Versatile Diffusion
Versatile Diffusion was proposed in Versatile Diffusion: Text, Images and Variations All in One Diffusion Model by Xingqian Xu, Zhangyang Wang, Eric Zhang, Kai Wang, Humphrey Shi .
The abstract from the paper is:
The recent advances in diffusion models have set an impressive milestone in many generation tasks. Trending works such as DALL-E2, Imagen, and Stable Diffusion have attracted great interest in academia and industry. Despite the rapid landscape changes, recent new approaches focus on extensions and performance rather than capacity, thus requiring separate models for separate tasks. In this work, we expand the existing single-flow diffusion pipeline into a multi-flow network, dubbed Versatile Diffusion (VD), that handles text-to-image, image-to-text, image-variation, and text-variation in one unified model. Moreover, we generalize VD to a unified multi-flow multimodal diffusion framework with grouped layers, swappable streams, and other propositions that can process modalities beyond images and text. Through our experiments, we demonstrate that VD and its underlying framework have the following merits: a) VD handles all subtasks with competitive quality; b) VD initiates novel extensions and applications such as disentanglement of style and semantic, image-text dual-guided generation, etc.; c) Through these experiments and applications, VD provides more semantic insights of the generated outputs.
Tips
You can load the more memory intensive βall-in-oneβ VersatileDiffusionPipeline that supports all the tasks or use the individual pipelines which are more memory efficient.
Pipeline | Supported tasks |
---|---|
VersatileDiffusionPipeline | all of the below |
VersatileDiffusionTextToImagePipeline | text-to-image |
VersatileDiffusionImageVariationPipeline | image variation |
VersatileDiffusionDualGuidedPipeline | image-text dual guided generation |
Make sure to check out the Schedulers guide to learn how to explore the tradeoff between scheduler speed and quality, and see the reuse components across pipelines section to learn how to efficiently load the same components into multiple pipelines.
VersatileDiffusionPipeline
class diffusers.VersatileDiffusionPipeline
< source >( tokenizer: CLIPTokenizer image_feature_extractor: CLIPImageProcessor text_encoder: CLIPTextModel image_encoder: CLIPVisionModel image_unet: UNet2DConditionModel text_unet: UNet2DConditionModel vae: AutoencoderKL scheduler: KarrasDiffusionSchedulers )
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
) — ACLIPTokenizer
to tokenize text. -
unet (UNet2DConditionModel) —
A
UNet2DConditionModel
to denoise the encoded image latents. -
scheduler (SchedulerMixin) —
A scheduler to be used in combination with
unet
to denoise the encoded image latents. Can be one of DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler. -
safety_checker (
StableDiffusionSafetyChecker
) — Classification module that estimates whether generated images could be considered offensive or harmful. Please refer to the model card for more details about a model’s potential harms. -
feature_extractor (
CLIPImageProcessor
) — ACLIPImageProcessor
to extract features from generated images; used as inputs to thesafety_checker
.
Pipeline for text-to-image generation using Stable Diffusion.
This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.).
dual_guided
< source >(
prompt: typing.Union[PIL.Image.Image, typing.List[PIL.Image.Image]]
image: typing.Union[str, typing.List[str]]
text_to_image_strength: float = 0.5
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.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = 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: int = 1
)
β
ImagePipelineOutput or tuple
Parameters
-
prompt (
str
orList[str]
) — The prompt or prompts to guide image generation. -
height (
int
, optional, defaults toself.image_unet.config.sample_size * self.vae_scale_factor
) — The height in pixels of the generated image. -
width (
int
, optional, defaults toself.image_unet.config.sample_size * self.vae_scale_factor
) — The width in pixels of the generated image. -
num_inference_steps (
int
, optional, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. -
guidance_scale (
float
, optional, defaults to 7.5) — A higher guidance scale value encourages the model to generate images closely linked to the textprompt
at the expense of lower image quality. Guidance scale is enabled whenguidance_scale > 1
. -
negative_prompt (
str
orList[str]
, optional) — The prompt or prompts to guide what to not include in image generation. If not defined, you need to passnegative_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
orList[torch.Generator]
, optional) — Atorch.Generator
to make generation deterministic. -
latents (
torch.FloatTensor
, optional) — Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor is generated by sampling using the supplied randomgenerator
. -
output_type (
str
, optional, defaults to"pil"
) — The output format of the generated image. Choose betweenPIL.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 calls everycallback_steps
steps during inference. The function is 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 is called. If not specified, the callback is called at every step.
Returns
ImagePipelineOutput or tuple
If return_dict
is True
, ImagePipelineOutput is returned, otherwise a tuple
is
returned where the first element is a list with the generated images.
The call function to the pipeline for generation.
Examples:
>>> from diffusers import VersatileDiffusionPipeline
>>> import torch
>>> import requests
>>> from io import BytesIO
>>> from PIL import Image
>>> # let's download an initial image
>>> url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg"
>>> response = requests.get(url)
>>> image = Image.open(BytesIO(response.content)).convert("RGB")
>>> text = "a red car in the sun"
>>> pipe = VersatileDiffusionPipeline.from_pretrained(
... "shi-labs/versatile-diffusion", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> generator = torch.Generator(device="cuda").manual_seed(0)
>>> text_to_image_strength = 0.75
>>> image = pipe.dual_guided(
... prompt=text, image=image, text_to_image_strength=text_to_image_strength, generator=generator
... ).images[0]
>>> image.save("./car_variation.png")
image_variation
< source >(
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.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = 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: int = 1
)
β
StableDiffusionPipelineOutput or tuple
Parameters
-
image (
PIL.Image.Image
,List[PIL.Image.Image]
ortorch.Tensor
) — The image prompt or prompts to guide the image generation. -
height (
int
, optional, defaults toself.image_unet.config.sample_size * self.vae_scale_factor
) — The height in pixels of the generated image. -
width (
int
, optional, defaults toself.image_unet.config.sample_size * self.vae_scale_factor
) — The width in pixels of the generated image. -
num_inference_steps (
int
, optional, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. -
guidance_scale (
float
, optional, defaults to 7.5) — A higher guidance scale value encourages the model to generate images closely linked to the textprompt
at the expense of lower image quality. Guidance scale is enabled whenguidance_scale > 1
. -
negative_prompt (
str
orList[str]
, optional) — The prompt or prompts to guide what to not include in image generation. If not defined, you need to passnegative_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
, optional) — Atorch.Generator
to make generation deterministic. -
latents (
torch.FloatTensor
, optional) — Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor is generated by sampling using the supplied randomgenerator
. -
output_type (
str
, optional, defaults to"pil"
) — The output format of the generated image. Choose betweenPIL.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 calls everycallback_steps
steps during inference. The function is 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 is called. If not specified, the callback is called at every step.
Returns
StableDiffusionPipelineOutput or tuple
If return_dict
is True
, StableDiffusionPipelineOutput is returned,
otherwise a tuple
is returned where the first element is a list with the generated images and the
second element is a list of bool
s indicating whether the corresponding generated image contains
βnot-safe-for-workβ (nsfw) content.
The call function to the pipeline for generation.
Examples:
>>> from diffusers import VersatileDiffusionPipeline
>>> import torch
>>> import requests
>>> from io import BytesIO
>>> from PIL import Image
>>> # let's download an initial image
>>> url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg"
>>> response = requests.get(url)
>>> image = Image.open(BytesIO(response.content)).convert("RGB")
>>> pipe = VersatileDiffusionPipeline.from_pretrained(
... "shi-labs/versatile-diffusion", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> generator = torch.Generator(device="cuda").manual_seed(0)
>>> image = pipe.image_variation(image, generator=generator).images[0]
>>> image.save("./car_variation.png")
text_to_image
< 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.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = 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: int = 1
)
β
StableDiffusionPipelineOutput or tuple
Parameters
-
prompt (
str
orList[str]
) — The prompt or prompts to guide image generation. -
height (
int
, optional, defaults toself.image_unet.config.sample_size * self.vae_scale_factor
) — The height in pixels of the generated image. -
width (
int
, optional, defaults toself.image_unet.config.sample_size * self.vae_scale_factor
) — The width in pixels of the generated image. -
num_inference_steps (
int
, optional, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. -
guidance_scale (
float
, optional, defaults to 7.5) — A higher guidance scale value encourages the model to generate images closely linked to the textprompt
at the expense of lower image quality. Guidance scale is enabled whenguidance_scale > 1
. -
negative_prompt (
str
orList[str]
, optional) — The prompt or prompts to guide what to not include in image generation. If not defined, you need to passnegative_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
, optional) — Atorch.Generator
to make generation deterministic. -
latents (
torch.FloatTensor
, optional) — Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor is generated by sampling using the supplied randomgenerator
. -
output_type (
str
, optional, defaults to"pil"
) — The output format of the generated image. Choose betweenPIL.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 calls everycallback_steps
steps during inference. The function is 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 is called. If not specified, the callback is called at every step.
Returns
StableDiffusionPipelineOutput or tuple
If return_dict
is True
, StableDiffusionPipelineOutput is returned,
otherwise a tuple
is returned where the first element is a list with the generated images and the
second element is a list of bool
s indicating whether the corresponding generated image contains
βnot-safe-for-workβ (nsfw) content.
The call function to the pipeline for generation.
Examples:
>>> from diffusers import VersatileDiffusionPipeline
>>> import torch
>>> pipe = VersatileDiffusionPipeline.from_pretrained(
... "shi-labs/versatile-diffusion", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> generator = torch.Generator(device="cuda").manual_seed(0)
>>> image = pipe.text_to_image("an astronaut riding on a horse on mars", generator=generator).images[0]
>>> image.save("./astronaut.png")
VersatileDiffusionTextToImagePipeline
class diffusers.VersatileDiffusionTextToImagePipeline
< source >( tokenizer: CLIPTokenizer text_encoder: CLIPTextModelWithProjection image_unet: UNet2DConditionModel text_unet: UNetFlatConditionModel vae: AutoencoderKL scheduler: KarrasDiffusionSchedulers )
Parameters
- vqvae (VQModel) — Vector-quantized (VQ) model to encode and decode images to and from latent representations.
-
bert (
LDMBertModel
) — Text-encoder model based onBERT
. -
tokenizer (
BertTokenizer
) — ABertTokenizer
to tokenize text. -
unet (UNet2DConditionModel) —
A
UNet2DConditionModel
to denoise the encoded image latents. -
scheduler (SchedulerMixin) —
A scheduler to be used in combination with
unet
to denoise the encoded image latents. Can be one of DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler.
Pipeline for text-to-image generation using Versatile 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.).
__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.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = 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: int = 1
**kwargs
)
β
StableDiffusionPipelineOutput or tuple
Parameters
-
prompt (
str
orList[str]
) — The prompt or prompts to guide image generation. -
height (
int
, optional, defaults toself.image_unet.config.sample_size * self.vae_scale_factor
) — The height in pixels of the generated image. -
width (
int
, optional, defaults toself.image_unet.config.sample_size * self.vae_scale_factor
) — The width in pixels of the generated image. -
num_inference_steps (
int
, optional, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. -
guidance_scale (
float
, optional, defaults to 7.5) — A higher guidance scale value encourages the model to generate images closely linked to the textprompt
at the expense of lower image quality. Guidance scale is enabled whenguidance_scale > 1
. -
negative_prompt (
str
orList[str]
, optional) — The prompt or prompts to guide what to not include in image generation. If not defined, you need to passnegative_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
, optional) — Atorch.Generator
to make generation deterministic. -
latents (
torch.FloatTensor
, optional) — Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor is generated by sampling using the supplied randomgenerator
. -
output_type (
str
, optional, defaults to"pil"
) — The output format of the generated image. Choose betweenPIL.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 calls everycallback_steps
steps during inference. The function is 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 is called. If not specified, the callback is called at every step.
Returns
StableDiffusionPipelineOutput or tuple
If return_dict
is True
, StableDiffusionPipelineOutput is returned,
otherwise a tuple
is returned where the first element is a list with the generated images.
The call function to the pipeline for generation.
Examples:
>>> from diffusers import VersatileDiffusionTextToImagePipeline
>>> import torch
>>> pipe = VersatileDiffusionTextToImagePipeline.from_pretrained(
... "shi-labs/versatile-diffusion", torch_dtype=torch.float16
... )
>>> pipe.remove_unused_weights()
>>> pipe = pipe.to("cuda")
>>> generator = torch.Generator(device="cuda").manual_seed(0)
>>> image = pipe("an astronaut riding on a horse on mars", generator=generator).images[0]
>>> image.save("./astronaut.png")
VersatileDiffusionImageVariationPipeline
class diffusers.VersatileDiffusionImageVariationPipeline
< source >( image_feature_extractor: CLIPImageProcessor image_encoder: CLIPVisionModelWithProjection image_unet: UNet2DConditionModel vae: AutoencoderKL scheduler: KarrasDiffusionSchedulers )
Parameters
- vqvae (VQModel) — Vector-quantized (VQ) model to encode and decode images to and from latent representations.
-
bert (
LDMBertModel
) — Text-encoder model based onBERT
. -
tokenizer (
BertTokenizer
) — ABertTokenizer
to tokenize text. -
unet (UNet2DConditionModel) —
A
UNet2DConditionModel
to denoise the encoded image latents. -
scheduler (SchedulerMixin) —
A scheduler to be used in combination with
unet
to denoise the encoded image latents. Can be one of DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler.
Pipeline for image variation using Versatile 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.).
__call__
< source >(
image: typing.Union[PIL.Image.Image, typing.List[PIL.Image.Image], torch.Tensor]
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.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = 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: int = 1
**kwargs
)
β
StableDiffusionPipelineOutput or tuple
Parameters
-
image (
PIL.Image.Image
,List[PIL.Image.Image]
ortorch.Tensor
) — The image prompt or prompts to guide the image generation. -
height (
int
, optional, defaults toself.image_unet.config.sample_size * self.vae_scale_factor
) — The height in pixels of the generated image. -
width (
int
, optional, defaults toself.image_unet.config.sample_size * self.vae_scale_factor
) — The width in pixels of the generated image. -
num_inference_steps (
int
, optional, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. -
guidance_scale (
float
, optional, defaults to 7.5) — A higher guidance scale value encourages the model to generate images closely linked to the textprompt
at the expense of lower image quality. Guidance scale is enabled whenguidance_scale > 1
. -
negative_prompt (
str
orList[str]
, optional) — The prompt or prompts to guide what to not include in image generation. If not defined, you need to passnegative_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
, optional) — Atorch.Generator
to make generation deterministic. -
latents (
torch.FloatTensor
, optional) — Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor is generated by sampling using the supplied randomgenerator
. -
output_type (
str
, optional, defaults to"pil"
) — The output format of the generated image. Choose betweenPIL.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 calls everycallback_steps
steps during inference. The function is 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 is called. If not specified, the callback is called at every step.
Returns
StableDiffusionPipelineOutput or tuple
If return_dict
is True
, StableDiffusionPipelineOutput is returned,
otherwise a tuple
is returned where the first element is a list with the generated images.
The call function to the pipeline for generation.
Examples:
>>> from diffusers import VersatileDiffusionImageVariationPipeline
>>> import torch
>>> import requests
>>> from io import BytesIO
>>> from PIL import Image
>>> # let's download an initial image
>>> url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg"
>>> response = requests.get(url)
>>> image = Image.open(BytesIO(response.content)).convert("RGB")
>>> pipe = VersatileDiffusionImageVariationPipeline.from_pretrained(
... "shi-labs/versatile-diffusion", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> generator = torch.Generator(device="cuda").manual_seed(0)
>>> image = pipe(image, generator=generator).images[0]
>>> image.save("./car_variation.png")
VersatileDiffusionDualGuidedPipeline
class diffusers.VersatileDiffusionDualGuidedPipeline
< source >( tokenizer: CLIPTokenizer image_feature_extractor: CLIPImageProcessor text_encoder: CLIPTextModelWithProjection image_encoder: CLIPVisionModelWithProjection image_unet: UNet2DConditionModel text_unet: UNetFlatConditionModel vae: AutoencoderKL scheduler: KarrasDiffusionSchedulers )
Parameters
- vqvae (VQModel) — Vector-quantized (VQ) model to encode and decode images to and from latent representations.
-
bert (
LDMBertModel
) — Text-encoder model based onBERT
. -
tokenizer (
BertTokenizer
) — ABertTokenizer
to tokenize text. -
unet (UNet2DConditionModel) —
A
UNet2DConditionModel
to denoise the encoded image latents. -
scheduler (SchedulerMixin) —
A scheduler to be used in combination with
unet
to denoise the encoded image latents. Can be one of DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler.
Pipeline for image-text dual-guided generation using Versatile 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.).
__call__
< source >(
prompt: typing.Union[PIL.Image.Image, typing.List[PIL.Image.Image]]
image: typing.Union[str, typing.List[str]]
text_to_image_strength: float = 0.5
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.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = 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: int = 1
**kwargs
)
β
ImagePipelineOutput or tuple
Parameters
-
prompt (
str
orList[str]
) — The prompt or prompts to guide image generation. -
height (
int
, optional, defaults toself.image_unet.config.sample_size * self.vae_scale_factor
) — The height in pixels of the generated image. -
width (
int
, optional, defaults toself.image_unet.config.sample_size * self.vae_scale_factor
) — The width in pixels of the generated image. -
num_inference_steps (
int
, optional, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. -
guidance_scale (
float
, optional, defaults to 7.5) — A higher guidance scale value encourages the model to generate images closely linked to the textprompt
at the expense of lower image quality. Guidance scale is enabled whenguidance_scale > 1
. -
negative_prompt (
str
orList[str]
, optional) — The prompt or prompts to guide what to not include in image generation. If not defined, you need to passnegative_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
orList[torch.Generator]
, optional) — Atorch.Generator
to make generation deterministic. -
latents (
torch.FloatTensor
, optional) — Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor is generated by sampling using the supplied randomgenerator
. -
output_type (
str
, optional, defaults to"pil"
) — The output format of the generated image. Choose betweenPIL.Image
ornp.array
. -
return_dict (
bool
, optional, defaults toTrue
) — Whether or not to return a ImagePipelineOutput instead of a plain tuple. -
callback (
Callable
, optional) — A function that calls everycallback_steps
steps during inference. The function is 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 is called. If not specified, the callback is called at every step.
Returns
ImagePipelineOutput or tuple
If return_dict
is True
, ImagePipelineOutput is returned, otherwise a tuple
is
returned where the first element is a list with the generated images.
The call function to the pipeline for generation.
Examples:
>>> from diffusers import VersatileDiffusionDualGuidedPipeline
>>> import torch
>>> import requests
>>> from io import BytesIO
>>> from PIL import Image
>>> # let's download an initial image
>>> url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg"
>>> response = requests.get(url)
>>> image = Image.open(BytesIO(response.content)).convert("RGB")
>>> text = "a red car in the sun"
>>> pipe = VersatileDiffusionDualGuidedPipeline.from_pretrained(
... "shi-labs/versatile-diffusion", torch_dtype=torch.float16
... )
>>> pipe.remove_unused_weights()
>>> pipe = pipe.to("cuda")
>>> generator = torch.Generator(device="cuda").manual_seed(0)
>>> text_to_image_strength = 0.75
>>> image = pipe(
... prompt=text, image=image, text_to_image_strength=text_to_image_strength, generator=generator
... ).images[0]
>>> image.save("./car_variation.png")