# K-Diffusion

[k-diffusion](https://github.com/crowsonkb/k-diffusion) is a popular library created by [Katherine Crowson](https://github.com/crowsonkb/). We provide `StableDiffusionKDiffusionPipeline` and `StableDiffusionXLKDiffusionPipeline` that allow you to run Stable DIffusion with samplers from k-diffusion.

Note that most the samplers from k-diffusion are implemented in Diffusers and we recommend using existing schedulers. You can find a mapping between k-diffusion samplers and schedulers in Diffusers [here](https://huggingface.co/docs/diffusers/api/schedulers/overview)


## StableDiffusionKDiffusionPipeline[[diffusers.StableDiffusionKDiffusionPipeline]]

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<docstring><name>class diffusers.StableDiffusionKDiffusionPipeline</name><anchor>diffusers.StableDiffusionKDiffusionPipeline</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py#L66</source><parameters>[{"name": "vae", "val": ": AutoencoderKL"}, {"name": "text_encoder", "val": ": CLIPTextModel"}, {"name": "tokenizer", "val": ": typing.Union[transformers.models.clip.tokenization_clip.CLIPTokenizer, transformers.models.clip.tokenization_clip_fast.CLIPTokenizerFast]"}, {"name": "unet", "val": ": UNet2DConditionModel"}, {"name": "scheduler", "val": ": KarrasDiffusionSchedulers"}, {"name": "safety_checker", "val": ": StableDiffusionSafetyChecker"}, {"name": "feature_extractor", "val": ": CLIPImageProcessor"}, {"name": "requires_safety_checker", "val": ": bool = True"}]</parameters><paramsdesc>- **vae** ([AutoencoderKL](/docs/diffusers/main/en/api/models/autoencoderkl#diffusers.AutoencoderKL)) --
  Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
- **text_encoder** (`CLIPTextModel`) --
  Frozen text-encoder. Stable Diffusion uses the text portion of
  [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
  the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
- **tokenizer** (`CLIPTokenizer`) --
  Tokenizer of class
  [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
- **unet** ([UNet2DConditionModel](/docs/diffusers/main/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel)) -- Conditional U-Net architecture to denoise the encoded image latents.
- **scheduler** ([SchedulerMixin](/docs/diffusers/main/en/api/schedulers/overview#diffusers.SchedulerMixin)) --
  A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
  [DDIMScheduler](/docs/diffusers/main/en/api/schedulers/ddim#diffusers.DDIMScheduler), [LMSDiscreteScheduler](/docs/diffusers/main/en/api/schedulers/lms_discrete#diffusers.LMSDiscreteScheduler), or [PNDMScheduler](/docs/diffusers/main/en/api/schedulers/pndm#diffusers.PNDMScheduler).
- **safety_checker** (`StableDiffusionSafetyChecker`) --
  Classification module that estimates whether generated images could be considered offensive or harmful.
  Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
  details.
- **feature_extractor** (`CLIPImageProcessor`) --
  Model that extracts features from generated images to be used as inputs for the `safety_checker`.</paramsdesc><paramgroups>0</paramgroups></docstring>

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

This model inherits from [DiffusionPipeline](/docs/diffusers/main/en/api/pipelines/overview#diffusers.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()](/docs/diffusers/main/en/api/loaders/textual_inversion#diffusers.loaders.TextualInversionLoaderMixin.load_textual_inversion) for loading textual inversion embeddings
- [load_lora_weights()](/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights) for loading LoRA weights
- [save_lora_weights()](/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights) for saving LoRA weights

> [!WARNING] > This is an experimental pipeline and is likely to change in the future.





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<docstring><name>encode_prompt</name><anchor>diffusers.StableDiffusionKDiffusionPipeline.encode_prompt</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py#L202</source><parameters>[{"name": "prompt", "val": ""}, {"name": "device", "val": ""}, {"name": "num_images_per_prompt", "val": ""}, {"name": "do_classifier_free_guidance", "val": ""}, {"name": "negative_prompt", "val": " = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "lora_scale", "val": ": typing.Optional[float] = None"}, {"name": "clip_skip", "val": ": typing.Optional[int] = None"}]</parameters><paramsdesc>- **prompt** (`str` or `List[str]`, *optional*) --
  prompt to be encoded
- **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`).
- **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.
- **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.</paramsdesc><paramgroups>0</paramgroups></docstring>

Encodes the prompt into text encoder hidden states.




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## StableDiffusionXLKDiffusionPipeline[[diffusers.StableDiffusionXLKDiffusionPipeline]]

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<docstring><name>class diffusers.StableDiffusionXLKDiffusionPipeline</name><anchor>diffusers.StableDiffusionXLKDiffusionPipeline</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py#L90</source><parameters>[{"name": "vae", "val": ": AutoencoderKL"}, {"name": "text_encoder", "val": ": CLIPTextModel"}, {"name": "text_encoder_2", "val": ": CLIPTextModelWithProjection"}, {"name": "tokenizer", "val": ": CLIPTokenizer"}, {"name": "tokenizer_2", "val": ": CLIPTokenizer"}, {"name": "unet", "val": ": UNet2DConditionModel"}, {"name": "scheduler", "val": ": KarrasDiffusionSchedulers"}, {"name": "force_zeros_for_empty_prompt", "val": ": bool = True"}]</parameters></docstring>



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<docstring><name>encode_prompt</name><anchor>diffusers.StableDiffusionXLKDiffusionPipeline.encode_prompt</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py#L206</source><parameters>[{"name": "prompt", "val": ": str"}, {"name": "prompt_2", "val": ": typing.Optional[str] = None"}, {"name": "device", "val": ": typing.Optional[torch.device] = None"}, {"name": "num_images_per_prompt", "val": ": int = 1"}, {"name": "do_classifier_free_guidance", "val": ": bool = True"}, {"name": "negative_prompt", "val": ": typing.Optional[str] = None"}, {"name": "negative_prompt_2", "val": ": typing.Optional[str] = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "pooled_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_pooled_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "lora_scale", "val": ": typing.Optional[float] = None"}, {"name": "clip_skip", "val": ": typing.Optional[int] = None"}]</parameters><paramsdesc>- **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.</paramsdesc><paramgroups>0</paramgroups></docstring>

Encodes the prompt into text encoder hidden states.




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