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<!--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/using-diffusers/img2img.md | https://huggingface.co/docs/diffusers/en/using-diffusers/img2img/ | .md | 83_0 | |
[[open-in-colab]]
Image-to-image is similar to [text-to-image](conditional_image_generation), but in addition to a prompt, you can also pass an initial image as a starting point for the diffusion process. The initial image is encoded to latent space and noise is added to it. Then the latent diffusion model takes a pr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/img2img.md | https://huggingface.co/docs/diffusers/en/using-diffusers/img2img/#image-to-image | #image-to-image | .md | 83_1 |
The most popular image-to-image models are [Stable Diffusion v1.5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5), [Stable Diffusion XL (SDXL)](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0), and [Kandinsky 2.2](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder). Th... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/img2img.md | https://huggingface.co/docs/diffusers/en/using-diffusers/img2img/#popular-models | #popular-models | .md | 83_2 |
Stable Diffusion v1.5 is a latent diffusion model initialized from an earlier checkpoint, and further finetuned for 595K steps on 512x512 images. To use this pipeline for image-to-image, you'll need to prepare an initial image to pass to the pipeline. Then you can pass a prompt and the image to the pipeline to generate... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/img2img.md | https://huggingface.co/docs/diffusers/en/using-diffusers/img2img/#stable-diffusion-v15 | #stable-diffusion-v15 | .md | 83_3 |
SDXL is a more powerful version of the Stable Diffusion model. It uses a larger base model, and an additional refiner model to increase the quality of the base model's output. Read the [SDXL](sdxl) guide for a more detailed walkthrough of how to use this model, and other techniques it uses to produce high quality image... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/img2img.md | https://huggingface.co/docs/diffusers/en/using-diffusers/img2img/#stable-diffusion-xl-sdxl | #stable-diffusion-xl-sdxl | .md | 83_4 |
The Kandinsky model is different from the Stable Diffusion models because it uses an image prior model to create image embeddings. The embeddings help create a better alignment between text and images, allowing the latent diffusion model to generate better images.
The simplest way to use Kandinsky 2.2 is:
```py
imp... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/img2img.md | https://huggingface.co/docs/diffusers/en/using-diffusers/img2img/#kandinsky-22 | #kandinsky-22 | .md | 83_5 |
There are several important parameters you can configure in the pipeline that'll affect the image generation process and image quality. Let's take a closer look at what these parameters do and how changing them affects the output. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/img2img.md | https://huggingface.co/docs/diffusers/en/using-diffusers/img2img/#configure-pipeline-parameters | #configure-pipeline-parameters | .md | 83_6 |
`strength` is one of the most important parameters to consider and it'll have a huge impact on your generated image. It determines how much the generated image resembles the initial image. In other words:
- 📈 a higher `strength` value gives the model more "creativity" to generate an image that's different from the i... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/img2img.md | https://huggingface.co/docs/diffusers/en/using-diffusers/img2img/#strength | #strength | .md | 83_7 |
The `guidance_scale` parameter is used to control how closely aligned the generated image and text prompt are. A higher `guidance_scale` value means your generated image is more aligned with the prompt, while a lower `guidance_scale` value means your generated image has more space to deviate from the prompt.
You can ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/img2img.md | https://huggingface.co/docs/diffusers/en/using-diffusers/img2img/#guidance-scale | #guidance-scale | .md | 83_8 |
A negative prompt conditions the model to *not* include things in an image, and it can be used to improve image quality or modify an image. For example, you can improve image quality by including negative prompts like "poor details" or "blurry" to encourage the model to generate a higher quality image. Or you can modif... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/img2img.md | https://huggingface.co/docs/diffusers/en/using-diffusers/img2img/#negative-prompt | #negative-prompt | .md | 83_9 |
There are some other interesting ways you can use an image-to-image pipeline aside from just generating an image (although that is pretty cool too). You can take it a step further and chain it with other pipelines. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/img2img.md | https://huggingface.co/docs/diffusers/en/using-diffusers/img2img/#chained-image-to-image-pipelines | #chained-image-to-image-pipelines | .md | 83_10 |
Chaining a text-to-image and image-to-image pipeline allows you to generate an image from text and use the generated image as the initial image for the image-to-image pipeline. This is useful if you want to generate an image entirely from scratch. For example, let's chain a Stable Diffusion and a Kandinsky model.
Sta... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/img2img.md | https://huggingface.co/docs/diffusers/en/using-diffusers/img2img/#text-to-image-to-image | #text-to-image-to-image | .md | 83_11 |
You can also chain multiple image-to-image pipelines together to create more interesting images. This can be useful for iteratively performing style transfer on an image, generating short GIFs, restoring color to an image, or restoring missing areas of an image.
Start by generating an image:
```py
import torch
from... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/img2img.md | https://huggingface.co/docs/diffusers/en/using-diffusers/img2img/#image-to-image-to-image | #image-to-image-to-image | .md | 83_12 |
Another way you can chain your image-to-image pipeline is with an upscaler and super-resolution pipeline to really increase the level of details in an image.
Start with an image-to-image pipeline:
```py
import torch
from diffusers import AutoPipelineForImage2Image
from diffusers.utils import make_image_grid, load_i... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/img2img.md | https://huggingface.co/docs/diffusers/en/using-diffusers/img2img/#image-to-upscaler-to-super-resolution | #image-to-upscaler-to-super-resolution | .md | 83_13 |
Trying to generate an image that looks exactly the way you want can be difficult, which is why controlled generation techniques and models are so useful. While you can use the `negative_prompt` to partially control image generation, there are more robust methods like prompt weighting and ControlNets. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/img2img.md | https://huggingface.co/docs/diffusers/en/using-diffusers/img2img/#control-image-generation | #control-image-generation | .md | 83_14 |
Prompt weighting allows you to scale the representation of each concept in a prompt. For example, in a prompt like "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", you can choose to increase or decrease the embeddings of "astronaut" and "jungle". The [Compel](https://github.com/damian0815/compel... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/img2img.md | https://huggingface.co/docs/diffusers/en/using-diffusers/img2img/#prompt-weighting | #prompt-weighting | .md | 83_15 |
ControlNets provide a more flexible and accurate way to control image generation because you can use an additional conditioning image. The conditioning image can be a canny image, depth map, image segmentation, and even scribbles! Whatever type of conditioning image you choose, the ControlNet generates an image that pr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/img2img.md | https://huggingface.co/docs/diffusers/en/using-diffusers/img2img/#controlnet | #controlnet | .md | 83_16 |
Running diffusion models is computationally expensive and intensive, but with a few optimization tricks, it is entirely possible to run them on consumer and free-tier GPUs. For example, you can use a more memory-efficient form of attention such as PyTorch 2.0's [scaled-dot product attention](../optimization/torch2.0#sc... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/img2img.md | https://huggingface.co/docs/diffusers/en/using-diffusers/img2img/#optimize | #optimize | .md | 83_17 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/unconditional_image_generation.md | https://huggingface.co/docs/diffusers/en/using-diffusers/unconditional_image_generation/ | .md | 84_0 | |
[[open-in-colab]]
Unconditional image generation generates images that look like a random sample from the training data the model was trained on because the denoising process is not guided by any additional context like text or image.
To get started, use the [`DiffusionPipeline`] to load the [anton-l/ddpm-butterfli... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/unconditional_image_generation.md | https://huggingface.co/docs/diffusers/en/using-diffusers/unconditional_image_generation/#unconditional-image-generation | #unconditional-image-generation | .md | 84_1 |
Diffusers' pipelines can be used as an inference engine for a server. It supports concurrent and multithreaded requests to generate images that may be requested by multiple users at the same time.
This guide will show you how to use the [`StableDiffusion3Pipeline`] in a server, but feel free to use any pipeline you w... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/create_a_server.md | https://huggingface.co/docs/diffusers/en/using-diffusers/create_a_server/#create-a-server | #create-a-server | .md | 85_0 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/advanced_inference/outpaint.md | https://huggingface.co/docs/diffusers/en/advanced_inference/outpaint/ | .md | 86_0 | |
Outpainting extends an image beyond its original boundaries, allowing you to add, replace, or modify visual elements in an image while preserving the original image. Like [inpainting](../using-diffusers/inpaint), you want to fill the white area (in this case, the area outside of the original image) with new visual elem... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/advanced_inference/outpaint.md | https://huggingface.co/docs/diffusers/en/advanced_inference/outpaint/#outpainting | #outpainting | .md | 86_1 |
Start by picking an image to outpaint with and remove the background with a Space like [BRIA-RMBG-1.4](https://hf.co/spaces/briaai/BRIA-RMBG-1.4).
<iframe
src="https://briaai-bria-rmbg-1-4.hf.space"
frameborder="0"
width="850"
height="450"
></iframe>
For example, remove the background from this image of a pair of s... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/advanced_inference/outpaint.md | https://huggingface.co/docs/diffusers/en/advanced_inference/outpaint/#image-preparation | #image-preparation | .md | 86_2 |
Once your image is ready, you can generate content in the white area around the shoes with [controlnet-inpaint-dreamer-sdxl](https://hf.co/destitech/controlnet-inpaint-dreamer-sdxl), a SDXL ControlNet trained for inpainting.
Load the inpainting ControlNet, ZoeDepth model, VAE and pass them to the [`StableDiffusionXLC... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/advanced_inference/outpaint.md | https://huggingface.co/docs/diffusers/en/advanced_inference/outpaint/#outpaint | #outpaint | .md | 86_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/outputs.md | https://huggingface.co/docs/diffusers/en/api/outputs/ | .md | 87_0 | |
All model outputs are subclasses of [`~utils.BaseOutput`], data structures containing all the information returned by the model. The outputs can also be used as tuples or dictionaries.
For example:
```python
from diffusers import DDIMPipeline
pipeline = DDIMPipeline.from_pretrained("google/ddpm-cifar10-32")
output... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/outputs.md | https://huggingface.co/docs/diffusers/en/api/outputs/#outputs | #outputs | .md | 87_1 |
BaseOutput
Base class for all model outputs as dataclass. Has a `__getitem__` that allows indexing by integer or slice (like a
tuple) or strings (like a dictionary) that will ignore the `None` attributes. Otherwise behaves like a regular
Python dictionary.
<Tip warning={true}>
You can't unpack a [`BaseOutput`] di... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/outputs.md | https://huggingface.co/docs/diffusers/en/api/outputs/#baseoutput | #baseoutput | .md | 87_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/outputs.md | https://huggingface.co/docs/diffusers/en/api/outputs/#imagepipelineoutput | #imagepipelineoutput | .md | 87_3 |
[[autodoc]] FlaxImagePipelineOutput: No module named 'flax' | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/outputs.md | https://huggingface.co/docs/diffusers/en/api/outputs/#flaximagepipelineoutput | #flaximagepipelineoutput | .md | 87_4 |
AudioPipelineOutput
Output class for audio pipelines.
Args:
audios (`np.ndarray`)
List of denoised audio samples of a NumPy array of shape `(batch_size, num_channels, sample_rate)`. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/outputs.md | https://huggingface.co/docs/diffusers/en/api/outputs/#audiopipelineoutput | #audiopipelineoutput | .md | 87_5 |
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/outputs.md | https://huggingface.co/docs/diffusers/en/api/outputs/#imagetextpipelineoutput | #imagetextpipelineoutput | .md | 87_6 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/attnprocessor.md | https://huggingface.co/docs/diffusers/en/api/attnprocessor/ | .md | 88_0 | |
An attention processor is a class for applying different types of attention mechanisms. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/attnprocessor.md | https://huggingface.co/docs/diffusers/en/api/attnprocessor/#attention-processor | #attention-processor | .md | 88_1 |
AttnProcessor
Default processor for performing attention-related computations.
AttnProcessor
Default processor for performing attention-related computations.
2_0
AttnAddedKVProcessor
Processor for performing attention-related computations with extra learnable key and value matrices for the text
encoder.
Att... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/attnprocessor.md | https://huggingface.co/docs/diffusers/en/api/attnprocessor/#attnprocessor | #attnprocessor | .md | 88_2 |
AllegroAttnProcessor2_0
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
used in the Allegro model. It applies a normalization layer and rotary embedding on the query and key vector. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/attnprocessor.md | https://huggingface.co/docs/diffusers/en/api/attnprocessor/#allegro | #allegro | .md | 88_3 |
AuraFlowAttnProcessor2_0
Attention processor used typically in processing Aura Flow.
FusedAuraFlowAttnProcessor2_0
Attention processor used typically in processing Aura Flow with fused projections. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/attnprocessor.md | https://huggingface.co/docs/diffusers/en/api/attnprocessor/#auraflow | #auraflow | .md | 88_4 |
CogVideoXAttnProcessor2_0
Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on
query and key vectors, but does not include spatial normalization.
FusedCogVideoXAttnProcessor2_0
Processor for implementing scaled dot-product attention for the CogVideoX mo... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/attnprocessor.md | https://huggingface.co/docs/diffusers/en/api/attnprocessor/#cogvideox | #cogvideox | .md | 88_5 |
CrossFrameAttnProcessor
Cross frame attention processor. Each frame attends the first frame.
Args:
batch_size: The number that represents actual batch size, other than the frames.
For example, calling unet with a single prompt and num_images_per_prompt=1, batch_size should be equal to
2, due to classifier-free guid... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/attnprocessor.md | https://huggingface.co/docs/diffusers/en/api/attnprocessor/#crossframeattnprocessor | #crossframeattnprocessor | .md | 88_6 |
CustomDiffusionAttnProcessor
Processor for implementing attention for the Custom Diffusion method.
Args:
train_kv (`bool`, defaults to `True`):
Whether to newly train the key and value matrices corresponding to the text features.
train_q_out (`bool`, defaults to `True`):
Whether to newly train query matrices corres... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/attnprocessor.md | https://huggingface.co/docs/diffusers/en/api/attnprocessor/#custom-diffusion | #custom-diffusion | .md | 88_7 |
FluxAttnProcessor2_0
Attention processor used typically in processing the SD3-like self-attention projections.
FusedFluxAttnProcessor2_0
Attention processor used typically in processing the SD3-like self-attention projections.
FluxSingleAttnProcessor2_0
Processor for implementing scaled dot-product attention (ena... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/attnprocessor.md | https://huggingface.co/docs/diffusers/en/api/attnprocessor/#flux | #flux | .md | 88_8 |
HunyuanAttnProcessor2_0
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
used in the HunyuanDiT model. It applies a s normalization layer and rotary embedding on query and key vector.
FusedHunyuanAttnProcessor2_0
Processor for implementing scaled do... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/attnprocessor.md | https://huggingface.co/docs/diffusers/en/api/attnprocessor/#hunyuan | #hunyuan | .md | 88_9 |
PAGIdentitySelfAttnProcessor2_0
Processor for implementing PAG using scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
PAG reference: https://arxiv.org/abs/2403.17377
PAGCFGIdentitySelfAttnProcessor2_0
Processor for implementing PAG using scaled dot-product attention (enabled by defau... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/attnprocessor.md | https://huggingface.co/docs/diffusers/en/api/attnprocessor/#identityselfattnprocessor20 | #identityselfattnprocessor20 | .md | 88_10 |
IPAdapterAttnProcessor
Attention processor for Multiple IP-Adapters.
Args:
hidden_size (`int`):
The hidden size of the attention layer.
cross_attention_dim (`int`):
The number of channels in the `encoder_hidden_states`.
num_tokens (`int`, `Tuple[int]` or `List[int]`, defaults to `(4,)`):
The context length of the i... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/attnprocessor.md | https://huggingface.co/docs/diffusers/en/api/attnprocessor/#ip-adapter | #ip-adapter | .md | 88_11 |
JointAttnProcessor2_0
Attention processor used typically in processing the SD3-like self-attention projections.
PAGJointAttnProcessor2_0
Attention processor used typically in processing the SD3-like self-attention projections.
PAGCFGJointAttnProcessor2_0
Attention processor used typically in processing the SD3-like... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/attnprocessor.md | https://huggingface.co/docs/diffusers/en/api/attnprocessor/#jointattnprocessor20 | #jointattnprocessor20 | .md | 88_12 |
LoRAAttnProcessor
Processor for implementing attention with LoRA.
LoRAAttnProcessor
Processor for implementing attention with LoRA.
2_0
LoRAAttnAddedKVProcessor
Processor for implementing attention with LoRA with extra learnable key and value matrices for the text encoder.
LoRAXFormersAttnProcessor
Proces... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/attnprocessor.md | https://huggingface.co/docs/diffusers/en/api/attnprocessor/#lora | #lora | .md | 88_13 |
LuminaAttnProcessor2_0
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
used in the LuminaNextDiT model. It applies a s normalization layer and rotary embedding on query and key vector. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/attnprocessor.md | https://huggingface.co/docs/diffusers/en/api/attnprocessor/#lumina-t2x | #lumina-t2x | .md | 88_14 |
MochiAttnProcessor2_0
Attention processor used in Mochi.
MochiVaeAttnProcessor2_0
Attention processor used in Mochi VAE. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/attnprocessor.md | https://huggingface.co/docs/diffusers/en/api/attnprocessor/#mochi | #mochi | .md | 88_15 |
SanaLinearAttnProcessor2_0
Processor for implementing scaled dot-product linear attention.
SanaMultiscaleAttnProcessor2_0
Processor for implementing multiscale quadratic attention.
PAGCFGSanaLinearAttnProcessor2_0
Processor for implementing scaled dot-product linear attention.
PAGIdentitySanaLinearAttnProce... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/attnprocessor.md | https://huggingface.co/docs/diffusers/en/api/attnprocessor/#sana | #sana | .md | 88_16 |
StableAudioAttnProcessor2_0
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
used in the Stable Audio model. It applies rotary embedding on query and key vector, and allows MHA, GQA or MQA. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/attnprocessor.md | https://huggingface.co/docs/diffusers/en/api/attnprocessor/#stable-audio | #stable-audio | .md | 88_17 |
SlicedAttnProcessor
Processor for implementing sliced attention.
Args:
slice_size (`int`, *optional*):
The number of steps to compute attention. Uses as many slices as `attention_head_dim // slice_size`, and
`attention_head_dim` must be a multiple of the `slice_size`.
SlicedAttnAddedKVProcessor
Processor for im... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/attnprocessor.md | https://huggingface.co/docs/diffusers/en/api/attnprocessor/#slicedattnprocessor | #slicedattnprocessor | .md | 88_18 |
XFormersAttnProcessor
Processor for implementing memory efficient attention using xFormers.
Args:
attention_op (`Callable`, *optional*, defaults to `None`):
The base
[operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to
use as the attention operator. It is recom... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/attnprocessor.md | https://huggingface.co/docs/diffusers/en/api/attnprocessor/#xformersattnprocessor | #xformersattnprocessor | .md | 88_19 |
XLAFlashAttnProcessor2_0
Processor for implementing scaled dot-product attention with pallas flash attention kernel if using `torch_xla`. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/attnprocessor.md | https://huggingface.co/docs/diffusers/en/api/attnprocessor/#xlaflashattnprocessor20 | #xlaflashattnprocessor20 | .md | 88_20 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/normalization.md | https://huggingface.co/docs/diffusers/en/api/normalization/ | .md | 89_0 | |
Customized normalization layers for supporting various models in 🤗 Diffusers. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/normalization.md | https://huggingface.co/docs/diffusers/en/api/normalization/#normalization-layers | #normalization-layers | .md | 89_1 |
AdaLayerNorm
Norm layer modified to incorporate timestep embeddings.
Parameters:
embedding_dim (`int`): The size of each embedding vector.
num_embeddings (`int`, *optional*): The size of the embeddings dictionary.
output_dim (`int`, *optional*):
norm_elementwise_affine (`bool`, defaults to `False):
norm_eps (`bool`... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/normalization.md | https://huggingface.co/docs/diffusers/en/api/normalization/#adalayernorm | #adalayernorm | .md | 89_2 |
AdaLayerNorm
Norm layer modified to incorporate timestep embeddings.
Parameters:
embedding_dim (`int`): The size of each embedding vector.
num_embeddings (`int`, *optional*): The size of the embeddings dictionary.
output_dim (`int`, *optional*):
norm_elementwise_affine (`bool`, defaults to `False):
norm_eps (`bool`... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/normalization.md | https://huggingface.co/docs/diffusers/en/api/normalization/#adalayernormzero | #adalayernormzero | .md | 89_3 |
AdaLayerNorm
Norm layer modified to incorporate timestep embeddings.
Parameters:
embedding_dim (`int`): The size of each embedding vector.
num_embeddings (`int`, *optional*): The size of the embeddings dictionary.
output_dim (`int`, *optional*):
norm_elementwise_affine (`bool`, defaults to `False):
norm_eps (`bool`... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/normalization.md | https://huggingface.co/docs/diffusers/en/api/normalization/#adalayernormsingle | #adalayernormsingle | .md | 89_4 |
AdaGroupNorm
GroupNorm layer modified to incorporate timestep embeddings.
Parameters:
embedding_dim (`int`): The size of each embedding vector.
num_embeddings (`int`): The size of the embeddings dictionary.
num_groups (`int`): The number of groups to separate the channels into.
act_fn (`str`, *optional*, defaults t... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/normalization.md | https://huggingface.co/docs/diffusers/en/api/normalization/#adagroupnorm | #adagroupnorm | .md | 89_5 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/utilities.md | https://huggingface.co/docs/diffusers/en/api/utilities/ | .md | 90_0 | |
Utility and helper functions for working with 🤗 Diffusers. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/utilities.md | https://huggingface.co/docs/diffusers/en/api/utilities/#utilities | #utilities | .md | 90_1 |
numpy_to_pil
Convert a numpy image or a batch of images to a PIL image. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/utilities.md | https://huggingface.co/docs/diffusers/en/api/utilities/#numpytopil | #numpytopil | .md | 90_2 |
pt_to_pil
Convert a torch image to a PIL image. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/utilities.md | https://huggingface.co/docs/diffusers/en/api/utilities/#pttopil | #pttopil | .md | 90_3 |
load_image
Loads `image` to a PIL Image.
Args:
image (`str` or `PIL.Image.Image`):
The image to convert to the PIL Image format.
convert_method (Callable[[PIL.Image.Image], PIL.Image.Image], *optional*):
A conversion method to apply to the image after loading it. When set to `None` the image will be converted
"RGB"... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/utilities.md | https://huggingface.co/docs/diffusers/en/api/utilities/#loadimage | #loadimage | .md | 90_4 |
export_to_gif | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/utilities.md | https://huggingface.co/docs/diffusers/en/api/utilities/#exporttogif | #exporttogif | .md | 90_5 |
export_to_video | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/utilities.md | https://huggingface.co/docs/diffusers/en/api/utilities/#exporttovideo | #exporttovideo | .md | 90_6 |
make_image_grid
Prepares a single grid of images. Useful for visualization purposes. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/utilities.md | https://huggingface.co/docs/diffusers/en/api/utilities/#makeimagegrid | #makeimagegrid | .md | 90_7 |
randn_tensor
A helper function to create random tensors on the desired `device` with the desired `dtype`. When
passing a list of generators, you can seed each batch size individually. If CPU generators are passed, the tensor
is always created on the CPU. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/utilities.md | https://huggingface.co/docs/diffusers/en/api/utilities/#randntensor | #randntensor | .md | 90_8 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/configuration.md | https://huggingface.co/docs/diffusers/en/api/configuration/ | .md | 91_0 | |
Schedulers from [`~schedulers.scheduling_utils.SchedulerMixin`] and models from [`ModelMixin`] inherit from [`ConfigMixin`] which stores all the parameters that are passed to their respective `__init__` methods in a JSON-configuration file.
<Tip>
To use private or [gated](https://huggingface.co/docs/hub/models-gate... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/configuration.md | https://huggingface.co/docs/diffusers/en/api/configuration/#configuration | #configuration | .md | 91_1 |
ConfigMixin
Base class for all configuration classes. All configuration parameters are stored under `self.config`. Also
provides the [`~ConfigMixin.from_config`] and [`~ConfigMixin.save_config`] methods for loading, downloading, and
saving classes that inherit from [`ConfigMixin`].
Class attributes:
- **config_name... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/configuration.md | https://huggingface.co/docs/diffusers/en/api/configuration/#configmixin | #configmixin | .md | 91_2 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/internal_classes_overview.md | https://huggingface.co/docs/diffusers/en/api/internal_classes_overview/ | .md | 92_0 | |
The APIs in this section are more experimental and prone to breaking changes. Most of them are used internally for development, but they may also be useful to you if you're interested in building a diffusion model with some custom parts or if you're interested in some of our helper utilities for working with 🤗 Diffuse... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/internal_classes_overview.md | https://huggingface.co/docs/diffusers/en/api/internal_classes_overview/#overview | #overview | .md | 92_1 |
<!--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
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/logging.md | https://huggingface.co/docs/diffusers/en/api/logging/ | .md | 93_0 | |
🤗 Diffusers has a centralized logging system to easily manage the verbosity of the library. The default verbosity is set to `WARNING`.
To change the verbosity level, use one of the direct setters. For instance, to change the verbosity to the `INFO` level.
```python
import diffusers
diffusers.logging.set_verbosity... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/logging.md | https://huggingface.co/docs/diffusers/en/api/logging/#logging | #logging | .md | 93_1 |
set_verbosity_error
Set the verbosity to the `ERROR` level.
set_verbosity_warning
Set the verbosity to the `WARNING` level.
set_verbosity_info
Set the verbosity to the `INFO` level.
set_verbosity_debug
Set the verbosity to the `DEBUG` level. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/logging.md | https://huggingface.co/docs/diffusers/en/api/logging/#base-setters | #base-setters | .md | 93_2 |
get_verbosity
Return the current level for the 🤗 Diffusers' root logger as an `int`.
Returns:
`int`:
Logging level integers which can be one of:
- `50`: `diffusers.logging.CRITICAL` or `diffusers.logging.FATAL`
- `40`: `diffusers.logging.ERROR`
- `30`: `diffusers.logging.WARNING` or `diffusers.logging.WARN`
- `2... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/logging.md | https://huggingface.co/docs/diffusers/en/api/logging/#other-functions | #other-functions | .md | 93_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/image_processor.md | https://huggingface.co/docs/diffusers/en/api/image_processor/ | .md | 94_0 | |
The [`VaeImageProcessor`] provides a unified API for [`StableDiffusionPipeline`]s to prepare image inputs for VAE encoding and post-processing outputs once they're decoded. This includes transformations such as resizing, normalization, and conversion between PIL Image, PyTorch, and NumPy arrays.
All pipelines with [`... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/image_processor.md | https://huggingface.co/docs/diffusers/en/api/image_processor/#vae-image-processor | #vae-image-processor | .md | 94_1 |
VaeImageProcessor
Image processor for VAE.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`. Can accept
`height` and `width` arguments from [`image_processor.VaeImageProcessor.preprocess`] method.
vae_scale_facto... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/image_processor.md | https://huggingface.co/docs/diffusers/en/api/image_processor/#vaeimageprocessor | #vaeimageprocessor | .md | 94_2 |
The [`VaeImageProcessorLDM3D`] accepts RGB and depth inputs and returns RGB and depth outputs.
VaeImageProcessor
Image processor for VAE.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`. Can accept
`height` an... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/image_processor.md | https://huggingface.co/docs/diffusers/en/api/image_processor/#vaeimageprocessorldm3d | #vaeimageprocessorldm3d | .md | 94_3 |
PixArtImageProcessor
Image processor for PixArt image resize and crop.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`. Can accept
`height` and `width` arguments from [`image_processor.VaeImageProcessor.preproce... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/image_processor.md | https://huggingface.co/docs/diffusers/en/api/image_processor/#pixartimageprocessor | #pixartimageprocessor | .md | 94_4 |
IPAdapterMaskProcessor
Image processor for IP Adapter image masks.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`.
vae_scale_factor (`int`, *optional*, defaults to `8`):
VAE scale factor. If `do_resize` is `Tru... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/image_processor.md | https://huggingface.co/docs/diffusers/en/api/image_processor/#ipadaptermaskprocessor | #ipadaptermaskprocessor | .md | 94_5 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/quantization.md | https://huggingface.co/docs/diffusers/en/api/quantization/ | .md | 95_0 | |
Quantization techniques reduce memory and computational costs by representing weights and activations with lower-precision data types like 8-bit integers (int8). This enables loading larger models you normally wouldn't be able to fit into memory, and speeding up inference. Diffusers supports 8-bit and 4-bit quantizatio... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/quantization.md | https://huggingface.co/docs/diffusers/en/api/quantization/#quantization | #quantization | .md | 95_1 |
BitsAndBytesConfig
This is a wrapper class about all possible attributes and features that you can play with a model that has been
loaded using `bitsandbytes`.
This replaces `load_in_8bit` or `load_in_4bit`therefore both options are mutually exclusive.
Currently only supports `LLM.int8()`, `FP4`, and `NF4` quanti... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/quantization.md | https://huggingface.co/docs/diffusers/en/api/quantization/#bitsandbytesconfig | #bitsandbytesconfig | .md | 95_2 |
GGUFQuantizationConfig
This is a config class for GGUF Quantization techniques.
Args:
compute_dtype: (`torch.dtype`, defaults to `torch.float32`):
This sets the computational type which might be different than the input type. For example, inputs might be
fp32, but computation can be set to bf16 for speedups. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/quantization.md | https://huggingface.co/docs/diffusers/en/api/quantization/#ggufquantizationconfig | #ggufquantizationconfig | .md | 95_3 |
TorchAoConfig
This is a config class for torchao quantization/sparsity techniques.
Args:
quant_type (`str`):
The type of quantization we want to use, currently supporting:
- **Integer quantization:**
- Full function names: `int4_weight_only`, `int8_dynamic_activation_int4_weight`,
`int8_weight_only`, `int8_dynamic_ac... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/quantization.md | https://huggingface.co/docs/diffusers/en/api/quantization/#torchaoconfig | #torchaoconfig | .md | 95_4 |
DiffusersQuantizer
Abstract class of the HuggingFace quantizer. Supports for now quantizing HF diffusers models for inference and/or
quantization. This class is used only for diffusers.models.modeling_utils.ModelMixin.from_pretrained and cannot be
easily used outside the scope of that method yet.
Attributes
quantiz... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/quantization.md | https://huggingface.co/docs/diffusers/en/api/quantization/#diffusersquantizer | #diffusersquantizer | .md | 95_5 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/video_processor.md | https://huggingface.co/docs/diffusers/en/api/video_processor/ | .md | 96_0 | |
The [`VideoProcessor`] provides a unified API for video pipelines to prepare inputs for VAE encoding and post-processing outputs once they're decoded. The class inherits [`VaeImageProcessor`] so it includes transformations such as resizing, normalization, and conversion between PIL Image, PyTorch, and NumPy arrays. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/video_processor.md | https://huggingface.co/docs/diffusers/en/api/video_processor/#video-processor | #video-processor | .md | 96_1 |
[[autodoc]] preprocess_video: No module named 'diffusers.video_processor.VideoProcessor'; 'diffusers.video_processor' is not a package
[[autodoc]] postprocess_video: No module named 'diffusers.video_processor.VideoProcessor'; 'diffusers.video_processor' is not a package | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/video_processor.md | https://huggingface.co/docs/diffusers/en/api/video_processor/#videoprocessor | #videoprocessor | .md | 96_2 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/activations.md | https://huggingface.co/docs/diffusers/en/api/activations/ | .md | 97_0 | |
Customized activation functions for supporting various models in 🤗 Diffusers. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/activations.md | https://huggingface.co/docs/diffusers/en/api/activations/#activation-functions | #activation-functions | .md | 97_1 |
GELU
GELU activation function with tanh approximation support with `approximate="tanh"`.
Parameters:
dim_in (`int`): The number of channels in the input.
dim_out (`int`): The number of channels in the output.
approximate (`str`, *optional*, defaults to `"none"`): If `"tanh"`, use tanh approximation.
bias (`bool`, d... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/activations.md | https://huggingface.co/docs/diffusers/en/api/activations/#gelu | #gelu | .md | 97_2 |
GEGLU
A [variant](https://arxiv.org/abs/2002.05202) of the gated linear unit activation function.
Parameters:
dim_in (`int`): The number of channels in the input.
dim_out (`int`): The number of channels in the output.
bias (`bool`, defaults to True): Whether to use a bias in the linear layer. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/activations.md | https://huggingface.co/docs/diffusers/en/api/activations/#geglu | #geglu | .md | 97_3 |
ApproximateGELU
The approximate form of the Gaussian Error Linear Unit (GELU). For more details, see section 2 of this
[paper](https://arxiv.org/abs/1606.08415).
Parameters:
dim_in (`int`): The number of channels in the input.
dim_out (`int`): The number of channels in the output.
bias (`bool`, defaults to True): W... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/activations.md | https://huggingface.co/docs/diffusers/en/api/activations/#approximategelu | #approximategelu | .md | 97_4 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_unclip.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_unclip/ | .md | 98_0 | |
Stable unCLIP checkpoints are finetuned from [Stable Diffusion 2.1](./stable_diffusion/stable_diffusion_2) checkpoints to condition on CLIP image embeddings.
Stable unCLIP still conditions on text embeddings. Given the two separate conditionings, stable unCLIP can be used
for text guided image variation. When combined ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_unclip.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_unclip/#stable-unclip | #stable-unclip | .md | 98_1 |
Stable unCLIP takes `noise_level` as input during inference which determines how much noise is added to the image embeddings. A higher `noise_level` increases variation in the final un-noised images. By default, we do not add any additional noise to the image embeddings (`noise_level = 0`). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_unclip.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_unclip/#tips | #tips | .md | 98_2 |
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