source stringclasses 273
values | url stringlengths 47 172 | file_type stringclasses 1
value | chunk stringlengths 1 512 | chunk_id stringlengths 5 9 |
|---|---|---|---|---|
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_xl/#stablediffusionxlimg2imgpipeline | .md | 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, et... | 174_4_0 |
/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 | .md | - [`~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.StableDiffusionXLLoraLoaderMixi... | 174_4_1 |
/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 | .md | - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion XL uses the text portion of
[CLIP](https://h... | 174_4_2 |
/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 | .md | 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.CLIPTextModelWithPr... | 174_4_3 |
/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 | .md | 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#transformer... | 174_4_4 |
/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 | .md | 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`... | 174_4_5 |
/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 | .md | 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.... | 174_4_6 |
/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 | .md | 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, et... | 174_5_0 |
/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 | .md | - [`~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.StableDiffusionXLLoraLoaderMixi... | 174_5_1 |
/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 | .md | - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion XL uses the text portion of
[CLIP](https://h... | 174_5_2 |
/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 | .md | 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.CLIPTextModelWithPr... | 174_5_3 |
/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 | .md | 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#transformer... | 174_5_4 |
/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 | .md | 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 co... | 174_5_5 |
/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 | .md | 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... | 174_5_6 |
/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 | .md | 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__ | 174_5_7 |
/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 | <!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | 175_0_0 |
/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 | 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.
--> | 175_0_1 |
/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 | .md | 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 Di... | 175_1_0 |
/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 | .md | 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> | 175_1_1 |
/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 | .md | 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.)... | 175_2_0 |
/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 | .md | - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
Args:
vae ([`AutoencoderKL`]):
Var... | 175_2_1 |
/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 | .md | 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 la... | 175_2_2 |
/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 | .md | 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_effici... | 175_2_3 |
/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 | .md | StableDiffusionPipelineOutput
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
nsfw_content_detected (`List[bool]`)
List indicating whether the c... | 175_3_0 |
/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 | <!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | 176_0_0 |
/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 | 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.
--> | 176_0_1 |
/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 | .md | 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... | 176_1_0 |
/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 | .md | 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> | 176_1_1 |
/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 | .md | 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 pipelin... | 176_2_0 |
/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 | .md | - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.FromSingleFileMixin.fro... | 176_2_1 |
/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 | .md | vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):... | 176_2_2 |
/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 | .md | 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... | 176_2_3 |
/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 | .md | - all
- __call__
- enable_attention_slicing
- disable_attention_slicing
- enable_xformers_memory_efficient_attention
- disable_xformers_memory_efficient_attention | 176_2_4 |
/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 | .md | StableDiffusionPipelineOutput
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
nsfw_content_detected (`List[bool]`)
List indicating whether the c... | 176_3_0 |
/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 | <!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | 177_0_0 |
/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 | 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.
--> | 177_0_1 |
/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 | .md | 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... | 177_1_0 |
/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 | .md | *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,... | 177_1_1 |
/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 | .md | 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... | 177_1_2 |
/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 | .md | 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 pr... | 177_1_3 |
/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 | .md | 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 ima... | 177_1_4 |
/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 | .md | <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 ... | 177_1_5 |
/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 | .md | 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 det... | 177_2_0 |
/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 | .md | 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 ... | 177_3_0 |
/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 | .md | 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 ([`UNetSpatioTempo... | 177_3_1 |
/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 | .md | 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 i... | 177_3_2 |
/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 | .md | 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)`. | 177_4_0 |
/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 | <!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | 178_0_0 |
/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 | 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.
--> | 178_0_1 |
/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 | .md | 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 Dif... | 178_1_0 |
/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 | .md | *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 con... | 178_1_1 |
/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 | .md | 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 sto... | 178_1_2 |
/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 | .md | 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 outperform... | 178_1_3 |
/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 | .md | 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.* | 178_1_4 |
/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 | .md | <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> | 178_1_5 |
/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 | .md | 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 pipeli... | 178_2_0 |
/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 | .md | - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.FromSingleFileMixin.fro... | 178_2_1 |
/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 | .md | - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co... | 178_2_2 |
/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 | .md | tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMSch... | 178_2_3 |
/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 | .md | [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-... | 178_2_4 |
/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 | .md | 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_a... | 178_2_5 |
/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 | .md | StableDiffusionPipelineOutput
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
nsfw_content_detected (`List[bool]`)
List indicating whether the c... | 178_3_0 |
/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 | .md | FlaxStableDiffusionImg2ImgPipeline
- all
- __call__ | 178_4_0 |
/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 | .md | [[autodoc]] FlaxStableDiffusionPipelineOutput: module diffusers.pipelines.stable_diffusion has no attribute FlaxStableDiffusionPipelineOutput | 178_5_0 |
/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 | <!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | 179_0_0 |
/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 | 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.
--> | 179_0_1 |
/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 | .md | [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 co... | 179_1_0 |
/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 | .md | *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... | 179_1_1 |
/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 | .md | 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... | 179_1_2 |
/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 | .md | 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 promisi... | 179_1_3 |
/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 | .md | This model was contributed by the community contributor [HimariO](https://github.com/HimariO) ❤️ . | 179_1_4 |
/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 | .md | 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 dow... | 179_2_0 |
/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 | .md | 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]`, *op... | 179_2_1 |
/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 | .md | 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.CLIPTextMod... | 179_2_2 |
/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 | .md | 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 ... | 179_2_3 |
/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 | .md | [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1... | 179_2_4 |
/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 | .md | 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 | 179_2_5 |
/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 | .md | 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 d... | 179_3_0 |
/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 | .md | 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... | 179_3_1 |
/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 | .md | - [`~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 o... | 179_3_2 |
/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 | .md | 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 ([... | 179_3_3 |
/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 | .md | [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_d... | 179_3_4 |
/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 | .md | 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 ... | 179_3_5 |
/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 | .md | 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_attenti... | 179_3_6 |
/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 | <!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | 180_0_0 |
/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 | 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.
--> | 180_0_1 |
/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 | .md | 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 Diffusi... | 180_1_0 |
/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 | .md | self-harm, and otherwise offensive content. Safe Stable Diffusion is an extension of Stable Diffusion that drastically reduces this type of content. | 180_1_1 |
/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 | .md | The abstract from the paper is: | 180_1_2 |
/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 | .md | *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 w... | 180_1_3 |
/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 | .md | 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)-contain... | 180_1_4 |
/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 | .md | 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.* | 180_1_5 |
/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 | .md | Use the `safety_concept` property of [`StableDiffusionPipelineSafe`] to check and edit the current safety concept:
```python
>>> from diffusers import StableDiffusionPipelineSafe | 180_2_0 |
/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 | .md | >>> 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, ch... | 180_2_1 |
/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 | .md | ```
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
>>>... | 180_2_2 |
/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 | .md | >>> 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... | 180_2_3 |
/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 | .md | 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 particul... | 180_3_0 |
/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 | .md | - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co... | 180_3_1 |
/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 | .md | tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMSch... | 180_3_2 |
/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 | .md | [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-... | 180_3_3 |
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