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Image-to-Image Generation

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Image-to-Image Generation

StableDiffusionImg2ImgPipeline

The Stable Diffusion model was created by the researchers and engineers from CompVis, Stability AI, runway, and LAION. The StableDiffusionImg2ImgPipeline lets you pass a text prompt and an initial image to condition the generation of new images using Stable Diffusion.

The original codebase can be found here: CampVis/stable-diffusion

StableDiffusionImg2ImgPipeline is compatible with all Stable Diffusion checkpoints for Text-to-Image

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

class diffusers.StableDiffusionImg2ImgPipeline

< >

( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPImageProcessor 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. Stable Diffusion uses the text portion of CLIP, specifically the clip-vit-large-patch14 variant.
  • tokenizer (CLIPTokenizer) — 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.
  • safety_checker (StableDiffusionSafetyChecker) — Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the model card for details.
  • feature_extractor (CLIPImageProcessor) — Model that extracts features from generated images to be used as inputs for 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 the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

In addition the pipeline inherits the following loading methods:

as well as the following saving methods:

__call__

< >

( prompt: typing.Union[str, typing.List[str]] = None image: typing.Union[torch.FloatTensor, PIL.Image.Image] = None strength: float = 0.8 num_inference_steps: typing.Optional[int] = 50 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.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None callback_steps: int = 1 cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None ) → StableDiffusionPipelineOutput 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.
  • image (torch.FloatTensor or PIL.Image.Image) — Image, or tensor representing an image batch, that will be used as the starting point for the process.
  • strength (float, optional, defaults to 0.8) — 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.
  • 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 will be modulated by strength.
  • 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).
  • 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://arxiv.org/abs/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.
  • 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.
  • 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.
  • callback (Callable, optional) — A function that will be called every callback_steps steps during inference. The function will be called with the following arguments: callback(step: int, timestep: int, latents: torch.FloatTensor).
  • callback_steps (int, optional, defaults to 1) — The frequency at which the callback function will be called. If not specified, the callback will be called at every step.
  • cross_attention_kwargs (dict, optional) — A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under self.processor in diffusers.cross_attention.

Returns

StableDiffusionPipelineOutput or tuple

StableDiffusionPipelineOutput if return_dict is True, otherwise a tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of bools denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the safety_checker`.

Function invoked when calling 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 = "runwayml/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[str, int, 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_dim must be a multiple of slice_size.

Enable sliced attention computation.

When this option is enabled, the attention module will split the input tensor in slices, to compute attention in several steps. This is useful to save some memory in exchange for a small speed decrease.

disable_attention_slicing

< >

( )

Disable sliced attention computation. If enable_attention_slicing was previously invoked, this method will go back to computing attention 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 the memory_efficient_attention() function of xFormers.

Enable memory efficient attention as implemented in xformers.

When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference time. Speed up at training time is not guaranteed.

Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention is used.

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

< >

( )

Disable memory efficient attention as implemented in xformers.

load_textual_inversion

< >

( pretrained_model_name_or_path: typing.Union[str, typing.Dict[str, torch.Tensor]] token: typing.Optional[str] = None **kwargs )

Parameters

  • pretrained_model_name_or_path (str or os.PathLike) — Can be either:

    • A string, the model id of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids should have an organization name, like "sd-concepts-library/low-poly-hd-logos-icons".
    • A path to a directory containing textual inversion weights, e.g. ./my_text_inversion_directory/.
  • weight_name (str, optional) — Name of a custom weight file. This should be used in two cases:

    • 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” form.
  • cache_dir (Union[str, os.PathLike], optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be 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 delete incompletely received files. Will attempt to resume the download if such a file exists.
  • proxies (Dict[str, str], optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., {'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 or not to only look at local files (i.e., do not try to download the model).
  • use_auth_token (str or bool, optional) — The token to use as HTTP bearer authorization for remote files. If True, will use the token generated when running diffusers-cli login (stored in ~/.huggingface).
  • revision (str, optional, defaults to "main") — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so revision can be any identifier allowed by git.
  • subfolder (str, optional, defaults to "") — In case the relevant files are located inside a subfolder of the model repo (either remote in huggingface.co or downloaded locally), you can specify the folder name here.
  • mirror (str, optional) — Mirror source to accelerate downloads in China. If you are from China and have an accessibility problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety. Please refer to the mirror site for more information.

Load textual inversion embeddings into the text encoder of stable diffusion pipelines. Both diffusers and Automatic1111 formats are supported (see example below).

This function is experimental and might change in the future.

It is required to be logged in (huggingface-cli login) when you want to use private or gated models.

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 first download the vector,

e.g. 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_ckpt

< >

( 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 on the Hub. Should be in the format "https://huggingface.co/<repo_id>/blob/main/<path_to_file>"
    • 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 under this dtype. If "auto" is passed the dtype will be automatically derived from the model’s weights.
  • 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 in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
  • resume_download (bool, optional, defaults to False) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
  • proxies (Dict[str, str], optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., {'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 or not to only look at local files (i.e., do not try to download the model).
  • use_auth_token (str or bool, optional) — The token to use as HTTP bearer authorization for remote files. If True, will use the token generated when running huggingface-cli login (stored in ~/.huggingface).
  • revision (str, optional, defaults to "main") — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so revision can be any identifier allowed by git.
  • use_safetensors (bool, optional ) — If set to True, the pipeline will be loaded from safetensors weights. If set to None (the default). The pipeline will load using safetensors if the safetensors weights are available and if safetensors is installed. If the to False the pipeline will not use safetensors.
  • extract_ema (bool, optional, defaults to False) — Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights or not. Defaults to False. Pass True to extract the EMA weights. EMA weights usually yield higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.
  • upcast_attention (bool, optional, defaults to None) — Whether the attention computation should always be upcasted. This is necessary when running stable
  • image_size (int, optional, defaults to 512) — The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Diffusion v2 Base. Use 768 for Stable Diffusion v2.
  • prediction_type (str, optional) — The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable Diffusion v2 Base. Use 'v_prediction' for Stable Diffusion v2.
  • num_in_channels (int, optional, defaults to None) — The number of input channels. If None, it will be automatically inferred.
  • scheduler_type (str, optional, defaults to ‘pndm’) — Type of scheduler to use. Should be one of ["pndm", "lms", "heun", "euler", "euler-ancestral", "dpm", "ddim"].
  • load_safety_checker (bool, optional, defaults to True) — Whether to load the safety checker or not. Defaults to True.
  • kwargs (remaining dictionary of keyword arguments, optional) — Can be used to overwrite load - and saveable variables - i.e. the pipeline components - of the specific pipeline class. The overwritten components are then directly passed to the pipelines __init__ method. See example below for more information.

Instantiate a PyTorch diffusion pipeline from pre-trained pipeline weights saved in the original .ckpt format.

The pipeline is set in evaluation mode by default using model.eval() (Dropout modules are deactivated).

Examples:

>>> from diffusers import StableDiffusionPipeline

>>> # Download pipeline from huggingface.co and cache.
>>> pipeline = StableDiffusionPipeline.from_ckpt(
...     "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_ckpt("./v1-5-pruned-emaonly")

>>> # Enable float16 and move to GPU
>>> pipeline = StableDiffusionPipeline.from_ckpt(
...     "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

< >

( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] **kwargs )

Parameters

  • pretrained_model_name_or_path_or_dict (str or os.PathLike or dict) — Can be either:

    • A string, the model id of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids should have an organization name, like google/ddpm-celebahq-256.
    • A path to a directory containing model weights saved using ~ModelMixin.save_config, e.g., ./my_model_directory/.
    • A torch state dict.
  • cache_dir (Union[str, os.PathLike], optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be 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 delete incompletely received files. Will attempt to resume the download if such a file exists.
  • proxies (Dict[str, str], optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., {'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 or not to only look at local files (i.e., do not try to download the model).
  • use_auth_token (str or bool, optional) — The token to use as HTTP bearer authorization for remote files. If True, will use the token generated when running diffusers-cli login (stored in ~/.huggingface).
  • revision (str, optional, defaults to "main") — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so revision can be any identifier allowed by git.
  • subfolder (str, optional, defaults to "") — In case the relevant files are located inside a subfolder of the model repo (either remote in huggingface.co or downloaded locally), you can specify the folder name here.
  • mirror (str, optional) — Mirror source to accelerate downloads in China. If you are from China and have an accessibility problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety. Please refer to the mirror site for more information.

Load pretrained attention processor layers (such as LoRA) into UNet2DConditionModel and CLIPTextModel).

This function is experimental and might change in the future.

It is required to be logged in (huggingface-cli login) when you want to use private or gated models.

save_lora_weights

< >

( save_directory: typing.Union[str, os.PathLike] unet_lora_layers: typing.Dict[str, torch.nn.modules.module.Module] = 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 = False )

Parameters

  • save_directory (str or os.PathLike) — Directory to which to save. Will be created if it doesn’t exist.
  • unet_lora_layers (Dict[str, torch.nn.Module]) — State dict of the LoRA layers corresponding to the UNet. Specifying this helps to make the serialization process easier and cleaner.
  • text_encoder_lora_layers (Dict[str, torch.nn.Module]) — State dict of the LoRA layers corresponding to the text_encoder. Since the text_encoder comes from transformers, we cannot rejig it. That is why we have to explicitly pass the text encoder LoRA state dict.
  • is_main_process (bool, optional, defaults to True) — Whether the process calling this is the main process or not. Useful when in distributed training like TPUs and 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 on distributed training like TPUs when one need to replace torch.save by another method. Can be configured with the environment variable DIFFUSERS_SAVE_MODE.

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

enable_model_cpu_offload

< >

( gpu_id = 0 )

Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to enable_sequential_cpu_offload, this method moves one whole model at a time to the GPU when its forward method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with enable_sequential_cpu_offload, but performance is much better due to the iterative execution of the unet.

enable_sequential_cpu_offload

< >

( gpu_id = 0 )

Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a torch.device('meta') and loaded to GPU only when their specific submodule has its forwardmethod called. Note that offloading happens on a submodule basis. Memory savings are higher than withenable_model_cpu_offload`, but performance is lower.

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. Stable Diffusion uses the text portion of CLIP, specifically the clip-vit-large-patch14 variant.
  • tokenizer (CLIPTokenizer) — Tokenizer of class CLIPTokenizer.
  • unet (FlaxUNet2DConditionModel) — 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 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 details.
  • feature_extractor (CLIPImageProcessor) — Model that extracts features from generated images to be used as inputs for the safety_checker.

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

This model inherits from FlaxDiffusionPipeline. 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.)

__call__

< >

( prompt_ids: array image: array params: typing.Union[typing.Dict, flax.core.frozen_dict.FrozenDict] prng_seed: PRNGKeyArray strength: float = 0.8 num_inference_steps: int = 50 height: typing.Optional[int] = None width: typing.Optional[int] = None guidance_scale: typing.Union[float, 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.array) — The prompt or prompts to guide the image generation.
  • image (jnp.array) — Array representing an image batch, that will be used as the starting point for the process.
  • params (Dict or FrozenDict) — Dictionary containing the model parameters/weights
  • prng_seed (jax.random.KeyArray or jax.Array) — Array containing random number generator key
  • strength (float, optional, defaults to 0.8) — 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 (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.
  • 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) — 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.
  • noise (jnp.array, 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. tensor will ge 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. NOTE: This argument exists because __call__ is not yet end-to-end pmap-able. It will be removed in a future release.

Returns

FlaxStableDiffusionPipelineOutput or tuple

FlaxStableDiffusionPipelineOutput if return_dict is True, otherwise a tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of bools denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the safety_checker`.

Function invoked when calling 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:])))
←Text-to-Image Inpaint→