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It is recommended to use this pipeline with checkpoints that have been specifically fine-tuned for inpainting, such
as [runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting). Default
text-to-image Stable Diffusion checkpoints, such as
[stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) are also compatible but they might be less performant.
<Tip>
Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
</Tip> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/inpaint.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/inpaint/#tips | #tips | .md | 173_2 |
StableDiffusionInpaintPipeline
Pipeline for text-guided image inpainting using 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
- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
Args:
vae ([`AutoencoderKL`, `AsymmetricAutoencoderKL`]):
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 ([`~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`.
- all
- __call__
- enable_attention_slicing
- disable_attention_slicing
- enable_xformers_memory_efficient_attention
- disable_xformers_memory_efficient_attention
- load_textual_inversion
- load_lora_weights
- save_lora_weights | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/inpaint.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/inpaint/#stablediffusioninpaintpipeline | #stablediffusioninpaintpipeline | .md | 173_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/stable_diffusion/inpaint.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/inpaint/#stablediffusionpipelineoutput | #stablediffusionpipelineoutput | .md | 173_4 |
FlaxStableDiffusionInpaintPipeline
- all
- __call__ | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/inpaint.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/inpaint/#flaxstablediffusioninpaintpipeline | #flaxstablediffusioninpaintpipeline | .md | 173_5 |
[[autodoc]] FlaxStableDiffusionPipelineOutput: module diffusers.pipelines.stable_diffusion has no attribute FlaxStableDiffusionPipelineOutput | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/inpaint.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/inpaint/#flaxstablediffusionpipelineoutput | #flaxstablediffusionpipelineoutput | .md | 173_6 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or 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/stable_diffusion/stable_diffusion_xl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_xl/ | .md | 174_0 |
|
Stable Diffusion XL (SDXL) was proposed in [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://huggingface.co/papers/2307.01952) by Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach.
The abstract from the paper is:
*We present SDXL, a latent diffusion model for text-to-image synthesis. Compared to previous versions of Stable Diffusion, SDXL leverages a three times larger UNet backbone: The increase of model parameters is mainly due to more attention blocks and a larger cross-attention context as SDXL uses a second text encoder. We design multiple novel conditioning schemes and train SDXL on multiple aspect ratios. We also introduce a refinement model which is used to improve the visual fidelity of samples generated by SDXL using a post-hoc image-to-image technique. We demonstrate that SDXL shows drastically improved performance compared the previous versions of Stable Diffusion and achieves results competitive with those of black-box state-of-the-art image generators.* | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_xl/#stable-diffusion-xl | #stable-diffusion-xl | .md | 174_1 |
- Using SDXL with a DPM++ scheduler for less than 50 steps is known to produce [visual artifacts](https://github.com/huggingface/diffusers/issues/5433) because the solver becomes numerically unstable. To fix this issue, take a look at this [PR](https://github.com/huggingface/diffusers/pull/5541) which recommends for ODE/SDE solvers:
- set `use_karras_sigmas=True` or `lu_lambdas=True` to improve image quality
- set `euler_at_final=True` if you're using a solver with uniform step sizes (DPM++2M or DPM++2M SDE)
- Most SDXL checkpoints work best with an image size of 1024x1024. Image sizes of 768x768 and 512x512 are also supported, but the results aren't as good. Anything below 512x512 is not recommended and likely won't be for default checkpoints like [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0).
- SDXL can pass a different prompt for each of the text encoders it was trained on. We can even pass different parts of the same prompt to the text encoders.
- SDXL output images can be improved by making use of a refiner model in an image-to-image setting.
- SDXL offers `negative_original_size`, `negative_crops_coords_top_left`, and `negative_target_size` to negatively condition the model on image resolution and cropping parameters.
<Tip>
To learn how to use SDXL for various tasks, how to optimize performance, and other usage examples, take a look at the [Stable Diffusion XL](../../../using-diffusers/sdxl) guide.
Check out the [Stability AI](https://huggingface.co/stabilityai) Hub organization for the official base and refiner model checkpoints!
</Tip> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_xl/#tips | #tips | .md | 174_2 |
StableDiffusionXLPipeline
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.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
- [`~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`].
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.
- all
- __call__ | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_xl/#stablediffusionxlpipeline | #stablediffusionxlpipeline | .md | 174_3 |
StableDiffusionXLImg2ImgPipeline
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.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
- [`~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`].
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.
- all
- __call__ | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_xl/#stablediffusionxlimg2imgpipeline | #stablediffusionxlimg2imgpipeline | .md | 174_4 |
StableDiffusionXLInpaintPipeline
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.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
- [`~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`].
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.
- all
- __call__ | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_xl/#stablediffusionxlinpaintpipeline | #stablediffusionxlinpaintpipeline | .md | 174_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/stable_diffusion/depth2img.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/depth2img/ | .md | 175_0 |
|
The Stable Diffusion model can also infer depth based on an image using [MiDaS](https://github.com/isl-org/MiDaS). This allows you to pass a text prompt and an initial image to condition the generation of new images as well as a `depth_map` to preserve the image structure.
<Tip>
Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
</Tip> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/depth2img.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/depth2img/#depth-to-image | #depth-to-image | .md | 175_1 |
StableDiffusionDepth2ImgPipeline
Pipeline for text-guided depth-based image-to-image generation using 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
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`].
- all
- __call__
- enable_attention_slicing
- disable_attention_slicing
- enable_xformers_memory_efficient_attention
- disable_xformers_memory_efficient_attention
- load_textual_inversion
- load_lora_weights
- save_lora_weights | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/depth2img.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/depth2img/#stablediffusiondepth2imgpipeline | #stablediffusiondepth2imgpipeline | .md | 175_2 |
StableDiffusionPipelineOutput
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
nsfw_content_detected (`List[bool]`)
List indicating whether the 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/stable_diffusion/depth2img.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/depth2img/#stablediffusionpipelineoutput | #stablediffusionpipelineoutput | .md | 175_3 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or 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/stable_diffusion/upscale.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/upscale/ | .md | 176_0 |
|
The Stable Diffusion upscaler diffusion model was created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/), and [LAION](https://laion.ai/). It is used to enhance the resolution of input images by a factor of 4.
<Tip>
Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
</Tip> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/upscale.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/upscale/#super-resolution | #super-resolution | .md | 176_1 |
StableDiffusionUpscalePipeline
Pipeline for text-guided image super-resolution using Stable Diffusion 2.
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.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
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.
low_res_scheduler ([`SchedulerMixin`]):
A scheduler used to add initial noise to the low resolution conditioning image. It must be an instance of
[`DDPMScheduler`].
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__
- enable_attention_slicing
- disable_attention_slicing
- enable_xformers_memory_efficient_attention
- disable_xformers_memory_efficient_attention | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/upscale.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/upscale/#stablediffusionupscalepipeline | #stablediffusionupscalepipeline | .md | 176_2 |
StableDiffusionPipelineOutput
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
nsfw_content_detected (`List[bool]`)
List indicating whether the 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/stable_diffusion/upscale.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/upscale/#stablediffusionpipelineoutput | #stablediffusionpipelineoutput | .md | 176_3 |
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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/stable_diffusion/svd.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/svd/ | .md | 177_0 |
|
Stable Video Diffusion was proposed in [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://hf.co/papers/2311.15127) by Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, Varun Jampani, Robin Rombach.
The abstract from the paper is:
*We present Stable Video Diffusion - a latent video diffusion model for high-resolution, state-of-the-art text-to-video and image-to-video generation. Recently, latent diffusion models trained for 2D image synthesis have been turned into generative video models by inserting temporal layers and finetuning them on small, high-quality video datasets. However, training methods in the literature vary widely, and the field has yet to agree on a unified strategy for curating video data. In this paper, we identify and evaluate three different stages for successful training of video LDMs: text-to-image pretraining, video pretraining, and high-quality video finetuning. Furthermore, we demonstrate the necessity of a well-curated pretraining dataset for generating high-quality videos and present a systematic curation process to train a strong base model, including captioning and filtering strategies. We then explore the impact of finetuning our base model on high-quality data and train a text-to-video model that is competitive with closed-source video generation. We also show that our base model provides a powerful motion representation for downstream tasks such as image-to-video generation and adaptability to camera motion-specific LoRA modules. Finally, we demonstrate that our model provides a strong multi-view 3D-prior and can serve as a base to finetune a multi-view diffusion model that jointly generates multiple views of objects in a feedforward fashion, outperforming image-based methods at a fraction of their compute budget. We release code and model weights at this https URL.*
<Tip>
To learn how to use Stable Video Diffusion, take a look at the [Stable Video Diffusion](../../../using-diffusers/svd) guide.
<br>
Check out the [Stability AI](https://huggingface.co/stabilityai) Hub organization for the [base](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid) and [extended frame](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt) checkpoints!
</Tip> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/svd.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/svd/#stable-video-diffusion | #stable-video-diffusion | .md | 177_1 |
Video generation is memory-intensive and one way to reduce your memory usage is to set `enable_forward_chunking` on the pipeline's UNet so you don't run the entire feedforward layer at once. Breaking it up into chunks in a loop is more efficient.
Check out the [Text or image-to-video](text-img2vid) guide for more details about how certain parameters can affect video generation and how to optimize inference by reducing memory usage. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/svd.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/svd/#tips | #tips | .md | 177_2 |
StableVideoDiffusionPipeline
Pipeline to generate video from an input image using Stable Video 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.).
Args:
vae ([`AutoencoderKLTemporalDecoder`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
image_encoder ([`~transformers.CLIPVisionModelWithProjection`]):
Frozen CLIP image-encoder
([laion/CLIP-ViT-H-14-laion2B-s32B-b79K](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K)).
unet ([`UNetSpatioTemporalConditionModel`]):
A `UNetSpatioTemporalConditionModel` to denoise the encoded image latents.
scheduler ([`EulerDiscreteScheduler`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/svd.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/svd/#stablevideodiffusionpipeline | #stablevideodiffusionpipeline | .md | 177_3 |
StableVideoDiffusionPipelineOutput
Output class for Stable Video Diffusion pipeline.
Args:
frames (`[List[List[PIL.Image.Image]]`, `np.ndarray`, `torch.Tensor`]):
List of denoised PIL images of length `batch_size` or numpy array or torch tensor of shape `(batch_size,
num_frames, height, width, num_channels)`. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/svd.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/svd/#stablevideodiffusionpipelineoutput | #stablevideodiffusionpipelineoutput | .md | 177_4 |
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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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--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/img2img.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/img2img/ | .md | 178_0 |
|
The Stable Diffusion model can also be applied to image-to-image generation by passing a text prompt and an initial image to condition the generation of new images.
The [`StableDiffusionImg2ImgPipeline`] uses the diffusion-denoising mechanism proposed in [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://huggingface.co/papers/2108.01073) by Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon.
The abstract from the paper is:
*Guided image synthesis enables everyday users to create and edit photo-realistic images with minimum effort. The key challenge is balancing faithfulness to the user input (e.g., hand-drawn colored strokes) and realism of the synthesized image. Existing GAN-based methods attempt to achieve such balance using either conditional GANs or GAN inversions, which are challenging and often require additional training data or loss functions for individual applications. To address these issues, we introduce a new image synthesis and editing method, Stochastic Differential Editing (SDEdit), based on a diffusion model generative prior, which synthesizes realistic images by iteratively denoising through a stochastic differential equation (SDE). Given an input image with user guide of any type, SDEdit first adds noise to the input, then subsequently denoises the resulting image through the SDE prior to increase its realism. SDEdit does not require task-specific training or inversions and can naturally achieve the balance between realism and faithfulness. SDEdit significantly outperforms state-of-the-art GAN-based methods by up to 98.09% on realism and 91.72% on overall satisfaction scores, according to a human perception study, on multiple tasks, including stroke-based image synthesis and editing as well as image compositing.*
<Tip>
Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
</Tip> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/img2img.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/img2img/#image-to-image | #image-to-image | .md | 178_1 |
StableDiffusionImg2ImgPipeline
Pipeline for text-guided image-to-image generation using 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.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)).
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`.
- all
- __call__
- enable_attention_slicing
- disable_attention_slicing
- enable_xformers_memory_efficient_attention
- disable_xformers_memory_efficient_attention
- load_textual_inversion
- from_single_file
- load_lora_weights
- save_lora_weights | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/img2img.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/img2img/#stablediffusionimg2imgpipeline | #stablediffusionimg2imgpipeline | .md | 178_2 |
StableDiffusionPipelineOutput
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
nsfw_content_detected (`List[bool]`)
List indicating whether the 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/stable_diffusion/img2img.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/img2img/#stablediffusionpipelineoutput | #stablediffusionpipelineoutput | .md | 178_3 |
FlaxStableDiffusionImg2ImgPipeline
- all
- __call__ | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/img2img.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/img2img/#flaxstablediffusionimg2imgpipeline | #flaxstablediffusionimg2imgpipeline | .md | 178_4 |
[[autodoc]] FlaxStableDiffusionPipelineOutput: module diffusers.pipelines.stable_diffusion has no attribute FlaxStableDiffusionPipelineOutput | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/img2img.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/img2img/#flaxstablediffusionpipelineoutput | #flaxstablediffusionpipelineoutput | .md | 178_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
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/stable_diffusion/adapter.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/adapter/ | .md | 179_0 |
|
[T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.08453) by Chong Mou, Xintao Wang, Liangbin Xie, Jian Zhang, Zhongang Qi, Ying Shan, Xiaohu Qie.
Using the pretrained models we can provide control images (for example, a depth map) to control Stable Diffusion text-to-image generation so that it follows the structure of the depth image and fills in the details.
The abstract of the paper is the following:
*The incredible generative ability of large-scale text-to-image (T2I) models has demonstrated strong power of learning complex structures and meaningful semantics. However, relying solely on text prompts cannot fully take advantage of the knowledge learned by the model, especially when flexible and accurate controlling (e.g., color and structure) is needed. In this paper, we aim to ``dig out" the capabilities that T2I models have implicitly learned, and then explicitly use them to control the generation more granularly. Specifically, we propose to learn simple and lightweight T2I-Adapters to align internal knowledge in T2I models with external control signals, while freezing the original large T2I models. In this way, we can train various adapters according to different conditions, achieving rich control and editing effects in the color and structure of the generation results. Further, the proposed T2I-Adapters have attractive properties of practical value, such as composability and generalization ability. Extensive experiments demonstrate that our T2I-Adapter has promising generation quality and a wide range of applications.*
This model was contributed by the community contributor [HimariO](https://github.com/HimariO) ❤️ . | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/adapter.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/adapter/#t2i-adapter | #t2i-adapter | .md | 179_1 |
StableDiffusionAdapterPipeline
Pipeline for text-to-image generation using Stable Diffusion augmented with T2I-Adapter
https://arxiv.org/abs/2302.08453
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:
adapter ([`T2IAdapter`] or [`MultiAdapter`] or `List[T2IAdapter]`):
Provides additional conditioning to the unet during the denoising process. If you set multiple Adapter as a
list, the outputs from each Adapter are added together to create one combined additional conditioning.
adapter_weights (`List[float]`, *optional*, defaults to None):
List of floats representing the weight which will be multiply to each adapter's output before adding them
together.
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.
tokenizer (`CLIPTokenizer`):
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`].
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
details.
feature_extractor ([`CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
- all
- __call__
- enable_attention_slicing
- disable_attention_slicing
- enable_vae_slicing
- disable_vae_slicing
- enable_xformers_memory_efficient_attention
- disable_xformers_memory_efficient_attention | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/adapter.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/adapter/#stablediffusionadapterpipeline | #stablediffusionadapterpipeline | .md | 179_2 |
StableDiffusionXLAdapterPipeline
Pipeline for text-to-image generation using Stable Diffusion augmented with T2I-Adapter
https://arxiv.org/abs/2302.08453
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
- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
Args:
adapter ([`T2IAdapter`] or [`MultiAdapter`] or `List[T2IAdapter]`):
Provides additional conditioning to the unet during the denoising process. If you set multiple Adapter as a
list, the outputs from each Adapter are added together to create one combined additional conditioning.
adapter_weights (`List[float]`, *optional*, defaults to None):
List of floats representing the weight which will be multiply to each adapter's output before adding them
together.
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.
tokenizer (`CLIPTokenizer`):
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`].
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
details.
feature_extractor ([`CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
- all
- __call__
- enable_attention_slicing
- disable_attention_slicing
- enable_vae_slicing
- disable_vae_slicing
- enable_xformers_memory_efficient_attention
- disable_xformers_memory_efficient_attention | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/adapter.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/adapter/#stablediffusionxladapterpipeline | #stablediffusionxladapterpipeline | .md | 179_3 |
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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
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/stable_diffusion/stable_diffusion_safe.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_safe/ | .md | 180_0 |
|
Safe Stable Diffusion was proposed in [Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models](https://huggingface.co/papers/2211.05105) and mitigates inappropriate degeneration from Stable Diffusion models because they're trained on unfiltered web-crawled datasets. For instance Stable Diffusion may unexpectedly generate nudity, violence, images depicting self-harm, and otherwise offensive content. Safe Stable Diffusion is an extension of Stable Diffusion that drastically reduces this type of content.
The abstract from the paper is:
*Text-conditioned image generation models have recently achieved astonishing results in image quality and text alignment and are consequently employed in a fast-growing number of applications. Since they are highly data-driven, relying on billion-sized datasets randomly scraped from the internet, they also suffer, as we demonstrate, from degenerated and biased human behavior. In turn, they may even reinforce such biases. To help combat these undesired side effects, we present safe latent diffusion (SLD). Specifically, to measure the inappropriate degeneration due to unfiltered and imbalanced training sets, we establish a novel image generation test bed-inappropriate image prompts (I2P)-containing dedicated, real-world image-to-text prompts covering concepts such as nudity and violence. As our exhaustive empirical evaluation demonstrates, the introduced SLD removes and suppresses inappropriate image parts during the diffusion process, with no additional training required and no adverse effect on overall image quality or text alignment.* | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_safe.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_safe/#safe-stable-diffusion | #safe-stable-diffusion | .md | 180_1 |
Use the `safety_concept` property of [`StableDiffusionPipelineSafe`] to check and edit the current safety concept:
```python
>>> from diffusers import StableDiffusionPipelineSafe
>>> pipeline = StableDiffusionPipelineSafe.from_pretrained("AIML-TUDA/stable-diffusion-safe")
>>> pipeline.safety_concept
'an image showing hate, harassment, violence, suffering, humiliation, harm, suicide, sexual, nudity, bodily fluids, blood, obscene gestures, illegal activity, drug use, theft, vandalism, weapons, child abuse, brutality, cruelty'
```
For each image generation the active concept is also contained in [`StableDiffusionSafePipelineOutput`].
There are 4 configurations (`SafetyConfig.WEAK`, `SafetyConfig.MEDIUM`, `SafetyConfig.STRONG`, and `SafetyConfig.MAX`) that can be applied:
```python
>>> from diffusers import StableDiffusionPipelineSafe
>>> from diffusers.pipelines.stable_diffusion_safe import SafetyConfig
>>> pipeline = StableDiffusionPipelineSafe.from_pretrained("AIML-TUDA/stable-diffusion-safe")
>>> prompt = "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c. leyendecker"
>>> out = pipeline(prompt=prompt, **SafetyConfig.MAX)
```
<Tip>
Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
</Tip> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_safe.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_safe/#tips | #tips | .md | 180_2 |
StableDiffusionPipelineSafe
Pipeline based on the [`StableDiffusionPipeline`] for text-to-image generation using Safe Latent Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipeline also inherits the following loading methods:
- [`~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`.
- all
- __call__ | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_safe.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_safe/#stablediffusionpipelinesafe | #stablediffusionpipelinesafe | .md | 180_3 |
StableDiffusionSafePipelineOutput
Output class for Safe Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
nsfw_content_detected (`List[bool]`)
List of flags denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, or `None` if safety checking could not be performed.
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images that were flagged by the safety checker any may contain "not-safe-for-work"
(nsfw) content, or `None` if no safety check was performed or no images were flagged.
applied_safety_concept (`str`)
The safety concept that was applied for safety guidance, or `None` if safety guidance was disabled
- all
- __call__ | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_safe.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_safe/#stablediffusionsafepipelineoutput | #stablediffusionsafepipelineoutput | .md | 180_4 |
<!--Copyright 2024 The GLIGEN Authors and 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/stable_diffusion/gligen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/gligen/ | .md | 181_0 |
|
The GLIGEN model was created by researchers and engineers from [University of Wisconsin-Madison, Columbia University, and Microsoft](https://github.com/gligen/GLIGEN). The [`StableDiffusionGLIGENPipeline`] and [`StableDiffusionGLIGENTextImagePipeline`] can generate photorealistic images conditioned on grounding inputs. Along with text and bounding boxes with [`StableDiffusionGLIGENPipeline`], if input images are given, [`StableDiffusionGLIGENTextImagePipeline`] can insert objects described by text at the region defined by bounding boxes. Otherwise, it'll generate an image described by the caption/prompt and insert objects described by text at the region defined by bounding boxes. It's trained on COCO2014D and COCO2014CD datasets, and the model uses a frozen CLIP ViT-L/14 text encoder to condition itself on grounding inputs.
The abstract from the [paper](https://huggingface.co/papers/2301.07093) is:
*Large-scale text-to-image diffusion models have made amazing advances. However, the status quo is to use text input alone, which can impede controllability. In this work, we propose GLIGEN, Grounded-Language-to-Image Generation, a novel approach that builds upon and extends the functionality of existing pre-trained text-to-image diffusion models by enabling them to also be conditioned on grounding inputs. To preserve the vast concept knowledge of the pre-trained model, we freeze all of its weights and inject the grounding information into new trainable layers via a gated mechanism. Our model achieves open-world grounded text2img generation with caption and bounding box condition inputs, and the grounding ability generalizes well to novel spatial configurations and concepts. GLIGEN’s zeroshot performance on COCO and LVIS outperforms existing supervised layout-to-image baselines by a large margin.*
<Tip>
Make sure to check out the Stable Diffusion [Tips](https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality and how to reuse pipeline components efficiently!
If you want to use one of the official checkpoints for a task, explore the [gligen](https://huggingface.co/gligen) Hub organizations!
</Tip>
[`StableDiffusionGLIGENPipeline`] was contributed by [Nikhil Gajendrakumar](https://github.com/nikhil-masterful) and [`StableDiffusionGLIGENTextImagePipeline`] was contributed by [Nguyễn Công Tú Anh](https://github.com/tuanh123789). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/gligen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/gligen/#gligen-grounded-language-to-image-generation | #gligen-grounded-language-to-image-generation | .md | 181_1 |
StableDiffusionGLIGENPipeline
Pipeline for text-to-image generation using Stable Diffusion with Grounded-Language-to-Image Generation (GLIGEN).
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.).
Args:
vae ([`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`.
- all
- __call__
- enable_vae_slicing
- disable_vae_slicing
- enable_vae_tiling
- disable_vae_tiling
- enable_model_cpu_offload
- prepare_latents
- enable_fuser | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/gligen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/gligen/#stablediffusiongligenpipeline | #stablediffusiongligenpipeline | .md | 181_2 |
StableDiffusionGLIGENTextImagePipeline
Pipeline for text-to-image generation using Stable Diffusion with Grounded-Language-to-Image Generation (GLIGEN).
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.).
Args:
vae ([`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.
processor ([`~transformers.CLIPProcessor`]):
A `CLIPProcessor` to procces reference image.
image_encoder ([`~transformers.CLIPVisionModelWithProjection`]):
Frozen image-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
image_project ([`CLIPImageProjection`]):
A `CLIPImageProjection` to project image embedding into phrases embedding space.
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`.
- all
- __call__
- enable_vae_slicing
- disable_vae_slicing
- enable_vae_tiling
- disable_vae_tiling
- enable_model_cpu_offload
- prepare_latents
- enable_fuser | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/gligen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/gligen/#stablediffusiongligentextimagepipeline | #stablediffusiongligentextimagepipeline | .md | 181_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/stable_diffusion/gligen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/gligen/#stablediffusionpipelineoutput | #stablediffusionpipelineoutput | .md | 181_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
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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/models/asymmetricautoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/asymmetricautoencoderkl/ | .md | 182_0 |
|
Improved larger variational autoencoder (VAE) model with KL loss for inpainting task: [Designing a Better Asymmetric VQGAN for StableDiffusion](https://arxiv.org/abs/2306.04632) by Zixin Zhu, Xuelu Feng, Dongdong Chen, Jianmin Bao, Le Wang, Yinpeng Chen, Lu Yuan, Gang Hua.
The abstract from the paper is:
*StableDiffusion is a revolutionary text-to-image generator that is causing a stir in the world of image generation and editing. Unlike traditional methods that learn a diffusion model in pixel space, StableDiffusion learns a diffusion model in the latent space via a VQGAN, ensuring both efficiency and quality. It not only supports image generation tasks, but also enables image editing for real images, such as image inpainting and local editing. However, we have observed that the vanilla VQGAN used in StableDiffusion leads to significant information loss, causing distortion artifacts even in non-edited image regions. To this end, we propose a new asymmetric VQGAN with two simple designs. Firstly, in addition to the input from the encoder, the decoder contains a conditional branch that incorporates information from task-specific priors, such as the unmasked image region in inpainting. Secondly, the decoder is much heavier than the encoder, allowing for more detailed recovery while only slightly increasing the total inference cost. The training cost of our asymmetric VQGAN is cheap, and we only need to retrain a new asymmetric decoder while keeping the vanilla VQGAN encoder and StableDiffusion unchanged. Our asymmetric VQGAN can be widely used in StableDiffusion-based inpainting and local editing methods. Extensive experiments demonstrate that it can significantly improve the inpainting and editing performance, while maintaining the original text-to-image capability. The code is available at https://github.com/buxiangzhiren/Asymmetric_VQGAN*
Evaluation results can be found in section 4.1 of the original paper. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/asymmetricautoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/asymmetricautoencoderkl/#asymmetricautoencoderkl | #asymmetricautoencoderkl | .md | 182_1 |
* [https://huggingface.co/cross-attention/asymmetric-autoencoder-kl-x-1-5](https://huggingface.co/cross-attention/asymmetric-autoencoder-kl-x-1-5)
* [https://huggingface.co/cross-attention/asymmetric-autoencoder-kl-x-2](https://huggingface.co/cross-attention/asymmetric-autoencoder-kl-x-2) | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/asymmetricautoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/asymmetricautoencoderkl/#available-checkpoints | #available-checkpoints | .md | 182_2 |
```python
from diffusers import AsymmetricAutoencoderKL, StableDiffusionInpaintPipeline
from diffusers.utils import load_image, make_image_grid
prompt = "a photo of a person with beard"
img_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/celeba_hq_256.png"
mask_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"
original_image = load_image(img_url).resize((512, 512))
mask_image = load_image(mask_url).resize((512, 512))
pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
pipe.vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5")
pipe.to("cuda")
image = pipe(prompt=prompt, image=original_image, mask_image=mask_image).images[0]
make_image_grid([original_image, mask_image, image], rows=1, cols=3)
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/asymmetricautoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/asymmetricautoencoderkl/#example-usage | #example-usage | .md | 182_3 |
AsymmetricAutoencoderKL
Designing a Better Asymmetric VQGAN for StableDiffusion https://arxiv.org/abs/2306.04632 . A VAE model with KL loss
for encoding images into latents and decoding latent representations into images.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
Parameters:
in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
out_channels (int, *optional*, defaults to 3): Number of channels in the output.
down_block_types (`Tuple[str]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
Tuple of downsample block types.
down_block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
Tuple of down block output channels.
layers_per_down_block (`int`, *optional*, defaults to `1`):
Number layers for down block.
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
Tuple of upsample block types.
up_block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
Tuple of up block output channels.
layers_per_up_block (`int`, *optional*, defaults to `1`):
Number layers for up block.
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
latent_channels (`int`, *optional*, defaults to 4): Number of channels in the latent space.
sample_size (`int`, *optional*, defaults to `32`): Sample input size.
norm_num_groups (`int`, *optional*, defaults to `32`):
Number of groups to use for the first normalization layer in ResNet blocks.
scaling_factor (`float`, *optional*, defaults to 0.18215):
The component-wise standard deviation of the trained latent space computed using the first batch of the
training set. This is used to scale the latent space to have unit variance when training the diffusion
model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
/ scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/asymmetricautoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/asymmetricautoencoderkl/#asymmetricautoencoderkl | #asymmetricautoencoderkl | .md | 182_4 |
AutoencoderKLOutput
Output of AutoencoderKL encoding method.
Args:
latent_dist (`DiagonalGaussianDistribution`):
Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`.
`DiagonalGaussianDistribution` allows for sampling latents from the distribution. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/asymmetricautoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/asymmetricautoencoderkl/#autoencoderkloutput | #autoencoderkloutput | .md | 182_5 |
DecoderOutput
Output of decoding method.
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):
The decoded output sample from the last layer of the model. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/asymmetricautoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/asymmetricautoencoderkl/#decoderoutput | #decoderoutput | .md | 182_6 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
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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/models/dit_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/dit_transformer2d/ | .md | 183_0 |
|
A Transformer model for image-like data from [DiT](https://huggingface.co/papers/2212.09748). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/dit_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/dit_transformer2d/#dittransformer2dmodel | #dittransformer2dmodel | .md | 183_1 |
DiTTransformer2DModel
A 2D Transformer model as introduced in DiT (https://arxiv.org/abs/2212.09748).
Parameters:
num_attention_heads (int, optional, defaults to 16): The number of heads to use for multi-head attention.
attention_head_dim (int, optional, defaults to 72): The number of channels in each head.
in_channels (int, defaults to 4): The number of channels in the input.
out_channels (int, optional):
The number of channels in the output. Specify this parameter if the output channel number differs from the
input.
num_layers (int, optional, defaults to 28): The number of layers of Transformer blocks to use.
dropout (float, optional, defaults to 0.0): The dropout probability to use within the Transformer blocks.
norm_num_groups (int, optional, defaults to 32):
Number of groups for group normalization within Transformer blocks.
attention_bias (bool, optional, defaults to True):
Configure if the Transformer blocks' attention should contain a bias parameter.
sample_size (int, defaults to 32):
The width of the latent images. This parameter is fixed during training.
patch_size (int, defaults to 2):
Size of the patches the model processes, relevant for architectures working on non-sequential data.
activation_fn (str, optional, defaults to "gelu-approximate"):
Activation function to use in feed-forward networks within Transformer blocks.
num_embeds_ada_norm (int, optional, defaults to 1000):
Number of embeddings for AdaLayerNorm, fixed during training and affects the maximum denoising steps during
inference.
upcast_attention (bool, optional, defaults to False):
If true, upcasts the attention mechanism dimensions for potentially improved performance.
norm_type (str, optional, defaults to "ada_norm_zero"):
Specifies the type of normalization used, can be 'ada_norm_zero'.
norm_elementwise_affine (bool, optional, defaults to False):
If true, enables element-wise affine parameters in the normalization layers.
norm_eps (float, optional, defaults to 1e-5):
A small constant added to the denominator in normalization layers to prevent division by zero. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/dit_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/dit_transformer2d/#dittransformer2dmodel | #dittransformer2dmodel | .md | 183_2 |
<!--Copyright 2024 The HuggingFace Team and The InstantX 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/models/controlnet_union.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_union/ | .md | 184_0 |
|
ControlNetUnionModel is an implementation of ControlNet for Stable Diffusion XL.
The ControlNet model was introduced in [ControlNetPlus](https://github.com/xinsir6/ControlNetPlus) by xinsir6. It supports multiple conditioning inputs without increasing computation.
*We design a new architecture that can support 10+ control types in condition text-to-image generation and can generate high resolution images visually comparable with midjourney. The network is based on the original ControlNet architecture, we propose two new modules to: 1 Extend the original ControlNet to support different image conditions using the same network parameter. 2 Support multiple conditions input without increasing computation offload, which is especially important for designers who want to edit image in detail, different conditions use the same condition encoder, without adding extra computations or parameters.* | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_union.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_union/#controlnetunionmodel | #controlnetunionmodel | .md | 184_1 |
By default the [`ControlNetUnionModel`] should be loaded with [`~ModelMixin.from_pretrained`].
```py
from diffusers import StableDiffusionXLControlNetUnionPipeline, ControlNetUnionModel
controlnet = ControlNetUnionModel.from_pretrained("xinsir/controlnet-union-sdxl-1.0")
pipe = StableDiffusionXLControlNetUnionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet)
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_union.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_union/#loading | #loading | .md | 184_2 |
ControlNetUnionModel
A ControlNetUnion model.
Args:
in_channels (`int`, defaults to 4):
The number of channels in the input sample.
flip_sin_to_cos (`bool`, defaults to `True`):
Whether to flip the sin to cos in the time embedding.
freq_shift (`int`, defaults to 0):
The frequency shift to apply to the time embedding.
down_block_types (`tuple[str]`, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
The tuple of downsample blocks to use.
only_cross_attention (`Union[bool, Tuple[bool]]`, defaults to `False`):
block_out_channels (`tuple[int]`, defaults to `(320, 640, 1280, 1280)`):
The tuple of output channels for each block.
layers_per_block (`int`, defaults to 2):
The number of layers per block.
downsample_padding (`int`, defaults to 1):
The padding to use for the downsampling convolution.
mid_block_scale_factor (`float`, defaults to 1):
The scale factor to use for the mid block.
act_fn (`str`, 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`, defaults to 1e-5):
The epsilon to use for the normalization.
cross_attention_dim (`int`, 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`].
encoder_hid_dim (`int`, *optional*, defaults to None):
If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
dimension to `cross_attention_dim`.
encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
attention_head_dim (`Union[int, Tuple[int]]`, defaults to 8):
The dimension of the attention heads.
use_linear_projection (`bool`, defaults to `False`):
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"`.
addition_embed_type (`str`, *optional*, defaults to `None`):
Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
"text". "text" will use the `TextTimeEmbedding` layer.
num_class_embeds (`int`, *optional*, defaults to 0):
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`.
upcast_attention (`bool`, defaults to `False`):
resnet_time_scale_shift (`str`, defaults to `"default"`):
Time scale shift config for ResNet blocks (see `ResnetBlock2D`). Choose from `default` or `scale_shift`.
projection_class_embeddings_input_dim (`int`, *optional*, defaults to `None`):
The dimension of the `class_labels` input when `class_embed_type="projection"`. Required when
`class_embed_type="projection"`.
controlnet_conditioning_channel_order (`str`, defaults to `"rgb"`):
The channel order of conditional image. Will convert to `rgb` if it's `bgr`.
conditioning_embedding_out_channels (`tuple[int]`, *optional*, defaults to `(48, 96, 192, 384)`):
The tuple of output channel for each block in the `conditioning_embedding` layer.
global_pool_conditions (`bool`, defaults to `False`): | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_union.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_union/#controlnetunionmodel | #controlnetunionmodel | .md | 184_3 |
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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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--> | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl/ | .md | 185_0 |
|
The variational autoencoder (VAE) model with KL loss was introduced in [Auto-Encoding Variational Bayes](https://arxiv.org/abs/1312.6114v11) by Diederik P. Kingma and Max Welling. The model is used in 🤗 Diffusers to encode images into latents and to decode latent representations into images.
The abstract from the paper is:
*How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions are two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.* | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl/#autoencoderkl | #autoencoderkl | .md | 185_1 |
By default the [`AutoencoderKL`] should be loaded with [`~ModelMixin.from_pretrained`], but it can also be loaded
from the original format using [`FromOriginalModelMixin.from_single_file`] as follows:
```py
from diffusers import AutoencoderKL
url = "https://huggingface.co/stabilityai/sd-vae-ft-mse-original/blob/main/vae-ft-mse-840000-ema-pruned.safetensors" # can also be a local file
model = AutoencoderKL.from_single_file(url)
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl/#loading-from-the-original-format | #loading-from-the-original-format | .md | 185_2 |
AutoencoderKL
A VAE model with KL loss for encoding images into latents and decoding latent representations into images.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
Parameters:
in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
out_channels (int, *optional*, defaults to 3): Number of channels in the output.
down_block_types (`Tuple[str]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
Tuple of downsample block types.
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
Tuple of upsample block types.
block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
Tuple of block output channels.
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
latent_channels (`int`, *optional*, defaults to 4): Number of channels in the latent space.
sample_size (`int`, *optional*, defaults to `32`): Sample input size.
scaling_factor (`float`, *optional*, defaults to 0.18215):
The component-wise standard deviation of the trained latent space computed using the first batch of the
training set. This is used to scale the latent space to have unit variance when training the diffusion
model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
/ scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
force_upcast (`bool`, *optional*, default to `True`):
If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE
can be fine-tuned / trained to a lower range without loosing too much precision in which case
`force_upcast` can be set to `False` - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix
mid_block_add_attention (`bool`, *optional*, default to `True`):
If enabled, the mid_block of the Encoder and Decoder will have attention blocks. If set to false, the
mid_block will only have resnet blocks
- decode
- encode
- all | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl/#autoencoderkl | #autoencoderkl | .md | 185_3 |
AutoencoderKLOutput
Output of AutoencoderKL encoding method.
Args:
latent_dist (`DiagonalGaussianDistribution`):
Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`.
`DiagonalGaussianDistribution` allows for sampling latents from the distribution. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl/#autoencoderkloutput | #autoencoderkloutput | .md | 185_4 |
DecoderOutput
Output of decoding method.
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):
The decoded output sample from the last layer of the model. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl/#decoderoutput | #decoderoutput | .md | 185_5 |
FlaxAutoencoderKL | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl/#flaxautoencoderkl | #flaxautoencoderkl | .md | 185_6 |
[[autodoc]] FlaxAutoencoderKLOutput: No module named 'flax' | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl/#flaxautoencoderkloutput | #flaxautoencoderkloutput | .md | 185_7 |
[[autodoc]] FlaxDecoderOutput: No module named 'flax' | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl/#flaxdecoderoutput | #flaxdecoderoutput | .md | 185_8 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or 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/models/overview.md | https://huggingface.co/docs/diffusers/en/api/models/overview/ | .md | 186_0 |
|
🤗 Diffusers provides pretrained models for popular algorithms and modules to create custom diffusion systems. The primary function of models is to denoise an input sample as modeled by the distribution \\(p_{\theta}(x_{t-1}|x_{t})\\).
All models are built from the base [`ModelMixin`] class which is a [`torch.nn.Module`](https://pytorch.org/docs/stable/generated/torch.nn.Module.html) providing basic functionality for saving and loading models, locally and from the Hugging Face Hub. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/overview.md | https://huggingface.co/docs/diffusers/en/api/models/overview/#models | #models | .md | 186_1 |
ModelMixin
Base class for all models.
[`ModelMixin`] takes care of storing the model configuration and provides methods for loading, downloading and
saving models.
- **config_name** ([`str`]) -- Filename to save a model to when calling [`~models.ModelMixin.save_pretrained`]. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/overview.md | https://huggingface.co/docs/diffusers/en/api/models/overview/#modelmixin | #modelmixin | .md | 186_2 |
FlaxModelMixin | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/overview.md | https://huggingface.co/docs/diffusers/en/api/models/overview/#flaxmodelmixin | #flaxmodelmixin | .md | 186_3 |
PushToHubMixin
A Mixin to push a model, scheduler, or pipeline to the Hugging Face Hub. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/overview.md | https://huggingface.co/docs/diffusers/en/api/models/overview/#pushtohubmixin | #pushtohubmixin | .md | 186_4 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or 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/models/flux_transformer.md | https://huggingface.co/docs/diffusers/en/api/models/flux_transformer/ | .md | 187_0 |
|
A Transformer model for image-like data from [Flux](https://blackforestlabs.ai/announcing-black-forest-labs/). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/flux_transformer.md | https://huggingface.co/docs/diffusers/en/api/models/flux_transformer/#fluxtransformer2dmodel | #fluxtransformer2dmodel | .md | 187_1 |
FluxTransformer2DModel
The Transformer model introduced in Flux.
Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
Args:
patch_size (`int`, defaults to `1`):
Patch size to turn the input data into small patches.
in_channels (`int`, defaults to `64`):
The number of channels in the input.
out_channels (`int`, *optional*, defaults to `None`):
The number of channels in the output. If not specified, it defaults to `in_channels`.
num_layers (`int`, defaults to `19`):
The number of layers of dual stream DiT blocks to use.
num_single_layers (`int`, defaults to `38`):
The number of layers of single stream DiT blocks to use.
attention_head_dim (`int`, defaults to `128`):
The number of dimensions to use for each attention head.
num_attention_heads (`int`, defaults to `24`):
The number of attention heads to use.
joint_attention_dim (`int`, defaults to `4096`):
The number of dimensions to use for the joint attention (embedding/channel dimension of
`encoder_hidden_states`).
pooled_projection_dim (`int`, defaults to `768`):
The number of dimensions to use for the pooled projection.
guidance_embeds (`bool`, defaults to `False`):
Whether to use guidance embeddings for guidance-distilled variant of the model.
axes_dims_rope (`Tuple[int]`, defaults to `(16, 56, 56)`):
The dimensions to use for the rotary positional embeddings. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/flux_transformer.md | https://huggingface.co/docs/diffusers/en/api/models/flux_transformer/#fluxtransformer2dmodel | #fluxtransformer2dmodel | .md | 187_2 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or 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/models/consisid_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/consisid_transformer3d/ | .md | 188_0 |
|
A Diffusion Transformer model for 3D data from [ConsisID](https://github.com/PKU-YuanGroup/ConsisID) was introduced in [Identity-Preserving Text-to-Video Generation by Frequency Decomposition](https://arxiv.org/pdf/2411.17440) by Peking University & University of Rochester & etc.
The model can be loaded with the following code snippet.
```python
from diffusers import ConsisIDTransformer3DModel
transformer = ConsisIDTransformer3DModel.from_pretrained("BestWishYsh/ConsisID-preview", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/consisid_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/consisid_transformer3d/#consisidtransformer3dmodel | #consisidtransformer3dmodel | .md | 188_1 |
[[autodoc]] ConsisIDTransformer3DModel | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/consisid_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/consisid_transformer3d/#consisidtransformer3dmodel | #consisidtransformer3dmodel | .md | 188_2 |
[[autodoc]] models.modeling_outputs.Transformer2DModelOutput | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/consisid_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/consisid_transformer3d/#transformer2dmodeloutput | #transformer2dmodeloutput | .md | 188_3 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or 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/models/autoencoder_oobleck.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoder_oobleck/ | .md | 189_0 |
|
The Oobleck variational autoencoder (VAE) model with KL loss was introduced in [Stability-AI/stable-audio-tools](https://github.com/Stability-AI/stable-audio-tools) and [Stable Audio Open](https://huggingface.co/papers/2407.14358) by Stability AI. The model is used in 🤗 Diffusers to encode audio waveforms into latents and to decode latent representations into audio waveforms.
The abstract from the paper is:
*Open generative models are vitally important for the community, allowing for fine-tunes and serving as baselines when presenting new models. However, most current text-to-audio models are private and not accessible for artists and researchers to build upon. Here we describe the architecture and training process of a new open-weights text-to-audio model trained with Creative Commons data. Our evaluation shows that the model's performance is competitive with the state-of-the-art across various metrics. Notably, the reported FDopenl3 results (measuring the realism of the generations) showcase its potential for high-quality stereo sound synthesis at 44.1kHz.* | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoder_oobleck.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoder_oobleck/#autoencoderoobleck | #autoencoderoobleck | .md | 189_1 |
AutoencoderOobleck
An autoencoder for encoding waveforms into latents and decoding latent representations into waveforms. First
introduced in Stable Audio.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
Parameters:
encoder_hidden_size (`int`, *optional*, defaults to 128):
Intermediate representation dimension for the encoder.
downsampling_ratios (`List[int]`, *optional*, defaults to `[2, 4, 4, 8, 8]`):
Ratios for downsampling in the encoder. These are used in reverse order for upsampling in the decoder.
channel_multiples (`List[int]`, *optional*, defaults to `[1, 2, 4, 8, 16]`):
Multiples used to determine the hidden sizes of the hidden layers.
decoder_channels (`int`, *optional*, defaults to 128):
Intermediate representation dimension for the decoder.
decoder_input_channels (`int`, *optional*, defaults to 64):
Input dimension for the decoder. Corresponds to the latent dimension.
audio_channels (`int`, *optional*, defaults to 2):
Number of channels in the audio data. Either 1 for mono or 2 for stereo.
sampling_rate (`int`, *optional*, defaults to 44100):
The sampling rate at which the audio waveform should be digitalized expressed in hertz (Hz).
- decode
- encode
- all | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoder_oobleck.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoder_oobleck/#autoencoderoobleck | #autoencoderoobleck | .md | 189_2 |
OobleckDecoderOutput
Output of decoding method.
Args:
sample (`torch.Tensor` of shape `(batch_size, audio_channels, sequence_length)`):
The decoded output sample from the last layer of the model.
OobleckDecoderOutput
Output of decoding method.
Args:
sample (`torch.Tensor` of shape `(batch_size, audio_channels, sequence_length)`):
The decoded output sample from the last layer of the model. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoder_oobleck.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoder_oobleck/#oobleckdecoderoutput | #oobleckdecoderoutput | .md | 189_3 |
AutoencoderOobleckOutput
Output of AutoencoderOobleck encoding method.
Args:
latent_dist (`OobleckDiagonalGaussianDistribution`):
Encoded outputs of `Encoder` represented as the mean and standard deviation of
`OobleckDiagonalGaussianDistribution`. `OobleckDiagonalGaussianDistribution` allows for sampling latents
from the distribution. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoder_oobleck.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoder_oobleck/#autoencoderoobleckoutput | #autoencoderoobleckoutput | .md | 189_4 |
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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
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/models/transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/transformer2d/ | .md | 190_0 |
|
A Transformer model for image-like data from [CompVis](https://huggingface.co/CompVis) that is based on the [Vision Transformer](https://huggingface.co/papers/2010.11929) introduced by Dosovitskiy et al. The [`Transformer2DModel`] accepts discrete (classes of vector embeddings) or continuous (actual embeddings) inputs.
When the input is **continuous**:
1. Project the input and reshape it to `(batch_size, sequence_length, feature_dimension)`.
2. Apply the Transformer blocks in the standard way.
3. Reshape to image.
When the input is **discrete**:
<Tip>
It is assumed one of the input classes is the masked latent pixel. The predicted classes of the unnoised image don't contain a prediction for the masked pixel because the unnoised image cannot be masked.
</Tip>
1. Convert input (classes of latent pixels) to embeddings and apply positional embeddings.
2. Apply the Transformer blocks in the standard way.
3. Predict classes of unnoised image. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/transformer2d/#transformer2dmodel | #transformer2dmodel | .md | 190_1 |
Transformer2DModel
A 2D Transformer model for image-like data.
Parameters:
num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
in_channels (`int`, *optional*):
The number of channels in the input and output (specify if the input is **continuous**).
num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).
This is fixed during training since it is used to learn a number of position embeddings.
num_vector_embeds (`int`, *optional*):
The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**).
Includes the class for the masked latent pixel.
activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward.
num_embeds_ada_norm ( `int`, *optional*):
The number of diffusion steps used during training. Pass if at least one of the norm_layers is
`AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are
added to the hidden states.
During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`.
attention_bias (`bool`, *optional*):
Configure if the `TransformerBlocks` attention should contain a bias parameter. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/transformer2d/#transformer2dmodel | #transformer2dmodel | .md | 190_2 |
Transformer2DModelOutput
The output of [`Transformer2DModel`].
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability
distributions for the unnoised latent pixels. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/transformer2d/#transformer2dmodeloutput | #transformer2dmodeloutput | .md | 190_3 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or 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/models/autoencoderkl_cogvideox.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl_cogvideox/ | .md | 191_0 |
|
The 3D variational autoencoder (VAE) model with KL loss used in [CogVideoX](https://github.com/THUDM/CogVideo) was introduced in [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://github.com/THUDM/CogVideo/blob/main/resources/CogVideoX.pdf) by Tsinghua University & ZhipuAI.
The model can be loaded with the following code snippet.
```python
from diffusers import AutoencoderKLCogVideoX
vae = AutoencoderKLCogVideoX.from_pretrained("THUDM/CogVideoX-2b", subfolder="vae", torch_dtype=torch.float16).to("cuda")
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl_cogvideox.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl_cogvideox/#autoencoderklcogvideox | #autoencoderklcogvideox | .md | 191_1 |
AutoencoderKLCogVideoX
A VAE model with KL loss for encoding images into latents and decoding latent representations into images. Used in
[CogVideoX](https://github.com/THUDM/CogVideo).
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
Parameters:
in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
out_channels (int, *optional*, defaults to 3): Number of channels in the output.
down_block_types (`Tuple[str]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
Tuple of downsample block types.
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
Tuple of upsample block types.
block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
Tuple of block output channels.
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
sample_size (`int`, *optional*, defaults to `32`): Sample input size.
scaling_factor (`float`, *optional*, defaults to `1.15258426`):
The component-wise standard deviation of the trained latent space computed using the first batch of the
training set. This is used to scale the latent space to have unit variance when training the diffusion
model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
/ scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
force_upcast (`bool`, *optional*, default to `True`):
If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE
can be fine-tuned / trained to a lower range without loosing too much precision in which case
`force_upcast` can be set to `False` - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix
- decode
- encode
- all | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl_cogvideox.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl_cogvideox/#autoencoderklcogvideox | #autoencoderklcogvideox | .md | 191_2 |
AutoencoderKLOutput
Output of AutoencoderKL encoding method.
Args:
latent_dist (`DiagonalGaussianDistribution`):
Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`.
`DiagonalGaussianDistribution` allows for sampling latents from the distribution. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl_cogvideox.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl_cogvideox/#autoencoderkloutput | #autoencoderkloutput | .md | 191_3 |
DecoderOutput
Output of decoding method.
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):
The decoded output sample from the last layer of the model. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl_cogvideox.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl_cogvideox/#decoderoutput | #decoderoutput | .md | 191_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/models/aura_flow_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/aura_flow_transformer2d/ | .md | 192_0 |
|
A Transformer model for image-like data from [AuraFlow](https://blog.fal.ai/auraflow/). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/aura_flow_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/aura_flow_transformer2d/#auraflowtransformer2dmodel | #auraflowtransformer2dmodel | .md | 192_1 |
AuraFlowTransformer2DModel
A 2D Transformer model as introduced in AuraFlow (https://blog.fal.ai/auraflow/).
Parameters:
sample_size (`int`): The width of the latent images. This is fixed during training since
it is used to learn a number of position embeddings.
patch_size (`int`): Patch size to turn the input data into small patches.
in_channels (`int`, *optional*, defaults to 16): The number of channels in the input.
num_mmdit_layers (`int`, *optional*, defaults to 4): The number of layers of MMDiT Transformer blocks to use.
num_single_dit_layers (`int`, *optional*, defaults to 4):
The number of layers of Transformer blocks to use. These blocks use concatenated image and text
representations.
attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.
num_attention_heads (`int`, *optional*, defaults to 18): The number of heads to use for multi-head attention.
joint_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
caption_projection_dim (`int`): Number of dimensions to use when projecting the `encoder_hidden_states`.
out_channels (`int`, defaults to 16): Number of output channels.
pos_embed_max_size (`int`, defaults to 4096): Maximum positions to embed from the image latents. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/aura_flow_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/aura_flow_transformer2d/#auraflowtransformer2dmodel | #auraflowtransformer2dmodel | .md | 192_2 |
<!--Copyright 2024 The HuggingFace Team and The InstantX 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/models/controlnet_flux.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_flux/ | .md | 193_0 |
|
FluxControlNetModel is an implementation of ControlNet for Flux.1.
The ControlNet model was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, Maneesh Agrawala. It provides a greater degree of control over text-to-image generation by conditioning the model on additional inputs such as edge maps, depth maps, segmentation maps, and keypoints for pose detection.
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.* | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_flux.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_flux/#fluxcontrolnetmodel | #fluxcontrolnetmodel | .md | 193_1 |
By default the [`FluxControlNetModel`] should be loaded with [`~ModelMixin.from_pretrained`].
```py
from diffusers import FluxControlNetPipeline
from diffusers.models import FluxControlNetModel, FluxMultiControlNetModel
controlnet = FluxControlNetModel.from_pretrained("InstantX/FLUX.1-dev-Controlnet-Canny")
pipe = FluxControlNetPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", controlnet=controlnet)
controlnet = FluxControlNetModel.from_pretrained("InstantX/FLUX.1-dev-Controlnet-Canny")
controlnet = FluxMultiControlNetModel([controlnet])
pipe = FluxControlNetPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", controlnet=controlnet)
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_flux.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_flux/#loading-from-the-original-format | #loading-from-the-original-format | .md | 193_2 |
FluxControlNetModel | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_flux.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_flux/#fluxcontrolnetmodel | #fluxcontrolnetmodel | .md | 193_3 |
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