Image-to-image (original) (raw)

The Stable Diffusion model can also be applied to image-to-image generation by passing a text prompt and an initial image to condition the generation of new images.

The StableDiffusionImg2ImgPipeline uses the diffusion-denoising mechanism proposed in SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations by Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon.

The abstract from the paper is:

Guided image synthesis enables everyday users to create and edit photo-realistic images with minimum effort. The key challenge is balancing faithfulness to the user input (e.g., hand-drawn colored strokes) and realism of the synthesized image. Existing GAN-based methods attempt to achieve such balance using either conditional GANs or GAN inversions, which are challenging and often require additional training data or loss functions for individual applications. To address these issues, we introduce a new image synthesis and editing method, Stochastic Differential Editing (SDEdit), based on a diffusion model generative prior, which synthesizes realistic images by iteratively denoising through a stochastic differential equation (SDE). Given an input image with user guide of any type, SDEdit first adds noise to the input, then subsequently denoises the resulting image through the SDE prior to increase its realism. SDEdit does not require task-specific training or inversions and can naturally achieve the balance between realism and faithfulness. SDEdit significantly outperforms state-of-the-art GAN-based methods by up to 98.09% on realism and 91.72% on overall satisfaction scores, according to a human perception study, on multiple tasks, including stroke-based image synthesis and editing as well as image compositing.

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!

StableDiffusionImg2ImgPipeline

class diffusers.StableDiffusionImg2ImgPipeline

( 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 ofDDIMScheduler, 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-guided image-to-image generation using Stable Diffusion.

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

The 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

( prompt: typing.Union[str, typing.List[str]] = 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 = 0.8 num_inference_steps: typing.Optional[int] = 50 timesteps: typing.List[int] = None sigmas: typing.List[float] = None guidance_scale: typing.Optional[float] = 7.5 negative_prompt: typing.Union[str, typing.List[str], NoneType] = None num_images_per_prompt: typing.Optional[int] = 1 eta: typing.Optional[float] = 0.0 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None prompt_embeds: typing.Optional[torch.Tensor] = None negative_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 clip_skip: 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 ) → 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.
image (torch.Tensor, PIL.Image.Image, np.ndarray, List[torch.Tensor], List[PIL.Image.Image], or List[np.ndarray]) —Image, numpy array or tensor representing an image batch to be used as the starting point. For both numpy array and pytorch tensor, the expected value range is between [0, 1] If it’s a tensor or a list or tensors, the expected shape should be (B, C, H, W) or (C, H, W). If it is a numpy array or a list of arrays, the expected shape should be (B, H, W, C) or (H, W, C) It can also accept image latents as image, but if passing latents directly it is not encoded again.
strength (float, optional, defaults to 0.8) — Indicates extent to transform the reference image. Must be between 0 and 1. image is used as a starting point and more noise is added the higher the strength. The number of denoising steps depends on the amount of noise initially added. When strength is 1, added noise is maximum and the denoising process runs for the full number of iterations specified in num_inference_steps. A value of 1 essentially ignores 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. This parameter is modulated by strength.
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.
guidance_scale (float, optional, defaults to 7.5) — A higher guidance scale value encourages the model to generate images closely linked to the textprompt 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.
prompt_embeds (torch.Tensor, 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.Tensor, 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.
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 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 inself.processor.
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.

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 bools indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content.

The call function to the pipeline for generation.

Examples:

import requests import torch from PIL import Image from io import BytesIO

from diffusers import StableDiffusionImg2ImgPipeline

device = "cuda" model_id_or_path = "stable-diffusion-v1-5/stable-diffusion-v1-5" pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16) pipe = pipe.to(device)

url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"

response = requests.get(url) init_image = Image.open(BytesIO(response.content)).convert("RGB") init_image = init_image.resize((768, 512))

prompt = "A fantasy landscape, trending on artstation"

images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images images[0].save("fantasy_landscape.png")

enable_attention_slicing

( slice_size: typing.Union[int, str, NoneType] = '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_dimmust 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( ... "stable-diffusion-v1-5/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 sliced attention computation. If enable_attention_slicing was previously called, attention is computed in one step.

enable_xformers_memory_efficient_attention

( attention_op: typing.Optional[typing.Callable] = None )

Parameters

attention_op (Callable, optional) — Override the default None operator for use as op argument to thememory_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)

pipe.vae.enable_xformers_memory_efficient_attention(attention_op=None)

disable_xformers_memory_efficient_attention

( )

Disable memory efficient attention from xFormers.

load_textual_inversion

( pretrained_model_name_or_path: typing.Union[str, typing.List[str], typing.Dict[str, torch.Tensor], typing.List[typing.Dict[str, torch.Tensor]]] token: typing.Union[typing.List[str], str, NoneType] = None tokenizer: typing.Optional[ForwardRef('PreTrainedTokenizer')] = None text_encoder: typing.Optional[ForwardRef('PreTrainedModel')] = 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.
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.
hf_token (str or bool, optional) — The token to use as HTTP bearer authorization for remote files. If True, the token generated fromdiffusers-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 = "stable-diffusion-v1-5/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 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 = "stable-diffusion-v1-5/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

( 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.
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 fromdiffusers-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.
original_config_file (str, optional) — The path to the original config file that was used to train the model. If not provided, the config file will be inferred from the checkpoint file.
config (str, optional) — Can be either:
- A string, the repo id (for example CompVis/ldm-text2im-large-256) of a pretrained pipeline hosted on the Hub.
- A path to a directory (for example ./my_pipeline_directory/) containing the pipeline component configs in Diffusers format.
disable_mmap (‘bool’, optional, defaults to ‘False’) — Whether to disable mmap when loading a Safetensors model. This option can perform better when the model is on a network mount or hard drive.
kwargs (remaining dictionary of keyword arguments, optional) — Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline class). The overwritten components are passed directly to the pipelines __init__ method. See example below for more information.

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

Examples:

from diffusers import StableDiffusionPipeline

pipeline = StableDiffusionPipeline.from_single_file( ... "https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix/AbyssOrangeMix.safetensors" ... )

pipeline = StableDiffusionPipeline.from_single_file("./v1-5-pruned-emaonly.ckpt")

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

load_lora_weights

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] adapter_name = None hotswap: bool = False **kwargs )

Parameters

pretrained_model_name_or_path_or_dict (str or os.PathLike or dict) — See lora_state_dict().
adapter_name (str, optional) — Adapter name to be used for referencing the loaded adapter model. If not specified, it will usedefault_{i} where i is the total number of adapters being loaded.
low_cpu_mem_usage (bool, optional) — Speed up model loading by only loading the pretrained LoRA weights and not initializing the random weights.
hotswap — (bool, optional) Defaults to False. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter in-place. This means that, instead of loading an additional adapter, this will take the existing adapter weights and replace them with the weights of the new adapter. This can be faster and more memory efficient. However, the main advantage of hotswapping is that when the model is compiled with torch.compile, loading the new adapter does not require recompilation of the model. When using hotswapping, the passed adapter_name should be the name of an already loaded adapter.
If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need to call an additional method before loading the adapter:

Load LoRA weights specified in pretrained_model_name_or_path_or_dict into self.unet andself.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

( save_directory: typing.Union[str, os.PathLike] unet_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None text_encoder_lora_layers: typing.Dict[str, torch.nn.modules.module.Module] = None is_main_process: bool = True weight_name: str = None save_function: typing.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 variableDIFFUSERS_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.

encode_prompt

( prompt device num_images_per_prompt do_classifier_free_guidance negative_prompt = None prompt_embeds: typing.Optional[torch.Tensor] = None negative_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
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 passnegative_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.

Encodes the prompt into text encoder hidden states.

get_guidance_scale_embedding

( 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.

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

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

StableDiffusionPipelineOutput

class diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput

( images: typing.Union[typing.List[PIL.Image.Image], numpy.ndarray] nsfw_content_detected: typing.Optional[typing.List[bool]] )

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 orNone if safety checking could not be performed.

Output class for Stable Diffusion pipelines.

FlaxStableDiffusionImg2ImgPipeline

class diffusers.FlaxStableDiffusionImg2ImgPipeline

( vae: FlaxAutoencoderKL text_encoder: FlaxCLIPTextModel tokenizer: CLIPTokenizer unet: FlaxUNet2DConditionModel scheduler: typing.Union[diffusers.schedulers.scheduling_ddim_flax.FlaxDDIMScheduler, diffusers.schedulers.scheduling_pndm_flax.FlaxPNDMScheduler, diffusers.schedulers.scheduling_lms_discrete_flax.FlaxLMSDiscreteScheduler, diffusers.schedulers.scheduling_dpmsolver_multistep_flax.FlaxDPMSolverMultistepScheduler] 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 ofFlaxDDIMScheduler, FlaxLMSDiscreteScheduler, FlaxPNDMScheduler, orFlaxDPMSolverMultistepScheduler.
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-guided image-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

( prompt_ids: Array image: Array params: typing.Union[typing.Dict, flax.core.frozen_dict.FrozenDict] prng_seed: Array strength: float = 0.8 num_inference_steps: int = 50 height: typing.Optional[int] = None width: typing.Optional[int] = None guidance_scale: typing.Union[float, jax.Array] = 7.5 noise: Array = None neg_prompt_ids: Array = None return_dict: bool = True jit: bool = False ) → FlaxStableDiffusionPipelineOutput or tuple

Parameters

prompt_ids (jnp.ndarray) — The prompt or prompts to guide image generation.
image (jnp.ndarray) — Array representing an image batch to be used as the starting point.
params (Dict or FrozenDict) — Dictionary containing the model parameters/weights.
prng_seed (jax.Array or jax.Array) — Array containing random number generator key.
strength (float, optional, defaults to 0.8) — Indicates extent to transform the reference image. Must be between 0 and 1. image is used as a starting point and more noise is added the higher the strength. The number of denoising steps depends on the amount of noise initially added. When strength is 1, added noise is maximum and the denoising process runs for the full number of iterations specified in num_inference_steps. A value of 1 essentially ignores 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. This parameter is modulated by strength.
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.
guidance_scale (float, optional, defaults to 7.5) — A higher guidance scale value encourages the model to generate images closely linked to the textprompt at the expense of lower image quality. Guidance scale is enabled when guidance_scale > 1.
noise (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. The array is generated by sampling using the supplied random generator.
return_dict (bool, optional, defaults to True) — Whether or not to return a FlaxStableDiffusionPipelineOutput instead of a plain tuple.
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.

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 bools 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 import jax.numpy as jnp from flax.jax_utils import replicate from flax.training.common_utils import shard import requests from io import BytesIO from PIL import Image from diffusers import FlaxStableDiffusionImg2ImgPipeline

def create_key(seed=0): ... return jax.random.PRNGKey(seed)

rng = create_key(0)

url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" response = requests.get(url) init_img = Image.open(BytesIO(response.content)).convert("RGB") init_img = init_img.resize((768, 512))

prompts = "A fantasy landscape, trending on artstation"

pipeline, params = FlaxStableDiffusionImg2ImgPipeline.from_pretrained( ... "CompVis/stable-diffusion-v1-4", ... revision="flax", ... dtype=jnp.bfloat16, ... )

num_samples = jax.device_count() rng = jax.random.split(rng, jax.device_count()) prompt_ids, processed_image = pipeline.prepare_inputs( ... prompt=[prompts] * num_samples, image=[init_img] * num_samples ... ) p_params = replicate(params) prompt_ids = shard(prompt_ids) processed_image = shard(processed_image)

output = pipeline( ... prompt_ids=prompt_ids, ... image=processed_image, ... params=p_params, ... prng_seed=rng, ... strength=0.75, ... num_inference_steps=50, ... jit=True, ... height=512, ... width=768, ... ).images

output_images = pipeline.numpy_to_pil(np.asarray(output.reshape((num_samples,) + output.shape[-3:])))

FlaxStableDiffusionPipelineOutput

class diffusers.pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput

( images: ndarray nsfw_content_detected: typing.List[bool] )

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.

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

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