higher resolution (1024x... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#würstchen-v2-comes-to-diffusers | #würstchen-v2-comes-to-diffusers | .md | 107_3 |
For the sake of usability, Würstchen can be used with a single pipeline. This pipeline can be used as follows:
```python
import torch
from diffusers import AutoPipelineForText2Image
from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS
pipe = AutoPipelineForText2Image.from_pretrained("warp-ai/wuerstch... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#text-to-image-generation | #text-to-image-generation | .md | 107_4 |
You can make use of `torch.compile` function and gain a speed-up of about 2-3x:
```python
prior_pipeline.prior = torch.compile(prior_pipeline.prior, mode="reduce-overhead", fullgraph=True)
decoder_pipeline.decoder = torch.compile(decoder_pipeline.decoder, mode="reduce-overhead", fullgraph=True)
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#speed-up-inference | #speed-up-inference | .md | 107_5 |
- Due to the high compression employed by Würstchen, generations can lack a good amount
of detail. To our human eye, this is especially noticeable in faces, hands etc.
- **Images can only be generated in 128-pixel steps**, e.g. the next higher resolution
after 1024x1024 is 1152x1152
- The model lacks the ability to ren... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#limitations | #limitations | .md | 107_6 |
WuerstchenCombinedPipeline
Combined Pipeline for text-to-image generation using Wuerstchen
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/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#wuerstchencombinedpipeline | #wuerstchencombinedpipeline | .md | 107_7 |
WuerstchenPriorPipeline
Pipeline for generating image prior for Wuerstchen.
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.)
The pipeline als... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#wuerstchenpriorpipeline | #wuerstchenpriorpipeline | .md | 107_8 |
WuerstchenPriorPipelineOutput
Output class for WuerstchenPriorPipeline.
Args:
image_embeddings (`torch.Tensor` or `np.ndarray`)
Prior image embeddings for text prompt | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#wuerstchenpriorpipelineoutput | #wuerstchenpriorpipelineoutput | .md | 107_9 |
WuerstchenDecoderPipeline
Pipeline for generating images from the Wuerstchen 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.)
Args:
to... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#wuerstchendecoderpipeline | #wuerstchendecoderpipeline | .md | 107_10 |
```bibtex
@misc{pernias2023wuerstchen,
title={Wuerstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models},
author={Pablo Pernias and Dominic Rampas and Mats L. Richter and Christopher J. Pal and Marc Aubreville},
year={2023},
eprint={2306.00637},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
`... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#citation | #citation | .md | 107_11 |
<!--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.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/ | .md | 108_0 | |
[PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis](https://huggingface.co/papers/2310.00426) is Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/#pixart-α | #pixart-α | .md | 108_1 |
Run the [`PixArtAlphaPipeline`] 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.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/#inference-with-under-8gb-gpu-vram | #inference-with-under-8gb-gpu-vram | .md | 108_2 |
PixArtAlphaPipeline
Pipeline for text-to-image generation using PixArt-Alpha.
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 ([`A... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/#pixartalphapipeline | #pixartalphapipeline | .md | 108_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/latent_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latent_diffusion/ | .md | 109_0 | |
Latent Diffusion was proposed in [High-Resolution Image Synthesis with Latent Diffusion Models](https://huggingface.co/papers/2112.10752) by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer.
The abstract from the paper is:
*By decomposing the image formation process into a sequential app... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latent_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latent_diffusion/#latent-diffusion | #latent-diffusion | .md | 109_1 |
LDMTextToImagePipeline
Pipeline for text-to-image generation using latent 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.).
Parameters:
vqvae ([`VQModel`])... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latent_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latent_diffusion/#ldmtexttoimagepipeline | #ldmtexttoimagepipeline | .md | 109_2 |
LDMSuperResolutionPipeline
A pipeline for image super-resolution using latent 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.).
Parameters:
vqvae ([`VQMode... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latent_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latent_diffusion/#ldmsuperresolutionpipeline | #ldmsuperresolutionpipeline | .md | 109_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/latent_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latent_diffusion/#imagepipelineoutput | #imagepipelineoutput | .md | 109_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/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/ | .md | 110_0 | |
[Lumina-Next : Making Lumina-T2X Stronger and Faster with Next-DiT](https://github.com/Alpha-VLLM/Lumina-T2X/blob/main/assets/lumina-next.pdf) from Alpha-VLLM, OpenGVLab, Shanghai AI Laboratory.
The abstract f... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#lumina-t2x | #lumina-t2x | .md | 110_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
from diffusers import LuminaText2ImgPipeline
import torch
pipeline = LuminaText2ImgPipeline.from_pretrained(
"Alpha-VLLM/Lumina-Next-SFT-di... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#inference-text-to-image | #inference-text-to-image | .md | 110_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/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#quantization | #quantization | .md | 110_3 |
LuminaText2ImgPipeline
Pipeline for text-to-image generation using Lumina-T2I.
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 ([`... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#luminatext2imgpipeline | #luminatext2imgpipeline | .md | 110_4 |
<!--Copyright 2024 The HuggingFace Team, The InstantX Team, and the XLabs 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
U... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_flux/ | .md | 111_0 | |
FluxControlNetPipeline is an implementation of ControlNet for Flux.1.
ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala.
With a ControlNet model, you can provide an additional control i... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_flux/#controlnet-with-flux1 | #controlnet-with-flux1 | .md | 111_1 |
FluxControlNetPipeline
The Flux pipeline for text-to-image generation.
Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
Args:
transformer ([`FluxTransformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([`FlowMatchEulerDiscreteScheduler... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_flux/#fluxcontrolnetpipeline | #fluxcontrolnetpipeline | .md | 111_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/controlnet_flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_flux/#fluxpipelineoutput | #fluxpipelineoutput | .md | 111_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/dit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/dit/ | .md | 112_0 | |
[Scalable Diffusion Models with Transformers](https://huggingface.co/papers/2212.09748) (DiT) is by William Peebles and Saining Xie.
The abstract from the paper is:
*We explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-u... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/dit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/dit/#dit | #dit | .md | 112_1 |
DiTPipeline
Pipeline for image generation based on a Transformer backbone instead of a UNet.
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:
transformer... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/dit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/dit/#ditpipeline | #ditpipeline | .md | 112_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/dit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/dit/#imagepipelineoutput | #imagepipelineoutput | .md | 112_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/ddpm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ddpm/ | .md | 113_0 | |
[Denoising Diffusion Probabilistic Models](https://huggingface.co/papers/2006.11239) (DDPM) by Jonathan Ho, Ajay Jain and Pieter Abbeel proposes a diffusion based model of the same name. In the 🤗 Diffusers library, DDPM refers to the *discrete denoising scheduler* from the paper as well as the pipeline.
The abstract... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ddpm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ddpm/#ddpm | #ddpm | .md | 113_1 |
DDPMPipeline
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/ddpm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ddpm/#ddpmpipeline | #ddpmpipeline | .md | 113_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/ddpm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ddpm/#imagepipelineoutput | #imagepipelineoutput | .md | 113_3 |
<!--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/ledits_pp.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ledits_pp/ | .md | 114_0 | |
LEDITS++ was proposed in [LEDITS++: Limitless Image Editing using Text-to-Image Models](https://huggingface.co/papers/2311.16711) by Manuel Brack, Felix Friedrich, Katharina Kornmeier, Linoy Tsaban, Patrick Schramowski, Kristian Kersting, Apolinário Passos.
The abstract from the paper is:
*Text-to-image diffusion m... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ledits_pp.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ledits_pp/#ledits | #ledits | .md | 114_1 |
LEditsPPPipelineStableDiffusion
Pipeline for textual image editing using LEDits++ with Stable Diffusion.
This model inherits from [`DiffusionPipeline`] and builds on the [`StableDiffusionPipeline`]. Check the superclass
documentation for the generic methods implemented for all pipelines (downloading, saving, runnin... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ledits_pp.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ledits_pp/#leditspppipelinestablediffusion | #leditspppipelinestablediffusion | .md | 114_2 |
LEditsPPPipelineStableDiffusion
Pipeline for textual image editing using LEDits++ with Stable Diffusion.
This model inherits from [`DiffusionPipeline`] and builds on the [`StableDiffusionPipeline`]. Check the superclass
documentation for the generic methods implemented for all pipelines (downloading, saving, runnin... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ledits_pp.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ledits_pp/#leditspppipelinestablediffusionxl | #leditspppipelinestablediffusionxl | .md | 114_3 |
LEditsPPDiffusionPipelineOutput
Output class for LEdits++ 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 t... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ledits_pp.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ledits_pp/#leditsppdiffusionpipelineoutput | #leditsppdiffusionpipelineoutput | .md | 114_4 |
LEditsPPInversionPipelineOutput
Output class for LEdits++ Diffusion pipelines.
Args:
input_images (`List[PIL.Image.Image]` or `np.ndarray`)
List of the cropped and resized input images as PIL images of length `batch_size` or NumPy array of shape `
(batch_size, height, width, num_channels)`.
vae_reconstruction_image... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ledits_pp.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ledits_pp/#leditsppinversionpipelineoutput | #leditsppinversionpipelineoutput | .md | 114_5 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogview3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogview3/ | .md | 115_0 | |
--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogview3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogview3/#limitations-under-the-license | #limitations-under-the-license | .md | 115_1 |
[CogView3: Finer and Faster Text-to-Image Generation via Relay Diffusion](https://huggingface.co/papers/2403.05121) from Tsinghua University & ZhipuAI, by Wendi Zheng, Jiayan Teng, Zhuoyi Yang, Weihan Wang, Jidong Chen, Xiaotao Gu, Yuxiao Dong, Ming Ding, Jie Tang.
The abstract from the paper is:
*Recent advancemen... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogview3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogview3/#cogview3plus | #cogview3plus | .md | 115_2 |
CogView3PlusPipeline
Pipeline for text-to-image generation using CogView3Plus.
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 ([`... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogview3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogview3/#cogview3pluspipeline | #cogview3pluspipeline | .md | 115_3 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.