AltDiffusion was proposed in AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities by Zhongzhi Chen, Guang Liu, Bo-Wen Zhang, Fulong Ye, Qinghong Yang, Ledell Wu.
The abstract from the paper is:
In this work, we present a conceptually simple and effective method to train a strong bilingual multimodal representation model. Starting from the pretrained multimodal representation model CLIP released by OpenAI, we switched its text encoder with a pretrained multilingual text encoder XLM-R, and aligned both languages and image representations by a two-stage training schema consisting of teacher learning and contrastive learning. We validate our method through evaluations of a wide range of tasks. We set new state-of-the-art performances on a bunch of tasks including ImageNet-CN, Flicker30k- CN, and COCO-CN. Further, we obtain very close performances with CLIP on almost all tasks, suggesting that one can simply alter the text encoder in CLIP for extended capabilities such as multilingual understanding.
AltDiffusion
is conceptually the same as Stable Diffusion.
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.
( vae: AutoencoderKL text_encoder: RobertaSeriesModelWithTransformation tokenizer: XLMRobertaTokenizer unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPImageProcessor requires_safety_checker: bool = True )
Parameters
RobertaSeriesModelWithTransformation
) —
Frozen text-encoder (clip-vit-large-patch14). XLMRobertaTokenizer
to tokenize text. UNet2DConditionModel
to denoise the encoded image latents. unet
to denoise the encoded image latents. Can be one of
DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler. 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. CLIPImageProcessor
to extract features from generated images; used as inputs to the safety_checker
. Pipeline for text-to-image generation using Alt Diffusion.
This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipeline also inherits the following loading methods:
.ckpt
files( prompt: typing.Union[str, typing.List[str]] = None 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 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 guidance_rescale: float = 0.0 clip_skip: typing.Optional[int] = None ) → ~pipelines.stable_diffusion.AltDiffusionPipelineOutput
or tuple
Parameters
str
or List[str]
, optional) —
The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds
. int
, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor
) —
The height in pixels of the generated image. int
, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor
) —
The width in pixels of the generated image. 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. float
, optional, defaults to 7.5) —
A higher guidance scale value encourages the model to generate images closely linked to the text
prompt
at the expense of lower image quality. Guidance scale is enabled when guidance_scale > 1
. str
or List[str]
, optional) —
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass negative_prompt_embeds
instead. Ignored when not using guidance (guidance_scale < 1
). int
, optional, defaults to 1) —
The number of images to generate per prompt. 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. torch.Generator
or List[torch.Generator]
, optional) —
A torch.Generator
to make
generation deterministic. torch.FloatTensor
, optional) —
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random generator
. torch.FloatTensor
, optional) —
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the prompt
input argument. torch.FloatTensor
, optional) —
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, negative_prompt_embeds
are generated from the negative_prompt
input argument. str
, optional, defaults to "pil"
) —
The output format of the generated image. Choose between PIL.Image
or np.array
. bool
, optional, defaults to True
) —
Whether or not to return a ~pipelines.stable_diffusion.AltDiffusionPipelineOutput
instead of a
plain tuple. Callable
, optional) —
A function that calls every callback_steps
steps during inference. The function is called with the
following arguments: callback(step: int, timestep: int, latents: torch.FloatTensor)
. int
, optional, defaults to 1) —
The frequency at which the callback
function is called. If not specified, the callback is called at
every step. dict
, optional) —
A kwargs dictionary that if specified is passed along to the AttentionProcessor
as defined in
self.processor
. float
, optional, defaults to 0.0) —
Guidance rescale factor from Common Diffusion Noise Schedules and Sample Steps are
Flawed. Guidance rescale factor should fix overexposure when
using zero terminal SNR. int
, optional) —
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings. Returns
~pipelines.stable_diffusion.AltDiffusionPipelineOutput
or tuple
If return_dict
is True
, ~pipelines.stable_diffusion.AltDiffusionPipelineOutput
is returned,
otherwise a tuple
is returned where the first element is a list with the generated images and the
second element is a list of bool
s indicating whether the corresponding generated image contains
“not-safe-for-work” (nsfw) content.
The call function to the pipeline for generation.
Examples:
>>> import torch
>>> from diffusers import AltDiffusionPipeline
>>> pipe = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion-m9", torch_dtype=torch.float16)
>>> pipe = pipe.to("cuda")
>>> # "dark elf princess, highly detailed, d & d, fantasy, highly detailed, digital painting, trending on artstation, concept art, sharp focus, illustration, art by artgerm and greg rutkowski and fuji choko and viktoria gavrilenko and hoang lap"
>>> prompt = "黑暗精灵公主,非常详细,幻想,非常详细,数字绘画,概念艺术,敏锐的焦点,插图"
>>> image = pipe(prompt).images[0]
Disables the FreeU mechanism if enabled.
Disable sliced VAE decoding. If enable_vae_slicing
was previously enabled, this method will go back to
computing decoding in one step.
Disable tiled VAE decoding. If enable_vae_tiling
was previously enabled, this method will go back to
computing decoding in one step.
( s1: float s2: float b1: float b2: float )
Parameters
float
) —
Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
mitigate “oversmoothing effect” in the enhanced denoising process. float
) —
Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
mitigate “oversmoothing effect” in the enhanced denoising process. float
) — Scaling factor for stage 1 to amplify the contributions of backbone features. float
) — Scaling factor for stage 2 to amplify the contributions of backbone features. Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.
The suffixes after the scaling factors represent the stages where they are being applied.
Please refer to the official repository for combinations of the values that are known to work well for different pipelines such as Alt Diffusion v1, v2, and Alt Diffusion XL.
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.
Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow processing larger images.
( prompt device num_images_per_prompt do_classifier_free_guidance negative_prompt = None prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None lora_scale: typing.Optional[float] = None clip_skip: typing.Optional[int] = None )
Parameters
str
or List[str]
, optional) —
prompt to be encoded
device — (torch.device
):
torch device int
) —
number of images that should be generated per prompt bool
) —
whether to use classifier free guidance or not 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
). 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. 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. float
, optional) —
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. int
, optional) —
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings. Encodes the prompt into text encoder hidden states.
( vae: AutoencoderKL text_encoder: RobertaSeriesModelWithTransformation tokenizer: XLMRobertaTokenizer unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPImageProcessor requires_safety_checker: bool = True )
Parameters
RobertaSeriesModelWithTransformation
) —
Frozen text-encoder (clip-vit-large-patch14). XLMRobertaTokenizer
to tokenize text. UNet2DConditionModel
to denoise the encoded image latents. unet
to denoise the encoded image latents. Can be one of
DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler. 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. CLIPImageProcessor
to extract features from generated images; used as inputs to the safety_checker
. Pipeline for text-guided image-to-image generation using Alt Diffusion.
This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipeline also inherits the following loading methods:
.ckpt
files( prompt: typing.Union[str, typing.List[str]] = None image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.FloatTensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.FloatTensor]] = 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 clip_skip: int = None ) → ~pipelines.stable_diffusion.AltDiffusionPipelineOutput
or tuple
Parameters
str
or List[str]
, optional) —
The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds
. torch.FloatTensor
, PIL.Image.Image
, np.ndarray
, List[torch.FloatTensor]
, List[PIL.Image.Image]
, or List[np.ndarray]
) —
Image
, numpy array or tensor representing an image batch to be used as the starting point. For both
numpy array and pytorch tensor, the expected value range is between [0, 1]
If it’s a tensor or a list
or tensors, the expected shape should be (B, C, H, W)
or (C, H, W)
. If it is a numpy array or a
list of arrays, the expected shape should be (B, H, W, C)
or (H, W, C)
It can also accept image
latents as image
, but if passing latents directly it is not encoded again. float
, optional, defaults to 0.8) —
Indicates extent to transform the reference image
. Must be between 0 and 1. image
is used as a
starting point and more noise is added the higher the strength
. The number of denoising steps depends
on the amount of noise initially added. When strength
is 1, added noise is maximum and the denoising
process runs for the full number of iterations specified in num_inference_steps
. A value of 1
essentially ignores image
. int
, optional, defaults to 50) —
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. This parameter is modulated by strength
. float
, optional, defaults to 7.5) —
A higher guidance scale value encourages the model to generate images closely linked to the text
prompt
at the expense of lower image quality. Guidance scale is enabled when guidance_scale > 1
. str
or List[str]
, optional) —
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass negative_prompt_embeds
instead. Ignored when not using guidance (guidance_scale < 1
). int
, optional, defaults to 1) —
The number of images to generate per prompt. 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. torch.Generator
or List[torch.Generator]
, optional) —
A torch.Generator
to make
generation deterministic. torch.FloatTensor
, optional) —
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the prompt
input argument. torch.FloatTensor
, optional) —
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, negative_prompt_embeds
are generated from the negative_prompt
input argument. str
, optional, defaults to "pil"
) —
The output format of the generated image. Choose between PIL.Image
or np.array
. bool
, optional, defaults to True
) —
Whether or not to return a ~pipelines.stable_diffusion.AltDiffusionPipelineOutput
instead of a
plain tuple. Callable
, optional) —
A function that calls every callback_steps
steps during inference. The function is called with the
following arguments: callback(step: int, timestep: int, latents: torch.FloatTensor)
. int
, optional, defaults to 1) —
The frequency at which the callback
function is called. If not specified, the callback is called at
every step. dict
, optional) —
A kwargs dictionary that if specified is passed along to the AttentionProcessor
as defined in
self.processor
. int
, optional) —
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings. Returns
~pipelines.stable_diffusion.AltDiffusionPipelineOutput
or tuple
If return_dict
is True
, ~pipelines.stable_diffusion.AltDiffusionPipelineOutput
is returned,
otherwise a tuple
is returned where the first element is a list with the generated images and the
second element is a list of bool
s indicating whether the corresponding generated image contains
“not-safe-for-work” (nsfw) content.
The call function to the pipeline for generation.
Examples:
>>> import requests
>>> import torch
>>> from PIL import Image
>>> from io import BytesIO
>>> from diffusers import AltDiffusionImg2ImgPipeline
>>> device = "cuda"
>>> model_id_or_path = "BAAI/AltDiffusion-m9"
>>> pipe = AltDiffusionImg2ImgPipeline.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))
>>> # "A fantasy landscape, trending on artstation"
>>> prompt = "幻想风景, artstation"
>>> images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images
>>> images[0].save("幻想风景.png")
Disables the FreeU mechanism if enabled.
( s1: float s2: float b1: float b2: float )
Parameters
float
) —
Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
mitigate “oversmoothing effect” in the enhanced denoising process. float
) —
Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
mitigate “oversmoothing effect” in the enhanced denoising process. float
) — Scaling factor for stage 1 to amplify the contributions of backbone features. float
) — Scaling factor for stage 2 to amplify the contributions of backbone features. Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.
The suffixes after the scaling factors represent the stages where they are being applied.
Please refer to the official repository for combinations of the values that are known to work well for different pipelines such as Alt Diffusion v1, v2, and Alt Diffusion XL.
( prompt device num_images_per_prompt do_classifier_free_guidance negative_prompt = None prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None lora_scale: typing.Optional[float] = None clip_skip: typing.Optional[int] = None )
Parameters
str
or List[str]
, optional) —
prompt to be encoded
device — (torch.device
):
torch device int
) —
number of images that should be generated per prompt bool
) —
whether to use classifier free guidance or not 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
). 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. 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. float
, optional) —
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. int
, optional) —
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings. Encodes the prompt into text encoder hidden states.
( images: typing.Union[typing.List[PIL.Image.Image], numpy.ndarray] nsfw_content_detected: typing.Optional[typing.List[bool]] )
Parameters
List[PIL.Image.Image]
or np.ndarray
) —
List of denoised PIL images of length batch_size
or NumPy array of shape (batch_size, height, width, num_channels)
. List[bool]
) —
List indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content or
None
if safety checking could not be performed. Output class for Alt Diffusion pipelines.