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CogView3PipelineOutput
Output class for CogView3 pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogview3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogview3/#cogview3pipelineoutput | #cogview3pipelineoutput | .md | 115_4 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky_v22.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky_v22/ | .md | 116_0 | |
Kandinsky 2.2 is created by [Arseniy Shakhmatov](https://github.com/cene555), [Anton Razzhigaev](https://github.com/razzant), [Aleksandr Nikolich](https://github.com/AlexWortega), [Vladimir Arkhipkin](https://github.com/oriBetelgeuse), [Igor Pavlov](https://github.com/boomb0om), [Andrey Kuznetsov](https://github.com/ku... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky_v22.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky_v22/#kandinsky-22 | #kandinsky-22 | .md | 116_1 |
KandinskyV22PriorPipeline
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 ([`P... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky_v22.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky_v22/#kandinskyv22priorpipeline | #kandinskyv22priorpipeline | .md | 116_2 |
KandinskyV22Pipeline
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:
scheduler (... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky_v22.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky_v22/#kandinskyv22pipeline | #kandinskyv22pipeline | .md | 116_3 |
KandinskyV22CombinedPipeline
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.)
... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky_v22.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky_v22/#kandinskyv22combinedpipeline | #kandinskyv22combinedpipeline | .md | 116_4 |
KandinskyV22ControlnetPipeline
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:
s... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky_v22.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky_v22/#kandinskyv22controlnetpipeline | #kandinskyv22controlnetpipeline | .md | 116_5 |
KandinskyV22PriorEmb2EmbPipeline
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:
pri... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky_v22.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky_v22/#kandinskyv22prioremb2embpipeline | #kandinskyv22prioremb2embpipeline | .md | 116_6 |
KandinskyV22Img2ImgPipeline
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:
sch... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky_v22.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky_v22/#kandinskyv22img2imgpipeline | #kandinskyv22img2imgpipeline | .md | 116_7 |
KandinskyV22Img2ImgCombinedPipeline
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, ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky_v22.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky_v22/#kandinskyv22img2imgcombinedpipeline | #kandinskyv22img2imgcombinedpipeline | .md | 116_8 |
KandinskyV22ControlnetImg2ImgPipeline
Pipeline for image-to-image generation using Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky_v22.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky_v22/#kandinskyv22controlnetimg2imgpipeline | #kandinskyv22controlnetimg2imgpipeline | .md | 116_9 |
KandinskyV22InpaintPipeline
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.)
Arg... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky_v22.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky_v22/#kandinskyv22inpaintpipeline | #kandinskyv22inpaintpipeline | .md | 116_10 |
KandinskyV22InpaintCombinedPipeline
Combined Pipeline for inpainting generation using Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky_v22.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky_v22/#kandinskyv22inpaintcombinedpipeline | #kandinskyv22inpaintcombinedpipeline | .md | 116_11 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/i2vgenxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/i2vgenxl/ | .md | 117_0 | |
[I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models](https://hf.co/papers/2311.04145.pdf) by Shiwei Zhang, Jiayu Wang, Yingya Zhang, Kang Zhao, Hangjie Yuan, Zhiwu Qin, Xiang Wang, Deli Zhao, and Jingren Zhou.
The abstract from the paper is:
*Video synthesis has recently made remarkable ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/i2vgenxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/i2vgenxl/#i2vgen-xl | #i2vgen-xl | .md | 117_1 |
* I2VGenXL always uses a `clip_skip` value of 1. This means it leverages the penultimate layer representations from the text encoder of CLIP.
* It can generate videos of quality that is often on par with [Stable Video Diffusion](../../using-diffusers/svd) (SVD).
* Unlike SVD, it additionally accepts text prompts as inp... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/i2vgenxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/i2vgenxl/#notes | #notes | .md | 117_2 |
I2VGenXLPipeline
Pipeline for image-to-video generation as proposed in [I2VGenXL](https://i2vgen-xl.github.io/).
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.).
Arg... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/i2vgenxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/i2vgenxl/#i2vgenxlpipeline | #i2vgenxlpipeline | .md | 117_3 |
I2VGenXLPipelineOutput
Output class for image-to-video pipeline.
Args:
frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
denoised
PIL image sequences of length `num_frames.` It can also be a Nu... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/i2vgenxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/i2vgenxl/#i2vgenxlpipelineoutput | #i2vgenxlpipelineoutput | .md | 117_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/pix2pix.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pix2pix/ | .md | 118_0 | |
[InstructPix2Pix: Learning to Follow Image Editing Instructions](https://huggingface.co/papers/2211.09800) is by Tim Brooks, Aleksander Holynski and Alexei A. Efros.
The abstract from the paper is:
*We propose a method for editing images from human instructions: given an input image and a written instruction that t... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pix2pix.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pix2pix/#instructpix2pix | #instructpix2pix | .md | 118_1 |
StableDiffusionInstructPix2PixPipeline
Pipeline for pixel-level image editing by following text instructions (based on Stable Diffusion).
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a partic... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pix2pix.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pix2pix/#stablediffusioninstructpix2pixpipeline | #stablediffusioninstructpix2pixpipeline | .md | 118_2 |
StableDiffusionXLInstructPix2PixPipeline
Pipeline for pixel-level image editing by following text instructions. Based on Stable Diffusion XL.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading o... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pix2pix.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pix2pix/#stablediffusionxlinstructpix2pixpipeline | #stablediffusionxlinstructpix2pixpipeline | .md | 118_3 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sdxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sdxl/ | .md | 119_0 | |
ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala.
With a ControlNet model, you can provide an additional control image to condition and control Stable Diffusion generation. For example, ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sdxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sdxl/#controlnet-with-stable-diffusion-xl | #controlnet-with-stable-diffusion-xl | .md | 119_1 |
StableDiffusionXLControlNetPipeline
Pipeline for text-to-image generation using Stable Diffusion XL with ControlNet guidance.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device,... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sdxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sdxl/#stablediffusionxlcontrolnetpipeline | #stablediffusionxlcontrolnetpipeline | .md | 119_2 |
StableDiffusionXLControlNetImg2ImgPipeline
Pipeline for image-to-image generation using Stable Diffusion XL with ControlNet guidance.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sdxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sdxl/#stablediffusionxlcontrolnetimg2imgpipeline | #stablediffusionxlcontrolnetimg2imgpipeline | .md | 119_3 |
StableDiffusionXLControlNetInpaintPipeline
Pipeline for text-to-image generation using Stable Diffusion XL.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sdxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sdxl/#stablediffusionxlcontrolnetinpaintpipeline | #stablediffusionxlcontrolnetinpaintpipeline | .md | 119_4 |
StableDiffusionPipelineOutput
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
nsfw_content_detected (`List[bool]`)
List indicating whether the c... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sdxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sdxl/#stablediffusionpipelineoutput | #stablediffusionpipelineoutput | .md | 119_5 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/flux/ | .md | 120_0 | |
Flux is a series of text-to-image generation models based on diffusion transformers. To know more about Flux, check out the original [blog post](https://blackforestlabs.ai/announcing-black-forest-labs/) by the creators of Flux, Black Forest Labs.
Original model checkpoints for Flux can be found [here](https://hugging... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/flux/#flux | #flux | .md | 120_1 |
* `max_sequence_length` cannot be more than 256.
* `guidance_scale` needs to be 0.
* As this is a timestep-distilled model, it benefits from fewer sampling steps.
```python
import torch
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/flux/#timestep-distilled | #timestep-distilled | .md | 120_2 |
* The guidance-distilled variant takes about 50 sampling steps for good-quality generation.
* It doesn't have any limitations around the `max_sequence_length`.
```python
import torch
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
pip... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/flux/#guidance-distilled | #guidance-distilled | .md | 120_3 |
* Flux Fill pipeline does not require `strength` as an input like regular inpainting pipelines.
* It supports both inpainting and outpainting.
```python
import torch
from diffusers import FluxFillPipeline
from diffusers.utils import load_image
image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/flux/#fill-inpaintingoutpainting | #fill-inpaintingoutpainting | .md | 120_4 |
**Note:** `black-forest-labs/Flux.1-Canny-dev` is _not_ a [`ControlNetModel`] model. ControlNet models are a separate component from the UNet/Transformer whose residuals are added to the actual underlying model. Canny Control is an alternate architecture that achieves effectively the same results as a ControlNet model ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/flux/#canny-control | #canny-control | .md | 120_5 |
**Note:** `black-forest-labs/Flux.1-Depth-dev` is _not_ a ControlNet model. [`ControlNetModel`] models are a separate component from the UNet/Transformer whose residuals are added to the actual underlying model. Depth Control is an alternate architecture that achieves effectively the same results as a ControlNet model ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/flux/#depth-control | #depth-control | .md | 120_6 |
* Flux Redux pipeline is an adapter for FLUX.1 base models. It can be used with both flux-dev and flux-schnell, for image-to-image generation.
* You can first use the `FluxPriorReduxPipeline` to get the `prompt_embeds` and `pooled_prompt_embeds`, and then feed them into the `FluxPipeline` for image-to-image generation.... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/flux/#redux | #redux | .md | 120_7 |
We can combine Flux Turbo LoRAs with Flux Control and other pipelines like Fill and Redux to enable few-steps' inference. The example below shows how to do that for Flux Control LoRA for depth and turbo LoRA from [`ByteDance/Hyper-SD`](https://hf.co/ByteDance/Hyper-SD).
```py
from diffusers import FluxControlPipeline... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/flux/#combining-flux-turbo-loras-with-flux-control-fill-and-redux | #combining-flux-turbo-loras-with-flux-control-fill-and-redux | .md | 120_8 |
When unloading the Control LoRA weights, call `pipe.unload_lora_weights(reset_to_overwritten_params=True)` to reset the `pipe.transformer` completely back to its original form. The resultant pipeline can then be used with methods like [`DiffusionPipeline.from_pipe`]. More details about this argument are available in [t... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/flux/#note-about-unloadloraweights-when-using-flux-loras | #note-about-unloadloraweights-when-using-flux-loras | .md | 120_9 |
Flux can generate high-quality images with FP16 (i.e. to accelerate inference on Turing/Volta GPUs) but produces different outputs compared to FP32/BF16. The issue is that some activations in the text encoders have to be clipped when running in FP16, which affects the overall image. Forcing text encoders to run with FP... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/flux/#running-fp16-inference | #running-fp16-inference | .md | 120_10 |
Quantization helps reduce the memory requirements of very large models by storing model weights in a lower precision data type. However, quantization may have varying impact on video quality depending on the video model.
Refer to the [Quantization](../../quantization/overview) overview to learn more about supported q... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/flux/#quantization | #quantization | .md | 120_11 |
The `FluxTransformer2DModel` supports loading checkpoints in the original format shipped by Black Forest Labs. This is also useful when trying to load finetunes or quantized versions of the models that have been published by the community.
<Tip>
`FP8` inference can be brittle depending on the GPU type, CUDA version, ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/flux/#single-file-loading-for-the-fluxtransformer2dmodel | #single-file-loading-for-the-fluxtransformer2dmodel | .md | 120_12 |
FluxPipeline
The Flux pipeline for text-to-image generation.
Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
Args:
transformer ([`FluxTransformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([`FlowMatchEulerDiscreteScheduler`]):
A sch... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/flux/#fluxpipeline | #fluxpipeline | .md | 120_13 |
FluxImg2ImgPipeline
The Flux pipeline for image inpainting.
Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
Args:
transformer ([`FluxTransformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([`FlowMatchEulerDiscreteScheduler`]):
A sche... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/flux/#fluximg2imgpipeline | #fluximg2imgpipeline | .md | 120_14 |
FluxInpaintPipeline
The Flux pipeline for image inpainting.
Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
Args:
transformer ([`FluxTransformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([`FlowMatchEulerDiscreteScheduler`]):
A sche... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/flux/#fluxinpaintpipeline | #fluxinpaintpipeline | .md | 120_15 |
FluxControlNetInpaintPipeline
The Flux controlnet pipeline for inpainting.
Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
Args:
transformer ([`FluxTransformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([`FlowMatchEulerDiscreteSched... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/flux/#fluxcontrolnetinpaintpipeline | #fluxcontrolnetinpaintpipeline | .md | 120_16 |
FluxControlNetImg2ImgPipeline
The Flux controlnet pipeline for image-to-image generation.
Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
Args:
transformer ([`FluxTransformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([`FlowMatchEul... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/flux/#fluxcontrolnetimg2imgpipeline | #fluxcontrolnetimg2imgpipeline | .md | 120_17 |
FluxControlPipeline
The Flux pipeline for controllable text-to-image generation.
Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
Args:
transformer ([`FluxTransformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([`FlowMatchEulerDiscret... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/flux/#fluxcontrolpipeline | #fluxcontrolpipeline | .md | 120_18 |
FluxControlImg2ImgPipeline
The Flux pipeline for image inpainting.
Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
Args:
transformer ([`FluxTransformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([`FlowMatchEulerDiscreteScheduler`]):... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/flux/#fluxcontrolimg2imgpipeline | #fluxcontrolimg2imgpipeline | .md | 120_19 |
FluxPriorReduxPipeline
The Flux Redux pipeline for image-to-image generation.
Reference: https://blackforestlabs.ai/flux-1-tools/
Args:
image_encoder ([`SiglipVisionModel`]):
SIGLIP vision model to encode the input image.
feature_extractor ([`SiglipImageProcessor`]):
Image processor for preprocessing images for t... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/flux/#fluxpriorreduxpipeline | #fluxpriorreduxpipeline | .md | 120_20 |
FluxFillPipeline
The Flux Fill pipeline for image inpainting/outpainting.
Reference: https://blackforestlabs.ai/flux-1-tools/
Args:
transformer ([`FluxTransformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([`FlowMatchEulerDiscreteScheduler`]):
A schedu... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/flux/#fluxfillpipeline | #fluxfillpipeline | .md | 120_21 |
<!-- Copyright 2024 The HuggingFace Team. All rights reserved. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/sana.md | https://huggingface.co/docs/diffusers/en/api/pipelines/sana/ | .md | 121_0 | |
[SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers](https://huggingface.co/papers/2410.10629) from NVIDIA and MIT HAN Lab, by Enze Xie, Junsong Chen, Junyu Chen, Han Cai, Haotian Tang, Yujun Lin, Zhekai Zhang, Muyang Li, Ligeng Zhu, Yao Lu, Song Han.
The abstract from the paper is:
... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/sana.md | https://huggingface.co/docs/diffusers/en/api/pipelines/sana/#sanapipeline | #sanapipeline | .md | 121_1 |
Quantization helps reduce the memory requirements of very large models by storing model weights in a lower precision data type. However, quantization may have varying impact on video quality depending on the video model.
Refer to the [Quantization](../../quantization/overview) overview to learn more about supported q... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/sana.md | https://huggingface.co/docs/diffusers/en/api/pipelines/sana/#quantization | #quantization | .md | 121_2 |
SanaPipeline
Pipeline for text-to-image generation using [Sana](https://huggingface.co/papers/2410.10629).
- all
- __call__ | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/sana.md | https://huggingface.co/docs/diffusers/en/api/pipelines/sana/#sanapipeline | #sanapipeline | .md | 121_3 |
SanaPAGPipeline
Pipeline for text-to-image generation using [Sana](https://huggingface.co/papers/2410.10629). This pipeline
supports the use of [Perturbed Attention Guidance
(PAG)](https://huggingface.co/docs/diffusers/main/en/using-diffusers/pag).
- all
- __call__ | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/sana.md | https://huggingface.co/docs/diffusers/en/api/pipelines/sana/#sanapagpipeline | #sanapagpipeline | .md | 121_4 |
SanaPipelineOutput
Output class for Sana pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/sana.md | https://huggingface.co/docs/diffusers/en/api/pipelines/sana/#sanapipelineoutput | #sanapipelineoutput | .md | 121_5 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/panorama.md | https://huggingface.co/docs/diffusers/en/api/pipelines/panorama/ | .md | 122_0 | |
[MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation](https://huggingface.co/papers/2302.08113) is by Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel.
The abstract from the paper is:
*Recent advances in text-to-image generation with diffusion models present transformative capabilities in i... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/panorama.md | https://huggingface.co/docs/diffusers/en/api/pipelines/panorama/#multidiffusion | #multidiffusion | .md | 122_1 |
While calling [`StableDiffusionPanoramaPipeline`], it's possible to specify the `view_batch_size` parameter to be > 1.
For some GPUs with high performance, this can speedup the generation process and increase VRAM usage.
To generate panorama-like images make sure you pass the width parameter accordingly. We recommend... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/panorama.md | https://huggingface.co/docs/diffusers/en/api/pipelines/panorama/#tips | #tips | .md | 122_2 |
StableDiffusionPanoramaPipeline
Pipeline for text-to-image generation using MultiDiffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipeline also inheri... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/panorama.md | https://huggingface.co/docs/diffusers/en/api/pipelines/panorama/#stablediffusionpanoramapipeline | #stablediffusionpanoramapipeline | .md | 122_3 |
StableDiffusionPipelineOutput
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
nsfw_content_detected (`List[bool]`)
List indicating whether the c... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/panorama.md | https://huggingface.co/docs/diffusers/en/api/pipelines/panorama/#stablediffusionpipelineoutput | #stablediffusionpipelineoutput | .md | 122_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/deepfloyd_if.md | https://huggingface.co/docs/diffusers/en/api/pipelines/deepfloyd_if/ | .md | 123_0 | |
DeepFloyd IF is a novel state-of-the-art open-source text-to-image model with a high degree of photorealism and language understanding.
The model is a modular composed of a frozen text encoder and three cascaded pixel diffusion modules:
- Stage 1: a base model that generates 64x64 px image based on text prompt,
- Stage... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/deepfloyd_if.md | https://huggingface.co/docs/diffusers/en/api/pipelines/deepfloyd_if/#overview | #overview | .md | 123_1 |
Before you can use IF, you need to accept its usage conditions. To do so:
1. Make sure to have a [Hugging Face account](https://huggingface.co/join) and be logged in.
2. Accept the license on the model card of [DeepFloyd/IF-I-XL-v1.0](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0). Accepting the license on the stage I ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/deepfloyd_if.md | https://huggingface.co/docs/diffusers/en/api/pipelines/deepfloyd_if/#usage | #usage | .md | 123_2 |
By default diffusers makes use of [model cpu offloading](../../optimization/memory#model-offloading) to run the whole IF pipeline with as little as 14 GB of VRAM.
```python
from diffusers import DiffusionPipeline
from diffusers.utils import pt_to_pil, make_image_grid
import torch
# stage 1
stage_1 = DiffusionPipelin... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/deepfloyd_if.md | https://huggingface.co/docs/diffusers/en/api/pipelines/deepfloyd_if/#text-to-image-generation | #text-to-image-generation | .md | 123_3 |
The same IF model weights can be used for text-guided image-to-image translation or image variation.
In this case just make sure to load the weights using the [`IFImg2ImgPipeline`] and [`IFImg2ImgSuperResolutionPipeline`] pipelines.
**Note**: You can also directly move the weights of the text-to-image pipelines to th... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/deepfloyd_if.md | https://huggingface.co/docs/diffusers/en/api/pipelines/deepfloyd_if/#text-guided-image-to-image-generation | #text-guided-image-to-image-generation | .md | 123_4 |
The same IF model weights can be used for text-guided image-to-image translation or image variation.
In this case just make sure to load the weights using the [`IFInpaintingPipeline`] and [`IFInpaintingSuperResolutionPipeline`] pipelines.
**Note**: You can also directly move the weights of the text-to-image pipelines... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/deepfloyd_if.md | https://huggingface.co/docs/diffusers/en/api/pipelines/deepfloyd_if/#text-guided-inpainting-generation | #text-guided-inpainting-generation | .md | 123_5 |
In addition to being loaded with `from_pretrained`, Pipelines can also be loaded directly from each other.
```python
from diffusers import IFPipeline, IFSuperResolutionPipeline
pipe_1 = IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0")
pipe_2 = IFSuperResolutionPipeline.from_pretrained("DeepFloyd/IF-II-L-v1.0")
... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/deepfloyd_if.md | https://huggingface.co/docs/diffusers/en/api/pipelines/deepfloyd_if/#converting-between-different-pipelines | #converting-between-different-pipelines | .md | 123_6 |
The simplest optimization to run IF faster is to move all model components to the GPU.
```py
pipe = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
pipe.to("cuda")
```
You can also run the diffusion process for a shorter number of timesteps.
This can either b... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/deepfloyd_if.md | https://huggingface.co/docs/diffusers/en/api/pipelines/deepfloyd_if/#optimizing-for-speed | #optimizing-for-speed | .md | 123_7 |
When optimizing for GPU memory, we can use the standard diffusers CPU offloading APIs.
Either the model based CPU offloading,
```py
pipe = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()
```
or the more aggressive layer based C... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/deepfloyd_if.md | https://huggingface.co/docs/diffusers/en/api/pipelines/deepfloyd_if/#optimizing-for-memory | #optimizing-for-memory | .md | 123_8 |
| Pipeline | Tasks | Colab
|---|---|:---:|
| [pipeline_if.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py) | *Text-to-Image Generation* | - |
| [pipeline_if_superresolution.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/deepfloyd... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/deepfloyd_if.md | https://huggingface.co/docs/diffusers/en/api/pipelines/deepfloyd_if/#available-pipelines | #available-pipelines | .md | 123_9 |
IFPipeline
- all
- __call__ | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/deepfloyd_if.md | https://huggingface.co/docs/diffusers/en/api/pipelines/deepfloyd_if/#ifpipeline | #ifpipeline | .md | 123_10 |
IFSuperResolutionPipeline
- all
- __call__ | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/deepfloyd_if.md | https://huggingface.co/docs/diffusers/en/api/pipelines/deepfloyd_if/#ifsuperresolutionpipeline | #ifsuperresolutionpipeline | .md | 123_11 |
IFImg2ImgPipeline
- all
- __call__ | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/deepfloyd_if.md | https://huggingface.co/docs/diffusers/en/api/pipelines/deepfloyd_if/#ifimg2imgpipeline | #ifimg2imgpipeline | .md | 123_12 |
IFImg2ImgSuperResolutionPipeline
- all
- __call__ | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/deepfloyd_if.md | https://huggingface.co/docs/diffusers/en/api/pipelines/deepfloyd_if/#ifimg2imgsuperresolutionpipeline | #ifimg2imgsuperresolutionpipeline | .md | 123_13 |
IFInpaintingPipeline
- all
- __call__ | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/deepfloyd_if.md | https://huggingface.co/docs/diffusers/en/api/pipelines/deepfloyd_if/#ifinpaintingpipeline | #ifinpaintingpipeline | .md | 123_14 |
IFInpaintingSuperResolutionPipeline
- all
- __call__ | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/deepfloyd_if.md | https://huggingface.co/docs/diffusers/en/api/pipelines/deepfloyd_if/#ifinpaintingsuperresolutionpipeline | #ifinpaintingsuperresolutionpipeline | .md | 123_15 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/shap_e.md | https://huggingface.co/docs/diffusers/en/api/pipelines/shap_e/ | .md | 124_0 | |
The Shap-E model was proposed in [Shap-E: Generating Conditional 3D Implicit Functions](https://huggingface.co/papers/2305.02463) by Alex Nichol and Heewoo Jun from [OpenAI](https://github.com/openai).
The abstract from the paper is:
*We present Shap-E, a conditional generative model for 3D assets. Unlike recent wo... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/shap_e.md | https://huggingface.co/docs/diffusers/en/api/pipelines/shap_e/#shap-e | #shap-e | .md | 124_1 |
ShapEPipeline
Pipeline for generating latent representation of a 3D asset and rendering with the NeRF method.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/shap_e.md | https://huggingface.co/docs/diffusers/en/api/pipelines/shap_e/#shapepipeline | #shapepipeline | .md | 124_2 |
ShapEImg2ImgPipeline
Pipeline for generating latent representation of a 3D asset and rendering with the NeRF method from an image.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular de... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/shap_e.md | https://huggingface.co/docs/diffusers/en/api/pipelines/shap_e/#shapeimg2imgpipeline | #shapeimg2imgpipeline | .md | 124_3 |
ShapEPipelineOutput
Output class for [`ShapEPipeline`] and [`ShapEImg2ImgPipeline`].
Args:
images (`torch.Tensor`)
A list of images for 3D rendering. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/shap_e.md | https://huggingface.co/docs/diffusers/en/api/pipelines/shap_e/#shapepipelineoutput | #shapepipelineoutput | .md | 124_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/audioldm2.md | https://huggingface.co/docs/diffusers/en/api/pipelines/audioldm2/ | .md | 125_0 | |
AudioLDM 2 was proposed in [AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining](https://arxiv.org/abs/2308.05734) by Haohe Liu et al. AudioLDM 2 takes a text prompt as input and predicts the corresponding audio. It can generate text-conditional sound effects, human speech and music.
Inspi... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/audioldm2.md | https://huggingface.co/docs/diffusers/en/api/pipelines/audioldm2/#audioldm-2 | #audioldm-2 | .md | 125_1 |
AudioLDM2 comes in three variants. Two of these checkpoints are applicable to the general task of text-to-audio generation. The third checkpoint is trained exclusively on text-to-music generation.
All checkpoints share the same model size for the text encoders and VAE. They differ in the size and depth of the UNet.
S... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/audioldm2.md | https://huggingface.co/docs/diffusers/en/api/pipelines/audioldm2/#choosing-a-checkpoint | #choosing-a-checkpoint | .md | 125_2 |
* Descriptive prompt inputs work best: use adjectives to describe the sound (e.g. "high quality" or "clear") and make the prompt context specific (e.g. "water stream in a forest" instead of "stream").
* It's best to use general terms like "cat" or "dog" instead of specific names or abstract objects the model may not be... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/audioldm2.md | https://huggingface.co/docs/diffusers/en/api/pipelines/audioldm2/#constructing-a-prompt | #constructing-a-prompt | .md | 125_3 |
* The _quality_ of the predicted audio sample can be controlled by the `num_inference_steps` argument; higher steps give higher quality audio at the expense of slower inference.
* The _length_ of the predicted audio sample can be controlled by varying the `audio_length_in_s` argument. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/audioldm2.md | https://huggingface.co/docs/diffusers/en/api/pipelines/audioldm2/#controlling-inference | #controlling-inference | .md | 125_4 |
* The quality of the generated waveforms can vary significantly based on the seed. Try generating with different seeds until you find a satisfactory generation.
* Multiple waveforms can be generated in one go: set `num_waveforms_per_prompt` to a value greater than 1. Automatic scoring will be performed between the gene... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/audioldm2.md | https://huggingface.co/docs/diffusers/en/api/pipelines/audioldm2/#evaluating-generated-waveforms | #evaluating-generated-waveforms | .md | 125_5 |
AudioLDM2Pipeline
Pipeline for text-to-audio generation using AudioLDM2.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
vae ([`AutoencoderKL`]):
Variational ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/audioldm2.md | https://huggingface.co/docs/diffusers/en/api/pipelines/audioldm2/#audioldm2pipeline | #audioldm2pipeline | .md | 125_6 |
AudioLDM2ProjectionModel
A simple linear projection model to map two text embeddings to a shared latent space. It also inserts learned
embedding vectors at the start and end of each text embedding sequence respectively. Each variable appended with
`_1` refers to that corresponding to the second text encoder. Otherwis... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/audioldm2.md | https://huggingface.co/docs/diffusers/en/api/pipelines/audioldm2/#audioldm2projectionmodel | #audioldm2projectionmodel | .md | 125_7 |
AudioLDM2UNet2DConditionModel
A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
shaped output. Compared to the vanilla [`UNet2DConditionModel`], this variant optionally includes an additional
self-attention layer in each Transformer block, as well as multipl... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/audioldm2.md | https://huggingface.co/docs/diffusers/en/api/pipelines/audioldm2/#audioldm2unet2dconditionmodel | #audioldm2unet2dconditionmodel | .md | 125_8 |
AudioPipelineOutput
Output class for audio pipelines.
Args:
audios (`np.ndarray`)
List of denoised audio samples of a NumPy array of shape `(batch_size, num_channels, sample_rate)`. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/audioldm2.md | https://huggingface.co/docs/diffusers/en/api/pipelines/audioldm2/#audiopipelineoutput | #audiopipelineoutput | .md | 125_9 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kolors.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kolors/ | .md | 126_0 | |
Kolors is a large-scale text-to-image generation model based on latent diffusion, developed by [the Kuaishou Kolors team](https://github.com/Kwai-Kolors/Kolors). Trained on billions of text-image pairs,... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kolors.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kolors/#kolors-effective-training-of-diffusion-model-for-photorealistic-text-to-image-synthesis | #kolors-effective-training-of-diffusion-model-for-photorealistic-text-to-image-synthesis | .md | 126_1 |
```python
import torch
from diffusers import DPMSolverMultistepScheduler, KolorsPipeline
pipe = KolorsPipeline.from_pretrained("Kwai-Kolors/Kolors-diffusers", torch_dtype=torch.float16, variant="fp16")
pipe.to("cuda")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=Tr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kolors.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kolors/#usage-example | #usage-example | .md | 126_2 |
Kolors needs a different IP Adapter to work, and it uses [Openai-CLIP-336](https://huggingface.co/openai/clip-vit-large-patch14-336) as an image encoder.
<Tip>
Using an IP Adapter with Kolors requires more than 24GB of VRAM. To use it, we recommend using [`~DiffusionPipeline.enable_model_cpu_offload`] on consumer G... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kolors.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kolors/#ip-adapter | #ip-adapter | .md | 126_3 |
KolorsPipeline
- all
- __call__ | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kolors.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kolors/#kolorspipeline | #kolorspipeline | .md | 126_4 |
KolorsImg2ImgPipeline
- all
- __call__ | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kolors.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kolors/#kolorsimg2imgpipeline | #kolorsimg2imgpipeline | .md | 126_5 |
<!-- Copyright 2024 The HuggingFace Team. All rights reserved. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/ | .md | 127_0 | |
[LTX Video](https://huggingface.co/Lightricks/LTX-Video) is the first DiT-based video generation model capable of generating high-quality videos in real-time. It produces 24 FPS videos at a 768x512 resolution faster than they can be watched. Trained on a large-scale dataset of diverse videos, the model generates high-r... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#ltx-video | #ltx-video | .md | 127_1 |
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