source stringclasses 273
values | url stringlengths 47 172 | file_type stringclasses 1
value | chunk stringlengths 1 512 | chunk_id stringlengths 5 9 |
|---|---|---|---|---|
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/overview/#pipelines | .md | </Tip>
The table below lists all the pipelines currently available in 🤗 Diffusers and the tasks they support. Click on a pipeline to view its abstract and published paper.
| Pipeline | Tasks |
|---|---|
| [aMUSEd](amused) | text2image |
| [AnimateDiff](animatediff) | text2video |
| [Attend-and-Excite](attend_and_e... | 102_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/overview/#pipelines | .md | | [AudioLDM2](audioldm2) | text2audio |
| [AuraFlow](auraflow) | text2image |
| [BLIP Diffusion](blip_diffusion) | text2image |
| [CogVideoX](cogvideox) | text2video |
| [Consistency Models](consistency_models) | unconditional image generation |
| [ControlNet](controlnet) | text2image, image2image, inpainting |
| [Cont... | 102_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/overview/#pipelines | .md | | [ControlNet with Stable Diffusion 3](controlnet_sd3) | text2image |
| [ControlNet with Stable Diffusion XL](controlnet_sdxl) | text2image |
| [ControlNet-XS](controlnetxs) | text2image |
| [ControlNet-XS with Stable Diffusion XL](controlnetxs_sdxl) | text2image |
| [Dance Diffusion](dance_diffusion) | unconditional a... | 102_1_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/overview/#pipelines | .md | | [DDIM](ddim) | unconditional image generation |
| [DDPM](ddpm) | unconditional image generation |
| [DeepFloyd IF](deepfloyd_if) | text2image, image2image, inpainting, super-resolution |
| [DiffEdit](diffedit) | inpainting |
| [DiT](dit) | text2image |
| [Flux](flux) | text2image |
| [Hunyuan-DiT](hunyuandit) | text2... | 102_1_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/overview/#pipelines | .md | | [Kandinsky 2.1](kandinsky) | text2image, image2image, inpainting, interpolation |
| [Kandinsky 2.2](kandinsky_v22) | text2image, image2image, inpainting |
| [Kandinsky 3](kandinsky3) | text2image, image2image |
| [Kolors](kolors) | text2image |
| [Latent Consistency Models](latent_consistency_models) | text2image |
|... | 102_1_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/overview/#pipelines | .md | | [Latte](latte) | text2image |
| [LEDITS++](ledits_pp) | image editing |
| [Lumina-T2X](lumina) | text2image |
| [Marigold](marigold) | depth |
| [MultiDiffusion](panorama) | text2image |
| [MusicLDM](musicldm) | text2audio |
| [PAG](pag) | text2image |
| [Paint by Example](paint_by_example) | inpainting |
| [PIA](pia... | 102_1_9 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/overview/#pipelines | .md | | [PixArt-Σ](pixart_sigma) | text2image |
| [Self-Attention Guidance](self_attention_guidance) | text2image |
| [Semantic Guidance](semantic_stable_diffusion) | text2image |
| [Shap-E](shap_e) | text-to-3D, image-to-3D |
| [Stable Audio](stable_audio) | text2audio |
| [Stable Cascade](stable_cascade) | text2image |
| [... | 102_1_10 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/overview/#pipelines | .md | | [Stable Diffusion XL](stable_diffusion/stable_diffusion_xl) | text2image, image2image, inpainting |
| [Stable Diffusion XL Turbo](stable_diffusion/sdxl_turbo) | text2image, image2image, inpainting |
| [Stable unCLIP](stable_unclip) | text2image, image variation |
| [T2I-Adapter](stable_diffusion/adapter) | text2image... | 102_1_11 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/overview/#pipelines | .md | | [Text2Video-Zero](text_to_video_zero) | text2video |
| [unCLIP](unclip) | text2image, image variation |
| [UniDiffuser](unidiffuser) | text2image, image2text, image variation, text variation, unconditional image generation, unconditional audio generation |
| [Value-guided planning](value_guided_sampling) | value guid... | 102_1_12 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/overview/#diffusionpipeline | .md | 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... | 102_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/overview/#diffusionpipeline | .md | Class attributes:
- **config_name** (`str`) -- The configuration filename that stores the class and module names of all the
diffusion pipeline's components.
- **_optional_components** (`List[str]`) -- List of all optional components that don't have to be passed to the
pipeline to function (should be overridden by sub... | 102_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/overview/#diffusionpipeline | .md | - __call__
- device
- to
- components
[[autodoc]] enable_freeu: No module named 'diffusers.pipelines.StableDiffusionMixin'
[[autodoc]] disable_freeu: No module named 'diffusers.pipelines.StableDiffusionMixin' | 102_2_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/overview/#flaxdiffusionpipeline | .md | [[autodoc]] FlaxDiffusionPipeline: No module named 'flax' | 102_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/overview/#pushtohubmixin | .md | PushToHubMixin
A Mixin to push a model, scheduler, or pipeline to the Hugging Face Hub. | 102_4_0 |
/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 | <!--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... | 103_0_0 |
/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 | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
--> | 103_0_1 |
/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 | .md | 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: | 103_1_0 |
/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 | .md | *Latent Diffusion models (LDMs) have achieved remarkable results in synthesizing high-resolution images. However, the iterative sampling process is computationally intensive and leads to slow generation. Inspired by Consistency Models (song et al.), we propose Latent Consistency Models (LCMs), enabling swift inference ... | 103_1_1 |
/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 | .md | Diffusion (rombach et al). Viewing the guided reverse diffusion process as solving an augmented probability flow ODE (PF-ODE), LCMs are designed to directly predict the solution of such ODE in latent space, mitigating the need for numerous iterations and allowing rapid, high-fidelity sampling. Efficiently distilled fro... | 103_1_2 |
/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 | .md | a high-quality 768 x 768 2~4-step LCM takes only 32 A100 GPU hours for training. Furthermore, we introduce Latent Consistency Fine-tuning (LCF), a novel method that is tailored for fine-tuning LCMs on customized image datasets. Evaluation on the LAION-5B-Aesthetics dataset demonstrates that LCMs achieve state-of-the-ar... | 103_1_3 |
/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 | .md | A demo for the [SimianLuo/LCM_Dreamshaper_v7](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7) checkpoint can be found [here](https://huggingface.co/spaces/SimianLuo/Latent_Consistency_Model).
The pipelines were contributed by [luosiallen](https://luosiallen.github.io/), [nagolinc](https://github.com/nagolinc), a... | 103_1_4 |
/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 | .md | 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 ... | 103_2_0 |
/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 | .md | - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.FromSingleFileMixin.fro... | 103_2_1 |
/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 | .md | - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co... | 103_2_2 |
/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 | .md | tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Currently only
supports... | 103_2_3 |
/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 | .md | Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcesso... | 103_2_4 |
/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 | .md | requires_safety_checker (`bool`, *optional*, defaults to `True`):
Whether the pipeline requires a safety checker component.
- all
- __call__
- enable_freeu
- disable_freeu
- enable_vae_slicing
- disable_vae_slicing
- enable_vae_tiling
- disable_vae_tiling | 103_2_5 |
/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 | .md | 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... | 103_3_0 |
/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 | .md | - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.FromSingleFileMixin.fro... | 103_3_1 |
/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 | .md | - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co... | 103_3_2 |
/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 | .md | tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Currently only
supports... | 103_3_3 |
/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 | .md | Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcesso... | 103_3_4 |
/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 | .md | requires_safety_checker (`bool`, *optional*, defaults to `True`):
Whether the pipeline requires a safety checker component.
- all
- __call__
- enable_freeu
- disable_freeu
- enable_vae_slicing
- disable_vae_slicing
- enable_vae_tiling
- disable_vae_tiling | 103_3_5 |
/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 | .md | 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... | 103_4_0 |
/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 | <!--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... | 104_0_0 |
/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 | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
--> | 104_0_1 |
/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 | .md | [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: | 104_1_0 |
/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 | .md | *Language-guided image editing has achieved great success recently. In this paper, for the first time, we investigate exemplar-guided image editing for more precise control. We achieve this goal by leveraging self-supervised training to disentangle and re-organize the source image and the exemplar. However, the naive a... | 104_1_1 |
/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 | .md | analyze it and propose an information bottleneck and strong augmentations to avoid the trivial solution of directly copying and pasting the exemplar image. Meanwhile, to ensure the controllability of the editing process, we design an arbitrary shape mask for the exemplar image and leverage the classifier-free guidance ... | 104_1_2 |
/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 | .md | framework involves a single forward of the diffusion model without any iterative optimization. We demonstrate that our method achieves an impressive performance and enables controllable editing on in-the-wild images with high fidelity.* | 104_1_3 |
/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 | .md | The original codebase can be found at [Fantasy-Studio/Paint-by-Example](https://github.com/Fantasy-Studio/Paint-by-Example), and you can try it out in a [demo](https://huggingface.co/spaces/Fantasy-Studio/Paint-by-Example). | 104_1_4 |
/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 | .md | 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... | 104_2_0 |
/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 | .md | <Tip>
Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components i... | 104_2_1 |
/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 | .md | 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... | 104_3_0 |
/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 | .md | vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
image_encoder ([`PaintByExampleImageEncoder`]):
Encodes the example input image. The `unet` is conditioned on the example image instead of a text prompt.
tokenizer ([`~transformers.CLIPTokenizer... | 104_3_1 |
/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 | .md | unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`Stabl... | 104_3_2 |
/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 | .md | Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcesso... | 104_3_3 |
/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 | .md | 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... | 104_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/ | .md | <!--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... | 105_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/ | .md | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
--> | 105_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinsky-21 | .md | 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... | 105_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinsky-21 | .md | The description from it's GitHub page is:
*Kandinsky 2.1 inherits best practicies from Dall-E 2 and Latent diffusion, while introducing some new ideas. As text and image encoder it uses CLIP model and diffusion image prior (mapping) between latent spaces of CLIP modalities. This approach increases the visual performa... | 105_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinsky-21 | .md | The original codebase can be found at [ai-forever/Kandinsky-2](https://github.com/ai-forever/Kandinsky-2).
<Tip>
Check out the [Kandinsky Community](https://huggingface.co/kandinsky-community) organization on the Hub for the official model checkpoints for tasks like text-to-image, image-to-image, and inpainting.
... | 105_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinsky-21 | .md | </Tip>
<Tip>
Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same com... | 105_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskypriorpipeline | .md | 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... | 105_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskypriorpipeline | .md | image_encoder ([`CLIPVisionModelWithProjection`]):
Frozen image-encoder.
text_encoder ([`CLIPTextModelWithProjection`]):
Frozen text-encoder.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
scheduler ([`UnCLI... | 105_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskypipeline | .md | 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 (... | 105_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskypipeline | .md | tokenizer ([`XLMRobertaTokenizer`]):
Tokenizer of class
scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
movq ([`VQModel`]):
MoVQ Decoder to gen... | 105_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskycombinedpipeline | .md | 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... | 105_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskycombinedpipeline | .md | tokenizer ([`XLMRobertaTokenizer`]):
Tokenizer of class
scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
movq ([`VQModel`]):
MoVQ Decoder to gen... | 105_4_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskycombinedpipeline | .md | prior_prior ([`PriorTransformer`]):
The canonical unCLIP prior to approximate the image embedding from the text embedding.
prior_image_encoder ([`CLIPVisionModelWithProjection`]):
Frozen image-encoder.
prior_text_encoder ([`CLIPTextModelWithProjection`]):
Frozen text-encoder.
prior_tokenizer (`CLIPTokenizer`):
Tokenize... | 105_4_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskycombinedpipeline | .md | prior_scheduler ([`UnCLIPScheduler`]):
A scheduler to be used in combination with `prior` to generate image embedding.
- all
- __call__ | 105_4_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskyimg2imgpipeline | .md | 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... | 105_5_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskyimg2imgpipeline | .md | Frozen text-encoder.
tokenizer ([`XLMRobertaTokenizer`]):
Tokenizer of class
scheduler ([`DDIMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
movq ([`VQModel`]):
MoVQ image encoder ... | 105_5_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskyimg2imgcombinedpipeline | .md | 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... | 105_6_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskyimg2imgcombinedpipeline | .md | tokenizer ([`XLMRobertaTokenizer`]):
Tokenizer of class
scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
movq ([`VQModel`]):
MoVQ Decoder to gen... | 105_6_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskyimg2imgcombinedpipeline | .md | prior_prior ([`PriorTransformer`]):
The canonical unCLIP prior to approximate the image embedding from the text embedding.
prior_image_encoder ([`CLIPVisionModelWithProjection`]):
Frozen image-encoder.
prior_text_encoder ([`CLIPTextModelWithProjection`]):
Frozen text-encoder.
prior_tokenizer (`CLIPTokenizer`):
Tokenize... | 105_6_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskyimg2imgcombinedpipeline | .md | prior_scheduler ([`UnCLIPScheduler`]):
A scheduler to be used in combination with `prior` to generate image embedding.
- all
- __call__ | 105_6_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskyinpaintpipeline | .md | 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:
... | 105_7_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskyinpaintpipeline | .md | Frozen text-encoder.
tokenizer ([`XLMRobertaTokenizer`]):
Tokenizer of class
scheduler ([`DDIMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
movq ([`VQModel`]):
MoVQ image encoder ... | 105_7_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskyinpaintcombinedpipeline | .md | 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... | 105_8_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskyinpaintcombinedpipeline | .md | tokenizer ([`XLMRobertaTokenizer`]):
Tokenizer of class
scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
movq ([`VQModel`]):
MoVQ Decoder to gen... | 105_8_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskyinpaintcombinedpipeline | .md | prior_prior ([`PriorTransformer`]):
The canonical unCLIP prior to approximate the image embedding from the text embedding.
prior_image_encoder ([`CLIPVisionModelWithProjection`]):
Frozen image-encoder.
prior_text_encoder ([`CLIPTextModelWithProjection`]):
Frozen text-encoder.
prior_tokenizer (`CLIPTokenizer`):
Tokenize... | 105_8_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky/#kandinskyinpaintcombinedpipeline | .md | prior_scheduler ([`UnCLIPScheduler`]):
A scheduler to be used in combination with `prior` to generate image embedding.
- all
- __call__ | 105_8_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs/ | .md | <!--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... | 106_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs/ | .md | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
--> | 106_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs/#controlnet-xs | .md | 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. | 106_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs/#controlnet-xs | .md | Like the original ControlNet model, you can provide an additional control image to condition and control Stable Diffusion generation. For example, if you provide a depth map, the ControlNet model generates an image that'll preserve the spatial information from the depth map. It is a more flexible and accurate way to co... | 106_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs/#controlnet-xs | .md | ControlNet-XS generates images with comparable quality to a regular ControlNet, but it is 20-25% faster ([see benchmark](https://github.com/UmerHA/controlnet-xs-benchmark/blob/main/Speed%20Benchmark.ipynb) with StableDiffusion-XL) and uses ~45% less memory.
Here's the overview from the [project page](https://vislearn... | 106_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs/#controlnet-xs | .md | *With increasing computing capabilities, current model architectures appear to follow the trend of simply upscaling all components without validating the necessity for doing so. In this project we investigate the size and architectural design of ControlNet [Zhang et al., 2023] for controlling the image generation proce... | 106_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs/#controlnet-xs | .md | with as little as 1% of the parameters of the base model achieves state-of-the art results, considerably better than ControlNet in terms of FID score. Hence we call it ControlNet-XS. We provide the code for controlling StableDiffusion-XL [Podell et al., 2023] (Model B, 48M Parameters) and StableDiffusion 2.1 [Rombach e... | 106_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs/#controlnet-xs | .md | This model was contributed by [UmerHA](https://twitter.com/UmerHAdil). ❤️
<Tip>
Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reus... | 106_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs/#stablediffusioncontrolnetxspipeline | .md | 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,... | 106_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs/#stablediffusioncontrolnetxspipeline | .md | - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`loaders.FromSingleFileMixin.from... | 106_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs/#stablediffusioncontrolnetxspipeline | .md | vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):... | 106_2_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs/#stablediffusioncontrolnetxspipeline | .md | A [`UNet2DConditionModel`] used to create a UNetControlNetXSModel to denoise the encoded image latents.
controlnet ([`ControlNetXSAdapter`]):
A [`ControlNetXSAdapter`] to be used in combination with `unet` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `... | 106_2_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs/#stablediffusioncontrolnetxspipeline | .md | [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more d... | 106_2_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs/#stablediffusioncontrolnetxspipeline | .md | about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
- all
- __call__ | 106_2_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs/#stablediffusionpipelineoutput | .md | 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... | 106_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/ | .md | <!--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... | 107_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/ | .md | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
--> | 107_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#würstchen | .md | <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... | 107_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#würstchen | .md | *We introduce Würstchen, a novel architecture for text-to-image synthesis that combines competitive performance with unprecedented cost-effectiveness for large-scale text-to-image diffusion models. A key contribution of our work is to develop a latent diffusion technique in which we learn a detailed but extremely compa... | 107_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#würstchen | .md | This highly compressed representation of an image provides much more detailed guidance compared to latent representations of language and this significantly reduces the computational requirements to achieve state-of-the-art results. Our approach also improves the quality of text-conditioned image generation based on ou... | 107_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#würstchen | .md | consists of 24,602 A100-GPU hours - compared to Stable Diffusion 2.1's 200,000 GPU hours. Our approach also requires less training data to achieve these results. Furthermore, our compact latent representations allows us to perform inference over twice as fast, slashing the usual costs and carbon footprint of a state-of... | 107_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#würstchen | .md | the end performance. In a broader comparison against SOTA models our approach is substantially more efficient and compares favorably in terms of image quality. We believe that this work motivates more emphasis on the prioritization of both performance and computational accessibility.* | 107_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#würstchen-overview | .md | 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... | 107_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#würstchen-overview | .md | range of 4x - 8x spatial compression. Würstchen takes this to an extreme. Through its novel design, we achieve a 42x spatial compression. This was unseen before because common methods fail to faithfully reconstruct detailed images after 16x spatial compression. Würstchen employs a two-stage compression, what we call St... | 107_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#würstchen-overview | .md | Autoencoder (more details can be found in the [paper](https://huggingface.co/papers/2306.00637)). A third model, Stage C, is learned in that highly compressed latent space. This training requires fractions of the compute used for current top-performing models, while also allowing cheaper and faster inference. | 107_2_2 |
/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 | .md | 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... | 107_3_0 |
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