#
Stable diffusion XL

Stable Diffusion XL은 Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, Robin Rombach에 의해 SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis에서 제안되었습니다.

논문 초록은 다음을 따릅니다:

text-to-image의 latent diffusion 모델인 SDXL을 소개합니다. 이전 버전의 Stable Diffusion과 비교하면, SDXL은 세 배 더큰 규모의 UNet 백본을 포함합니다: 모델 파라미터의 증가는 많은 attention 블럭을 사용하고 더 큰 cross-attention context를 SDXL의 두 번째 텍스트 인코더에 사용하기 때문입니다. 다중 종횡비에 다수의 새로운 conditioning 방법을 구성했습니다. 또한 후에 수정하는 image-to-image 기술을 사용함으로써 SDXL에 의해 생성된 시각적 품질을 향상하기 위해 정제된 모델을 소개합니다. SDXL은 이전 버전의 Stable Diffusion보다 성능이 향상되었고, 이러한 black-box 최신 이미지 생성자와 경쟁력있는 결과를 달성했습니다.

##
팁

- Stable Diffusion XL은 특히 786과 1024사이의 이미지에 잘 작동합니다.
- Stable Diffusion XL은 아래와 같이 학습된 각 텍스트 인코더에 대해 서로 다른 프롬프트를 전달할 수 있습니다. 동일한 프롬프트의 다른 부분을 텍스트 인코더에 전달할 수도 있습니다.
- Stable Diffusion XL 결과 이미지는 아래에 보여지듯이 정제기(refiner)를 사용함으로써 향상될 수 있습니다.

###
이용가능한 체크포인트:

- Text-to-Image (1024x1024 해상도): StableDiffusionXLPipeline을 사용한 stabilityai/stable-diffusion-xl-base-1.0
- Image-to-Image / 정제기(refiner) (1024x1024 해상도): StableDiffusionXLImg2ImgPipeline를 사용한 stabilityai/stable-diffusion-xl-refiner-1.0

##
사용 예시

SDXL을 사용하기 전에 `transformers`, `accelerate`, `safetensors` 와 `invisible_watermark`를 설치하세요. 다음과 같이 라이브러리를 설치할 수 있습니다:

    pip install transformers
    pip install accelerate
    pip install safetensors
    pip install invisible-watermark>=0.2.0

###
워터마커

Stable Diffusion XL로 이미지를 생성할 때 워터마크가 보이지 않도록 추가하는 것을 권장하는데, 이는 다운스트림(downstream) 어플리케이션에서 기계에 합성되었는지를 식별하는데 도움을 줄 수 있습니다. 그렇게 하려면 invisible_watermark 라이브러리를 통해 설치해주세요:

    pip install invisible-watermark>=0.2.0

`invisible-watermark` 라이브러리가 설치되면 워터마커가 기본적으로 사용될 것입니다.

생성 또는 안전하게 이미지를 배포하기 위해 다른 규정이 있다면, 다음과 같이 워터마커를 비활성화할 수 있습니다:

    pipe = StableDiffusionXLPipeline.from_pretrained(..., add_watermarker=False)

###
Text-to-Image

text-to-image를 위해 다음과 같이 SDXL을 사용할 수 있습니다:

    from diffusers import StableDiffusionXLPipeline
    import torch

    pipe = StableDiffusionXLPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
    )
    pipe.to("cuda")

    prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
    image = pipe(prompt=prompt).images[0]

###
Image-to-image

image-to-image를 위해 다음과 같이 SDXL을 사용할 수 있습니다:

    import torch
    from diffusers import StableDiffusionXLImg2ImgPipeline
    from diffusers.utils import load_image

    pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
    )
    pipe = pipe.to("cuda")
    url = "https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/aa_xl/000000009.png"

    init_image = load_image(url).convert("RGB")
    prompt = "a photo of an astronaut riding a horse on mars"
    image = pipe(prompt, image=init_image).images[0]

###
인페인팅

inpainting를 위해 다음과 같이 SDXL을 사용할 수 있습니다:

    import torch
    from diffusers import StableDiffusionXLInpaintPipeline
    from diffusers.utils import load_image

    pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
    )
    pipe.to("cuda")

    img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
    mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"

    init_image = load_image(img_url).convert("RGB")
    mask_image = load_image(mask_url).convert("RGB")

    prompt = "A majestic tiger sitting on a bench"
    image = pipe(prompt=prompt, image=init_image, mask_image=mask_image, num_inference_steps=50, strength=0.80).images[0]

###
이미지 결과물을 정제하기

base 모델 체크포인트에서, StableDiffusion-XL 또한 고주파 품질을 향상시키는 이미지를 생성하기 위해 낮은 노이즈 단계 이미지를 제거하는데 특화된 refiner 체크포인트를 포함하고 있습니다. 이 refiner 체크포인트는 이미지 품질을 향상시키기 위해 base 체크포인트를 실행한 후 “두 번째 단계” 파이프라인에 사용될 수 있습니다.

refiner를 사용할 때, 쉽게 사용할 수 있습니다

- 1.) base 모델과 refiner을 사용하는데, 이는 Denoisers의 앙상블을 위한 첫 번째 제안된 eDiff-I를 사용하거나
- 2.) base 모델을 거친 후 SDEdit 방법으로 단순하게 refiner를 실행시킬 수 있습니다.

참고: SD-XL base와 refiner를 앙상블로 사용하는 아이디어는 커뮤니티 기여자들이 처음으로 제안했으며, 이는 다음과 같은 `diffusers`를 구현하는 데도 도움을 주셨습니다.

- SytanSD
- bghira
- Birch-san
- AmericanPresidentJimmyCarter

####
1.) Denoisers의 앙상블

base와 refiner 모델을 denoiser의 앙상블로 사용할 때, base 모델은 고주파 diffusion 단계를 위한 전문가의 역할을 해야하고, refiner는 낮은 노이즈 diffusion 단계를 위한 전문가의 역할을 해야 합니다.

2.)에 비해 1.)의 장점은 전체적으로 denoising 단계가 덜 필요하므로 속도가 훨씬 더 빨라집니다. 단점은 base 모델의 결과를 검사할 수 없다는 것입니다. 즉, 여전히 노이즈가 심하게 제거됩니다.

base 모델과 refiner를 denoiser의 앙상블로 사용하기 위해 각각 고노이즈(high-nosise) (즉 base 모델)와 저노이즈 (즉 refiner 모델)의 노이즈를 제거하는 단계를 거쳐야하는 타임스텝의 기간을 정의해야 합니다. base 모델의 `denoising_end`와 refiner 모델의 `denoising_start`를 사용해 간격을 정합니다.

`denoising_end`와 `denoising_start` 모두 0과 1사이의 실수 값으로 전달되어야 합니다. 전달되면 노이즈 제거의 끝과 시작은 모델 스케줄에 의해 정의된 이산적(discrete) 시간 간격의 비율로 정의됩니다. 노이즈 제거 단계의 수는 모델이 학습된 불연속적인 시간 간격과 선언된 fractional cutoff에 의해 결정되므로 ‘강도’ 또한 선언된 경우 이 값이 ‘강도’를 재정의합니다.

예시를 들어보겠습니다. 우선, 두 개의 파이프라인을 가져옵니다. 텍스트 인코더와 variational autoencoder는 동일하므로 refiner를 위해 다시 불러오지 않아도 됩니다.

    from diffusers import DiffusionPipeline
    import torch

    base = DiffusionPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
    )
    pipe.to("cuda")

    refiner = DiffusionPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-refiner-1.0",
        text_encoder_2=base.text_encoder_2,
        vae=base.vae,
        torch_dtype=torch.float16,
        use_safetensors=True,
        variant="fp16",
    )
    refiner.to("cuda")

이제 추론 단계의 수와 고노이즈에서 노이즈를 제거하는 단계(즉 base 모델)를 거쳐 실행되는 지점을 정의합니다.

    n_steps = 40
    high_noise_frac = 0.8

Stable Diffusion XL base 모델은 타임스텝 0-999에 학습되며 Stable Diffusion XL refiner는 포괄적인 낮은 노이즈 타임스텝인 0-199에 base 모델로 부터 파인튜닝되어, 첫 800 타임스텝 (높은 노이즈)에 base 모델을 사용하고 마지막 200 타입스텝 (낮은 노이즈)에서 refiner가 사용됩니다. 따라서, `high_noise_frac`는 0.8로 설정하고, 모든 200-999 스텝(노이즈 제거 타임스텝의 첫 80%)은 base 모델에 의해 수행되며 0-199 스텝(노이즈 제거 타임스텝의 마지막 20%)은 refiner 모델에 의해 수행됩니다.

기억하세요, 노이즈 제거 절차는 높은 값(높은 노이즈) 타임스텝에서 시작되고, 낮은 값 (낮은 노이즈) 타임스텝에서 끝납니다.

이제 두 파이프라인을 실행해봅시다. `denoising_end`과 `denoising_start`를 같은 값으로 설정하고 `num_inference_steps`는 상수로 유지합니다. 또한 base 모델의 출력은 잠재 공간에 있어야 한다는 점을 기억하세요:

    prompt = "A majestic lion jumping from a big stone at night"

    image = base(
        prompt=prompt,
        num_inference_steps=n_steps,
        denoising_end=high_noise_frac,
        output_type="latent",
    ).images
    image = refiner(
        prompt=prompt,
        num_inference_steps=n_steps,
        denoising_start=high_noise_frac,
        image=image,
    ).images[0]

이미지를 살펴보겠습니다.

원래의 이미지 | Denoiser들의 앙상블
--- | ---

동일한 40 단계에서 base 모델을 실행한다면, 이미지의 디테일(예: 사자의 눈과 코)이 떨어졌을 것입니다:

앙상블 방식은 사용 가능한 모든 스케줄러에서 잘 작동합니다!

####
2.) 노이즈가 완전히 제거된 기본 이미지에서 이미지 출력을 정제하기

일반적인 `StableDiffusionImg2ImgPipeline` 방식에서, 기본 모델에서 생성된 완전히 노이즈가 제거된 이미지는 refiner checkpoint를 사용해 더 향상시킬 수 있습니다.

이를 위해, 보통의 “base” text-to-image 파이프라인을 수행 후에 image-to-image 파이프라인으로써 refiner를 실행시킬 수 있습니다. base 모델의 출력을 잠재 공간에 남겨둘 수 있습니다.

    from diffusers import DiffusionPipeline
    import torch

    pipe = DiffusionPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
    )
    pipe.to("cuda")

    refiner = DiffusionPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-refiner-1.0",
        text_encoder_2=pipe.text_encoder_2,
        vae=pipe.vae,
        torch_dtype=torch.float16,
        use_safetensors=True,
        variant="fp16",
    )
    refiner.to("cuda")

    prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"

    image = pipe(prompt=prompt, output_type="latent" if use_refiner else "pil").images[0]
    image = refiner(prompt=prompt, image=image[None, :]).images[0]

원래의 이미지 | 정제된 이미지
--- | ---

refiner는 또한 인페인팅 설정에 잘 사용될 수 있습니다. 아래에 보여지듯이 StableDiffusionXLInpaintPipeline 클래스를 사용해서 만들어보세요.

Denoiser 앙상블 설정에서 인페인팅에 refiner를 사용하려면 다음을 수행하면 됩니다:

    from diffusers import StableDiffusionXLInpaintPipeline
    from diffusers.utils import load_image

    pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
    )
    pipe.to("cuda")

    refiner = StableDiffusionXLInpaintPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-refiner-1.0",
        text_encoder_2=pipe.text_encoder_2,
        vae=pipe.vae,
        torch_dtype=torch.float16,
        use_safetensors=True,
        variant="fp16",
    )
    refiner.to("cuda")

    img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
    mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"

    init_image = load_image(img_url).convert("RGB")
    mask_image = load_image(mask_url).convert("RGB")

    prompt = "A majestic tiger sitting on a bench"
    num_inference_steps = 75
    high_noise_frac = 0.7

    image = pipe(
        prompt=prompt,
        image=init_image,
        mask_image=mask_image,
        num_inference_steps=num_inference_steps,
        denoising_start=high_noise_frac,
        output_type="latent",
    ).images
    image = refiner(
        prompt=prompt,
        image=image,
        mask_image=mask_image,
        num_inference_steps=num_inference_steps,
        denoising_start=high_noise_frac,
    ).images[0]

일반적인 SDE 설정에서 인페인팅에 refiner를 사용하기 위해, `denoising_end`와 `denoising_start`를 제거하고 refiner의 추론 단계의 수를 적게 선택하세요.

###
단독 체크포인트 파일 / 원래의 파일 형식으로 불러오기

`from_single_file()`를 사용함으로써 원래의 파일 형식을 `diffusers` 형식으로 불러올 수 있습니다:

    from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
    import torch

    pipe = StableDiffusionXLPipeline.from_single_file(
        "./sd_xl_base_1.0.safetensors", torch_dtype=torch.float16
    )
    pipe.to("cuda")

    refiner = StableDiffusionXLImg2ImgPipeline.from_single_file(
        "./sd_xl_refiner_1.0.safetensors", torch_dtype=torch.float16
    )
    refiner.to("cuda")

###
모델 offloading을 통해 메모리 최적화하기

out-of-memory 에러가 난다면, `StableDiffusionXLPipeline.enable_model_cpu_offload()`을 사용하는 것을 권장합니다.

    - pipe.to("cuda")
    + pipe.enable_model_cpu_offload()

그리고

    - refiner.to("cuda")
    + refiner.enable_model_cpu_offload()

###
torch.compile 로 추론 속도를 올리기

`torch.compile`를 사용함으로써 추론 속도를 올릴 수 있습니다. 이는 ca. 20% 속도 향상이 됩니다.

    + pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
    + refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=True)

###
torch < 2.0 일 때 실행하기

참고 Stable Diffusion XL을 `torch`가 2.0 버전 미만에서 실행시키고 싶을 때, xformers 어텐션을 사용해주세요:

    pip install xformers

    +pipe.enable_xformers_memory_efficient_attention()
    +refiner.enable_xformers_memory_efficient_attention()

##
StableDiffusionXLPipeline

###
class diffusers.StableDiffusionXLPipeline

< source >

( vae: AutoencoderKL text_encoder: CLIPTextModel text_encoder_2: CLIPTextModelWithProjection tokenizer: CLIPTokenizer tokenizer_2: CLIPTokenizer unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers image_encoder: CLIPVisionModelWithProjection = None feature_extractor: CLIPImageProcessor = None force_zeros_for_empty_prompt: bool = True add_watermarker: typing.Optional[bool] = None )

Parameters

- 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, specifically the clip-vit-large-patch14 variant.
- text_encoder_2 (`CLIPTextModelWithProjection`) — Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of CLIP, specifically the laion/CLIP-ViT-bigG-14-laion2B-39B-b160k variant.
- tokenizer (`CLIPTokenizer`) — Tokenizer of class CLIPTokenizer.
- tokenizer_2 (`CLIPTokenizer`) — Second Tokenizer of class CLIPTokenizer.
- unet (`UNet2DConditionModel`) — Conditional U-Net architecture to denoise the encoded image latents.
- scheduler (`SchedulerMixin`) — A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of `DDIMScheduler`, `LMSDiscreteScheduler`, or `PNDMScheduler`.
- force_zeros_for_empty_prompt (`bool`, optional, defaults to `"True"`) — Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of `stabilityai/stable-diffusion-xl-base-1-0`.
- add_watermarker (`bool`, optional) — Whether to use the invisible_watermark library to watermark output images. If not defined, it will default to True if the package is installed, otherwise no watermarker will be used.

Pipeline for text-to-image generation using Stable Diffusion XL.

This model inherits from `DiffusionPipeline`. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

The pipeline also inherits the following loading methods:

- `load_textual_inversion()` for loading textual inversion embeddings
- `from_single_file()` for loading `.ckpt` files
- `load_lora_weights()` for loading LoRA weights
- `save_lora_weights()` for saving LoRA weights
- `load_ip_adapter()` for loading IP Adapters

####
__call__

< source >

( prompt: typing.Union[str, typing.List[str]] = None prompt_2: typing.Union[str, typing.List[str], NoneType] = None height: typing.Optional[int] = None width: typing.Optional[int] = None num_inference_steps: int = 50 timesteps: typing.List[int] = None sigmas: typing.List[float] = None denoising_end: typing.Optional[float] = None guidance_scale: float = 5.0 negative_prompt: typing.Union[str, typing.List[str], NoneType] = None negative_prompt_2: typing.Union[str, typing.List[str], NoneType] = None num_images_per_prompt: typing.Optional[int] = 1 eta: float = 0.0 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.Tensor] = None prompt_embeds: typing.Optional[torch.Tensor] = None negative_prompt_embeds: typing.Optional[torch.Tensor] = None pooled_prompt_embeds: typing.Optional[torch.Tensor] = None negative_pooled_prompt_embeds: typing.Optional[torch.Tensor] = None ip_adapter_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None ip_adapter_image_embeds: typing.Optional[typing.List[torch.Tensor]] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True cross_attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None guidance_rescale: float = 0.0 original_size: typing.Optional[typing.Tuple[int, int]] = None crops_coords_top_left: typing.Tuple[int, int] = (0, 0) target_size: typing.Optional[typing.Tuple[int, int]] = None negative_original_size: typing.Optional[typing.Tuple[int, int]] = None negative_crops_coords_top_left: typing.Tuple[int, int] = (0, 0) negative_target_size: typing.Optional[typing.Tuple[int, int]] = None clip_skip: typing.Optional[int] = None callback_on_step_end: typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['latents'] **kwargs ) →
`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput` or `tuple`

Parameters

- prompt (`str` or `List[str]`, optional) — The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead.
- prompt_2 (`str` or `List[str]`, optional) — The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is used in both text-encoders
- height (`int`, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The height in pixels of the generated image. This is set to 1024 by default for the best results. Anything below 512 pixels won’t work well for stabilityai/stable-diffusion-xl-base-1.0 and checkpoints that are not specifically fine-tuned on low resolutions.
- width (`int`, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The width in pixels of the generated image. This is set to 1024 by default for the best results. Anything below 512 pixels won’t work well for stabilityai/stable-diffusion-xl-base-1.0 and checkpoints that are not specifically fine-tuned on low resolutions.
- num_inference_steps (`int`, optional, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
- timesteps (`List[int]`, optional) — Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. Must be in descending order.
- sigmas (`List[float]`, optional) — Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used.
- denoising_end (`float`, optional) — When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be completed before it is intentionally prematurely terminated. As a result, the returned sample will still retain a substantial amount of noise as determined by the discrete timesteps selected by the scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a “Mixture of Denoisers” multi-pipeline setup, as elaborated in Refining the Image Output
- guidance_scale (`float`, optional, defaults to 5.0) — Guidance scale as defined in Classifier-Free Diffusion Guidance. `guidance_scale` is defined as `w` of equation 2. of Imagen Paper. Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
- negative_prompt (`str` or `List[str]`, optional) — The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
- negative_prompt_2 (`str` or `List[str]`, optional) — The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
- num_images_per_prompt (`int`, optional, defaults to 1) — The number of images to generate per prompt.
- eta (`float`, optional, defaults to 0.0) — Corresponds to parameter eta (η) in the DDIM paper: https://huggingface.co/papers/2010.02502. Only applies to `schedulers.DDIMScheduler`, will be ignored for others.
- generator (`torch.Generator` or `List[torch.Generator]`, optional) — One or a list of torch generator(s) to make generation deterministic.
- latents (`torch.Tensor`, optional) — Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`.
- prompt_embeds (`torch.Tensor`, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument.
- negative_prompt_embeds (`torch.Tensor`, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.
- pooled_prompt_embeds (`torch.Tensor`, optional) — Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled text embeddings will be generated from `prompt` input argument.
- negative_pooled_prompt_embeds (`torch.Tensor`, optional) — Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` input argument.
- ip_adapter_image — (`PipelineImageInput`, optional): Optional image input to work with IP Adapters.
- ip_adapter_image_embeds (`List[torch.Tensor]`, optional) — Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not provided, embeddings are computed from the `ip_adapter_image` input argument.
- output_type (`str`, optional, defaults to `"pil"`) — The output format of the generate image. Choose between PIL: `PIL.Image.Image` or `np.array`.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a `~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput` instead of a plain tuple.
- cross_attention_kwargs (`dict`, optional) — A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in diffusers.models.attention_processor.
- guidance_rescale (`float`, optional, defaults to 0.0) — Guidance rescale factor proposed by Common Diffusion Noise Schedules and Sample Steps are Flawed `guidance_scale` is defined as `φ` in equation 16. of Common Diffusion Noise Schedules and Sample Steps are Flawed. Guidance rescale factor should fix overexposure when using zero terminal SNR.
- original_size (`Tuple[int]`, optional, defaults to (1024, 1024)) — If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled. `original_size` defaults to `(height, width)` if not specified. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952.
- crops_coords_top_left (`Tuple[int]`, optional, defaults to (0, 0)) — `crops_coords_top_left` can be used to generate an image that appears to be “cropped” from the position `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting `crops_coords_top_left` to (0, 0). Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952.
- target_size (`Tuple[int]`, optional, defaults to (1024, 1024)) — For most cases, `target_size` should be set to the desired height and width of the generated image. If not specified it will default to `(height, width)`. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952.
- negative_original_size (`Tuple[int]`, optional, defaults to (1024, 1024)) — To negatively condition the generation process based on a specific image resolution. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952. For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- negative_crops_coords_top_left (`Tuple[int]`, optional, defaults to (0, 0)) — To negatively condition the generation process based on a specific crop coordinates. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952. For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- negative_target_size (`Tuple[int]`, optional, defaults to (1024, 1024)) — To negatively condition the generation process based on a target image resolution. It should be as same as the `target_size` for most cases. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952. For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, optional) — A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of each denoising step during the inference. with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
- callback_on_step_end_tensor_inputs (`List`, optional) — The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the `._callback_tensor_inputs` attribute of your pipeline class.

Returns

`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput` or `tuple`

`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput` if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images.

Function invoked when calling the pipeline for generation.

Examples:

    >>> import torch
    >>> from diffusers import StableDiffusionXLPipeline

    >>> pipe = StableDiffusionXLPipeline.from_pretrained(
    ...     "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
    ... )
    >>> pipe = pipe.to("cuda")

    >>> prompt = "a photo of an astronaut riding a horse on mars"
    >>> image = pipe(prompt).images[0]

####
encode_prompt

< source >

( prompt: str prompt_2: typing.Optional[str] = None device: typing.Optional[torch.device] = None num_images_per_prompt: int = 1 do_classifier_free_guidance: bool = True negative_prompt: typing.Optional[str] = None negative_prompt_2: typing.Optional[str] = None prompt_embeds: typing.Optional[torch.Tensor] = None negative_prompt_embeds: typing.Optional[torch.Tensor] = None pooled_prompt_embeds: typing.Optional[torch.Tensor] = None negative_pooled_prompt_embeds: typing.Optional[torch.Tensor] = None lora_scale: typing.Optional[float] = None clip_skip: typing.Optional[int] = None )

Parameters

- prompt (`str` or `List[str]`, optional) — prompt to be encoded
- prompt_2 (`str` or `List[str]`, optional) — The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is used in both text-encoders
- device — (`torch.device`): torch device
- num_images_per_prompt (`int`) — number of images that should be generated per prompt
- do_classifier_free_guidance (`bool`) — whether to use classifier free guidance or not
- negative_prompt (`str` or `List[str]`, optional) — The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
- negative_prompt_2 (`str` or `List[str]`, optional) — The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
- prompt_embeds (`torch.Tensor`, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument.
- negative_prompt_embeds (`torch.Tensor`, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.
- pooled_prompt_embeds (`torch.Tensor`, optional) — Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled text embeddings will be generated from `prompt` input argument.
- negative_pooled_prompt_embeds (`torch.Tensor`, optional) — Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` input argument.
- lora_scale (`float`, optional) — A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
- clip_skip (`int`, optional) — Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings.

Encodes the prompt into text encoder hidden states.

####
get_guidance_scale_embedding

< source >

( w: Tensor embedding_dim: int = 512 dtype: dtype = torch.float32 ) →
`torch.Tensor`

Parameters

- w (`torch.Tensor`) — Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
- embedding_dim (`int`, optional, defaults to 512) — Dimension of the embeddings to generate.
- dtype (`torch.dtype`, optional, defaults to `torch.float32`) — Data type of the generated embeddings.

Returns

`torch.Tensor`

Embedding vectors with shape `(len(w), embedding_dim)`.

See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298

##
StableDiffusionXLImg2ImgPipeline

###
class diffusers.StableDiffusionXLImg2ImgPipeline

< source >

( vae: AutoencoderKL text_encoder: CLIPTextModel text_encoder_2: CLIPTextModelWithProjection tokenizer: CLIPTokenizer tokenizer_2: CLIPTokenizer unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers image_encoder: CLIPVisionModelWithProjection = None feature_extractor: CLIPImageProcessor = None requires_aesthetics_score: bool = False force_zeros_for_empty_prompt: bool = True add_watermarker: typing.Optional[bool] = None )

Parameters

- 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, specifically the clip-vit-large-patch14 variant.
- text_encoder_2 (`CLIPTextModelWithProjection`) — Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of CLIP, specifically the laion/CLIP-ViT-bigG-14-laion2B-39B-b160k variant.
- tokenizer (`CLIPTokenizer`) — Tokenizer of class CLIPTokenizer.
- tokenizer_2 (`CLIPTokenizer`) — Second Tokenizer of class CLIPTokenizer.
- unet (`UNet2DConditionModel`) — Conditional U-Net architecture to denoise the encoded image latents.
- scheduler (`SchedulerMixin`) — A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of `DDIMScheduler`, `LMSDiscreteScheduler`, or `PNDMScheduler`.
- requires_aesthetics_score (`bool`, optional, defaults to `"False"`) — Whether the `unet` requires an `aesthetic_score` condition to be passed during inference. Also see the config of `stabilityai/stable-diffusion-xl-refiner-1-0`.
- force_zeros_for_empty_prompt (`bool`, optional, defaults to `"True"`) — Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of `stabilityai/stable-diffusion-xl-base-1-0`.
- add_watermarker (`bool`, optional) — Whether to use the invisible_watermark library to watermark output images. If not defined, it will default to True if the package is installed, otherwise no watermarker will be used.

Pipeline for text-to-image generation using Stable Diffusion XL.

This model inherits from `DiffusionPipeline`. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

The pipeline also inherits the following loading methods:

- `load_textual_inversion()` for loading textual inversion embeddings
- `from_single_file()` for loading `.ckpt` files
- `load_lora_weights()` for loading LoRA weights
- `save_lora_weights()` for saving LoRA weights
- `load_ip_adapter()` for loading IP Adapters

####
__call__

< source >

( prompt: typing.Union[str, typing.List[str]] = None prompt_2: typing.Union[str, typing.List[str], NoneType] = None image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None strength: float = 0.3 num_inference_steps: int = 50 timesteps: typing.List[int] = None sigmas: typing.List[float] = None denoising_start: typing.Optional[float] = None denoising_end: typing.Optional[float] = None guidance_scale: float = 5.0 negative_prompt: typing.Union[str, typing.List[str], NoneType] = None negative_prompt_2: typing.Union[str, typing.List[str], NoneType] = None num_images_per_prompt: typing.Optional[int] = 1 eta: float = 0.0 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.Tensor] = None prompt_embeds: typing.Optional[torch.Tensor] = None negative_prompt_embeds: typing.Optional[torch.Tensor] = None pooled_prompt_embeds: typing.Optional[torch.Tensor] = None negative_pooled_prompt_embeds: typing.Optional[torch.Tensor] = None ip_adapter_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None ip_adapter_image_embeds: typing.Optional[typing.List[torch.Tensor]] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True cross_attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None guidance_rescale: float = 0.0 original_size: typing.Tuple[int, int] = None crops_coords_top_left: typing.Tuple[int, int] = (0, 0) target_size: typing.Tuple[int, int] = None negative_original_size: typing.Optional[typing.Tuple[int, int]] = None negative_crops_coords_top_left: typing.Tuple[int, int] = (0, 0) negative_target_size: typing.Optional[typing.Tuple[int, int]] = None aesthetic_score: float = 6.0 negative_aesthetic_score: float = 2.5 clip_skip: typing.Optional[int] = None callback_on_step_end: typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['latents'] **kwargs ) →
`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput` or `tuple`

Parameters

- prompt (`str` or `List[str]`, optional) — The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead.
- prompt_2 (`str` or `List[str]`, optional) — The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is used in both text-encoders
- image (`torch.Tensor` or `PIL.Image.Image` or `np.ndarray` or `List[torch.Tensor]` or `List[PIL.Image.Image]` or `List[np.ndarray]`) — The image(s) to modify with the pipeline.
- strength (`float`, optional, defaults to 0.3) — Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. Note that in the case of `denoising_start` being declared as an integer, the value of `strength` will be ignored.
- num_inference_steps (`int`, optional, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
- timesteps (`List[int]`, optional) — Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. Must be in descending order.
- sigmas (`List[float]`, optional) — Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used.
- denoising_start (`float`, optional) — When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and it is assumed that the passed `image` is a partly denoised image. Note that when this is specified, strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline is integrated into a “Mixture of Denoisers” multi-pipeline setup, as detailed in Refine Image Quality.
- denoising_end (`float`, optional) — When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be completed before it is intentionally prematurely terminated. As a result, the returned sample will still retain a substantial amount of noise (ca. final 20% of timesteps still needed) and should be denoised by a successor pipeline that has `denoising_start` set to 0.8 so that it only denoises the final 20% of the scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a “Mixture of Denoisers” multi-pipeline setup, as elaborated in Refine Image Quality.
- guidance_scale (`float`, optional, defaults to 7.5) — Guidance scale as defined in Classifier-Free Diffusion Guidance. `guidance_scale` is defined as `w` of equation 2. of Imagen Paper. Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
- negative_prompt (`str` or `List[str]`, optional) — The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
- negative_prompt_2 (`str` or `List[str]`, optional) — The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
- num_images_per_prompt (`int`, optional, defaults to 1) — The number of images to generate per prompt.
- eta (`float`, optional, defaults to 0.0) — Corresponds to parameter eta (η) in the DDIM paper: https://huggingface.co/papers/2010.02502. Only applies to `schedulers.DDIMScheduler`, will be ignored for others.
- generator (`torch.Generator` or `List[torch.Generator]`, optional) — One or a list of torch generator(s) to make generation deterministic.
- latents (`torch.Tensor`, optional) — Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`.
- prompt_embeds (`torch.Tensor`, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument.
- negative_prompt_embeds (`torch.Tensor`, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.
- pooled_prompt_embeds (`torch.Tensor`, optional) — Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled text embeddings will be generated from `prompt` input argument.
- negative_pooled_prompt_embeds (`torch.Tensor`, optional) — Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` input argument.
- ip_adapter_image — (`PipelineImageInput`, optional): Optional image input to work with IP Adapters.
- ip_adapter_image_embeds (`List[torch.Tensor]`, optional) — Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not provided, embeddings are computed from the `ip_adapter_image` input argument.
- output_type (`str`, optional, defaults to `"pil"`) — The output format of the generate image. Choose between PIL: `PIL.Image.Image` or `np.array`.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a `~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput` instead of a plain tuple.
- cross_attention_kwargs (`dict`, optional) — A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in diffusers.models.attention_processor.
- guidance_rescale (`float`, optional, defaults to 0.0) — Guidance rescale factor proposed by Common Diffusion Noise Schedules and Sample Steps are Flawed `guidance_scale` is defined as `φ` in equation 16. of Common Diffusion Noise Schedules and Sample Steps are Flawed. Guidance rescale factor should fix overexposure when using zero terminal SNR.
- original_size (`Tuple[int]`, optional, defaults to (1024, 1024)) — If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled. `original_size` defaults to `(height, width)` if not specified. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952.
- crops_coords_top_left (`Tuple[int]`, optional, defaults to (0, 0)) — `crops_coords_top_left` can be used to generate an image that appears to be “cropped” from the position `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting `crops_coords_top_left` to (0, 0). Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952.
- target_size (`Tuple[int]`, optional, defaults to (1024, 1024)) — For most cases, `target_size` should be set to the desired height and width of the generated image. If not specified it will default to `(height, width)`. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952.
- negative_original_size (`Tuple[int]`, optional, defaults to (1024, 1024)) — To negatively condition the generation process based on a specific image resolution. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952. For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- negative_crops_coords_top_left (`Tuple[int]`, optional, defaults to (0, 0)) — To negatively condition the generation process based on a specific crop coordinates. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952. For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- negative_target_size (`Tuple[int]`, optional, defaults to (1024, 1024)) — To negatively condition the generation process based on a target image resolution. It should be as same as the `target_size` for most cases. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952. For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- aesthetic_score (`float`, optional, defaults to 6.0) — Used to simulate an aesthetic score of the generated image by influencing the positive text condition. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952.
- negative_aesthetic_score (`float`, optional, defaults to 2.5) — Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952. Can be used to simulate an aesthetic score of the generated image by influencing the negative text condition.
- clip_skip (`int`, optional) — Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings.
- callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, optional) — A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of each denoising step during the inference. with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
- callback_on_step_end_tensor_inputs (`List`, optional) — The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the `._callback_tensor_inputs` attribute of your pipeline class.

Returns

`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput` or `tuple`

`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput` if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images.

Function invoked when calling the pipeline for generation.

Examples:

    >>> import torch
    >>> from diffusers import StableDiffusionXLImg2ImgPipeline
    >>> from diffusers.utils import load_image

    >>> pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
    ...     "stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16
    ... )
    >>> pipe = pipe.to("cuda")
    >>> url = "https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/aa_xl/000000009.png"

    >>> init_image = load_image(url).convert("RGB")
    >>> prompt = "a photo of an astronaut riding a horse on mars"
    >>> image = pipe(prompt, image=init_image).images[0]

####
encode_prompt

< source >

( prompt: str prompt_2: typing.Optional[str] = None device: typing.Optional[torch.device] = None num_images_per_prompt: int = 1 do_classifier_free_guidance: bool = True negative_prompt: typing.Optional[str] = None negative_prompt_2: typing.Optional[str] = None prompt_embeds: typing.Optional[torch.Tensor] = None negative_prompt_embeds: typing.Optional[torch.Tensor] = None pooled_prompt_embeds: typing.Optional[torch.Tensor] = None negative_pooled_prompt_embeds: typing.Optional[torch.Tensor] = None lora_scale: typing.Optional[float] = None clip_skip: typing.Optional[int] = None )

Parameters

- prompt (`str` or `List[str]`, optional) — prompt to be encoded
- prompt_2 (`str` or `List[str]`, optional) — The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is used in both text-encoders
- device — (`torch.device`): torch device
- num_images_per_prompt (`int`) — number of images that should be generated per prompt
- do_classifier_free_guidance (`bool`) — whether to use classifier free guidance or not
- negative_prompt (`str` or `List[str]`, optional) — The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
- negative_prompt_2 (`str` or `List[str]`, optional) — The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
- prompt_embeds (`torch.Tensor`, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument.
- negative_prompt_embeds (`torch.Tensor`, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.
- pooled_prompt_embeds (`torch.Tensor`, optional) — Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled text embeddings will be generated from `prompt` input argument.
- negative_pooled_prompt_embeds (`torch.Tensor`, optional) — Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` input argument.
- lora_scale (`float`, optional) — A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
- clip_skip (`int`, optional) — Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings.

Encodes the prompt into text encoder hidden states.

####
get_guidance_scale_embedding

< source >

( w: Tensor embedding_dim: int = 512 dtype: dtype = torch.float32 ) →
`torch.Tensor`

Parameters

- w (`torch.Tensor`) — Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
- embedding_dim (`int`, optional, defaults to 512) — Dimension of the embeddings to generate.
- dtype (`torch.dtype`, optional, defaults to `torch.float32`) — Data type of the generated embeddings.

Returns

`torch.Tensor`

Embedding vectors with shape `(len(w), embedding_dim)`.

See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298

##
StableDiffusionXLInpaintPipeline

###
class diffusers.StableDiffusionXLInpaintPipeline

< source >

( vae: AutoencoderKL text_encoder: CLIPTextModel text_encoder_2: CLIPTextModelWithProjection tokenizer: CLIPTokenizer tokenizer_2: CLIPTokenizer unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers image_encoder: CLIPVisionModelWithProjection = None feature_extractor: CLIPImageProcessor = None requires_aesthetics_score: bool = False force_zeros_for_empty_prompt: bool = True add_watermarker: typing.Optional[bool] = None )

Parameters

- 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, specifically the clip-vit-large-patch14 variant.
- text_encoder_2 (`CLIPTextModelWithProjection`) — Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of CLIP, specifically the laion/CLIP-ViT-bigG-14-laion2B-39B-b160k variant.
- tokenizer (`CLIPTokenizer`) — Tokenizer of class CLIPTokenizer.
- tokenizer_2 (`CLIPTokenizer`) — Second Tokenizer of class CLIPTokenizer.
- unet (`UNet2DConditionModel`) — Conditional U-Net architecture to denoise the encoded image latents.
- scheduler (`SchedulerMixin`) — A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of `DDIMScheduler`, `LMSDiscreteScheduler`, or `PNDMScheduler`.
- requires_aesthetics_score (`bool`, optional, defaults to `"False"`) — Whether the `unet` requires a aesthetic_score condition to be passed during inference. Also see the config of `stabilityai/stable-diffusion-xl-refiner-1-0`.
- force_zeros_for_empty_prompt (`bool`, optional, defaults to `"True"`) — Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of `stabilityai/stable-diffusion-xl-base-1-0`.
- add_watermarker (`bool`, optional) — Whether to use the invisible_watermark library to watermark output images. If not defined, it will default to True if the package is installed, otherwise no watermarker will be used.

Pipeline for text-to-image generation using Stable Diffusion XL.

This model inherits from `DiffusionPipeline`. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

The pipeline also inherits the following loading methods:

- `load_textual_inversion()` for loading textual inversion embeddings
- `from_single_file()` for loading `.ckpt` files
- `load_lora_weights()` for loading LoRA weights
- `save_lora_weights()` for saving LoRA weights
- `load_ip_adapter()` for loading IP Adapters

####
__call__

< source >

( prompt: typing.Union[str, typing.List[str]] = None prompt_2: typing.Union[str, typing.List[str], NoneType] = None image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None mask_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None masked_image_latents: Tensor = None height: typing.Optional[int] = None width: typing.Optional[int] = None padding_mask_crop: typing.Optional[int] = None strength: float = 0.9999 num_inference_steps: int = 50 timesteps: typing.List[int] = None sigmas: typing.List[float] = None denoising_start: typing.Optional[float] = None denoising_end: typing.Optional[float] = None guidance_scale: float = 7.5 negative_prompt: typing.Union[str, typing.List[str], NoneType] = None negative_prompt_2: typing.Union[str, typing.List[str], NoneType] = None num_images_per_prompt: typing.Optional[int] = 1 eta: float = 0.0 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.Tensor] = None prompt_embeds: typing.Optional[torch.Tensor] = None negative_prompt_embeds: typing.Optional[torch.Tensor] = None pooled_prompt_embeds: typing.Optional[torch.Tensor] = None negative_pooled_prompt_embeds: typing.Optional[torch.Tensor] = None ip_adapter_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None ip_adapter_image_embeds: typing.Optional[typing.List[torch.Tensor]] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True cross_attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None guidance_rescale: float = 0.0 original_size: typing.Tuple[int, int] = None crops_coords_top_left: typing.Tuple[int, int] = (0, 0) target_size: typing.Tuple[int, int] = None negative_original_size: typing.Optional[typing.Tuple[int, int]] = None negative_crops_coords_top_left: typing.Tuple[int, int] = (0, 0) negative_target_size: typing.Optional[typing.Tuple[int, int]] = None aesthetic_score: float = 6.0 negative_aesthetic_score: float = 2.5 clip_skip: typing.Optional[int] = None callback_on_step_end: typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['latents'] **kwargs ) →
`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput` or `tuple`

Parameters

- prompt (`str` or `List[str]`, optional) — The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead.
- prompt_2 (`str` or `List[str]`, optional) — The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is used in both text-encoders
- image (`PIL.Image.Image`) — `Image`, or tensor representing an image batch which will be inpainted, i.e. parts of the image will be masked out with `mask_image` and repainted according to `prompt`.
- mask_image (`PIL.Image.Image`) — `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted to a single channel (luminance) before use. If it’s a tensor, it should contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
- height (`int`, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The height in pixels of the generated image. This is set to 1024 by default for the best results. Anything below 512 pixels won’t work well for stabilityai/stable-diffusion-xl-base-1.0 and checkpoints that are not specifically fine-tuned on low resolutions.
- width (`int`, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The width in pixels of the generated image. This is set to 1024 by default for the best results. Anything below 512 pixels won’t work well for stabilityai/stable-diffusion-xl-base-1.0 and checkpoints that are not specifically fine-tuned on low resolutions.
- padding_mask_crop (`int`, optional, defaults to `None`) — The size of margin in the crop to be applied to the image and masking. If `None`, no crop is applied to image and mask_image. If `padding_mask_crop` is not `None`, it will first find a rectangular region with the same aspect ration of the image and contains all masked area, and then expand that area based on `padding_mask_crop`. The image and mask_image will then be cropped based on the expanded area before resizing to the original image size for inpainting. This is useful when the masked area is small while the image is large and contain information irrelevant for inpainting, such as background.
- strength (`float`, optional, defaults to 0.9999) — Conceptually, indicates how much to transform the masked portion of the reference `image`. Must be between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores the masked portion of the reference `image`. Note that in the case of `denoising_start` being declared as an integer, the value of `strength` will be ignored.
- num_inference_steps (`int`, optional, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
- timesteps (`List[int]`, optional) — Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. Must be in descending order.
- sigmas (`List[float]`, optional) — Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used.
- denoising_start (`float`, optional) — When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and it is assumed that the passed `image` is a partly denoised image. Note that when this is specified, strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline is integrated into a “Mixture of Denoisers” multi-pipeline setup, as detailed in Refining the Image Output.
- denoising_end (`float`, optional) — When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be completed before it is intentionally prematurely terminated. As a result, the returned sample will still retain a substantial amount of noise (ca. final 20% of timesteps still needed) and should be denoised by a successor pipeline that has `denoising_start` set to 0.8 so that it only denoises the final 20% of the scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a “Mixture of Denoisers” multi-pipeline setup, as elaborated in Refining the Image Output.
- guidance_scale (`float`, optional, defaults to 7.5) — Guidance scale as defined in Classifier-Free Diffusion Guidance. `guidance_scale` is defined as `w` of equation 2. of Imagen Paper. Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
- negative_prompt (`str` or `List[str]`, optional) — The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
- negative_prompt_2 (`str` or `List[str]`, optional) — The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
- prompt_embeds (`torch.Tensor`, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument.
- negative_prompt_embeds (`torch.Tensor`, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.
- pooled_prompt_embeds (`torch.Tensor`, optional) — Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled text embeddings will be generated from `prompt` input argument.
- negative_pooled_prompt_embeds (`torch.Tensor`, optional) — Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` input argument.
- ip_adapter_image — (`PipelineImageInput`, optional): Optional image input to work with IP Adapters.
- ip_adapter_image_embeds (`List[torch.Tensor]`, optional) — Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not provided, embeddings are computed from the `ip_adapter_image` input argument.
- num_images_per_prompt (`int`, optional, defaults to 1) — The number of images to generate per prompt.
- eta (`float`, optional, defaults to 0.0) — Corresponds to parameter eta (η) in the DDIM paper: https://huggingface.co/papers/2010.02502. Only applies to `schedulers.DDIMScheduler`, will be ignored for others.
- generator (`torch.Generator`, optional) — One or a list of torch generator(s) to make generation deterministic.
- latents (`torch.Tensor`, optional) — Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`.
- output_type (`str`, optional, defaults to `"pil"`) — The output format of the generate image. Choose between PIL: `PIL.Image.Image` or `np.array`.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a `StableDiffusionPipelineOutput` instead of a plain tuple.
- cross_attention_kwargs (`dict`, optional) — A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in diffusers.models.attention_processor.
- original_size (`Tuple[int]`, optional, defaults to (1024, 1024)) — If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled. `original_size` defaults to `(height, width)` if not specified. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952.
- crops_coords_top_left (`Tuple[int]`, optional, defaults to (0, 0)) — `crops_coords_top_left` can be used to generate an image that appears to be “cropped” from the position `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting `crops_coords_top_left` to (0, 0). Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952.
- target_size (`Tuple[int]`, optional, defaults to (1024, 1024)) — For most cases, `target_size` should be set to the desired height and width of the generated image. If not specified it will default to `(height, width)`. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952.
- negative_original_size (`Tuple[int]`, optional, defaults to (1024, 1024)) — To negatively condition the generation process based on a specific image resolution. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952. For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- negative_crops_coords_top_left (`Tuple[int]`, optional, defaults to (0, 0)) — To negatively condition the generation process based on a specific crop coordinates. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952. For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- negative_target_size (`Tuple[int]`, optional, defaults to (1024, 1024)) — To negatively condition the generation process based on a target image resolution. It should be as same as the `target_size` for most cases. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952. For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- aesthetic_score (`float`, optional, defaults to 6.0) — Used to simulate an aesthetic score of the generated image by influencing the positive text condition. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952.
- negative_aesthetic_score (`float`, optional, defaults to 2.5) — Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952. Can be used to simulate an aesthetic score of the generated image by influencing the negative text condition.
- clip_skip (`int`, optional) — Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings.
- callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, optional) — A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of each denoising step during the inference. with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
- callback_on_step_end_tensor_inputs (`List`, optional) — The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the `._callback_tensor_inputs` attribute of your pipeline class.

Returns

`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput` or `tuple`

`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput` if `return_dict` is True, otherwise a `tuple.` tuple. When returning a tuple, the first element is a list with the generated images.

Function invoked when calling the pipeline for generation.

Examples:

    >>> import torch
    >>> from diffusers import StableDiffusionXLInpaintPipeline
    >>> from diffusers.utils import load_image

    >>> pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
    ...     "stabilityai/stable-diffusion-xl-base-1.0",
    ...     torch_dtype=torch.float16,
    ...     variant="fp16",
    ...     use_safetensors=True,
    ... )
    >>> pipe.to("cuda")

    >>> img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
    >>> mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"

    >>> init_image = load_image(img_url).convert("RGB")
    >>> mask_image = load_image(mask_url).convert("RGB")

    >>> prompt = "A majestic tiger sitting on a bench"
    >>> image = pipe(
    ...     prompt=prompt, image=init_image, mask_image=mask_image, num_inference_steps=50, strength=0.80
    ... ).images[0]

####
encode_prompt

< source >

( prompt: str prompt_2: typing.Optional[str] = None device: typing.Optional[torch.device] = None num_images_per_prompt: int = 1 do_classifier_free_guidance: bool = True negative_prompt: typing.Optional[str] = None negative_prompt_2: typing.Optional[str] = None prompt_embeds: typing.Optional[torch.Tensor] = None negative_prompt_embeds: typing.Optional[torch.Tensor] = None pooled_prompt_embeds: typing.Optional[torch.Tensor] = None negative_pooled_prompt_embeds: typing.Optional[torch.Tensor] = None lora_scale: typing.Optional[float] = None clip_skip: typing.Optional[int] = None )

Parameters

- prompt (`str` or `List[str]`, optional) — prompt to be encoded
- prompt_2 (`str` or `List[str]`, optional) — The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is used in both text-encoders
- device — (`torch.device`): torch device
- num_images_per_prompt (`int`) — number of images that should be generated per prompt
- do_classifier_free_guidance (`bool`) — whether to use classifier free guidance or not
- negative_prompt (`str` or `List[str]`, optional) — The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
- negative_prompt_2 (`str` or `List[str]`, optional) — The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
- prompt_embeds (`torch.Tensor`, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument.
- negative_prompt_embeds (`torch.Tensor`, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.
- pooled_prompt_embeds (`torch.Tensor`, optional) — Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled text embeddings will be generated from `prompt` input argument.
- negative_pooled_prompt_embeds (`torch.Tensor`, optional) — Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` input argument.
- lora_scale (`float`, optional) — A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
- clip_skip (`int`, optional) — Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings.

Encodes the prompt into text encoder hidden states.

####
get_guidance_scale_embedding

< source >

( w: Tensor embedding_dim: int = 512 dtype: dtype = torch.float32 ) →
`torch.Tensor`

Parameters

- w (`torch.Tensor`) — Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
- embedding_dim (`int`, optional, defaults to 512) — Dimension of the embeddings to generate.
- dtype (`torch.dtype`, optional, defaults to `torch.float32`) — Data type of the generated embeddings.

Returns

`torch.Tensor`

Embedding vectors with shape `(len(w), embedding_dim)`.

See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298

###
각 텍스트 인코더에 다른 프롬프트를 전달하기

Stable Diffusion XL는 두 개의 텍스트 인코더에 학습되었습니다. 기본 동작은 각 프롬프트에 동일한 프롬프트를 전달하는 것입니다. 그러나 일부 사용자가 품질을 향상시킬 수 있다고 지적한 것처럼 텍스트 인코더마다 다른 프롬프트를 전달할 수 있습니다. 그렇게 하려면, `prompt_2`와 `negative_prompt_2`를 `prompt`와 `negative_prompt`에 전달해야 합니다. 그렇게 함으로써, 원래의 프롬프트들(`prompt`)과 부정 프롬프트들(`negative_prompt`)를 `텍스트 인코더`에 전달할 것입니다.(공식 SDXL 0.9/1.0의 OpenAI CLIP-ViT/L-14에서 볼 수 있습니다.) 그리고 `prompt_2`와 `negative_prompt_2`는 `text_encoder_2`에 전달됩니다.(공식 SDXL 0.9/1.0의 OpenCLIP-ViT/bigG-14에서 볼 수 있습니다.)

    from diffusers import StableDiffusionXLPipeline
    import torch

    pipe = StableDiffusionXLPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
    )
    pipe.to("cuda")

    # OAI CLIP-ViT/L-14에 prompt가 전달됩니다
    prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
    # OpenCLIP-ViT/bigG-14에 prompt_2가 전달됩니다
    prompt_2 = "monet painting"
    image = pipe(prompt=prompt, prompt_2=prompt_2).images[0]

< > Update on GitHub