sayakpaul/diffusers-qa-chatbot-artifacts · Datasets at Hugging Face

text stringlengths 0 5.54k
negative_prompt="bad quality, worse quality",
num_frames=16,
guidance_scale=7.5,
num_inference_steps=25,
generator=torch.Generator("cpu").manual_seed(42),
)
frames = output.frames[0]
export_to_gif(frames, "animation.gif")
masterpiece, bestquality, sunset.
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. AnimateDiffPipeline class diffusers.AnimateDiffPipeline < source > ( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel motion_adapter: MotionAdapter scheduler: Union feature_extractor: CLIPImageProcessor = None image_encoder: CLIPVisionModelWithProjection = None ) Parameters vae (AutoencoderKL) —
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder (CLIPTextModel) —
Frozen text-encoder (clip-vit-large-patch14). tokenizer (CLIPTokenizer) —
A CLIPTokenizer to tokenize text. unet (UNet2DConditionModel) —
A UNet2DConditionModel used to create a UNetMotionModel to denoise the encoded video latents. motion_adapter (MotionAdapter) —
A MotionAdapter to be used in combination with unet to denoise the encoded video 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-video generation. This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.). The pipeline also inherits the following loading methods: load_textual_inversion() for loading textual inversion embeddings load_lora_weights() for loading LoRA weights save_lora_weights() for saving LoRA weights load_ip_adapter() for loading IP Adapters __call__ < source > ( prompt: Union = None num_frames: Optional = 16 height: Optional = None width: Optional = None num_inference_steps: int = 50 guidance_scale: float = 7.5 negative_prompt: Union = None num_videos_per_prompt: Optional = 1 eta: float = 0.0 generator: Union = None latents: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None ip_adapter_image: Union = None output_type: Optional = 'pil' return_dict: bool = True callback: Optional = None callback_steps: Optional = 1 cross_attention_kwargs: Optional = None clip_skip: Optional = None ) → TextToVideoSDPipelineOutput or tuple Parameters prompt (str or List[str], optional) —
The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds. height (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) —
The height in pixels of the generated video. width (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) —
The width in pixels of the generated video. num_frames (int, optional, defaults to 16) —
The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds
amounts to 2 seconds of video. num_inference_steps (int, optional, defaults to 50) —
The number of denoising steps. More denoising steps usually lead to a higher quality videos 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 text
prompt at the expense of lower image quality. Guidance scale is enabled when guidance_scale > 1. negative_prompt (str or List[str], optional) —
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass negative_prompt_embeds instead. Ignored when not using guidance (guidance_scale < 1). eta (float, optional, defaults to 0.0) —
Corresponds to parameter eta (η) from the DDIM paper. Only applies
to the DDIMScheduler, and is ignored in other schedulers. generator (torch.Generator or List[torch.Generator], optional) —
A torch.Generator to make
generation deterministic. latents (torch.FloatTensor, optional) —
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video
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. Latents should be of shape
(batch_size, num_channel, num_frames, height, width). prompt_embeds (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. negative_prompt_embeds (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.
ip_adapter_image — (PipelineImageInput, optional): Optional image input to work with IP Adapters. output_type (str, optional, defaults to "pil") —
The output format of the generated video. Choose between torch.FloatTensor, PIL.Image or
np.array. return_dict (bool, optional, defaults to True) —
Whether or not to return a TextToVideoSDPipelineOutput instead
of a plain tuple. callback (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). callback_steps (int, optional, defaults to 1) —
The frequency at which the callback function is called. If not specified, the callback is called at
every step. cross_attention_kwargs (dict, optional) —
A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined in
self.processor. clip_skip (int, optional) —
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings. Returns
TextToVideoSDPipelineOutput or tuple

If return_dict is True, TextToVideoSDPipelineOutput is
returned, otherwise a tuple is returned where the first element is a list with the generated frames.
The call function to the pipeline for generation. Examples: Copied >>> import torch
>>> from diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler
>>> from diffusers.utils import export_to_gif

>>> adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2")
>>> pipe = AnimateDiffPipeline.from_pretrained("frankjoshua/toonyou_beta6", motion_adapter=adapter)
>>> pipe.scheduler = DDIMScheduler(beta_schedule="linear", steps_offset=1, clip_sample=False)
>>> output = pipe(prompt="A corgi walking in the park")
>>> frames = output.frames[0]
>>> export_to_gif(frames, "animation.gif") disable_freeu < source > ( ) Disables the FreeU mechanism if enabled. disable_vae_slicing < source > ( ) Disable sliced VAE decoding. If enable_vae_slicing was previously enabled, this method will go back to
computing decoding in one step. disable_vae_tiling < source > ( ) Disable tiled VAE decoding. If enable_vae_tiling was previously enabled, this method will go back to
computing decoding in one step. enable_freeu < source > ( s1: float s2: float b1: float b2: float ) Parameters s1 (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. s2 (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. b1 (float) — Scaling factor for stage 1 to amplify the contributions of backbone features. b2 (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 Stable Diffusion v1, v2, and Stable Diffusion XL. enable_vae_slicing < source > ( ) 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_vae_tiling < source > ( ) 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. encode_prompt < source > ( prompt device num_images_per_prompt do_classifier_free_guidance negative_prompt = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None lora_scale: Optional = None clip_skip: Optional = None ) Parameters prompt (str or List[str], optional) —
prompt to be encoded
device — (torch.device):
torch device num_images_per_prompt (int) —
number of images that should be generated per prompt do_classifier_free_guidance (bool) —
whether to use classifier free guidance or not negative_prompt (str or List[str], optional) —
The prompt or prompts not to guide the image generation. If not defined, one has to pass
negative_prompt_embeds instead. Ignored when not using guidance (i.e., ignored if guidance_scale is
less than 1). 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. lora_scale (float, optional) —
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. clip_skip (int, optional) —
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings. Encodes the prompt into text encoder hidden states. enable_freeu disable_freeu enable_vae_slicing disable_vae_slicing enable_vae_tiling disable_vae_tiling AnimateDiffPipelineOutput class diffusers.pipelines.animatediff.AnimateDiffPipelineOutput < source > ( frames: Union )
DiffEdit DiffEdit: Diffusion-based semantic image editing with mask guidance is by Guillaume Couairon, Jakob Verbeek, Holger Schwenk, and Matthieu Cord. The abstract from the paper is: Image generation has recently seen tremendous advances, with diffusion models allowing to synthesize convincing images for a large variety of text prompts. In this article, we propose DiffEdit, a method to take advantage of text-conditioned diffusion models for the task of semantic image editing, where the goal is to edit an image based on a text query. Semantic image editing is an extension of image generation, with the additional constraint that the generated image should be as similar as possible to a given input image. Current editing methods based on diffusion models usually require to provide a mask, making the task much easier by treating it as a conditional inpainting task. In contrast, our main contribution is able to automatically generate a mask highlighting regions of the input image that need to be edited, by contrasting predictions of a diffusion model conditioned on different text prompts. Moreover, we rely on latent inference to preserve content in those regions of interest and show excellent synergies with mask-based diffusion. DiffEdit achieves state-of-the-art editing performance on ImageNet. In addition, we evaluate semantic image editing in more challenging settings, using images from the COCO dataset as well as text-based generated images. The original codebase can be found at Xiang-cd/DiffEdit-stable-diffusion, and you can try it out in this demo. This pipeline was contributed by clarencechen. ❤️ Tips The pipeline can generate masks that can be fed into other inpainting pipelines. In order to generate an image using this pipeline, both an image mask (source and target prompts can be manually specified or generated, and passed to generate_mask())
and a set of partially inverted latents (generated using invert()) must be provided as arguments when calling the pipeline to generate the final edited image. The function generate_mask() exposes two prompt arguments, source_prompt and target_prompt
that let you control the locations of the semantic edits in the final image to be generated. Let’s say,
you wanted to translate from “cat” to “dog”. In this case, the edit direction will be “cat -> dog”. To reflect
this in the generated mask, you simply have to set the embeddings related to the phrases including “cat” to
source_prompt and “dog” to target_prompt. When generating partially inverted latents using invert, assign a caption or text embedding describing the

negative_prompt="bad quality, worse quality",

num_frames=16,

guidance_scale=7.5,

num_inference_steps=25,

generator=torch.Generator("cpu").manual_seed(42),

)

frames = output.frames[0]

export_to_gif(frames, "animation.gif")

masterpiece, bestquality, sunset.

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. AnimateDiffPipeline class diffusers.AnimateDiffPipeline < source > ( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel motion_adapter: MotionAdapter scheduler: Union feature_extractor: CLIPImageProcessor = None image_encoder: CLIPVisionModelWithProjection = None ) Parameters vae (AutoencoderKL) —

Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder (CLIPTextModel) —

Frozen text-encoder (clip-vit-large-patch14). tokenizer (CLIPTokenizer) —

A CLIPTokenizer to tokenize text. unet (UNet2DConditionModel) —

A UNet2DConditionModel used to create a UNetMotionModel to denoise the encoded video latents. motion_adapter (MotionAdapter) —

A MotionAdapter to be used in combination with unet to denoise the encoded video 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-video generation. This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods

implemented for all pipelines (downloading, saving, running on a particular device, etc.). The pipeline also inherits the following loading methods: load_textual_inversion() for loading textual inversion embeddings load_lora_weights() for loading LoRA weights save_lora_weights() for saving LoRA weights load_ip_adapter() for loading IP Adapters __call__ < source > ( prompt: Union = None num_frames: Optional = 16 height: Optional = None width: Optional = None num_inference_steps: int = 50 guidance_scale: float = 7.5 negative_prompt: Union = None num_videos_per_prompt: Optional = 1 eta: float = 0.0 generator: Union = None latents: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None ip_adapter_image: Union = None output_type: Optional = 'pil' return_dict: bool = True callback: Optional = None callback_steps: Optional = 1 cross_attention_kwargs: Optional = None clip_skip: Optional = None ) → TextToVideoSDPipelineOutput or tuple Parameters prompt (str or List[str], optional) —

The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds. height (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) —

The height in pixels of the generated video. width (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) —

The width in pixels of the generated video. num_frames (int, optional, defaults to 16) —

The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds

amounts to 2 seconds of video. num_inference_steps (int, optional, defaults to 50) —

The number of denoising steps. More denoising steps usually lead to a higher quality videos 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 text

prompt at the expense of lower image quality. Guidance scale is enabled when guidance_scale > 1. negative_prompt (str or List[str], optional) —

The prompt or prompts to guide what to not include in image generation. If not defined, you need to

pass negative_prompt_embeds instead. Ignored when not using guidance (guidance_scale < 1). eta (float, optional, defaults to 0.0) —

Corresponds to parameter eta (η) from the DDIM paper. Only applies

to the DDIMScheduler, and is ignored in other schedulers. generator (torch.Generator or List[torch.Generator], optional) —

A torch.Generator to make

generation deterministic. latents (torch.FloatTensor, optional) —

Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video

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. Latents should be of shape

(batch_size, num_channel, num_frames, height, width). prompt_embeds (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. negative_prompt_embeds (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.

ip_adapter_image — (PipelineImageInput, optional): Optional image input to work with IP Adapters. output_type (str, optional, defaults to "pil") —

The output format of the generated video. Choose between torch.FloatTensor, PIL.Image or

np.array. return_dict (bool, optional, defaults to True) —

Whether or not to return a TextToVideoSDPipelineOutput instead

of a plain tuple. callback (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). callback_steps (int, optional, defaults to 1) —

The frequency at which the callback function is called. If not specified, the callback is called at

every step. cross_attention_kwargs (dict, optional) —

A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined in

self.processor. clip_skip (int, optional) —

Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that

the output of the pre-final layer will be used for computing the prompt embeddings. Returns

TextToVideoSDPipelineOutput or tuple

If return_dict is True, TextToVideoSDPipelineOutput is

returned, otherwise a tuple is returned where the first element is a list with the generated frames.

The call function to the pipeline for generation. Examples: Copied >>> import torch

>>> from diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler

>>> from diffusers.utils import export_to_gif

>>> adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2")

>>> pipe = AnimateDiffPipeline.from_pretrained("frankjoshua/toonyou_beta6", motion_adapter=adapter)

>>> pipe.scheduler = DDIMScheduler(beta_schedule="linear", steps_offset=1, clip_sample=False)

>>> output = pipe(prompt="A corgi walking in the park")

>>> frames = output.frames[0]

>>> export_to_gif(frames, "animation.gif") disable_freeu < source > ( ) Disables the FreeU mechanism if enabled. disable_vae_slicing < source > ( ) Disable sliced VAE decoding. If enable_vae_slicing was previously enabled, this method will go back to

computing decoding in one step. disable_vae_tiling < source > ( ) Disable tiled VAE decoding. If enable_vae_tiling was previously enabled, this method will go back to

computing decoding in one step. enable_freeu < source > ( s1: float s2: float b1: float b2: float ) Parameters s1 (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. s2 (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. b1 (float) — Scaling factor for stage 1 to amplify the contributions of backbone features. b2 (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 Stable Diffusion v1, v2, and Stable Diffusion XL. enable_vae_slicing < source > ( ) 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_vae_tiling < source > ( ) 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. encode_prompt < source > ( prompt device num_images_per_prompt do_classifier_free_guidance negative_prompt = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None lora_scale: Optional = None clip_skip: Optional = None ) Parameters prompt (str or List[str], optional) —

prompt to be encoded

device — (torch.device):

torch device num_images_per_prompt (int) —

number of images that should be generated per prompt do_classifier_free_guidance (bool) —

whether to use classifier free guidance or not negative_prompt (str or List[str], optional) —

The prompt or prompts not to guide the image generation. If not defined, one has to pass

negative_prompt_embeds instead. Ignored when not using guidance (i.e., ignored if guidance_scale is

less than 1). 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. lora_scale (float, optional) —

A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. clip_skip (int, optional) —

Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that

the output of the pre-final layer will be used for computing the prompt embeddings. Encodes the prompt into text encoder hidden states. enable_freeu disable_freeu enable_vae_slicing disable_vae_slicing enable_vae_tiling disable_vae_tiling AnimateDiffPipelineOutput class diffusers.pipelines.animatediff.AnimateDiffPipelineOutput < source > ( frames: Union )

DiffEdit DiffEdit: Diffusion-based semantic image editing with mask guidance is by Guillaume Couairon, Jakob Verbeek, Holger Schwenk, and Matthieu Cord. The abstract from the paper is: Image generation has recently seen tremendous advances, with diffusion models allowing to synthesize convincing images for a large variety of text prompts. In this article, we propose DiffEdit, a method to take advantage of text-conditioned diffusion models for the task of semantic image editing, where the goal is to edit an image based on a text query. Semantic image editing is an extension of image generation, with the additional constraint that the generated image should be as similar as possible to a given input image. Current editing methods based on diffusion models usually require to provide a mask, making the task much easier by treating it as a conditional inpainting task. In contrast, our main contribution is able to automatically generate a mask highlighting regions of the input image that need to be edited, by contrasting predictions of a diffusion model conditioned on different text prompts. Moreover, we rely on latent inference to preserve content in those regions of interest and show excellent synergies with mask-based diffusion. DiffEdit achieves state-of-the-art editing performance on ImageNet. In addition, we evaluate semantic image editing in more challenging settings, using images from the COCO dataset as well as text-based generated images. The original codebase can be found at Xiang-cd/DiffEdit-stable-diffusion, and you can try it out in this demo. This pipeline was contributed by clarencechen. ❤️ Tips The pipeline can generate masks that can be fed into other inpainting pipelines. In order to generate an image using this pipeline, both an image mask (source and target prompts can be manually specified or generated, and passed to generate_mask())

and a set of partially inverted latents (generated using invert()) must be provided as arguments when calling the pipeline to generate the final edited image. The function generate_mask() exposes two prompt arguments, source_prompt and target_prompt

that let you control the locations of the semantic edits in the final image to be generated. Let’s say,

you wanted to translate from “cat” to “dog”. In this case, the edit direction will be “cat -> dog”. To reflect

this in the generated mask, you simply have to set the embeddings related to the phrases including “cat” to

source_prompt and “dog” to target_prompt. When generating partially inverted latents using invert, assign a caption or text embedding describing the