#
Text-to-image

The Stable Diffusion model was created by researchers and engineers from CompVis, Stability AI, Runway, and LAION. The StableDiffusionPipeline is capable of generating photorealistic images given any text input. It’s trained on 512x512 images from a subset of the LAION-5B dataset. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and can run on consumer GPUs. Latent diffusion is the research on top of which Stable Diffusion was built. It was proposed in High-Resolution Image Synthesis with Latent Diffusion Models by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer.

The abstract from the paper is:

By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. However, since these models typically operate directly in pixel space, optimization of powerful DMs often consumes hundreds of GPU days and inference is expensive due to sequential evaluations. To enable DM training on limited computational resources while retaining their quality and flexibility, we apply them in the latent space of powerful pretrained autoencoders. In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text or bounding boxes and high-resolution synthesis becomes possible in a convolutional manner. Our latent diffusion models (LDMs) achieve a new state of the art for image inpainting and highly competitive performance on various tasks, including unconditional image generation, semantic scene synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs. Code is available at https://github.com/CompVis/latent-diffusion.

Make sure to check out the Stable Diffusion Tips section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!

If you’re interested in using one of the official checkpoints for a task, explore the CompVis, Runway, and Stability AI Hub organizations!

##
StableDiffusionPipeline

###
class diffusers.StableDiffusionPipeline

< source >

( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPImageProcessor image_encoder: CLIPVisionModelWithProjection = None requires_safety_checker: bool = True )

Parameters

- 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).
- tokenizer (CLIPTokenizer) — A `CLIPTokenizer` to tokenize text.
- unet (UNet2DConditionModel) — A `UNet2DConditionModel` to denoise the encoded image latents.
- scheduler (SchedulerMixin) — A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler.
- safety_checker (`StableDiffusionSafetyChecker`) — Classification module that estimates whether generated images could be considered offensive or harmful. Please refer to the model card for more details about a model’s potential harms.
- feature_extractor (CLIPImageProcessor) — A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.

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 the following loading methods:

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

####
__call__

< source >

( prompt: Union = None height: Optional = None width: Optional = None num_inference_steps: int = 50 timesteps: List = None guidance_scale: float = 7.5 negative_prompt: Union = None num_images_per_prompt: Optional = 1 eta: float = 0.0 generator: Union = None latents: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None ip_adapter_image: Union = None output_type: Optional = 'pil' return_dict: bool = True cross_attention_kwargs: Optional = None guidance_rescale: float = 0.0 clip_skip: Optional = None callback_on_step_end: Optional = None callback_on_step_end_tensor_inputs: List = ['latents'] **kwargs ) →
StableDiffusionPipelineOutput or `tuple`

Parameters

- prompt (`str` or `List[str]`, optional) — The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
- height (`int`, optional, defaults to `self.unet.config.sample_size * self.vae_scale_factor`) — The height in pixels of the generated image.
- width (`int`, optional, defaults to `self.unet.config.sample_size * self.vae_scale_factor`) — The width in pixels of the generated image.
- 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.
- guidance_scale (`float`, optional, defaults to 7.5) — A higher guidance scale value encourages the model to generate images closely linked to the text `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
- negative_prompt (`str` or `List[str]`, optional) — The prompt or prompts to guide what to not include in image generation. If not defined, you need to pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
- 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) from the DDIM paper. Only applies to the DDIMScheduler, and is ignored in other schedulers.
- generator (`torch.Generator` or `List[torch.Generator]`, optional) — A `torch.Generator` to make generation deterministic.
- latents (`torch.FloatTensor`, 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 is generated by sampling using the supplied random `generator`.
- prompt_embeds (`torch.FloatTensor`, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, text embeddings are generated from the `prompt` input argument.
- negative_prompt_embeds (`torch.FloatTensor`, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument. ip_adapter_image — (`PipelineImageInput`, optional): Optional image input to work with IP Adapters.
- output_type (`str`, optional, defaults to `"pil"`) — The output format of the generated image. Choose between `PIL.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 in `self.processor`.
- guidance_rescale (`float`, optional, defaults to 0.0) — Guidance rescale factor from Common Diffusion Noise Schedules and Sample Steps are Flawed. Guidance rescale factor should fix overexposure when using zero terminal SNR.
- 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`, optional) — A function that calls at the end of each denoising steps during the inference. The function is called 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

StableDiffusionPipelineOutput or `tuple`

If `return_dict` is `True`, StableDiffusionPipelineOutput is returned, otherwise a `tuple` is returned where the first element is a list with the generated images and the second element is a list of `bool`s indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content.

The call function to the pipeline for generation.

Examples:

    >>> import torch
    >>> from diffusers import StableDiffusionPipeline

    >>> pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
    >>> pipe = pipe.to("cuda")

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

####
enable_attention_slicing

< source >

( slice_size: Union = 'auto' )

Parameters

- slice_size (`str` or `int`, optional, defaults to `"auto"`) — When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If `"max"`, maximum amount of memory will be saved by running only one slice at a time. If a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` must be a multiple of `slice_size`.

Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor in slices to compute attention in several steps. For more than one attention head, the computation is performed sequentially over each head. This is useful to save some memory in exchange for a small speed decrease.

⚠️ Don’t enable attention slicing if you’re already using `scaled_dot_product_attention` (SDPA) from PyTorch 2.0 or xFormers. These attention computations are already very memory efficient so you won’t need to enable this function. If you enable attention slicing with SDPA or xFormers, it can lead to serious slow downs!

Examples:

    >>> import torch
    >>> from diffusers import StableDiffusionPipeline

    >>> pipe = StableDiffusionPipeline.from_pretrained(
    ...     "runwayml/stable-diffusion-v1-5",
    ...     torch_dtype=torch.float16,
    ...     use_safetensors=True,
    ... )

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

####
disable_attention_slicing

< source >

( )

Disable sliced attention computation. If `enable_attention_slicing` was previously called, attention is computed in one step.

####
enable_vae_slicing

< source >

( )

Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.

####
disable_vae_slicing

< source >

( )

Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to computing decoding in one step.

####
enable_xformers_memory_efficient_attention

< source >

( attention_op: Optional = None )

Parameters

- attention_op (`Callable`, optional) — Override the default `None` operator for use as `op` argument to the `memory_efficient_attention()` function of xFormers.

Enable memory efficient attention from xFormers. When this option is enabled, you should observe lower GPU memory usage and a potential speed up during inference. Speed up during training is not guaranteed.

⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient attention takes precedent.

Examples:

    >>> import torch
    >>> from diffusers import DiffusionPipeline
    >>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp

    >>> pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16)
    >>> pipe = pipe.to("cuda")
    >>> pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
    >>> # Workaround for not accepting attention shape using VAE for Flash Attention
    >>> pipe.vae.enable_xformers_memory_efficient_attention(attention_op=None)

####
disable_xformers_memory_efficient_attention

< source >

( )

Disable memory efficient attention from xFormers.

####
enable_vae_tiling

< source >

( )

Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow processing larger images.

####
disable_vae_tiling

< source >

( )

Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to computing decoding in one step.

####
load_textual_inversion

< source >

( pretrained_model_name_or_path: Union token: Union = None tokenizer: Optional = None text_encoder: Optional = None **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike` or `List[str or os.PathLike]` or `Dict` or `List[Dict]`) — Can be either one of the following or a list of them:
  - A string, the model id (for example `sd-concepts-library/low-poly-hd-logos-icons`) of a pretrained model hosted on the Hub.
  - A path to a directory (for example `./my_text_inversion_directory/`) containing the textual inversion weights.
  - A path to a file (for example `./my_text_inversions.pt`) containing textual inversion weights.
  - A torch state dict.
- token (`str` or `List[str]`, optional) — Override the token to use for the textual inversion weights. If `pretrained_model_name_or_path` is a list, then `token` must also be a list of equal length.
- text_encoder (CLIPTextModel, optional) — Frozen text-encoder (clip-vit-large-patch14). If not specified, function will take self.tokenizer.
- tokenizer (CLIPTokenizer, optional) — A `CLIPTokenizer` to tokenize text. If not specified, function will take self.tokenizer.
- weight_name (`str`, optional) — Name of a custom weight file. This should be used when:
  - The saved textual inversion file is in 🤗 Diffusers format, but was saved under a specific weight name such as `text_inv.bin`.
  - The saved textual inversion file is in the Automatic1111 format.
- cache_dir (`Union[str, os.PathLike]`, optional) — Path to a directory where a downloaded pretrained model configuration is cached if the standard cache is not used.
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to resume downloading the model weights and configuration files. If set to `False`, any incompletely downloaded files are deleted.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- local_files_only (`bool`, optional, defaults to `False`) — Whether to only load local model weights and configuration files or not. If set to `True`, the model won’t be downloaded from the Hub.
- token (`str` or bool, optional) — The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from `diffusers-cli login` (stored in `~/.huggingface`) is used.
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier allowed by Git.
- subfolder (`str`, optional, defaults to `""`) — The subfolder location of a model file within a larger model repository on the Hub or locally.
- mirror (`str`, optional) — Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not guarantee the timeliness or safety of the source, and you should refer to the mirror site for more information.

Load Textual Inversion embeddings into the text encoder of StableDiffusionPipeline (both 🤗 Diffusers and Automatic1111 formats are supported).

Example:

To load a Textual Inversion embedding vector in 🤗 Diffusers format:

    from diffusers import StableDiffusionPipeline
    import torch

    model_id = "runwayml/stable-diffusion-v1-5"
    pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")

    pipe.load_textual_inversion("sd-concepts-library/cat-toy")

    prompt = "A <cat-toy> backpack"

    image = pipe(prompt, num_inference_steps=50).images[0]
    image.save("cat-backpack.png")

To load a Textual Inversion embedding vector in Automatic1111 format, make sure to download the vector first (for example from civitAI) and then load the vector

locally:

    from diffusers import StableDiffusionPipeline
    import torch

    model_id = "runwayml/stable-diffusion-v1-5"
    pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")

    pipe.load_textual_inversion("./charturnerv2.pt", token="charturnerv2")

    prompt = "charturnerv2, multiple views of the same character in the same outfit, a character turnaround of a woman wearing a black jacket and red shirt, best quality, intricate details."

    image = pipe(prompt, num_inference_steps=50).images[0]
    image.save("character.png")

####
from_single_file

< source >

( pretrained_model_link_or_path **kwargs )

Parameters

- pretrained_model_link_or_path (`str` or `os.PathLike`, optional) — Can be either:
  - A link to the `.ckpt` file (for example `"https://huggingface.co/<repo_id>/blob/main/<path_to_file>.ckpt"`) on the Hub.
  - A path to a file containing all pipeline weights.
- torch_dtype (`str` or `torch.dtype`, optional) — Override the default `torch.dtype` and load the model with another dtype.
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- cache_dir (`Union[str, os.PathLike]`, optional) — Path to a directory where a downloaded pretrained model configuration is cached if the standard cache is not used.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to resume downloading the model weights and configuration files. If set to `False`, any incompletely downloaded files are deleted.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- local_files_only (`bool`, optional, defaults to `False`) — Whether to only load local model weights and configuration files or not. If set to `True`, the model won’t be downloaded from the Hub.
- token (`str` or bool, optional) — The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from `diffusers-cli login` (stored in `~/.huggingface`) is used.
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier allowed by Git.
- use_safetensors (`bool`, optional, defaults to `None`) — If set to `None`, the safetensors weights are downloaded if they’re available and if the safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors weights. If set to `False`, safetensors weights are not loaded.

Instantiate a DiffusionPipeline from pretrained pipeline weights saved in the `.ckpt` or `.safetensors` format. The pipeline is set in evaluation mode (`model.eval()`) by default.

Examples:

    >>> from diffusers import StableDiffusionPipeline

    >>> # Download pipeline from huggingface.co and cache.
    >>> pipeline = StableDiffusionPipeline.from_single_file(
    ...     "https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix/AbyssOrangeMix.safetensors"
    ... )

    >>> # Download pipeline from local file
    >>> # file is downloaded under ./v1-5-pruned-emaonly.ckpt
    >>> pipeline = StableDiffusionPipeline.from_single_file("./v1-5-pruned-emaonly")

    >>> # Enable float16 and move to GPU
    >>> pipeline = StableDiffusionPipeline.from_single_file(
    ...     "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt",
    ...     torch_dtype=torch.float16,
    ... )
    >>> pipeline.to("cuda")

####
load_lora_weights

< source >

( pretrained_model_name_or_path_or_dict: Union adapter_name = None **kwargs )

Parameters

- pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`) — See lora_state_dict().
- kwargs (`dict`, optional) — See lora_state_dict().
- adapter_name (`str`, optional) — Adapter name to be used for referencing the loaded adapter model. If not specified, it will use `default_{i}` where i is the total number of adapters being loaded.

Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.unet` and `self.text_encoder`.

All kwargs are forwarded to `self.lora_state_dict`.

See lora_state_dict() for more details on how the state dict is loaded.

See load_lora_into_unet() for more details on how the state dict is loaded into `self.unet`.

See load_lora_into_text_encoder() for more details on how the state dict is loaded into `self.text_encoder`.

####
save_lora_weights

< source >

( save_directory: Union unet_lora_layers: Dict = None text_encoder_lora_layers: Dict = None transformer_lora_layers: Dict = None is_main_process: bool = True weight_name: str = None save_function: Callable = None safe_serialization: bool = True )

Parameters

- save_directory (`str` or `os.PathLike`) — Directory to save LoRA parameters to. Will be created if it doesn’t exist.
- unet_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`) — State dict of the LoRA layers corresponding to the `unet`.
- text_encoder_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`) — State dict of the LoRA layers corresponding to the `text_encoder`. Must explicitly pass the text encoder LoRA state dict because it comes from 🤗 Transformers.
- is_main_process (`bool`, optional, defaults to `True`) — Whether the process calling this is the main process or not. Useful during distributed training and you need to call this function on all processes. In this case, set `is_main_process=True` only on the main process to avoid race conditions.
- save_function (`Callable`) — The function to use to save the state dictionary. Useful during distributed training when you need to replace `torch.save` with another method. Can be configured with the environment variable `DIFFUSERS_SAVE_MODE`.
- safe_serialization (`bool`, optional, defaults to `True`) — Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.

Save the LoRA parameters corresponding to the UNet and text encoder.

####
disable_freeu

< source >

( )

Disables the FreeU mechanism if enabled.

####
enable_freeu

< source >

( s1: float s2: float b1: float b2: float )

Parameters

- s1 (`float`) — Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to mitigate “oversmoothing effect” in the enhanced denoising process.
- s2 (`float`) — Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to mitigate “oversmoothing effect” in the enhanced denoising process.
- b1 (`float`) — Scaling factor for stage 1 to amplify the contributions of backbone features.
- b2 (`float`) — Scaling factor for stage 2 to amplify the contributions of backbone features.

Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.

The suffixes after the scaling factors represent the stages where they are being applied.

Please refer to the official repository for combinations of the values that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.

####
encode_prompt

< source >

( prompt device num_images_per_prompt do_classifier_free_guidance negative_prompt = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None lora_scale: Optional = None clip_skip: Optional = None )

Parameters

- 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.FloatTensor`, 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.FloatTensor`, 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.

Encodes the prompt into text encoder hidden states.

####
fuse_qkv_projections

< source >

( unet: bool = True vae: bool = True )

Parameters

- unet (`bool`, defaults to `True`) — To apply fusion on the UNet.
- vae (`bool`, defaults to `True`) — To apply fusion on the VAE.

Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value) are fused. For cross-attention modules, key and value projection matrices are fused.

This API is 🧪 experimental.

####
get_guidance_scale_embedding

< source >

( w embedding_dim = 512 dtype = torch.float32 ) →
`torch.FloatTensor`

Parameters

- timesteps (`torch.Tensor`) — generate embedding vectors at these timesteps
- embedding_dim (`int`, optional, defaults to 512) — dimension of the embeddings to generate dtype — data type of the generated embeddings

Returns

`torch.FloatTensor`

Embedding vectors with shape `(len(timesteps), embedding_dim)`

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

####
unfuse_qkv_projections

< source >

( unet: bool = True vae: bool = True )

Parameters

- unet (`bool`, defaults to `True`) — To apply fusion on the UNet.
- vae (`bool`, defaults to `True`) — To apply fusion on the VAE.

Disable QKV projection fusion if enabled.

This API is 🧪 experimental.

##
StableDiffusionPipelineOutput

###
class diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput

< source >

( images: Union nsfw_content_detected: Optional )

Parameters

- images (`List[PIL.Image.Image]` or `np.ndarray`) — List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width, num_channels)`.
- nsfw_content_detected (`List[bool]`) — List indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content or `None` if safety checking could not be performed.

Output class for Stable Diffusion pipelines.

##
FlaxStableDiffusionPipeline

###
class diffusers.FlaxStableDiffusionPipeline

< source >

( vae: FlaxAutoencoderKL text_encoder: FlaxCLIPTextModel tokenizer: CLIPTokenizer unet: FlaxUNet2DConditionModel scheduler: Union safety_checker: FlaxStableDiffusionSafetyChecker feature_extractor: CLIPImageProcessor dtype: dtype = <class 'jax.numpy.float32'> )

Parameters

- vae (FlaxAutoencoderKL) — Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
- text_encoder (FlaxCLIPTextModel) — Frozen text-encoder (clip-vit-large-patch14).
- tokenizer (CLIPTokenizer) — A `CLIPTokenizer` to tokenize text.
- unet (FlaxUNet2DConditionModel) — A `FlaxUNet2DConditionModel` 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 `FlaxDDIMScheduler`, `FlaxLMSDiscreteScheduler`, `FlaxPNDMScheduler`, or `FlaxDPMSolverMultistepScheduler`.
- safety_checker (`FlaxStableDiffusionSafetyChecker`) — Classification module that estimates whether generated images could be considered offensive or harmful. Please refer to the model card for more details about a model’s potential harms.
- feature_extractor (CLIPImageProcessor) — A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.

Flax-based pipeline for text-to-image generation using Stable Diffusion.

This model inherits from FlaxDiffusionPipeline. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.).

####
__call__

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( prompt_ids: array params: Union prng_seed: Array num_inference_steps: int = 50 height: Optional = None width: Optional = None guidance_scale: Union = 7.5 latents: Array = None neg_prompt_ids: Array = None return_dict: bool = True jit: bool = False ) →
FlaxStableDiffusionPipelineOutput or `tuple`

Parameters

- prompt (`str` or `List[str]`, optional) — The prompt or prompts to guide image generation.
- height (`int`, optional, defaults to `self.unet.config.sample_size * self.vae_scale_factor`) — The height in pixels of the generated image.
- width (`int`, optional, defaults to `self.unet.config.sample_size * self.vae_scale_factor`) — The width in pixels of the generated image.
- 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.
- guidance_scale (`float`, optional, defaults to 7.5) — A higher guidance scale value encourages the model to generate images closely linked to the text `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
- latents (`jnp.ndarray`, 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 array is generated by sampling using the supplied random `generator`.
- jit (`bool`, defaults to `False`) — Whether to run `pmap` versions of the generation and safety scoring functions.

  This argument exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a future release.

- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a FlaxStableDiffusionPipelineOutput instead of a plain tuple.

Returns

FlaxStableDiffusionPipelineOutput or `tuple`

If `return_dict` is `True`, FlaxStableDiffusionPipelineOutput is returned, otherwise a `tuple` is returned where the first element is a list with the generated images and the second element is a list of `bool`s indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content.

The call function to the pipeline for generation.

Examples:

    >>> import jax
    >>> import numpy as np
    >>> from flax.jax_utils import replicate
    >>> from flax.training.common_utils import shard

    >>> from diffusers import FlaxStableDiffusionPipeline

    >>> pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
    ...     "runwayml/stable-diffusion-v1-5", revision="bf16", dtype=jax.numpy.bfloat16
    ... )

    >>> prompt = "a photo of an astronaut riding a horse on mars"

    >>> prng_seed = jax.random.PRNGKey(0)
    >>> num_inference_steps = 50

    >>> num_samples = jax.device_count()
    >>> prompt = num_samples * [prompt]
    >>> prompt_ids = pipeline.prepare_inputs(prompt)
    # shard inputs and rng

    >>> params = replicate(params)
    >>> prng_seed = jax.random.split(prng_seed, jax.device_count())
    >>> prompt_ids = shard(prompt_ids)

    >>> images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
    >>> images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))

##
FlaxStableDiffusionPipelineOutput

###
class diffusers.pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput

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( images: ndarray nsfw_content_detected: List )

Parameters

- images (`np.ndarray`) — Denoised images of array shape of `(batch_size, height, width, num_channels)`.
- nsfw_content_detected (`List[bool]`) — List indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content or `None` if safety checking could not be performed.

Output class for Flax-based Stable Diffusion pipelines.

####
replace

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( **updates )

“Returns a new object replacing the specified fields with new values.