Stable diffusion pipelines
Stable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from CompVis, Stability AI and LAION. 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. You can learn more details about it in the specific pipeline for latent diffusion that is part of 🤗 Diffusers.
For more details about how Stable Diffusion works and how it differs from the base latent diffusion model, please refer to the official launch announcement post and this section of our own blog post.
Tips:
- To tweak your prompts on a specific result you liked, you can generate your own latents, as demonstrated in the following notebook:
Overview:
Pipeline | Tasks | Colab | Demo |
---|---|---|---|
pipeline_stable_diffusion.py | Text-to-Image Generation | 🤗 Stable Diffusion | |
pipeline_stable_diffusion_img2img.py | Image-to-Image Text-Guided Generation | 🤗 Diffuse the Rest | |
pipeline_stable_diffusion_inpaint.py | Experimental – Text-Guided Image Inpainting | Coming soon |
Tips
How to load and use different schedulers.
The stable diffusion pipeline uses PNDMScheduler scheduler by default. But diffusers
provides many other schedulers that can be used with the stable diffusion pipeline such as DDIMScheduler, LMSDiscreteScheduler, EulerDiscreteScheduler, EulerAncestralDiscreteScheduler etc.
To use a different scheduler, you can either change it via the ConfigMixin.from_config() method or pass the scheduler
argument to the from_pretrained
method of the pipeline. For example, to use the EulerDiscreteScheduler, you can do the following:
>>> from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
>>> pipeline = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
>>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
>>> # or
>>> euler_scheduler = EulerDiscreteScheduler.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
>>> pipeline = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", scheduler=euler_scheduler)
How to convert all use cases with multiple or single pipeline
If you want to use all possible use cases in a single DiffusionPipeline
you can either:
- Make use of the Stable Diffusion Mega Pipeline or
- Make use of the
components
functionality to instantiate all components in the most memory-efficient way:
>>> from diffusers import (
... StableDiffusionPipeline,
... StableDiffusionImg2ImgPipeline,
... StableDiffusionInpaintPipeline,
... )
>>> text2img = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
>>> img2img = StableDiffusionImg2ImgPipeline(**text2img.components)
>>> inpaint = StableDiffusionInpaintPipeline(**text2img.components)
>>> # now you can use text2img(...), img2img(...), inpaint(...) just like the call methods of each respective pipeline
StableDiffusionPipelineOutput
class diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput
< source >( images: typing.Union[typing.List[PIL.Image.Image], numpy.ndarray] nsfw_content_detected: typing.Optional[typing.List[bool]] )
Parameters
-
images (
List[PIL.Image.Image]
ornp.ndarray
) — List of denoised PIL images of lengthbatch_size
or numpy array of shape(batch_size, height, width, num_channels)
. PIL images or numpy array present the denoised images of the diffusion pipeline. -
nsfw_content_detected (
List[bool]
) — List of flags denoting whether the corresponding generated image likely represents “not-safe-for-work” (nsfw) content, orNone
if safety checking could not be performed.
Output class for Stable Diffusion pipelines.
StableDiffusionPipeline
class diffusers.StableDiffusionPipeline
< source >( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel scheduler: typing.Union[diffusers.schedulers.scheduling_ddim.DDIMScheduler, diffusers.schedulers.scheduling_pndm.PNDMScheduler, diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler, diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler, diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler, diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler] 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 (
CLIPFeatureExtractor
) — Model that extracts features from generated images to be used as inputs for thesafety_checker
.
Pipeline for text-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.)
__call__
< source >(
prompt: typing.Union[str, typing.List[str]]
height: typing.Optional[int] = None
width: typing.Optional[int] = None
num_inference_steps: int = 50
guidance_scale: float = 7.5
negative_prompt: typing.Union[str, typing.List[str], NoneType] = None
num_images_per_prompt: typing.Optional[int] = 1
eta: float = 0.0
generator: typing.Optional[torch._C.Generator] = None
latents: 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: typing.Optional[int] = 1
)
→
StableDiffusionPipelineOutput or tuple
Parameters
-
prompt (
str
orList[str]
) — The prompt or prompts to guide the 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) — Guidance scale as defined in Classifier-Free Diffusion Guidance.guidance_scale
is defined asw
of equation 2. of Imagen Paper. Guidance scale is enabled by settingguidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the textprompt
, usually at the expense of lower image quality. -
negative_prompt (
str
orList[str]
, optional) — The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). -
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) — 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 will ge generated by sampling using the supplied randomgenerator
. -
output_type (
str
, optional, defaults to"pil"
) — The output format of the generate image. Choose between PIL:PIL.Image.Image
ornp.array
. -
return_dict (
bool
, optional, defaults toTrue
) — Whether or not to return a StableDiffusionPipelineOutput instead of a plain tuple. -
callback (
Callable
, optional) — A function that will be called everycallback_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 thecallback
function will be called. If not specified, the callback will be called at every step.
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.
enable_attention_slicing
< source >( slice_size: typing.Union[str, int, NoneType] = 'auto' )
Parameters
-
slice_size (
str
orint
, optional, defaults to"auto"
) — When"auto"
, halves the input to the attention heads, so attention will be computed in two steps. If a number is provided, uses as many slices asattention_head_dim // slice_size
. In this case,attention_head_dim
must be a multiple ofslice_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 sliced attention computation. If enable_attention_slicing
was previously invoked, this method will go
back to computing attention in one step.
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 sliced VAE decoding. If enable_vae_slicing
was previously invoked, this method will go back to
computing decoding in one step.
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.
Disable memory efficient attention as implemented in xformers.
StableDiffusionImg2ImgPipeline
class diffusers.StableDiffusionImg2ImgPipeline
< source >( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel scheduler: typing.Union[diffusers.schedulers.scheduling_ddim.DDIMScheduler, diffusers.schedulers.scheduling_pndm.PNDMScheduler, diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler, diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler, diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler, diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler] 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 (
CLIPFeatureExtractor
) — Model that extracts features from generated images to be used as inputs for thesafety_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.)
__call__
< source >(
prompt: typing.Union[str, typing.List[str]]
init_image: typing.Union[torch.FloatTensor, PIL.Image.Image]
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.Optional[torch._C.Generator] = 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: typing.Optional[int] = 1
)
→
StableDiffusionPipelineOutput or tuple
Parameters
-
prompt (
str
orList[str]
) — The prompt or prompts to guide the image generation. -
init_image (
torch.FloatTensor
orPIL.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 referenceinit_image
. Must be between 0 and 1.init_image
will be used as a starting point, adding more noise to it the larger thestrength
. The number of denoising steps depends on the amount of noise initially added. Whenstrength
is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified innum_inference_steps
. A value of 1, therefore, essentially ignoresinit_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 bystrength
. -
guidance_scale (
float
, optional, defaults to 7.5) — Guidance scale as defined in Classifier-Free Diffusion Guidance.guidance_scale
is defined asw
of equation 2. of Imagen Paper. Guidance scale is enabled by settingguidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the textprompt
, usually at the expense of lower image quality. -
negative_prompt (
str
orList[str]
, optional) — The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). -
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) — A torch generator to make generation deterministic. -
output_type (
str
, optional, defaults to"pil"
) — The output format of the generate image. Choose between PIL:PIL.Image.Image
ornp.array
. -
return_dict (
bool
, optional, defaults toTrue
) — Whether or not to return a StableDiffusionPipelineOutput instead of a plain tuple. -
callback (
Callable
, optional) — A function that will be called everycallback_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 thecallback
function will be called. If not specified, the callback will be called at every step.
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.
enable_attention_slicing
< source >( slice_size: typing.Union[str, int, NoneType] = 'auto' )
Parameters
-
slice_size (
str
orint
, optional, defaults to"auto"
) — When"auto"
, halves the input to the attention heads, so attention will be computed in two steps. If a number is provided, uses as many slices asattention_head_dim // slice_size
. In this case,attention_head_dim
must be a multiple ofslice_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 sliced attention computation. If enable_attention_slicing
was previously invoked, this method will go
back to computing attention in one step.
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.
Disable memory efficient attention as implemented in xformers.
StableDiffusionInpaintPipeline
class diffusers.StableDiffusionInpaintPipeline
< source >( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel scheduler: typing.Union[diffusers.schedulers.scheduling_ddim.DDIMScheduler, diffusers.schedulers.scheduling_pndm.PNDMScheduler, diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler] 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 (
CLIPFeatureExtractor
) — Model that extracts features from generated images to be used as inputs for thesafety_checker
.
Pipeline for text-guided image inpainting using Stable Diffusion. This is an experimental feature.
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.)
__call__
< source >(
prompt: typing.Union[str, typing.List[str]]
image: typing.Union[torch.FloatTensor, PIL.Image.Image]
mask_image: typing.Union[torch.FloatTensor, PIL.Image.Image]
height: typing.Optional[int] = None
width: typing.Optional[int] = None
num_inference_steps: int = 50
guidance_scale: float = 7.5
negative_prompt: typing.Union[str, typing.List[str], NoneType] = None
num_images_per_prompt: typing.Optional[int] = 1
eta: float = 0.0
generator: typing.Optional[torch._C.Generator] = None
latents: 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: typing.Optional[int] = 1
)
→
StableDiffusionPipelineOutput or tuple
Parameters
-
prompt (
str
orList[str]
) — The prompt or prompts to guide the image generation. -
image (
PIL.Image.Image
) —Image
, or tensor representing an image batch which will be inpainted, i.e. parts of the image will be masked out withmask_image
and repainted according toprompt
. -
mask_image (
PIL.Image.Image
) —Image
, or tensor representing an image batch, to maskimage
. White pixels in the mask will be repainted, while black pixels will be preserved. Ifmask_image
is a PIL image, it will be converted to a single channel (luminance) before use. If it’s a tensor, it should contain one color channel (L) instead of 3, so the expected shape would be(B, H, W, 1)
. -
height (
int
, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The height in pixels of the generated image. -
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) — Guidance scale as defined in Classifier-Free Diffusion Guidance.guidance_scale
is defined asw
of equation 2. of Imagen Paper. Guidance scale is enabled by settingguidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the textprompt
, usually at the expense of lower image quality. -
negative_prompt (
str
orList[str]
, optional) — The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). -
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) — 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 will ge generated by sampling using the supplied randomgenerator
. -
output_type (
str
, optional, defaults to"pil"
) — The output format of the generate image. Choose between PIL:PIL.Image.Image
ornp.array
. -
return_dict (
bool
, optional, defaults toTrue
) — Whether or not to return a StableDiffusionPipelineOutput instead of a plain tuple. -
callback (
Callable
, optional) — A function that will be called everycallback_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 thecallback
function will be called. If not specified, the callback will be called at every step.
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.
enable_attention_slicing
< source >( slice_size: typing.Union[str, int, NoneType] = 'auto' )
Parameters
-
slice_size (
str
orint
, optional, defaults to"auto"
) — When"auto"
, halves the input to the attention heads, so attention will be computed in two steps. If a number is provided, uses as many slices asattention_head_dim // slice_size
. In this case,attention_head_dim
must be a multiple ofslice_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 sliced attention computation. If enable_attention_slicing
was previously invoked, this method will go
back to computing attention in one step.
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.
Disable memory efficient attention as implemented in xformers.
StableDiffusionImageVariationPipeline
class diffusers.StableDiffusionImageVariationPipeline
< source >( vae: AutoencoderKL image_encoder: CLIPVisionModelWithProjection unet: UNet2DConditionModel scheduler: typing.Union[diffusers.schedulers.scheduling_ddim.DDIMScheduler, diffusers.schedulers.scheduling_pndm.PNDMScheduler, diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler, diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler, diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler, diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler] 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.
-
image_encoder (
CLIPVisionModelWithProjection
) — Frozen CLIP image-encoder. Stable Diffusion Image Variation uses the vision portion of CLIP, specifically the clip-vit-large-patch14 variant. - 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 (
CLIPFeatureExtractor
) — Model that extracts features from generated images to be used as inputs for thesafety_checker
.
Pipeline to generate variations from an input image 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.)
__call__
< source >(
image: typing.Union[PIL.Image.Image, typing.List[PIL.Image.Image], torch.FloatTensor]
height: typing.Optional[int] = None
width: typing.Optional[int] = None
num_inference_steps: int = 50
guidance_scale: float = 7.5
num_images_per_prompt: typing.Optional[int] = 1
eta: float = 0.0
generator: typing.Optional[torch._C.Generator] = None
latents: 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: typing.Optional[int] = 1
)
→
StableDiffusionPipelineOutput or tuple
Parameters
-
image (
PIL.Image.Image
orList[PIL.Image.Image]
ortorch.FloatTensor
) — The image or images to guide the image generation. If you provide a tensor, it needs to comply with the configuration of thisCLIPFeatureExtractor
-
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) — Guidance scale as defined in Classifier-Free Diffusion Guidance.guidance_scale
is defined asw
of equation 2. of Imagen Paper. Guidance scale is enabled by settingguidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the textprompt
, usually at the expense of lower image quality. -
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) — 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 will ge generated by sampling using the supplied randomgenerator
. -
output_type (
str
, optional, defaults to"pil"
) — The output format of the generate image. Choose between PIL:PIL.Image.Image
ornp.array
. -
return_dict (
bool
, optional, defaults toTrue
) — Whether or not to return a StableDiffusionPipelineOutput instead of a plain tuple. -
callback (
Callable
, optional) — A function that will be called everycallback_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 thecallback
function will be called. If not specified, the callback will be called at every step.
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.
enable_attention_slicing
< source >( slice_size: typing.Union[str, int, NoneType] = 'auto' )
Parameters
-
slice_size (
str
orint
, optional, defaults to"auto"
) — When"auto"
, halves the input to the attention heads, so attention will be computed in two steps. If a number is provided, uses as many slices asattention_head_dim // slice_size
. In this case,attention_head_dim
must be a multiple ofslice_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 sliced attention computation. If enable_attention_slicing
was previously invoked, this method will go
back to computing attention in one step.
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.
Disable memory efficient attention as implemented in xformers.
StableDiffusionUpscalePipeline
class diffusers.StableDiffusionUpscalePipeline
< source >( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel low_res_scheduler: DDPMScheduler scheduler: typing.Union[diffusers.schedulers.scheduling_ddim.DDIMScheduler, diffusers.schedulers.scheduling_pndm.PNDMScheduler, diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler] max_noise_level: int = 350 )
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.
- low_res_scheduler (SchedulerMixin) — A scheduler used to add initial noise to the low res conditioning image. It must be an instance of DDPMScheduler.
-
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.
Pipeline for text-guided image super-resolution using Stable Diffusion 2.
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.)
__call__
< source >(
prompt: typing.Union[str, typing.List[str]]
image: typing.Union[torch.FloatTensor, PIL.Image.Image, typing.List[PIL.Image.Image]]
num_inference_steps: int = 75
guidance_scale: float = 9.0
noise_level: int = 20
negative_prompt: typing.Union[str, typing.List[str], NoneType] = None
num_images_per_prompt: typing.Optional[int] = 1
eta: float = 0.0
generator: typing.Optional[torch._C.Generator] = None
latents: 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: typing.Optional[int] = 1
)
→
StableDiffusionPipelineOutput or tuple
Parameters
-
prompt (
str
orList[str]
) — The prompt or prompts to guide the image generation. -
image (
PIL.Image.Image
or ListPIL.Image.Image
ortorch.FloatTensor
) —Image
, or tensor representing an image batch which will be upscaled. * -
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) — Guidance scale as defined in Classifier-Free Diffusion Guidance.guidance_scale
is defined asw
of equation 2. of Imagen Paper. Guidance scale is enabled by settingguidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the textprompt
, usually at the expense of lower image quality. -
negative_prompt (
str
orList[str]
, optional) — The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). -
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) — 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 will ge generated by sampling using the supplied randomgenerator
. -
output_type (
str
, optional, defaults to"pil"
) — The output format of the generate image. Choose between PIL:PIL.Image.Image
ornp.array
. -
return_dict (
bool
, optional, defaults toTrue
) — Whether or not to return a StableDiffusionPipelineOutput instead of a plain tuple. -
callback (
Callable
, optional) — A function that will be called everycallback_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 thecallback
function will be called. If not specified, the callback will be called at every step.
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.
enable_attention_slicing
< source >( slice_size: typing.Union[str, int, NoneType] = 'auto' )
Parameters
-
slice_size (
str
orint
, optional, defaults to"auto"
) — When"auto"
, halves the input to the attention heads, so attention will be computed in two steps. If a number is provided, uses as many slices asattention_head_dim // slice_size
. In this case,attention_head_dim
must be a multiple ofslice_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 sliced attention computation. If enable_attention_slicing
was previously invoked, this method will go
back to computing attention in one step.
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.
Disable memory efficient attention as implemented in xformers.