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/pixart_sigma.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart_sigma/#pixart-σ | .md | </Tip>
<Tip>
You can further improve generation quality by passing the generated image from [`PixArtSigmaPipeline`] to the [SDXL refiner](../../using-diffusers/sdxl#base-to-refiner-model) model.
</Tip> | 144_1_9 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart_sigma.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart_sigma/#inference-with-under-8gb-gpu-vram | .md | Run the [`PixArtSigmaPipeline`] with under 8GB GPU VRAM by loading the text encoder in 8-bit precision. Let's walk through a full-fledged example.
First, install the [bitsandbytes](https://github.com/TimDettmers/bitsandbytes) library:
```bash
pip install -U bitsandbytes
```
Then load the text encoder in 8-bit:
... | 144_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart_sigma.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart_sigma/#inference-with-under-8gb-gpu-vram | .md | text_encoder = T5EncoderModel.from_pretrained(
"PixArt-alpha/PixArt-Sigma-XL-2-1024-MS",
subfolder="text_encoder",
load_in_8bit=True,
device_map="auto",
)
pipe = PixArtSigmaPipeline.from_pretrained(
"PixArt-alpha/PixArt-Sigma-XL-2-1024-MS",
text_encoder=text_encoder,
transformer=None,
device_map="balanced"
)
```
Now,... | 144_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart_sigma.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart_sigma/#inference-with-under-8gb-gpu-vram | .md | device_map="balanced"
)
```
Now, use the `pipe` to encode a prompt:
```python
with torch.no_grad():
prompt = "cute cat"
prompt_embeds, prompt_attention_mask, negative_embeds, negative_prompt_attention_mask = pipe.encode_prompt(prompt)
```
Since text embeddings have been computed, remove the `text_encoder` and `pi... | 144_2_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart_sigma.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart_sigma/#inference-with-under-8gb-gpu-vram | .md | def flush():
gc.collect()
torch.cuda.empty_cache()
del text_encoder
del pipe
flush()
```
Then compute the latents with the prompt embeddings as inputs:
```python
pipe = PixArtSigmaPipeline.from_pretrained(
"PixArt-alpha/PixArt-Sigma-XL-2-1024-MS",
text_encoder=None,
torch_dtype=torch.float16,
).to("cuda") | 144_2_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart_sigma.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart_sigma/#inference-with-under-8gb-gpu-vram | .md | latents = pipe(
negative_prompt=None,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
prompt_attention_mask=prompt_attention_mask,
negative_prompt_attention_mask=negative_prompt_attention_mask,
num_images_per_prompt=1,
output_type="latent",
).images | 144_2_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart_sigma.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart_sigma/#inference-with-under-8gb-gpu-vram | .md | del pipe.transformer
flush()
```
<Tip>
Notice that while initializing `pipe`, you're setting `text_encoder` to `None` so that it's not loaded.
</Tip>
Once the latents are computed, pass it off to the VAE to decode into a real image:
```python
with torch.no_grad():
image = pipe.vae.decode(latents / pipe.vae.co... | 144_2_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart_sigma.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart_sigma/#inference-with-under-8gb-gpu-vram | .md | image = pipe.image_processor.postprocess(image, output_type="pil")[0]
image.save("cat.png")
```
By deleting components you aren't using and flushing the GPU VRAM, you should be able to run [`PixArtSigmaPipeline`] with under 8GB GPU VRAM.
; 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... | 145_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/ | .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.
--> | 145_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#perturbed-attention-guidance | .md | [Perturbed-Attention Guidance (PAG)](https://ku-cvlab.github.io/Perturbed-Attention-Guidance/) is a new diffusion sampling guidance that improves sample quality across both unconditional and conditional settings, achieving this without requiring further training or the integration of external modules. | 145_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#perturbed-attention-guidance | .md | PAG was introduced in [Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance](https://huggingface.co/papers/2403.17377) by Donghoon Ahn, Hyoungwon Cho, Jaewon Min, Wooseok Jang, Jungwoo Kim, SeonHwa Kim, Hyun Hee Park, Kyong Hwan Jin and Seungryong Kim.
The abstract from the paper is: | 145_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#perturbed-attention-guidance | .md | *Recent studies have demonstrated that diffusion models are capable of generating high-quality samples, but their quality heavily depends on sampling guidance techniques, such as classifier guidance (CG) and classifier-free guidance (CFG). These techniques are often not applicable in unconditional generation or in vari... | 145_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#perturbed-attention-guidance | .md | we propose a novel sampling guidance, called Perturbed-Attention Guidance (PAG), which improves diffusion sample quality across both unconditional and conditional settings, achieving this without requiring additional training or the integration of external modules. PAG is designed to progressively enhance the structure... | 145_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#perturbed-attention-guidance | .md | generating intermediate samples with degraded structure by substituting selected self-attention maps in diffusion U-Net with an identity matrix, by considering the self-attention mechanisms' ability to capture structural information, and guiding the denoising process away from these degraded samples. In both ADM and St... | 145_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#perturbed-attention-guidance | .md | and even unconditional scenarios. Moreover, PAG significantly improves the baseline performance in various downstream tasks where existing guidances such as CG or CFG cannot be fully utilized, including ControlNet with empty prompts and image restoration such as inpainting and deblurring.* | 145_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#perturbed-attention-guidance | .md | PAG can be used by specifying the `pag_applied_layers` as a parameter when instantiating a PAG pipeline. It can be a single string or a list of strings. Each string can be a unique layer identifier or a regular expression to identify one or more layers.
- Full identifier as a normal string: `down_blocks.2.attentions.... | 145_1_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#perturbed-attention-guidance | .md | - Full identifier as a RegEx: `down_blocks.2.(attentions|motion_modules).0.transformer_blocks.0.attn1.processor`
- Partial identifier as a RegEx: `down_blocks.2`, or `attn1`
- List of identifiers (can be combo of strings and ReGex): `["blocks.1", "blocks.(14|20)", r"down_blocks\.(2,3)"]`
<Tip warning={true}> | 145_1_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#perturbed-attention-guidance | .md | <Tip warning={true}>
Since RegEx is supported as a way for matching layer identifiers, it is crucial to use it correctly otherwise there might be unexpected behaviour. The recommended way to use PAG is by specifying layers as `blocks.{layer_index}` and `blocks.({layer_index_1|layer_index_2|...})`. Using it in any oth... | 145_1_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#animatediffpagpipeline | .md | AnimateDiffPAGPipeline
Pipeline for text-to-video generation using
[AnimateDiff](https://huggingface.co/docs/diffusers/en/api/pipelines/animatediff) and [Perturbed Attention
Guidance](https://huggingface.co/docs/diffusers/en/using-diffusers/pag).
This model inherits from [`DiffusionPipeline`]. Check the superclass ... | 145_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#animatediffpagpipeline | .md | 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`] fo... | 145_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#animatediffpagpipeline | .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 ([clip-vit-large-patch14](https://huggingface.co/openai/clip-v... | 145_2_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#animatediffpagpipeline | .md | tokenizer (`CLIPTokenizer`):
A [`~transformers.CLIPTokenizer`] to tokenize text.
unet ([`UNet2DConditionModel`]):
A [`UNet2DConditionModel`] used to create a UNetMotionModel to denoise the encoded video latents.
motion_adapter ([`MotionAdapter`]):
A [`MotionAdapter`] to be used in combination with `unet` to denoise the... | 145_2_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#animatediffpagpipeline | .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__ | 145_2_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#hunyuanditpagpipeline | .md | HunyuanDiTPAGPipeline
Pipeline for English/Chinese-to-image generation using HunyuanDiT and [Perturbed Attention
Guidance](https://huggingface.co/docs/diffusers/en/using-diffusers/pag).
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements ... | 145_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#hunyuanditpagpipeline | .md | library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
HunyuanDiT uses two text encoders: [mT5](https://huggingface.co/google/mt5-base) and [bilingual CLIP](fine-tuned by
ourselves)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode ... | 145_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#hunyuanditpagpipeline | .md | `sdxl-vae-fp16-fix`.
text_encoder (Optional[`~transformers.BertModel`, `~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
HunyuanDiT uses a fine-tuned [bilingual CLIP].
tokenizer (Optional[`~transformers.BertTokenizer`, `~transformers.C... | 145_3_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#hunyuanditpagpipeline | .md | transformer ([`HunyuanDiT2DModel`]):
The HunyuanDiT model designed by Tencent Hunyuan.
text_encoder_2 (`T5EncoderModel`):
The mT5 embedder. Specifically, it is 't5-v1_1-xxl'.
tokenizer_2 (`MT5Tokenizer`):
The tokenizer for the mT5 embedder.
scheduler ([`DDPMScheduler`]):
A scheduler to be used in combination with Hunyu... | 145_3_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#kolorspagpipeline | .md | KolorsPAGPipeline
- all
- __call__ | 145_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionpaginpaintpipeline | .md | StableDiffusionPAGInpaintPipeline
Pipeline for text-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 in... | 145_5_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionpaginpaintpipeline | .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... | 145_5_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionpaginpaintpipeline | .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... | 145_5_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionpaginpaintpipeline | .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... | 145_5_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionpaginpaintpipeline | .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/runwayml/stable-diffusion-v1-5) for more d... | 145_5_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionpaginpaintpipeline | .md | 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__ | 145_5_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionpagpipeline | .md | StableDiffusionPAGPipeline
Pipeline for text-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 ... | 145_6_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionpagpipeline | .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... | 145_6_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionpagpipeline | .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... | 145_6_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionpagpipeline | .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... | 145_6_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionpagpipeline | .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/runwayml/stable-diffusion-v1-5) for more d... | 145_6_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionpagpipeline | .md | 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__ | 145_6_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionpagimg2imgpipeline | .md | StableDiffusionPAGImg2ImgPipeline
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 pip... | 145_7_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionpagimg2imgpipeline | .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... | 145_7_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionpagimg2imgpipeline | .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... | 145_7_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionpagimg2imgpipeline | .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... | 145_7_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionpagimg2imgpipeline | .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/runwayml/stable-diffusion-v1-5) for more d... | 145_7_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionpagimg2imgpipeline | .md | 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__ | 145_7_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusioncontrolnetpagpipeline | .md | StableDiffusionControlNetPAGPipeline
Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, e... | 145_8_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusioncontrolnetpagpipeline | .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... | 145_8_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusioncontrolnetpagpipeline | .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... | 145_8_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusioncontrolnetpagpipeline | .md | tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
Provides additional conditioning to the `unet` during the denoising process. If yo... | 145_8_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusioncontrolnetpagpipeline | .md | additional conditioning.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whethe... | 145_8_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusioncontrolnetpagpipeline | .md | Please refer to the [model card](https://huggingface.co/runwayml/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`. | 145_8_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusioncontrolnetpaginpaintpipeline | .md | StableDiffusionControlNetPAGInpaintPipeline
Pipeline for image inpainting using Stable Diffusion with ControlNet guidance.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, et... | 145_9_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusioncontrolnetpaginpaintpipeline | .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... | 145_9_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusioncontrolnetpaginpaintpipeline | .md | - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
<Tip>
This pipeline can be used with checkpoints that have been specifically fine-tuned for inpainting
([runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting)) as well as
default text-to-image Stable D... | 145_9_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusioncontrolnetpaginpaintpipeline | .md | ([runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)). Default text-to-image
Stable Diffusion checkpoints might be preferable for ControlNets that have been fine-tuned on those, such as
[lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint... | 145_9_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusioncontrolnetpaginpaintpipeline | .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`]):... | 145_9_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusioncontrolnetpaginpaintpipeline | .md | unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
Provides additional conditioning to the `unet` during the denoising process. If you set multiple
ControlNets as a list, the outputs from each ControlNet are added ... | 145_9_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusioncontrolnetpaginpaintpipeline | .md | 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 harmf... | 145_9_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusioncontrolnetpaginpaintpipeline | .md | 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__ | 145_9_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlpagpipeline | .md | StableDiffusionXLPAGPipeline
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.) ... | 145_10_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlpagpipeline | .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... | 145_10_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlpagpipeline | .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... | 145_10_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlpagpipeline | .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... | 145_10_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlpagpipeline | .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... | 145_10_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlpagpipeline | .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`].
force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
Whether the negative prompt embeddings shall b... | 145_10_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlpagpipeline | .md | `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.
... | 145_10_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlpagimg2imgpipeline | .md | StableDiffusionXLPAGImg2ImgPipeline
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,... | 145_11_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlpagimg2imgpipeline | .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... | 145_11_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlpagimg2imgpipeline | .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... | 145_11_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlpagimg2imgpipeline | .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... | 145_11_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlpagimg2imgpipeline | .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... | 145_11_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlpagimg2imgpipeline | .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`... | 145_11_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlpagimg2imgpipeline | .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.... | 145_11_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlpaginpaintpipeline | .md | StableDiffusionXLPAGInpaintPipeline
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,... | 145_12_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlpaginpaintpipeline | .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... | 145_12_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlpaginpaintpipeline | .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... | 145_12_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlpaginpaintpipeline | .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... | 145_12_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlpaginpaintpipeline | .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... | 145_12_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlpaginpaintpipeline | .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... | 145_12_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlpaginpaintpipeline | .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... | 145_12_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlpaginpaintpipeline | .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__ | 145_12_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlcontrolnetpagpipeline | .md | StableDiffusionXLControlNetPAGPipeline
Pipeline for text-to-image generation using Stable Diffusion XL with ControlNet guidance.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular devi... | 145_13_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlcontrolnetpagpipeline | .md | - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.FromSingleFileMixin... | 145_13_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlcontrolnetpagpipeline | .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... | 145_13_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlcontrolnetpagpipeline | .md | text_encoder_2 ([`~transformers.CLIPTextModelWithProjection`]):
Second frozen text-encoder
([laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
tokenizer_2 ([`~transformers.CLIPToke... | 145_13_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlcontrolnetpagpipeline | .md | unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
Provides additional conditioning to the `unet` during the denoising process. If you set multiple
ControlNets as a list, the outputs from each ControlNet are added ... | 145_13_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlcontrolnetpagpipeline | .md | A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
Whether the negative prompt embeddings should always be set to 0. Also see the... | 145_13_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlcontrolnetpagpipeline | .md | `stabilityai/stable-diffusion-xl-base-1-0`.
add_watermarker (`bool`, *optional*):
Whether to use the [invisible_watermark](https://github.com/ShieldMnt/invisible-watermark/) library to
watermark output images. If not defined, it defaults to `True` if the package is installed; otherwise no
watermarker is used.
- all
-... | 145_13_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlcontrolnetpagimg2imgpipeline | .md | StableDiffusionXLControlNetPAGImg2ImgPipeline
Pipeline for image-to-image generation using Stable Diffusion XL with ControlNet guidance.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or sav... | 145_14_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlcontrolnetpagimg2imgpipeline | .md | The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`... | 145_14_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlcontrolnetpagimg2imgpipeline | .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 uses the text portion of
[CLIP](https://hugg... | 145_14_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlcontrolnetpagimg2imgpipeline | .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... | 145_14_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlcontrolnetpagimg2imgpipeline | .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... | 145_14_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlcontrolnetpagimg2imgpipeline | .md | controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets
as a list, the outputs from each ControlNet are added together to create one combined additional
conditioning.
scheduler ([`SchedulerMixin`]):
A schedul... | 145_14_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlcontrolnetpagimg2imgpipeline | .md | [`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_... | 145_14_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlcontrolnetpagimg2imgpipeline | .md | 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 de... | 145_14_7 |
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