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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 in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
--> | /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/kuznetsoffandrey), and [Denis Dimitrov](https://github.com/denndimitrov).
The description from it's GitHub page is:
*Kandinsky 2.2 brings substantial improvements upon its predecessor, Kandinsky 2.1, by introducing a new, more powerful image encoder - CLIP-ViT-G and the ControlNet support. The switch to CLIP-ViT-G as the image encoder significantly increases the model's capability to generate more aesthetic pictures and better understand text, thus enhancing the model's overall performance. The addition of the ControlNet mechanism allows the model to effectively control the process of generating images. This leads to more accurate and visually appealing outputs and opens new possibilities for text-guided image manipulation.*
The original codebase can be found at [ai-forever/Kandinsky-2](https://github.com/ai-forever/Kandinsky-2).
<Tip>
Check out the [Kandinsky Community](https://huggingface.co/kandinsky-community) organization on the Hub for the official model checkpoints for tasks like text-to-image, image-to-image, and inpainting.
</Tip>
<Tip>
Make sure to check out the schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
</Tip> | /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 ([`PriorTransformer`]):
The canonical unCLIP prior to approximate the image embedding from the text embedding.
image_encoder ([`CLIPVisionModelWithProjection`]):
Frozen image-encoder.
text_encoder ([`CLIPTextModelWithProjection`]):
Frozen text-encoder.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
scheduler ([`UnCLIPScheduler`]):
A scheduler to be used in combination with `prior` to generate image embedding.
image_processor ([`CLIPImageProcessor`]):
A image_processor to be used to preprocess image from clip.
- all
- __call__
- interpolate | /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 (Union[`DDIMScheduler`,`DDPMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
movq ([`VQModel`]):
MoVQ Decoder to generate the image from the latents.
- all
- __call__ | /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.)
Args:
scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
movq ([`VQModel`]):
MoVQ Decoder to generate the image from the latents.
prior_prior ([`PriorTransformer`]):
The canonical unCLIP prior to approximate the image embedding from the text embedding.
prior_image_encoder ([`CLIPVisionModelWithProjection`]):
Frozen image-encoder.
prior_text_encoder ([`CLIPTextModelWithProjection`]):
Frozen text-encoder.
prior_tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
prior_scheduler ([`UnCLIPScheduler`]):
A scheduler to be used in combination with `prior` to generate image embedding.
prior_image_processor ([`CLIPImageProcessor`]):
A image_processor to be used to preprocess image from clip.
- all
- __call__ | /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:
scheduler ([`DDIMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
movq ([`VQModel`]):
MoVQ Decoder to generate the image from the latents.
- all
- __call__ | /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:
prior ([`PriorTransformer`]):
The canonical unCLIP prior to approximate the image embedding from the text embedding.
image_encoder ([`CLIPVisionModelWithProjection`]):
Frozen image-encoder.
text_encoder ([`CLIPTextModelWithProjection`]):
Frozen text-encoder.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
scheduler ([`UnCLIPScheduler`]):
A scheduler to be used in combination with `prior` to generate image embedding.
- all
- __call__
- interpolate | /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:
scheduler ([`DDIMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
movq ([`VQModel`]):
MoVQ Decoder to generate the image from the latents.
- all
- __call__ | /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, etc.)
Args:
scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
movq ([`VQModel`]):
MoVQ Decoder to generate the image from the latents.
prior_prior ([`PriorTransformer`]):
The canonical unCLIP prior to approximate the image embedding from the text embedding.
prior_image_encoder ([`CLIPVisionModelWithProjection`]):
Frozen image-encoder.
prior_text_encoder ([`CLIPTextModelWithProjection`]):
Frozen text-encoder.
prior_tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
prior_scheduler ([`UnCLIPScheduler`]):
A scheduler to be used in combination with `prior` to generate image embedding.
prior_image_processor ([`CLIPImageProcessor`]):
A image_processor to be used to preprocess image from clip.
- all
- __call__ | /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.)
Args:
scheduler ([`DDIMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
movq ([`VQModel`]):
MoVQ Decoder to generate the image from the latents.
- all
- __call__ | /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.)
Args:
scheduler ([`DDIMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
movq ([`VQModel`]):
MoVQ Decoder to generate the image from the latents.
- all
- __call__ | /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.)
Args:
scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
movq ([`VQModel`]):
MoVQ Decoder to generate the image from the latents.
prior_prior ([`PriorTransformer`]):
The canonical unCLIP prior to approximate the image embedding from the text embedding.
prior_image_encoder ([`CLIPVisionModelWithProjection`]):
Frozen image-encoder.
prior_text_encoder ([`CLIPTextModelWithProjection`]):
Frozen text-encoder.
prior_tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
prior_scheduler ([`UnCLIPScheduler`]):
A scheduler to be used in combination with `prior` to generate image embedding.
prior_image_processor ([`CLIPImageProcessor`]):
A image_processor to be used to preprocess image from clip.
- all
- __call__ | /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 agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
--> | /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 strides benefiting from the rapid development of diffusion models. However, it still encounters challenges in terms of semantic accuracy, clarity and spatio-temporal continuity. They primarily arise from the scarcity of well-aligned text-video data and the complex inherent structure of videos, making it difficult for the model to simultaneously ensure semantic and qualitative excellence. In this report, we propose a cascaded I2VGen-XL approach that enhances model performance by decoupling these two factors and ensures the alignment of the input data by utilizing static images as a form of crucial guidance. I2VGen-XL consists of two stages: i) the base stage guarantees coherent semantics and preserves content from input images by using two hierarchical encoders, and ii) the refinement stage enhances the video's details by incorporating an additional brief text and improves the resolution to 1280×720. To improve the diversity, we collect around 35 million single-shot text-video pairs and 6 billion text-image pairs to optimize the model. By this means, I2VGen-XL can simultaneously enhance the semantic accuracy, continuity of details and clarity of generated videos. Through extensive experiments, we have investigated the underlying principles of I2VGen-XL and compared it with current top methods, which can demonstrate its effectiveness on diverse data. The source code and models will be publicly available at [this https URL](https://i2vgen-xl.github.io/).*
The original codebase can be found [here](https://github.com/ali-vilab/i2vgen-xl/). The model checkpoints can be found [here](https://huggingface.co/ali-vilab/).
<Tip>
Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. Also, to know more about reducing the memory usage of this pipeline, refer to the ["Reduce memory usage"] section [here](../../using-diffusers/svd#reduce-memory-usage).
</Tip>
Sample output with I2VGenXL:
<table>
<tr>
<td><center>
library.
<br>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/i2vgen-xl-example.gif"
alt="library"
style="width: 300px;" />
</center></td>
</tr>
</table> | /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 inputs.
* It can generate higher resolution videos.
* When using the [`DDIMScheduler`] (which is default for this pipeline), less than 50 steps for inference leads to bad results.
* This implementation is 1-stage variant of I2VGenXL. The main figure in the [I2VGen-XL](https://arxiv.org/abs/2311.04145) paper shows a 2-stage variant, however, 1-stage variant works well. See [this discussion](https://github.com/huggingface/diffusers/discussions/7952) for more details. | /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.).
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer (`CLIPTokenizer`):
A [`~transformers.CLIPTokenizer`] to tokenize text.
unet ([`I2VGenXLUNet`]):
A [`I2VGenXLUNet`] to denoise the encoded video latents.
scheduler ([`DDIMScheduler`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents.
- all
- __call__ | /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 NumPy array or Torch tensor of shape
`(batch_size, num_frames, channels, height, width)` | /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 agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
--> | /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 tells the model what to do, our model follows these instructions to edit the image. To obtain training data for this problem, we combine the knowledge of two large pretrained models -- a language model (GPT-3) and a text-to-image model (Stable Diffusion) -- to generate a large dataset of image editing examples. Our conditional diffusion model, InstructPix2Pix, is trained on our generated data, and generalizes to real images and user-written instructions at inference time. Since it performs edits in the forward pass and does not require per example fine-tuning or inversion, our model edits images quickly, in a matter of seconds. We show compelling editing results for a diverse collection of input images and written instructions.*
You can find additional information about InstructPix2Pix on the [project page](https://www.timothybrooks.com/instruct-pix2pix), [original codebase](https://github.com/timothybrooks/instruct-pix2pix), and try it out in a [demo](https://huggingface.co/spaces/timbrooks/instruct-pix2pix).
<Tip>
Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
</Tip> | /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 particular device, etc.).
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
more details about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
- __call__
- all
- load_textual_inversion
- load_lora_weights
- save_lora_weights | /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 or saving, running on a particular device, etc.)
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
- [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion XL uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
text_encoder_2 ([` CLIPTextModelWithProjection`]):
Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
specifically the
[laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_2 (`CLIPTokenizer`):
Second Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
requires_aesthetics_score (`bool`, *optional*, defaults to `"False"`):
Whether the `unet` requires a aesthetic_score condition to be passed during inference. Also see the config
of `stabilityai/stable-diffusion-xl-refiner-1-0`.
force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of
`stabilityai/stable-diffusion-xl-base-1-0`.
add_watermarker (`bool`, *optional*):
Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to
watermark output images. If not defined, it will default to True if the package is installed, otherwise no
watermarker will be used.
is_cosxl_edit (`bool`, *optional*):
When set the image latents are scaled.
- __call__
- all | /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 |
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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specific language governing permissions and limitations under the License.
--> | /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, if you provide a depth map, the ControlNet model generates an image that'll preserve the spatial information from the depth map. It is a more flexible and accurate way to control the image generation process.
The abstract from the paper is:
*We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with "zero convolutions" (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.*
You can find additional smaller Stable Diffusion XL (SDXL) ControlNet checkpoints from the 🤗 [Diffusers](https://huggingface.co/diffusers) Hub organization, and browse [community-trained](https://huggingface.co/models?other=stable-diffusion-xl&other=controlnet) checkpoints on the Hub.
<Tip warning={true}>
🧪 Many of the SDXL ControlNet checkpoints are experimental, and there is a lot of room for improvement. Feel free to open an [Issue](https://github.com/huggingface/diffusers/issues/new/choose) and leave us feedback on how we can improve!
</Tip>
If you don't see a checkpoint you're interested in, you can train your own SDXL ControlNet with our [training script](../../../../../examples/controlnet/README_sdxl).
<Tip>
Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
</Tip> | /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, etc.).
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
text_encoder_2 ([`~transformers.CLIPTextModelWithProjection`]):
Second frozen text-encoder
([laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
tokenizer_2 ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
Provides additional conditioning to the `unet` during the denoising process. If you set multiple
ControlNets as a list, the outputs from each ControlNet are added together to create one combined
additional conditioning.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
Whether the negative prompt embeddings should always be set to 0. Also see the config of
`stabilityai/stable-diffusion-xl-base-1-0`.
add_watermarker (`bool`, *optional*):
Whether to use the [invisible_watermark](https://github.com/ShieldMnt/invisible-watermark/) library to
watermark output images. If not defined, it defaults to `True` if the package is installed; otherwise no
watermarker is used.
- all
- __call__ | /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, running on a particular device, etc.)
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
text_encoder_2 ([` CLIPTextModelWithProjection`]):
Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
specifically the
[laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_2 (`CLIPTokenizer`):
Second Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets
as a list, the outputs from each ControlNet are added together to create one combined additional
conditioning.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
requires_aesthetics_score (`bool`, *optional*, defaults to `"False"`):
Whether the `unet` requires an `aesthetic_score` condition to be passed during inference. Also see the
config of `stabilityai/stable-diffusion-xl-refiner-1-0`.
force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of
`stabilityai/stable-diffusion-xl-base-1-0`.
add_watermarker (`bool`, *optional*):
Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to
watermark output images. If not defined, it will default to True if the package is installed, otherwise no
watermarker will be used.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
- all
- __call__ | /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 device, etc.)
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion XL uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
text_encoder_2 ([` CLIPTextModelWithProjection`]):
Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
specifically the
[laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_2 (`CLIPTokenizer`):
Second Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
- all
- __call__ | /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 corresponding generated image contains "not-safe-for-work" (nsfw) content or
`None` if safety checking could not be performed. | /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 |
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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 agreed to in writing, software distributed under the License is distributed on
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specific language governing permissions and limitations under the License.
--> | /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://huggingface.co/black-forest-labs). Original inference code can be found [here](https://github.com/black-forest-labs/flux).
<Tip>
Flux can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details. Additionally, Flux can benefit from quantization for memory efficiency with a trade-off in inference latency. Refer to [this blog post](https://huggingface.co/blog/quanto-diffusers) to learn more. For an exhaustive list of resources, check out [this gist](https://gist.github.com/sayakpaul/b664605caf0aa3bf8585ab109dd5ac9c).
</Tip>
Flux comes in the following variants:
| model type | model id |
|:----------:|:--------:|
| Timestep-distilled | [`black-forest-labs/FLUX.1-schnell`](https://huggingface.co/black-forest-labs/FLUX.1-schnell) |
| Guidance-distilled | [`black-forest-labs/FLUX.1-dev`](https://huggingface.co/black-forest-labs/FLUX.1-dev) |
| Fill Inpainting/Outpainting (Guidance-distilled) | [`black-forest-labs/FLUX.1-Fill-dev`](https://huggingface.co/black-forest-labs/FLUX.1-Fill-dev) |
| Canny Control (Guidance-distilled) | [`black-forest-labs/FLUX.1-Canny-dev`](https://huggingface.co/black-forest-labs/FLUX.1-Canny-dev) |
| Depth Control (Guidance-distilled) | [`black-forest-labs/FLUX.1-Depth-dev`](https://huggingface.co/black-forest-labs/FLUX.1-Depth-dev) |
| Canny Control (LoRA) | [`black-forest-labs/FLUX.1-Canny-dev-lora`](https://huggingface.co/black-forest-labs/FLUX.1-Canny-dev-lora) |
| Depth Control (LoRA) | [`black-forest-labs/FLUX.1-Depth-dev-lora`](https://huggingface.co/black-forest-labs/FLUX.1-Depth-dev-lora) |
| Redux (Adapter) | [`black-forest-labs/FLUX.1-Redux-dev`](https://huggingface.co/black-forest-labs/FLUX.1-Redux-dev) |
All checkpoints have different usage which we detail below. | /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.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
out = pipe(
prompt=prompt,
guidance_scale=0.,
height=768,
width=1360,
num_inference_steps=4,
max_sequence_length=256,
).images[0]
out.save("image.png")
``` | /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)
pipe.enable_model_cpu_offload()
prompt = "a tiny astronaut hatching from an egg on the moon"
out = pipe(
prompt=prompt,
guidance_scale=3.5,
height=768,
width=1360,
num_inference_steps=50,
).images[0]
out.save("image.png")
``` | /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/resolve/main/cup.png")
mask = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/cup_mask.png")
repo_id = "black-forest-labs/FLUX.1-Fill-dev"
pipe = FluxFillPipeline.from_pretrained(repo_id, torch_dtype=torch.bfloat16).to("cuda")
image = pipe(
prompt="a white paper cup",
image=image,
mask_image=mask,
height=1632,
width=1232,
max_sequence_length=512,
generator=torch.Generator("cpu").manual_seed(0)
).images[0]
image.save(f"output.png")
``` | /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 would, by using channel-wise concatenation with input control condition and ensuring the transformer learns structure control by following the condition as closely as possible.
```python
# !pip install -U controlnet-aux
import torch
from controlnet_aux import CannyDetector
from diffusers import FluxControlPipeline
from diffusers.utils import load_image
pipe = FluxControlPipeline.from_pretrained("black-forest-labs/FLUX.1-Canny-dev", torch_dtype=torch.bfloat16).to("cuda")
prompt = "A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts."
control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")
processor = CannyDetector()
control_image = processor(control_image, low_threshold=50, high_threshold=200, detect_resolution=1024, image_resolution=1024)
image = pipe(
prompt=prompt,
control_image=control_image,
height=1024,
width=1024,
num_inference_steps=50,
guidance_scale=30.0,
).images[0]
image.save("output.png")
```
Canny Control is also possible with a LoRA variant of this condition. The usage is as follows:
```python
# !pip install -U controlnet-aux
import torch
from controlnet_aux import CannyDetector
from diffusers import FluxControlPipeline
from diffusers.utils import load_image
pipe = FluxControlPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to("cuda")
pipe.load_lora_weights("black-forest-labs/FLUX.1-Canny-dev-lora")
prompt = "A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts."
control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")
processor = CannyDetector()
control_image = processor(control_image, low_threshold=50, high_threshold=200, detect_resolution=1024, image_resolution=1024)
image = pipe(
prompt=prompt,
control_image=control_image,
height=1024,
width=1024,
num_inference_steps=50,
guidance_scale=30.0,
).images[0]
image.save("output.png")
``` | /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 would, by using channel-wise concatenation with input control condition and ensuring the transformer learns structure control by following the condition as closely as possible.
```python
# !pip install git+https://github.com/huggingface/image_gen_aux
import torch
from diffusers import FluxControlPipeline, FluxTransformer2DModel
from diffusers.utils import load_image
from image_gen_aux import DepthPreprocessor
pipe = FluxControlPipeline.from_pretrained("black-forest-labs/FLUX.1-Depth-dev", torch_dtype=torch.bfloat16).to("cuda")
prompt = "A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts."
control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")
processor = DepthPreprocessor.from_pretrained("LiheYoung/depth-anything-large-hf")
control_image = processor(control_image)[0].convert("RGB")
image = pipe(
prompt=prompt,
control_image=control_image,
height=1024,
width=1024,
num_inference_steps=30,
guidance_scale=10.0,
generator=torch.Generator().manual_seed(42),
).images[0]
image.save("output.png")
```
Depth Control is also possible with a LoRA variant of this condition. The usage is as follows:
```python
# !pip install git+https://github.com/huggingface/image_gen_aux
import torch
from diffusers import FluxControlPipeline, FluxTransformer2DModel
from diffusers.utils import load_image
from image_gen_aux import DepthPreprocessor
pipe = FluxControlPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to("cuda")
pipe.load_lora_weights("black-forest-labs/FLUX.1-Depth-dev-lora")
prompt = "A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts."
control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")
processor = DepthPreprocessor.from_pretrained("LiheYoung/depth-anything-large-hf")
control_image = processor(control_image)[0].convert("RGB")
image = pipe(
prompt=prompt,
control_image=control_image,
height=1024,
width=1024,
num_inference_steps=30,
guidance_scale=10.0,
generator=torch.Generator().manual_seed(42),
).images[0]
image.save("output.png")
``` | /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.
* When use `FluxPriorReduxPipeline` with a base pipeline, you can set `text_encoder=None` and `text_encoder_2=None` in the base pipeline, in order to save VRAM.
```python
import torch
from diffusers import FluxPriorReduxPipeline, FluxPipeline
from diffusers.utils import load_image
device = "cuda"
dtype = torch.bfloat16
repo_redux = "black-forest-labs/FLUX.1-Redux-dev"
repo_base = "black-forest-labs/FLUX.1-dev"
pipe_prior_redux = FluxPriorReduxPipeline.from_pretrained(repo_redux, torch_dtype=dtype).to(device)
pipe = FluxPipeline.from_pretrained(
repo_base,
text_encoder=None,
text_encoder_2=None,
torch_dtype=torch.bfloat16
).to(device)
image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/style_ziggy/img5.png")
pipe_prior_output = pipe_prior_redux(image)
images = pipe(
guidance_scale=2.5,
num_inference_steps=50,
generator=torch.Generator("cpu").manual_seed(0),
**pipe_prior_output,
).images
images[0].save("flux-redux.png")
``` | /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
from image_gen_aux import DepthPreprocessor
from diffusers.utils import load_image
from huggingface_hub import hf_hub_download
import torch
control_pipe = FluxControlPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
control_pipe.load_lora_weights("black-forest-labs/FLUX.1-Depth-dev-lora", adapter_name="depth")
control_pipe.load_lora_weights(
hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"), adapter_name="hyper-sd"
)
control_pipe.set_adapters(["depth", "hyper-sd"], adapter_weights=[0.85, 0.125])
control_pipe.enable_model_cpu_offload()
prompt = "A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts."
control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")
processor = DepthPreprocessor.from_pretrained("LiheYoung/depth-anything-large-hf")
control_image = processor(control_image)[0].convert("RGB")
image = control_pipe(
prompt=prompt,
control_image=control_image,
height=1024,
width=1024,
num_inference_steps=8,
guidance_scale=10.0,
generator=torch.Generator().manual_seed(42),
).images[0]
image.save("output.png")
``` | /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 [this PR](https://github.com/huggingface/diffusers/pull/10397). | /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 FP32 inference thus removes this output difference. See [here](https://github.com/huggingface/diffusers/pull/9097#issuecomment-2272292516) for details.
FP16 inference code:
```python
import torch
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16) # can replace schnell with dev
# to run on low vram GPUs (i.e. between 4 and 32 GB VRAM)
pipe.enable_sequential_cpu_offload()
pipe.vae.enable_slicing()
pipe.vae.enable_tiling()
pipe.to(torch.float16) # casting here instead of in the pipeline constructor because doing so in the constructor loads all models into CPU memory at once
prompt = "A cat holding a sign that says hello world"
out = pipe(
prompt=prompt,
guidance_scale=0.,
height=768,
width=1360,
num_inference_steps=4,
max_sequence_length=256,
).images[0]
out.save("image.png")
``` | /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 quantization backends and selecting a quantization backend that supports your use case. The example below demonstrates how to load a quantized [`FluxPipeline`] for inference with bitsandbytes.
```py
import torch
from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, FluxTransformer2DModel, FluxPipeline
from transformers import BitsAndBytesConfig as BitsAndBytesConfig, T5EncoderModel
quant_config = BitsAndBytesConfig(load_in_8bit=True)
text_encoder_8bit = T5EncoderModel.from_pretrained(
"black-forest-labs/FLUX.1-dev",
subfolder="text_encoder_2",
quantization_config=quant_config,
torch_dtype=torch.float16,
)
quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
transformer_8bit = FluxTransformer2DModel.from_pretrained(
"black-forest-labs/FLUX.1-dev",
subfolder="transformer",
quantization_config=quant_config,
torch_dtype=torch.float16,
)
pipeline = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
text_encoder_2=text_encoder_8bit,
transformer=transformer_8bit,
torch_dtype=torch.float16,
device_map="balanced",
)
prompt = "a tiny astronaut hatching from an egg on the moon"
image = pipeline(prompt, guidance_scale=3.5, height=768, width=1360, num_inference_steps=50).images[0]
image.save("flux.png")
``` | /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, and `torch` version that you are using. It is recommended that you use the `optimum-quanto` library in order to run FP8 inference on your machine.
</Tip>
The following example demonstrates how to run Flux with less than 16GB of VRAM.
First install `optimum-quanto`
```shell
pip install optimum-quanto
```
Then run the following example
```python
import torch
from diffusers import FluxTransformer2DModel, FluxPipeline
from transformers import T5EncoderModel, CLIPTextModel
from optimum.quanto import freeze, qfloat8, quantize
bfl_repo = "black-forest-labs/FLUX.1-dev"
dtype = torch.bfloat16
transformer = FluxTransformer2DModel.from_single_file("https://huggingface.co/Kijai/flux-fp8/blob/main/flux1-dev-fp8.safetensors", torch_dtype=dtype)
quantize(transformer, weights=qfloat8)
freeze(transformer)
text_encoder_2 = T5EncoderModel.from_pretrained(bfl_repo, subfolder="text_encoder_2", torch_dtype=dtype)
quantize(text_encoder_2, weights=qfloat8)
freeze(text_encoder_2)
pipe = FluxPipeline.from_pretrained(bfl_repo, transformer=None, text_encoder_2=None, torch_dtype=dtype)
pipe.transformer = transformer
pipe.text_encoder_2 = text_encoder_2
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
guidance_scale=3.5,
output_type="pil",
num_inference_steps=20,
generator=torch.Generator("cpu").manual_seed(0)
).images[0]
image.save("flux-fp8-dev.png")
``` | /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 scheduler to be used in combination with `transformer` to denoise the encoded image latents.
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
text_encoder_2 ([`T5EncoderModel`]):
[T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_2 (`T5TokenizerFast`):
Second Tokenizer of class
[T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast).
- all
- __call__ | /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 scheduler to be used in combination with `transformer` to denoise the encoded image latents.
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
text_encoder_2 ([`T5EncoderModel`]):
[T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_2 (`T5TokenizerFast`):
Second Tokenizer of class
[T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast).
- all
- __call__ | /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 scheduler to be used in combination with `transformer` to denoise the encoded image latents.
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
text_encoder_2 ([`T5EncoderModel`]):
[T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_2 (`T5TokenizerFast`):
Second Tokenizer of class
[T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast).
- all
- __call__ | /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 ([`FlowMatchEulerDiscreteScheduler`]):
A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
text_encoder_2 ([`T5EncoderModel`]):
[T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_2 (`T5TokenizerFast`):
Second Tokenizer of class
[T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast).
- all
- __call__ | /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 ([`FlowMatchEulerDiscreteScheduler`]):
A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
text_encoder_2 ([`T5EncoderModel`]):
[T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_2 (`T5TokenizerFast`):
Second Tokenizer of class
[T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast).
- all
- __call__ | /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 ([`FlowMatchEulerDiscreteScheduler`]):
A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
text_encoder_2 ([`T5EncoderModel`]):
[T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_2 (`T5TokenizerFast`):
Second Tokenizer of class
[T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast).
- all
- __call__ | /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`]):
A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
text_encoder_2 ([`T5EncoderModel`]):
[T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_2 (`T5TokenizerFast`):
Second Tokenizer of class
[T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast).
- all
- __call__ | /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 the SIGLIP model.
image_embedder ([`ReduxImageEncoder`]):
Redux image encoder to process the SIGLIP embeddings.
text_encoder ([`CLIPTextModel`], *optional*):
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
text_encoder_2 ([`T5EncoderModel`], *optional*):
[T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
tokenizer (`CLIPTokenizer`, *optional*):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_2 (`T5TokenizerFast`, *optional*):
Second Tokenizer of class
[T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast).
- all
- __call__ | /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 scheduler to be used in combination with `transformer` to denoise the encoded image latents.
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
text_encoder_2 ([`T5EncoderModel`]):
[T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_2 (`T5TokenizerFast`):
Second Tokenizer of class
[T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast).
- all
- __call__ | /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:
*We introduce Sana, a text-to-image framework that can efficiently generate images up to 4096×4096 resolution. Sana can synthesize high-resolution, high-quality images with strong text-image alignment at a remarkably fast speed, deployable on laptop GPU. Core designs include: (1) Deep compression autoencoder: unlike traditional AEs, which compress images only 8×, we trained an AE that can compress images 32×, effectively reducing the number of latent tokens. (2) Linear DiT: we replace all vanilla attention in DiT with linear attention, which is more efficient at high resolutions without sacrificing quality. (3) Decoder-only text encoder: we replaced T5 with modern decoder-only small LLM as the text encoder and designed complex human instruction with in-context learning to enhance the image-text alignment. (4) Efficient training and sampling: we propose Flow-DPM-Solver to reduce sampling steps, with efficient caption labeling and selection to accelerate convergence. As a result, Sana-0.6B is very competitive with modern giant diffusion model (e.g. Flux-12B), being 20 times smaller and 100+ times faster in measured throughput. Moreover, Sana-0.6B can be deployed on a 16GB laptop GPU, taking less than 1 second to generate a 1024×1024 resolution image. Sana enables content creation at low cost. Code and model will be publicly released.*
<Tip>
Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
</Tip>
This pipeline was contributed by [lawrence-cj](https://github.com/lawrence-cj) and [chenjy2003](https://github.com/chenjy2003). The original codebase can be found [here](https://github.com/NVlabs/Sana). The original weights can be found under [hf.co/Efficient-Large-Model](https://huggingface.co/Efficient-Large-Model).
Available models:
| Model | Recommended dtype |
|:-----:|:-----------------:|
| [`Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers`](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers) | `torch.bfloat16` |
| [`Efficient-Large-Model/Sana_1600M_1024px_diffusers`](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px_diffusers) | `torch.float16` |
| [`Efficient-Large-Model/Sana_1600M_1024px_MultiLing_diffusers`](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px_MultiLing_diffusers) | `torch.float16` |
| [`Efficient-Large-Model/Sana_1600M_512px_diffusers`](https://huggingface.co/Efficient-Large-Model/Sana_1600M_512px_diffusers) | `torch.float16` |
| [`Efficient-Large-Model/Sana_1600M_512px_MultiLing_diffusers`](https://huggingface.co/Efficient-Large-Model/Sana_1600M_512px_MultiLing_diffusers) | `torch.float16` |
| [`Efficient-Large-Model/Sana_600M_1024px_diffusers`](https://huggingface.co/Efficient-Large-Model/Sana_600M_1024px_diffusers) | `torch.float16` |
| [`Efficient-Large-Model/Sana_600M_512px_diffusers`](https://huggingface.co/Efficient-Large-Model/Sana_600M_512px_diffusers) | `torch.float16` |
Refer to [this](https://huggingface.co/collections/Efficient-Large-Model/sana-673efba2a57ed99843f11f9e) collection for more information.
Note: The recommended dtype mentioned is for the transformer weights. The text encoder and VAE weights must stay in `torch.bfloat16` or `torch.float32` for the model to work correctly. Please refer to the inference example below to see how to load the model with the recommended dtype.
<Tip>
Make sure to pass the `variant` argument for downloaded checkpoints to use lower disk space. Set it to `"fp16"` for models with recommended dtype as `torch.float16`, and `"bf16"` for models with recommended dtype as `torch.bfloat16`. By default, `torch.float32` weights are downloaded, which use twice the amount of disk storage. Additionally, `torch.float32` weights can be downcasted on-the-fly by specifying the `torch_dtype` argument. Read about it in the [docs](https://huggingface.co/docs/diffusers/v0.31.0/en/api/pipelines/overview#diffusers.DiffusionPipeline.from_pretrained).
</Tip> | /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 quantization backends and selecting a quantization backend that supports your use case. The example below demonstrates how to load a quantized [`SanaPipeline`] for inference with bitsandbytes.
```py
import torch
from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, SanaTransformer2DModel, SanaPipeline
from transformers import BitsAndBytesConfig as BitsAndBytesConfig, AutoModel
quant_config = BitsAndBytesConfig(load_in_8bit=True)
text_encoder_8bit = AutoModel.from_pretrained(
"Efficient-Large-Model/Sana_1600M_1024px_diffusers",
subfolder="text_encoder",
quantization_config=quant_config,
torch_dtype=torch.float16,
)
quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
transformer_8bit = SanaTransformer2DModel.from_pretrained(
"Efficient-Large-Model/Sana_1600M_1024px_diffusers",
subfolder="transformer",
quantization_config=quant_config,
torch_dtype=torch.float16,
)
pipeline = SanaPipeline.from_pretrained(
"Efficient-Large-Model/Sana_1600M_1024px_diffusers",
text_encoder=text_encoder_8bit,
transformer=transformer_8bit,
torch_dtype=torch.float16,
device_map="balanced",
)
prompt = "a tiny astronaut hatching from an egg on the moon"
image = pipeline(prompt).images[0]
image.save("sana.png")
``` | /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 agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
--> | /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 image quality. However, user controllability of the generated image, and fast adaptation to new tasks still remains an open challenge, currently mostly addressed by costly and long re-training and fine-tuning or ad-hoc adaptations to specific image generation tasks. In this work, we present MultiDiffusion, a unified framework that enables versatile and controllable image generation, using a pre-trained text-to-image diffusion model, without any further training or finetuning. At the center of our approach is a new generation process, based on an optimization task that binds together multiple diffusion generation processes with a shared set of parameters or constraints. We show that MultiDiffusion can be readily applied to generate high quality and diverse images that adhere to user-provided controls, such as desired aspect ratio (e.g., panorama), and spatial guiding signals, ranging from tight segmentation masks to bounding boxes.*
You can find additional information about MultiDiffusion on the [project page](https://multidiffusion.github.io/), [original codebase](https://github.com/omerbt/MultiDiffusion), and try it out in a [demo](https://huggingface.co/spaces/weizmannscience/MultiDiffusion). | /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 a width value of 2048 which is the default.
Circular padding is applied to ensure there are no stitching artifacts when working with panoramas to ensure a seamless transition from the rightmost part to the leftmost part. By enabling circular padding (set `circular_padding=True`), the operation applies additional crops after the rightmost point of the image, allowing the model to "see” the transition from the rightmost part to the leftmost part. This helps maintain visual consistency in a 360-degree sense and creates a proper “panorama” that can be viewed using 360-degree panorama viewers. When decoding latents in Stable Diffusion, circular padding is applied to ensure that the decoded latents match in the RGB space.
For example, without circular padding, there is a stitching artifact (default):
But with circular padding, the right and the left parts are matching (`circular_padding=True`):
<Tip>
Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
</Tip> | /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 inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
more details about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
- __call__
- all | /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 corresponding generated image contains "not-safe-for-work" (nsfw) content or
`None` if safety checking could not be performed. | /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 agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
--> | /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 2: a 64x64 px => 256x256 px super-resolution model, and
- Stage 3: a 256x256 px => 1024x1024 px super-resolution model
Stage 1 and Stage 2 utilize a frozen text encoder based on the T5 transformer to extract text embeddings, which are then fed into a UNet architecture enhanced with cross-attention and attention pooling.
Stage 3 is [Stability AI's x4 Upscaling model](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler).
The result is a highly efficient model that outperforms current state-of-the-art models, achieving a zero-shot FID score of 6.66 on the COCO dataset.
Our work underscores the potential of larger UNet architectures in the first stage of cascaded diffusion models and depicts a promising future for text-to-image synthesis. | /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 model card will auto accept for the other IF models.
3. Make sure to login locally. Install `huggingface_hub`:
```sh
pip install huggingface_hub --upgrade
```
run the login function in a Python shell:
```py
from huggingface_hub import login
login()
```
and enter your [Hugging Face Hub access token](https://huggingface.co/docs/hub/security-tokens#what-are-user-access-tokens).
Next we install `diffusers` and dependencies:
```sh
pip install -q diffusers accelerate transformers
```
The following sections give more in-detail examples of how to use IF. Specifically:
- [Text-to-Image Generation](#text-to-image-generation)
- [Image-to-Image Generation](#text-guided-image-to-image-generation)
- [Inpainting](#text-guided-inpainting-generation)
- [Reusing model weights](#converting-between-different-pipelines)
- [Speed optimization](#optimizing-for-speed)
- [Memory optimization](#optimizing-for-memory)
**Available checkpoints**
- *Stage-1*
- [DeepFloyd/IF-I-XL-v1.0](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0)
- [DeepFloyd/IF-I-L-v1.0](https://huggingface.co/DeepFloyd/IF-I-L-v1.0)
- [DeepFloyd/IF-I-M-v1.0](https://huggingface.co/DeepFloyd/IF-I-M-v1.0)
- *Stage-2*
- [DeepFloyd/IF-II-L-v1.0](https://huggingface.co/DeepFloyd/IF-II-L-v1.0)
- [DeepFloyd/IF-II-M-v1.0](https://huggingface.co/DeepFloyd/IF-II-M-v1.0)
- *Stage-3*
- [stabilityai/stable-diffusion-x4-upscaler](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler)
**Google Colab**
[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/deepfloyd_if_free_tier_google_colab.ipynb) | /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 = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
stage_1.enable_model_cpu_offload()
# stage 2
stage_2 = DiffusionPipeline.from_pretrained(
"DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16
)
stage_2.enable_model_cpu_offload()
# stage 3
safety_modules = {
"feature_extractor": stage_1.feature_extractor,
"safety_checker": stage_1.safety_checker,
"watermarker": stage_1.watermarker,
}
stage_3 = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-x4-upscaler", **safety_modules, torch_dtype=torch.float16
)
stage_3.enable_model_cpu_offload()
prompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says "very deep learning"'
generator = torch.manual_seed(1)
# text embeds
prompt_embeds, negative_embeds = stage_1.encode_prompt(prompt)
# stage 1
stage_1_output = stage_1(
prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, generator=generator, output_type="pt"
).images
#pt_to_pil(stage_1_output)[0].save("./if_stage_I.png")
# stage 2
stage_2_output = stage_2(
image=stage_1_output,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
generator=generator,
output_type="pt",
).images
#pt_to_pil(stage_2_output)[0].save("./if_stage_II.png")
# stage 3
stage_3_output = stage_3(prompt=prompt, image=stage_2_output, noise_level=100, generator=generator).images
#stage_3_output[0].save("./if_stage_III.png")
make_image_grid([pt_to_pil(stage_1_output)[0], pt_to_pil(stage_2_output)[0], stage_3_output[0]], rows=1, rows=3)
``` | /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 the image-to-image pipelines
without loading them twice by making use of the [`~DiffusionPipeline.components`] argument as explained [here](#converting-between-different-pipelines).
```python
from diffusers import IFImg2ImgPipeline, IFImg2ImgSuperResolutionPipeline, DiffusionPipeline
from diffusers.utils import pt_to_pil, load_image, make_image_grid
import torch
# download image
url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
original_image = load_image(url)
original_image = original_image.resize((768, 512))
# stage 1
stage_1 = IFImg2ImgPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
stage_1.enable_model_cpu_offload()
# stage 2
stage_2 = IFImg2ImgSuperResolutionPipeline.from_pretrained(
"DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16
)
stage_2.enable_model_cpu_offload()
# stage 3
safety_modules = {
"feature_extractor": stage_1.feature_extractor,
"safety_checker": stage_1.safety_checker,
"watermarker": stage_1.watermarker,
}
stage_3 = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-x4-upscaler", **safety_modules, torch_dtype=torch.float16
)
stage_3.enable_model_cpu_offload()
prompt = "A fantasy landscape in style minecraft"
generator = torch.manual_seed(1)
# text embeds
prompt_embeds, negative_embeds = stage_1.encode_prompt(prompt)
# stage 1
stage_1_output = stage_1(
image=original_image,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
generator=generator,
output_type="pt",
).images
#pt_to_pil(stage_1_output)[0].save("./if_stage_I.png")
# stage 2
stage_2_output = stage_2(
image=stage_1_output,
original_image=original_image,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
generator=generator,
output_type="pt",
).images
#pt_to_pil(stage_2_output)[0].save("./if_stage_II.png")
# stage 3
stage_3_output = stage_3(prompt=prompt, image=stage_2_output, generator=generator, noise_level=100).images
#stage_3_output[0].save("./if_stage_III.png")
make_image_grid([original_image, pt_to_pil(stage_1_output)[0], pt_to_pil(stage_2_output)[0], stage_3_output[0]], rows=1, rows=4)
``` | /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 to the image-to-image pipelines
without loading them twice by making use of the [`~DiffusionPipeline.components()`] function as explained [here](#converting-between-different-pipelines).
```python
from diffusers import IFInpaintingPipeline, IFInpaintingSuperResolutionPipeline, DiffusionPipeline
from diffusers.utils import pt_to_pil, load_image, make_image_grid
import torch
# download image
url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/person.png"
original_image = load_image(url)
# download mask
url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/glasses_mask.png"
mask_image = load_image(url)
# stage 1
stage_1 = IFInpaintingPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
stage_1.enable_model_cpu_offload()
# stage 2
stage_2 = IFInpaintingSuperResolutionPipeline.from_pretrained(
"DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16
)
stage_2.enable_model_cpu_offload()
# stage 3
safety_modules = {
"feature_extractor": stage_1.feature_extractor,
"safety_checker": stage_1.safety_checker,
"watermarker": stage_1.watermarker,
}
stage_3 = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-x4-upscaler", **safety_modules, torch_dtype=torch.float16
)
stage_3.enable_model_cpu_offload()
prompt = "blue sunglasses"
generator = torch.manual_seed(1)
# text embeds
prompt_embeds, negative_embeds = stage_1.encode_prompt(prompt)
# stage 1
stage_1_output = stage_1(
image=original_image,
mask_image=mask_image,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
generator=generator,
output_type="pt",
).images
#pt_to_pil(stage_1_output)[0].save("./if_stage_I.png")
# stage 2
stage_2_output = stage_2(
image=stage_1_output,
original_image=original_image,
mask_image=mask_image,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
generator=generator,
output_type="pt",
).images
#pt_to_pil(stage_1_output)[0].save("./if_stage_II.png")
# stage 3
stage_3_output = stage_3(prompt=prompt, image=stage_2_output, generator=generator, noise_level=100).images
#stage_3_output[0].save("./if_stage_III.png")
make_image_grid([original_image, mask_image, pt_to_pil(stage_1_output)[0], pt_to_pil(stage_2_output)[0], stage_3_output[0]], rows=1, rows=5)
``` | /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")
from diffusers import IFImg2ImgPipeline, IFImg2ImgSuperResolutionPipeline
pipe_1 = IFImg2ImgPipeline(**pipe_1.components)
pipe_2 = IFImg2ImgSuperResolutionPipeline(**pipe_2.components)
from diffusers import IFInpaintingPipeline, IFInpaintingSuperResolutionPipeline
pipe_1 = IFInpaintingPipeline(**pipe_1.components)
pipe_2 = IFInpaintingSuperResolutionPipeline(**pipe_2.components)
``` | /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 be done with the `num_inference_steps` argument:
```py
pipe("<prompt>", num_inference_steps=30)
```
Or with the `timesteps` argument:
```py
from diffusers.pipelines.deepfloyd_if import fast27_timesteps
pipe("<prompt>", timesteps=fast27_timesteps)
```
When doing image variation or inpainting, you can also decrease the number of timesteps
with the strength argument. The strength argument is the amount of noise to add to the input image which also determines how many steps to run in the denoising process.
A smaller number will vary the image less but run faster.
```py
pipe = IFImg2ImgPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
pipe.to("cuda")
image = pipe(image=image, prompt="<prompt>", strength=0.3).images
```
You can also use [`torch.compile`](../../optimization/torch2.0). Note that we have not exhaustively tested `torch.compile`
with IF and it might not give expected results.
```py
from diffusers import DiffusionPipeline
import torch
pipe = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
pipe.to("cuda")
pipe.text_encoder = torch.compile(pipe.text_encoder, mode="reduce-overhead", fullgraph=True)
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
``` | /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 CPU offloading.
```py
pipe = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
pipe.enable_sequential_cpu_offload()
```
Additionally, T5 can be loaded in 8bit precision
```py
from transformers import T5EncoderModel
text_encoder = T5EncoderModel.from_pretrained(
"DeepFloyd/IF-I-XL-v1.0", subfolder="text_encoder", device_map="auto", load_in_8bit=True, variant="8bit"
)
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained(
"DeepFloyd/IF-I-XL-v1.0",
text_encoder=text_encoder, # pass the previously instantiated 8bit text encoder
unet=None,
device_map="auto",
)
prompt_embeds, negative_embeds = pipe.encode_prompt("<prompt>")
```
For CPU RAM constrained machines like Google Colab free tier where we can't load all model components to the CPU at once, we can manually only load the pipeline with
the text encoder or UNet when the respective model components are needed.
```py
from diffusers import IFPipeline, IFSuperResolutionPipeline
import torch
import gc
from transformers import T5EncoderModel
from diffusers.utils import pt_to_pil, make_image_grid
text_encoder = T5EncoderModel.from_pretrained(
"DeepFloyd/IF-I-XL-v1.0", subfolder="text_encoder", device_map="auto", load_in_8bit=True, variant="8bit"
)
# text to image
pipe = DiffusionPipeline.from_pretrained(
"DeepFloyd/IF-I-XL-v1.0",
text_encoder=text_encoder, # pass the previously instantiated 8bit text encoder
unet=None,
device_map="auto",
)
prompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says "very deep learning"'
prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)
# Remove the pipeline so we can re-load the pipeline with the unet
del text_encoder
del pipe
gc.collect()
torch.cuda.empty_cache()
pipe = IFPipeline.from_pretrained(
"DeepFloyd/IF-I-XL-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16, device_map="auto"
)
generator = torch.Generator().manual_seed(0)
stage_1_output = pipe(
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
output_type="pt",
generator=generator,
).images
#pt_to_pil(stage_1_output)[0].save("./if_stage_I.png")
# Remove the pipeline so we can load the super-resolution pipeline
del pipe
gc.collect()
torch.cuda.empty_cache()
# First super resolution
pipe = IFSuperResolutionPipeline.from_pretrained(
"DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16, device_map="auto"
)
generator = torch.Generator().manual_seed(0)
stage_2_output = pipe(
image=stage_1_output,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
output_type="pt",
generator=generator,
).images
#pt_to_pil(stage_2_output)[0].save("./if_stage_II.png")
make_image_grid([pt_to_pil(stage_1_output)[0], pt_to_pil(stage_2_output)[0]], rows=1, rows=2)
``` | /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_if/pipeline_if_superresolution.py) | *Text-to-Image Generation* | - |
| [pipeline_if_img2img.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py) | *Image-to-Image Generation* | - |
| [pipeline_if_img2img_superresolution.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py) | *Image-to-Image Generation* | - |
| [pipeline_if_inpainting.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py) | *Image-to-Image Generation* | - |
| [pipeline_if_inpainting_superresolution.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting_superresolution.py) | *Image-to-Image Generation* | - | | /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 in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
--> | /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 work on 3D generative models which produce a single output representation, Shap-E directly generates the parameters of implicit functions that can be rendered as both textured meshes and neural radiance fields. We train Shap-E in two stages: first, we train an encoder that deterministically maps 3D assets into the parameters of an implicit function; second, we train a conditional diffusion model on outputs of the encoder. When trained on a large dataset of paired 3D and text data, our resulting models are capable of generating complex and diverse 3D assets in a matter of seconds. When compared to Point-E, an explicit generative model over point clouds, Shap-E converges faster and reaches comparable or better sample quality despite modeling a higher-dimensional, multi-representation output space.*
The original codebase can be found at [openai/shap-e](https://github.com/openai/shap-e).
<Tip>
See the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
</Tip> | /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:
prior ([`PriorTransformer`]):
The canonical unCLIP prior to approximate the image embedding from the text embedding.
text_encoder ([`~transformers.CLIPTextModelWithProjection`]):
Frozen text-encoder.
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
scheduler ([`HeunDiscreteScheduler`]):
A scheduler to be used in combination with the `prior` model to generate image embedding.
shap_e_renderer ([`ShapERenderer`]):
Shap-E renderer projects the generated latents into parameters of a MLP to create 3D objects with the NeRF
rendering method.
- all
- __call__ | /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 device, etc.).
Args:
prior ([`PriorTransformer`]):
The canonical unCLIP prior to approximate the image embedding from the text embedding.
image_encoder ([`~transformers.CLIPVisionModel`]):
Frozen image-encoder.
image_processor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to process images.
scheduler ([`HeunDiscreteScheduler`]):
A scheduler to be used in combination with the `prior` model to generate image embedding.
shap_e_renderer ([`ShapERenderer`]):
Shap-E renderer projects the generated latents into parameters of a MLP to create 3D objects with the NeRF
rendering method.
- all
- __call__ | /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 agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
--> | /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.
Inspired by [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview), AudioLDM 2 is a text-to-audio _latent diffusion model (LDM)_ that learns continuous audio representations from text embeddings. Two text encoder models are used to compute the text embeddings from a prompt input: the text-branch of [CLAP](https://huggingface.co/docs/transformers/main/en/model_doc/clap) and the encoder of [Flan-T5](https://huggingface.co/docs/transformers/main/en/model_doc/flan-t5). These text embeddings are then projected to a shared embedding space by an [AudioLDM2ProjectionModel](https://huggingface.co/docs/diffusers/main/api/pipelines/audioldm2#diffusers.AudioLDM2ProjectionModel). A [GPT2](https://huggingface.co/docs/transformers/main/en/model_doc/gpt2) _language model (LM)_ is used to auto-regressively predict eight new embedding vectors, conditional on the projected CLAP and Flan-T5 embeddings. The generated embedding vectors and Flan-T5 text embeddings are used as cross-attention conditioning in the LDM. The [UNet](https://huggingface.co/docs/diffusers/main/en/api/pipelines/audioldm2#diffusers.AudioLDM2UNet2DConditionModel) of AudioLDM 2 is unique in the sense that it takes **two** cross-attention embeddings, as opposed to one cross-attention conditioning, as in most other LDMs.
The abstract of the paper is the following:
*Although audio generation shares commonalities across different types of audio, such as speech, music, and sound effects, designing models for each type requires careful consideration of specific objectives and biases that can significantly differ from those of other types. To bring us closer to a unified perspective of audio generation, this paper proposes a framework that utilizes the same learning method for speech, music, and sound effect generation. Our framework introduces a general representation of audio, called "language of audio" (LOA). Any audio can be translated into LOA based on AudioMAE, a self-supervised pre-trained representation learning model. In the generation process, we translate any modalities into LOA by using a GPT-2 model, and we perform self-supervised audio generation learning with a latent diffusion model conditioned on LOA. The proposed framework naturally brings advantages such as in-context learning abilities and reusable self-supervised pretrained AudioMAE and latent diffusion models. Experiments on the major benchmarks of text-to-audio, text-to-music, and text-to-speech demonstrate state-of-the-art or competitive performance against previous approaches. Our code, pretrained model, and demo are available at [this https URL](https://audioldm.github.io/audioldm2).*
This pipeline was contributed by [sanchit-gandhi](https://huggingface.co/sanchit-gandhi) and [Nguyễn Công Tú Anh](https://github.com/tuanh123789). The original codebase can be
found at [haoheliu/audioldm2](https://github.com/haoheliu/audioldm2). | /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.
See table below for details on the three checkpoints:
| Checkpoint | Task | UNet Model Size | Total Model Size | Training Data / h |
|-----------------------------------------------------------------|---------------|-----------------|------------------|-------------------|
| [audioldm2](https://huggingface.co/cvssp/audioldm2) | Text-to-audio | 350M | 1.1B | 1150k |
| [audioldm2-large](https://huggingface.co/cvssp/audioldm2-large) | Text-to-audio | 750M | 1.5B | 1150k |
| [audioldm2-music](https://huggingface.co/cvssp/audioldm2-music) | Text-to-music | 350M | 1.1B | 665k |
| [audioldm2-gigaspeech](https://huggingface.co/anhnct/audioldm2_gigaspeech) | Text-to-speech | 350M | 1.1B |10k |
| [audioldm2-ljspeech](https://huggingface.co/anhnct/audioldm2_ljspeech) | Text-to-speech | 350M | 1.1B | | | /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 familiar with.
* Using a **negative prompt** can significantly improve the quality of the generated waveform, by guiding the generation away from terms that correspond to poor quality audio. Try using a negative prompt of "Low quality." | /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 generated waveforms and prompt text, and the audios ranked from best to worst accordingly.
The following example demonstrates how to construct good music and speech generation using the aforementioned tips: [example](https://huggingface.co/docs/diffusers/main/en/api/pipelines/audioldm2#diffusers.AudioLDM2Pipeline.__call__.example).
<Tip>
Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
</Tip> | /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 Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.ClapModel`]):
First frozen text-encoder. AudioLDM2 uses the joint audio-text embedding model
[CLAP](https://huggingface.co/docs/transformers/model_doc/clap#transformers.CLAPTextModelWithProjection),
specifically the [laion/clap-htsat-unfused](https://huggingface.co/laion/clap-htsat-unfused) variant. The
text branch is used to encode the text prompt to a prompt embedding. The full audio-text model is used to
rank generated waveforms against the text prompt by computing similarity scores.
text_encoder_2 ([`~transformers.T5EncoderModel`, `~transformers.VitsModel`]):
Second frozen text-encoder. AudioLDM2 uses the encoder of
[T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the
[google/flan-t5-large](https://huggingface.co/google/flan-t5-large) variant. Second frozen text-encoder use
for TTS. AudioLDM2 uses the encoder of
[Vits](https://huggingface.co/docs/transformers/model_doc/vits#transformers.VitsModel).
projection_model ([`AudioLDM2ProjectionModel`]):
A trained model used to linearly project the hidden-states from the first and second text encoder models
and insert learned SOS and EOS token embeddings. The projected hidden-states from the two text encoders are
concatenated to give the input to the language model. A Learned Position Embedding for the Vits
hidden-states
language_model ([`~transformers.GPT2Model`]):
An auto-regressive language model used to generate a sequence of hidden-states conditioned on the projected
outputs from the two text encoders.
tokenizer ([`~transformers.RobertaTokenizer`]):
Tokenizer to tokenize text for the first frozen text-encoder.
tokenizer_2 ([`~transformers.T5Tokenizer`, `~transformers.VitsTokenizer`]):
Tokenizer to tokenize text for the second frozen text-encoder.
feature_extractor ([`~transformers.ClapFeatureExtractor`]):
Feature extractor to pre-process generated audio waveforms to log-mel spectrograms for automatic scoring.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded audio latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded audio latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
vocoder ([`~transformers.SpeechT5HifiGan`]):
Vocoder of class `SpeechT5HifiGan` to convert the mel-spectrogram latents to the final audio waveform.
- all
- __call__ | /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. Otherwise, it is from the first.
Args:
text_encoder_dim (`int`):
Dimensionality of the text embeddings from the first text encoder (CLAP).
text_encoder_1_dim (`int`):
Dimensionality of the text embeddings from the second text encoder (T5 or VITS).
langauge_model_dim (`int`):
Dimensionality of the text embeddings from the language model (GPT2).
- forward | /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 multiple cross-attention layers. It also allows for up
to two cross-attention embeddings, `encoder_hidden_states` and `encoder_hidden_states_1`.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
Parameters:
sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
Height and width of input/output sample.
in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.
out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
Whether to flip the sin to cos in the time embedding.
freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
The tuple of downsample blocks to use.
mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):
Block type for middle of UNet, it can only be `UNetMidBlock2DCrossAttn` for AudioLDM2.
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`):
The tuple of upsample blocks to use.
only_cross_attention (`bool` or `Tuple[bool]`, *optional*, default to `False`):
Whether to include self-attention in the basic transformer blocks, see
[`~models.attention.BasicTransformerBlock`].
block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
The tuple of output channels for each block.
layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
If `None`, normalization and activation layers is skipped in post-processing.
norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
The dimension of the cross attention features.
transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1):
The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
[`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
[`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
num_attention_heads (`int`, *optional*):
The number of attention heads. If not defined, defaults to `attention_head_dim`
resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
class_embed_type (`str`, *optional*, defaults to `None`):
The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
`"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
num_class_embeds (`int`, *optional*, defaults to `None`):
Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
class conditioning with `class_embed_type` equal to `None`.
time_embedding_type (`str`, *optional*, defaults to `positional`):
The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
time_embedding_dim (`int`, *optional*, defaults to `None`):
An optional override for the dimension of the projected time embedding.
time_embedding_act_fn (`str`, *optional*, defaults to `None`):
Optional activation function to use only once on the time embeddings before they are passed to the rest of
the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.
timestep_post_act (`str`, *optional*, defaults to `None`):
The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
time_cond_proj_dim (`int`, *optional*, defaults to `None`):
The dimension of `cond_proj` layer in the timestep embedding.
conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer.
conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer.
projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when
`class_embed_type="projection"`. Required when `class_embed_type="projection"`.
class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time
embeddings with the class embeddings.
- forward | /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 agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
--> | /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, Kolors exhibits significant advantages over both open-source and closed-source models in visual quality, complex semantic accuracy, and text rendering for both Chinese and English characters. Furthermore, Kolors supports both Chinese and English inputs, demonstrating strong performance in understanding and generating Chinese-specific content. For more details, please refer to this [technical report](https://github.com/Kwai-Kolors/Kolors/blob/master/imgs/Kolors_paper.pdf).
The abstract from the technical report is:
*We present Kolors, a latent diffusion model for text-to-image synthesis, characterized by its profound understanding of both English and Chinese, as well as an impressive degree of photorealism. There are three key insights contributing to the development of Kolors. Firstly, unlike large language model T5 used in Imagen and Stable Diffusion 3, Kolors is built upon the General Language Model (GLM), which enhances its comprehension capabilities in both English and Chinese. Moreover, we employ a multimodal large language model to recaption the extensive training dataset for fine-grained text understanding. These strategies significantly improve Kolors’ ability to comprehend intricate semantics, particularly those involving multiple entities, and enable its advanced text rendering capabilities. Secondly, we divide the training of Kolors into two phases: the concept learning phase with broad knowledge and the quality improvement phase with specifically curated high-aesthetic data. Furthermore, we investigate the critical role of the noise schedule and introduce a novel schedule to optimize high-resolution image generation. These strategies collectively enhance the visual appeal of the generated high-resolution images. Lastly, we propose a category-balanced benchmark KolorsPrompts, which serves as a guide for the training and evaluation of Kolors. Consequently, even when employing the commonly used U-Net backbone, Kolors has demonstrated remarkable performance in human evaluations, surpassing the existing open-source models and achieving Midjourney-v6 level performance, especially in terms of visual appeal. We will release the code and weights of Kolors at <https://github.com/Kwai-Kolors/Kolors>, and hope that it will benefit future research and applications in the visual generation community.* | /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=True)
image = pipe(
prompt='一张瓢虫的照片，微距，变焦，高质量，电影，拿着一个牌子，写着"可图"',
negative_prompt="",
guidance_scale=6.5,
num_inference_steps=25,
).images[0]
image.save("kolors_sample.png")
``` | /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 GPUs.
</Tip>
<Tip>
While Kolors is integrated in Diffusers, you need to load the image encoder from a revision to use the safetensor files. You can still use the main branch of the original repository if you're comfortable loading pickle checkpoints.
</Tip>
```python
import torch
from transformers import CLIPVisionModelWithProjection
from diffusers import DPMSolverMultistepScheduler, KolorsPipeline
from diffusers.utils import load_image
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
"Kwai-Kolors/Kolors-IP-Adapter-Plus",
subfolder="image_encoder",
low_cpu_mem_usage=True,
torch_dtype=torch.float16,
revision="refs/pr/4",
)
pipe = KolorsPipeline.from_pretrained(
"Kwai-Kolors/Kolors-diffusers", image_encoder=image_encoder, torch_dtype=torch.float16, variant="fp16"
)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True)
pipe.load_ip_adapter(
"Kwai-Kolors/Kolors-IP-Adapter-Plus",
subfolder="",
weight_name="ip_adapter_plus_general.safetensors",
revision="refs/pr/4",
image_encoder_folder=None,
)
pipe.enable_model_cpu_offload()
ipa_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/kolors/cat_square.png")
image = pipe(
prompt="best quality, high quality",
negative_prompt="",
guidance_scale=6.5,
num_inference_steps=25,
ip_adapter_image=ipa_image,
).images[0]
image.save("kolors_ipa_sample.png")
``` | /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-resolution videos with realistic and varied content. We provide a model for both text-to-video as well as image + text-to-video usecases.
<Tip>
Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
</Tip>
Available models:
| Model name | Recommended dtype |
|:-------------:|:-----------------:|
| [`LTX Video 0.9.0`](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.safetensors) | `torch.bfloat16` |
| [`LTX Video 0.9.1`](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.1.safetensors) | `torch.bfloat16` |
Note: The recommended dtype is for the transformer component. The VAE and text encoders can be either `torch.float32`, `torch.bfloat16` or `torch.float16` but the recommended dtype is `torch.bfloat16` as used in the original repository. | /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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